Report No. 44321-BD Bangladesh Poverty Assessment for Bangladesh Creating Opportunities and Bridging the East-West Divide October 21, 2008 Poverty Reduction, Economic Management, Finance and Private Sector Development Sector Unit South Asia Region Document of the World Bank Table of Contents ~~~ ~~~ Preface and Acknowledgements............................................................................... V Executive Summary............................................................................................. vi-xviii 1. Poverty.growth and inequality......................................................................... 1 2. Creatingjobs - linking growthand poverty reduction............................................ 17 3. Profilingthe poor: characteristicsand determinants of poverty................................ 33 4. Laggingregions in Bangladesh:is there an east-west economicdivide? ..................... 53 5. Creating humancapital: bridgingthe access and quality gap .............................. 73 6. Are the poor protected?Vulnerability and the role of safety nets............................. 89 List of References................................................................................................. 105 Annexes.............................................................................................................. List of Tables Table 1: Poverty HeadcountRates (%) viii Table 1-1: PovertyHeadcountRates(%) 2 Table 1-2: Depthand severityof poverty 3 Table 1-3: Meanreal (rural2005 Dhaka prices) per capita monthlyconsumption 5 Table 1-4: Gini indexof per capita expenditure 6 Table 1-5: Growthof real per capita expenditure (%) 7 Table 2-1: Overviewof labor market, 2000-2005 19 Table 2-2: Structureof the Labor Market by sectors, 2000-2005 22 Table 2-3: Men and Women in the Labor Market 23 Table 2-4: Returnsto education,2005 25 Table 2-5: East-Westdifferences in basic labor marketfeatures 28 Table 2-6: Employmentand income 29 Table 3-1:Trends in basic assets and amenities 33 Table 3-2: Demographiccharacteristicsof households 36 Table 3-3:Trendsof povertyand landownership in rural areas 38 Table 3-4: Educationof householdhead and poverty 39 Table 3-5: Poverty rate and populationshare by occupationof householdhead in 2005 40 Table 3-6: % of householdsreceiving remittancesby division 41 Table4-1: Coefficientsof locationvariables (2005) 56 Table 5-1: Trends in service utilization, 1996-1997and 2004 74 Table 5-2: Incidenceof public healthexpenditure,2005 75 Table 5-3: Inequalityin early age mortality, 1985-1996 and 1994-2003 76 Table 5-4: Trends in inequality in prevalenceof malnutrition, 1996-97and 2004 (% malnourished) 77 Table 5-5: Earlyage mortalityand prevalenceof malnutritionby division 78 Table 5-6: Serviceutilizationby division, 2007 79 Table 5-7: Primaryand secondary school gross enrolmentrates,2005 81 Table 5-8: Changes in the average years of education,2000-2005 82 Table 6-1: Povertyimpactof shocks 90 Table 6-2: Householdresponsesto the food priceshock 92 Table 6-3: Financialallocationsfor major social safety net programs 96 Table 6-4: Householdsparticipating in at least one program(%) 99 Table 6-5: Distributionof beneficiariesand povertyrates 99 .. 11 List of Figures Figure 1: Povertyheadcounttrends for divisions ix Figure2: Improvementsin non-expenditurewelfare indicatorsof households:2000-2005 xi Figure 1-1: Densityand cumulativedistributionof per capita expenditure (2000and 2005) 3 Figure 1-2: Long-termpovertytrends (headcount rates) 4 Figure 1-3:Trendsof GDP per capita and PrivateConsumptionper capita 5 Figure 1-4: Growth IncidenceCurve for per capita expenditure (2000-05) 6 Figure 1-5:Growth-inequalitydecompositionsof poverty headcountchanges 7 Figure 1-6: Relativeand absolute inequality 8 Figure 1-7:Annual averagegrowth in GDP and reduction in poverty headcount 9 Figure 1-8: Poverty headcounttrends for divisions 13 Figure 1-9: Growth in rural and urban per capita consumption(2000-2005) 14 Figure2-1: Dimensionsof Growth in Bangladesh 18 Figure2-2: DemographicSituation in Bangladesh 21 Figure2-3: IncomeGrowthDecomposition2000-05 24 Figure2- 4: Gender gap for wage workers 25 Figure2-5: Wage and poverty changes by division, 2000-2005 26 Figure2-6: Decompositionof Changes in GDP per Capita into Components,etc. 27 Figure3-1:% reduction in poverty headcount(2000-05)by landownership 38 Figure3-2: Main Occupationof Household Head (% of population) 40 Figure3-3: Povertyrate (%) by occupation of householdheads 40 Figure3-4: % of populationreceivingremittanceby quintile, 2005 41 Figure3-5: Extreme poverty rate (%) by landownershipand occupation of householdhead 45 Figure3-6: Extreme povertyrate (%) by educationof householdhead 45 Figure 4-1 Povertyheadcountrates from HIES samples for districts (old boundaries) 54 Figure 4-2: Landtype (by levelof inundation)by thanas 58 Figure4-3: Cyclone risk by thanas 58 Figure4-4: Employmentin formal sector firms (2006) 61 Figure4-5: Clusteringof manufacturingfirms (2006) 61 Figure4-6: The rivers in Bangladesh 61 Figure 4-7: IR-LIRgaps in log real per capita expenditures(2000 and 2005) 62 Figure4-8: Urban-Ruralgaps in log real per capita expenditures (2000and 2005) 62 Figure 4-9: Changes in employment (2003-2006) 66 Figure 4-10: Changes in employment in Dhaka city and surrounding areas (2003-2006) 66 Figure 5-1: Gross and net enrolment rates in Bangladesh,2000-2005 80 Figure 5-2: Cross-countrycomparisonof the incidenceof public educationspending 83 ... 111 Figure5-3: Gross primaryenrolment(2005) 85 Figure5-4: Gross secondaryenrolment(2005) 85 Figure6-1:How consumptionis distributed (2005) 90 Figure6-2: Beneficiaries'preferenceon cash versus food 97 Figure6-3: Distributionof beneficiariesacross consumptiongroups 99 List of Boxes Box 1.1: Poverty measurementin Bangladesh 2 Box 1.2: Risingconsumptionamongthe extreme poor between2000 and 2005 6 Box 1.3: The concept of elasticity of povertyto growthand the choice of methodology 11 Box 2.1: Improvingwomen's participationin the laborforce: implicationsfor policy 32 Box 3.1:Improvementsin non-expenditurewelfare indicatorsof households:2000-2005 34 Box 3.2: Genderdimensionsof poverty 37 Box 3.3: Migrationand remittances-findings of a study and policy innovations 42 Box 3.4: The impactof microfinanceon householdwelfare -evidencefrom research 43 Box 3.5: What factors determinedynamic movements in and out of poverty? 44 Box 3.6: Causesof progressand decline: what do the poor say? 44 Box 3.7: The extreme poor in urbanareas 46 Box 3.8: Poverty impactof remittancesand RMG industry: resultsfrom a CGE model 48 Box4.1: Trends in regionaldisparities-what do locationeffects on householdconsumptionsuggest? 55 Box 4.2: Spatialassociation betweenmicrofinanceexpansionand povertyreduction 57 Box 4.3: DecomposingIR-LIRgaps in per capita expendituresof households 63 Box4.4: Factors influencingthe locationdecisionsof start-up nonfarmenterprises? 66 Box 4.5: Dispersionof economicactivities to peri-urbanareas surroundingDhaka 67 Box 4.6: Stimulatingregionalgrowth and reducing urbanconcentrationin the primarycity 70 Box 4.7: The Jamuna MultipurposeBridge-connecting Rajshahiwith the east 71 Box 5.1: Educationand healthservice perceptionsamongthe poor 84 Box 6.1: Impactof rising rice prices on householdwelfare in Bangladesh 91 Box 6.2: Bangladesh'svulnerabilityto naturaldisasters and seasonalshocks 95 Box 6.3: Some key lessonsfrom employmentgeneration programsacross countries 98 Box 6.3: A wide variety of publicsafety net programs 95 Box 6.4: Creating a centralagency as part of safety net reform:the case of the Philippines 100 Box 6.5: Innovationsby MFlsto reducevulnerabilityamongthe extreme poor 102 iv Preface and Acknowledgements This report was written by Ambar Narayan and Hassan Zaman (task team leaders), with significant contributions from Tahrat Shahid, Andy Kotikula, Umar Serajuddin, Nobuo Yoshida, Marcin Jan Sasin, Samer Al-Sammai and Forhad Shilpi. The report draws from a rich set o f background papers, whose authors included (in addition to the above team members) Shaikh Ahmed, Peter Davis, Bazlul Khondker, Agnes Quisumbing, Hossain Zillur Rahman, Salim Raihan, Binayak Sen, Manohar Sharma and Nistha Sinha. Qaiser Khan, Syed Josh and Andrea Vermehren contributed to the chapter on safety nets, Tania Dmytraczenko provided inputs into the chapter on human development and Paul Dorosh (World Bank) contributed to the analysis of rural poverty. Mehar Akhter Khan, Mildred Gonsalvez and Oxana Bricha provided capable assistance for the report. The World Bank and the Bangladesh Bureau o f Statistics (BBS) have had a long-standing partnership on poverty measurement and analysis using data from successive rounds o f the Household Income Expenditure Surveys (HIES), including HIES 2005. The analyses o f poverty trends and patterns usingmultiple rounds o f HIES that underpin the storyline o f this report was conducted in partnership with BBS in 2006. In this context, acknowledgements are especially due to the late Syed Nizamuddin for overseeing the early stages o f the HIES 2005 process on behalf o f the World Bank and Faizuddin Ahmed for his extensive work with survey data inclose collaboration with the World Bank team. A number o f current and ex-officials o f BBS were instrumental in supporting and facilitating the work, and the team would especially like to thank A. EkramulHaque (Director-General o fBBS), Shamsul Alam and A. R. Sikder. The report benefited from detailed comments by officials from Bangladesh Bank and the Economics Relations Division, Ministry o f Finance, Government o f Bangladesh on an earlier draft. The report also benefited from advice and guidance by Tara Vishwanath and Vinaya Swaroop (World Bank)at various stages o f preparation and their detailed comments on different drafts. Peer reviewers for the report were Wahiduddin Mahmud (Dhaka University), Peter Lanjouw and Emmanuel Skoufias (World Bank). The team i s deeply grateful for their insightful comments and to Martin Ravallion and Tamar Atinc (World Bank) for their comments at the concept note stage. The report benefited from extensive discussions with government officials, researchers and development practitioners outside the government and development partners. In particular, acknowledgments are due to participants at the workshop dedicated to the late Syed Nizamuddin inAugust 2007, where the preliminaryfindings from the backgroundpapers of the report were discussed with participants from a wide cross-section o f government officials and academics. Discussions andpresentations were also made at other forums inDhaka, at the local donor group on poverty, Dhaka University, Independent University o f Bangladesh, PKSF and BRAC. Support and advice from Department for International Development (DFID), United Kingdom, particularly Sayeeda Tauhid, is gratefully acknowledged. Financial resources from DfID financed most o f the external background papers done for the report and the workshop disseminating the background papers. The team thanks Sadiq Ahmed and Ernest0 May (previous and current Sector Director, SASPF, respectively), and Zhu Xian (Country Director for Bangladesh) for their continued support through the preparation o f the report. V ExecutiveSummary 1. Bangladesh represents a success story among developing countries. Poverty incidence, which was as high as 57 percent at the beginning o f the 199Os, had declined to 49 percent in 2000. This trend accelerated subsequently, reducing the poverty headcount rate to 40 percent in 2005. The primary contributing factor was robust and stable economic growth along with no worsening o f inequality. Respectable GDP growth that started at the beginning o f the 1990s continued into the new millennium and averaged above 5 percent annually between 2000 and 2005. Inequality, as measuredby the Gini coefficient o f consumption, remained stable between 2000 and 2005. 2. Recent shocks to the Bangladeshi economy in the form of natural disasters and rising food prices have partially dampened the rapid progress in reducing poverty. The year 2007 saw two natural disasters - floods and a devastating cyclone within a few months o f each other. Another significant shock has been the steep rise in food prices, including the main staple, rice, which has revealed the risk posed by global price volatility for a net food-importing country like Bangladesh. Estimates in this report suggest that the impact o f the food price shock has likely negatedsome (but not all) o f the reduction inpoverty brought about by economic growth between 2005 and2008. 3. The average annual rate of poverty reduction in Bangladesh during 2000-2005 was the second highest among South Asian countries for a comparable period. This was partly due to GDP growth that compared well with the region, along with stable consumption inequality. The pace o f poverty reduction in Bangladesh is, however, much lower than in faster-growing East Asian countries like China, Thailand, and Vietnam, which underscores the importance o f higher growth for achieving even faster reduction inpoverty.' 4. The reduction in consumption poverty was also accompanied by impressive gains in other indicators of wellbeing. For example, Bangladesh i s on course to meet the year 2015 Millennium Development Goals (MDGs) for infant and child mortality and has already met the MDGof gender parity inprimaryand secondary schooling. Impressive improvements inaccess to sanitation andthe quality o f housing since 2000, particularly inrural areas, reflect broad-based gains instandard o fliving for the poor. 5. Poverty reduction from 2000 to 2005 can be attributed to a combination of factors that add up to a story of significant social and economic transformation. The economic transformation i s closely related to rapid GDP growth and the urbanization process in recent times - manifested in rising returns to human and physical assets, rising labor productivity and wages, the shift from low return agricultural labor to nonfarm employment and growth inexport industries. Increasing flow o f remittances has been another key factor contributing to poverty reduction. Equally important are some o f the forces that have emerged from social transformations occurring over time. A fall inthe number o f dependents ina household, linked to past reductions in fertility, has been an important contributor in raising per capita incomes. Increases in labor force participation and educational attainment, particularly among women, have contributed as well. 6. For all its progress, however, Bangladesh remains a poor country - with an estimated 56 million people in poverty in 2005 and disparities in incomes and human capabilities across income and occupational groups, gender, and regions. Underlying the national poverty story are vast differences between regions. Dhaka, Chittagong, and Sylhet divisions in the eastern part o f the country experienced rapid poverty reduction. In the West, meanwhile, gains were much vi smaller for Rajshahi and nonexistent for Barisal and Khulna. Recent shocks - natural disasters and food price rises - have also highlighted vulnerabilities with likely adverse impacts on poverty, at least in the short run. Sustaining and improving the pace o f poverty reduction and human development, addressing the constraints faced by economically lagging regions and cushioning the impact o f shocks therefore remain enduringchallenges. 7. A significant finding of the report is the changing pattern of regional inequality. While regional inequality in incomekonsumption in Bangladesh has been historically significant until the early 199Os, it was inducedmainly by large differences between the greater Dhaka region and the rest of the country." Recent years, in contrast, have seen a growing divergence between the eastern andwestern parts o f the country. Between2000 and 2005, while most regions inthe East have moved closer to the greater Dhaka region in terms o f incomes and poverty, the West has been increasingly laggingbehind. 8. There is evidence to suggest that the eastern region has increasingly benefited from integration with growth poles, namely Dhaka and Chittagong, in contrast to the more isolated West and Southwest. Two large rivers crisscrossing the country appear to act as natural boundaries between these two parts o f the country by imposing a strong barrier to connectivity. In addition, a combination of factors contribute to stagnant incomes in lagging regions - the relative lack o f remittance income, inadequate public infrastructure like electricity and roads to markets, the lack o f growth poles within these regions, anddeficiencies inassets andendowments among households. 9. Growing inequality among regions is a feature in many developing countries experiencing rapid gains in income and poverty. InSouth Asia, rising regional gaps have been observed in countries like India, Sri Lanka, and Pakistan. The pattern o f spatial differences in India and Pakistan - much larger countries with multiple urban growth centers - are very different from those in Bangladesh."' But there are similarities between Sri Lanka and Bangladesh, most importantly inthe divergence between the geographic areas integrated with the capital city and the rest o f the country. The Bangladeshi story, however, appears to have at least one unique aspect among South Asian countries: the natural boundaries created by rivers playing a key role inlimiting the integrationo f certain geographic areas with growing areas. 10. In addition to the structural causes of poverty, recurring community-wide shocks have a significant accumulated impact. Some o f these are seasonal, while others are more unpredictable, like the major floods and tropical cyclone that occurred in 2007. There is some evidence to suggest that severe and repeated community-wide shocks contribute to poverty traps incertain areas o f the country. The recent steep rise inrice prices, while benefiting a relatively small group o f (larger) farmers, has had an especially severe impact on the poorest households. The frequency and severity o f such large shocks calls for safety nets programs to play a critical role. By (at least partly) mitigating the impact o f the shocks, a well-functioning safety net system would ensure that the considerable gains Bangladesh has achieved through rapid economic and social transformation are not eroded. 11. Specific areas for policy focus which are elaborated in the report include measures to: (i)promote growth by sustaining increases in labor productivity and job creation in manufacturing and services; (ii)expand opportunities in lagging regions by improving connectivity with growth poles and investinginhuman capital; (iii) facilitate migration from poor areas given the poverty-reducing impact o f remittances; (iv) stimulate women's participation in the labor force (v) sustain Bangladesh's past successes in reducing fertility; (vi) improve poor vii households access to and quality o f education, health and nutrition services; (vii) strengthen the coordination, targeting and coverage o f safety net programs. I. Poverty,growth, and inequality in recentyears 12. The last two rounds o f Household Income and Expenditure Surveys (HIES) show that the percentage o f population with per capita consumption below the upper poverty line declined by 18 percent during2000- 2005, while that below the lower poverty line (the National 48.9 40.0 34.3 25.1 threshold for extreme poverty) declined by 27 percent Urban 35.2 28.4 19.9 14.6 (from 34 to 25 percent) (Table 1). While the rural- Rural 52.3 43.8 37.9 28.6 urban gap narrowed, the rural poverty rate in 2005 was still more than one and a halftimes the urbanrate. 13. The fall in poverty headcount rates was significantly more than population growth during 2000-2005 leading to a decline in the number of poor people. The size o f the population below the upper poverty and the lower poverty lines declined by nearly 6 million and 8.3 million respectively. The levels and distribution o f consumptionamong the poor improved as well, as evident from reductions in poverty gap and squared poverty gap measures by 30 and 37 percent respectively. 14. Growth in consumption, fueled by robust GDP growth, was the dominant force in reducingpoverty. Real per capita consumption expenditure from HIES increased at an average annual rate o f 2.3 percent, with a higher increase for rural thanurban areas. The National Income Accounts shows that the growth o f private consumption per capita was the highest during2000- 2005. Decompositions o f changes in poverty indicate that consumption growth (from HIES) accountedfor nearly all the reductioninnationalpoverty headcount. 15. Consumption growth occurred at similar rates across the board, resulting in stable relative inequality as measuredby the nationalGiniindex o fper capitareal consumption (which remained at 0.31 from 2000 to 2005). However, similar consumption growth rates for the rich andpoor alike imply an increase inthe size o fthe rich-poor gaps. The absolute Gini index ofper capita real consumption increased by 13 percent between 2000 and 2005 for the national population.iv 16. Bangladesh is on track to achieve the MDG of halving poverty from the 1990 level. If GDP were to continue growing at the same average rate as between 2000 and 2005 (5.3 percent per year), Bangladesh would meet the MDGtarget o f halving poverty and extreme poverty rates between 1990 and 2015. However, since these projections depend on estimates o f elasticity extrapolated from historical data, they are imperfect guides for the future, and the actual poverty impact o f growth would depend on how distributional changes evolve over time. Realizing these projections would also depend on the country being able to sustain its recent trends in reducing fertility and population growth. Severe shocks, like the recent rise in food prices, could also erode some o f the gains from economic growth and slow the pace o f poverty reduction. The frequency o f such shocks, how long they last and how rapidly the economy bounces back from them will therefore influence the future pace o f poverty reduction. ... Vlll 17. There are sharp variations in the rate of I Figure1:Povertyheadcounttrends for divisions poverty reduction across regions, with the eastern part of the country far outpacing the West and southwest. The largest decline in poverty occurred for Dhaka, Chittagong, and Sylhet divisions, while Barisal and Khulna saw little change (Figure 1). Dhaka and Chittagong divisions, with just over half the country's population in 2000, contributed 79 percent of the reduction in national poverty. All divisions with , high consumption growth also saw substantial m20W02W5 reductions in poverty and there was no apparent Source: HIES (2000,2005) association between growth and distributionalchanges. II. Linkinggrowth andpoverty: theroleof thelabor market 18. Economic growth in Bangladesh has been driven primarily by factor accumulation - of both labor and capital. With public investmentremaining almost unchanged as a share of GDP, private investment has enabled capital accumulation, which has in turn improved labor productivity, raising real wages andhousehold incomes. 19. Labor force participation rates remained steady between 2000 and 2005 at about half the working age population, which is low by world standards. The participation rate was just above 10percent for women compared to above 80 percent for men (HIES 2005). While the last two rounds of Labor Force Survey show a higher female participation rate, this was still low by international Standards." About 5.6 million new jobs were created -just enough to maintain an unchangedemployment rate giventhe number ofnew entrants into the labor market during2000- 2005. 20. Between 2000 and 2005, important structural changes occurred in the labor market. Firstly, the share of agriculture intotal employment declined - agricultural employment grew at 0.7 percent per year compared to 5 and 2.8 percent for services and manufacturing respectively. Secondly, there was a movement away from low productivity jobs in apculture to more productivejobs, and especially to salaried employment inthe private sector. Thirdly, there was a strong rural-urban shift in employment - consistent with a similar shift inpopulation share that actually accounted for almost 9 percent of the total change inpoverty between 2000 and 2005." These trends are consistent with the shnking share o f agriculture in GDP while the share of services and industry are increasing. Despite slow growth, the contribution of apculture to poverty reduction was significant because of the size o f the sector, which also made for a large contribution of rural areas. 21. Demographic transition is a key force shaping the labor market, creating opportunities as well as challenges. Although population growth has recently moderated to about 1.5 percent per year, the working age populationhas been expanding at 2.5-2.8 percent into the 2000s. A potential asset for income generation and growth, this also creates a major challenge for the labor market as an estimated 22 million new entrants between 2005 and 2015 will needto be absorbed. 22. Poverty is most prevalent among daily agricultural wage workers and subsistence farmers while the better-off tend to be engaged in salaried employment or nonfarm self- employment. Daily wage labor accounts for a third of all workers and i s characterized by low ix wages, especially in agriculture. Among salaried workers (about 20 percent o f all employed), those working for government or community organizations have relatively higher education and earnings thanthose inthe private sector. 23. Women are playing an increasingly important role in the Bangladesh labor market. Women's participation rates, employment, working hours, level o f education, and income have all increased much more than those o f men between 2000 and 2005. Women's earnings fi-om salaried employment increased by about 60 percent in 5 years, with changes in education level accounting for a substantialpart o f the change. However, the labor market continues to be highly segmented along gender lines. Women's participation in wage work i s extremely low, and women are also significantly less likely than men to be self-employed in nonfarm activities. While women earn significantly less than men at the same type o f job, the gender gap narrowed between 2000 and 2005, although mainly for better-off, salaried workers. Overall, the growth in women's participation and incomes has been largely concentratedinthe middle andhigher end of the income distribution. 24. Wage growth and labor productivity increases played a key role in poverty reduction between 2000 and 2005. Ninety percent o f the growth inreal income per capita i s attributable to wage growth and the rest to an increase in the share o f working age population in total population. Growth in real wages was evenly spread across the wage distribution, which is consistent with relative inequality inconsumption remaining stable from 2000 to 2005. Wages in salaried employment inthe private sector had the highest growth, and among economic sectors, wage growth was most significant inthe services. Consistent with rising wages, increase in labor productivity accounted for a major share o f the growth in GDP per capita, while an almost unchanged employment rate had no contribution. About half o f the productivity increase is attributable to inter-sectoral mobility o f workers -related to outflows from low-productivity daily wage jobs in agriculture and expansion o f private sector salaried employment. Productivity growth was strong inmanufacturing butjob creation was slow, which is an important reason why growth inoverall employment just about kept pace with that o fthe working age population. 25. Differences in wage growth and job-creation patterns help explain the widening East- West gap in poverty. Wages grew robustly inthe eastern part o f the country but stagnated inthe West. While both East and West created employment to match the rise in working age population, the East created many more jobs that are more stable (salaried), better paid, and in a robustly growing nonfarm sector. In contrast, a large proportion o f jobs created in the West consisted o f daily wage work or apcultural self-employment. A smaller and declining urban premium for wages in the West suggests weaker agglomeration effects - likely related to the absence o f urbangrowth poles and poor connectivity to markets. III.Aprofle of thepoor: characteristicsanddeterminants 26. Most non-consumption indicators of welfare showed significant improvements between 2000 and 2005 (Figure 2). A sharp improvement inmost welfare indicators was a feature for the population as a whole but especially so for extreme poor households. For instance, housing conditions improved especially inrural areas, with a larger percentage o f households having walls and roofs o f corrugated iron sheets or cement as opposed to straw. Similarly, the poor made rapid gains inaccess to sanitation. However, notwithstandingthese improvements, large gaps between the poor and non-poor remain for most indicators. X 27. The poor in Bangladesh have several distinct characteristics. The likelihood o f poverty i s higher when a household has a larger number o f dependents, has low levels of education, or when the household i s headed by a female whose husband does not send remittances. A household whose headis engaged in daily wage work is significantly more likely to be poor compared to all others. For rural households, ownership of agricultural I 1 II land raises household per capita consumption with land ---- Livestock ownership (%) - 1 size. urban ho~seholdsare likely to be ofthe hexagon for 2005 lie outside those forZ000, better-off if the head i s engaged in Sourcee:NiEStzodo, 2005) I alone.all 6diniensiuns. I nonfarm self-employment or ifthey ownsomeform of business. 28. The rapid growth of international remittances (20 percent annually during 2000-2005) played its part in poverty reduction, primarily in regions like Sylhet and Chittagong. The poverty rate among households receiving remittances from abroad i s 17 percent compared to 42 percent among the rest. There are stark geographic disparities: in2005,24 percent o f households in Chittagong division and 16 percent in Sylhet received remittances, compared to less than 5 percent in Rajshahi, Khulna, and Barisal. Simulations with a computable general equilibrium model (CGE) attribute a little above 15 percent o f the poverty decline to the effect o f growth o f foreign remittances duringthis period."" 29. Microfinance membership expansion at the thana level and household consumption levels are found to be positively correlated. While the HIES data limitsthe scope for analyzing the impact o f microfinance on household welfare, a number o f studies using smaller data sets have found significant positive impacts o f microfinance membership on various dimensions o f household welfare that are consistent with the positive correlations inthis report. 30. Poverty reduction during 2000-2005 occurred partly due to substantial improvements in key household attributes, with even the poorest o f all households experiencing some o f these gains. These include changes in household characteristics like demographics and education attainments. The average household size fell sharply, mainly due to a decline in the number o f children, which reduced the dependency ratio. The proportion o f household heads with secondary education levels or above rose from 27 to 31percent. 31. Increases in returns to attributes like occupations and assets also contributed significantly to poverty reduction, implyingthat households were able to get more out o f their existing endowments and occupations. Returns to agricultural labor, farming, and landownership improved significantly for rural households, as did returns to non-agncultural daily labor and self- employment for urban households. The findings are consistent with rising labor productivity and earnings being important drivers o f poverty reduction (see section I1 above), which in turn suggest an improvement inthe general economic environment. X i IV. Regional inequality: the "East-Westeconomic divide" 32. The geographic location of a household clearly influences its likelihood of being poor. Location outside Dhaka district (by the "old" classification o f 17 districts) i s disadvantageous for a household even after controlling for the effect o f household attributes. But since the early- 1990s, and especially between 2000 and2005, the gap with Dhaka has shrunk for most regions in the East but not for the West. Between 2000 and 2005, the HIES samples from most o f the eastern districts showed significant reductionin...poverty, with the highest reductions occurring in districts that were among the poorest in2000."" Incontrast, almost allwesterndistricts have had much smaller (or no) reductions with no pattern o f convergence. 33. Remoteness from local markets and Dhaka city, and lack of infrastructure (like electricity) are characteristics of poor areas that partly explain why location matters for household welfare. These characteristics also tend to occur together, which suggest that only certain areas in the country are likely to possess the combination o f factors necessary to attract high-return economic activities. 34. Two metropolitan cities have emerged as the main centers of economic activity of the country - Dhaka with a population o f 10 million andto a lesser extent, Chittagong, the main port city, with a population o f 3.4 million. Dhaka alone accounts for 80 percent o f the country's Ready Made Garments output andhalf o f manufacturing sector employment. A large increase in formal sector employment between 2003 and 2006 inthe greater Dhaka region, relative to the rest o f the country, suggests that agglomeration has increased in recent years.'" However, even as concentration has increased in the greater Dhaka region, there i s a growing trend o f dispersion within this region - from the core o f the city to outlying areas o f Dhaka city, particularly to the north and west. 35. The two major rivers, Ganges and Brahmaputra, create significant barriers to the connectivity of the West to the growth poles. Thus territories to the east o f the rivers are defined as the integrated region (IR) - covering the divisions o f Chittagong, Sylhet, and most o f Dhaka. Areas to the west o f Brahmaputra (Rajshahi Division) and south o f Ganges (Barisal and Khulna divisions, and the greater Faridpur districts in Dhaka division), which are separated from Dhaka and Chittagong by one o f the two rivers, are defined as the less integrated region (LIR). The IR-LIR distinction almost coincides with the so-called East-West divide- with the only exception o f greater Faridpur district, which was considered a part o f "East" earlier. 36. The natural barriers imposed by the river seem to matter - while the urban-rural gap in average per capita consumption has declined between 2000 and 2005, the IR-LIR gap has increased. Moreover, the poor households inIR experienced a much higher consumption growth compared with those inLIR. The widening gap i s partly due to physical andhuman endowments improving more in IR than LIR. Inaddition, households inLIR make substantially lower returns on their endowments compared to those belonging to the same expenditure quantile in IR; and between 2000 and 2005 this returns gap has increased for the bottom 40 percent. 37. An analysis of the endowment and return gaps suggests the following stylized story on the divergence between the East and West (or IR and LIR). Increasing agglomeration o f high-returneconomic activities at the major growth centers have led to strong spillover effects, higher incomes, and narrowing of urban-rural differences within the eastern part o f the country. Inthe greater Dhakaregion, growthhasbeenincreasingly dispersedoutwardfrom the maincity - due to a combination o f increasing agglomeration costs in the main city and spillovers to surrounding markets - which has also helped to reduce the urban-rural gap within the eastern xii region. On the other hand, East-West differences in income and poverty have expanded on account o f the western region being handicapped by the absence o f growth poles, poor connectivity with urban centers in the East, and deficient public infrastructure and markets. These factors have led to higher wage growth and higher-returnjob creation inthe East compared to the West (see section 11). While the better-endowed households from the West can respond to the economic opportunities in the East by migrating, the poor are mostly unable to overcome barriers to their mobility. Thus the differences inreturns to endowments between the eastern and western parts o f the country are especially high for the poor and increasing over time. The geographically skewed distribution o f remittances is also a key factor behind the divergence across the `two halves' o f Bangladesh. V. Improvements in human capability 38. In keeping with its progress in reducing income poverty, Bangladesh has seen rapid gains in a number of key education and health outcomes. The country is well on the way to achieving its MDGs for outcomes like infant and child mortality andhas already met the MDGo f gender parity inprimary and secondary schooling." Nevertheless, a number o f obstacles remain inachieving accessto education andhealth services for the poor, as inequalities inopportunities and outcomes persist across different wealth and income groups, gender, and regions. As seen earlier, education improves nonfarm employment opportunities, increases earnings o f workers, and enhances the mobility o f the poor from lagging regions. Poor health contributes to a vicious cycle o fpoverty, malnutritionandhigher morbidity, which often leads to families remaining poor across generations. Most importantly, better education and health are critical objectives in themselves, with interrelated effects on other development outcomes. 39. There is clear evidence of persistingand, in some cases, increasinginequalitiesin access to healthand education. For example, data from Demographic andHealth Surveys (DHS) show that although childhood immunization coverage has gone up, the increase occurred primarily among better-off households. A lack o f adequate care during child delivery continues to be a challenge across all wealth quintiles, but especially so for the poor, with poor women's access to such services showing very little change from 1996-1997 to 2004. A gender gap is also evident in provider choice for child treatment, with girls more likely to receive treatment from public sector providers than from private providers. On the positive side, vitamin A supplementation to children under 5 saw both an increase incoverage and a decline ininequality. 40. Malnutrition indicators have improved but remain high compared to other countries and for the poor in particular. Malnutrition remains higher than many African countries with comparable per capita incomes. Improvements in feeding practices - specifically `timely complementary feeding' - have contributed to a reduction in the proportion of the populationthat i s underweight, from 56 percent in 1996 to 47 percent in 2004. However, better-off households have tended to benefit from gains innutrition to a greater extent than poor households. Absolute inequality also increased between girls and boys in terms o f nutrition indicators. Malnutrition needs to be redressed before a child has reached the age o f two in order to avoid permanent negative health consequences. The current rise in food prices i s likely to reverse some o f the gains made thus far intackling this problem. 41. There has been mixedprogressineducation outcomessince 2000. There was little change inprimarygross enrolment since a 90percent enrolment rate was already attainedin2000. There was substantial growth in gross secondary enrolment, although this was accompanied by declining completion rates. The rich-poor gap in attainment i s wider for higher levels o f education, due to the rising costs o f moving up in the educational system and the higher ... x111 opportunity cost for poor children going to school in lieu o f working to supplement household incomes. On the whole, boys fiom poor households appear to be getting left behind in the gains that the country has made in educational attainment, compared to girls in poor households and boys inbetter-off households. 42. Per capita public expenditure on both education and health is relatively low and the extent to which spending reaches the poor varies by the type of service. Comparisons with neighboring countries and others at similar stages o f development show that per capita education spending i s relatively low in Bangladesh. Moreover, targeting o f both primary and secondary spending, including two significant conditional cash transfer programs, can be significantly improved to benefit the poor. Similarly, analysis o f public spending on health suggests that expenditures are low relative to other countries. Spending on family planning, communicable diseases, and maternal health are almost distribution-neutral, while the incidence o f spending on adult curative care favors the non-poor.xi 43. The quality of public service provision appears to be low in both health and education, which is likely to disproportionately affect the poor. Highrates o f absenteeism are found in public healthcare, with facilities inrural areas - that are likely to serve large numbers o f the poor -bearingthe bruntofthe problem.'" In education, highprivate expenditures, particularly for secondary level and above, indicate an increasing demand for private school and tuition services. Large differences inprivate spending between the poor and non-poor contribute to rich-poor gaps in education outcomes. Additionally, the recent rise in food prices may have compelled households, particularly the poor, to further reduce their spending on education. 44. Regional variations in both education and health outcomes show that districts with higher poverty rates, ironically, tend to have better outcomes. Some indicators o f human capital (e.g. number o f years o f schooling inthe working age population) did seem to grow faster inthe East than inthe West from 2000 to 2005. Even so, the spatial pattern of education and health outcomes is not easy to explain, particularly in the context o f faster poverty reduction and accelerated progress in human development for the country as a whole. Some factors may help resolve this puzzle, such as possible differences in social norms between different parts o f the country (e.g. greater conservatism in Sylhet limiting female gains inhuman development). Given the limitations o f HIES data, these questions call for more detailed analysis using alternate data sources. VI. Vulnerability to shocks and the role of safety nets 45. A large concentration of population within a relatively narrow band of consumption around the poverty line suggests that many in Bangladesh are vulnerable to falling into poverty as a result o f even a small shock. For example, a 5 percent shock to consumption, distributed equally throughout the population, would increase the share o f the population below the lower and upper poverty lines by 11 and 16 percent respectively. A shock that disproportionately affects the lower part o f the distributionwould have an even larger impact onpoverty. 46. An example of such a shock is the recent steep rise in rice prices o f nearly 40 percent between April 2007 andMarch2008. A survey conductedby the World Bank inJuly 2008 found that a significant majority o f households have had to respond to the price shock by cutting back on their food intake, consuming lower quality food, or reducing spending on non-food items. According to the HIES, the share o f rice in a householdbudget averages around 24 percent for an average Bangladeshi household and significantly higher for the extreme poor. Since nominal wages are slow to adjust and more than 80 percent o f households are net buyers of rice, increases xiv in rice prices are likely to have a significant adverse impact on real incomes. Assuming a uniform 5 percent wage increase for all, a 3 percent real income loss for the average household i s estimated, which translates to a roughly 3 percentage point increase inthe poverty rate. 47. The magnitude of the impact implies that the food price shock is likely to have negated some (but not all) of the reduction in poverty achieved between 2005 and 2008 due to strong ... and stable economic growth.""' More important than the aggregate poverty impact o f the price shock i s its role in worsening the income or consumption distribution. The adverse impact i s much higher for households that were already poor than for those who were better-off, and for vulnerable groups like daily wage workers and subsistence farmers compared to others. Those likely to benefit are farmers with more than 1.5 acres o f land, who constitute less than a quarter o f all households. 48. Among household-specific sources of shock, health shocks, especially among income earners, are particularly important contributors to poverty. Households with lower endowments (in terms of education, land ownership or asset ownership) and households with poorer demographic attributes are likely to be more vulnerable to certain types o f shocks. Sudden illnesses lead to poverty due to lack o f earnings and expensive medical treatment. Moreover, economic shocks such as the recent rise infood prices makes poor households switch to cheaper, less nutritious food items and contributes to malnutrition and illhealth. 49. Bangladesh also suffers from recurring community-wide and external shocks. Large areas in the northwest are subject to a seasonal phenomenon called Mongu, which occurs during the lean agricultural season inOctober and November every year and contributes to highchronic poverty. Other shocks are more unpredictable, like the 2007 floods that affected 46 o f the country's 64 districts and Cyclone Sidr in the same year that devastated parts o f Barisal and Khulna divisions. There i s some evidence that the areas at a highrisk o fnatural disasters are also more likely to be poor and have lower access to markets and infrastructure - conditions that are likely to exacerbate the impact o f a natural disaster and contribute to poverty traps. 50. Given the high incidence of shocks and the large vulnerable population, safety net programs have an important role to play. Such programs transfer resources directly as a source o f income for the extreme poor; they mitigate the risk o f households falling further into poverty as a result o f a shock andhave the potential to enhance humancapital gains when linked to education and health programs. The government has raised safety net expenditures steadily since the mid-1990sY funding a wide spectrum o f programs - a mix o f conditional and unconditional cash and food transfers, subsidies, and targeted assistance to specific groups. A dominant share o f resources i s spent on unconditional programs, out o f which in-kind (food) transfers constitute the largest part. A small share o f in-kind transfer programs provide food fortified with essential nutrients. One o f the major programs used to respond to the recent food price crisis was distribution of subsidized coarse rice rations ingovernment markets. 51. Evidence suggests that safety net programs are still inadequate to address the vast needs of the poor. Only about 13 percent o f households (including 23 percent o f the poorest 10 percent) benefit from at least one safety net program. The benefit amounts are small - for example, the food benefit from VGF i sjust 21 percent o f the lower poverty line. Targeting errors compound the problem o f low coverage among the poor; for example, the poorest divisions have much lower proportion o f population covered by safety nets than do better-off areas such as Sylhet. A lack o f safety net coverage in urban areas i s a critical gap in the system. Moreover, multiple implementing agencies undertake programs in a largely uncoordinated manner, thereby limitingthe ability to make strategic choices withbudgetary resources. xv VII. Theroad ahead: implicationsfor apoverty reductionstrategy 52. Future gains in reducing poverty will require productivity growth in agriculture and job creation in the industrial and services sectors. Inparticular, manufacturing employment would need to expand faster than during 2000-2005 to absorb the large number o f estimated new entrants inthe labor market expected by 2015. Improving labor productivity inagriculture would be important to raise earnings o f agricultural wage workers and subsistence farmers. Promoting diversification into higher value-added crops, greater mechanization, and timely dissemination o f new technology can raise agricultural productivity and incomes. Accelerating private sector, export-led growth would require a policy environment that improves returns to investments - including a stable macroeconomic environment, infrastructure improvements, and rule o f law. Policies to raise women's employment and incomes can have significant dividends in terms o f household income and poverty, given the vast potential that remains untapped due to the low participation o f women in the workforce. Specific areas for policy focus could include better enforcement o f existing laws, continued focus on higher education for women, and creation o f support systems to facilitate women's participationinthe labor force.xiv 53. Sustaining the reduction in poverty will require slowing population growth rates and providing better quality options in schooling and health care. Investments in future generations will create the conditions for higher growth and poverty reduction in the long run. Buildinghumancapacity among the poor remains a key priority inorder for the poor to shift to occupations with higher returns. Public education expenditures ought to be raised in line with countries at similar stages o f development. These additional resources could be spent on raising the quality o f public schooling, which would help reduce the rich-poor gaps in education outcomes. In health, demand-side interventions targeted to the poor such as vouchers and conditional cash transfer programs - that provide greater incentives for households to seek care - can be considered for reducing the rich-poor and regional disparities. Sustaining Bangladesh's past successes in reducing fertility will be critical - given that demographic dividends played a key role in reducing poverty between 2000 and 2005. Hence sustaining past successes in partnering with NGOsinthe area o fpopulation control i s crucial. 54. Rapid urbanization in Bangladesh creates opportunities for growth and poverty reduction, as i s apparent inthe eastern part o f the country. To gain the most out o f this process, urban policy would need to induce improvements in: (a) basic infrastructure and.services in the largest cities in the East to reduce agglomeration costs; and (b) growth prospects o f smaller towns/cities, particularly in lagging regions, so that they emerge as alternate growth centers. Coordination between urban policies at various levels and strengthening o f urban municipal government can help address these two objectives simultaneously. 55. Narrowing the economic gap between integrated and less integrated or lagging regions would require improving endowments and returns to the endowments in lagging regions. Investments to improve humancapital o f the poor inlaggingregions would enable them to access better opportunities in growing regions and improving credit access to household enterprises would raise productive investment. Raising the returns to endowments involves improving the investment climate for nonfarm enterprises in lagging regions, for which enhancing o f the availability andquality o f infrastructure, including roads and electricity, would be key. 56. Investments in interregional transport infrastructure and spatially targeted incentives can improve returns to endowments and facilitate migration to growth centers. Improving connectivity o f remote areas with Dhaka and local markets would generate economic benefits in lagging regions. More specifically, improving connectivity across the large river dividing the xvi Southwest and the East is likely to have a large impact, as the Jamuna Bridge seems to have had by connectingthe Northwest to the East. Special Economic Zones (SEZs) can stimulate growth in lagging regions if the supporting infrastructure (particularly power) can be made available. New programs such as PKSF's international migration credit facility for poor households in lagging areas may become the catalyst for significantly higher remittance flows to the western part of Bangladesh. 57. In prioritizinglsequencing across different types o f interventions for lagging regions, it is useful to distinguish between policies likely to yield rapid returns and those with longer-run benefits. Investments inpublic infrastructure are likely to induce relatively rapid economic gains in lagging regions. Efforts to improve humancapital would yield longer-run dividends in the form o f higher mobility across regions and sectors among the poor and better growth prospects for lagging regions. Depending on the type o f intervention and the area targeted, spatially targeted programs can produce short- or long-run benefits. Insequencing, complementarities and synergies across interventions also need to be taken into account. For example, conditional cash transfer programs in lagging regions can serve the objective o f long-term human development and addressing the immediate need for social protection. At the same time, investing inhuman development in lagging regions may not yield desired results without complementary improvements ininfrastructure to spur the nonfarm sector and improve returns to education. 58. An urgent priority should also be to make the existing safety net system more coordinated and strategic. To this end, some key steps would be: (a) improving coordination among overlapping programs administered by multiple Ministries by, for instance, establishing a national umbrella body; (b) developing key strategic objectives, including identifying priority areas and households groups, and (c) building capacity for program administration among local governments. The resources saved due to higher efficiency and the newly proposed programs could then be used to fill some o f the critical gaps in the system, such as the lack o f coverage among the urban poor and low coverage among the poor in lagging regions. Safety net interventions can be linked with the longer-term objectives o f income generation and human development. Linking safety nets with measures to improve the access o f the poor to livelihood- enhancing assets i s likely to induce sustainable increases inincome. Increased use o f conditional cash transfers can enhance the linkages between safety nets and humandevelopment, particularly for urban areas and lagging regions. Raising the share o f fortified food in in-kind transfer programs can help meet the twin goals o f income support and nutritional supplementation. Spurred by the recent food crisis, the Philippines i s currently in the midst o f such a comprehensive reform o f the national safety net system. 59. The themes presented in this report could form the basis of a nationally owned development strategy. Much o f the poverty reduction achieved inBangladesh during2000-2005 i s linked to strong and stable economic growth, which has appreciably raised the returns to physical and human endowments. Continued poverty reduction would require sustaining this process and spreading it to lagging regions and sectors o f the country. Investments in ensuring access by the poor and the quality o f education and health services will stimulate growth and builda more equitable Bangladesh. Improving the coverage and effectiveness o f social safety net programs needs to be an urgent priority as well, since the accumulated impact o f repeated shocks can significantly erode the gains from economic growth. Translating these ideas into tangible improvements in poor peoples' lives will require effective implementation o f policies and programs. This remains a shared responsibility o f society at large, including the Government, civil society, the private sector and development partners. xvii I Cross-country comparisons o f poverty incidence are, however, indicative at best - every country uses a different national poverty line and countries conduct their household surveys indifferent years. `I See, for instance, Ravallion and Wodon (1999), World Bank (2002). iii different parts o f these countries - in contrast to a relatively clear distinction in the cases o f Sri Lanka and Bangladesh One important difference i s that in India and Pakistan, economically lagging regions can be found scattered inmany betweenthe dynamic region surrounding the capital cities and far-flung areas. iv The absolute Gini Index depends just on the size o f rich-poor gaps in consumption, while the relative Gini index measures the gaps relative to the mean o f the distribution. While the absolute index is closer to a layman's perception o f inequality, the relative index is more meaningful for comparisons over time when the average levels o f consumption can change significantly. The female labor force participation rate from Labor Force Surveys (LFS) was reported as 26 percent in 2002/2003 and 24 percent in 1999/2000 (World Bank, 2007~). The LFS-HIES difference may be partly due to the fact that HIES may not account fully for female unpaid work in crop and non-crop production, cottage industries, small trade, and farming. Almost halfo f the women counted as economically active inLFS are unpaid family workers. There was a 23 percent increase inurban share o f the population from 2000 to 2005, continuing a historical trend - the urban population share increased by 22 percent from 1995-1996 to 2000 and by 15 percent from 1991-1992 to 1995-1996. vii The inflow o f remittances in Bangladesh increased from $1.9 billion in 2000 to $3.8 billion in 2005 (around 20 percent annually), while RMGexports grew from $4.8 billion to $6.9 billion (9 percent annually). "jiiSince the survey i s not designed to be representative at the (old) district level, these results should be seen as applying to the HIES sample for each district, rather than its population. ix From Economic Census (2003,2006). World Bank(2007~)"To the MDGs and Beyond: Accountability and Institutional Innovation inBangladesh." xi Wagstaff (2003). xii Chaudhury and Hammer (2003). Given that GDP grew at around 6 percent annually during 2005-2008, the poverty rate would have been expected to decline by around 5 percentage points between 2005 and 2008 (using the elasticity o f poverty reduction to growth estimated inchapter 1 o f this report) as a normalresponse to GDP growth. But with the impact o f the food price shock (equivalent to 3 percentage point increase inpoverty rate) factored in,the poverty rate would have declined by roughly 2 percentage points over the same period. Also see World Bank country gender assessment for Bangladesh (World Bank, 2008d). xviii I. Poverty,Growth,andInequality 1. Bangladesh has been successful in achieving significant poverty reduction since 1990, as successive rounds o f Household Income and Expenditure Surveys (HIES) conducted by the Bangladesh Bureau o f Statistics (BBS) have shown. This chapter will focus on changes in poverty incidence between 2000 and 2005 (the last two rounds o f the HIES), and bring out the relationship between changes in poverty, consumption growth, and distributional changes - nationally as well as for urbadrural sectors and regions withinthe country. 2. The main source o f information for this chapter and indeed most o f the report i s HIES - the official nationally representative source for measuring consumption poverty in Bangladesh - that necessarily limits the time horizon for the analysis to the period up to 2005. Since the underlying factors determining poverty tend to change slowly over time, analysis o f the period until 2005 is valuable inidentifying the pathways to poverty reduction andpolicies that can make a difference. 3. At the same time, it i s important to recognize that a series o f shocks affected Bangladesh in 2007-2008, including two natural disasters and commodity price shocks in the international market, which have strained the government's finances, slowed growth, and affected income distribution by disproportionately affecting certain geographic areas and groups. These events are likely to have reduced the rate o f poverty reduction in 2007 well below what would be expected during a "normal" year in Bangladesh. While the extent o f poverty impact cannot be estimatedyet with any degree o f accuracy, section I1o f this chapter and a subsequent chapter will speculate briefly on its magnitude and distribution, extrapolating from the information in surveys up to 2005. As the Bangladeshi economy adjusts to or rebounds from shocks, as it has on numerous occasions in the past, the findings o f this report can help understand how future economic growth translates into welfare gains among different groups, sectors, and regions. 4. Section Ibelow presents the poverty trends at the national, rural, andurban levels, how these relate to growth and distributional changes, and how they compare with the experiences o f other developing countries. Section I1presents results from projecting poverty trends under different GDP growth scenarios, given the historical relationship between poverty reduction, inequality and growth seen in Bangladesh during the last 15 years. Section I11 focuses on the regional pattern o f poverty reduction -how the regional differences compare between 2000 and 2005, and how these patterns are inturnlinked to growth and inequality changes inspecific regions. I. Poverty, growth, and inequality in recentyears 5. Poverty headcount rates based on both upper and lower poverty lines usingthe Cost o f Basic Needs (CBN) method (see Box 1.1) show that the proportion o f poor in the population declined significantly between 2000 and 2005. Moreover, the improvements were not limited to reductions in the proportion of poor in the total population, but also in the size o f the poor population and level and distribution o f consumption among the poor. The improvements occurred at similar rates for urban and rural areas. Furthermore, the extent o f poverty reduction inBangladeshbetween 2000 and2005 was onpar or higher thanwhat was seeninother countries in South Asia during similar periods. This was partly due to GDP growth rates that compared well with the region, as well as almost no change inconsumption inequality duringthisperiod. Trends inpoverty -national, rural, and urban 6. National poverty headcount rate declined by 18 percent (or 9 percentage points) between 2000 and 2005. In 2005, 40 percent of Bangladesh's population was poor (per capita 1 consumption below the upperpoverty line) as compared to 49 percent in 2000 (Table 1-1). The percentage decline in poverty rate was higher in urban areas (24 percent) than rural areas (19 percent),' which translated to reductions o f 6.8 and 8.5 percentage points for urban and rural areas respectively. 7. A notable feature o f poverty reduction between 2000 and 2005 was a sizeable decline in the incidence o f extreme poverty. The percentage o f population under the lower poverty line, the threshold for extreme poverty, fell by 27 percent (or 9 percentage points) from 34 percent o f the population in 2000 to 25 percent in 2005. A fall o f 27 percent (or 5 percentage Urban 35.2 28.4 19.9 14.6 points) occurred inurbanareas andthat o f Rural I 52.3 43.8 37.9 28.6 25 percent (9 percentage points) in rural areas (see Table 1-1). The percentage decline in extreme poverty rate was thus more than that in the poverty rate, consistent with the growth in per capita consumption for the bottom two deciles being higher than that for the third and fourth deciles (see below). 8. The fall inpoverty headcount rates was large enough to significantly reduce the number o f people inpoverty or extreme poverty. The size o f the population below the upper poverty andthe lower poverty lines declined by nearly 6 million and 8.3 million respectively between 2000 and 2005. However, an estimated 56 million Bangladeshis were still below the (upper) poverty line in2005, 35 million among whom were below the lower or extreme poverty line (see Annex 1, Table A-1.1). The reductions were statistically significant - at 95 percent level o f confidence for national and rural poverty and at 90 percent level for urban poverty (see Annex 1, Figure A-1.1). 2 percent for urban and rural Table 1-2: Depth and severity ofpoverty respectively. These trends suggest Poverty gap Squaredpovertygap significant improvement among those 2000 2005 2000 2005 below the poverty line. A fall in National 12.8 9.0 4.6 2.9 poverty gap indicates that the average Urban 9.0 6.5 3.3 2.1 consumption o f the poor has improved, Rural 13.7 9.8 4.9 3.1 whilea declinein squared gap Source: HIES 2000 and 2005; using poverty lines estimated with HIES 2005) anddeflatedto adjust for inflationduring2000-05 10. While the rural-urban gap inpoverty rate closed slightly between 2000 and 2005, the gap still remains considerable. Rural poverty rate in2005 was 44 percent, compared to the urbanpoverty rate o f 28 percent. Rural areas account for 75 percent o f the total population o f Bangladesh, but are home to 82 percent o f the poor people inthe country. Sensitivity of poverty trends to methods of measurement 11. The reduction inpoverty headcount is robust to a wide range o f choices for poverty lines. Figure 1-1shows the changes in the distribution o f per capita consumption expenditure between 2000 and 2005. The density curves show that the distribution o f per capita expenditures has shifted slightly downward and to the right, consistent with a rise in real consumption levels for the entire population. The cumulative distribution curves show that for a wide range o f poverty lines, the reductioninpoverty rate between 2000 and 2005 i s significant and almost unchanged.2 'igure 1-1: Density and cumulativedistributionof per capita expenditure(2000 and 2005) PL: RuralDhaka PL :ourre HIES (2000 and2005) Note. The poverty rate is givenby the vertical coordinate(Y-axis) ofthe pointwhere the cumulativedistributionfunctions intersect 12. H o w sensitive are the poverty trends to different methods o f adjustment for household composition and size? Per capita expenditures have been used so far to measure welfare at individual level and to estimate poverty rates, a choice o f methodology that ignores the * The cumulative distribution of per capita consumption expenditures drawn separately for urban and rural areas show that the estimated change in urban poverty is more sensitive to the placement of the poverty line than that in rural poverty (see Annex 1, Figure A-1.2). 3 composition o f households (for instance, treats adults and children equally in terms o f consumption needs) and economies o f scale in consumption for larger households (certain types o f consumption items are lumpy and/or shared betweenhousehold members - see also chapter 3). Given the difficulty inquantifying these effects ina way that most analysts andpolicymakers can agree on, they are routinely excluded from poverty measures adopted by most countries including Bangladesh. However, it i s still important to see whether such adjustments for household composition and size make a significant difference to the poverty trends. 13. The decline in poverty i s found to be robust to a range o f adjustments for household composition and scale effects (see Annex 1, section I1for a discussion and results). With the poverty line unchanged from before, poverty estimates after the adjustments are lower than those with unadjusted expenditures. However, for all reasonable adjustments for household composition and scale effects, the decline in poverty between 2000 and 2005 i s significant and comparable to the decline with unadjusted expenditures. 14. Therefore, there is unequivocal evidence to suggest that the substantial poverty reduction between 2000 and 2005 in Bangladesh i s not an artifact o f the choice o f poverty line or welfare measure (per capita expenditures). Rather, the decline was significant enough to be robust to almost any reasonable choice o f poverty line and/or welfare measure. How does the latest period measure up against the previous decade? 15. Longer-term figures show that the gains during2000-2005, far from being an aberration, can be seen as continuing a trend from the decade o f the 1990s. The proportion o f population below the upper poverty line had fallen from 57 percent in 1991-1992 to 49 percent in 2000, while that below the lower poverty line declined from 41 to 34 percent (Figure l-2).3 The years 2000-2005 saw an acceleration o f this trend, particularly innational and rural poverty reduction. While rural and urban areas experienced similar reduction inpoverty rates over the 15 year horizon, the years between 1991-1992 and 1995-1996 saw the largest decline in urban poverty and 2000-2005 the largest decline inrural poverty. igure1-2: Long-termpoverty trends (headcount rates) Upper Poverty Line I Lower Poverty Line I 3 20- r 0) I 0 3 1991/92 1995196 2000 2005 1991/92 1995/96 2000 -*-..Rural+Urban --c-National ..-+-..Rural+Urban+National iurce: HIES (different rounds) ,te: Headcountrates calculated usingthe Upperand Lower Poverty Lines of 2005, adjusted for price changes between years. Also see Annex 1, Table A-1.2 4 Drivers of poverty reduction: growth and distributional changes consumption expenditure. Real per capita consumption expenditure from Table 1-3: Mean real (rural 2005 Dhaka prices) per HIES increased by 12 percent between capita monthlyconsumption 2000 and 2005 - an average annual 2000 2005 Cumu- Average growth rate o f 2.3 percent (Table 1-3). lative annual The increase in percentage terms was change (YO) growth(YO) 1210 11.9 2.3% higher for rural areas than urban areas. National 1082 ~~~~l 1103 985 12.0 2.3% In spite o f that, the percentage reduction o f urban poverty was higher urban~ 1465 1535 4.8 0.9% s ~ HIES~2000 and2005. ~ ~ , because urban inequality declined Note: To obtain real consumption, nominal consumption expenditures are increased deflated by price indices to adjust for inflation over time and by upper povertylines to adjust for regionalprice differences. 17. The estimates o f consumption growth from Figure 1-3: Trends of GDP per capita and HIES are also consistent with Bangladesh's Private Consumptionper capita macroeconomic performance during the 2000- 2005. Annual average growth inreal GDP per capita was 3.3 percent and that o f private consumption per capita was 2.8 percent. The growth in the private consumption component was also higher during 2000-2005 than any 250 - previous period since 1990, which is consistent zoo. with 2000-2005 being the years o f highest m. poverty reduction (Figure 1-3). 18. Growth has also been accompanied by a shift in the relative importance o f different 0 , , , , , , , , , , , , , , , sectors in the Bangladeshi economy. The 3 4 4 f i E W i t services sector now accounts for more than 50 urce: World DevelopmentIndicator2007 percent o f GDP while the share o f agriculture has declined from 25 to 19 percent between 2000 and 2005. Industry accounted for 26 percent o f GDP in 2005 with the Ready Made Garments (RMG) sector being the main source o f manufacturing growth. These shifts are no doubt an important part o f the explanation for the pattern o fpoverty inBangladeshreduction- across urban andrural areas and regions. Chapter 2 o f this report analyzes the contributions o f different sectors increating employment andreducing poverty. 19. HIES data shows that growth inconsumption occurred across the board for the poor andnon- poor alike. Real per capita consumption o f the poorest and richest population deciles grew by 14 percent between 2000 and 2005, and that o f the second-poorest and second-richest deciles by 12 and 11percent respectively. 5 20. The Growth Incidence Curves (GICs, see Figure 1-4) indicate that annual average growth o f per capita consumption during 2000-2005 was highest for the bottom 20 percent and top 10 percent o f the population. A comparison o f the mean of growth rates o f percentiles o f per capita consumption with the growth rate of mean per capita consumption (Figure 1-4) suggests that growth was nearly equitable across consumption groups in percentage terms. Growth was more pro-poor in urban areas than in rural areas (Annex 1, Figure A-1.3). T h i s i s consistent with the mean o f growth rates being slightly higher than the growth rate o f mean consumption for urban areas, while the converse i s true for rural areas (Figure 1-4). 21. The pattern o f consumption growth shown in Figure 1-4 and Figure A-1.3 (Annex 1) also implied large gains among the extreme poor (the bottom three deciles o f the population) as well as among the very poorest (the bottom decile), consistent with the large reduction in extreme poverty rate mentioned above (see Box 1.2). Real per capita expenditures indicate a greater than I Distribution average improvement inthe economic status of the bottom ofper capita expenditures three deciles and the bottom decile between 2000 and 2005. Between 2000 and 2005, average real per capita expenditures of the bottom three deciles and the bottom decile grew at annual average rates of 2.5 and 2.9 percent respectively, compared with 2.4 percent for the whole population. Among urban households, expenditure growth among the bottom three deciles (2.1 percent) was much higher than that for the entire urbanpopulation (1 percent), while among rural households growth was more uniform across expenditure groups. The growth also appears to have been equitably distributed among the extreme poor. The distribution of expenditures among the bottom three deciles shifted rightwards from 2000 to 2005 in a way that suggests growth occurred for the entire distribution of the 22. Consistent with the growth trends, relative inequality as measured by the national Gini index o f per capita real National consumption showed no change Urban 0.3 1 0.37 0.37 0.35 between 2000 and 2005 (Table 1-4). Rural 0.25 0.27 0.27 0.28 The urban gini fell and the rural gini Source: HIES(different rounds) increased over this period, but these Note: 1) Nominal consumption are adjusted for spatialhgional price differences(deflatedby UpperPL) to obtain "real" ginis for each year changes were quite small. 2) Gini index for year t i s half the ratio of mean absolute deviations (MAD)ofpercapitaexp to the meanofthe distributioninyear t. 6 reduction - 1991-1992 to 1995-1996 and 2000 to 2005 (See Figure 1-2) - also saw relatively highannual growth inreal per Table1-5: Growth ofreal per capita expenditure (%) capita consumption (Table 1-5). The (relative) Gini of per 3.84 capita consumption increasedby more than 15 percent between 1991/92- 95/96 1995/96 2000 - 0.48 1991-1992 and 1995-1996,but remained stable thereafter (Table 2.27 1-4). This explains why poverty reduction between 1991-1992 2000 - 2005 Source: HES 1991-92,1995-96, and and 1995-1996 was smaller than what was seen for 2000-2005, HIES2CW 2005. National Rural Urban -8 ; - u I -6.8 ~ -12 -4 O i l , 1 -12 -12 $ 5 $ g 15 = - ~~~ L L $ E .g e L 0 - .E5E 0 ;! q ju: i C s 2 j f ;I 2@ sS 5 i g g 2 % B g [ .E =52 g"g 2 :2 6 5 . @ E .E p E Hm B s e .E Changesinurbaninequality 25. The small decline in urban inequality (around 5 percent reduction in the Gini index) merits some discussion, since it contradicts the conventional wisdom that rapid economic growth and urbanizationi s likely to lead to an increaseinurban inequality. 26. Firstly, it must be stressed that the decline in urban inequality i s marginal and the more relevant policy issue is that urbaninequality in2005 i s still as much as 25 percent higher than that inrural areas (Table 1-4). Secondly, the change in the overall urban inequality measure masks highvariation across urban areas indifferentdivisions, with inequality risingfor urbanareas with highconsumption growth (Sylhet and Chittagongdivisions) and falling where consumption was stagnant (Khulna, Rajshahi, and Dhaka divisions). Disaggregating the urbanareas differently, all the reduction in inequality occurred in metropolitan areas, while inequality remainedunchanged for urban municipalities. The wide variation in trends across different urban areas suggests that local factors unique to specific areas may have played an important role in explaining these 7 trends. Thirdly, the fall in consumption inequality in some o f the urban areas is consistent with the pattern o f income and employment growth in Bangladesh. As chapter 2 will show, income growth was high in the nonfarm sector, which tends to be concentrated in urban areas, and particularly inthe services sector where many o f the urban poor are employed. This would have contributed to reductionininequality insome urbanareas with a large services sector. 27. The urban inequality trend may also be affected by data or sampling issues. For example, if "respondent bias" (a higher incidence o f non-responses among relatively wealthy respondents to a survey -more likely to occur inurban areas) had increased between 2000 and 2005, the estimated change ininequality would not be accurate. However, there i s no evidence that `respondent bias' has changed over these years. A more serious concern is that due to the updating o f the urban sampling frame between 2000 and 2005, a few areas classified as "rural" in 2000 are "urban" in 2005, which can yield misleading results when consumption in urban and rural areas are compared over time. While such data problems cannot be ruled out completely, the fact that the urban Gini remained stable from 1995-1996 to 2000 as well seems to suggest that average urban inequality has stabilized in Bangladesh for quite some time and the period o f 2000-2005 hasjust seen a continuation o f that trend. Relative versus absolute inequality 28. Notably, the measure o f inequality (Gini index o f per capita consumption) used so far i s relative, implyingthat it remains unchanged if inequality relative to the mean of the distribution does not change. In contrast, absolute inequality measures the size of the gap in consumption between different group^.^ Each measure has its pros and cons: while the absolute index i s a more intuitive concept, the relative index i s more meaningful for comparisons over time especially when the average levels o f consumption can change significantly. In contrast to the relative Gini indices in Table 1-4, the absolute Gini index o f per capita real consumption increased by 13 percent and 15 percent for the national population and rural population respectively between 2000 and 2005, while remaining stable for the urbanpopulation (see Annex 1, Table A-1.4). Figure 1-6: Relative and absolute inequality - ratio of selected percentiles and gaps between percentiles of per capita real expenditures Ratioof percentiles of real per capita exp:2000 and Absolute differences between percentiles of real 2005 per c a p b exp: 2000 and 2005 4 , I 1.600 , pW/pIO pW/p50 p50/p10 p75/p25 p75/p50 p50/p25 p90-plO ~ 9 0 . ~ 5 0p5opIO p75p25 p75p50 p50-p25 Wtios of pctiles of real per capitaexp Gaps between pctiles of real per capita exp IJ2000 rn 2005 Source: HIES (2000,2005) Note; pNrefers to the percentile o f per capital real consumption expenditure. The difference between relative and absolute Gini indices i s illustrated by one example: if everyone's per capita expenditure increased by the same proportion, relative Gini would remain unchanged while the absolute Gini would increase (since the gaps would increase, given an initial distribution that i s unequal). 8 29. The distinction between changes in relative and absolute inequality i s also seen from Figure 1-6. The ratios between different percentiles o f per capita real consumption have remained almost unchanged from 2000 to 2005, which i s consistent with stable relative Gini indices. But the absolute sizes o f the differences between higher and lower percentiles have increased from 2000 to 2005, consistent with the increase in the absolute Gini index. The gap in consumption between the upper and lower ends o f the distribution i s higher for the urban than for the rural population (see Annex 1, Figure A-1.4). This gap has widened for the rural population - consistent with the trends o f absolute Giniindices. Growth and poverty reduction - a cross-country comparison 30. Cross-country comparisons o f poverty incidence can only be indicative - every country uses a different nationalpoverty line reflecting its own consumptionpattern and national consensus on measurement method and calorie threshold, and countries do not conduct their household surveys duringthe same years. Evenwith these caveats, cross-country comparisons on the rate o fpoverty reduction can be instructive.' The average annual rate o fpoverty reduction inBangladesh during 2000-2005 was second only to that for India - among all South Asian countries for which data is available over (roughly) comparable periods (Figure 1-7). 31. GDP growth also appears to have had a r Figure 1-7: Annual average growthin GDP and - larger impact on poverty-in Bangladesh than reductioninpovertyheadcount in other South Asian countries with the 5.0, it exception o f Nepal, as seen from the ratio o f 4.0 the height o f the dark bar to the light bar for 3.0 each country in Figure 1-7 - a rough measure -P o f responsiveness o f poverty to GDP growth 2.0 (see Section I11 for more precise elasticity 1.o estimates for Bangladesh). Stable relative 0.0 inequality explains why Bangladesh during 2000-05 reduced poverty at a rate close to India's and higher than all other countries in the region, even though the annual average 0 Annualgrowthof percapitagdp mAnnualrateof povertyreduction GDP growth in Bangladesh has been lower Source: See Annex 1, Table A-1.5 than that for India and Sri Lanka.6 Note: 1) Poverty lines are defined differently across countries; so - . . . I poverty.headcount ratios are not comparable across countries. 2)-Annual rates o f reduction in poverty are negative of the % 32. l+Or -- - most South Asian countries, I values shownbythe dark bars. . . . . . acceleration o f growth since early 1990s has been accompanied by increasing inequality; whereas for Bangladesh, inequality has stabilized since the mid-1990s (see Table 1-4 above). The precise reasons for this difference are difficult to identify without an in-depth analysis covering all countries that i s outside the purview o f this report. The growth process in Bangladesh, however, offers some clues. The main stimulus to economic growth inBangladesh since the late 1980s has been labor-intensive manufacturing, particularly RMG, micro- and small-scale nonfarm enterprises, and remittances from migrant workers - all o f which typically provide economic opportunities for the poor and continued to drive poverty reduction during 2000-2005 (see chapters 2 and 3). Nevertheless, inequality increased till the mid-1990s because the sectors The inconsistencies are especially problematic when comparing the poverty level of one country with that of another. The problems are less severe incomparing the extent ofpoverty reduction across countries (over somewhat comparable periods), since that involves measuring the change ineach country with the poverty line held constant inreal terms. Bangladesh also outperforms African countries with a similar level of GDP per capita, such as Kenya, Mauritania and Burkina Faso, in poverty reduction (see Annex 1, Table A-1.5). This i s because Bangladesh has a higher rate of per capita GDP growth than all three countries, as well as a higher ratio of poverty reductionto growth (elasticity) thantwo ofthe countries. 9 experiencing higher growth had more unequal incomes to start with (relative to the agricultural sector) and because growth was not strong enough to raise wages in the informal labor markets. Since 2000, labor productivity and wages in all sectors have increased (see chapter 2), which has helpedstabilize consumption inequality and accelerate poverty reduction. 33. The pace o f poverty reduction in Bangladesh is, however, much lower than that for fast- growing East Asian countries, like China, Thailand and Vietnam (Annex 1, Table A-1.5). While all three countries experiencedhigher per capita GDP growth rates than Bangladesh, two o f these countries also have comparable elasticity o f poverty to growth. This suggests that if Bangladesh i s able to attain GDP growth comparable to East Asian levels, it may match the pace o f poverty reduction seen in these countries. Vietnam i s a telling example; while both Bangladesh and Vietnam had similar poverty rates o f nearly 58 percent around 1990, the poverty rate o f Bangladesh was twice that o f Vietnam in 2005. Vietnam's annual GDP growth was on average 2.5 percentage points higher thanBangladesh's inthis period - while Bangladesh's real per capita income increased by nearly 50 percent, Vietnam's quadrupled from 1990 to 2005. 34. In concludingthis section, it is usefulto recap the main findings. Bangladesh experienced substantial poverty reduction during the last 15 years (between 1991-1992 and ZOOS), with the pace accelerating during 2000-2005. Rapid growth in consumption has been the primary contributing factor; growth has occurred at similar rates for the poor and non-poor alike, w h c h i s consistent with relative inequality remaining almost unchanged for the country as a whole. At the same time, given the large disparity in the initial (2000) distribution o f consumption, similar growth rates for all consumption groups necessarily imply an increase in the average size of the gap in consumption between the poor and the non-poor. Absolute inequality or the size o f the gap in consumption between different groups has expanded for the country as a whole. For rural areas, consumption growth has been the dominant force in reducing poverty; whereas in urban areas, a marginal reductionininequalityhas also had a sizeable poverty reducingimpact. 35. Poverty reduction in Bangladesh in 2000-2005 compares well with other South Asian countries in recent years, with an annual average rate o f reduction second to only that for India. Bangladesh could achieve this in part due to strong growth, and in part due to no appreciable increase in inequality, with the result that GDP growth had a higher impact on poverty in Bangladesh than for all countries in the region with the exception o f Nepal. However, as Asia becomes more regionally integrated, it i s natural for Bangladesh to "look East." Comparisons with Vietnam, China, and Thailand underscore the importance of higher growth to make even further reductions inpoverty. IL Projecting recent trends in growth, inequality, andpoverty into thefuture 36. The emerging patterns o f growth and inequality changes are encouraging for poverty reduction in Bangladesh. An interesting question in this context i s whether, and under what conditions, can Bangladesh achieve the Millennium Development Goal (MDG) o f halving the proportiono fpeople living inextreme poverty from the 1990 levelby the year 2015? 37. A simple way to estimate future poverty trends i s by applying a growth elasticity o f poverty, i.e. the percentage reduction inpoverty obtained with a one percent growth inconsumption, along with different scenarios for GDP growth. Different methodologies for estimating growth elasticity can yield different poverty projections for the same growth scenario. Three commonly used methodologies are employed on data for earlier years to yield poverty projections for 2005, which are then compared with the actual poverty rate o f 2005 to select the most appropriate method for estimating elasticity. Projections for poverty trends are then produced using one 10 selected method (as developed by Datt and Ravallion, 1992), for different scenarios o f future GDP growth (Box 1.3; also see Annex 1, Section 11). - - those usingthe RegressionandBourguignonmethodsare -1.62 and-1.79 respectively. Projectedpoverty trends 38. Three alternate growth scenarios are considered: real GDP growth rates o f 4.5, 5.3 and 7.5 percent per annum. The annual growth rate o f 5.3 percent i s the baseline scenario, given that this was the annual average growth o f real GDP between 2000 and 2005. Since the net elasticity o f poverty was estimated with respect to growth in per capita expenditure from HIES, the GDP growth rates have to be converted into growth inper capita consumption.' 39. If GDP were to grow at the current rate (5.3 percent annually) between 2005 and 2015, the incidence o f poverty (with respect to upper poverty lines) would decline to 27 percent by 2015, which means Bangladesh will meet the MDG of halvingpoverty rates between 1990and 2015. If the country were to grow at only 4.5 percent per annum, poverty reduction would likely not meet the MDGtarget. Conversely, if the country were to instead grow at 7.5 percent per annum over this period, the incidence o f poverty would decline to 22 percent by 2015, well below the MDG target. Using poverty estimates based on the lower poverty lines, the incidence o f extreme poverty inBangladesh would decline to 15 percent in2015 under the 4.5 percent growth scenario, and to 9 percent under the 7.5 percent growth scenario. Thus for both the high-case and low-case growth scenarios, Bangladesh would be well on track to halve extreme poverty by 2015 from the 1990 level (see Annex 1, Figure A-1.5 for all projections). Caveats to the projections 40. History may be an imperfect guide. Firstly, the above projections are extrapolated from historical data - an imperfect guide for the future. Actual poverty reduction for any growth rate can be quite different from what was experienced historically if the distributional impact o f growth turns out to be different from what was seen in recent years. Therefore, the projections here must not be interpreted as definitive future trends, but rather as showing the future path o f poverty reduction ifthe association between growth and inequality duringthe last decade were to hold for the future. But, for example, ifinequality were to increase faster than what it had during Between 2000 and 2005, the annual growth rate of per capita household consumption expenditure was 2.3 percent in comparison to the GDP growth rate of 5.3 percent. For other scenarios, the same conversion rate is applied: 4.5 percent and 7.5 percent of real GDP growth rates are converted to 1.9 percent and 3.2 percent of per capita household consumption expenditure. 11 the last decade, poverty reduction would be lower than what i s projected here for the same GDP growth rate. 41. Shocks may affect growth and responsiveness of poverty to growth. Secondly, these projections do not take into account the poverty impact o f shocks that may occur ina certain year. As mentioned earlier, the year 2007 has seentwo natural disasters - serious floods and a cyclone -estimated to reduce GDP growth by at least 1percentage point from pre-shock projections. The elasticity o f poverty reduction to growth calculated here suggests that even a one-percentage point loss in GDP growth would lower the extent o f reduction inpoverty headcount in 2007 by 0.7 percentage points (or 14 percent).' Eventhese estimates will be inaccurate ifthe shocks have led to distributionalchanges different from what was seen duringthe years prior to 2005. 42. The recent international commodity price increases, mainly for food and fuel, would have added to the adverse impact on poverty due to the larger share o f food inthe consumption basket o f the poor. For instance, the nearly 40 percent increase in the price o f rice between April 2007 andMarch 2008, accompanied by a 5 percent nominal wage increase for all workers, would have led to a 3 percent average loss inreal income for households and raised poverty rate by around 3 percentage points from the baseline poverty rate o f 2005 (see chapter 6 for more details). Given that GDP grew at around 6 percent annually during 2005-2008, the poverty rate would have been expected to decline by around 5 percentage points between 2005 and 2008 (using the elasticity o f poverty reduction to growth estimated earlier) as a normal response to GDP growth. Instead, with the impact o f the food price shock factored in, the net decline inpoverty rate between 2005 and2008 would have beenroughly 2 percentage points (from 40 to 38 percent). This impliesthat some of the reduction in poverty occurring as a result of strong and stable GDP growth since 2005 has been negated by thefoodprice shock. The frequency o f such shocks, how long they last (especially relevant for commodity price rises), and how rapidly the economy bounces back from them would influence the future pace o f poverty reduction and therefore the rate o f progress towards the MDGtarget. 43. Continued decline infertility will be crucial to meet theseprojections. An important dnver o f poverty reductionbetween 2000 and 2005 was a sizable reduction inhousehold size. Average household size fell from 5.2 in 2000 to 4.9 in 2000, mainly a reflection o f reduction in fertility rate (see chapter 3). The population growth rate fell from 2.9 percent annually inthe 1970s to 1.5 percent by the late 1990s. Estimates suggest that ifhousehold size had not changedbetween 2000 and 2005, the reduction inpoverty would have been about halfof the actual reduction (see section 111, Annex 1). The projections so far, being extrapolated from the poverty trends from 2000 to 2005, implicitly assume that the reduction inhousehold size would continue till 2015. However, it is still usefulto examine what would happenifthis assumption were not to holdtrue. 44. Ifhousehold size i s assumed to not decline between 2005 and 2015, the simulations suggest that poverty reduction during this period would be around 7-10 percentage points less than the projections presented earlier (see section 111, Annex 1). For example, if GDP were to grow at an annual average rate o f 5.3 percent, poverty rate i s estimated to fall to 34 percent in 2015 if household size remains unchanged from 2005, compared to 27 percent in the earlier projections. Even if GDP were to grow at 7.5 percent annually, the MDG target o f halving poverty between 1990 and 2015 would not be met ifhousehold size were to stop declining from 2005 onwards. The primary reason for the higher poverty projections is a much lower impact o f GDP growth on the per capita consumption expenditure. No change in household size essentially implies no * This assumes a pre-shock GDP growthof 7 percentfor 2007, which would bereducedto 6 percentdue to the natural disasters, which would inturnreducethe rateofreductioninpovertyheadcountrate from 4.6 to 3.9 percent for 2007. 12 reduction in population growth, which reduces the growth rate o f per capita expenditure for any given GDP growth, resultinginpoverty reductionbeing much less responsive to GDP growth.' 45. The assumption o f no decline in household size from 2005 i s no doubt unrealistic, given Bangladesh's history o f sustained decline infertility - total fertility rate fell from 7 in 1975 to 3.2 in 1999-2000. This has reduced not only household size, but also the dependency ratio within households (see chapter 3). Rather, this sensitivity analysis i s most useful in highlighting how crucial it is for the country to continue along the path o f fertility reduction in order to achieve a sustained andsizeable reductioninpoverty. III.Thechangingpatternof povertyacrossregions 46. While poverty reduction has occurred for both rural and urban areas, the reduction has been highly uneven across regions. The largest decline inpoverty incidence occurred for the Dhaka division, followed by Chittagong and Sylhet. Incontrast, poverty headcount stagnated inBarisal and increased slightly for Khulna. As a result o f this unequal pattern o f poverty reduction, regional differences were quite sharp in 2005. The poverty headcount rate ranged from a low o f 32 percent in Dhaka and 34 percent in Chittagong and Sylhet to over 50 percent in Barisal and Rajshahi (Figure 1-8). Dhaka and Chittagong divisions, with just over half the country's population in 2000, contributed 79 percent o f the reduction in national poverty headcount between 2000 and2005. 47. Decomposition exercises also reveal that Figure1-8: Povertyheadcounttrends for divisions I within-division effects explain all o f the aggregate poverty reduction and population shifts between divisions or the interaction between the two effects play negligible roles. In contrast, when poverty change i s decomposed by urbadrural, population shift from rural to urban areas accounts for as much as 9 percent o f the reduction in national poverty rate (see Annex 1, Table A- 1.6)." The large effect o f population shift L > arises from a nearly 23 percent increase in W 2000 0 2005 urban share o f the population from 2000 to Source: HIES (2000,2005) I 2005 (Annex 1, Figure A-1.6). This continues a historical trend- the urban population share increased by 22 percent from 1995-1996 to 2000 and by 15 percent from 1991-1992 to 1995- 1996. The results also suggest that the rural-urban population shift occurred more within each division rather thanacross divisions. Changes in regional and urban-rural gaps 48. There i s evidence that average differences between divisions, but not between urban and rural areas, increased from 2000 to 2005. Cumulative growth in average per capita real expenditure was 12 percent for rural areas and 5 percent for urban areas (Table 1-3). But the highest growth inmeanandmedianper capita expenditures occurred for Dhaka and Sylhet divisions, which also Interestingly, when household size is held constant (between 2000 and 2005), the elasticity o f poverty to per capita consumption growth actually increases slightly in absolute value, indicating that the reduction inhousehold size had a small negative impact onpoverty by increasing inequality (see section 111, Annex 1). loThese are lower than each sector's share intotal population in 2000 (80 and 20 percent respectively) - a phenomenon that i s explained by the effect o f the rural-urban population shift on poverty. 13 had the highest expenditure levels in2000. Rajshahi and Barisal, which had the lowest per capita expenditure levels in2000, experienced far lower growth (see Annex 1, Figure A-1.8). 49. Thus the urban-rural gap in average per capita consumption expenditure appears to have shrunk while inequality between divisions has increased between 2000 and 2005. Decompositions o f an inequality index (Theil inequality o f per capita consumption) confirm this trend: the share o f between-division inequality in total inequality increased from 2.4 to 4.5 percent, while that o f within-division inequality fell slightly. In contrast, the share o f the between-group component intotal inequality fell when the groups are defined as urbdrural (see Annex 1, FigureA-1.8) 50. Growth and inequality changes played varying roles across divisions in explaining the poverty trends shown in Figure 1-8. The largest poverty reduction (of 31 percent) in Dhaka division occurred mainly due to high consumption growth. Poverty reduction in Chittagong and Sylhet (of 26 and 20 percent respectively) was driven by consumption growth, while increases in inequality dampened some o f that impact. A modest reduction in poverty (of 10 percent) in Rajshahi was driven by marginal growth in consumption, with little or no change in inequality within. BarisalandKhulna saw no reductioninpoverty on account of anemic growth along with increasing inequalitywithin each division.l1 Urbanand rural trends within divisions 51. Since the HIES data i s not strictly representative for areas smaller thandivisions, these results are subject to large standard errors and must be interpreted with caution. At the same time, the sample sizes are large enough for some levels o f disaggregation for useful comparisons over time andacross space-thus providing some insightsinto the patternso fchanges within divisions. 52. Consumption growth varies widely within rural and urban areas across divisions (Figure 1- Figure 1-9: Growth inrural and urban per capita consumption (2000-2005) 9). Real per capita consumption growth during 2000-2005 ranged from 3-4 percent for rural 65% - Barisal, Khulna, and Chittagong to 11percent for Rajshahi and Sylhet and 27 percent for rural 45% - Dhaka. Urban consumption growth rates range 25% - - from -12 percent inurban Khulna to 62 percent in urban Sylhet. While Chittagong and Sylhet urban 5% - I' n r ,L I' areas registered real per capita consumption 15% growth o f more than 35 percent, other urban areas experienced either no growth (urban Dhaka) or negative growth (urban Barisal, Khulna, and Rajshahi). Poverty reduction in Dhaka and Rajshahi divisions (relatively modest for Source: HIES (2000,2005) Rajshahi) occurred mainly due to rural consumption growth; whereas urban growth was the primary factor behindpoverty reduction inChittagong and Sylhet divisions. 53. The lack o f consumption growth inurban areas o f Dhaka division merits closer examination, especially inthe light o f evidence inchapter 4 that suggests increasing concentration of economic "While the contributionof within-division inequalityto totalinequalitydeclined slightly, inequalitywithin the division actuallyincreasedfor all divisions other than Dhakabetween2000 and 2005 (see Annex 1, Table A-1.7). Consistent with this, the increase in mean expenditureswas higher than that in median expendituresfor all divisions other than Dhaka(Annex 1, FigureA-1.8). 14 activities in areas surrounding Dhaka city. More disaggregated results provide some clues to what might be happening. Per capita consumption growth was strong for the urbanmunicipalities and rural areas o f Dhaka division, but nearly zero for the Dhaka metropolitan area between 2000 and 2005 (see Annex 1, Table A-1.8). This is in turn consistent with a trend o f economic activities andjob creation spreading outwards from the core city to the outlying areas, most likely due to increasing agglomeration costs inthe city (see chapter 4). At the same time, increasing integration o f the surrounding areas including satellite towns and rural areas with the city has brought benefits to those areas. All these factors taken together seem to explain why rapid growth and poverty reduction in the rural areas and urban municipalities o f Dhaka division co- exist with stagnation inthe Dhakametropolitanarea. 54. Summarizingthe results from this section, certain regional trends appear to be emerging. On the average, differences between divisions (geographic regions), rather than between urban and rural areas, appear to have increased from 2000 to 2005. Inequality between divisions also increased proportionately more than that within divisions, suggesting no evidence for convergence among regions for the country as a whole in consumptionlevel and poverty. While there was little change inpopulation shares among divisions during2000-2005, a relatively large rural-to-urban shift inpopulation share accounted for as much as 9 percent o f the total change in poverty between 2000 and2005. 55. A discernible pattern seems to have emerged in the rising inequality between divisions - the eastern parts o f the country have far outpaced the areas to the West and Southwest in terms o f poverty reduction, a trend that merits closer examination. The largest decline inpoverty occurred for Dhaka division, followed in descending order o f magnitude by Chittagong, Sylhet and Rajshahi, while Barisal and Khulna had no reduction. Poverty reduction was dnven primarilyby growth - while rural consumption growth was the primary driver in Dhaka and Rajshahi, urban consumption growth was the dominant factor inChittagongand Sylhet divisions. I K Conclusion: a roadmapfor the rest of the report 56. Rapid poverty reduction in Bangladesh during 2000-2005, which occurred inboth urban and rural areas, was a result o f strong growth in consumption, which occurred at similar rates for the poor and non-poor, resulting in an elasticity o f poverty reduction to growth that was higher than most South Asian countries. If the GDP growth rate seen during 2000-2005 i s maintained (or bettered), and if the trend in inequality is similar to what was seen in during the last decade, Bangladesh would attain the MDGtarget o f halving its poverty and extreme poverty rate from the 1990 level by 2015. This prediction also hinges on whether the country i s able to sustain its past successes inreducing fertility and consumption growth. Understanding the links between growth and poverty reduction would require a thorough analysis o f the labor market - in terms o f the trends and patterns inemployment, wages, productivity, and income shares o f key sectors o f the economy, which will help identify future opportunities and address constraints to poverty reduction. This is the subject o f chapter 2 o f this report. 57. Notwithstanding the progress achieved, the country continues to face significant challenges. While relative inequality has not worsened, similar rates o f consumption growth for upper and lower ends o f the distribution imply that the size o f the gaps between the rich and the poor has widened. The poor in Bangladesh, particularly the 25 percent o f the population below the lower poverty line, still consume at very low levels. Identifyingthe factors - individual, household, and community- or area-specific - that limit the opportunities o f the poor would be critical to inform policies to alleviate poverty, which i s the subject o f chapter 3. A related question would be how 15 endowments or characteristics o f households, and the economic returns from these, have evolved to explainthe rapidpoverty reductioninrecent years. 58. The regionally disaggregated analysis in this chapter hints that the gap in poverty incidence between Dhaka and the rest o f the country that persisted through the 1990s has evolved into a regional (East-West) divide by 2005. How robust this trend i s to more disaggregated analysis, andwhat factors are responsible for certain regions to lagbehindthe rest o fthe country, are some of the important questions addressed in chapter 4. Related to this i s the question o f how the emerging regionaltrends inpoverty are related to increasing integrationandeconomic dynamism incertain parts o f the country; and ifthis were true, why parts o f the country are excluded from this process. 59. Another important question inthis context - addressed in chapter 5 - i s whether the regional patterns inconsumption growth andpoverty are also mirrored by other indicators o f welfare, such as health and education outcomes. Chapter 5 will analyze in-depth how human development indicators have evolved in recent years, how they relate to household level and regional patterns o f consumption poverty, and what these trends and patterns would imply for the future economic prospects o f the poor. The high vulnerability o f the country to shocks - including household- specific events, frequent natural disasters and economic shocks like the recent food price increases - makes safety net programs all the more critical for sustaining the pace o f poverty reduction. Chapter 6 analyzes the nature andpattern o f shocks andthe vulnerabilities they create, and concludes by examining the adequacy and effectiveness o f public safety net programs relative to the needs o f the poor andvulnerable. 16 2. Creating Jobs Linking Growth and Poverty Reduction - 1. Chapter 1 provided an overview o f the main trends in poverty and inequality in Bangladesh. This chapter begins the search for explanations o f these trends with a closer look at changes in labor market conditions. A number o f specific questions are explored here. H o w did economic growth in Bangladesh translate into broad-based poverty reduction, in the light o f the results o f chapter l? To what extent do changes in labor market participation, employment and returns to labor and human capital explain the time trends inpoverty? Which sectors contributed the most to income growth and poverty reduction, and did poverty reduction take place within sectors or as a result o f employment shifts across sectors? Finally, how do these trends vary by gender or geography, in the context o f the large regional gaps in poverty reduction noted in chapter l? 2. Addressing the sorts o f questions that are listed above requires combining micro andmacro data from different sources, which can potentially be problematic. There i s no single consistent data source that contains all the necessary information for analyzing the links among growth, employment, productivity, and poverty. Information on economic growth is derived from the System o f National Accounts (SNA), employment and labor income from the HIES, and estimates o f poverty from the consumption data in HIES. For the analysis to tell a consistent story, it i s therefore imperative that data from different sources are comparable and compatible, income and consumption aggregates from the same survey are consistent, andhousehold surveys are comparable over time. The data sets for Bangladesh are not perfectly compatible, which is to be expected. However, on balance, they provide a relatively good basis for analysis (see Annex 2, section I).* I. Economicgrowth and the labor market in Bangladesh 3. For the past decade, economic growth in Bangladesh has been robust and stable. Respectable growth performance that began in the early 1990s with the introduction o f political andeconomic reforms3 continued into the new millennium,with recordhighGDP growth rates o f above 5 percent per year between 2000 and 2005 and beyond (Figure 2-1). This has meant an average increase o f above three percent annually inper capita income. Inaddition, the volatility o f GDP changes inBangladesh over past decades has been one o f the lowest inthe world. Income stability i s beneficialto the poor, who usually have fewer means o f coping with shocks. Nature of GDP growth in Bangladesh 4. A notable feature of economic growth inBangladesh has been its broad-basednature. This means that all sectors - agriculture, industry, and services - are expanding and contributing to overall growth. However, over the longer term several trends are evident in terms o f the relative performance o f sectors (Figure 2-1). The share o f the agricultural sector is gradually shrinking, while that o f industry has been consistently in~reasing.~The latter has occurred due to the growth o f the manufacturing sector in particular - led by the expansion o f exports o f the Ready Made 'The chapter is based on the report "Bangladesh: the Role of Employmentand Earnings in Shared Growth. A World Bank Labor Market Study," World Bank (2008a). This report inturn draws from anumber of World Bank reports on Bangladesh-includingWorld Bank 2003a, 2003b, 2005a and2007a. Also see chapter 1, World Bank (2008a). Refoms included political democratization, prudent macroeconomic management, trade openness and integration with the world economy, as well as deregulationand greatermarket orientation. Term "industry" is usedinthis chapterto denotemining,manufacturing, utility services,and construction. 17 Garments (RMG) sector (currently 17 percent o f GDP) - and construction (currently 8 percent o f GDP). The services sector has grownto account for a little more than halfo f GDP. Source:BBS I 5. Onthe expenditure side, external demand (export, particularly o f Ready Made Garments or RMG) has been instrumental inthe expansion o f industrial production. A simple decomposition reveals it contributed about one quarter o f incremental GDP growth over the past decade, which leaves three quarters o f the GDP growth to be explained by domestic demand directed towards domestically produced goods and service^.^ 6. A growth accounting exercise indicates that economic expansion has been drivenprimarily by factor accumulation; growth rates o f capital stock and effective labor alike have accelerated over the past 15 years (World Bank, 2007a).6 Rising growth in capital stock has contributed to the increase in labor productivity and wages discussed later in this chapter. Notably, investment by the private sector is largely responsible for the economy increasing its productive capacity. With public investment levels unchanged at about 6.5 percent o f GDP, private investment, facilitated by a stable and more market-friendly environment, surged from 10 percent in the beginning o f the 1990s to over 18 percent o f GDP currently. This has been financed entirely from increased domestic savings, while rising foreign export earning from RMGhas made it possible for domestic firms to bringinmore imported capital goods. The potential o f foreign savings (e.g., inthe form ofFDI)remains underutili~ed.~ II. Overview of the labor market 7. Overall, according to surveys, the labor market dimension o f the business environment in Bangladesh compares favorably with that o f its neighbors. Private sector firms do not report particular problems with the hiringand firing of workers and their staffing level i s believed to be close to optimal (World Bank, 2003b). The relatively flexible labor market has facilitated the economic transition. 8. However, other dimensions o f the investment climate, namely infrastructure bottlenecks and deficiencies in rule o f law, act as constraints to business expansion and employment growth according to a recent investment climate survey (World Bank, 2008b). For example, the demand This simple decomposition ignores any potential multipliereffects. Growth o f capital stock rose from an average rate o f 4 percent peryear during the 1980s to 6.6 percent during the past 15 years. Growth o f effective labor also increased by almost 1 percentagepoint over the same period. The contribution of total factor productivity, which measures the efficiency with which capital and labor are used to produce output, to growth has been small. 'WithFDI at 1.3 percent o f GDP, Bangladesh ranks 92"d among 134non-OECD countries for which such data exists. 18 for electricity has far outstripped the supply and the lack o f dependable sources has compelled many firms to incur the costs o f own generators or power outages. Despite progress over the past year, inefficiencies at Chittagong port have significantly increasedthe costs o f foreign trade, thus limitingfirms' growthpotential. Law and order problemshave beenreportedby entrepreneurs to be important areas o f concern ina number o f studies (see World Bank, 2003a and2003b). 9. Labor force participation rates remained relatively steady during the 2000s; just about half o f the working age population was in the labor force in 2000 and 2005, which i s low by world standards. Labor force participationrate was only about 10percent for women comparedto more than 80 percent for men. The estimate for women must however be treated with caution since female labor force participation rates from the last two rounds o f the Labor Force Survey was more than twice the rate reported here from HIES data.8 Notwithstanding the discrepancy between surveys, women's participation in the Bangladeshi labor market i s low by international standards - likely due to a combination o f factors ranging from tradition to the low bargaining power o f women within households and society and the physically demanding nature o f work, particularly for daily wage activities like construction andharvesting(see World Bank, 2008d). Unemployment and underemployment 10. During the 2000s, economic Table 2-1: Overview oflabormarket, 2000-2005 growth in Bangladesh was accompanied by the creation of about 5.6 million new Annualized jobs. With a growing working age 2000 2005 real growth" population, this was however just Population' (mn), o/w 128.9 141.8 1.9% enough to maintain the overall % urban 20% 25% 6.3% Working age population' (mn) 72.6 83.6 2.9% employment rate at an unchanged level. % of total 56% 59% Unemployment rates are similar in Employment3(mn) 37.5 43.1 2.8% Bangladesh to other low-income employment rate," % 52% 52% countries and countries in South Asia. Unemployment rate,' % 6.9% 1.5% Unemployment tends to be concentrated Underemployment rate6 9% 9% among the younger age groups, the more Hours worked7, per week 48.4 47.2 -1.2(*) educated, and women. However, the Meanearnings per worker7," _ _ 2,553 3,364 0.9% unemployment rate does not provide a Medianearnings per w~rker'~'' 1,739 2,223 0.3% meaningful measure o f labor market Medianhourly rate," Tkhour 8.8 11.3 0% slack - first because it is highly sensitive Labor income (%o f total income) 74% 74% 3.7% to how labor market participation i s Literacy rate,'" YO 47% 55% Years o f education9 3.7 4.4 +0.7(*) measured; and second, in the absence o f Poverty rate 49% 40% -9%p(*) a comprehensive social security system Notes: (*) Absolute change over five year period; (1) from WDI database; only the better-off can afford not to (2) aged 15-64; (3) ineconomic activity duringpastyear; (4) within 15-64 work, while the rest needs to survive by year category; (5) not reliable, ILO definition, basedon 7-days reporting working in low intensity, low paid daily period; (6) % of employed working 20 hours per week or less (yearly equivalent per year); (7) in all activities; (8) Able to read and write; (9) wage jobs, or self-employment. Average for working age population. Illiterate individuals have been assigned zero years of schooling; (IO) Annualized real growth derived 11. from absolute levels, (11) Tk per month; (12) in main activity Underemployment i s more Sources: Basedon HIES2000,2005; World DevelopmentIndicators. common than unemployment, and a better indicator o fthe stalk o fthe labor market. On average, throughout the year, about 9 percent o fthe employedwork less than20 hours a week (Table 2-1). Underemploymenti s more prevalent The female labor force participation rate from Labor Force Surveys (LFS) was reported as 26 percent in 2002103 and 24 percent in 1999/2000 (World Bank, 2008d). The LFS-HIES difference may be partly due to the fact that HIES may not account fully for female unpaid work incrop and non-crop production, cottage industries, small trade, and farming. Almost half o f the women counted as economically active inLFS are unpaid family workers. 19 among women and in rural areas, primarily due to seasonality. On the whole, the people o f Bangladesh seem to work long hours -particularly men and urbanites - with an average o f more than47hoursa week. Occupation types and characteristics 12. Labor income constitutes about three quarters o f the total income, which i s within a range typically found in other countries, and its sources are about equally divided between wage employment and self-employment (Table 2-1, also refer to Annex 2, Table A-2.1 for paragraphs 12-15). There are four distinct types o fjobs that people are engaged in, namely: (i) daily labor or daily wage labor, (ii) salaried jobs, (iii) non-agriculture self-employment, and (iv) farming or "self-employment inagriculture." 13. Daily wage labor, which accounts for about a third o f all workers, consists o f daily wage employment in agriculture (recruited mostly from the rural landless) and outside o f agriculture. Usual activities include harvesting, construction work, rickshaw-pulling and so on. The variation inwages is minimal, the rewards to educationare almost non-existent, andvery few workers have any education. Consequently, their income is very low andpoverty rates are highest. 14. Salariedjobs - about one-fifth o f workers - consist o f two distinct groups: those working for the government (or community organizations) and the rest. The former (about 8 percent o f all workers) are characterized by relatively high education and very high relative earnings and wages. The latter group, namely private sector salaried employees, earns hourly wages that are not much higher than those o f the daily workers. But their hours are more regular and long (over 57 hours per week on average), perhaps to make up for the low wages. 15. The self-employed outside o f agriculture - about 20 percent o f all employed - consist o f individual own-account workers, employers, and those engaged in family enterprises (and employing no outside employees). Among them, employers stand out as a group with much higher earnings, long working hours, and low poverty rates. Income o f the self-employed compares favorably with income fi-om salariedjobs inthe private sector. 16. Finally, the self-employed in agriculture account for about a quarter o f all workers. Land tenancy arrangements are widespread: about 45 percent o f the crop-producing households rent-in some o f the land that they cultivate, and about 20 percent rent-inall o f it. Median earnings for farmers are lower than that o f most occupation groups and close to that for agricultural daily wage labor. Subsistence farmers have the lowest earnings among all occupation groups by far. III. Laborforcesize,compositionandeducationstatus 17. A growing population continues to put pressure on the labor market and the demographic transition i s one o f the key forces shaping the economic and labor market landscape. The population has been growing rapidly for decades, at about 2.5 percent per year.' Although this growth has recently moderated to about 2 percent per year, between 2000 and 2005 about 13 million people have been added to the total population (Figure 2-2). Despite falling total fertility rates, increasing life expectancy and lower child mortality have supported a robust population growth. 20 Demographic challenges for the labor market 18. The demographc trends in Bangladesh create opportunities as well as challenges. The working age populationhas been expanding even more rapidly than the total population, growing at rates o f 2.5-2.8 percent into the ~ O O O S . ' ~While this can be an asset for income generation and growth, it poses a major challenge for the labor market to absorb a large wave of new entrants every year. The "bulge" among the 5-14 age group in the 2005 population (Figure 2-2, upper panel) indicates that the working age population will expand rapidly over the next decade. The UNpopulationprojections show that the annual growthrate o f the working age population will remain over 2 percent till 2015 even as annual population growth rate slows down to around 1.5 percent (Figure 2-2, lower panel). This will add an estimated 22 million to the working age populationbetween 2005 and 2015. 19. In such an environment migration can be an important employment option, helping to ease the labor Figure 2-2: DemographicSituationin market pressures caused by demographics. Government Bangladesh statistics on documented migration estimate that 3.7 95-99 90-94 -- -- 1 R L R q million Bangladeshi have emigrated during the past 30 80-64 75-79 years and about 3 million - or 6 percent o fthe in-country 70-74 65-69 economically active population - are currently living 60-64 55-59 abroad. Bangladesh i s also a rapidly urbanizing society. 50-54 45-49 As seen in chapter 1, the share o f urban in total 40-44 35-39 30-34 population rose from 20 percent in 2000 to 25 percent in 26-29 20-24 2005, which suggests a significant rate o f rural-to-urban 16-19 10-14 migration. '' 5-9 0-4 mllon10 -8 -6 -4 -2 0 2 4 6 8 10 Changes inskills of the labor force ' Population(LHS)and 20. InBangladesh, all types of labor market outcomes, its growth (IUS) ototalw orkng age & &total including type o f occupation, total earnings, wages, and I d w o r k n g a g e 250 2 5% work intensity, are correlated with education and skills. Chapter 5 o f this report includes a detailed discussion o f 200 2 0% the trends and patterns in education outcomes and its 150 1 5% gender dimensions. A rapid growth in school enrolment, 100 1 0% particularly for females (see chapter 5), has had an impact on the quality o f the Bangladeshi labor force. The 50 0 5% average number o f years o f completed formal education 0 0 0% 4-A-A-Aa-NNNNNNNNNNN grew by 0.7 years from 2000 to 2005. Female education o m o m o m o ~ o m o m o m o m levels have increased at a faster rate (by almost 1 year ;ources Based on HIES 2000,2005, over the period) thanmale education (0.5 years). INpopulation projections 21. Increases in the education level of the labor force have however not been spread evenly across the population - the largest absolute increases have been concentrated amongst the richer population groups (chapter 5). In spite of the progress, only 55 percent o f those aged 15-64 can read and write, while the average number o f years spent in formal education i s only 4.4 years. loIn this chapter, the working age population is the population aged between 15 and 64 years. Labor force or, equivalently, the economically active population is the part o f the population that either works or is available for work. The unemployed are a part o f the labor force that does not work but i s avaiIable for work. Occasionally, the term "labor force" i s used to mean country's (potential) humadlabor resources in general - thus it i s closer to the meaning o f "working age population" -but this shouId be evident from the context. I 'The apparent "dent" among prime-aged men in the population pyramid derived from survey data (Figure 2-2) probably reflects missing household members who have migrated and thus remained unreported inthe survey. 21 Men still had over a year more education than females on the average, which matters for women's labor market participationand outcomes. I K Structural changesin the labor market 22. Between 2000 and 2005 important structural changes occurred in the labor market. The main processes were: (i) a gradual decline of agriculture and a rise o f services; (ii)movement a away from low productivity jobs (in agriculture) to more productive jobs (outside apculture) including salaried employment; (iii) strong employment growth in urban areas; (iv) a sizeable increase in women's labor market participation. These processes are closely intertwined and related to the rapid reductioninpoverty between 2000 and 2005. Sectoral shifts inthe labor market 23. The agricultural sector has declined in importance over time. The share o f agriculture in total employment declined from 51 to 46 percent between 2000 and 2005, while total labor income from agriculture grew at only 0.4 percent annually (using HIES, see Table 2-2). Even so, the absolutenumber ofpeople engagedinagriculture grew by 0.7 percent annually over the same period. Thus apculture continues to be an important sector, employing almost half o f the population and providing over 30 percent o f income. Industry's share in total employment stayed almost unchanged at 22-23 percent o f total employment during 2000-2005. While manufacturing employment grew by an average o f 2.8 percent annually, employment in the construction sector grew at 7.5 percent annually reflecting the rapidprocess o f urbanizationgoing on in the country. A similar trend was seen for incomes - average labor income from manufacturing grew at just above 2 percent annually compared to above 5 percent for construction. contributions to both income and employment came from the IShareoftotalIAnnualized.I . _. 2005 services sector. Employment and income in this sector has been expanding by over 5 and 7 percent per year respectively. Trade and catering, as well as the public and community sectors, contributed ohmanufacturing 18% 18% 2.8% 20% 2.1% the most. o/w construction 4% 5% 7.5% 5% 5.1% Services 27% 31% 5.4% 41% 7.4% 25. Therehasbeena away Notes: (1) Oftotal incomeoriginating in agivensector Sources: BasedonHIES 2000,2005; World DevelopmentIndicators. productivity employment (mainly in agriculture) into more productive, often salaried, employment (see Annex 2, Table A-2.1). As landless daily labor continued to move from rural to urban areas, the absolute number of daily wage workers in agriculture fell, while the share o f those outside agriculture increased. However, the most robust trend was an increase in salaried jobs, where employment grew by almost 5 percent per year and real incomes by over 7 percent. These increases took place predominantly in the private sector - although a non-negligible contribution came from women newly employedinthe public sector. 26. The non-apcultural self-employment sector appears to be undergoing a gradual consolidation - mainly due to an outflow from low-productivity own-account self-employment 22 into salaried employment and towards micro and small enterprises. Between 2000 and 2005, the extent o f self-employment per household has fallen by some 10 percent.I2 While own account self-employment has declined in importance, employment and income in family enterprises have grown rapidly - at 6 and 8 percent average annual rates respectively (Annex 2, Table A-2.1). Women are seen to be increasingly takingpart inhousehold businesses (see below). Urbanand ruraltrends 27. Urban areas are undergoing strong growth. Between 2000 and 2005 the urban population grew ffom 20 percent to 25 percent o f the total population and, accordingly, the urban labor market has been expanding rapidly (total income grew by 7.7 percent per year, or 1.3 percent in per capita terms). Currently about 40 percent o f labor income originates in urban areas - an increase from 32 percent in 2000. Salaried employment, particularly in the private sector, was a key drivingforce. Still, self-employment remains the main source o f income. 28. Income generation inrural areas has been aided by the expansion o f non-farm activities. In 2005, the rural non-farm sector accounted for 55 percent o f income and employment in rural areas. Incontrast to previous decades, this change came about inlarge partthrough the expansion o f salaried and wage employment in the non-farm sector, rather than that o f individual self- empl~yment.'~The rural non-farm sector i s important for poverty reduction - poverty rates among households inthis sector are as much as 10percentage points lower than for the rest o f the rural population - and i s expected to continue to be so, given the limited availability o f land for cultivation. Women andthe labor market 29. Themost dynamicchangesin the Table 2-3: Men andWomeninthe Labor Market labor market have occurred among Men Women women (see Table 2-3). Between 2000 Annuali Annuali zed real zed real and 2005, female employment grew at 2000 2005 growth' 2000 2005 growth' 4*3percentper year and the income Labor force generated by women grew at over 10 Employment rate, % 82% 82% 2.6% 11% 12% 4.3% Yearsofeducation 4.5 5.1 +0.5(*) 2.9 3.8 +0.9(*) percent a year. Hours o f work by Eamingsandhours women have increased significantly, by Medlanearnings 1,918 2,398 -0.1% 625 1,000 4.9% 3.5 hours per week, compared with a Medianhourly rate 9.4 11.8 0.0% 4.4 6.9 4.6% decline for men o f 1.9 hours.14 Higher Hours worked 49.5 47.6 -1.9(*) 40.1 43.6 +3.5(*) shares than average growth of wages has also Jobtypes shares Dailylabor 33% 32% 2.3% 33% 26% -0.7% contributed to the increase in women's Salaried 18% 20% 4.5% 35% 36% 4.8% income. Non-agric. self-empl. 22% 20% 1.0% 10% 13% 10.2% Amiculture self-emuI* 27% 28% 2.9% 22% 25% 7.5% 30. sixty percent of new jobs for r s h a r e s & g r o w t h 95%! 93% 3.2% 5% 7% 11.6%1 women during this period Were created Nom: (*)Absolute changeover five year period (1) For employment ratesand in areas, withnearly shares ofjoh types figures are derived fromabsolute numbers. (2) ~ r o w tohf urban halfof work agriculkral self-employment for women is likely to be slightly overestimated due to in salaried employment in the private dataincomparability. Source: Basedon HIES 2000,2005. sector, typically in the textile and apparel industry.Although manufacturingremains important, inthe 2000s much o f the additional Total non-agricultural self-employment has grown by an annual average rate o f 1.6 percent even as per household figure has fallen, due to increase innumber o f households from 2000 to 2005 (see Annex 2, Table A-2.1). The share o f non-agricultural self-employment intotal employment has fallen from 21to 20 percent. l3 As seen in paragraph 26, incomes and employment in individual (own-account) self-employment appear to be declining. l4The growth inhours worked for women mightbe somewhat overestimated due to data incompatibility inthis area. 23 employment growth came from other quarters. First, the public sector has beenactively recruiting women (mainly as teachers andhealth workers); l5second, their participationinself-employment, particularly as members or owners o f household enterprises, has been expanding. Women are increasingly participatinginhousehold businesses; the share o f firms with female members grew from 5 percent to 9 percent between 2000 and 2005. Women are somewhat more likely to use formal channels as primary sources o f financing, which may be a consequence o f the gender preference o f microcredit schemes inBangladesh. 31. However, shifts towards greater labor market participation among women were concentrated among better-off and more educated households. This weakens the pro-poor impact o f these changes. For example, the number o f working women with some education almost doubled over the period, compared with almost no growth for those with no education. Again, this is unlike previous increases in female employment associated with the rise in the garments industry,which were generally concentratedtowards poorer and less educated women. V. Trendsandpatterns in earnings and wages 32. Average earnings per worker (as derived from HIES) have been growing by a gradual 0.9 percent a year. However, real income per capita has been growing twice as fast (1.9 percent per year) owing to an increase in the share o f people working. A simple decomposition shows that growth inwage (rates) accounted for 90 percent o f growth intotal income per capita (Figure 2-3), with increase inthe share o fworking age individuals contributing slightly over 10percent. Other employment related variables - employment rate and hours o f work - had a small and ambiguous impact.l6 33. Wage inequality has remained stable" Figure 2-3: Income Growth Decomposition suggesting that increases in real wages have been relatively evenly spread across the wage Contribution to changes In Income per person distribution. But there i s substantial variation by increases in real income from salaried , 1 I I I I II employment (2.4 percent per year), houriy rate , 0 hoursworked I , I particularly in the private sector (3.4 percent). uenpbymntnte I mshareofwomage 1 Among sectors, earnings increased noticeably , I I I I I I 1 , I , ,1 ,' 1 1 only for workers inservices, while earnings in , , , , , , , / \ I , I agricultural activities (as well as in 2 $ 3 5 $ 1 Q u I 0 ) - 4 0 ) g g g g $ $ manufacturing and construction) seems sources: Basedon HIES 2000,2005 Women's incomeandwages 34. For women, incomes from salaried employment have increased at a rapid rate; in five years, the average income for women in this type o f employment has increased by about 60 percent. Oaxaca-Blinder decomposition shows that changes in characteristics, namely the l5This may be in part due to quotas for women being introduced in the public sector. For example, the primary education ministry recently recruited an additional 14,000 teachers, around 60 percent among who were women. See Annex 2, section I1for a brieftechnical note on the decomposition. l7Gini coefficient o findividualwages rose from 0.49to 0.50over the period, which isnot statistically significant. 24 increase in education level, accounted for between 30 to 70 percent o f the overall change in women's wages over the period.l8 35. The large gender wage gap is gradually Figure 2- 4: Gender gap for wage workei narrowing, though mainly for the better-off, salaried +gender wagegap 2000 workers. Average male wages are significantly higher I -genderwagegap 2005 Y than female wages. Interestingly, the pattern o f wage gap seems to have changed. Whereas in 2000 the highest disparities were recorded at the top end o f the distribution, the situation has reversed over the period (Figure 2- 4). What factors explain differences in earnings? 36. To better understand what accounts for differences in levels and growth o f wages, earning functions were estimated for the Bangladeshi labor market and various sub-population &oups. As expected, the main factors explaining the differences are (i) education; (ii) (iii) and (iv) geographic location (see Annex 2, gender; sector; Table A-2.2). 37. The returns to (an additional year 08education are, onaverage, between 5.5 to 6percent (Table 2-4)." They are higher for women, probably reflecting the relative scarcity o f education within this group. The returns are also lower in rural areas, where the prevailing production techniques do not reward skills very highly. They are particularly low for the daily labor sector, reflecting the "commodity" character o f labor exchanged inthis segment of the market. Overall, returns to education in the labor market have remained stable since 2000. Female rates seem to have declined slightly, perhaps due to increases ineducation among female labor over the period. 38. The educational wage premium increases Table 2-4: Returns to education, 2005 with the level of education (Table 2-4). Additional year, inpercent total male female Furthermore, rates of return to education in `Average 5.5** 4.9** l.O** countries around the world and in Asia - ranging ' ofwhich in from a higho f 20 percent for primary education to Primary 3.6** 2.9** 3.9** a low o f 16 percent for secondary education - are Junior secondary (SSC) 6.2** 5.6** 9.2** well in excess o f the returns reDorted for Seniorsecondav(HSC) 5 9 * * 7.3** 4.4** Bangladesh. Among South Asian couitries, only . 9.2** 8.6** 8.4** India records a lower return to primary education wage 2.1** 2.2** 1.0** (i.e. 3 percent).20 Public sector 6.5** 5.9** 8.4** Urban areas l.l**1.2** 9.3** 39. Women are at a disadvantage in the labor Average 2000 6.0** 4.6** 8.8** market, earning as much as 60 percent less than Sources: Based on HIES 2000,2005 decomposition shows that in 2000, about 30 percent o f the overall gender wage gap could be explained by differences in the characteristics o f male and female wage employees. The remaining two thirds o f the gender gap was due to "differences incoefficients," which inthis type See Annex 2, section I1for a brieftechnical note on the Oaxaca-Blinder decomposition referred to here. *' The exact coefficients depend on the detailed specification, as well as cost o f living and other adjustments. Although the data used inthe India study is for 1995. Interestingly, primary schooling has the highest returninmost countries, butnot in South Asian countries. 25 o f analysis is often ascribed to gender discrimination." By 2005, the difference in male and female wages due to characteristics had disappeared and the remaining gender gap consists almost entirely o f the unexplained component. Women have significantly better opportunities in urban areas and in salaried employment, particularly in the public sector jobs.22 Therefore, the current urban shift provides a chance for women to access productive employment on better terms. 40. Public sector wage workers earn substantial premiums in the labor market, and although there i s some evidence that these premiums have been declining in recent times, a public sector worker still earns wages over 50 percent higher than an equivalent worker in the private sector, not counting other benefits associated with public sector e m p l ~ y m e n t .The ~ ~ structure o f salaries inthis sector is flatter thanaverage. Thepublic sector premiumis significantly higher for women. Public sector premium is also higherinrural areas, where government jobs are less prevalent. 41. Location matters in determining wages. Regional premiums in wagelearnings regressions vary from between -8 percent (Rajshahi) and 15 percent (Chittagong), relative to the reference district (Barisal). Dhaka and Sylhet are the other two divisions with positive premiums. The location premiums on wages are broadly consistent with the regional pattern o f poverty and growth discussed in Chapter 1. The importance o f location i s discussed inmore detail in section VI11inthe context o fsystematic differences betweenthe eastern andwestern parts o fthe country. VI. Poverty and the labor market 42. The poor depend on labor income for their livelihood. Somewhat expectedly for a country without extensive social safety nets, lack o f labor income i s associated with highrisk or poverty. Importantly, the poor derive a greater share of their income from labor than the non-poor (Le. 85 percent for the bottom quintile compared with 70 percent for the highest), which underscores the importance o f adequate returns to labor for poverty reduction. daily wage sector; accordingly, the poverty rate inthis sector (60 percent) is very high. The association between poverty Figure 2-5: Wage andpoverty and employment as a daily wage laborer i s shown clearly in changesby division, 2ooo~2005 0 povertychange wage change Chapter 3 through a multivariate analysis identifying the correlates o f poverty. Outside daily wage, poverty incidence 5% (25-30 percent) i s similar betweenjob categories; the better 0% off tend to work in salaried employment or in self- employment outside o f agriculture. Household endowments -5% o f human andphysical capital (especially land) are important determinants o f poverty - primarily because these -``Oh endowments are closely associated with employment types o f household members(see Chapter 3). -,5% Source HIES2000,2005 "Obviously, amuchmoresophisticatedanalysisthanoursisneededtoattributetheseresultsto"true" discrimination. ''Asevident from a much smaller (negative) gender coefficients inwage regressions within these sub-markets. 23These findings may explain the massive oversubscription for public sectorjob vacancies. 26 and 2005 also experienced more reduction in poverty (Chittagong, Sylhet in Figure 2-5). However, Dhaka division seems to be an outlier with marginal real wage gains and a large decline o fpoverty. Growth,productivityand employment 45. Examining the relative importance o f labor productivity versus employment growth in explaining GDP growth i s useful to see whether the poor have been able to take advantage o f opportunities in the growing sectors. Increasing productivity (value added per worker) has accounted for as much as 75 percent o f the growth o f GDP per capita (Figure 2-6, left panel). Although as mentioned earlier, growth in Bangladesh was accompanied by the creation o f 5.6 millionnewjobs between 2000 and 2005, the employment rate (the share o f employed among the working age population) did not change significantly, and its contribution to the growth in GDP per capita was nearly zero. An increase in the working age population as a share o f the total population (i.e. the fall independency ratio) accounted for around 25 percent o f the growth inper capita GDP.24 46. Productivity growth has been strong in the industry sector, with some growth occurring in agriculture (Figure 2-6, middle panel). Inservices, productivity grew more slowly than elsewhere, which is a typical finding across countries. Onthe other hand, the level o f productivity inservices i s the highest among all three sectors, w h l e the level o f productivity inagriculture i s very low. 47. About halfo f the total productivity increase can be associated with intersectoral mobility o f workers (Figure 2-6, middle panel). The most important channel i s the outflow o f low- productivity daily wage jobs in agriculture to (mostly daily wage) jobs in services - a phenomenon related to rural-urban migration and expansion o f non-farm employment. Within industry, the manufacturing sector did not expand its employment, pursuing productivity gains instead, which was likely necessitated by the garment sector having to face greater international competition after the expiry o f favorable MFA quotas. On the other hand, the construction sector, driven by ongoing urbanization, has been creating new jobs and positively contributed to the overall increase inGDP per capita.25 Figure2-6: Decompositionof Changesin ;DPper Capita into Components Productivityby Sector PovertyChangesby Sector Contrlbutlonto chmgws In ODP per aptla Contrlbutlonto growth In GW pw Change In poverty asoclated with worker. by sactor pvetty changer w Rhln sector mfbwloutfbw tonmmreclor Agrkukure by broad Sector I l l Mustry 0% -1% -2% Source: HIES 2000,2005 24 *'SeeAnnex Annex 2, section I1for technical details on the decomposition of change GDP. See 2, section I1for abrieftechnical note on the decomposition o f productivitychange. 27 Contributions of different sectors to poverty reduction 48. How far have the poor benefited from the expansion o f growing sectors? The question is potentially important, given the policy debates on the trade-off between focusing interventions on the sectors where most o f the poor are found (such as agriculture) and focusing more on higher- earning sectors so that these are able to accommodate more workers from low-paying sectors. An indirect way to examine this i s by decomposing overall poverty reduction during 2000-2005 into changes in poverty within specific sectors, and that due to changes in the share o f the people "attached" to each sector.26 49. The analysis shows that a large proportion o f the poverty reductionbetween 2000 and 2005 took place within economic sectors (Figure 2-6, right panel). Services, which grew fastest, accounted for the highest share o f poverty decline. Despite slow growth, the contribution o f agriculture was significant because o f the size o f the sector. Interms o f intersectoral flows, the flow from agnculture into services had a small but non-negligible role in poverty reduction - consistent with how intersectoral mobility o f workers contributed to productivity increase^.^' The changes between 2000 and 2005 suggest that while raising productivity in agnculture i s critical for reducing poverty, growth o f other higher-productivity sectors i s also important for sustained poverty reduction- all the more because productivity increase in agnculture would be hard to achieve beyond a point given the shortage o f land and the large share of the workforce employedinthis sector. VII. Labor market outcomes and lagging regions 50. As seen in chapter 1, between 2000 and 2005, there has been a sizable reduction in Table 2-5: East-Westdifferencesinbasiclabor poverty in the divisions o f Dhaka, Chittagong, marketfeatures and Sylhet, which are mostly inthe eastern part 2000 2005 Working age population,% o ftotal growth' o f the country and almost no poverty reduction 55 58 2.9 % inKhulna, Barisal, and Rajshahi that are inthe 58 60 2.8 % west. Taking each region as a whole, poverty Urbanization,%o f total population rate fell from 46 to 33 percent in the East 25 30 5.7 % 13 17 7.9 % compared with a decline from 53 to 50 percent Years of education, (aged 15+) inthe West (Table 2-5). 3.6 4.5 0.9 y 51. Labor market differences are just as important in explaining the East-West gap as they are in helping understand overall poverty reduction. Table 2-5 shows the basic characteristics o f the labor force in the two regions, which are not very different. The working age population has grown at similar 80 78 2.2 % rates in both regions between 2000 and 2005. Nominalwages3,average Wmonth Both regions are undergoing rapid urbanization, 2,820 3,789 1.5 % although the East was still almost twice as West 21224 2;821 -0.1 % urbanized as the West in2005. Accumulation o f annual growthinreal Note: (1) y-years, over 5 years, %-annual growth rate; (2) t e r n ; (3) nominal labor income/month human capital appears to have been slightly Source: HIES (2000,2005) ''Seean 26 Annex 2 for a brieftechnical note on the decomposition of change inpoverty. In analogous decomposition usingjob categories, the results are consistent with those from decompositions using sectors: most of the poverty reduction took place within categories, with comparable contributions from wage and non- wage employment; inflows into salariedjobs played a small butkey role as well. 28 higherinthe East, with average years o feducation (among those above age 15) improvingby one year in the East during 2000-2005 compared with 0.3 years in the West, although there i s no significant East-West gap inlevels o f humancapital (see chapter 5). 52. Employment grew in both regions - somewhat faster in the East - generally in step with population growth. Employment rates are comparable and have not changed much over the period. The share o f labor income intotal income in the East i s significantly lower than that in the West (Table 2-5), which likely reflects better access to other income sources in the East, particularly self-employment andforeign remittances. 53. The most significant East-West differences are in evolution o f wages and structure o f the economy. Real wages have been growing robustly in the East, while they have stagnated in the West (Table 2-5). Total labor income growth inthe East was twice that o f the West (Table 2-5). More disaggregated analysis shows that the slow growth o f wages in the West is mostly explained by stagnation inthe urbanareas. 54. In terms of economic structure, the West derives a larger share o f income from agricultural - I Table 2-6: Employmentandincome Employment I activities, compared to the eastern part o f the country where services dominate (Table 2-6). Structure Growth Labor markets and the types o f jobs they offer are (2005) 2000-05(%)I Sectors East West East West also very different between regions. In the East, Agriculture 40 53 0.9 0.5 salaried jobs dominate, while in the West farming Industry 26 20 4.0 3.8 remains an important activity and the share o f low- 33 27 5.3 5.6 paid daily waged workers i s high (Table 2-6). ~ ~ ~ ~ e s Dailywage 26 39 0.6 3.3 Moreover, these differences seem to have been Salaried 28 13 5.4 2.6 reinforced between 2000 and 2005. In the East, Nonagrself-em~l 22 17 2.7 -0.1 growth o f salaried employment and total income self-empl 24 31 4.1 2.7 from salaries was rapid, while the structure o f the Totallaborincome labor market inthe West was relatively static. Structure Growth (2005) 2000-05 (Yo)* Sectors East West East West 55. Regression analysis to identify the Agriculture 25 43 -0.1 0.8 determinants o f wages28 finds higher returns to Industry 26 21 3.4 1.7 education in the East than in the West, with the 48 37 8.6 4.8 difference being even larger for urban areas. The Job types gender disadvantage and public sector premium are Dailywage 16 22 0.6 3.2 Salaried 33 21 8.6 4.4 lower in the East, which likely point to better Nonamself-emd 36 26 4.7 -1.6 integration o f the labor market. A significant ``East premium" (or West's disadvantage) for both years reflects the large East-West gap inwages even after netting out the effect o f individual factors that matter for wages. Location matters for wages mainly due to a better economic environment on the average in the East, due to factors like better connectivity to markets, access to infrastructure and agglomeration economies (see Chapter 4). Because o f these factors, the East i s better able to attract higher-return economic activities, resulting in large differences in the economic structure o f the two regions as shown above. 56. Interestingly, the "urban premium" for wages i s significantly smaller in the West and has been declining from 2000 to 2005. This, along with relatively low returns to education inurban '*For resultsof the earningfunctionestimations for EastandWest, refer to Table A-2.3, Annex 2. 29 areas o f the West, would suggest that agglomeration effects (that would lead to concentration o f high-return economic activities in urban areas) are much stronger in the East. Chapter 4 examines this in greater detail and identifies the lack o f "growth poles" in the West as an important factor contributingto its lagging economic performance. WIL Conclusion -summary of mainfindings and implicationsforpolicy 57. Urbanization and the associated expansion o f the services sector have been important factors in shaping the development process inBangladesh in the 2000s. The urbanpopulation i s growing at a rate triple that o f the national average. This i s accompanied by a shift away from agriculture, with daily wage labor in agriculture migrating to urban areas to take up non- agricultural work largely in the services sector. This raises new challenges for poverty reduction efforts, particularly inurbanareas. 58. Growth has been broad based and led by private investment. Labor market activity indicators, such as labor force participation rate and employment rate remained unchanged, reflecting the fact that employment creation was just about enough to keep pace with the size o f the workmg age population. Recent increases in income per capita are linked predominantly to rising labor productivity and labor incomes. Withinagriculture, growth was linkedto risinglabor productivity - albeit from a low initial level - and rising labor productivity was a key contributor to growth in the manufacturing sector as well. Intersectoral mobility o f people, primarily from agriculture (especially agricultural daily wage workers) to services contributed significantly to growth as well, along with risingemployment inthe services sector. 59. Although some long-standing segmentation i s seen between public and private employment and between rural and urban areas, the labor market is relatively flexible and facilitates Bangladesh's economic transition. Firms do not report particular problems with the hiring and firing o f workers and their staffing level is believed to be close to optimal. If labor market regulations are not a main barrier to creation o f goodjobs, addressing barriers outside the labor market could be a more effective a policy instrument. Thus, in addition to investment in productive assets (human capital and credit), buildinga conducive environment for the returns to these assets (such as stable macroeconomic environment, trade openness, infrastructure, and rule o f law) would also improve labor market outcomes like employment and earnings. 60. Increases inreal wages have been evenly spread across the wage distribution, which partly explains why consumption inequality remained stable from 2000 to 2005. Growth in earnings however varied substantially across job types, sectors and regions - highest among salaried employees in the private sector and in the services sector. Across divisions, the pattern o f earnings growth i s consistent with the regionalvariations inpoverty reductionnoted inchapter 1. 61. The widening East-West gap inpoverty in Bangladesh can be better understood in light o f stark regional differences inwage growth andjob-creation patterns. Wages have grown robustly in the East but stagnated in the West. Both East and West managed to create employment to match the rise inworking age population, but with important differences. The East created many more "good jobs" - that are more stable (salaried), better paid, and ina robustly growing nonfarm sector (including self-employment). Incontrast, a large proportion o f thejobs created inthe West consisted o f daily wage work or agricultural self-employment. Lower gender and public-private differences in earnings in the East indicate a better-functioning labor market with fewer distortions. A smaller and declining urban premium for wages in the West suggests weaker agglomeration effects - likely related to the absence o f urban growth poles andpoor connectivity to markets. Chapter 4 includes a detailed discussion o f how differences in connectivity and 30 market access contributes to the regional economic divide and what that implies for the design o f policies to reduce this divide. 62. While intersectoral or rural-urban flows have played some role inpoverty reduction, most o f the poverty reduction has taken place within economic sectors and ruralhrban areas. Poverty reduction efforts would therefore need to focus on areas and sectors where the poor currently are. This will involve promoting continued productivity growth in agnculture where wages remain low. Diversification into higher value added crops, use o f new seed varieties, andtechnology are key in this respect. Promoting nonfarm employment among rural households would help raise incomes and reduce poverty, since poverty incidence i s significantly lower among rural households engaged inthe nonfarm sector than among other rural households. 63. The importance o f nonfarm employment, in rural and urban areas alike, underscores the need for rapid job creation in services and manufacturing sectors to reduce poverty in Bangladesh. Large-scale job creation i s all the more critical given the demographic transition in Bangladesh, which would imply a large number o f new entrants (estimated as more than 20 million) in the labor market between 2005 and 2015. Services had the largest contribution to poverty reduction, through rising employment (including attracting labor from agnculture into this sector) and labor incomes. This suggests that the services sector, particularly inurbanareas and in the private sector, has large potential to provide employment opportunities for the poor. Higheremployment growth inmanufacturingsector beyondwhat has been seen inthe first part o f the 2000s would also be neededto sustain the pace o f poverty reduction and provide employment to the largenumber o fnew entrants to the labor market. 64. At the household level, the main determinant o f labor market outcomes is endowment in productive assets - both physical as well as financial capital and human capital. First, although impressive expansion o f microcredit i s helping many entrepreneurs (many o f them women), lack o f capital for small and medium enterprises remains an issue. Second, improvements in levels o f education (mostly secondary level) have also been an important factor inthe growth o f real wages between 2000 and 2005. The impact o f education on wages has been particularly highfor women and helpedto narrow the gender wage gap slightly. However, returns to education inBangladesh are low compared with other countries, probably reflecting differences in education quality - which makes improving the quality o f education a crucial policy priority. 65. Women are playing an increasingly important role in the Bangladesh labor market. Their participationrates, working hours, levels o f education, and income levels have all increased at a much faster pace than those for men. Moreover, an increasing share o f women's income derives from salaried employment as well as from household enterprises, often with more formal sources o f financing. At the same time, their labor market participation remains small by international standards, with much potential for improvement. Furthermore, growth in women's participation and incomes has been concentrated inthe middle and higher end o f the income distribution, rather thanamongpoorerwomen. 66. Improving employment among women in Bangladesh will have a significant impact on aggregate income growth and household poverty. The recent country gender assessment (World Bank 2008d) makes a broad argument infavor of linkingthe discourse on women's employment to the macroeconomic policy agenda, given the vast income growth potential that remains untapped due to the low participation o fwomen inthe workforce. The findingso f this report also suggest the need to focus in a number o f specific areas, including better enforcement o f existing laws, continued focus on higher education for women, and creation o f support systems to facilitate women's participation in the labor force (Box 2.1). Improving education outcomes 31 among women and the current urban shift in economic activities hold promise for the future, in terms o f providing opportunities for women to take part in productive employment on better terms. impromgacce 32 3. Profilingthe Poor: Characteristicsand Determinantsof Poverty 1. This chapter explores the multidimensional nature o f poverty and the factors that are associated with poverty in Bangladesh. Income or consumption poverty i s often strongly associated with attributes such as demographics, education, land ownership, occupation and employment status. Beyond individual household attributes, the characteristics o f the area a household i s located incan also affect the economic status o f households. The exact combination o f factors that keeps a household below the poverty line is unknown, but a comprehensive analysis o f the key correlates can suggest specific constraints to household incomes that in turn informpolicy interventions to reduce poverty. 2. How poverty is related to characteristics o f households and areas where a household is located and how these relationships have changed over time also help understand what factors explain the rapid poverty reduction in Bangladesh between 2000 and 2005. Chapter 2 has addressed this question along a key dimension - namely how changes in labor productivity and employment patterns have contributed to growth and poverty reduction. This chapter complements the findings o f chapter 2 ina framework incorporating a broad set o f household and spatial factors that are likely to influence a household's economic status. Multivariate regressions o f household consumption on a range o f individual, household, and location-specific attributes help in identifying the key determinants o f household welfare and the processes underlyingthe changes in poverty incidence over time. The chapter also includes an in-depth analysis o f the poorest group (extreme poor) o f households, to examine the changes in their welfare over time and the factors likely contributingto these changes. I. Non-income dimensionsof welfare how Bangladeshi households havefared - 3. Household welfare i s influenced by a range o f characteristics other than consumption. Improvements inthese indicators would show a positive trend inthe wellbeing o f the population, going beyond the relatively narrow measure o f consumption on non-durable goods. One would also expect these characteristics to be correlated with consumption; households with a higher consumption level are likely to live in a better house, built with superior materials, and equipped with features such as electricity and improved latrine. Therefore, improvements in these indicators over time would also serve as a consistency check for the consumption poverty trends analyzed inchapter 1. 1 ble3-1: Trendsinbasi assets and amenities All households Bottom5 deciles Bottom3 deciles 2000 2005 2000 2005 2000 2005 Average real value of livestock (tks) 4280 5281 3222 4653 2623 3919 Livestock ownership (%) 35.2 40.3 33.6 43.3 31.6 42.5 Wall of dwelling (YOwith cement / CI sheet) 37.7 55.2 21.4 39.5 17.4 33.9 Roof of dwelling (% with cement / CI sheet) 76.4 89.9 68.1 84.2 64.5 81.6 Safe latrine use ("?) 52.0 69.3 35.2 55.6 29.4 50.0 Electricityconnection(YO) 31.2 44.2 14.6 25.4 10.0 20.2 TV ownership (%) 15.8 26.5 3.6 10.1 1.8 6.7 Phone ownership (%) 1.5 12.2 0.1 1.5 0.0 0.9 Source:HIES 2000.2005 33 4. A number o f non-consumption indicators o f welfare show significant improvements between 2000 and 2005, for the general population and the poor alike (Table 3-1). The gains in six key areas like asset ownership, electricity access, safe latrine access, literacy levels, and occupational characteristics - for the general population and the poorest 50 percent o f the population - are visually represented inBox 3.1. Earlier work on poverty in Bangladesh shows that poverty and quuZity of housing i s closely correlated. For example, households who live in houses with straw roofs are typically extremely poor (Hossain, 1995). It i s therefore sigmficant that housing conditions have improved dramatically between 2000 and 2005, with a larger percentage o f households with walls and roofs o f corrugated iron sheets and cement that are more resilient to adverse weather conditions (Table 3-1). Housing conditions have improved substantially for the poor and extremely poor households as well. Box 3.1: improvementsin non-expenditureNelfare indi _.__ - -~ Ur . r C (a) .illhouseholds hexagonfor 2005 lie outside thosefor 2000, which indicates improwmentsalong all six dtmeiistons. Figure (b) indicatesthat householdsinthe bottom50 percentofthepopulation made even moregains behvcen2000 and2005 $ource Serajuddtnet a1(20073 5. Access to hygienic sanitation facilities i s closely associated with a reduced disease burden and better health outcomes. Between 2000 and 2005, the percentage o f households with access to a safe toilet has increased from 52 percent to 69 percent (Table 3-1). At the same time, the differences betweenpoor and non-poor remain significant. In2005, households who do not have access to safe toilet are nearly twice as likely to be poor than those who do. 6. Also significant is the increase in the share o f households with electricity connections, from 31 to 44 percent during 2000-2005 (Table 3-1). That said, most households suffer fiom regular power outages as there has been virtually no additional generation capacity during this period (World Bank, 2007a). There has also been a sharp rise in the percentage o f households with access to a phone (landline andor mobile) -from 2 percent of the population in 2000 to 13 percent in 2005 - mainly due to expansion o f the mobile phone network. This i s especially 34 evident in rural areas, where access to a mobile phone is about 20 times higher than a land line. However, among the poorest 50 percent o f the population, phone ownership while rising, remains very low at less than 2 percent. 7. An important household asset, especially in rural areas, i s livestock ownership. Between 2000 and 2005, the average livestock asset value inreal terms increased by about 20 percent for all households. For poorer households (e.g. the bottom five deciles) the increase was almost 50 percent. The increase appears to have come both from existing owners increasing their livestock holdings andfrom a higher number o fhouseholds owning livestock. II. Whatfactors influence the likelihood to bepoor? 8. To address the question o f what household-specific factors correlate with a household's economic status, bivariate analysis i s complemented with multivariate regressions - to measure the relationship between each attribute and household consumption independent o f the effects o f other attributes. Regressions o f (log of) per capita expenditures on a set o f household and location-specific attributes are run separately for urban and rural samples.' Given that the main objective i s to identify how each factor influences a household's economic status, the set o f independent variables is limited to those that are likely to be exogenous - more likely to be determinants rather than the results o f a household's per capita consumption. The 2005 results are compared with the corresponding figures in2000 whenever such comparisoni s instructive, to get an idea o f how the determinants o f poverty have evolved over this period and how these changes may have affected consumption poverty. Householddemographicsandpoverty 9. Cross-country evidence suggests that larger households - who are commonly households with a large number o f children- are more likely to be poor (Lanjouw and Ravallion, 1995). This also appears to be the case for Bangladesh - the multivariate regressions for 2005 suggest that number o f infants, children and adults are negatively correlated with per capita expenditures in 2005 (Annex 3, Table A-3.1). The negative association is much stronger with number o f infants or children than that o f adults, and strongest with the number o f infants.* These results are intuitive inthat higher dependency within a householdwould be associated with higher likelihood o f poverty. Table 3-2 shows that poor households had a larger average household size than non- poor households inboth 2000 and 2005. This i s because the average number o f children ina poor household i s higher than that in non-poor households which, combined with a slightly smaller average number o f adults for poor households, leads to a significantly higher average dependency ratio for poor households. 10. One caveat i s important to state and merits more analysis. The strong correlation between household size/composition andpoverty i s partly a result o fthe reference welfare measure used in Bangladesh being consumption per capita, which does not take into account economies o f scale andequivalence scales inconsumption (see chapter l).3 effects were to be incorporated, Ifthese 'See Annex 3 for a detailed description o f the regression models, variables, and results, and Table A-3.1 (Annex 3) for the results with HIES 2005. The model specification follows Ravallion and Wodon (1999), who estimated similar models on HIES data from earlier years. All regression results in this chapter are from columns (1) and (3) o f Table A - 3.1; the results incolumns (2) and (4) are relevant for chapter 4. *Inthe regressions, an additional child (age 1-14) inthe household was associated with around 18 percent lower per capita household expenditures; an additional infant (age less than one) was associated with 20 and 40 percent lowerper capita expenditures inrural and urban householdsrespectively Economies of scale in consumption refers to the fact that larger households are likely to obtain the sume level o f welfare per person with a lower expenditure per capita than a small household, due to the fact that certain types of 35 the welfare level o f large households relative to that o f small households would likely turn out to be higher than what their per capita consumption suggest. However, given the size o f the effect o f these demographic variables on consumption, the coefficients are likely to be still significant, albeit weaker than what appears here, even $reasonable adjustments for scale effects were to be incorporated. This seems to be supported by the analysis described in section 11, Annex 3. The results there indicate that households identified as poor after reasonable adjustments for economies of scale in consumption have on average a larger household size and higher dependency ratio than non-poor households (Table A, Annex 3). The gaps between poor and non-poor, however, become narrower with scale adjustments - this i s expected since such adjustment by definitionraises the measuredwelfare o f larger households. All households Poor households Non-poor households Demographics 2000 2005 2000 2005 2000 2005 HouseholdSize 5.18 4.85 5.4 5.2 5.0 4.6 Dependency Ratio 0.77 0.69 0.99 0.91 0.60 0.57 Numberofchildren 2.1 1.8 2.5 2.3 1.6 1.5 NumberofMaleAdults 1.6 1.5 1.4 1.4 1.7 1.6 NumberofFemaleAdults 1.5 1.5 1.5 1.5 1.6 1.6 Headfemale 0.09 0.10 0.08 0.08 0.09 0.12 HeadNon-Muslim 0.09 0.12 0.08 0.13 0.11 0.11 Age ofhead(years) 44.5 45.3 43.2 43.5 45.6 46.4 11. Notably, as seen inchapter 1, a sharp fall inhousehold size from 2000 to 2005 has played an important role in increasing per capita expenditures and reducingpoverty. The national average household size declined from 5.18 to 4.85 and the dependency ratio declined from 0.77 to 0.69. The declines in household size and the dependency ratio for poor households were similar to those o f the entire population. 12. Besides reflecting a decline in the population growth rate, household size may decline over time also due to household members splitting from ajoint family structure or migrating.4 Table 3-2 however suggests that the decline in household size in Bangladesh is associated with a decline in the number of children rather than the number o f adults. Therefore the decline in household size appears to represent a more fundamental demographic shift than household splitting or migration. The decline is also consistent with Bangladesh lowering its population growth rate from 2.9 percent per year in the 1970s to 1.5 percent currently. A decline in total fertility rate (TFR) from 7 in 1975 to 2.7 in 2007 i s also roughly consistent with the fall in the numbero fchildreninsurveyhouseholdsfrom 2000 to 2005. 13. Religion and age of household head also affect the economic status o f households. Households with non-Muslim heads tend to be poorer (Table 3-2), as also indicated by the negative coefficient o f the variable in multivariate regressions. Household per capita expenditures increase with the age o f the household head, the effect declining with increasing age. expenditure are lumpy (rent for housing i s one example) or on "shared" or public goods (see Lanjouw and Ravallion, 1995). Equivalence scale refers to adjustments to allow for the fact that consumption requirements are likely to vary by age, gender, sector (urbadrural), and even occupation or climate. InHIES, any householdmember who hasbeenaway for morethanthreemonths isnot apart ofthat household. 36 14. FromTable 3-2, poverty incidence appears to be slightly lower among households headedby women - female-headed households account for 8 percent o f poor households but 12 percent o f non-poor households. However, regressions suggest a more nuanced story about the association between gender of household head andpoverty. Among urban households, controlling for other attributes, female-headed households are significantly more likely to be poor (see Box 3.2), while the correlation is insignificant for rural households. The correlation between the gender o f the household head andhousehold economic status i s also affected by how one distinguishes between de facto and de jure female headed households (Buvinic and Gupta, 1997). Careful disaggregation by the marital status o f female household heads suggest that female-headed households face daunting economic challenges when the head i s widowed, divorced, or separated -inotherwords, lesslikelytohaveanadultmaleinthehousehold (Box3.2). HIES2005 ind female-headed for household attributes (including eholds are likelyto have lower per c le hardships in the 15. Interestingly, adjustment for economies o f scale in consumption also affects the association between gender o f household head and poverty. With economies o f scale above a certain threshold, poverty incidence appears to be significantly higher among female-headed households thanmale-headedhouseholds (see section 11, Annex 3), which is the opposite o fthe result with no economies o f scale inTable 3-2. This seems to suggest that female-headed households tend to be smaller on the average than male-headed households, which would imply that their average welfare (or poverty incidence) relative to male-headed households may be overstated in the absence o f scale adjustments for household consumption. These households have smaller household size and a smaller number o f male adults than the average household, suggestingmale migration. 37 16. Lack o f asset ownership istypically an important Table 3-3:Trends of povertyand land ownership in rural areas characteristic o f poor Population households in rural areas. poverty Rate Distribution Accordingly, land ownership Land size 2000 2005 2000 2005 is the most common targeting <0.05 acre 63.5 56.8 48.0 45.8 for anti-poverty Functionally landless0.05-0.5 acre 59.7 48.8 13.0 15.9 programs in Bangladesh. Marginal 0.5-1.5 acres 47.2 35.1 17.5 18.8 Poverty rate for the landless S-2*5 35.4 23.7 9.2 8.8 was 57 percent in2005 Mediudarge: 2.5 acresormore 20.7 12.8 12.4 10.7 compared to 24 percent for s ~ ~HIES:2000, 2005 e distribution in rural areas; and (ii)positive correlation between land ownership and the rate o f poverty reduction. The distribution o f land holding has been stable; in both 2000 and2005, around 61percent o fhouseholds in rural Bangladesh have less than 0.5 acres o f land (Table 3-3).6 While poverty rate declined among all land ownership groups, the fall in headcount rate was progressively greater for higher land ownership (Figure 3- 1). Poverty fell by 11percent among landless households and j 8 percent - among I Source: HIES2000,2005 mediudlarge landowners. Landownership i s thus an important determinant o f the likelihood o f a household to climb out o f poverty. But the fact that poverty incidence declined substantially even among the most disadvantaged (landless) i s a testimony to the broad-based poverty reductioninBangladeshbetween 2000 and 2005. Poverty and the educational attainment of household heads 18. Education i s a key determinant o f wage rates and household income inboth HIES 2000 and 2005 (Al-Samarrai, 2007a). Not surprisingly, Table 3-4 shows that poverty rates inboth2000 and 2005 are much lower when household heads attain higher levels o f education. The multivariate regressions show that per capita household expenditure increases with education o f household head (Annex 3, Table A-3.1). Rural households with heads who have had even minimal education (below fifth grade) have 13 percent higher per capita expenditures than households where the head has no education, and the education "premium" increases to 30 percent when the head has an education level o f tenth grade or above. The education premiums - especially for Land ownership of 0.5 acres or below is also the commonly used targeting criteria for NGO and public safety net programsinBangladesh. 38 levels o f fifth grade or higher - are even larger for urban households, reflecting greater opportunities for educated workers inurbanareas. The regressions show a similar direction o f impact of Table 3-4::Educationof householdhead and oovertv poverty spouse's (of the household head) t Poverty Rateh Population Distribution D,...arh. D n education on per capita 2000 2005 2000 2005 expenditures. The coefficients are NoEducation tlon 63.2 b5.2 54.7 54.1 57.3 53.5 smaller than those for the Primary 40.3 35.1 15.4 15.5 householdhead's education, but are Secondary7 30 21.4 19.9 22.1 still sizeable, increase with higher Higher Secondary condary 8.8 8.5 5.9 3.6 levels o f education and higher for Graduate and`above ` we 3.1 4.3 1.6 5.3 Source: HIES2000,2005 20. A spouse's (of the household head) education has gained in importance as a determinant o f household per capita consumptionover time. Inregressions using HIES 1988-89 (Ravallion and Wodon, 1999), education of spouse beyond "Class 5" was not significant; whereas for both 2000 and 2005, all education levels o f spouse have positive and significant effects on consumption (Annex 3, Table A-3.1). This is consistent with the progress made by Bangladeshi women over the last 15 years, interms o f increasedparticipationineconomic activities (see chapter 2) that has ledto higher returns to their education. 21. How has the education level among householdheads changed over time? Table 3-4 suggests two important trends fiom 2000 to 2005: (i)improving education levels, which would be expected to reduce poverty; and (ii) lower poverty incidence in 2005 than in 2000 for the same education levels. The proportion o f household heads with education o f secondary level or above has risenfrom 27 percent in2000 to 31percent in2005, while that o f those with no education has declined from 57 to 54 percent. At the same time, significant poverty reduction has occurred among all education levels - consistent with earlier findings that poverty reduction has been broad-based(see chapter 1). Occupational status of household head and poverty 22. Occupational status of household members is a key determinant o fpoverty (as seen inchapter 2). Earlier work in Bangladesh shows that agricultural wage laborers are typically the poorest occupational group (Hossain, 1995); in2005 this group has the highest poverty rate (66 percent) (Annex 2, Table A-2.1). Nearly a thlrd o f total employment i s in the daily wage sector. The poverty rate among households when the household head works as agricultural daily wage labor i s 72 percent, compared to 60 percent when the head works as non-agricultural daily wage labor (Table 3-5). In comparison, the poverty rate is around 33 percent among the second poorest group -households headedby the self-employed. 'The highest education among other members (besides the head and spouse) i s measuredby the difference between the maximum education o f any household member and that o f the head or spouse (whichever is higher). The coefficients are positive and significant in the regressions (the reference group being those with a difference o f zero), larger for urban households and increase with the size of the difference. 39 23. The multivariate regressions confirm that rural households headed by daily wage workers are significantly worse off, relative to the reference group o f households headed by self-employed farmers. Non-agricultural self-employment o f the household head has a positive and significant effect on welfare for urban households. Salaried employment o f household head has a marginal positive effect only for urban households.' Finally, the presence (and number) of nonfarm enterprises in the household has a strong association with per capita expenditures. In summary, urban households engaged in non-agricultural self- employment, especially in Poverty rate (%) Population share (%) household enterprises, are Rural Urban Total Rural Urban Total significantly better off Self: agriculture 33 27 33 29 6 23 than those who are not; Self: non-agriculture 38 23 33 17 31 20 while employment indaily Salariedemployee 27 17 22 10 31 15 wage work, especially in Dailywage: agriculture 72 72 72 19 5 16 agnculture, i s strongly Daily wage: non- associated with poverty agriculture 60 55 59 12 15 13 among rural households. 24. Labor outflow from agnculture to more remunerative non-agncultural igure3-2: Main Occupation of HouseholdHead (% employment had contributed to the decline of population) - in poverty in the 1990s (Mahmud, 2006; 30 r--- Sen et al, 2007). During 2000-2005 the 25 20 share o f agriculture in totaI employment 15 continued to fall (see chapter 2). 40 i o percent o f household heads reported 5 agnculture as their main occupation in 0 2005, down from 46 percent in 2005 (Figure 3-2). At the same time, the proportion o f household heads in non- 02000 m2005 1 agricultural salaried employment grew the Figure 3-3: Poverty rate (%) by occupation of most - consistent with the outflow o f labor householdheads from agriculture to salariedjobs (mainly in the urban services sector) reported in chapter 2. The shift out o f agnculture was even more pronounced for households in the bottom 50 percent (and bottom 30 percent) and contributed to poverty reduction, given that the average daily wage in non-agncultural employment was about 40 percent higher than that in agnculture (Annex 2, Table A-2.1). 25. Poverty reduction occurred within the agriculture sector as well from 2000 to 2005, as shown by the sectoral urce HIES2000,2005 decomposition results in chapter 2. This is also seen from the increase inper capita expenditures * This IS consistent with the finding in chapter 2 that salaned employment in the pnvate sector i s not hghly remunerative in companson with occupations other than daily wage labor. Average monthly earning o f salaned employees inthe pnvate sector is lower than that for the self-employed outside agnculture 40 among households headed by agricultural daily wage workers and farmers. Between 2000 and 2005, poverty incidence declined among households headed by all occupational categories (Figure 3-3). The results in chapter 2, as well as the decompositions later inthis chapter, suggest that improvements inlabor productivity within all sectors drove the reductioninpoverty. Remittances and poverty 26. Remittances have been a key driver o f poverty reduction in several countries and its role Figure3-4: %ofpopulationreceiving appears to have grown over the past decade remittanceby quintile, 2005 (World Bank, 2005b). In Bangladesh, central bank data shows that official remittances from I Remittances foreign countries grew from $2 billion in 2002 to above $6 billion in 2007.9 In2006, international 20 0 remittances amounted to just less than 9 percent o f 2 15 P GDP; international and domestic remittances ; 10 accounted for around 8 and 5 percent o f s 5 household's total consumption respectively in 0 HIES 2005 data. HIES 2005 shows a strong Pooresl Q2 Q3 @4 Richest positive correlation between receiving foreign ConsumptionQulnlile remittances and household expenditures. While Source:HIES2005 domestic remittances are received by rich and poor alike, foreign remittances go mostly to better o f f households (Figure 3-4). The poverty rate amongreceivers o f foreign remittances i s 17percent compared to 42 percent amongthe rest. 27. There are stark geographic disparities in the incidence o f foreign remittances. Twenty four percent o f households in Chittagong and 16 percent o f those in Sylhet received remittances from abroad Domestic Intemational in 2005, compared to less than 5 percent o f 2000 2005 2000 2005 households in Rajshahi, Khulna, and Barisal (Table Bansal 37.2 29.5 8.2 5.2 3-6). This East-West divide has changed little Chittagong 16.1 25.3 20.7 24.2 between 2000 and 2005 and roughly mirrors the Dhaka 17.5 13.5 8.2 7.8 pattern o f poverty in Bangladesh in 2005. The Khulna 21.0 24.1 1.8 3.9 distribution o f domestic remittances i s greater than Rajshahi 13.6 27.0 2.2 1.3 even that o f international remittances (Table 3-6). Sylhet 33.3 10.4 17.4 15.7 The incidence o f domestic remittances increased by Total 18.9 21.1 8.6 8.8 12 percent between 2000 and 2005, suggesting increasedinternal migration. lo 28. Regressions show that remittances are associated with higher household consumption for both urban and rural areas; the correlation with foreign remittances i s nearly three times larger than that with domestic remittances (Annex 3, Table 3.1). The regression coefficients must however be interpreted with caution, since the direction o f the causality is unclear. This i s because migration to foreign lands inparticular may require large investments upfront, which the relatively better off households are more likely to be able to afford. The coefficient of remittances could be capturing this reverse effect, rather than the impact o f remittances on The true size of remittances, includingunrecordedflows, i s believedto be larger. *OThe relatively low incidence of domestic remittances inDhaka division is likely due to the fact that most domestic migration occur into the urban areas of the division; whereas in Sylhet it is probably due to high rates of international (as opposedto domestic) migrationfrom the division. 41 household welfare. Recognizing that the large upfront costs o f international migration may constrain poor households, certain innovative actions have been recently initiated by the government (see Box 3.3) 29. International migration for employment appears to have become more prevalent in Bangladesh even among those with relatively low education and skills. About two thirds o f the officially registered international out-migrants (2 16,025 individuals) in2002 were "semi-skilled" or "unskilled" (Siddiqui and Abrar, 2003), suggesting that even the poor are able to gain from the ongoing globalization o f labor markets. Furthermore, as returns to employment abroad are relatively high, migration enables these households to not only finance essential consumption o f family members in Bangladesh but also make investments that contribute to income gains at home. Sharma (2007) - using survey data from 500 households across 20 communities - finds evidence that households with migrants have higher expenditures and save a substantial portion o f the remittances they receive (relative to comparable non-migrant households), which can finance investments inproductive activities (see Box 3.3). usingapropensity score are quite clear, the 1 ngaccess to foreign is opportunity for a vast number of ecognizing this, the p e n t of Monga affected ultra-poor by providing five Monga-prone northern districts (Lalmonirhat, and will be runby selected partner organizations.Ultra- n these areas and their family members are eligible to participants to secure overseas employment, arrange and The effect of location on household welfare 30. As reported in chapter 1, the incidence o f poverty shows a clear regional pattern, which suggests that geographic location o f a household plays a key role in determining its economic status. Thus it comes as no surprise to find that most o f the location dummy variables (at the level o f "old" districts) are significant inthe multivariate regressions o f per capita consumption, implyingthat the economic condition o f households are indeed influenced by the characteristics o f their location (Annex 3, Table A-3.1). After controlling for household characteristics, location o f a household inmost o f the outlying (old) districts (with the sole exceptions o f Sylhet-rural and Kushtia-urban) i s associated with lower consumption relative to Dhaka district in2005. A critical question in this context - what specific geographic or spatial factors are likely responsible for these unobservedlocation effects - i s addressed in chapter 4. 42 Microfinance and poverty 31. The microfinance revolution in recent years i s important to consider for any analysis o f poverty. Inadequate information on savings and credit in HIES makes it impossible to identify the effect o fbeinga microfinance client on a household's economic status. Instead, data obtained from PKSF (Palli Karma Sahayak Foundation, a microfinance apex institution) on changes in microfinance coverage at the sub-district (thana) level i s merged with HIES data to look at correlations between the geographic coverage o f microfinance and poverty. Microfinance membership expansion at the thana level and household consumption levels are found to be positively correlated (see detailed results in chapter 4). These correlations however do not necessarily imply a causal link between microfinance expansion andpoverty reduction; and there are other important caveats that apply due to the nature o f data used (see chapter 4). 32. To ascertain the extent o f the impact on poverty, panel data on household consumption and microfinance membership over time would be ideal. Even in the absence o f such data at the national level, having a more detailed credit and savings module infuture rounds o fHIES surveys will at least allow the correlations betweenhouseholds' access to microfinance and welfare to be measured. 33. Although the lack o f data limits the scope for national level analysis, a number o f studies using smaller data sets have found a significant positive impact o f microfinance on various dimensions o f household welfare in Bangladesh. While differing views exist about the impact o f microfinance on consumption poverty o f member households, there i s consensus to a large degree that microcredit reduces the variability o f consumption o f borrowers and therefore the impact o f income shocks (see Box 3.4, andalso chapter 6). The extent to which microcredi ingpoverty inBangladeshhas been several studies. Khandker (200 and extreme poverty rates dropped faster among microcredit borrowers than am attributableto microcredit pro to come from its redu er, 1998; Zaman, 199 What factors influence transitions in and out of poverty? 34. HIES 2005 has been used so far to identify the key determinants o f poverty and track their changes over time, but not to identify the factors responsible for households transiting in and out o f poverty, which requires panel data that tracks the same set o f households over time. However, a household panel survey by International Food Policy Research Institute (IFPRI) can shed some light on this question on a sample that is not representative for the country as a whole, but large 43 and diverse enough to yield useful insights (see chapter 6 for a more detailed description o f this study). The results are broadly consistent with those from the cross-sectional analysis from HIES data above. Education, ownership o f assets including land, household demographics, and community-specific or location effects all turn out to be important in determining a household's ability to move out o f poverty or being chronically poor, along with the incidence o f shocks like illness o f earningmembers (Box 3.5). 35. Qualitative studies can be useful inproviding insights on poverty transitions o f households - particularly on the processes through which various factors influence economic mobility. A companion study o f the IFPRI survey (Davis, 2006), which involved interviews in 116 focus groups across 11districts, yields findings that are consistent with those o f the IFPRIpanel. Life cycle events, as well as household-specific shocks, are found to be leading causes o f economic decline (or stagnation) o f households; whereas improvements inliving standards are often linked to factors that improve links to product, credit, or labor markets (Box 3.6). cline: what do the poor say of business activities, and daughters working, III. Whathappenedtotheextremelypoorhouseholdsbetween2000and2005? 36. A notable feature inBangladesh during2000-2005 i s the sizeable decline inthe incidence o f extreme poverty (the proportion o f the population below the lower poverty line), from 34 percent to 25 percent (see chapter 1). This seems to be somewhat at odds with the long-held view among many that extreme poverty in Bangladesh i s resistant to changes due to the fact that households are trapped in a vicious cycle o f low capability and incomes. L o w asset base, vulnerability to ecological and life cycle events, and lack o f agency have been put forth as explanations for the chronic nature o fpoverty by various authors (Sen andHulme, 2005). 37. Reductioninextreme poverty was due to higher thannational-average growth inexpenditures among the bottom three deciles, which was also equitably distributed within this group (see chapter 1). Between 2000 and 2005, average real per capita expenditures o f the bottom three 44 deciles grew at an annual average rate o f 2.5 percent, compared with 2.4 percent for the whole population. The expenditure growth i s also matched by an overall increase in the standard o f living o f households inthe bottom three deciles along dimensions like asset ownership, access to electricity and sanitation, literacy and occupational characteristics (see Annex 3, Figure A-2.1). Given a national extreme poverty rate o f 25 percent, most o f the bottom three decile households are still too poor to meet their daily caloric intake requirement. Clearly the malnutrition risks, and that o f inter-generational transmission o fpoverty, are highinlight o f inadequate diets. However, the tangible improvements in the quality o f their lives, relative to the past, including improved ownership o f assets and humandevelopment, provides some hope for the future. Figure 3-5: Extremepoverty rate (YO)by Figure 3-6: Extreme poverty rate (YO)by andownershipand occupation ofhouseholdhead education of household head class 10and above Non-Agself-enpbyed aass 6 to 9 aass 5 Mednm& Iarge !..andholder SdLandhoMer Below class 5 MarguralLandholder FunctionallyLandless No Bucation 0 10 20 30 40 50 w c e HIES 2000.2005 Characteristics of the extreme poor 38. Landlessness, low education, and employment as daily wage labor are the most significant factors influencing the likelihood o f a household to be extremely poor (Serajuddin et al, 2007). The extreme poverty rate i s nearly 40 percent for landless households and declines progressively by landownership, to 6 percent for those owning more than 2.5 acres (Figure 3-5). The extreme poverty rate i s 36 percent when the household head lacks any education, compared to just 4 percent for education o f high school level and above; 78 percent o f the extreme poor live in households where the head has no education (Figure 3-6). Nearly 60 percent o f extreme poor households rely on daily wage labor for sustenance; extreme poverty rate i s nearly double that o f the nationalaverage whenthe householdheadis employedas daily wage labor (Figure 3-5). 39. While the extreme poor inurban areas share many o f the characteristics o f the rural extreme poor, there are some specific features about their conditions. These are most commonly related to inadequate housing with high risk o f eviction, poor living conditions, limited access to basic services inpoor settlements, difficult employment conditions, particularly for women, and social problems inpoor urban communities (see Box 3.7). Although the rural extreme poor are likely to face many o f these problems in some form, crowding in urban areas and high living costs (including landprices) create conditions that are especially detrimental to the quality of life o f the poor. What led to the fall in extreme poverty rate between 2000 and 2005? 40. Between 2000 and 2005, extreme poverty rate declined for all land ownership, educational, and occupational groups (Figure 3-5 and Figure 3-6). This suggests a broad-based poverty 45 reduction process, with economic growth yielding benefits for even the most disadvantaged households. At the same time, the gains among the extreme poor were unevenly distributed across regions, consistent with the emerging East-West gap referred to repeatedly inthis report. ercent of women earn income for their famil ith 28 percent and 18 l e to work due to lack o .BU-IEDextremepoor, moreover, and PRA studies found For the ISource: BangladeshNational Social Protection Project program document (World Bank, 2008 draft) 41. Much o f the reduction in extreme poverty - particularly in rural areas - is attributable to income growth within the agricultural sector, consistent with what was found for overall poverty reduction(see also chapter 2). This i s evident from the lower rates o f extreme poverty associated with agricultural occupations in2005 compared to 2000 (see Figure3-5). 42. A shift from agnculture to non-agnculture also contributed to gains among the extreme poor as it has among the general population (see chapter 2). For the bottom three expenditure deciles (roughly corresponding to the extreme poor group o f 2000), the proportion of household heads whose main occupation was agricultural day labor fell from 36 to 33 percent and that o f farmers from 19 to 16 percent, while that o f non-agricultural day labor, salaried workers, or non- agricultural self-employed increased.l1 This intersectoral shift would have increased welfare since average wage rates for non-agricultural day laborers was about 40 percent higher thanthose for agricultural day laborers (Serajuddin et al, 2007). Complementarities between agricultural and non-agricultural sectors may be playing a beneficial role. For example, Sen et a1 (2007) argues that the reduction in agnculture activity (especially self-employed agnculture) i s to a '' The shift out of agriculture among the poorest is even more pronounced if we consider the main occupation in the household (as defined by the maximum number o f hours worked in an occupation by any household member) instead o f the main occupation of the household head. 46 certain extent attributable to the increased apcultural productivity that has freed up farm household labor for nonfarm activities. IV. Explainingpoverty changes between 2000 and 2005: resultsfrom a decomposition 43. The multivariateregression results from HIES datasets o f 2000 and 2005 can help understand the processes underlyingthe reduction inpoverty duringthis period. A useful framework for this exercise involves decomposing the growth in per capita real consumption (using the Oaxaca Blinder method) between 2000 and 2005 into growth due to changes in (i) householdand location endowments and (ii) returns to these endowments. The decompositions suggest somewhat different stories for the rural and urban samples (see summary results inAnnex 3, Table A-3.2).12 Among rural households, increasing returns over time had as strong an impact on the observed consumption growth as did changes in household and location characteristics. Among urban households, changes incharacteristics played a larger role than that inreturns or coefficients on the aggregate. Changesinendowmentsand returnsto factors influencingpoverty 44. Among household endowments or characteristics, changes inhousehold size and education o f household members contributed the most to consumption growth. This i s consistent with the findings earlier in this report that reduction in household size and improvement in education o f the labor force helped raise incomes and consumption. For example, chapter 1 finds that if household size had not changed between 2000 and 2005, poverty reduction would have been almost half o f what it actually was. The effect o f increase in education was particularly strong for urbanhouseholds, indicatinga higher skillspremiuminurbanareas, as mentionedearlier. 45. With regard to returns to endowments, changes in returns to household size, other demographic variables, and geographic location contributed more to the consumption growth o f rural than urban households; and changes in the returns to land ownership contributed to consumption growth among only rural households. On the whole, rural poverty reduction appears to be related to an across-the-board improvement in returns for most household and location endowments. This suggests an improving ability among rural households to utilize the resources available to them, which inturn suggests improvements in the economic environment and higher productivity during this period. The rise in returns was more muted for urban households, with improvements in endowments - including education and land ownership - playing a more important role. 46. For both rural and the urban households, the effects o f changes in returns to occupations clearly dominate that o f changes in occupational characteristics. For rural households, the increases inreturns to apcultural labor and farming are substantial and explains why poverty has declined significantly among households headedby an agricultural day laborer or farmer (as seen inFigure3-3). Forurbanhouseholds, returns to non-agricultural daily labor andself-employment improved significantly, and returns to non-farm enterprises improved to a lesser degree. The results from the decomposition are consistent with the findings o f chapter 2 - that labor productivity growth, risinglabor incomes and increased earnings from nonfarm self-employment inurbanareascontributed to reducingpoverty. 47. Among urban households, the coefficients on receiving remittances (domestic and foreign) increased sharply from 2000 to 2005, contributing significantly to urban consumption growth. While the coefficient on remittances and consumption does not necessarily measure the impact o f '*For detailed results, includingthe decomposition results for all variables, see Kotikula et a1(2007). 47 remittances on poverty (for reasons mentioned earlier), simulations using a computable general equilibrium (CGE) model for Bangladesh appears to confirm that foreign remittances played a keyrole inpovertyreductionbetween2000 and2005. 48. The CGE results indicate that almost a quarter o f the poverty decline i s attributable to the combined effects o f growth o f foreign remittances and RMG exports, both o f which exhibited strong growth (around 20 and 9 percent annually, respectively) during this period (Box 3.8). Between the two, remittance growth played a greater role inreducing poverty than RMG export growth. The positive impact o f international remittances i s not surprising, since inother countries remittances have been found to influence growth and poverty by raising consumption o f households and generating large multiplier effects due to the fact that remittances are more likely to be spent on domestically produced goods.l3 and remittances. between 2000 and indicating that remittance Explainingconsumptiongrowth amongthe extremepoor 49. For households in extreme poverty (the bottom three deciles o f each year), the effect o f changes inreturns were more important than changes incharacteristics. l4For rural households in the bottom three deciles, the contribution o f changes in returns to consumption growth were roughly twice that o f changes in endowments or characteristics. For urban households, changes inreturns hada significant contributioninconsumption growth only for the bottom20percent of households. More specifically, improving returns to apcultural labor inrural areas and to non- agricultural labor in urban areas contributed substantially to consumption growth. Increased returns in such occupations that employ a large proportion o f the extreme poor suggest that the poor contributed, andbenefited from, the economic growth process inBangladesh. Geographicor locationeffects-trends andcontributions to consumptiongrowth 50. Given the role playedby location effects inexplaininghouseholdconsumption, time trends o f these effects help understand whether and how the pattern of regional disparities has changed l3See Hanson and Woodruff (2003), Cox et a1(2003), Ratha (2003) and Adams (2006), l4For detailed results, refer to Serajuddin et a1(2007). 48 over the years. The 2005 results (Annex 3, Table A-3.1) are qualitatively similar to those from the earlier study (Ravallion and Wodon, 1999) usinghousehold data from late 1980s and early- 1 9 9 0 ~ 'Comparisons between their results and those for 2005 suggest that on the aggregate, ~ there has been some reduction in the "disadvantage" o f being located in a district other than Dhaka.l6This trend is seen more clearly for a shorter time period (2000-2005), during which the location effect on consumption has reduced for a majority of (old) districts. The decomposition results show that the aggregate negative effect o f being located in any (old) district other than Dhaka has reduced from 2000 to 2005, which has contributed positively to poverty reduction (Annex 3, Table A-3.2). 51. A more disaggregated picture, however, reveals a more nuanced story, broadly consistent with the emerging East-West divide mentioned in chapter 1. The districts whose location disadvantages relative to Dhaka district increased or remained unchanged are mostly in the westedsouthwestern part o f the country; whereas the location disadvantages o f the eastern districts (including those neighboring Dhaka) relative to Dhaka seem to be narrowing. The changes inthe location effect o f the first group o f districts contributed negatively to consumption growth, while that o f the second group contributed positively to consumption growth (see Kotikula et al, 2007). As mentioned earlier, chapter 4 will analyze in greater detail how geographic locationhas affected consumption growth andwhat factors underlie these effects. v. Conclusion 52. A sharp reduction in consumption poverty in Bangladesh during 2000-2005 was also mirrored by substantial improvements in living conditions - including housing characteristics, and access to sanitation facilities, electricity, and communications. Even highly disadvantaged households - in terms o f land ownership, educational attainment and occupation - were able to improve their welfare, consistent with the large gains inconsumptionamong the extreme poor (as seen in chapter 1). That said, reduction in consumption poverty, while occurring among the wealthy and poor alike in certain parts o f the country, was not as equitably distributed among geographic regions. 53. The poor in Bangladesh are more likely to belong to households with a larger number o f dependents, lower education, and with the household head engaged in daily wage labor. Poor households are also more likely to be landless or fbnctionally landless and less likely to receive domestic or foreign remittances. There are differences between urban and rural households. Land ownership is relatively more important as a determinant o f poverty and remittances less strongly correlated with welfare among rural households. Nonfarm self-employment (compared with other occupations) and ownership o f a household enterprise have a positive impact on household's economic status inurban areas but not inrural areas. Remittances, particularly from international migrants, have a strong positive impact on a household's economic status. 54. The geographic location o f a household has strong impact on its likelihood to be poor - a finding similar to what that in a previous study usingdata from more than 15 years back. Being located in a district outside Dhaka, with the exception o f one or two districts, is found to be disadvantageous for a household, even after controlling for household level attributes. This study used similar models as ours to explain variations in household expenditures (see Annex 3). The results were quite similar: inthe rural sample for 1988, there was only one district (Chittagong) whose location effect was not significantly different from that o f Dhaka; while in2005 this is true for only Sylhet. l6There are difficulties in making exact comparisons between the results o f Ravallion and Wodon (1999) and results for 2000 and 2005 - because o f some differences in the specifications, which are in turn related to changes in the household survey over time (see Annex 3). 49 55. Decompositions measuring the relative impact o f changes in returns to and levels o f endowments provide clues on why poverty incidence fell during 2000-2005. Key factors contributing to poverty reduction were changes in some household characteristics (a smaller number of dependents and improvements in education) and an increase in returns to different occupations. Among the extreme poor, consumption growth is explained mainly by improving returns to their endowments and occupations, including daily wage labor. The returns to owning more agricultural land increased, particularly in rural areas. Over a longer time horizon o f 15 years, an important development has been an increase inthe returns to women's education, which is consistent with increasingparticipation o f women ineconomic activities. 56. Thus, between 2000 and 2005, there were substantial improvements in key household attributes that influence the likelihood o f poverty, with even the poorest o f all households experiencing some o f these gains. Equally important for poverty reduction was the increase in returns to household characteristics - suggesting that households were able to get more out o f their existing endowments and occupations, perhaps because of better opportunities created by sustained economic growth during this period. These findings are consistent with chapter 2, which identifies rising productivity and earnings as important drivers o f poverty reduction. Poverty reduction is also linked to labor outflow from agricultural daily wage work to the non- agricultural sector - particularly (as seen in chapter 2) to salaried employment in the services sector. Inaddition, the rapid growth o f international remittances appears to have played a part in poverty reduction, although the distribution o f remittances continues to be skewed between regions within the country. 57. What do these results imply for policies to sustain and improve the pace o f poverty reduction? As mentioned in chapter 2, improving labor productivity in apculture i s critical to raise earnings o f agricultural wage workers. Given the population pressure on land, increasing access to land and achieving higher agricultural labor productivity would require accelerated growth in the non-agricultural sectors to absorb workers from low return agricultural wage employment. Increasing productivity would also require better water management, improved seed availability, timely access to fertilizer, and investments in research and extension services. The relatively highreturns to non-apcultural self employment underscore the importance o f this sector for poverty reduction. The rise in returns from and growth o f household-based nonfarm enterprises may be linked to the rapid spread o f microfinance. Further improving the access to finance for small enterprises, particularly in urban areas where microfinance i s less prevalent, i s likely to spur their growth. 58. Increasing education attainments will clearly have high dividends interms o f higher earnings and reduced poverty. As women's participation inthe labor force increases, there are increasing economic benefits o f women's education to the household - to complement the social and intra- household benefits associated with women's education. As education levels increase, the poor are also increasingly able to migrate out o f agriculture daily wage labor into (predominantly) salaried employment inservices. A fall independency ratios within households played a key role in reducing poverty between 2000 and 2005, indicating that sustaining Bangladesh's past successesinreducing fertility is crucial for poverty reduction. 59. Given the important role played by remittances in reducing poverty, especially from household members who have migrated to foreign countries, addressing the constraint faced by the poor to migration can be an area for policy intervention. This chapter mentioned a recent innovation by the government to facilitate organized contract migration from the very poor Monga-prone greater Rangpurdistricts. Learning from this experiment, other such interventions can be introduced - to improve household welfare by facilitating international and domestic 50 migration from areas where economic opportunities are extremely limited. Improvingthe human capital o f the poor, through better quality education and improved vocational training and health services, will also contribute towards raisingthe returns to and the poverty impact o fmigration. 60. As Bangladesh urbanizes rapidly, improving the conditions o f the urban poor in terms o f better sources o f income, quality o f life, and human development i s likely to become more o f a challenge. The incidence of extreme poverty inurbanareas has been reducing as rapidly as it has for the country as a whole. Gains in consumptiodincome, however, would not be enough to significantly improve the conditions o f low-income households, given high living costs in urban areas, housing shortages, lack o f basic services in low-income settlements, and crime and violence. Poverty reduction efforts must therefore focus on expanding access to housing, basic amenities, security, and health and education services to the urban poor. Safety net programs designed to protect the poorest against loss of income are necessary, given that these are almost non-existent in urban areas. Conditional cash transfer programs could enhance human development outcomes and provide a source o f regular income for the urban poor. Better availability o f childcare would help women cope better with the competing needs at employment andhome and increase their participationinthe labor force. 61. For the country as a whole, between 2000 and 2005 there has been an encouraging trend o f reduction inthe economic gap between the greater Dhaka region (old district) and areas outside this region. A closer examination, however, reveals that such convergence has occurred largely among the eastern districts and not inmost areas inthe west and southwest o f the country, which has resulted in the East-West gap referred to earlier in this report. As chapter 4 will show, significant consumption gains among the poor were largely limited to the eastern part o f the country that has better access to major urban growth centers o f the country. Chapter 4 will also address the critical question o f what types of spatial characteristics explain this dichotomy between different parts o fthe country. 51 52 4. Lagging Regions in Bangladesh: Is there an East-West Economic Divide? 1. As earlier chapters have reported, significant disparities exist in poverty incidence and its reduction (over the period 2000-2005) between different regions o f Bangladesh, resulting inwhat can be roughly described as an "east-west divide." Chapter 3 has shown that geographic location o f a household has a strong influence on its economic condition - to the extent that in 2005, location in any district outside Dhaka i s associated with lower household consumption levels, even after accounting for the effect o f a range o f household level attributes that typically influence household welfare. Given these results, a substantive analysis o f poverty inBangladesh would require examining the interactions between geographic or spatial characteristics and household economic status ingreater detail. 2. Regional disparities can also arise in non-income dimensions o f welfare, most notably in humandevelopment that is a key determinant o fwelfare andthe future economic prospects o f a country and regions within. A subsequent chapter (chapter 5) will analyze the changes, inequalities and determinants o f human development and their implications for future generations. It will in fact turn out that the regional gaps in income and consumption in Bangladesh are often at odds with achievements in education and health, suggesting that the processes underlying human development are at least in part different from those that explain economic gaps between regions. The focus o f this chapter, however, will be on economic inequality between regions, as captured by differences in household consumption, growth and poverty, and the terms "lagging" or "less-integrated" regions will refer to these differences. 3. Following up from chapter 3, this chapter has a few interrelated objectives. The first is to examine trends inregional disparities from 2000 to 2005 - the main time period o f focus for this report - ingreater detail and level o f disaggregation than what has been done so far. The second objective i s to "unpack" the so-called location effects mentionedinchapter 3, to understand better why certain parts o f the country are more likely to be better-off and experience greater reduction inpovertythanothers. This is not aneasy questionto address, giventhe range ofsocial, political and economic factors - including historical events and trends - that may influence current inequality patterns. Recognizing that a complete accounting o f the forces that shape regional . differences i s beyond the scope o f this report, the focus will instead be on a narrower question: what are the specific factors potentially influencing local economies - such as infrastructure, connectivity to markets and microfinance coverage - that explain (at least in part) the "unobserved" effects o f location on household welfare? 4. The third objective will be to look deeper into the emerging spatial trend in Bangladesh through the lens o f access to major economic centers. The key question would be: to what extent is the so-called "east-west divide" related to access to the country's major urban centers, the capital city o f Dhaka and the port city o f Chittagong. The "growth pole" effect on regional development cannot be measured directly with available information. However, looking at how economic activities are concentrated, how these patterns have evolved over time and how they may be related to the spatial factors mentioned above can help identify the constraints faced by the economically lagging regions of the country. Informed by the analysis, the chapter will conclude with a discussion onpolicy options to engender growth and poverty reduction inlagging regions, with reference to a few international experiences that are likely to be relevant. I. Regional disparitiesbelow the division level: trendsandpatterns 5. The disaggregation o f poverty incidence by divisions in chapter 1 indicated that on average the gaps between regions have expanded from 2000 to 2005 and contribute more to aggregate 53 inequality in2005 than in2000. However, a division is too large and heterogeneous an area to be useful as a unit for discerning trends in regional differences. More refinement would require disaggregating poverty incidence at the level o f the old districts or zillus (17 for the country).' Since the survey is not designed to be representative at the (old) district level, these results should be seen as applying to the HIES sample for each district, rather than its population. At the same time, the sample sizes are large enough for most o f the old districts to provide an indicative picture o f how regionaltrends in poverty have evolved during2000-2005.2 Figure4-1 Povertyheadcount rates from HIESsamples for distri&s.@ld boundaries)--_ I I __I__" -xx_-- I- ---__ -__.- 2000 2005 Note: Since HIES i s not representative o f the population at district level, the maps represent the poverty rate for the HIESsample from each district. Darker colors indicate higher poverty headcount rate. Source: HIES 2000 and 2005 Divergingtrends betweenthe eastern and western districts 6. Figure 4-1 compares the regional disparities inpoverty for 2000 and 2005, by grouping each district on the basis o f its poverty rate (as measured by the HIES sample for that district). Between 2000 and 2005, the average gap inpoverty incidence between Dhaka andthe rest o f the country has narrowed to some degree - a larger number o f districts have poverty rates close to that for Dhaka in2005 than in2000.3 Moreover, there are no districts in2005 with the extremely highpoverty rates that are observedfor four districts in2000. 7. But along with overall narrowing o f the gap with Dhaka, a divergence between the east and the west (with a few exceptions4) has emerged and are brought into clearer focus when districts are grouped by poverty rates. As before, "east" i s defined as the divisions o f Dhaka, Chittagong, and Sylhet and "west" as the divisions o f Khulna, Rajshah, and Barisal. All the eastern districts had significant reductions inpoverty; but the highest reductions occurred in some o f the poorest districts in 2000 (Mymensingh andNoakhali), which suggests a trend o f convergence among the eastern districts (see Annex 4, Table A-4.1). The pattern o f reduction also suggests increasing spillover effects from Dhaka district - that has historically had the lowest poverty incidence - on ' SeeAnnex 4, Figures A-4.1to see how the old district boundaries map to the divisions; and FigureA-4.2 and Table A-4.1 for how the old districts are sub-divided into the new districts. Incontrast to the old districts, the currenthew districts are too numerous (64) for the HIES sample of eachdistrict to be sufficient for even rough analysis. In2000,povertyrate ofthe HIESsample from Dhakadistrict was below 30percent-lowest inthe country. A notable exception is the district ofKushtia inthe west, which hadone o fthe largest poverty reductions. 54 surrounding areas: four out o f the five districts bordering Dhaka district experienced poverty reduction well above the national a~erage.~ 8. In contrast, most districts inthe west have seen much less reduction in poverty, and there i s no pattern o f convergence. While some poverty reduction occurred in Dinajpur and Rangpur in the northwest, which were among the poorest districts in 2000, large areas in the southwest (in Khulna andBarisal divisions) have actually grown poorer, andother areas have stagnated. Comparing"location effects" on consumptionbetween2000 and2005 9. The two major trends described here - reduction in the average gap between Dhaka district and the rest o f the country and the different paths taken by eastern and western parts o f the country - can be identified more formally, separately for rural and urbanareas, from the poverty determinant analysis conducted for chapter 3. 10. Inthe regressions to measure how household andlocation characteristics influence household (per capita) consumption levels, most location effects are significant in both years for rural and urban households. Households located in districts other than Dhaka district are likely to have lower per capita consumption (relative to being inDhaka district), net o f the effect o f household attributes (see Annex 4, Table A-4.2). Changes in location effects between 2000 and 2005 suggest some degree o f convergence in the location effect on consumption in a majority o f districts, as mentioned inchapter 3 (see Annex 4, Table A-4.2). However, the pattern o f changes is consistent with the trend o f east-west divergence discussed earlier (see Box 4.1). To see ifdistrict-level locati four and increased fo increased for the rest. The four districtsborderingDhakadistrict with large reductions inpoverty are Mymensingh, Faridpur and Jamalpur (Dhaka division), and Comilla (Chittagong division). Pabna (Rajshahi division), borderingDhakato the west, is the only exception, where povertystagnated. 55 II. Explaining"locationeffects" withthecharacteristicsof locations 11. An important question is to what extent the location effects shown above are explained by location-specific attributes, such as availability o f infrastructure, basic facilities and connectivity/access to urban markets.6 The multivariate regression analysis o f poverty determinants for 2005 i s a useful framework to examine this question. Variables that can serve as indicators o f connectivity to markets (for rural communities), electricity coverage, the size o f the nonfarm sector, and incidence o f andincrease incoverage o f microfinance (all at the thana level) are introduced in the regressions used in chapter 3.7 The results should be seen as identifying location-specific correlates rather than determinants o f household consumption - since there i s no way to differentiate between whether certain characteristics o f an area are causes o f economic progress or developed as a result o f economic progress inthese areas (see Annex 4 for description o f variables and caveats).8 12. Most o f the community or thana level location variables are significant correlates o f household consumption for rural areas, but less so for urban areas. Travel times to different markets and expansion inmicrofinancemembership are significant correlates o f consumption for rural households. Adding these variables, along with indicators for electricity coverage and the importance o f the nonfarm sector, also reduce the size o f (but do not completely eliminate) the location effects measured by district dummies - suggesting that the location characteristics partially account for the disadvantage o f a household being located outside Dhaka district (see Annex 4). Access to markets and infrastructure Table 4-1: Coefficients of location 13. Table 4-1 shows the coefficients o f location variables in reg] ision (20( L variables when they are added one at a time to the Urban basic model specification -to avoid possible "biases" Rural in the coefficients due to multicollinearity when all Travel time to thanaHQ -0.065 (`00 mins) variables are introducedtogether. (4.02)** Travel time to district HQ -0.008 (`00 mins) 14. Travel time to urban centers - a reasonable proxy (3.22)** for access to markets - is an important correlate o f Travel time to Dhaka -0.042 (`00 mins) poverty in rural Bangladesh. Per capita expenditure (4.44) ** i s significantly higher for a rural household when it i s % ofhouseholdswith 0.004 0.001 electricity inThana located in a community better connected to markets, (3.86)** (2.14)* after controlling for household characteristics and the %ofhouseholdsowning agricultural landinThana -0.004 -0.001 (unobserved) effects o f being located in a particular district (Table 4-1). While more analysis i s necessary, the findings suggest that there are economic benefits for households in areas with better accessto the local market andthe largest urbancenter o f the country. Locatiodgeographic factors, like infrastructure availability and access to markets turn out to be highly important in some other countries -Sri Lanka being one example -in explaining spatial differences in economic growth and poverty '(forThebasic the Sri Lanka case, see World Bank, 2007b). regression specifications are incolumns (1) and (3) o f Table A-3.1, Annex 3. The results o f the "extended" regressions, with the location characteristics, are incolumns (2) and (4) o f the same table. * This type o f "placement bias" can occur, for instance, if an economically powerful area receives more public goods due to greater political power or on account o fits economic/strategic importance for the central government. 56 15. ElectriJication also appears to be associated with lower poverty, particularly in rural areas. The coefficient on the percentage o f households ina thana with electricity connections i s positive and significant for both rural and urban regression, when introduced singly (without other location characteristics) into the regressions. The effect i s larger for rural households than urban households, primarilybecause connectivity to electricity i s muchmore uneveninrural areas. The association between household consumption and proportion households in the thana owning agricultural land - a proxy for the size ofthe non-fam sector - i s quite weak, regardless o f whether the variable i s introduced singly or with other location characteristics inthe regressions. The geographic coverage of microfinance 16. In the multivariate regressions, the increase in thana-level microfinance coverage during 2003-2005 i s positively associated with increases in consumption (see Box 4.2).9 An important caveat to these results is that they represent correlations between household consumption and microfinance coverage in an area, and not between a household's participation in microfinance and its consumption. The thana level variables - the only recourse in the absence o f household level data on microfinance in HIES - may be capturing this household effect indirectly, in addition to other benefits and externalities that could arise from microfinance expansion in an area. Box 4.2: Spatial association ision and poverty reduction A majority of microfinance ee Increaseinmicrofinancemembershipand * BRAC, ASA, and Grameen Bank, povertyreduction has declined slightly W 11 between 2003 and 2005, 64 to 61 percent (see Annex 4, Table A-4.3). umber of microfinance memberhouseholds incre 2005. Has microfinance expan reduction? The re such a link, which is supp Households in thanas with microfinance coverage experi poverty reduction - consistent for rural households (yello Note: Increase inmembership refers to % change in Microfinance membership also seemed members in a thana between2000 and 2005. The faster in areas that were on average PO membership figures o f2000 are obtained by projecting graph). The two results together suggest that, on back from the annual rate of growth during2003-05. average, microfinance expansion occurred in areas that Source: PKSF data (2003,2005); HIES (2000,2005) were initially poorer andsubsequentlyexperiencedhigherthanaveragereduction inpoverty. Source:Kotikula, Narayan, andZaman(2007) 17. To put these results incontext, microfinance access has increased rapidly inrecent years (see Zaman 2006 for more details). Thana-level cross-tabulations suggest that microfinance expansion has occurred inareas that were (a) poorer on the average inthe year 2000, and (b) have subsequently experienced higher than average reduction in poverty - consistent with the regression coefficients quoted above (see Box 4.2). Maps o f microfinance membership at the (old) district level show that coverage was higher among the poorer areas in the west in 2003, The negative correlation between microfinance coverage in a thana and consumption i s even harder to interpret. Higher microfinance coverage is higher in areas with greater poverty would be consistent with a conscious effort by MFIsinthe recent past to expand coverage inpoorer areas or an adverse impact of microfinance on poverty (unlikely based on evidence from numerous other studies). 57 whde larger increases in microfinance membership more in the east than in the west (see Annex 4, Figures A-4.3 and4.4). Area-specific risk of natura1disasters 18. Large parts o f Bangladesh are vulnerable to natural disasters including floods, droughts, and cyclones, which are mainly to do with the topography and location o f areas (see chapter 6). Given that, does the risk o f natural disasters explain some o f the so-called location effects on household welfare? The risk o f floods, primarily associated with the elevation of an area, is spread among locations throughout the country (Figure 4-2), whereas areas with high risk o f cyclones are concentrated in the south, primarily in the (old) districts o f Khulna, Barisal, Noakhali, andChittagong (Figure 4-3). Figure4-2: Landtype (by level ofinundation) by Figure 4-3: Cyclone risk bythanas thanas HIGHLAND f LOULAND MEDIUM HCHlAND MEDIUM LClWLAtlD Wind Risk VERY LOWLAWD Source Governmentof Bangladesh Source: World FoodProgram Note' Highland(no flood), Medium Highland(0-90cmflood), Note: The riskof cyclone takes into account storm surge and MediumLowland(90-180 cm flood), Lowland (> 180cm flood), wind damage. andBottomland(permanently wet). 19. While none o f the disaster risk variables have any impact on household welfare in urban areas, higher risk o f cyclone inan area i s associated with greater poverty among rural households. Moreover, introducing a dummy variable for thanas with highrisk o f cyclone into the basic rural regression specification significantly reduces the size o f the location effects on household consumption for the four most cyclone-prone districts. Thus the risk o f cyclones appears to partly account for the location disadvantages o f these districts, relative to Dhaka district. There i s no linear relationship between flood risk and poverty, partly because lowlands more susceptible to floods also benefit from the beneficial impact o f floods on soil fertility. The only clear result seems to be that very low or "permanently wet" lands and highlands tend to be worse o f f than areas with intermediate elevation. 20. The correlation between higher risk of cyclone and rural poverty should not be interpreted as the impact o f cyclones onpoverty, since the coefficient may reflect the effect o f other unobserved 58 factors that happen to be highly correlated with cyclone risk. For example, the effect o f cyclone risk on consumption disappears when introduced into the regressions with the infrastructure- related variables mentioned earlier (travel time to markets and electricity availability), because these variables turn out to be highly correlated with cyclone risk. Inother words this suggests that high cyclone risk areas are more likely to be poorly connected to markets and electricity." Thus while the impact o f cyclone risk on rural poverty is hardto quantify,the fact that highrisk areas are more likely to be poor and deficient in infrastructure and market connectivity is revealing, since better infrastructure would likely mitigate the economic impact if and when a disaster were to occur. Seasonaldeprivation inthe northwest 21. Another important area-specific phenomenon to take into account is that o f Monga, which affects large areas in the northwest part o f the country, primarily in the greater Rangpur area. Monga refers to a form o f severe deprivation during the lean agricultural season o f mid- September to November, corresponding to the post-planting and pre-harvesting o f the major Amon rice crop, which also induces high chronic poverty. Roughly 7 percent o f the total population inBangladesh inhabits the Monga-affecteddistricts (for more information, see chapter 6). The consequences o f Monga likely explain why location inRangpur (old) district had strong negative impact on household consumption in the poverty determinant regressions - the largest among all districts in2000 and the second-largest in2005 (see Annex 4, Table A-4.2). 22. In concluding this section, it is useful to summarize the main findings. Location characteristics partially account for the district level location effects inmultivariate regressions - in other words, they help explain why location in a certain district influences a household's economic status, net o f the effects o f household attributes. These include characteristics specific to the rural community (like connectivity to the nearest market or large urban centers) and attributes o f a larger area (incidence o f electric connections or microfinance membership in a thana). 23. Travel times to markets - especially the nearest market and Dhaka city - turn out to be important determinantso f the economic status o f rural households, and electricity coverage inan area also seems to matter. Infrastructure and related constraints that characterize poor areas are strongly correlated to each other as well, which i s to say they often occur simultaneously in the same areas. There i s some association between the expansion o f microfinance membership and reduction in poverty in rural areas. On the whole, the location factors considered here are more influential for the welfare o f rural households than for urbanhouseholds. This is partly because travel times are available for rural areas only and would arguably also be less relevant for urban areas. Rural areas with a higher risk o f cyclones are more likely to be poor and have lower access to markets and infrastructure - which suggests high vulnerability o f these areas, as well as the possibility that the risk o f large natural disasters, even if such events are infrequent, has an adverse impact on the regional economy. Other than these characteristics, the geographic pattern o f severe seasonal deprivation (Monga) contributes to chronic poverty in large parts o f the northwest. loThe correlation coefficients o f high cyclone risk in a thana with (i) time from a community to Dhaka i s 0.46, travel and (ii) percentage o fhouseholdsinthe thana with electricity connection is -0.20. 59 III. Regionalinequality-theeffect of "growthpoles"" 24. The discussion in this chapter indicates substantial differences in living standards across locations - across administrative divisions and at the district level - with a notable "east-west divide." Factors like remoteness from markets and towns and lack o f infrastructure (like electricity) are found to be important characteristics o f poor areas and are strongly correlated to each other. This in turn seems to suggest that certain areas in Bangladesh have a combination o f factors likely to lead to concentration o f economic activities, giving rise to so-called "growth poles." Therefore, an intuitive approach to analyze the importance o f location - particularly for informing policy discussions - i s to examine whether access and connectivity to such growth poles are important inexplainingregional differences inliving standards. The "growth poles" of Bangladesh 25. There is strong evidence to suggest that two metropolitan cities have emerged as the main centers o f economic activity o f the country: Dhaka, the capital city with a population o f around 12 million andto a lesser extent, Chittagong, the main port city with a population o f 3.4 million. These two cities account for 88 percent o f the nation's metropolitan area population and 41 percent o f the total urban population. Estimates based on HIES 2000 and 2005 indicate that the average real per capita expenditures in these two cities is about 40 percent higher than in other metropolitan areas. The dominant seats o f major administrative and economic functions, Dhaka and Chittagong act as the main domestic and international trading hubs, attracting large numbers o f migrants from other parts o f the country, as well. There i s also evidence that spatial concentration inDhaka city and its surrounding areas has increased inrecent years. Spatial concentration of economic activities 26. A substantialpart o fthe economic activities inBangladesh is clustered aroundDhaka and to a lesser extent, Chittagong (Figure 4-4). Urban areas account for 72 percent o f the employment in firms with 10 or more Total Persons Employed (TPE) - a reasonable measure o f formal sector employment. As Figure4-4 shows, a large majority o f these are in Dhaka and Chittagong cities andtheir surrounding areas.l2 Dhaka has seen an eightfold increase inits populationsince 1970, accounting for a third o f the country's urban population with its 12 million residents, and is the fastest growing mega-city o f the world (UNestimates). Its "urban primacy" (share o f total urban population) exceeds that o f most global comparators (World Bank, 2007a).I3 27. The concentration o f economic activity around Dhaka and Chittagong is also seen from the spatial distribution o f employment by industry. Employment inthe largest category o f industry- "agro processing industry" (including Ready Made Garments) - is concentrated inDhaka and to a lesser extent in Chittagong (Annex 4, Figure A-4.5). The same spatial pattern is seen for employment in manufacturing firms with TPE o f 10 o f more. Dhaka alone accounts for 80 percent o f the country's Ready Made Garments (RMG) output and half o f manufacturing sector employment. Complementary business services, particularly finance andreal estate account for a much higher share o f total employment inDhaka relative to the rest o f the country (World Bank, 2007a). Most o f the medium and small towns/cities do not have significant employment in formal sector manufacturing (Annex 4, Figure A-4.6). `IThis section draws substantially from Shilpi (2008). Maps and figures on spatial concentration are from Bangladesh Urban Strategy Notes (World Bank, 2008c draft). 12Dhaka SMA accounts for about 80 percent o f total formal sector employment and number o f establishments in all SMAs in Bangladesh. l3 For instance, the urban primacy o f the largest cities o f India, Pakistan and Korea are 6, 22 and 23 percent respectively. InSri Lanka, the greater Colombo area has an urban primacy similar to Dhaka's (World Bank, 200%). 60 Figure4-4: Employmentin formal sector Figure4-5: Clusteringof manufacturingfirms firms (2006) (2006) Note: Employmentinall firms with 1O+ Total Persons Note: Blue indicatespositivevalue ofLocal Moranz-score Employed(TPE). 1dot represents2000 workers. Dotsare (clustering), with darkerblue indicatinghigherclustering. randomly placedwithin eachUpazila Source: Economic Census (2006) 28. The clustering o f manufacturingemployment (infirms with TPE of 10 or more) is seen more formally by mapping Local Moran statistics for upuzilus - a measure o f correlation between manufacturingemployments inneighboring upuzilus.l4 Looking across the country, clustering i s seen only around Dhaka and to a much smaller extent, around Chittagong - indicating that agglomeration takes place predominantly inDhaka andits surroundmg areas (Figure 4-5). l5 Access to growth poles and household welfare 29. Given the clear pre-eminence o f these two urban centers as growth hubs, a key question would be whether access to these Figure 4-6: The riversin urban centers is associated with higher incomes and lower Bangladesh poverty. The major rivers Ganges and Brahmaputra slice Bangladesh into three parts (Figure 4-6). The natural borders defined by these two rivers appear to be an intuitive way of grouping regions in terms o f their access to Dhaka and Chittagong. Territories lying to the east o f the Ganges and Brahmaputra are defined as the integrated region (IR)- covering the divisions o f Chittagong, Sylhet, and most o f Dhaka (except for the greater Faridpur districts). Areas to the west o f Brahmaputra (Rajshahi Division) and south of Ganges (Barisal andKhulna divisions, andthe greater Faridpur districts inDhaka division) that are separated from Dhaka and Chittagong by one o f the two rivers, are defined as the less integrated region (LIR). l4Positive value o f the L-Mstatistic indicates that employment inmanufacturingin an upazila is associated with higher manufacturing employment inneighboring upazilas. l5The green areas, indicating negative values o f the Local Moran statistic, are likely to be the "boundaries" o f the clusters o fmanufacturing employment. 61 30. The IR-LIRdistinction almost coincides with the so-called East-West divide - with the only exception o f greater Faridpur district, which i s considered a part o f West but i s inLIR. Usingthe rivers to differentiate between regions has the advantage that the presence o f a large river i s an "exogenous" factor, and one that i s also strongly correlated with travel time to Dhaka, which was identified insection I1as a key factor inexplainingregional differences inpoverty. 31. The incidence o f poverty differs substantially between IRand LIR. According to HIES 2005, the poverty rate in IR was 33 percent compared to 50 percent inLIR. The gap was smaller in 2000, when the poverty headcount rate was 46 percent in IR and 53 percent inLIR. At the mean o f the expenditure distribution, the gap between IR andLIR was 9 percent in2000 and 17percent in2005; andthe urban-rural gap was 27 percent in2000 and 19percent in2005. Thus between 2000 and 2005, while the rural-urban gap has declined across Bangladesh suggesting greater integration inthe economy, the IR-LIRgap has increased. Figure 4-7: IR-LIRgaps inlogreal per capita Figure 4-8: Urban-Rural gaps in log real per expenditures(2000 and 2005) capita expenditures(2000 and 2005) I P.,a"CI. / - - 2 W - - M o s Source: Shilpi (2008) using HIES (2000and 2005) 32. IR-LIR gap in per capita expenditures. Figure 4-7 displays the IR-LIR gap in real per capita expenditure by all per capita expenditure quantiles (from 5th to 95th), which i s positive for all quantiles for both years. In 2000, the IR-LIR gap was increasing in expenditures quantiles (e.g. about 11percent at 20th percentile and 19percent at 80th percentile). In2005, however, the gaps are similar for the rich and the poor (e.g. around 16 percent at 20th and 80th percentiles). Compared with 2000, the IR-LIRgap in2005 increasedfor all quantiles below the 55thpercentile and decreased for all quantiles above the 55th percentile. Thus the poor households in IR experienced a muchfaster rate of consumption growth compared with their counterparts in the LIR. The increasing IR-LIRgap among poor households stands insharp contrast with the decline in the urban-rural gap among all households between 2000 and2005 (Figure 4-8). 33. Endowmentsversus returns in explaining IR-LIR gaps. To analyze what factors explain the IR-LIR differences, the IR-LIR gaps in the distribution o f per capita expenditures are decomposed into gaps owing to (i) differences in household and location endowments, and (ii) differences in returns to these endowments, using a quantile decomposition technique on HIES data from 2000 and 2005.16 Economic literature suggests that while regional differences in endowments may indicate "sorting" o f households by observable attributes among locations or regions, differences in returns can occur when there are costs o f migration between regions andor unobserved heterogeneity across households and locations. The latter can be attributed to sorting o f households by unobserved characteristics among locations, agglomeration economies l6This technique was pioneered by Machado and Mata (2005). Nguyen et a1 (2007) applied this technique to separate out the contribution of endowments/covariates and returnsto urban-rural inequality inVietnam. 62 in densely populated urban centers, or the externalities created by public services and infrastructure ina region.l7 34. Because the above effects may affect households differently depending on their economic status, returns to observed household attributes are likely to vary across households depending on their position inthe welfare distribution. Ravallion and Wodon (1999) showed that both sorting and return effects are important in explaining average regional gaps in welfare in Bangladesh during the early 1990s. The analysis here focuses on a more recent period; and the quantile regression approach allows the returns to household attributes to vary with a household's position inthe expenditure distribution(see Annex 4, section I1for a discussion of the motivation for the empirical exercise). 35. From 2000 to 2005, the endowment effect shifted upward for all quantiles (see Box 4.3), which suggests that physical and human endowments improved more in IR than LIR for almost all households. Due to this improvement, in2005 the entire distribution o fhouseholds inIRhave better attributes than those inthe LIR, with the exception o f the bottom decile. This i s consistent with an earlier finding that the average years o f education among the working age population increased faster in the East than in the West between 2000 and 2005 (chapter 2). At the same time, chapter 5 finds that educational attainment indicators in the western divisions are on par with, and inmany cases surpassed, those inthe east in2005. This suggests that the IR-LIRgaps in endowments are more significant for household characteristics such as physical assets, demographics, and occupational characteristics thanthey are for education. 36. The return effects show that the poorest 40 percent o f households in LIR get much smaller returns to their attributes compared with the same group in IR region, and this gap has increased from 2000 to 2005. This is in turn consistent with the patterns o f growth in wage and labor income shown in chapter 2 -robust inthe East but stagnant inthe West. While the return effects for upper quantiles have declined fiom 2000 to 2005, they are still substantial, accounting for at least half o f the total IR-LIRgap inlog per capita expenditures. The direction o f the endowment and returns effects indirectly suggest some degree o f sorting by observable attributes o f households, w h c h is to say selective migration of households and individuals with better attributesfrom LIR to IR (see Box 4.3). See for example, Roy (1951) for location sorting; Fujita et a1 (1999) for agglomeration economies; Ravallion and Jalan (1999) for the externalities o f public infrastructure in a region; and Kanbur and Rapoport (2005) for the costs o f migrationand how proximity can influence migration flows. For more references see Annex 4, section 11. 63 Figure A Endowment effects for IR-LIR Gap (2000 Figure B Return eflects for IR-LIR Gap (2000 and and 2005) and 95% ConfidenceIntervals 2005) and 95% Confidence Intervals - - P"C.nI. P.rC."D* .2WO -1005 I .2WO -2005 38. None o f the above factors affect all households and locations equally. The sorting o f unobserved attributes i s likely to be more important for relatively better-off households and urban areas where agglomeration forces can attract high-return economic activities. On the other hand, the differences inreturns between rural areas across regions for better-off households are likely to reflect the differences in attributes like public infiastructure and market access across regions. Barriers to migration are more likely to affect poorer households. 39. The IR-LIRdifferences in returns can be examined in the light o f the above arguments (see Annex 4, section 111). Differences inreturns for better-off households are smaller for rural areas o fthe two regions, which suggests sorting of households and economic activities with unobserved and better attributes to urban areas within IR." T h i s i s consistent with the pattern o f agglomeration o f high-returneconomic activities in the main urban center o f IR, namely Dhaka and its surrounding areas (see Figure 4-5 above). Higher urbanagglomeration inIR compared to LIR is also consistent with an earlier finding (chapter 2) that the urban premium on wages is muchhigher for the eastern divisions than for the west. Between 2000 and 2005, the urban-rural gap in returns for better-off households in IR increased sharply, suggesting increasing l8This is also consistent with the sorting by observed characteristics of households, resulting in the characteristics improving faster inIR as seen above (see paragraph 32 above). 64 agglomeration in the urban areas within IR, with Dhaka playing a major role given its size relative to other urbanareas. 40. Butthe gaps inreturn effects across rural areas inIR and LIR are still substantial, even for the upper quantiles - suggesting that differences in availability of public capital and market access play a key role in sustaining differential returns across regions. This i s also consistent with the findings o f section I1that characteristics like travel time to Dhaka and access to infrastructure partially account for the effects o f locationonhousehold expenditures. The average travel time to Dhaka for households in IR i s 33 percent lower than that in LIR. Forty-five percent o f households inIR are connected to electricity, compared to 22 percent o f those inLIR. Given the importance o f market access and infrastructure for the private sector, differences in these indicators explain why employment in the nonfarm sector (including self-employment) grew muchmore rapidly inthe East than inthe West between 2000 and 2005 (chapter 2). 41. The significant IR-LIR gaps inreturns for households at the lower half o f the distribution are consistent with barriers to mobilityfaced by thepoor. There is virtually no difference inreturns to observed household attributes across urban and rural areas within each region for poor households, which seems to suggest no serious barrier to their mobility within IR or LIR.l9The results suggest that while within each region, migration helps to equate returns for poorer households, the barriers created by the rivers impose significant costs to migration between regions - for example by hindering short-term migration and commuting, which in turn can limit the formation o f migration networks (see Annex 4, section 111). These barriers contribute to sustaining and even widening the differences in returns between IR and LIR among the poorer households. Increase inspatial concentration over timeZo 42. The results above suggest increasing agglomeration inthe urban areas within IR. This seems to be supportedby Economic Census data on employment in firms with Total Persons Employed (TPE) o f 10 or more. Figure 4-9 below shows a clear trend o f increasing concentration inurban and peri-urban areas, especially Dhaka city and its surrounding areas between 2003 and 2006.2' Large increase in formal sector employment occurred in Dhaka and its surrounding areas, along with smaller increases in other large cities. These were accompanied by reductions in employment in some o f the country's outlying areas and no discernible change in most parts o f the country. Disaggregation by sectors reveals that the largest increases in formal sector employment have occurred inagro-processing industries that includes RMG. 43. What has led to the high concentration o f economic activities in urban and peri-urban areas surrounding growth poles? Important factors influencing a firm's decision to locate in an area appear to be urbanization and spatial (or agglomeration) economies, the degree o f competition, and specialization and diversity o f economic activities. Market size and specialization in the nearest growth pole exert significant influence on where a new entrant i s likely to locate; such spillover effects are relatively weak for small and medium sized cities (see Box 4.4). l 9This is also broadly consistent with an earlier finding (see chapter 1): while the urban share of the population increasedby 23 percentbetween2000 and 2005, the rural-urbanpopulationshift occurred more within divisions than acrossdivisions. Maps andfigures usingEconomicCensus are taken fromWorld Bank(2008c, draft). Most of the data fromDhakaandother urban areas for the Economic Census of 2003 was actuallycollectedin 2001. Thus the time period for comparison for urban areas where most of the firms are located (2001-2006) roughly correspondswith that for HIES surveys(2000-2005). 65 and specialization in the nearest urban centers, and tend to be much townsthanfor large cities. sourc ural InvestmentClimate Asses 44. An interesting pattern emerges from the spatial trends in employment within the greater Dhaka area. Large increases in formal sector employment occurred in the outlying areas to the north and west of Dhaka City Corporation, while employment inthe city center o f Dhaka (close to the river inthe south) declined, although it still remains extremely dense (Figure 4-10). Figure4-9: Changes in employment Figure 4-10: Changes in employment in Dhaka (2003-2006) city and surroundingareas (2003-2006) Fi;J 3 P 1 s~a 0 *iPq ~~~ 3cm qi Note Employmentin firms with TPE of IO+, greenindicatespositivechange,with darker greenindicatinghighervalues, red indicatesnegativechange, with darkerredindicatinghigherabsolutevalues Source EconomicCensus (2003 and 2006) 66 45. Thus even as there i s a tendency towards higher concentration in the greater Dhaka region, within the region there is a trend o f dispersion from the core o f the city to outlying areas, particularly to the north and west. This may be a result o f increasing agglomeration costs in the main city (Box 4.5). Another question i s why the dispersion occurred primarily to the north and west o f the main city, and whether this i s related to infrastructure and other factors that influence business climate (Box 4.5). The simultaneous trends o f increasing concentration in the greater Dhaka region and dispersion from the core towards the outlying areas within this region are consistent with the regional poverty trends in section I- four out o f the five districts bordering Dhaka district experienced poverty reduction well above the national average. Box 4.5: Dispersion With very high concen Bank, 2007). her availability of lan oit the spillovers and var) have seen rapidexpansion road access to Dhaka and the Explaining regional disparities: the importance of local factors 46. The factors suggested by the analysis above - lack o f availability o f public infrastructure, inadequate market access and lack o f integration with the growth poles - represent the significant economic disadvantages faced by LIR vis-A-vis IR on the average. Inaddition, a range of local conditions are important inexplaining regionaldisparities inpoverty, including differences across areas within IR and LIR, which are not hlly captured by the broad IR-LIR (or east-west) approach adopted here. As noted in section 11, severe seasonal deprivation (Mongu) contributes to chronic poverty in the greater Rangpur region and natural disasters like cyclones are likely to have had a lasting economic impact inthe southern coastal districts. Ecological factors like river erosion on both sides o f Brahmaputra contribute to relatively highpoverty inJamalpur district in IR, as does the presence of depressed land with large water bodies (called Haor) inNetrokona and Sunamganj districts in IR (in the old districts o f Mymensingh and Sylhet respectively). '*At Bangladesh's income and urbanization levels, Henderson's estimate suggest an optimal primacy rate o f 21 percent for Dhaka compared to its actual primacy o f 32 percent, which translated to a 2 percentage point loss in GDP growth inhiscross-country model (World Bank 2007a). 67 Quality o f land also contributes to economic deprivation - for example, the intrusion o f saline water into the southern coastal districts that prevents multiple cropping.23 47. A comprehensive analysis o f the local factors contributing to poverty is not possible with current sources o f data, given that HIES data i s not even representative at the level o f old districts. To address this need, a poverty mapping exercise is about to be completed, which will provide poverty estimates at the upuzilu (sub-district) level by combining census and HIES data usingthe statistical technique o f small area estimation. The poverty map will help identify poor areas more accurately and help understand how differences in local characteristics (such as infrastructure, service delivery, agro-climatic conditions and so on) contribute to differences in poverty incidence across geographic areas. Such analysis can be valuable in designing policies that offer solutions tailored to the constraints faced by local economies in different parts o f the country. IV. Implicationsfor policies to reduce regional inequality 48. Regional inequality in economic development i s common around the world. Over the years, countries have implemented three types o f policies to address regional inequality within a country. The first involves spatially "blind" policies such as tax and transfers, intergovernmental transfers, reforms in housing and landmarkets and investments inhuman capital. Although these policies may not be designed with any spatial considerations, they can have varying impacts across regions. For instance, a progressive income tax system along with similar sub-regional fiscal revenue sharing system can benefit the relatively laggingregions; and investments inhealth and education can produce a skilled labor force to the benefit o fboth leading and lagging regions. The second set o f policies aims at improving connectivity among regions and incentives to foster mobility o f people in lagging regions. The third set o f policies involves providing spatially targeted incentives to firms andpeople to locate inlaggingregions. Historical experience (mostly with developed countries) suggests that the first set o f policies tend to be most effective in promoting regional convergence in economic devel~pment.~~However, most developing countries including Bangladesh find it difficult to adequately finance even basic government services, leaving little room for fiscal transfers to address regional inequalities. 49. The set o f policy instruments needed to foster development o f the lagging regions ultimately depends on the factors that drive the regional differences in the first place. In this respect, Bangladesh offers a few advantages. First, the country i s densely populated making investment in connectivity feasible and yielding highreturns. Second, much o f the interregional differences in Bangladesh are due to factors unrelated to ethnic divisions or other non-economic barriers to migration, which are potentially harder to address than economic barriers. Indeed, the findings o f section I11suggest fewer barriers to spatial mobility o f the poor within regions. Improving endowmentsin lagging regions 50. As differences in physical and human endowments contribute significantly to the IR-LIR expenditure gap, narrowing o f this gap would require investing in productive assets and enhancing employment opportunities and human development in LIR. Indirect evidence suggesting that householdshndividuals with better attributes are more likely to migrate from LIR to IR also implies that investments to improve human capital o f the poor would enhance their ability to access better opportunities in growing regions. Improving credit access to household 23We are grateful to ProfessorWahiduddin Mahmudfor pointing out these examples in his comments on an earlier draft o fthe report. 24See World DevelopmentReport (2009). 68 nonfarm enterprises (microfinance) can also reduce the welfare differences within each region. Given the widespread availability o f microfinance, investments in promoting small and medium enterprise lending are likely to have greater returns. 51. Investments inhuman development may not be sufficient by itself to close the economic gap between IR and LIR, given that human development outcomes in LIR are already on par or superior to those in many areas o f IR (see chapter 5). The fact that differences in returns to endowments contribute significantly to the IR-LIR gap suggests the need for other types o f interventionas well. Improvingreturnsto endowments:urbanpoliciesand regionaldevelopment 52. Substantial differences in returns to household attributes persist across IR and LIR regions. Reduction o f the IR-LIR gap would thus require policy interventions that affect the rates o f returns, which the findings o f this chapter suggest must include steps to improve the access to public services and market connectivity for lagging regions. The question o f how to improve returns to factors in lagging regions i s closely linked to two broad policy questions. The first i s how to gain the most out o f the urbanization process ongoing inBangladesh andspread economic dynamism beyond the major growth poles, including urbanareas inLIR. The second and related question is how to stimulate economic growth inLIR, by improving links to IR andwithin LIR. 53. Rapid urbanization in Bangladesh creates opportunities for poverty reduction, as apparent from the benefits generated by the growth poles in the eastern part o f the country. Careful urban management policies would help in gaining the most out o f this process and spreading the benefits to lagging areas. The economic dynamism o f Dhaka and its spillovers to surrounding areas makes it a magnet for economic activities and migrants; but the city's already stretched services and infrastructure imposes costs on enterprises and therefore on the economy as a whole (World Bank, 2007a). On the other hand, the urban areas in LIR lag behind those in IR both in terms o f household characteristics and returns to those, suggesting that weak agglomeration and spillover effects inthe former. LIR lacks growth poles like Dhaka and Chittagong; and smaller towns are especially deficient in terms o f attracting economic activities when compared to the large metros and peri-urban areas. The urban policy challenge therefore has two elements: (i) improving the access to basic infrastructure and services in major metropolitan areas; and (ii) improving the prospects o f smaller towns and secondary cities to emerge as growth centers. 54. Recent development literature suggest that spreading growth to smaller towns and cities and reducing the agglomeration costs inthe main growth centers require coordination between urban policies at various levels. Optimal urban concentration levels are achieved when there are institutional mechanisms internalizing the benefits and costs o f agglomeration in migrants' and enterprises' private decisions. A combination o f integrated urban planning by proactive autonomous local governments and well-hnctioning urban land markets can create interconnecting incentives that allow cities to achieve optimal sizes and improve infrastructure and services (see Box 4.6).25 For Bangladesh, other World Bank reports have identified a number o f specific areas for attention with regard to urban policies.26 Examples o f reforms in urban policies include increased devolution o f key services to city governments to improve accountability, enhancing their own revenue sources, correcting policy biases that have worked against the emergence o f smaller cities, and creating the right incentive structures and competition among cities. *'SeeKrugman (1999); Henderson and Becker (2000); Henderson and Wang (2005). 26See World Bank (2007a) and World Bank (2008b, draft). 69 55. International experiences also suggest that investments in interregional transport infrastructure can promote regional development by improving connectivity o f remote areas to growth poles (Box 4.6). Experience with such strategies has, however, beenmixed. For instance, improving connectivity between regions may lead to further concentration o f activities inleading regions (see Box 4.6) as has happened in the case o f Italy, Brazil and China. Transport improvements also may not be enough to promote regional growth and require complementary investments in other factors that matter for investment climate. That said, improved connectivity is likely to reduce interregional differences in living standards by improving agricultural specialization andcommercializationandthe mobility o f people. 56. InBangladesh, investments ininterregional transport and communication systems - including bridges across major rivers - can improve links between smaller and larger markets, attract investment to secondary cities and towns and reduce the pressure on Dhaka metro (World Bank, 2007a). To be effective, however, these investments will need to be complemented with improvements in the urban infrastructure and public services o f the lagging region, for which urbanmanagement policies would play a key role as described above. 57. Investment in infrastructure, especially in developing inter-regional highway systems, i s likely to improve connectivity between IR and LIR and increase access o f LIR residents to growth poles. The findings o f this chapter also suggest that improving connectivity with Dhaka and the local markets and improving access to services like electricity are likely to generate economic benefits in remote areas. The aforementioned N M I C A (2007) survey finds lack o f connectivity andpoor access to and quality o f electricity to be among the major constraints facing nonfarm enterprises. L o w demand is identified as one o f the most binding constraints by nearly 40 percent o f rural and small town enterprises. Most enterprises face daily power outages and lose about 3 to 4 percent o f their sales revenue due to power outages. Unreliable electric supply affects enterprises located insmall towns disproporti~nately.~~ 58. Since the two large rivers act as natural barriers to connectivity between LIR and IR, investment inriver bridges - particularly connecting the southwestern part o f the country (Barisal ''Eighteenpercent o f enterprises in small towns mention access to electricity and unreliability of its supply due to power outages to be a major constraint, compared with 10percent inpen-urban areas (World Bank, 2008b) 70 and Khulna) to the east - would have a large impact on connectivity. The experience o f Jamuna Bridge is instructive; the completion o f this bridge in 1998 brought enormous benefits to the northwest, which used to be among the poorest regions o f the country. The bridge i s likely to be an important reason why poverty reduction in northwest during 2000-2005 outpaced that in the southwest, which remains separated from the rest o fthe country by the two large rivers (Box 4.7). ' the world, the Jamuna(Brahmaputr than Rajshahi in 2000, but 59. Given that lack o f migration networks may limit the poor's mobility ability to improve returns to their endowments by migrating from LIR, programs to bridge the information gaps between IR and LIR (such as publicizingjob andhousing information o f IR inLIR) can facilitate mobility. The Government has recently taken innovative steps to facilitate organized contract migration from the Monga-prone greater Rangpur districts in the northwest to foreign countries (see chapter 3). Innovations along similar lines can also be piloted to facilitate domestic migration from poor rural areas inLIR. 60. Spatially targeted incentives have been tried inmany countries, through investment subsidies, tax rebates, location regulations, and provision o f services in designated areas such as special economic zones (SEZs). Experience with spatially targeted incentives in India and China, particularly Special Economic Zones (SEZs), shows that these incentives are more likely to succeed when they reinforce geographical advantages. For instance, the successful SEZs in China were established along the main coastal cities which were simultaneously opened to international trade and investments. Experiences like these, as well as Bangladesh's own experiences with SEZs, can guide future policy in designing spatially targeted incentives to stimulate growth centers in the less integrated region. Spatially targeted programs can also be used to create rural livelihoods in the nonfarm sector and improving credit access to household nonfarm enterprises in lagging regions. Such interventions are especially necessary to address structural poverty in certain areas, for example areas affected by seasonal shocks likeMonga or natural disasters. 71 72 5. Creating HumanCapital: Bridgingthe Access and Quality Gap 1. Previous chapters have highlighted that investments in human capital development are critical to reduce poverty in Bangladesh. For example, higher education among household members enhances employment opportunities in the non-farm sector and reduces the likelihood o f poverty. Improving human capital endowments is also likely to enhance the mobility of the poor from lagging regions, as mentioned inchapter 4. Other than its impact on consumption poverty, human development i s a critical objective in itself, with interrelated effects on other development outcomes - for example, improved nutrition levels among children lead to better schooling outcomes, healthier adults, and higher lifetime earnings. 2. This chapter discusses a number o f gains and challenges evident in human development in Bangladesh, focusing on the health and education sectors. Bangladesh has seen gains across the population distribution in education and health outcomes, and is well on the way to achieving its Millennium Development Goals in areas such as infant and child mortality and even met its MDG in gender parity inprimaryand secondary schoolingby 2005 (10 years ahead o f time). However, a range o f inequalities in opportunities and outcomes persist inboth sectors, and the poor still face many challenges inaccessingquality educationandhealthservices. 3. The rich-poor gap is evident, for example, in that better-off households have tended to benefit from gains in nutrition to a greater degree than poor households. Boys from poor households, meanwhile, appear to be gettingleft behind inthe gains that the country has made ineducational attainment compared to both girls in poor households and boys in better-off households. In addition to income and gender disparities, somewhat unexpected variations exist inboth health outcomes and education outcomes across divisions. Inparticular higherpoverty rates at the division level, interestingly, seem to coincide with both better healthandeducation outcomes. I. Healthsector:gainsandongoingchallenges 4. As discussed in Chapter 1, Bangladesh experienced a substantial decline in poverty over the past decade. This section explores whether Bangladesh has seen corresponding gains in the health status o f poor households during that period. The country has made some progress in improving health outcomes and service utilization, including considerable gains in reducing the gender gap ininfant mortality rates, for example. The rich-poor gap (proxied by the top and bottom quintiles respectively), however, remains unchanged and has even worsened for a number o f indicators. In particular, this section discusses inequalities in health access and outcomes based on Demographic and Health Survey (DHS) data from 1996-1997,2004, and 2007. Access: service utilization, availability and spending 5. Policy changes in 1998 focused on improving poor households' utilization o f primary or essential care services through the Essential Services Package (ESP). The ESP was also intended to reduce inequalities between poor and better-off households. Although the period between 1996-1997 and 2004 saw an increase in households' overall use o f health services such as family planning, antenatal care, and immunization o f children, the absolute rich-poor gap widened for most services (Table 5.1). Gender gaps inutilization, moreover, exist mainly amongthe poor. 'World Bank (2007~)"To the MDGsandBeyond:AccountabilityandInstitutionalInnovationinBangladesh." 73 6. The Expanded Program on Immunization or EPI, a priority program which aims to immunize all children under one year o f age against the six vaccine-preventable diseases, has led to substantial improvements inchildhood immunization coverage (including tetanus toxoid immunization) over the past decade. Full basic immunization coverage rose from 47 percent in 1996-1997 to 58 percent in 2004 for the bottom20 percent wealth quintile and 67 percent to 87 percent for the top 20 percent wealth quintile.2 While there have been gains across the board, this improvement occurred primarily among better-off households. L o w coverage o f measles immunizationamong poor children i s a factor behindthis widening inequality, with measles immunization actually dropping for the poor during the same period from 62 percent to 60 percent. The percentage o f children with full basic immunization coverage also shows a gender gap among the poor: 55 percent for poor girls versus 60 percent for poor boys in2004. Inthe case o f rich children, however, the coverage among girls is higher than that among boys (see Table 5-1 for furtherdetails). Curative care for childrenwith fever 12-23 months: Public Y Contraceptive prevalence rate (YOcurrently married) 44.7 50.0 5.3 38.8 48.5 9.7 Antenatal care by medically trained person' 24.9 81.1 56.2 16.0 62.3 46.3 Tetanus toxoid (%) 77.4 92.2 14.8 70.0 90.2 20.2 Delivery attended by medically trained person' 3.3 39.4 36.1 1.8 29.8 28.0 Delivery at home (YOwomen) 97.6 67.9 -29.7 98.5 80.5 -18 8. Despite good access to family planning services, however, poor pregnant women's contact with the health system i s very limited incomparison to better-off pregnant women. In2004, only 34 percent o f all poor pregnant women utilized antenatal care services as compared to 84 percent o f all rich mothers. The DHSdefines socioeconomic status interms of assets or wealth, rather than income or consumption that are unavailable from the survey; households are classified into quintiles based on their wealth or assets index using the principal components approach described inFilmer and Pritchett (2001). 74 Furthermore, the percentage o f pregnant women accessing antenatal care by a trained medical provider increased for all quintiles between 1996-1997 and 2004, but most o f this increase was for better-off women. The gap between rich and poor pregnant women receiving antenatal care by medically trained providers between 1996-1997 and 2004 grew from 46 percentage points to 56 percentage points (see Table 5-1). 9. The lack o f use o f professional facilities for childbirth remains a key factor behindmaternal mortality among the poor. Not surprisingly, inequality i s also evident in the type o f provider used. Women inpoor households are more likely to use government health facilities for antenatal care and better-off women are more likely to use private providers. Lack o f adequate care during child delivery across wealth quintiles continues to be problematic in Bangladesh and has been linked to the country's high rate o f maternal mortality. An increase in trained attendance at birth (from 30 percent to 39 percent) and a reduction in births delivered at home (from 81 percent to 68 percent) were seen among women in the top quintile between 1996-1997 and 2004. Poor women, however, experienced very little change intheir use o f these services during this period. The bottom quintile o f women saw an increase from 1.8 percent to only 3.3 percent in trained attendance at birth 1996-1997 and 2004, and deliveries at home during this period declined by only around 1percentage point for this group. 10. A larger percentage o fboys than girls receive treatment from private providers. Girlsare more likely to receive treatment from government providers, suggesting that households, poor and richalike, are more willing to pay for better quality care for boys than girls. Regardless o f gender and type o f provider, the poor are far less likely to seek treatment compared to the non-poor for the same illness. For example, there was a 30.2 percent gap inuse o f medical treatment for fever between children inthe bottom and top quintiles in2004. Quintiles Poverty status 1 1 2 1 3 1 4 1 5 Poor I Non-poor Total Panel 1: Using World Bank (2002) assumptions to break down health spending byfunction Family planning and communicablediseases 20.8 20.0 19.8 20.2 19.2 40.8 59.2 100 Adult curativecare 15.1 17.6 18.2 23.9 25.2 33.0 67.0 100 Maternal health 18.7 19.9 19.4 20.8 21.2 38.7 61.3 100 Child health 25.9 21.2 18.5 18.4 16.0 47.0 53.0 100 Total 19.7 19.5 18.9 21.1 20.8 39.3 60.7 100 Panel 2: Using Health Economics Unit 2004) assumptions to break down health s,7ending byfunction Maternal and child health, family planning, counseling 22.1 20.6 19,1 19.2 19.0 42.7 57.3 100 Adult curativecare 15.9 16.6 21.1 23.0 23.4 32.6 67.4 100 Total 18.2 18.0 20.4 21.7 21.8 36.3 63.7 100 11. Total health expenditures in Bangladesh relative to GDP are comparable to other countries in the region. Expenditures per capita and adjusted for purchasing power parity, however, suggest that Bangladesh is ahead o f only Myanmar in terms o f health spending (Begum and Dmytraczenko 2008). A benefit incidence analysis using two different approaches from the World Bank and the National Health Account (see Table 5-2) suggests that public spending on each quintile's share o f expenditures on family planning, communicable diseases and maternal health i s about equal to their population share. However, 75 even when the poor receive less than their share o f spending, they may benefit more. Studies show that while public spending has no significant effect on the health o f the non-poor, it has a positive marginal impact on the health o fthe poor (Wagstaff 2003). 12. Outside o f income and gender inequalities, the general quality of public service provision continues to be low. Highrates o f absenteeism are evident in public health care, reaching as high as 40 percent at the sub-district level, with small facilities inrural areas bearingthe brunt o f the problem (Chaudhury and Hammer 2003). Availability o f humanresources does not compare favorably with neighboring India and Pakistan, with Bangladesh having 26 physicians per 100,000 population compared to 60 per 100,000 in India and 74 per 100,000 in Paki~tan.~Bangladesh, however, has a comprehensive network o f health facilities across its 64 districts, with a 50-200 bed hospital ineach district. Results from a recent national level survey show inequalities in both experiences and perceptions across a range o f services, both in health andeducation (see Box 5.1 insection 11). Outcomes: healthandnutrition 13. Bangladesh has made significant gains in terms o f reducingfertility and mortality rates. Data from DHS 2007 show that the country's total fertility rate i s 2.7 births per woman, down from 5.1 births per woman in the mid-1980s. However, there are some signs o f this trend leveling o f f in recent years. Improved access to family planningservice due to Bangladesh's successful doorstep delivery program led to a sharp increase and reduction in inequality in the contraceptive prevalence rate. This rate rose, for example, from 39 percent in 1996-1997 to 45 percent in 2004 amongst the bottom 20 percent o f poor married women. The gap incontraceptive prevalence rates between the bottom 20 percent wealth quintile andtop 20 percent wealth quintile also narrowedover the sameperiod. 1994-2003 1985-1996 Bottom Top Gap (Bottom- Bottom Top Gap (Bottom- Mortality rates 20% 20% Overall TOP) 20% 20% Overall TOP) Infant (0-1year) 89.7 64.8 72.4 24.9 96.5 56.6 89.6 39.9 Child (1-5 years) 26.0 41.9 Under-5 121.1 71.5 96.6 49.6 141.3 76.1 127.8 65.2 Infant - girls 85.8 57.7 64.3 28.1 93 59.8 84.3 33.2 Infant -boys 93.5 72.1 80.2 21.4 99.8 53.5 94.9 46.3 Child - girls 29.0 47.0 Child -boys 24.0 36.9 Under-5 - girls 118.8 64.5 91.0 54.3 149.9 79.8 127.3 70.1 Under-5 - boys 123.3 78.8 101.9 44.5 133.2 72.5 128.3 60.7 14. Successes in family planning, moreover, helped women space their births and reduce the associated risko f infant mortality. Under-5 mortality rate has declined steeply -from 128 per 1000live birthsduring 1985-96 to 97 during 1994-2003, and subsequently to 65 during 2002-2006 (from the 2007 round o f DHS). Rich-poor differences ininfant and under-5 mortality have also shown a declining trend (see Table 5-3). In infant mortality, for example, the rich-poor gap declined from 40 percentage points in the period o f 1985-96 to 25 in 1994-2003. Moreover, between 1985-96 and 1994-2003, under-5 mortality fell more for girls than for boys to the extent that the rate was significantly lower for girls than for boys in 1994- 2003. This was driven by steep declines ininfant and child (age 1-5 years) mortality for girls. However, girls' risk o f dyingbetweenthe ages o f 1and 5 continued to be higherthan that for boys. Furthermore, the WHO `Global Atlas o f the HealthWorkforce' (2004). 76 rich-poor gap in female under-5 mortality continues to be greater than that for male under-5 mortality, suggestingthat girls' survival i s more strongly correlated with household income levels. 15. There have also been significant improvements in feeding practices, which play an important role in preventing malnutrition among infants. The percentage o f infants receiving timely complementingfeeding has nearly doubled for poor households (to 57.8 percent) and increased more than three times for better off households (to 71.3 percent) between 1996-1997 and 2004. Although the rich-poor gap has increased for this indicator, a gender gap among infants i s not evident. 16. Malnutrition among children inparticular is an issue o f critical importance for a developing country, with numerous adverse short- and long-term impacts. Inthe short term, malnutrition increases children's vulnerability to diseases and therefore the risk o f mortality. In the long term, fetal or childhood malnutrition increases the likelihood o f chronic noninfectious diseases in adulthood. Studies using longitudinal data show that malnourished children receive less education4 - either because their parents investless ineducation or because they have higher rates o f absenteeism from school due to illness. Poor nutritional status may delay school entry, impair cognitive development, and potentially reduce lifetime earnings due to its impact on childhood learning.5 2004 1996/97 Gap Gap Bottom TOP (Bottom- Bottom Top (Bottom- 20% 20% Overall Top) 20% 20% Overall Top) Stuntingamong childrenunder5 years 54.4 25.1 43.1 29.3 61.1 34.8 54.7 26.3 (moderateand severe) Underweightamong childrenunder 5 59.3 30 47.5 29.3 65.2 37.6 56.4 27.6 (moderateand severe) Stunting-girls 53.6 27.1 43.5 26.5 61.8 44.4 55.1 17.4 Stunting-boys 55.1 22.7 42.6 32.4 60.3 35.4 54.2 24.9 Underweight-girls 59 31.3 48.6 27.7 67.9 38 58.2 29.9 Underweight-bow 59.6 28.4 46.4 31.2 62.5 37.2 54.7 25.3 Note; Quintiles of wealth index Source: Gwatkin and others (2007). Analysis of Bangladesh DHSs,respective years. 17. Child malnutrition and child underweight rates in Bangladesh have declined significantly since the early 1990s. Underweight rates declined at a rate o f 3.6 percent per year during the 1990s, a pace similar to that o f Sri Lanka and better than that o f India. However, unequal progress in child malnutrition rates remains a challenge. Stunting rates have declined, but most o f the decline occurred among children in better-off households, with the rich-poor gap between stunting rates among children under 5 years growing from 26 percentage points in 1996-1997 to 29 in 2004 (see Table 5-4). Although better-off households have experienced more gains in nutritional status than poor households, a strikingly high proportion (25-30 percent) o f children in the richest quintile were malnourished in 2004. The overall malnutrition rate inBangladesh remains highby international standards - higher, for example, compared to sub-Saharan countries with similarlevels o fper capita income (see Annex 5, Table A-5.1). A study usinglongitudinaldata from Cebu, Philippinesfoundthatbetter-nourishedchildrenwere morelikely to start school earlierand less likely to repeat grades (Glewweandothers, 2001). Another study usingdata from rural Pakistanfoundthat malnutritiondecreasedthe probability ofever attendingschool(Aldermanandothers, 2003). 'A study ofadultidenticaltwins inthe United States foundthat, aftercontrolling for genetic andother endowments sharedby such twins, low birthweight hada large impacton schoolingandwages (Behrman andRosenzweig,2004). 77 18. Disaggregating trends by sex also demonstrates increasing absolute inequality in nutritional outcomes. The difference between the top and bottom quintiles in stunting rate among girls increased from 17 to 27 percentage points, and that in underweight rate among boys increased from 25 to 31 percentage points. A gender gap persists as well - the percentage o f girls who are underweight exceeds the percentage o f underweight boys by 2.2 percent in 2004, marginally lower than the 3.5 percent gap in 1996-1997 (see Table 5-4). Interestingly, in 1996-1997, girls were both more stunted and underweight than boys in the same wealth quintiles. By 2004, however, girls from the bottom 20 percent wealth quintile were less stunted and underweight than boys inthe same quintile but girls from the top 20 percent wealth quintile remained worse offthanboys intheir wealth quintile. Regional variations in health outcomes 19. Bangladesh's Health, Nutrition and Population Strategic Investment Planor HNP SIP for 2003-2010 aims to link expenditures to individual district performance in order to reach either poor districts or districts with poor health indicators. This geographic targeting may well be effective since regional variations are evident across a number o f health indicators (see Table 5-5). For example, in 2004, Dhaka division had an infant mortality rate o f 75 deaths per 1,000 live births versus Barisal's 61, despite having a lower poverty headcount ratio o f 32 percent versus Barisal's 52 percent in 2005. A high degree o f concentration is evident for the 2007 figures on child malnutrition as well. Sylhet and Chittagong stand out as consistently having some o f the worst outcomes (among the highest child and under-5 mortality rates and stuntingrates) in2004 and 2007, while Khulna stands out as having the best outcomes (lowest child andunder-5 mortality, stuntingand underweight rates). 2004 2007 2005 Infant Child Under-5 Stunting Underweight Poverty mortality mortality mortality (under 5 (under 5 headcount rate rate rate years ) years) rate Barisal 61 32 92 47 46 52 Chittagong 68 39 103 46 42 34 Dhaka 75 27 99 44 40 32 Khulna 66 13 78 35 34 46 Rajshahi 70 17 86 42 43 51 Sylhet 100 29 126 45 42 34 21. The mismatch between poverty and human development outcomes at the spatial level, especially with respect to health indicators, has been documented in earlier work (BIDS 2001; Sen and Hulme 2006). The fact that such mismatches continue to persist even in the context o f higher growth, faster poverty reduction, and accelerated progress in human development witnessed during 2000-2005 i s striking. 78 Table 5-6: Service utilizationby division, 2007 Childhood Antenatal careby Delivery attendedby Vitamin A dose immunization(YO medicallytrained medicallytrained -children(% 13-23 months), person(%women person(YOwomen Poverty children9-59 fullbasic pregnantinpast 5 givingbirth inpast 5 headcount months) coverage years) years) ratein2005 Barisal 84.9 90.2 43.6 13.4 52 Chittagong 86.0 77.2 52.4 18.5 34 Dhaka 89.7 82.4 48.2 19.8 32 Khulna 90.7 88.9 62.6 26.6 46 Rajshahi 88.8 85.6 55.0 15.4 51 Sylhet 87.5 70.8 46.9 10.9 34 22. The extent o f NGO program coverage could be one explanation behind the spatial divide in health indicators. Lagging regions o f Bangladesh ("lagging" defined in income-poverty terms) actually have a much higher concentration o f NGO activities than income-affluent regions. From DHS data (2004), the proportion o f rural respondents covered by NGOs ranges fiom 18 percent in Sylhet and 23 percent in Chittagong division to 34-35 percent in Rajshahi and Khulna. Analysis using DHS health data shows the relative advantage o f NGO membership across poverty categories (see Annex 5, Table A-5.2) and higher marginal effects o f NGO membership on health outcomes after taking into account household and community level controls (see Annex 5, Table A-5.3). Depending on choice o f health indicators, the relative advantage o fNGO membership over non-members can be inthe order o f 3-11percent. There are a number o f plausible explanations for this "NGO effect"- such as higher awareness among NGO member households and/or higher expenditures by NGOs on services for member households and spillover effects on non-NGO members residing in the same community (Dev et a1 2002, Munshi and Myaux 2006). The analysis here, however, cannot identify which o f these are more important in explainingwhy NGO membership seems to matter for health outcomes inBangladesh. 23. However, NGO presence is clearly not the only explanation behind these somewhat counter-intuitive trends. Another possible explanation for the slower progress inhealth indicators inChittagongand Sylhet is that these regions have a greater historical backlog o f relatively conservative social norms, as expressed in higher desired family size, more restrictive attitudes on women's physical mobility and related indicators o f female empowerment.6 These issues merit further explorations infbture research. IL Education sector:gains and ongoingchallenges 24. Investment in education in Bangladesh is associated with higher returns in the labor market and higher productivity in the agricultural sector. Higher education among household members - including the householdhead, hisher spouse as well as other members o f the household-has significantly positive impact on the household's economic status (chapter 3). Research on Bangladesh has shown significant intra-household externalities of education - education o f household members, and especially that of women, has a significant positive impact on the earnings o f even uneducated members o f a household.' Education i s also associated with better health outcomes through income effects as well as direct effects Higher initial disadvantages of women-related health and demographic indicators in Chittagong and Sylhet date back to the 1974 Census, which may be reflective of the long-termimpact ofpre-existing, relatively conservative socialnorms. * This section draws substantially from Al-Samarrai (2007b, 2007c), backgroundpapers for this report Researchusing 1995-1996HIES data for Bangladeshfinds that an illiterate adult has significantly more nonfarmearnings when living in a family with at least one literate member (holding a range of personal attributes constant); and that these effects are strongest for women (Basu et a12001). 79 from better knowledge and health practices. Investment in education i s thus a key component in the government's h v e to improve welfare among the poor. 25. As with other countries, primary school enrolment is correlated with socioeconomic status and gender in Bangladesh. Current policy focuses on improving quality o f education while maintaining and improving upon the gains in access achieved during the 1990s. This section explores the extent to which the government has so far been successhl inachieving these goals. Enrolmentandcompletion 26. The 1980s and 1990s saw a steady growth inprimary school enrolment, while the 1990s saw a sharp rise insecondary and higher secondary school levels. Primary enrolments increasedby 4 percent annually duringthe 1980sand 1990s, increasing from 8.2 million students in 1980to 16.8 million in 1998. At the secondary and higher secondary levels enrolments almost tripled from 3 to 11 million students between 1990 and2000. Female enrolment at the secondary level rose at a faster rate than male enrolment, inpart due to the introduction o f a country-wide female secondary education stipends scheme. In 1990, girls represented only 27 percent o f secondary enrolment compared to 51percent in2000. The gender gap also narrowed at the primary school level but more modestly. Female enrolment represented 40 percent o f total primary enrolment in 1980compared to 45 percent in 1998 (see World Bank 2002). Figure 5-1: Gross and net enrolment rates in Bangladesh,2000-2005 Source: HIES (2000 and2005) 27. Bangladesh has seen little change inprimary gross enrolment rates since 2000, when it was close to 90 percent (see Figure 5-l), although improvements in net rates indicate that more children o f primary school age were attending primary school in 2005. Improvements in primary school completion rates since 2000, moreover, were driven largely by increases in enrolment during the 1990s. Enrolment rates and educational attainment amongst boys from poor households have not kept pace with these gains. Between 2000 and 2005, the rich-poor gap in net primary school enrolment between boys grew from 15 to 16 percentage points, while the corresponding gap between girls narrowed from 14 to 13 percentage points. 28. At the secondary level, the country has experienced substantial growth in enrolment but declining completion rates. Secondary gross enrolment rate rose from 20 percent in 2000 to 30 percent in 2005. Figure 5-1 demonstrates that gross enrolment rates at higher secondary level are low and appear to have declined since 2000. The higher secondary gross enrolment rate was 54 percent in 2000 compared to 41 percent in 2005. Interestingly, this decline has been concentrated amongst the non-poor with the largest decline occurring in the richest 40 percent o f households. It is unclear why this has occurred, but it is 80 possible that declines in secondary completion have affected enrolment in higher secondary. These low completion rates suggest that improving grade progression at already existing schools would be critical for improving educational attainment. Primary (Classes 1-5) Secondary (Classes 6-10) Higher sec (Classes 11-12) male female total male female total male female total Quintiles 1 I 1 83 77 25 35 30 9 7 8 2 91 91 91 36 48 41 15 13 14 3 98 98 98 54 67 60 24 15 20 4 101 102 101 14 83 78 45 37 42 5 102 96 99 99 96 98 101 95 98 Poor 80 81 83 31 41 36 12 10 11 Non-poor 100 99 100 74 82 18 56 53 55 Rural 90 93 91 55 63 59 32 24 29 Urban 92 93 93 68 73 71 13 75 74 Total 90 93 92 58 66 62 42 39 41 29. Despite declines amongst wealthier households, the gap between rich andpoor generally widens with years o f education. For example, the gross enrolment rate gap between the poorest andrichest quintiles i s 22 percentage points at primary and 90 percentage points at higher secondary in 2005 (see Table 5-7). The widening o f the enrolment gap is due to higher rates o f dropping out from school amongst the poor, brought about by factors such as the rising costs o f education as students move up the system, the need for young members o f the household to work to supplement household incomes and lower levels o f investment in education by poor households. 30. While gender parity inenrolment rates at these levels is not uncommoninother developing countries, it is more unusual in South Asia. In2004, no other country in the region had achieved gender parity in primary school enrolment rates. Also striking are the larger gender gaps in favor of girls in poor households than in non-poor households (see Table 5-7). This implies that ultra-poor households send more o f their daughters to primary school compared to their sons. A study conducted in northern Bangladesh found that the ultra-poor depend heavily on the labor o f their sons from an early age, frequently preventing them from going to school (BRAC and SCUK 2005). However, failure to complete secondary education i s particularly pronounced among women with 8 percent having dropped out o f secondary school and 10percent not starting secondary school at all (of those eligible to attend) in2005. 31. Completion rates. Importantly, the majority o f children who complete their primary schooling (57 percent o f children o f secondary school age in 2005) continue on to secondary, implyingthat any further expansion o f secondary schooling i s likely to be constrained by a lack o f primary school graduates. Over a half o f all secondary non-starters and three quarters o f all dropouts are from poor households. This reflects the significantly higher costs o f secondary schooling compared to primary schooling in Bangladesh, as richer families make greater use o fprivate tuition to facilitate progress through secondary school. 32. It i s unclear why net enrolment figures between secondary and higher secondary differ so dramatically between gross and net enrolment (see Figure 5-1). However, it is possible that this i s due to the fact that Bangladeshi households typically send their children to primary school later than the 81 officially starting age o f 6 years - this may have more pronounced effects when combined with poor completion through to the secondary level. Those who do complete each level may well be doing so at later than official starting age, leading to significantly lower numbers in higher secondary net enrolment figures compared to gross enrolment. 33. Overall education levels. As a whole, the Table5-8: Changesinthe avera :years ofeducation, - average years o f education for individuals 2000-2005 aged between 16 and 40 has increased by 2000 2005 nearly a whole year (fi-om 4.2 to 5.0 years) Age group male female total male female total between 2000 and 2005, and completion 16 to 20 5.8 5.3 5.6 6.0 6.3 6.1 rates have improved for all cohorts o f 21 to 25 6.0 4.0 4.9 6.5 5.4 5.9 children during this period (see Table 5-8). 26 to 30 4.7 2.6 3.6 5.8 4.1 4.8 The improvement in education attainment 31 to 35 3.8 2.2 3.0 4.8 3.2 4.0 among the adult population i s consistent with 36 to 40 4.0 2.1 3.1 4.5 2.7 3.6 the finding of chapter 3 that education among Poor 2.7 1.5 2.0 3.0 2.3 2.7 household heads has increased. Chapter 3 Non-poor 6.6 5.0 5.8 7.0 5.9 6.4 also indicates that the improvement in Total 5.0 3.4 4.2 5.6 4.5 5.0 education "endowments" among household Source: HIES(: 10 and2005) members has had a significant poverty- Notes: There is no informationinthe HIES on the number o f years o f reducing impact. On average, moreover, the education completed for individuals that are not literate. To generate years of education these individuals are assumed to have zero years o f education. increase in women's education levels over Reported statistics include individuals that were still in school at the time o f the same period has been twice that o f men the survey and therefore have not completed their education. (from 3.4 i o 4.5 years o f education). Nevertheless, differences ineducation levels between poor and non- poor have remained large andrelatively stable since 2000 (3.7 years in2000 to 3.8 years in2005). 34. Cross-country comparisons. Overall, Bangladesh compares relatively poorly to other developing countries andto other South Asian countries interms o f primary and secondary enrolment rates. InSouth Asia, only Pakistan had a lower enrolment rate in 2004 than Bangladesh, with an overall primary enrolment rate o f 82 percent. Enrolment rates in tertiary institutions are also relatively low compared to developing country averages. Public spendingon education 35. Average government per-student spending in 2005 for primary and secondary education was approximately $20 and $31 respectively. Government per-student spending at both levels i s low compared to other countries in the region and countries at similar stages o f development (see Figure 5-2 for comparisons o f incidence o f public education spending). For example, spending in India was approximately 3 times as high at the primary school level in 2002. These low levels of spending may be contributingto Bangladesh's highdropout and low completionrates. 36. Benefit incidence analysis shows that, overall, public recurrent education expenditure i s not pro-poor. The bottom quintile represented 20 percent o f school-going children and yet received only 15 percent o f public recurrent education expenditure in 2005. Separation by level o f schooling shows even more striking results from 2005, with the bottom quintile representing 27 percent o f primary school-going children but receiving 24 percent o f public recurrent education expenditures, 19 percent o f secondary school-going children and receiving 12 percent, and 14 percent o f higher secondary students and receivingonly 3 percent. 37. Government spending on education in Bangladesh includes two significant social safety net programs. The primary school stipends program, introduced in 2003 (with predecessor programs initiated in1994), is designedto provide stipends to poor studentsinrural schools andcoveredupto 40 percent of rural students attending government-supported schools in2006. However, targeting occurs within schools 82 and therefore the poorest 40 percent o f primary school students nationally would not necessarily be selected for the program. For example, the poorest 40 percent o f students in a relatively affluent rural school may not contain any students defined as poor at the national level. Hence the primary stipend program may be poorly targeted. Figure5-2: Cross-countrycomparisonofthe incidenceofpublic educationspending 0 10 20 30 40 50 60 70 80 90 100 Source: Davoodiet al (2003) Notes: Nepaltertiary andtotal figures only include universityand no other tertiary. Nepal and Indiaquintilescalculated at the individual level whereas Pakistanis at the householdlevel. 38. The secondary school stipends scheme, initiated in 1994, i s intended to provide eligible female students with tuition-free education as well as a small stipend. Its aim i s to increase female access to secondary education, improve education quality and reduce fertility rates by delaying marriage. In2005, 2.5 million female students were participating inthe stipends program, with participation rates tending to be slightly higher for the poorest quintile (68 percent o f the poorest quintile versus 63 percent o f the wealthiest quintile participated among all o f the students attending eligible schools in 2005). Given that the program i s not poverty-targeted, this may simply reflect the greater reliance o f poor students on the stipendcomparedto their wealthier counterparts. 39. Public vs.private spending. Average household spending on primary education is similar to levels o f government spending at $20 per head for both in 2005, although differences between poor and non-poor students are very high. A similar pattern emerges at secondary and higher secondary although levels o f spending are much higher and household education expenditure ($68 per head in 2005) tends to be much greater than government per-student spending ($31 per head in 2005). Large differences between the poor and non-poor inprivate spending likely contribute to gaps ineducation outcomes between poor and non-poor students. 40. High private expenditures, particularly for secondary and higher levels o f education, are consistent with an increasing role o f private education services. While the use o f private education services was 83 lower for the poor than for the general population ina 2006 national survey on perceptions o f governance (conducted by Power and Participation Research Centre or PPRC), as many as 34 percent o f the poor reported such interaction. The substantial use o f private services in education survey reveals aspirations for higher quality education from private providers or a demand for private tutoring, both o f which seem to indicate that public education is perceived to be of low quality (Box 5.1). Determinantsof EducationalAttainment 41. Analysis o f householdfactors contributing to educational attainment since 2000 shows that income, levels o f education, religious affiliation, characteristics o f household heads, and geographic location all play important roles. 42. Data from the 2000 and 2005 HouseholdIncome andExpenditure Surveys reveal a number o f factors contributing to educational attainment (see Annex 5, Table A-5.4). Household expenditure per capita - a proxy for household income - i s found to have a positive impact on educational attainment although attainment appears to be relatively inelastic to expenditures. Interestingly,the income/expenditure impact appears to be slightly stronger for girls than for boys, suggesting that improvements in the household's economic status are likely to benefit girls more thanboys. 43. Age also appears to be a significant factor, with younger cohorts more likely to have higher educational attainment than older cohorts due to the educational expansion o f the 1990s. Gender continues to be an important determinant o f attainment, with women tending to have higher attainment thanmen, althoughthe magnitude ofthis effect is small. 44. Education of the household head and partner has positive and statistically significant effects on attainment. Attainment i s higher in households where the head i s female, but preferences in female- headed households strongly favor male household members. A boy living in a female-headed household i s 7 percentage points more likely to go beyond secondary schooling compared to a boy living in a male- headed household (as opposed to 3 percentage points among girls inthe same scenario). This greater male preference infemale-headed householdsmay be due to a greater need for these households to have a well- educated male to provide access to markets and social services. The overall number of children in the household tends to have a negative impact on educational attainment as well, likely due to smaller resources available to invest inthe education o f each child ina larger family. 45. Individuals residing inareas with more secondary schools have a higher probability o f continuing to secondary, but the supply o f secondary schools appears only to be statistically significant for boys' attainment. This more serious supply constraint for boys may arise from the strong incentives given to schools for enrolling girls through the nationwide government stipends programs for eligible girls at the 84 secondary level. Additionally, division dummies were found to have significant effects. These are discussed ingreater detail below. Regional variations in educational attainment 46. Regional differences are evident in enrolment rates. However, just like health outcomes, enrolment rates do not mirror regionalpatterns inconsumption poverty (see Figure 5-3 and Figure 5-4). Despite the positive correlation between household expenditures and educational attainment, this relationship is not evident at an aggregated, divisional level. Khulna and Barisal divisions, in spite o f being the poorest, have higher primary enrollment rates among both boys and girls than Dhaka, Chittagong and Sylhet. Along with the best health outcomes (as seen above), Khulna also has the highest enrolment rates at both primary and secondary level in 2005. Patterns insecondary school enrolment may be partially explained by differences inthe incidence o f secondary schools across areas, as suggested by the regression results cited earlier - for instance, the number o f secondary school age children per secondary school inKhulna i s 717 compared to 1,352 inSylhet, which has the lowest secondary enrollment rate. 47. The coefficients o f division dummies in the regressions o f school attainment are broadly consistent withthe regionalpatterns inenrollment seenabove. After controlling for household level determinants o f school attainment, households in Dhaka division are likely to have lower education attainment relative to all other divisions except for Sylhet; while the positive location effect is the highest for Khulna, Barisal and Rajshahi inthat order (see Annex 5, Table A-5.4). Thus it appears that when the effects o f household attributes on school attainment are netted out, being located in an economically lagging division has a positive effect on education outcomes. 48. The strong negative effect for Sylhet appears to be a primarily female phenomenon - girls were 8 percent less likely to go to school in Sylhet than girls in Dhaka division in 2005. Gender gaps in enrolment rates have historically been high in Sylhet, with the male primary gross enrolment rate at 15 percentage points higher than the female rate o f 76 percent in 2000 (countrywide female primary gross enrolment was 93 percent in the same year). Gender gaps in primary and secondary education have narrowed substantially in Sylhet since 2000, to the extent that gross primary enrollment among females have surpassed that among males in2005 (see Figure 5-3). 1 Figure5-3: Grossprimaryenrolment(2005) Figure5-4: Gross secondary enrolment(2005) 'E 20 0 -._ I f- mb Male 0 Female w Male Female , Source: Al-Samarrai (2007) usingHIES (2005) 49. The fact that economic opportunities in a region do not cast a positive influence on education outcomes is somewhat o f puzzle, and especially so given that returns to endowments including education are Zower in lagging regions, which should dampen the demand for education (see chapter 4). More complex phenomena may however be at work: for example, the ability to migrate from a lagging region i s linked to better human (and physical) endowments (see chapter 4), which can serve as an incentive for staying in school. Conversely, the greater labor market opportunities in the more vibrant Dhaka and 85 Chittagong divisions may translate into a greater demand for child labor, which would raise the opportunity cost o f attending school, particularly for poorer households. 50. Other factors that could explain the paradox include arguments similar to what were offered (in section I) the context o f regional patterns inhealth outcomes. These include the greater concentration in and longer presence o f NGOs in the economically lagging regions and the impact o f their awareness- raising activities on social outcomes; positive spillover effects on non-NGO members in the same community; and differences inhistorical social norms, particularly as it relates to the empowerment and mobility o f women. The last factor i s likely to be particularly relevant for Sylhet, as mentioned earlier in the context o fhealthoutcomes insection I.Given that the HIESdata do not allow a close examinationof these questions, more detailed analysis using alternate data sources will be necessary to explain this apparent paradox between the spatial patterns o f income poverty andhumandevelopment. III.Concludingremarks:policy implications 51. Bangladesh has seen substantial improvements in both health and education outcomes, but inequalities continue to pose significant challenges. Moreover, malnutrition rates remain high by international standards, even compared to countries with similar levels o f per capita income, and hold back improvements inhealth outcomes, particularly inc h l d and maternal mortality rates. The recent rise in food prices, and the switch to lower quality food and lower intake, is likely to have worsened this problem. The rich-poor gaps in health outcomes and utilization o f primary care services have either remained unchanged or widened between 1996-1997 and 2004, the period before and after a shift in health, nutrition, and population sector policy in 1998. Gender gaps among the poor are also evident, most notably in immunization coverage and inprovider choice for treatment o f childhood illnesses. These inequalities suggest a wide scope for demand-side interventions in reaching the poor, as i s being considered under the current health strategy. 52. Services delivered through household or community outreach show the smallest rich-poor gaps in areas such as contraceptive use, tetanus toxoid for pregnant mothers, vitamin A supplementation o f children, and use o f ORS among children experiencing diarrhea. As health outreach efforts successfully raise demand for health services and change households' preferences towards utilizing these services, however, service delivery may successfully move away from a doorstep delivery model to clinic-centered services. Services that currently show some o f the widest rich-poor gap are those that require visits to clinics or medicalproviders and out-of-pocket expenditures, such as antenatal care, delivery by medically trained provider, postnatal care and curative care for children. If costs are the biggest constraint to poor households' use o f services, vouchers may be effective inincreasing utilization by the poor. 53. Conditional Cash Transfer or CCT programs, another type o f demand-side intervention, may also provide incentives for households to seek care. These provide cash transfers to poor families, conditional on their use o f preventive and curative health services, school enrollment, and nutrition supplementation. Evaluations o f CCTs implemented in Latin America show that such programs effectively increase utilizationo fhealth services among poor households. 54. Another approach in a country like Bangladesh, where wide division-level variations in health outcomes are evident, i s geographic targeting o f health expenditures so that resources can be directed to areas most in need. Poverty maps and nutrition maps can provide indicators o f poverty andor under- nutrition for districts or even villages and thus can be usedto fine-tune the allocation and effectiveness o f expenditures. 55. In education, primary gross enrolment rates have stagnated since 2000, although there have been improvements in net rates. Enrolment rates amongst the poorest boys have not kept pace with rates for 86 boys in less poor households. Given the strong positive relationship between poverty and education, this i s a disturbingtrend. Emphasis should be placed on this group if poverty and education MDGs are to be achieved. Primary school completion rates since 2000 have improved, with female completion rates seeing the most significant increase. These increases were largely driven by a rise in enrolment rates in the 199Os,but given the stagnation o fprimaryschool enrolment, further increases incompletion will need to come about through improvements instudent retention. 56. Inaddition, secondary level education has seen substantial growth inenrolment but a slight decline in completion, resulting ina growing proportion o f secondary level dropouts. This is particularly true o f the poor. This may be due to relatively low government spending per-student in Bangladesh compared to other countries, leading to poor quality o f schooling. Moreover, the incidence of public education expenditure is not, on the whole, pro-poor. This combined with differential household expenditures, result inwide gaps between spending on poor and non-poor students. Differences inhousehold spending are largelyexplained by higher levels o f spending on private tuition amongst non-poor households, which is most striking at the secondary level. The current stagnation in primary enrolment rates and high numbers of secondary school dropouts (despite gains in secondary school enrolment) suggest that focusing on children's progression through the school system would be most effective in increasing completionrates. 57. A focus on skills development or vocational education i s essential to enhance the quality o f the labor force inBangladesh and mitigate the impact o f dropouts from secondary education. The main challenge would be to overcome the inadequate orientation o f the current skills development system to the labor market. A recent World Bank study (World Bank, 2006b) finds that formal providers o f technical and vocational education andtrainingdo not have strong linkages with the private sector employers who drive the changing patterns o f labor demand, w h c h would be necessary to ensure that skill development courses are relevant anduseful to trainees and employers alike.g 58. As with health, findings at the divisional level appear to contradict the negative associations between poverty and educational attainment seen at the individual level, possibly due to the fact that labor market opportunities are higher in wealthier divisions that have more economic opportunities. This i s most extreme in Chittagong, which exhibited one o f the largest declines in the incidence o f poverty despite a fall inprimary enrolment rates. Future policy options should address the sources o f these division-level variations, which may be related to the effects o fNGOpresence or local socio-cultural factors. The World Bank (2006b) study interviewedover 2300 graduates of Vocational Education and Training (VET) institutions. Less than 10 percent of individuals who graduated in 2003 from VET institutionswere employed two years later. Close to half of the employedgraduates took at least ayear to find ajob andratesof returnfor graduatesseem to be below those for graduates from the generaleducation system. 87 88 6. Are the Poor Protected?Vulnerabilityand the Role of Safety Nets 1. As seen inChapter 1, the percentage o f Bangladeshis living inpoverty (consumption below the upper poverty line) fell from 57 percent in 1991-1992 to 49 percent in 2000 and 40 percent in 2005. The percentage o f population in extreme poverty (consumption below the lower poverty line) fell from 50 percent in 1991-1992 to 25 percent in 2005; between 2000 and 2005, the number o f people in extreme poverty fell by 8.3 million. However, around 56 million Bangladeshis were still living in poverty in 2005, including 35 million who were inextreme poverty. 2. Previous chapters have shown that much o f the poverty reduction achieved so far in Bangladesh is linked to higher and more stable economic growth during the past 15 years and especially during the period 2000-2005, which has raised the returns to household endowments including labor. Other factors, like a rapid expansion o f microfinance and substantial improvements in human development have also played their part by improving the income-generating capacity o f the poor. Improving and sustaining economic growth i s clearly a necessary condition for Bangladesh to reduce poverty further and attain the MDG target of halving poverty from the 1990 levels. At the same time programs that involve direct transfer o f resources to the poor also have an important role to play in ensuring that the poor are able to meet their basic needs, cope with the impact o f economic shocks, and invest inhumandevelopment to be able benefit from andparticipate inthe growth process. 3. Social safety net programs are important ways to effect such transfers, where the primary objectives are to reduce the deprivations associated with deep poverty and mitigate the risk o f households falling into (or further into) poverty as a result o f a shock - whether at the household or community level. Shocks can affect all sections o f the population, but typically leave the most damaging impact on the poor -inthe formofseveredeprivationinthe short runand, inmany cases, lastingharmto their economic prospects that increases the likelihood o f chronic poverty. By mitigating the impact o f shocks, well- functioning safety nets reduce short-term vulnerability as well as improve long-term growth prospects among the poor - by reducing the compulsion among households to adopt coping strategies in the aftermath o f a shock that leads to loss o f human and physical capital and income-generating capacity. With these benefits in mind, the Government of Bangladesh has instituted a number o f safety net programs to provide cash and in-kindtransfers to the poor, with expenditures on these programs growing steadily since the mid-1990s. 4. Given the importance o f safety nets inpromoting equity and fostering inclusive growth, this chapter reviews these programs, in the context of the nature and pattern o f shocks and vulnerability to these shocks. Section Ibelow discusses available evidence on the incidence o f and vulnerability to shocks, its correlates and patterns, drawing from recent research based on panel data. Section I1 discusses key findings on safety net programs, drawing primarily from HIES 2005 that concluded a special module on targetingandcoverage o fsafety net programs and administrative data onprograms. Section I11concludes the chapter, along with recommendations that are informed by the analysis. I. Natureof shocksandvulnerabilityinBangladesh The vulnerable population: estimates fromHIESand recent events 5. The size o f the vulnerable population - at the risk o f falling into or deeper into poverty - i s large in Bangladesh. This is even suggested by cross-section data like HIES 2005, which cannot show movements in and out o f poverty over time. A high concentration o f consumption expenditures around poverty lines (Figure 6-1) implies that shocks can cause large movements inpoverty rates. The relative positions o f the upper and lower poverty lines and the density curve also suggest a large population consuming between the upper and lower poverty line levels, which implies that even a small shock can send a large number of individuals, many o f whom are already poor, into extreme deprivation. 89 6. Simulations with HIES 2005 confirm these I Figure 6-1: How consumption is distributed (2005) perceptions (Table 6-1). A 5 percent shock to consumption, distributed equally throughout the population, would increase the share o f population below the lower and upper poverty lines by 11 and 16 percent respectively, raising the number o f poor and extreme poor by 6.3 and 5.7 million respectively. Even a 2 percent shock to consumption would raise extreme poverty rate by 6 percent. Moreover, most large shocks do not affect the consumption o f everyone equally. Thus the actual welfare and distributional impacts may be quite different from what these simulations suggest. Source: HIES(2005) 8. The impact o f the natural disasters Table 6-1: Poverty impact of shocks has been exacerbated by shocks due to simulationsusingHIES 2005 steep rises in commodity prices, notably Shock to Yo increase Yo increase in fuel and food prices. While the full fioans,ump Increase Increase in # inpoverty extreme in# poor extreme poor impact o f rising international prices i s HCR poverty HCR not faced by the Bangladeshi consumer, b2% 4.7 6.3 2,613,200 2,196,200 the rise in the global price o f rice and 5% 11.4 16.2 6,310,600 5,657,300 edible oils in particular has contributed 7% 15.7 23.3 8,715,300 8,145,400 to food price inflation rising to 14 Source,,HIES (2005) Note:*; A "shock" of x% assumes x% in reduction in consumption for the entire percent in early 2008. Given the larger population. The impact of recentrice priceincreases 9. Retail prices o f rice increasedby around 38.8 percent inrural areas and 36.8 percent inurban areas o f Bangladesh from April 2007 to March 2008, which would have had a substantial short-run welfare impact, since rice accounts for around 24 percent o f total expenditure o f an average Bangladeshi household (and about a third for poor households). The nature o f impact also depends on the distribution o fnet buyers and sellers o frice inthe population and how responsive wages are to price increases. 10. Results from simulations based on HIES 2005. Simulations with survey data suggest the food price shock has a significantly adverse impact on welfare, given that only 17 percent o f Bangladesh households are net sellers o f rice and wages for most workers (including daily wage labor and manufacturing sector workers) are unlikely to keep up with rapid price increases. Assuming no wage increase, the 38.8 percent rural and 36.8 percent urban increase in rice prices would lead to a 5 percent real income loss for the average household and 11 percent for those in the bottom quintile. Nominal wages would have to increase by an average o f 14percent (20 percent for the bottom quintile)to leave the welfare level unchanged after the rice price increase. Ifwages adjust partially (a 5 percent increase for everyone), average real income would decline by around 3 percent for the population and 8 percent for the bottom quintile (see Box 6.1). 90 11. The impact is also unevenly distributed among different groups - larger for urban than rural households and for poor than nonpoor households (Box 6.1). Households more likely to suffer include those headed by daily wage workers and functionally landless, while the only groups that benefit are households headed by farmers andor owning more than 1.5 acres o f cultivable land. The results translate into significant poverty impact - about 3 percentage points with a 5 percent nominal wage increase - with the 2005poverty rates as the baseline. The estimated increase inextreme poverty rate is higher, implying that the poorest households bear the biggest brunt o f the impact - which suggests the critical need for safety nets for such households inthe face o f price shocks. Takinginto account the reduction inpoverty that would have occurred between 2005 and 2008 due to the strong and stable GDP growth (6 percent annually or more) duringthis period, the simulations suggest that the food price shock would have eroded some (but not all) o f the gains inpoverty reduction since 2005 (see Box 6.1). Simulations using HIE of the recent histo occupation groups, workers are affecte 1.5 acres of cultiva incomewould declineby around3 percentfor thepopulation and 8 percentfor the bottomquintile. a) by quintilesofperc by occupationofhouseholdhead U U -12.0 -12.0 I I Agri day Farmer Non-agri Non-agri Salaried Bottom 2nd 3rd 4th 5th labor day self-enpl labor Quintiler Oocupatlonof hhold head 0 Whout wage response W h wage increase(5%) 0 Withoutwage response With wage increase (5%) iource: HIES (ZOOS); for details onthe model andresults see Annex 6, Section I. does not take into 2008, when GDP 91 December March-April June2008 2007 2008 Source Wodd Bank Suny, BangladeshFoodPnceImpact Suney, ZOOS 12. Because o f the skewed nature o f the impact, inequality would also increase due to the rice price shock. The rise in rice prices appears to have been stabilized by May/June 2008 by the "bumper" Boro rice harvest; but the new equilibrium price i s likely to stay above the old equilibrium price, so that the welfare impact are likely to persist inthe near future. 13. Insightsfrom a rapid survey. A survey o f 2,000 households conducted by the World Bank inJuly 2008 focusing on the impacts o f the food price rises finds evidence consistent with results o f the simulations discussed above.' More than 95 percent o f survey households reported to have been adversely affected by the price increase, and the number o f households suffering from a shortage o f food has increased substantially (see Box 6.1). The impact was felt particularly acutely by day laborers, and landless andmarginally landless households. Households reported their wages or salaries lagging behind food price increases, leading to a decline in real income for most. The survey also suggests that the adversity that peaked inMarch-April 2008 persisted through June even as prices stabilized after the Boro rice harvest. 14. The survey offered important insights into how households have Table 6-2: Householdresponsesto thc ood pri shock ( b) responded to the shock. More than 75 Urban Rural Total percent o f households had to cut back Switch to lower quality food 87 88 88 on their food intake and nearly 90 Reduce nonfood expenditures 86 87 86 percent o f households lowered the Reduce quantity o f food intake 72 78 76 quality o f food they consumed andfor Take out loans 46 60 55 reducednonfood expenditures (Table 6- Spend savings/sell/pawn belongings 44 47 45 2). Reduced food intake may have also Decrease education expenses* 33 43 39 exacerbated the high rates o f child Work more I increaseproduction 25 40 34 malnutrition that exist in Bangladesh Take children out o f school* 7 9 8 even in normal times (see chapter 5), Assistance from community members 1 9 6 which can in turn have irreversible Stop loanpayment 3 6 5 health consequences for affected *: % o fhouseholdswith schoolgoingchildren Source:World BankFoodPricelmGct RapidSurvey (July, 2008) children in the years to come. A recent global assessment suggests that an additional 44 million individuals worldwide will be undernourished in 2008 alone due to the rise infood prices (World Bank, 2008e). The survey sample comprisedof 2,000 households,including 1,200 rural and 800 urbanhouseholds. The sample was designed to ensure that the data captureda wide spectrumof the Bangladeshisociety. The rural survey was conductedin all six divisions inthecountry, andincluded12districts. The urbansurveywas fieldedinsix districtsinfour divisionsofthe country. 92 15. Significant numbers o f households reported taking out new loans, dipping into household savings, selling or pawning belongings and working more hours. Some o f the measures households were compelled to adopt may have adverse consequences for the human capital o f future generations. More thana thirdofhouseholdswith school-going childrenreportreducing education-related expenditures, and nearly 8 percent o f such households took their children out o f school. The extent this matters depends on the lengtho f time a child i s taken out o f school - the longer the period the greater the risks to the child's development andBangladesh's humancapital formation. 16. The rice price rise is not the only global shock affecting Bangladesh; the rising price o f oil is a potentially significant source o f risk as well. The Bangladeshi consumer i s largely insulated from fluctuations in international oil prices by administered domestic prices supported by large subsidies (particularly for kerosene, diesel, and fertilizers). The direct impact o f fuel price rise on household real income is much less than that o f a rice price rise - expenditure on kerosene accounts for around 1percent o fhousehold expenditure (less than 2 percent for the rural poor who are the largest users), while transport costs account for 2-3 percent. Preliminary analysis also suggests that the direct impact o f fuel and fertilizer price rises on agricultural production costs o f farmers is likely to be small.2 That said, if the price rise i s significant for fuel and fertilizers, it will be an additional source o f hardship for the poor who already face the considerable impact o f rice price increases. Insightson shocks andvulnerabilityfrom a longitudinalstudy inruralBangladesh 17. What are the major sources o f vulnerability and transitions in and out o f poverty in Bangladesh? And related to that, what factors determine the extent to which households are vulnerable to shocks? A background paper for this report using a longitudinal dataset from Bangladesh lends some insights into these questions, by analyzing the patterns and causes o f dynamic movements in and out o f poverty (Quisumbing, 2007). The study involved re-surveying a sample o f households in 102 villages located in 14 districts who were interviewed as part o f a number o f different surveys between 1994 and 2000. The most recent follow-up survey, conducted in2006-2007, was on a sample o f 1,787 core households from the original survey alongwith 365 householdswho were "splits" from the originalhousehold. The survey was complemented by a qualitative study to examine perceptions o f changes on a sub-sample o f the survey communities (see Annex 6, Section I1for more details). 18. Incidence of shocks by type. Inthe survey, shocks are defined as adverse events that lead to a loss o f household income, a reduction in consumption, a loss o f productive assets, and/or serious concedanxiety about household welfare. Shocks are classified into a number o f broad categories: agro- climatic, economic, political/social/legal, crime, health, and life-cycle related (Annex 6, Section 11). 19. More than half o f all households had been affected by shocks in the last 10 years prior to the survey (1997-2006/07). The most frequently reported shocks for all households are iZZness shocks (expenses relatedto illness and/or foregone income), dowry and wedding-related expenses, andjoods (see Annex 6, Table A-6.1). Illness shocks account for at least 22 percent o f most commonly reported shocks with expenses related to illness perceived as more detrimental to household welfare than income losses. Dowry and wedding-related expenses account for 16-23 percent o f reported shocks while flood-related shocks accounts for 13 percent o f reported shocks in all sites. The relative frequencies with which different types o f shocks occur are broadly consistent with the insights from the focus group discussions (FGDs) conductedina sub-sample o fsurvey communities (Davis, 2006).3 20. Impact of shocks on consumption; Illness-related income losses and death o f a household member appear to have the most unambiguously adverse impact on consumption. Shocks can also have different The ratio of agriculturalexpenses on irrigation, fuel, and fertilizersto total householdexpendituresi s less than 5 percent for all expenditurequintiles. Fifty percentof all focus groups listeddowry as responsiblefor householddecline or remaininginpoverty, followed closelyby illness or injury (48 percent). Flooding,however, was mentionedless frequently (25 percent). 93 impacts on households, depending on their initial characteristics. For example, in certain villages, livestock deaths and division o f property have a significant adverse impact on household consumption when the head has less than four years o f schooling, but not otherwise. In others, dowry and wedding expenses have a greater negative impact when the household's land ownership is lower than the median landholding. These suggest that households with lower endowments - in terms o f education, land ownership, or asset ownership - are likely to be more vulnerable to certain types o f shocks. 21. This analysis o f movements in and out o f poverty suggests the following. First, schooling o f household head and ownership o f assets including land, besides being important determinants o f consumption, also influences the impact o f shocks on household consumption - inother words, the extent o f vulnerability o f households to specific shocks. Second, illness shocks - in particular, the income foregone when an income earner falls ill- are frequent and tend to have serious adverse impacts on household consumption. Third,two key demographic categories - children below age 15 and males and females above 55 - turn out to be significant, pointing to the importance o f life-cycle and demographic factors in creating and transmittingpoverty. Fourth, preliminary evidence suggests that dowry expenses represent a substantial drain on household resources, which i s also consistent with the findings from the qualitative work. Lastly, unobservable community effects are consistently significant, pointing to the important role o f locality-specific factors in affecting movements o f households in and out o f poverty. These factors are similar to those identified earlier as determinants o fpoverty status in2005. Community-wide shocks - floods, cyclones, and seasonal deprivation 22. Bangladesh i s one o f the most vulnerable countries in the world to natural disasters including floods, droughts, and cyclones (see Box 6.2). Much o f the vulnerability has to do with Bangladesh's geography and location. Eighty percent o f the country consists o f floodplains created by more than 300 rivers and channels, including three major rivers: the Ganges, the Brahmaputra, and the Meghna. The southern part o f the country i s also particularly vulnerable to cyclones (see chapter 4). In2007, as mentioned above, the country was stricken by serious floods and a cyclone whose combined impact on the economy was severe (Box 6.2). 23. Serious community-wide shocks, particularly when they occur repeatedly, increase the likelihood o f poverty traps inaffected areas due to several reasons. Severe shocks often compel the vulnerable to cope with immediate needs by selling remaining productive assets, accumulating high-interest loans, and removing children from school, all o f which adversely affect their long-term economic potential. As seen in chapter 4, the areas inBangladesh at a highrisk of natural disasters (particularly a cyclone) are also more likely to be poor and have lower access to markets and infrastructure. These conditions tend to exacerbate the impact o f a natural disaster and contribute to poverty traps, which would in turn prolong the recovery from a disaster and sustain chronic poverty inthe longrun. 24. Another form o f shock that occurs with nearly predictable regularity i s to do with seasonality in agriculture. Large areas in the northwest are subject to a phenomenon called Monga - a form o f deprivation during the lean agricultural season in October and November - causing enormous suffering every year and inducing high chronic poverty. An estimated 5 million extreme poor live inthe Monga- affected districts o f Rangpur, Gaibandha, Kurigram, Lalmonirhat and Nilphamariwhere agriculture i s the mainstay o f local economies (see Box 6.2). II. Addressingpoverty andvulnerabilitythroughtransfers: publicsafety netprograms 25. The sizes o f the extreme poor population and the vulnerable population in Bangladesh, the high incidence o f shocks including community-wide shocks, and the adverse impact o f shocks on the likelihood o f poverty (see section Iabove) all add up to a compelling case for safety nets to be a critical priority area for public policy. Other than addressing the key objective o f reducing short-term deprivation, safety nets also improve long-term economic prospects o f households when they function 94 well and flexibly respond to community-wide shocks. With safety nets addressing their basic needs inthe aftermath o f a shock, households would be less likely to take decisions - such as taking children out o f school or selling off productive assets like livestock - that increase the likelihood o f chronic poverty in the longrun. erability to natural disast t, by 2050, Banglade nes, more severe droug ftheDhakaandSylhetdivisi estimated 11million people 30 out of the 64 emand and wages. Although 26. Natural disasters cause immediate deprivationby wiping out livelihoods, due to the immense loss o f private assets - housing stock, durable household goods, and livestock - and disruption o f the local economy. Borrowing from informal sources often insulates the poor in the immediate aftermath of natural disasters but risks leaving them highly indebted.4 Social protectionprograms like cash grants and food aid can be highly beneficial in this context, by reducing the need for coping strategies that lead to long-termpoverty traps. See Dasgupta(2007). 95 27. Bangladesh has a wide spectrum o f social safety net programs, including both cash and in-kind (or food) programs. The composition o f programs is a mix o f conditional and unconditional cash and food programs, subsidies and targeted funds. Public safety net programs are focused on rural areas, with little coverage o f the urban poor. However, during the recent rise in food prices, the Government set up subsidized rice distribution outlets, including in urban centers. Self-targeting methods were used to screen out non-poor households through a combination o f rationing, queuing, and the provision o f coarse rice. Financing trends and composition of public safety net programs 28. Total public spending on social safety net programs was less than 1percent o f GDP till the late 1 9 9 0 ~ ~ but increased to 1.6 percent inthe allocations for 2007/08. A dominant share o f safety net resources are spent on unconditional programs, out of which in-kind (food) transfers constitute the largest part. Expenditure allocations for food transfers programs are much higher thanthat for cash transfer programs, for both conditional and unconditionalprograms. In2007-08, Vulnerable Group Feeding (VGF) was the single largest programwith the highest allocation(see Table 6-3) and5 millionbeneficiaries. The second and third largest programs, both in terms o f expenditures and number o f beneficiaries, are the Old Age Allowance and the Vulnerable Group Development (VGD) programs. Over time, the government has increasingly shifted resources away fi-om food programs towards cash transfer programs. For example, the Food-For-Education program (FFE) has been discontinued and replaced by Cash-For-Education, and Cash-For-Work i s gradually replacingthe Food-For-Work (FFW) program (Table 6-3). Table 6-3: Financial alloca )nsfor n jor soci; safety :t prog Ims (m ions of Program 1999-00 2000-01 01-02 02-03 03-04 04-05 05-06 06-07 07-08 Foodfor Work (FFW) 8060 8682 6728 4123 2047 4040 2197 2638 2609 GratuitousRelief(GR) & Test Relief (TR) 2280 2315 2319 2201 1896 1623 2687 2602 3019 Vulnerable GroupDevelopment (VGD) 2720 1952 2367 1865 1751 1736 2447 2580 2859 Vulnerable Group Feeding(VGF) 2290 2914 1250 992 1490 811 1883 3040 5643 Allowancefor Widow 250 245 239 370 777 964 1022 1102 1522 Honorariumfor FreedomFighters 150 147 275 263 155 175 318 424 503 Old Age Allowance 490 490 477 686 1555 2109 2385 2712 3136 PrimaryEducationStipendProject (PESP) 0 1099 954 5485 3749 4219 3356 3305 3270 Source: Various sources. 1999-00 baseyear Note: see Annex 6, Table C for abriefdescriptionof the objectives andother informationon eachprogram. Program coverage, efficiency, and targeting 29. Size, type, and frequency of benefits. The benefit amount for cash transfer programs has increased marginally inreal terms over the past d e ~ a d e .These allowances are still small relative to the needs o f a ~ typical poor beneficiary.6 For instance, the food benefit from VGF and the standard benefit fi-om cash transfer programs arejust 21 and 30 percent o f the lower poverty line respectively. Eventhe benefit from the VGD program - three times the amount o f wheat provided by VGF - amounts to only 62 percent of the lower poverty line.' 30. Low levels o f benefits that do not meet the minimumbasic requirements are often the norm inmany developing countries because o f limited financing, trade-offs between coverage and benefit size, and the important concern o f not lowering the incentives to work. Recent studies from India and Pakistan show For example, the size of the Old Age Benefit has increasedfrom Taka 100per beneficiary when it was launchedin 1997-1998 to Taka 220 in2007-2008, which is also anincreaseinrealterms. Taka 220 is the standardbenefitfor all majorcashprograms. The VGF food transfer (1Okg of wheat per beneficiary per month) i s equivalent to Taka 150 (assuming the value of a kg of wheat to be Taka 15, from FPMU Oct 06), while the typicalbenefitfromcashtransfer programs i s Taka 220. 'The populationweighted averageof stratumlevel lowerpovertylines for Bangladeshis Taka 718/person/month,at 2005 prices. 96 that programs with low levels o f benefits are also subject to less leakage than programs with higher benefits, perhapsbecause o f self-targeting. 31. Benefits are usually paid at regular intervals. While food transfers (except VGD) provide benefits for a short period depending on seasonal or emergency needs, all major cash transfer programs operate for longer periods. While entry to a program i s based on certain criteria and exit policies are also defined, very few programs have a strategy for graduating beneficiaries out o f the program. VGD and the stipend program are among the exceptions. Under the VGD program, the beneficiary saves money over the program cycle, while NGOs provide program participants with skills training and access to credit. Only secondary school girls are eligible for the FSESP stipend and continue receiving it - conditional on attendance and satisfactory academic performance - until they complete secondary school. The subsidized food rations provided in select `Open Market Sales' markets provide limited quantities o f coarse rice after standing ina long queue. It i s likely that this form o f `self-targeting' i s also likely to lead to less leakage to the non-poor. 32. Beneficiaries appear to prefer cash over in-kind Figure 6-2: Beneficiaries' preference on cash transfers, most likely because cash provides flexibility in versus food deciding what to spend on. About 75 percent o f all beneficiaries prefer cash over kind (Figure 6-2) although 1 1 80% - the preference for cash declines gradually for poorer 1 beneficiaries. However, administrative bottlenecks can lead to delays in payments and fluctuations in the amount o f money received in several cash based programs - in several programs, participants received payments every second month or at longer time intervals (Ahmed 2007). Loweit 2n; 3rd 4th top Total There are also drawbacks with bank account payments as Consumption qulntlles many poor households do not have a bank account and Refers cash transfers 0 Refers in-kindtransfers require daily remuneration. Food-based programs (e.g. Source: HIES 2005 food-for-work or subsidized food sales) become the program o f choice duringemergency responses due to these considerations. 33. A recent IFPRI evaluation o f safety net programs inBangladesh shows that the choice o f food versus cash distribution also depends upon the outcome which one wants to affect and the type o f household which joins the program (Ahmed et a1 2007). While the cash-based program (RMP) had a greater beneficial impact on household savings and female empowerment measures, the food ration program (IGVGD) has a greater impact in increasing household income and a combined food- and cash-based program (FSVGD) appeared more successful in raising women's caloric intake. Moreover, the type o f food provided appears to have intra-household gender implications. The IGVGD and FFA programs provide rice, which has a greater impact on male calorie intake, while the female benefit more when wheat flour is provided inthe FSVGD program. 34. Safety net programs in response to the recent food price shock. The government has taken a number o f relief measures in 2008 to mitigate the impact o f the rice price shock on the poor. Traditional government safety nets programs are basedmainly inrural areas and are typically not set up as a response to a price crisis. While the coverage o f several food-based safety net programs in rural areas was expanded after the rice price shock, just 16 percent o f rural and 4 percent o f urban households in the previously mentioned World Bank rapid survey o f 2008 report benefiting from at least one safety net program. To augment traditional programs, the Government set up Open Market Sales (OMS) outlets in urbanareas. 35. The OMS outlets, where key food items, mainly rice, were sold at subsidized rates, appear to have been reasonably effective inbenefiting the poor and the vulnerable through its self-selection mechanism. While a quarter o f all urbanhouseholds used OMS, as many as 43 percent o f poor urban families - daily 97 laborers - accessedthese markets. Fifty six percent o f urbanhouseholds claiming to be severely affected by the price crisis purchasedfood grains from the OMS outlets, compared tojust 13 percent o fthose who were mildly affected. That said, the coverage o f OMS was still insufficient relative to the vast needs o f the urbanpopulation- about 68 percent o f urbanhouseholds who ate less or skipped at least one meal ina month had not used OMS outlets in MarcWApril 2008. This may be because o f lack o f purchasing power, even at subsidized prices, among some o f the poorest households; and some households may not have found the price differential between OMS and the free market to be enough to justify the time spent inaccessinganOMS outlet. 36. The Government has budgeted significant resources on existing food based safety net programs inthe FY09 budget, with expenditures scaled up fiom 2.2 to 2.8 percent o f GDP. The most significant new safety net initiative included in the FY09 budget is an employment guarantee program for the ultra-poor inselectedparts ofthe country. An individual who meets certain eligibility criteriawill beprovidedwork or entitled to receive unemployment benefits for 100 days a year. The success o f this program will depend on how well it i s targeted andimplementedon the ground. sights from a recent program like enerationProgram. Some of these the country. Furthermore maps (currently beingupdated ectively identifylng the g with high poverty where the 37. The experiences o f large scale public works programs across countries are mixed and provide useful lessons for Bangladesh's program (see Box 6.3, and section I11in Annex 6 for more details). The most recent example i s India's vast National Rural Employment Guarantee (NREG) scheme. Given that this program i s still in its early days, its impacts and lessons are not yet fully understood. The limited evidence from aggregate data and small studies/social audits indicates its vast potential as well as pitfalls inimplementation, many of which are instructive for Bangladesh (see Box A-6.1, Annex 6). However, going by what i s known so far, Bangladesh's program appears to differ from NREG in a number o f important ways. A crucial difference is with regard to this program's intent to target extreme poor households using certain household criteria, while NREG (backed by an Act o f parliament) guarantees wage employment (up to a maximum o f 100 daydyear for a household) within a program district to every rural householdthat has an adult willing to work as casual labor at the minimumwage. 38. Targeting criteria in Government programs. Programs use different criteria for targeting benefits, and these are not applied universally. Programs such as VGD, VGF, and Old Age Allowance target income poverty but use different sets o f criteria. The startingpoint o f how targeting is done i s a guideline prepared by the implementingministry, which sets the targeting criteria, the total number o f beneficiaries (including upper limits on the number o f male and female beneficiaries), the distribution by district or union parishads (UP), and the amount and duration o f transfer per beneficiary. Usually the criteria include income level, asset, and household structure, and demographic features. Based on such criteria, local bodies (UP), inconsultation with other local agencies and community, identify beneficiaries. 98 39. Coverage and targeting in practice. About 13 percent o f households benefit from at least one safety net program, with targeted programs accounting for 8 percent o f households (Table 6-4). The coverage rate among rural households i s 15 percent compared to 5 percent among urban households (Table 6-5). The VGF program has the highest coverage o f all programs, followed by old age pensions, VGD, andTR. 40. Participation in safety net programs decreases with income/consumption. About 22 percent o f households in the lowest consumption quintile benefit from targeted programs, which declines progressively for higher quintiles and i s only 4 percent for the top quintile. While such progressive incidence o f benefits is a positive feature, a strong area o f concern i s the low rate o f coverage among households in the bottom quintile, given that the extreme poverty rate for the country is 25 percent (Chapter 1). Even among the bottom 10 percent o f the population, the combined coverage o f all safety net programs i s just 23 percent, which includes 16percent covered by targeted programs. 41. In spite o f overall coverage being progressive, a Figure 6-3: Distribution of beneficiaries across sizeable share o f the benefits goes to the nonpoor, consumptiongroups although the outcomes differ considerably across 100% programs. For example, 41 percent o f the beneficiaries 80% o f targeted programs are non-poor (i.e. top three quintiles -seeFigure6-3). Giventheverylowrateofcoverage of 60% safety nets inthe population, these errors o f inclusion (of 40% non-poor beneficiaries) are quite high. 0% 42. In terms o f which program i s able to deliver the maximum share o f its benefits to the bottom quintile, TR appears to do the best (39 percent o f its beneficiaries are bonomquintile 2nd 0 3rd 0 4th Top quintile from the bottom quintile), followed by VGF (36 percent). Estimates from surveys other than HIES (2005) suggest a Source: HIES (2005) somewhat better targeting performance with Gothirds o f VGD program participants coming from the bottom 30 percent o f the population (Ahmed et a12007). 43. Regional distribution of beneficiaries. Coverage o f safety net programs also vanes significantly by region and does not correlate well with division level poverty rates. For example, Sylhet with a poverty rate significantly lower than the national average has the highest coverage o f safety Table 6-5: Distribution of Lneficiariesand poverty rates nets among all divisions. Incontrast, Khulna, I Poverty %of households who are which has the second-highest poverty rate in Headcount (% beneficiaries (%) Division I I 2005 the country, has the lowest coverage o f safety 2005 2005 nets (Table 6-5). L o w coverage among the Urban total population o f certain districts also Dhaka 46.7 32.0 4.9 translates to low coverage among the poorest. Barisal 53.1 52.0 5.0 Forty one and 28 percent o f households from Chittagong 45.7 34.0 5.7 the poorest 10 percent o f the population Khulna 45.1 45.7 11.0 4.2 participate in safety net programs in Sylhet Rajshahi 56.7 51.2 12.1 13.0 6.7 and Chittagong respectively, compared to 15 Sylhet 42.4 33.8 22.4 24.3 11.3 percent in Barisal and Khulna (see Annex 6, National 48.9 40.0 13.0 15.5 5.5 Table A-6.2). 99 44. Leakage of benefits from safety net programs. The evidence on leakage i s somewhat mixed and suggests highvariation across programs. The weight o f evidence points towards higher leakage from food transfer programs, likely because o f the large number o f intermediaries inthe system and the difficulties surrounding procurement, distribution, andstorage o f food. 45. Towards a unifiedtargeting system. Having a national targeting system inplace i s useful as it can be used to coordinate and integrate programs, as well as respond quickly to the needs o f specific target groups or geographical areas (e.g. areas affected by disasters). Inorder to reduce targeting errors, improve transparency, and reduce multiple targeting methods, countries like the Philippines are considering nationaltargetingsystemsbased on a Proxy Means Test (PMT) (see Box 6.4). 46. The PMT method involves identifying key correlates o f poverty from household data and usingtheir relative importance in a formula to yield a score that proxies the consumption o f households. Data on these correlates, which are intended to be relatively easily observable, are collected from households applying for the program. The formula i s used to select households below a certain predetermined cut-off score. The experience o f other countries suggests that PMT typically needs to be complemented by a comprehensive outreach campaign to maximize participation o f the poor. While this method requires significant administrative capacity, the data requirements are lighter than those required for means testing (income data). 47. In Bangladesh, a mixed method - using a combination o f geographical targeting, PMT, and community validation o f the targeting results - could be an approach the Government could introduce for its national programs. In such a targeting system, the P M T can be used to identify the initial list o f beneficiaries and a community-led effort can be used to validate the list and adjudicate complaints and appeals, to reduce errors due to misreporting and take into account the special circumstances faced by specific small groups that a P M T by its very nature cannot. At the same time, a mixed method would achieve a balance between the subjectivity inherent ina community-based process andthe objectivity o f a PMT, so that the original rationale behind introducing a PMT i s not lost. Administration of safety net programs 48. Many o f the weaknesses identified in the safety net system are linked to how the programs are administered and coordinated. Thefirst major constraint i s the lack o f a single policymaking authority for safety net programs inBangladesh. Programs are planned and implementedby thirteen ministries, as well as the Bangladesh Bank and Palli Karma-Sahayak Foundation(PKSF). There is little coordinationamong the ministries in planning, targeting and implementing programs. The patchwork o f social protection initiatives - at least 30 known programs delivered through multiple agencies - make it difficult to determine the accountability of the implementing agencies. There i s also considerable overlap in IO0 programs across Ministries, in terms o f objectives and target population. For example, education stipend programs are run by three different ministries, and multiple ministries are involved in emergency- or disaster-related programs. The Philippineshas recently rationalized its multiple safety net programs under one central agency and designated a Cabinet-level post to headthis agency. Inthe wake o f the food price crisis, it i s also investing more resources into ensuring accurate targeting o f safety net resources by investinginhouseholdtargeting systems. Box 6.4 discusses these reforms inmore depth. 49. A second important issue is the large number o f intermediaries involved in the delivery system programs, which reduces efficiency and increases opportunities for leakage. For example, the funds allocated for the VGD program flow through four separate layers before they reach the IGVGD beneficiaries in the form o f food (see Annex 6, Figure A-6.1). Inaddition, VGD and other food transfer programs depend on the public food distribution system, with food being loaded and unloaded at a number o f points before finally being delivered to beneficiaries. PESP also involves a large number o f intermediaries involved inselecting students, disbursing stipends, and monitoringthe program. 50. A third key constraint i s the weak capacity o f local Governments (UP) primarily responsible for implementation o f programs. The government has recently introduced measures to strengthen the capacity and authority o f local governments. Additional discretionary resources would be made available to UPSconditional on them implementing a host o f financial and social controls to enhance transparency and accountability o f the resources. In addition, one component o f the World Bank's LGSP project finances a pilot to provide social assistance through local governments. Roleof non-governmentinstitutionsinsafety nets 51. In addition to public safety net programs, many o f the anti-poverty programs administered by non- government institutions including MFIs also act as safety nets that protect the consumption o f households, particularly during shocks. As seen in Chapter 3, there i s much evidence to suggest that microfinance reduces the consumption variability o f borrowers across time or seasons - a clear indicator o f reduced vulnerability to shocks' - most likely due to increased access to credit and creation o f income-generating assets among microfinance borrowers. There i s also evidence that MFIs enable their members to cope better with natural disasters (e.g. the floods o f 1998). 52. As a part o f their efforts to expand outreach among the poorest, a number o f initiatives have been adopted by MFIs like Grameen Bank, ASA, BRAC and PKSF, many o f which combine safety net type interventions with flexible microfinance products (see Box 6.5). The rationale for combining such approaches is that the primary objective o f microfinance- developing sustainable livelihoods - cannot be met among the extreme poor unless a household i s first able to meet its minimumneeds, which includes coping with the effect o f shocks. Some o f these programs are implemented through a partnership between MFIs, the government, and international donors in certain cases - a notable example being the Income Generation for Vulnerable Group Development (IGVGD) program (Box 6.5). MFIs have also become increasingly active inthe severely Monga-prone areas, experimenting with a number o f initiatives to address chronic poverty and vulnerability caused by seasonal deprivation inthese areas. III.Summaryandimplicationsforpolicy 53. Safety net programs have an important role to play inBangladesh, given the highincidence o f shocks reported by households and the large size o f the population at the risk o f falling into (or deeper into) poverty as a result o f shocks. There i s also a highconcentration o f the Bangladeshi population around the poverty line which suggests that many are vulnerable to falling into poverty as a result o f even a small shock. Among household-specific shocks, health shocks, especially when these occur to income earners, are particularly important contributors to poverty. Sharp income shocks, such as the recent rise in food * See Morduch(1999), Pitt andKhandker(1998) andZaman (1999). 101 prices, aggravate poverty, contribute to malnutrition, and can have irreversible human capital consequences, especially for infants under the age o f two. These highlight the need for a safety net systemthat can be scaled up and flexibly adjusted to mitigate the impact o f sudden shocks. 54. Bangladesh also suffers from recurring climate-related shocks. Some o f these are seasonal - large areas in the northwest are subject to a phenomenon called Monga, which occurs during the lean apcultural season in October and November every year and contribute to high chronic poverty. Others are more unpredictable, like major floods and tropical cyclones. The most recent floods in 2007 - less than a decade after the severe floods o f 1998 - affected 46 o f the country's 64 districts, including some o f the poorest areas inthe worst-affected Dhaka, Sylhet, and Rajshahi divisions. 55. The Government o f Bangladesh has steadily raisedthe expenditures on these programs since the mid- 199Os, with safety net expenditures close to 2 percent o f GDP in the past few years. Several large programs are relatively well-targeted and some link immediate safety net needs to longer term income generation and healthneeds. However, evidence inthis chapter suggests that safety net programs are still inadequate to address the vast needs o f the poor and the vulnerable. Coverage o f these programs taken together is low and, coupled with targeting errors, imply that many o f the poorest households are excluded from any assistance. There are stark mismatches between the spatial distribution o f poverty and that o f coverage o f safety net programs, with two o f the poorest divisions having a much lower rate o f coverage among its population than Sylhet or Dhaka. Correcting these mismatches therefore needs to be a critical element in a poverty-reducing strategy for the lagging areas o f the country. Extreme poor in urban areas are exposed to a highdegree of risk linked to factors that include lack o f housing and basic amenities, insecurity, and highcost o f living (see chapter 3), with little or no access to safety nets. 56. Increasing the linkages between safety net programs, human development o f children, and income generation activities (including access to microfinance) would also improve the long-term impact o f safety nets on poverty. Programs such as IGVGD, which link safety nets with longer run income generation opportunities, illustrate the benefits o f investing inthese linkages. Conditional cash transfers 102 (CCTs) linkedto education andor healthoutcomes can enhance the incentives among urbanpoor families to keep their children in school andor utilize health services. Such programs can also be effective in laggingregions where returns to education are low (see chapters 2 and 4). 57. The lack o f an overall coordinating authority constrains a coherent approach to poverty-targeted programs. The large number o f programs, Ministries, and intermediaries involved increases the administrative costs o f delivering benefits. Targeting criteria are often hard to verify and apply, which leads to leakage o fbenefits to the non-poor. The Philippines i s turningthe recent food price crisis into an opportunity to reform their safety net system. Programs are being consolidated under one new national umbrella body headed by a Cabinet level position. Steps towards moving to a uniform targeting system are being taken, as i s the establishment o f a large database o f the poor. A conditional cash transfer program is being piloted providing cash payments on condition that households invest in the education andhealthcare of their children. The sequencing andnature o f these reforms offer lessons that are highly relevant for Bangladesh. 58. A number o f reforms are currently being considered to strengthen the safety net system in Bangladesh. A new targeting method using Proxy Means Tests i s likely to be introduced for a cash transfer program inurban areas. This pilot program will link cash transfers to the human development o f children. It will be important to frame these new initiatives under a national social protection strategy which lays the basis for consolidating and coordinating programs, improving their administrative efficiency and coverage among the poor. 59. While this chapter has primarily focused on public safety nets with a view towards informing the government's efforts to improve the system, the important role played by non-government institutions must be underscoredas well. Inparticular, the MFIs' initiatives are critical to test innovative approaches and identify the kind of interventions that are most effective. The lessons gained from such experimentation can then be used to scale up the interventions -by the government, the MFIs themselves ,or throughjoint efforts o f the kindthat are already occurring (as inthe case o f IGVGD). 60. Safety net programs in Bangladesh mostly aim to enhance the capacity o f households to cope with risks, rather than provide ex ante insurance against the adverse impact o f risks. In the longer term, a comprehensive social protection strategy should also include appropriate mechanisms to reduce ex ante risks for households - particularly in the informal sector where pensions and insurance are largely unavailable. While designingsuch programs in a country with a large informal sector will not be easy, the extensive coverage o f microfinance in Bangladesh provides a possible base from which broader financial services, including specific types o f insurance, can be extended to the informal sector. The Government and microfinance institutions are now piloting some efforts inthis direction. 103 104 List of references Adams J. R. (2006). "Remittances and Poverty in Ghana." World Bank Policy Research Paper 3838. World Bank, Washington DC. Ahmed S.S. (2007). "Social Safety Nets in Bangladesh." Background paper for Bangladesh Poverty Assessment. Mimeo (draft). World Bank, Washington DC. Ahmed S.S. (2005). "Delivery Mechanism of Cash Transfer Programs to the Poor in Bangladesh." Social Protection Discussion Paper 0520. World Bank, Washington DC. Alderman, H., S. Appleton, L. Haddad, L. Song, and Y. Yohannes (2003). "Reducing Child Malnutrition: How Far Does Income Growth Take Us," World Bank Economic Review, 17(1): 107-131. Al-Samarrai, S. (2007a). "Changes in employment in Bangladesh, 2000-2005: the impacts on poverty and gender equity." Backgroundpaper for Poverty Assessment of Bangladesh. Mimeo. World Bank, Washington DC. Al-Samarrai, S. (200%). "Education spending and equity inBangladesh." Backgroundpaper for Poverty Assessment of Bangladesh. Mimeo. World Bank,Washington DC. Al-Samarrai, S. (2007~). "Changes in educational attainment in Bangladesh, 2000-2005." Background paper for Poverty Assessment of Bangladesh. Mimeo. World Bank, Washington DC. Bangladesh Bureau o f Statistics (2006). Preliminary Household Income and Expenditure Survey 2005 report. Government ofBangladesh Bangladesh Bureau of Statistics (2008). Household Income and Expenditure Survey report. Government of Bangladesh Bangladesh Institute of Development Studies (BIDS) (2001). "Fighting Human Poverty: BangladeshHumanDevelopment Report 2000". PlanningCommission/ BIDS, Dhaka. Barrett, C. and P. Dorosh (1996). "Farmers' welfare and changing food prices: nonparametric evidence fiom rice inMadagascar," American Journal of Agricultural Economics 78: 656-669. Basu, K, A. Narayan, and M. Ravallion (2001). "Is Literacy Shared within Household? Theory andEvidence for Bangladesh," Labor Economics, Vo18 (6): 649-665. Bayer, P, S. Khan, and C. Timmins (2007). "Non-parametric Identification and Estimation in a Generalized Roy Model." Mimeo. Begum, T. and T. Dmytraczenko (2008). "Public Expenditureand Institutional Review of HNP Sector." Background paper for Poverty Assessment of Bangladesh. Draft. World Bank, Washington DC. Behnnan, J. R. and M.R. Rosenzweig (2004). "Returns to birth weight." Review of Economics and Statistics 86(2): 586-601. Bongaarts, J. and S. C. Watkins (1996). "Social interactions and contemporary fertility transitions,"Population and Development Review 22(4):639-682. Bourguignon, Frangois (2002). "The growth elasticity of poverty reduction: explaining heterogeneity across countries and time periods," DELTA Working Papers 2002-03, DELTA (Ecole normale supirieure). 105 BRAC and Save the Children (UK) (2005). "Inheriting extreme poverty: Household aspirations, community attitudes and childhood in northern Bangladesh." Dhaka, BRAC, and Save the Children (UK): 40. Budd, J. W. (1993). "Changing Food Prices and Rural Welfare: a Nonparametric examinationof the CBte d'Ivoire," Economic Development and Cultural Change41(3): 587-603. Buvinic, M. and G. R. Gupta (1997). "Female-headed households and female-maintained families: are they worth targeting to reduce poverty in developing countries?" Economic Development and Cultural ChangeVol. 45. Chaudhury, N. and J. Hammer (2003). "Ghost Doctors: Absenteeism in Bangladeshi Health Facilities." Policy ResearchWorking PaperNo. 3065. World Bank, Washington DC. Cox, E., Alejandro, and M. Ureta (2003). "International Migration, Remittances, and Schooling: Evidence fiom El Salvador." Journal of Development Economics 72(2): 429-61. Dahl, G. (2002). "Mobility and the Return to Education: Testing a Roy Model with Multiple Markets." Econometrica Vol. 70(6). Datt, G. andRavallion, M. (1992). "Growth andredistribution components of changes inpoverty measures : A decomposition with applications to Brazil and India in the 1980s," Journal of Development Economics, Elsevier 38(2): 275-295. Davis, P. (2006). "Discussions among the poor: Exploring poverty dynamics with focus groups inBangladesh." Background paper for Bangladesh PovertyAssessment. Mimeo (draft). World Bank, Washington DC. Davoodi, H. R., E. R. Tiongson, et a1 (2003). "How useful are benefit incidence analyses of public education andhealthspending." IMFWorking paper No: WPlO31227: 48. Deaton, A. (1997). The Analysis of Household Surveys: A Microeconometric Approach to Development Policy (Johns Hopkins University Press: Washington DC). Deaton, A. (1989). "Saving and Liquidity Constraints." NBER Working Papers 3196, National Bureau of Economic Research. Deolalikar, A. and R. Gaiha (1993). "Persistent, expected and innate poverty: estimates for semiarid rural South India 1975-1984," Cambridge Journal of Economics 17: 409-421. Deshpande, S. K. (1988). "Local-level management of a rural anti-poverty program: a case study of the Employment Guarantee Scheme of Maharashtra." Dissertation. Fellow Program in Management, IndianInstitute of Management, Bangalore. Dev, M.S., K.S. James, andB. Sen (2002). "Causes of fertility decline inIndia and Bangladesh," Centrefor Economic and Social Studies. Working PaperNo. 45. Drkze, J. "NREGA: Dismantlingthe Contractor Raj." TheHindu (November 20,2007). Drkze, J. and S. Kidambi. "Long roadto employment guarantee" TheHindu (August 7,2007). Fafchamps, M. and F. Shilpi (2005). "Cities and Specialization: Evidence from South Asia." Economic Journal, Vol. 115. Filmer, D. and L.Pritchett (2001). "Estimating wealth effect without expenditure data- or tears: An applicationto educational enrollments instates of India." Demography (38: 115-132). Fujita, M., P. Krugman and A. Venables (1999). The Spatial Economy: Cities, Regions, and International Trade. MIT Press, Cambridge and London. 106 Glewwe, P., M. Gragnolati, and H. Zaman (2000). "Who Gained from Vietnam's Boom in the 1990s? An Analysis of Poverty and Inequality Trends." World Bank Policy Research Working PaperNo. 2275. Glewwe, P., H. Jacoby and E. &ng (2001). "Early Childhood Nutrition and Academic Achievement: A LongitudinalAnalysis," Journal of Public Economics, 81:345-368. Gwatkin, D., S. Rutstein, K. Johnson, E. Suliman, A. Wagstaff, and A. Amouzou (2007). "Socioeconomic differences inhealth, nutrition andpopulation: Bangladesh 1996-97, 1999-2000, 2004." World Bank, Washington DC. Hanson, G. H. and C.Woodruff (2003). "Emigration and educational attainment in Mexico." Mimeo. University of California at San Diego, CA. Hashemi, S., S. Schuler, and A. &ley (1996). "Rural Credit Programmes and Women's Empowerment inBangladesh, WorldDevelopment, Vol. 24 (4). " Henderson, J. V. (2003). "The Urbanization Process and Economic Growth: the So-What Question," Journal of Economic Growth,Vol. 8. Henderson, V. and H. Wang (2005). "Aspects of the Rural-Urban Transformation of Countries," Journal of Economic Geography, Vol. 5. Henderson, V. andR. Becker (2000). "Political Economy o f City Sizes and Formation, Journal of UrbanEconomics, Vol. 48. Hoddinott, J. and A. Quisumbing (2003). "Methods for Microeconometric f i s k and Vulnerability Assessments." Social Protection Working Paper No. 0324, World Bank Social Protection Discussion Paper Series. Hossain M. (1995) "Socio-economic characteristics of the poor" in Rahman and Hossain (eds) Rethinking ruralpoverty. UPL, Bangladesh. Ivanic, M. and W. Martin (2008). "Implications of Higher Global Food Prices for Poverty in Low-Income Countries." Policy ResearchWorking Paper 4594. World Bank, Washington DC. Jalan, J. and M.Ravallion (2002). "Geographic Poverty Traps? A Micro Model of Consumption Growth inRural China," Journal of Applied Econometrics, Vol. 17. Jitsuchon, S. (2004). "Small Area Estimation Poverty Map for Thailand." Paper presented at the SMERU Research Institute and Ford Foundation International Seminar, "Mapping Poverty in Southeast Asia," Jakarta, December 1-2. Kanbur, R and H. Rapoport (2005). "Migration Selectivity and the Evolution of Spatial Inequality," Journal of Economic Geography, Vol. 5(1). Khandker, S. (2005). "Micro-finance and Poverty: Evidence Using Panel Data from Bangladesh," WorldBank Economic Review, Vol. 19 (2). Khondker, B. and S. Raihan(2007). "Poverty Impacts o f Remittance and RMGinBangladesh: A Computable General Equilibrium Analysis." Background paper for Bangladesh Poverty Assessment. Mimeo (draft). Kotikula, A., A. Narayan, and H. Zaman (2007). "A Profile of Poverty in Bangladesh: Household Attributes, Location Effects and Changes over Time," Background paper for BangladeshPoverty Assessment. Mimeo. SouthAsia Region, World Bank, WashingtonDC. Krugman, P. (1999). "The role of geography in development." International Regional Science Review, Vol. 22(2). 107 Lanjouw, P. and M. Ravallion (1995). "Poverty and household size," Economic Journal, Vol. 105. Machado, J.A.F. and J. Mata (2005). "Counterfactual Decomposition of Changes in Wage DistributionsusingQuantile Regression," Journal of Applied Econometrics, Vol. 20 (4). Mahmud, W. (2006). "Employment, Incomes and Poverty: Prospects of Pro-poor Growth in Bangladesh," in Growth and Poverty: The Development Experience of Bangladesh, S. Ahmed and W. Mahmud (eds). Dhaka: University Press Limited. Mathur, L. (2007). "Employment Guarantee: Progress So Far," Economic and Political Weekly, 42(52): 17. Matin, I.and D. Hulme (2003). "Programs for the Poorest: Learning from the IGVGD Program inBangladesh." WorldDevelopment, Vol. 30. Morduch, J. (1998). "Does Microfinance Really Help the Poor? New Evidence on Flagship ProgramsinBangladesh." Working Paper. Munshi, K. and J. Myaux (2006). "Social Norms and the Fertility Transition.'' Journal of Development Economics 80(1): 1-38. Nguyen, B.T, J.W. Albrecht, S. B. Vroman, and M. D. Westbrook (2007). "A Quantile Regression Decomposition of Urban-rural Inequality in Vietnam." Journal of Development Economics, Vol. 83. Overman, H.G, P. Rice and A. J. Venables (2007). "Economic Linkages across Space." CEP Discussion PaperNo. 805. Phlipose, P. (2007) Indian Express. . Pitt, M. and S. Khandker (1998). "The Impact of Group-Based Credit Programs on Poor Households in Bangladesh: Does the Gender of Participants Matter?" Journal of Political Economy; Vol. 106 (5). Pitt, M., S. Khandker, and J. Cartwright (2006). "Empowering Women with Micro Finance: Evidence from Bangladesh." Economic Development and Cultural Change 54(4): 791-831. Quisumbing, A. (2007). "Poverty transitions, shocks, and consumption in rural Bangladesh: Preliminary results from a longitudinal household survey." Background paper for Bangladesh Poverty Assessment. Mimeo (draft). World Bank, Washington DC. Rahman, H.and S. Ali (2005). "Quality Improvements inPrimary Education: Micro Insightsfor a Macro Agenda." PPRC EducationPolicy Brief, Dhaka, Bangladesh. Rashid, S. (2002). "Dynamics of agricultural wage and rice price in Bangladesh." MTID discussionpapers44. InternationalFoodPolicy ResearchInstitute (IFPRI). Ratha, D. (2003). "Worker's Remittances: An Important and Stable Source of External Development Finance" in World Bank, Global Development Finance: Striving for Stability in Development Finance, Volume I : Analysis and Statistical Appendix (157-1 75). World Bank, Washington, DC. Ravallion, M. (1990). "Reaching The Poor Through Rural Public Employment; A Survey O f Theory And Evidence." World BankDiscussion Papers 94. World Bank, WashingtonDC.. Ravallion, Mand Q. Wodon (1999). "Poor Areas or Poor People?" Journal of Regional Sciences, VOl. 39(4). 108 Ravallion, M. and J. Jalan (1999). "China's Lagging Poor Areas," American Economic Review Papers and Proceedings, Vol. 89(2). Ravallion, M., S. Chen, and G. Datt, (1993). "Is poverty increasing in the developing world?" Working Paper Series 1146, Policy ResearchDepartment.World Bank, Washington DC. Ravallion, M. and S. Chen (2004). "How Have the World's Poorest Fared Since the Early 1980s?" WorldBank Research Observer, 19(2): 141-170. Roy, A. D (1951). "Some Thoughts on the Distribution of Earnings." Oxford Economic Papers, Vo1.3. Sen, B. and D. Hulme (2005). Chronic Poverty in Bangladesh: Tales of Ascent, Descent, Marginality and Persistence. DhakdManchester: Bangladesh Institute of Development Studies / CPRC / IDPM. Sen, B., M. K. Mujeri, and Q. Shahabuddin (2007). "Explaining Pro-Poor Growth in Bangladesh: Puzzles, Evidence, and Implications" in Delivering On The Promise Of Pro-Poor Growth: Insights and Lessons from Country Experiences, T. Besley and L. J. Cord (eds.). Palgrave, Macmillan andWorld Bank. Serajuddin, U., H. Zaman, A. Narayan (2008). "Pro-Poorest Growth in Bangladesh: Evidence between 2000 and 2005." Background paper for Bangladesh Poverty Assessment. Mimeo (draft). World Bank, Washington DC. Sharma, M. (2007). "International Migration, Remittance, and Householdwellbeing: A study of twenty communities in Bangladesh." Background paper for Bangladesh Poverty Assessment. Mimeo (draft). World Bank, Washington DC. Shilpi, F. (2008). "Migration, Sorting and Regional Inequality: Evidence from Bangladesh." Background paper for Bangladesh Poverty Assessment. Mimeo. World Bank, Washington DC. Siddiqi, T. and C. Arbar (2001). Migrant Worker Remittances and Microfinance in Bangladesh. InternationalLabor office, Dhaka. Smith and Blundell (1986). "An Exogeneity Test for the Simultaneous Equation Tobit Model," Econometrica, 54: 679-685. Wagstaff, Adam (2003). "Child health on a dollar a day: some tentative cross-country comparisons," Social Scienceand Medicine, 57(9): 1529-1538. World Bank (2002a). Poverty in Pakistan: vulnerabilities, social gaps, and rural dynamics. Washington DC. World Bank (2002b). Poverty in Bangladesh: Building on Progress. Report No. 24299-BD. Washington DC. World Bank (2003a). Bangladesh, Development Policy Review: Impressive Achievements but Continuing Challenges. Report No. 26154-BD. Washington DC. World Bank (2003b). Improving the Investment Climate in Bangladesh. WashingtonDC. World Bank (2005a). Bangladesh: Growth and Export Competitiveness. Report No. 31394-BD. Washington DC. World Bank (2005b). Global Economic Prospects 2006: Economic Implications of Remittances and Migration. Washington DC. World Bank (2006a). Economics and Governance of Nongovernmental Organizations in Bangladesh. ReportNo. 35861-BD. Washington DC. 109 World Bank (2006b). The Bangladesh Vocational Educational and Training System: an Assessment. SouthAsia Region. WashingtonDC. World Bank (2007a). Bangladesh: Strategy for Sustained Growth. Report No. 38289-BD. WashingtonDC. World Bank (2007b). Sri Lanka Poverty Assessment: Engendering Growth with Equity: Opportunities and Challenges. ReportNo. 36568-1k. SouthAsia Region. WashingtonDC. World Bank (2007~). To the MDGs and Beyond: Accountability and Institutional Innovation in Bangladesh. SouthAsiaRegion. WashingtonDC. World Bank (2008a). Bangladesh: the Role of Employment and Earnings in Shared Growth. A WorldBank Labor Market Study. Draft report. DC. World Bank (2008b). Bangladesh Rural Investment Climate Assessment. Draft. Washington World Bank(2008~).Bangladesh Urban Strategy Notes. Draft. WashingtonDC. World Bank (2008d). Whispers to Voices: Gender and Social Transformation in Bangladesh. SouthAsia Region. WashingtonDC. World Bank (2008e). Rising Food and Fuel Prices: Addressing the Risks tofuture generations. WashingtonDC. World Bank (2009). WorldDevelopment Report: Reshaping Economic Geography. Washington, DC. Zaman, H. (1999). "Assessing the Impact of Micro-Credit on Poverty and Vulnerability in Bangladesh." PolicyResearchWorking PaperNo. 2145. World Bank, WashingtonDC. Zaman H. (2006). "Microfinance in Bangladesh: Growth, Achievements and Lessons" in D. Narayan and E. Glinskaya (eds.) Ending poverty in South Asia: Ideas that work. Oxford University Press. 110 Annex 1:Poverty, Growth, and Inequality I. Poverty measurement in Bangladesh -a brief overview The World Bank and the Bangladesh Bureau o f Statistics (BBS) have had a long-standing partnership on poverty measurement issues using data from successive rounds o f the Household Income Expenditure Surveys (HIES). The World Bank supported the design and implementation o f the 2005 HIES and a Bank team worked closely with BBS analysts on deriving nationally representativepoverty andinequality estimates. Intuitively, Cost o f Basic Needs (CBN) poverty lines represent the level o fper capita expenditure at which a household can be expected to meet their basic needs (food and non-food). As prices and consumption patterns vary between different geographical areas, poverty lines are estimated for each o f 16 different geographical areas or sampling strata. To ensure that comparisons over time are made on the basis o f poverty lines that represent the same purchasing power, CBN poverty lines estimated for the new base year o f 2005 were then deflated by an appropriate price index to derive poverty lines for 2000. In the course o f the BBS-World Bank collaboration, a number o f methodological issues were examined closely, which can be classified into two broad categories: (i) updating the pre-existing poverty lines to 2005, using price indices to adjust for changes in cost o f living; and (ii) re-estimating poverty lines using the 2005 data and deflating these lines with price indices to obtain comparable poverty figures for previous survey years. Under (ii), a number o f different approaches were tried out, including estimating a single poverty line for the country and calculating appropriate spatial price indices to adjust for geographic differences incost o fliving, inlieu o f estimating poverty lines separately for each stratum. BBS in consultation with the Planning Commission decided on one method out o f the different options explored, which involves re-estimating poverty lines from HIES 2005 for 16 different strata usingthe Cost o f Basic Needs (CBN) method - similar to that used to derive poverty lines based on HIES 1991-92. Re-basing the poverty lines using 2005 data - as opposed to just updating the previous lines for cost o f living - ensures that these are based on the latest underlyingsampling frame (using Census 2001), andalso conforms to the view that poverty lines should be re-based every 10-15 years to reflect changes in consumption patterns.' Box 1below describes the exact steps involved inimplementingthe selected method. I which has been usedin consumption. The first was calculatedas the a whose total consumptionwas equal to their 0 rty lines to 2000 were ' The sampling frame o f 2005 HIES, based on the 2001 census, i s likely to better reflect the current economic and demographic situation. Poverty lines based on this frame will yield better comparison with future poverty estimates since the same sampling frame will be also used for future surveys untilthe Census of 2011becomes available. 111 Due to its similarity with earlier methods employed in Bangladesh, the selected method also yields a high degree o f consistency with the results obtained from previously used poverty lines. The poverty trends obtained usingother methodological options serve as important cross-checks for the robustness o f poverty trends to the choice o f particular poverty lines or methods. The results o f this analysis, including the poverty estimates and trends derived from the method recommended by BBS, were endorsed by a Steering Committee set up by the government on August 13,2006.* II.Adjustingconsumption expenditurefor householdcomposition andeconomiesof scale: an investigation In order to measure welfare at the individual level and to estimate poverty rates, per capita expenditures are used in Bangladesh, ignoring the composition o f households (the number o f adults and children, for instance) and the economies o f scale in consumption that may be available to largerhouseholds. Such effects are hard to quantify ina universally acceptable form, andtherefore excluded fi-ompoverty measures adopted by many countries including Bangladesh. Having said that, it i s important to see how sensitive the poverty estimates - particularly the trends over time - are to these adjustments. Two specific types o f adjustment are considered for this sensitivity analysis. Firstly, household consumption expenditures are adjusted by weighting all household members younger than 18 by a factor s (4)and all adult household members as 1. While this may appear as oversimplifying the problem o f adjusting for the composition o f a household, using a more complex equivalence scale, for instance one that differentiates between sex and various age categories, i s even more problematic for a number o freasons. First,no consensus on such a scale exists for Bangladesh to the best o f our knowledge. Second, because such scales are generally based on nutritional requirements, they are applicable only to food consumption and not to the vast array o f other items that enter into household consumption. Third, even a simple adjustment i s sufficient for the purpose at hand- namely to show the sensitivity o f poverty trends to such an adjustment. Secondly, household consumption is adjusted by a "scale factor" that captures the fact that the "true" welfare o f larger households may be higher than what i s suggested by unadjusted per capita expenditures, given the economies o f scale in consumption. While there are sound arguments in the economic literature in favor o f such scale adjustments (see, for instance, Lanjouw and Ravallion 1995), making such adjustment i s also problematic because it i s hard to define the precise adjustment applicable to Bangladesh. To examine the sensitivity o f poverty trends to household composition and scale effects, a somewhat arbitrary method i s adopted that involves considering a number of different scenarios - similar to the approach used inrelated literature for other developing co~ntries.~The number of equivalent adults in a household i s givenby AE = (A+ s.C)`; where A i s the number o f adults, C is the number o f children (below age 18) inthe household, the parameter s is the expenditure on a Chaired by the Planning Secretary, the meeting counted among its participants the Director General, BBS, Member GED, DG BIDS, Research Director BIDS, representatives from other government departments and Dhaka University, and the World Bank. See, for example, "Poverty inPakistan: vulnerabilities, social gaps and rural dynamics," World Bank (2002a). 112 child relative to that on an adult; and the parameter, t (between 0 and l), economies o fmeasures scale. When t is set to unity, the expenditure measure does not adjust for household size. Total household expenditure for each household is then divided by AE to arrive at an adjusted adult equivalent expenditure for the household, which i s then compared to the poverty lines used inthis report to arrive at adjustedpoverty estimates. For the purpose o fthis analysis, s i s set between 0.7 and 1, while t i s set between 0.8 and 1. Since economies o f scale primarily arise from the existence o f shared public goods inthe household, t should be high when a substantial fraction o f household expenditure i s on private rather than shared goods. Since households in Bangladesh spend a large proportion o f their budget on food (around 53 percent on the average) that i s essentially a private good, economies o f scale are likely to be limited, so that t should be set at or close to 1. For s, estimates used for other countries suggest that a range o f 0.7 to 1is a reasonable assumption. Figure A shows national poverty trends for four of such scenarios - for all possible combinations when FigureA: Povertyheadcountratefor s is set at 1 and 0.7 and t at 1 and 0.9. Poverty differentadjustments for household compositionandscale effects headcount estimates are lower when s and t are set at ..--,- less than unity for obvious reasons; however, the 6 50 - trend o f change from 2000 to 2005 is very similar 5 40- for all tested values o f s and t. The fact that 0 2 30- comparative trends across time are quite robust to S such scale adjustment suggests that analysis using r"20- -- m unadjusted poverty estimates, w h c h i s used 2 10, throughout this report, i s a reasonable methodology 2000 2005 to adopt. That said, the issue o f household -* -(S=l; t=l) - - c ( s = l ; t=.9) composition and economies o f scale in consumption - . - A - - . (S=.7;!=1) -(S=.7;!=.9) is one that merits more rigorous analysis in the Source: HIES (2000,2005) course o f future poverty work inBangladesh. III. Methodologiesfor estimatinggrowthelasticityof poverty Regression method. This method involves estimating y , p ,and 6 from regressions using growth rates o f stratum average poverty rates, per capita consumption expenditures, and Gini coefficients. Regression o f the growth rate o f poverty rates on the growth rates o f per capita consumption expenditure and Gini coefficient provides y (the gross elasticity o f poverty to growth) and p (the elasticity o fpoverty to inequality). Regressiono fthe growth rate o f Gini coefficients on the growth rate o f per capita consumption expenditures provides 6 (the elasticity o f inequality to growth). To run the regressions, a database including poverty rates, Gini coefficients, and mean expenditures at the stratum level using HES 1991-1992, HES 1995-1996, HIES 2000, and HIES 2005 i s constructed. Bourguignon (2002)'s method. This method estimates y and p by assuming the distribution o f per capita consumption expenditure i s log normal. The assumption provides a simple formula deriving both y and p using basic statistics from the distribution o f per capita expenditure. 6 (the elasticity o f inequality to growth) i s calculated by dividing the percentage change o f an inequality measure (standard deviation o f expenditure) by the percentage change o fmeanexpenditure. For this analysis, only HIES 2005 data i s used. Datt-Ravallion (1992) method. This method follows the well-known growth decomposition method. For this analysis, per capita expenditure data for 2000 and 2005 are used. This 113 method first creates a hypothetical distribution o f per capita expenditure as ifthe expenditure grew at the same rate among all households between 2000 and2005. Since this hypothetical distribution o f expenditure and the 2000 distribution share the same distributional properties, the difference in poverty rate between the two distributions can be attributed solely to economic growth between 2000 and 2005. On the other hand, since the hypothetical distribution and the 2005 distribution share the same mean expenditure, the difference in poverty rates can be attributed solely to a change indistributiodinequality between2000 and 2005. Deriving the direct impact i s trivial from the first comparison. The indirect impact i s derived fiom dividing the percentage change inpoverty rate from the second comparison by the percentage change inmeanexpenditure. Each methodology has its pros and cons. The regression method assumes that the relationship among growth, poverty, and inequality at the national level can be estimated by the variations at the stratum level and over time; however, there is no simple way to test the validity o f this assumption. Also, small sample biases are inevitable: at most, 48 observations are available for regressions (16 strata times 3 years - one year needs to be dropped due to the need to compute growth rates for all variables). On the other hand, since this method uses multi-year data, the growth elasticity tends to reflect a more stable and long-term relationship among growth, inequality, andpoverty. The validity o f Bourguignon (2002)'s method hinges on the assumption that the distribution o f per capita expenditure can be approximated by a log normal distribution. Bourguignon (2002) empirically tested this method for projection using a large cross-country data and found this method achieved fairly good accuracy. Inthe Datt-Ravallion(1992) method, the validity ofthe growth elasticity is subject to how closely the expenditure data form the select two surveys can predict the future relationship among growth, inequality and poverty, or more precisely, the elasticity o f poverty to growth. If the selected two surveys were to reflect some extraordinary circumstances, the projection based on this method would be biased. This method, however, has merit as well. Since distributional properties are highly multidimensional, the impact o f a change in distribution i s difficult to be measured by one aspect o f distribution such as Gini coefficient or standard deviation. The Datt- Ravallion method k l l y captures the distributional impact by comparing two distributions directly. Regression Bourguignon Datt-Ravallion Comparing the estimates from three -2.26 -1.79 -1.62 methods. The 2005 poverty rate is Direct (y ) Indirect (pa) simulated with the estimated net growth elasticity o f poverty and the Net Elasticity ( A ) -1.62 -1.79 -1.51 Projected2005 actual poverty rate o f 2000. Table A poverty rate (%) 39.5 38.5 40.1 shows that the simulations usingDatt- Actual 2005 Ravallion (1992)'s method i s closest poverty rate (%) 40.0 to the actual 2005 poverty rate Source: StaffestimationusingHIES 1991-1992, 1995-1996,2000, estimated directly from HIES 2005. 2005. Note: "NA" refersto "Not Available". "Actual 2005 poverty rate" Regression method also provides fairly refers to the poverty rate estimated from HIES 2005. "Projected good projections; however, the 2005 poverty rate" refersto the poverty rate estimated from the indirect effect via inequality seems to actual 2000 poverty rate and the net growth elasticity. 114 be too highinthe light o f the fact that the Gini coefficient did not change much between 2000 and 2005.4 Projections based on Bourguignon (2002)'s method matches the actual poverty rate o f2005 less closely than for the other two methods. This mightbe inline with the fact that log o f per capita expenditure does not pass a normality test. As a result, Datt-Ravallion (1992)'s method, which yields a net elasticity estimate o f -1.51, i s selected for projecting future poverty rates. Nevertheless, the similarity in estimates based on the Datt-Ravallion and regression methods confirms that the estimates are reasonably stable across different methods. Projections bused on the selected elasticity: Three alternate growth scenarios were considered - namely that real GDP in Bangladesh would grow by 4.5, 5.3 and 7.5 percent per annum - to forecast the headcount index o f poverty in the year 2015. The annual growth rate o f 5.3 percent is a baseline scenario since that was the average annual growth rate o f real GDP between 2000 and 2005. The GDP growth rates have to be converted to the growth o f per capita expenditure from household surveys to apply the estimates o f net elasticity o f poverty to growth. The ratio o f the annual growth rate o f per capita household expenditure in 2000-2005 (2.3 percent) to the annual GDP growth in2000-2005 (5.3 percent) was used to convert GDP growth rates to growth rates o f per capita household e~penditure.~The elasticity estimate (-1.5 1)was then applied to the per capita consumption growth rates to obtain poverty projections. The simulations indicate, for example, that if real GDP were to grow at 5.3 percent between 2005 and 2015, the incidence o f poverty would decline from 40 percent to 27 percent (see FigureA-1.5 for all projections). An analysis ofrobustness: assumingno change in householdsize An important drivingforce o f poverty reductionbetween 2000 and 2005 is a sizable reduction in the average household size between 2000 and 2005 (from 5.18 to 4.85), which is likely a reflection of reduction in the fertility rate in preceding years. The above projections implicitly assume that the large reduction in both the fertility rate and household size would continue till 2015. These assumptions may not be unrealistic, but to gauge the impact, it would be useful to experiment another extreme scenario: no change inhousehold size since 2005 onward. Such an assumption affects (i) the estimation o f growth elasticity and (ii) estimated growth the rate o fper capita expenditure for each projected GDP growth rate. Estimation of growth elasticity. To project the growth elasticity under the scenario that the average household size were to be unchanged between 2000 and 2005, the household size o f HIES2005 data was artificially inflated so that the quintile average household sizes would be the same as those o f HIES2000. The adjustment was made separately for each quintile because there are huge differences in household size reduction between 2000 and 2005 across quintiles. An "adjusted" per capita household expenditure was computed by dividing the household expenditure by the adjusted household size. Since the adjusted per capita expenditure is smaller thanthe actual one, the nationalpoverty headcount rate in2005 would increase to 45.2 percent from 40 percent, suggestingthat household size reduction accounted for about half of the poverty reductionbetween 2000 and 2005. Ifupperpovertylines are usedto adjustpercapita consumptionexpenditure for spatialprice differences, Gini coefficients in2000 and 2005 are 0.307 and 0.309,respectively. In the previous Bangladesh Poverty Assessment, projection of national poverty rates was done after projecting poverty rates for urban and rural areas separately. This approach was not adopted this time because there is simply noway to isolate migration effects from urbanandrural growth elasticity estimates. 115 Using the nedudjusted distribution o f per capita expenditure, the overall growth elasticity becomes -1.6 (compared to -1.51 with the actual distribution). The direct effect i s -1.61, which i s almost identical to the earlier estimate; the indirect effect i s 0.01, compared to 0.12 from the earlier estimate. Thus the adverse impact o f inequality on poverty reduction i s lower when the household size i s held constant, compared to the actual case where household size fell significantly. As a result, the responsiveness o f poverty to per capita consumption growth i s higher when household size is heldconstant compared to the actual case. Projected growth rate of per capita expenditure. As mentioned earlier, to project the poverty headcount rate, GDP growth rate needs to be translated to a growth rate o f per capita household expenditure. For the previous projections, the ratio o f the annual growth rate of per capita household expenditure in 2000-2005 to the annual GDP growth in2000-2005 (2.3/5.3) was used for this conversion. But now, with the average household size held constant between 2000 and 2005, the annual growth rate o f per capita household expenditure declines to 0.9, which yields a conversion ratio o f (0.9/5.3). This implies that the same annual GDP growth scenarios o f 4.5, 5.3 and 7.5 percent correspond to muchlower rates o f growth o fper capita household expenditure. would slow down significantly if the average household Table B: Projectedhousehold size does not change from 2005 to 2015. Even if GDP headcountrate in 2015 if grows at 7.5 percent annually, the projected poverty . size does not changeafter 2005 (%) headcount rate would be 32 percent in 2015 compared to Annual Assumption inhhsize 22 percent if household size were to decline at the current GDP rate. Samepace rate (%) No change as till 2005 It is easy to see that the main reason for the lower 5.3 34.3 27.0 projections i s a much lower impact o f GDP growth on the 4.5 35.1 28.9 growth o f per capita expenditure. No change inhousehold 6.0 33.5 25.4 size implies a high population growth, which reduces the 7.5 32.0 22.1 .Source:Staff estimation using thewDI 2008 growth rate per capita household expenditure (and per and HIES2000,2005 data. 116 I K Figures and tablesreferred to in main text FigureA-1.1: 95% ConfidenceIntervalsfor poverty eadcounts(2000 and 2005) "I I I I II I ource: HIES (2000 and2005) I IFigure A-1.2: Cumulativedistributionof expendituresfor urban and rural areas Source: HIES (2000 and2005) Note: The povertyrate is givenbythe Y-axis of the point where the cumulative distributionfunctions intersectsthe povertyline. IFigureA-1.3: Growth IncidenceCurves(2000-2005) -Rural and Urban GIC Rural GIC Urban average annualgrowthratesof mnsurnptimexpenditurn2000-05 averageannualgrowthratesof mnsurnpttmexpenditure 2000-05 I `1 0 20 40Percentiles80 80 100 ... .. Growthincidencecurve -Growth rate inmean -Growth tncldence curve-Growth rate in mean Mean of growth rates -Meanofgrowm rates INote: Growthis consideredpro-poor ifgrowth rateinmean y=w *e*a => Ay=Aw *he *Aa => wC=Aw/Ay, ec=Ae/Ay, ac=Aa/Ay, where Y is total value added, E is total employment, A is total working population, and Nis total population. Thus w corresponds to output per worker ("productivity"), e to the employment rate, a to an (inverse of) the dependency ratio, and wc,ec, ac to respective contributions. 4. Decomposition of change in productivity by sector (Section VI) The decomposition is w= E,(o,*E,) => Am= C,wc,+ C,cC,, where o would correspond to productivity (in sector i) and E,to a share of employment in sector i. Shapley decomposition is usedto arrive at respectiveadditive contributions wcIand e`,. 5. Decomposition of poverty reduction into changes in poverty within sector and changes inthe share of the people "attached" to each sector (Section VI) Change inoverall poverty is equivalent to [ZI~ 1 , 2 0 0 5 ( ~ 1 . 2 0 0 5 ~ ~ i , 2 0 0 0 )@i,2005-N1,2000) + zi (PI~~OOS-PZ~~S)] where P, is a poverty headcount for the total population in year t, PI,,is a poverty measure in sector i in year t, and Nl,,is the share of poor households "attached" to sector i at time t. The expressionon the left corresponds to a poverty reduction within sectors, while that on the right corresponds to a contribution of inter-sectoral changes. A household is "attached" to a sector from which it derives most of its income. 123 III.Tablesreferred toinchapter TableA-2.1: Structureofthe Labor Market, 2000-2005 Shareo ftotal hualiz 2005 emPloPent ed real oftotal labor income Years o f Earnings' Poverty educatio Hourly Hours 2000 2005 share growth n median growth12 rate2 worked3 rate4 change Employment 100% 100% 2.8% 100% 3.7% 4.2 2,223 0.9% 11.3 47.2 38% -9% 'Waged employment5 53% 53% 3.0% 48% 5.0% 4.3 2,200 2.0% 10.0 48.5 46% -8% Daily labor 33% 32% 2.0% 19% 1.8% 1.8 1,827 [0.3?'0] 9.2 43.4 60% -8% Agriculture 19% 16% -1.2% 8% -1.8% 1.3 1,600 -0.3% 8.5 39.6 66% -7% Non-agriculture 14% 16% 5.9% 11% 4.8% 2.3 2,200 -0.4% 10.0 47.2 55% -7% SaIaried 20% 22% 4.6% 29% 7.5% 7.8 3,378 2.4% 13.9 55.9 25% -6% Publicsector6 4% 4% 4.2% 7% 5.4% 10.7 5,654 0.8% 24.2 52.8 9% -6% Communitysector' 3% 4% 6.8% 7% 10.0% 10.5 4,138 1.7% 17.5 52.3 18% -3% Private sector 13% 14% 4.1% 15% 7.5% 6.1 2,735 10.7 57.8 32% -6% Self-emolovment . " 47Yo 47% 2.6% 52% 2.5% 4.1 2.257 1O.OYol , .3.4%, 13.7 45.4 29% -10% Non-agriculture 21% 20% 1.6% 31% 2.6% 5.0 3,152 [1.0Y0] 13.9 54.6 28% -10% Individual(omaccom) 12% 11% 0.7% 13% 1.8% 4.2 3.104 (1 0%1 13.7 53.0 35% -11% I . _ Family' 3% 4% 6.1% 3% 8.0% 4.9 2,167 1.6% 10.8 52.2 27% -18% Employers' 5% 5% 0.5% 15% 2.4% 7.1 5,417 [2.2%] 20.6 60.5 11% -3% Agriculture 27% 27% 3.4% 21% 2.4% 3.5 1,588 -0.7% 13.6 37.9 29% -9% o/w subsistence" .. 1.8% .. -2.4% 3.2 954 -4.1% 8.4 34.8 30% -13% Sectors Agriculture 51% 46% 0.7% 32% 0.4% 2.8 1,638 -0.2% 10.0 39.4 43% -1% Industry" 22% 23% 3.9% 27% 2.9% 4.5 2,511 -0.9% 11.3 52.2 37% -8% o/w manufachiring 18% 18% 2.8% 20% 2.1% 4.8 2500 -0.6% 10.6 54.0 35% -7% o/w cons!ruc!ion 4% 5% 7.5% 5% 5.1% 3.3 2500 -2.4% 12.5 45.4 46% -6% Services 27% 31% 5.4% 41% 7.4% 6.0 3,167 1.7% 13.5 53.9 31% -8% Notes: (1) Wmonth, from all activities; (2) Tk, median, in main activity, i.e. in the activity with the highest earnings; (3) In all activities; (4) Basedon consumptionand on "upper" poverty line; (5) Job categoriesin mainactivity; (6) Governmentorganizations, state owned enterprises, local governments; (7) "Autonomous bodies", NOS;(8) Householdenterpriseswith two or more members from the households and with no outside employees; (9) Household enterprises employing outside workers; (10) Members of householdsthat derive more than 90 percentof its income from agriculture and consume more than 50 percent of own agricultural production; this is an arbitrarily definition, hence absolute numbers has not been show; (1 1) Includes construction, (12) In mean earnings. When growth of mean was of different sign or was different by more than 3 percentagepoints from that of median, the estimates are presentedinbrackets. Sources: BasedonHIES 2000,2005; World DevelopmentIndicators Table A-2.2: EarningFunctionin Bangladesh,2000 and 2005 2005 All Daily Salaried o/wpubl Non-agri Agricult. 2ooo 2005 workers wage job ~ c s e c t self-emp self-emp Men urban Rural Salariedjob 0.335 - 0.274 0.474 0.120 0.384 Non-agric.self-empl. 0.416 0.439 0.281 0.355 0.397 Agriculture self-empl -0.284 -0.177 -1.149 -1.069 -0.191 Age 0.062 0.058 0.057 0.040 0.078 0.060 0.050 0.053 0.062 0.052 0.057 0.056 Age squared -0,001 -0.001 -0,001 0.000 -0.001 -0.001 -0.001 -0.001 -0,001 -0,001 -0.001 -0,001 Woman -1.113 -0.960 -0.967 -0.849 -0.388 -0.164 -0.908 -1.630 -0.657 -1.107 Years ofeducation 0.058 0.059 0.050 0.021 0.068 0.065 0.057 0.032 0.047 0.065 0.072 0.038 Muslim2 -0.006 0.007 0.020 0.108 0.140 0.168 0.084 -0.214 0.063 -0.129 0.082 0.011 Married 0.088 0.101 0.110 0.129 0.042 0.017 0.137 0.071 0.109 -0.056 0.212 0.059 Publicsectorjob 0.371 0.497 0.347 0.241 0.290 0.492 0.162 0.481 Urbanjob 0.418 0.351 0.187 0.234 0.097 0.057 0.325 -0.396 0.178 0,133 - Repional dummies. - outDut sumressed' I 11 R2 0.347 0.295 0.344 0.282 0.455 0.427 0.299 0.256 0.283 0.398 0.428 0.293 Notes: (1) Coefficientsfrom regressionof logmonthlyearnings from mainemployment/activity are not correctedfor potentialselection bias; (2) All coefficientslistedare statistically significantat lpercent, except "Muslim" -which is not significantanywhere, except in salariedjobs and inurbanareas and for men- at 5 percent;(3) The typical valuesfor regional dummies found inthe above regressions are : Barisal(0 - basecategory), Chittagong (+19%), Dhaka(+4%), Khulna (-3%), Rajshahi(-8%), Sylhet (110%). It is worth mentioningthat these regional wage premiumsare stronglv correlatedwith regional Dover& rates. 124 Table A-2.3: East-West differencesinearning function Nationwide 2005 2000 2005 East West Age 0.061 0.054 0.054 0.056 -0.001 -0.001 -0.001 -0.001 employment/activity (notcorrectedfor potentialselectionbias); All coefficientslisted are statisticallysignificantat lpercent, except "Muslim", which is not significant (2) The coefficientsfor nationwideregressions are slightly differentfromthose in TableA-2.2becausethe regionaldummies are differenthere(adummy for "west" 125 Annex 3: Profilingthe Poor: Characteristicsand Determinantsof Poverty I. Determinantsof povertyfrom multivariateregressions The model 1. The model specification follows Ravallion and Wodon (1999) (henceforth R-W) to a large extent, which involves estimating separate regressions between urban and rural samples, with district dummies in each regression. This implies that the coefficients are constant within each urban and rural sample, but each district may have different intercepts, captured by the district dummies. The econometric model canbe written as follows. 2. Y is natural logarithm o f per capita consumption, X represents household attributes, and D denotes dummy variables for each district, following the old classification o f districts (old zillas). The classification o f old districtdzillas - as opposed to the "new" districts - is retained for two reasons. Firstly, this allows for a direct comparison with R-W results that are based on HES 1991-92and 1988- useful to infer indirectly the long-term dynamic changes inthe correlates o f consumption poverty. Secondly, ifthe current classification (64 new districtdzillas instead o f 17 old zillas) were used, the number o f locatioddistrict dummies inthe regression would become so large as to make interpretation o f the results difficult; and the number o f households in each location would be too small to yield statistically significant effects o f district dummies. Note that the classification o f districts is used here just to estimate the effect o f location attributes by some criterion o f geographic disaggregation. In this context, whether the particular unit o f analysis captures current administrative arrangements i s less important, as long as this unit remains unchanged over time. 3. Equation (1) and (2) are estimated separately with ordinary least squared (OLS), where standard errors are corrected for cluster effect within district. The equations take a linear form. The Basic Model-model (1) andmodel (3) inTable A-6, Annex-are the baseline specifications for rural and urban samples respectively. All the independent variables are the same with the exception that the rural sample includes the number o f livestock. Inmodels (2) and (4), local area attributes are added - going beyond the specification o f R-W - to see the relative importance o f specific characteristics o f geographic areas as correlates o fpoverty (these regressions are referred to inchapter 4 o f the report). The variables in the regressions 4. The dependent variable in these regressions is the natural log o f per capita household consumption. This variable i s the sum o f food and non-food expenditures (excluding durable goods) and is expressed in real terms by adjusting for spatial price differences using the upper poverty lines. 5. Independent variables. The full set o f regressors can be found in Table A-6, Annex; they include the number o f infants, children and adults in a household, gender, marital status, age, religion, education level and occupation o f household head, education level o f the household head's spouse and agricultural land owned by the household. The difference between the education o f head (or o f spouse) and the maximum education inthe household i s added to capture potential gains from higher education among other members o f the family. All the above variables are identical to those used in the specification o f R-W, with the exception of the occupation variables, where the differences are due to changes in the occupation codes between 126 surveys. Variables that combine types o f employment (self-employed, salaried, daily wage, etc.) with sector o f employment (agriculture andnon-agriculture) are used instead inour regressions. 6. Inaddition to the above, 16 dummy variables are included - one for each (old) district, with Dhaka being the omitted or reference district - to capture the location effects on household consumption. This list o f location dummies is identical to the R-W specification (see Table A-4, Annex for a full list o f location dummies andhow the old districts map onto current districts). 7. Other independent variables included here - and not present in the R-W specification - are the number o f non-farm enterprises in a household and dummies for households that receive domestic and international remittances. In addition, as mentioned above, the specifications for the rural samples include the number o f chicken and cattle owned by the household. These variables are included to take into account the potential effect o f a few key household attributes- remittances are often claimed to play an important role inhousehold consumption, the ability to diversify into nonfarm enterprises may be associated with lower poverty, and ownership o f livestock may enhance incomes and enable consumption-smoothing at the time o f shocks. 8. The omitted dummies deJine a reference household, which i s characterized as a married Muslimcouple, who are landless, childless, have no education and are living inDhaka district. Other members o f the reference household are also illiterate. The head o f the household i s engaged in farming (self-employment in agriculture), and the household does not receive any remittances, either domestic or abroad. 9. Inmodels (2) and (4), additional location characteristics are added to the specifications o f (1) and (3) - the travel times to Dhaka, zilla headquarter, and thana headquarter (from HIES community survey); percentage o fhouseholds inthana with electricity connection and percentage owning agricultural land (from the PopulationCensus, 2001). These variables attempt to capture broad indicators o f availability o f infrastructure, access to markets and the size o f the nonfarm sector in a particular location. In addition, two location-specific variables related to access to microfinance derived from the PKSF database (see Section 11) are also included. These are (i) the coverage o f microfinance at the thana level and (ii) the increase in microfinance coverage from 2003 to 2005 at the thana level. The objective is to see what patterns o f correlation emerge between these indicators and household consumptiodpoverty, and how these results can be interpreted given the limitations o f a single-shot, cross-section type analysis. II.The impact of adjustmentsfor economies of scale in household consumption onpoverty correlates To examine the sensitivity of poverty correlates to household composition and scale effects (following Annex I),the number o f adult equivalent members for each household i s defined by AE = (A+ s.C)'; where A is the number o f adults, C is the number o f children (below age IS) in the household, the parameter s i s the expenditure on a child relative to that on an adult; and the parameter, t (between 0 and l), measures economies o f scale. When t i s set to unity, the expenditure measure does not adjust for household size. Total household expenditure for each household i s then divided by AE to arrive at an adjusted adult equivalent expenditure for the household, which is then compared to the poverty lines to identify poor households. 'The occupation variables used in R-W are (i) agricultural worker with land; (ii)fisheryiforestryllivestock worker; (iii) tenant farmer; (iv) owner farmer; (v) servant and day-laborer; (vi) transportation and communication; (vii) salesman, broker, middleman, etc; (viii) factory worker, artisan, petty trader, small businessman, executive official, professors, teacher; and (ix) retiredperson, student, non-working. 127 For the purpose o f this analysis, since economies o f scale are the area o f focus, s i s set at 1while t i s set at 0.8 and 0.9 (see Annex 1 for a rationale of why taking a value o f t close to 1 i s appropriate). Table A shows that the poor have a larger household size and number o f (and proportion of) dependents than the nonpoor for both parameter values oft. Proportion o f female headed households is identical for poor and nonpoor households when t=0.9, but significantly higher for poor households when t=0.8. Therefore, as the size o f the economies o f scale in consumption increases, female-headed households appear to be at a greater disadvantage relative to male-headedhouseholds. s=l, e0.9 s=l, e0.s Poor Non-poor Poor Non-poor 2000 2005 2000 2005 2000 2005 2000 2005 Household Size 5.3 5.1 5.1 4.8 5.0 4.8 5.2 4.9 Dependency Ratio 1.06 0.97 0.65 0.61 1.09 1.02 0.71 0.65 Number o f children 2.5 2.3 1.8 1.6 2.5 2.3 2.0 1.7 Number of Male Adults 1.3 1.3 1.7 1.6 1.2 1.2 1.7 1.6 Number of Female Adults 1.4 1.4 1.6 1.6 1.4 1.4 1.6 1.6 Headfemale 0.10 0.10 0.08 0.10 0.12 0.13 0.08 0.10 III.Figuresandtablesreferred tointhemaintext 1 Figure A-2.1: Non-Expenditure Welfare indicatorso f bottom3 decilesbetween 2000 and 2005 LAie,rockounmhipyo) Source: HIES 2000.2005 Table A-3.1: Regressionsof logofper E (1) (2) (3) (4) Rural- Urban- Rural-Basic Extended Urban-Basic Extended Mymensingh -0.108 -0.014 -0.114 -0.065 (12.96)** (0.49) (11.83)** (1.62) Faridpur -0.072 -0.004 -0.062 -0.042 (8.24)** (0.16) (7.16)** (0.89) TangaWJamalpur -0.236 -0.152 -0.269 -0.180 (30.45)** (6.86)** (24.03)** (4.02)** 128 Chittagong -0.045 0.108 -0.027 -0.025 (3.72)** (2.1 1) (2.93)** (0.84) Comilla -0.069 -0.014 -0.130 -0.094 (10.76)** (1.03) (13.08)** (2.64)* Sylhet 0.017 0.068 -0.066 -0.109 (1.70) (1.78) (6.76)** (2.35)* Noakhali -0.274 -0.212 -0.086 -0.056 (6.21)** (5.68)** (2.54)* (1.04) Khulna -0.276 -0.138 -0.416 -0.397 (28.96)** (5.08)** (54.34)** (20.98)** Jessore -0.281 -0.149 -0.334 -0.267 (24.37)** (4.66)** (55.95)** (5.59)** BarisalPatuakhali -0.358 -0.140 -0.226 -0.153 (36.47)** (2.85)* (27.68)** (4.25)** Kushtia -0.041 0.032 0.135 0.205 (7.46)** (1.42) (14.02)** (5.70)** Rajshahi -0.287 -0.169 -0.255 -0.199 (19.97)** (5.85)** (16.00)** (5.81)** Rangpur -0.318 -0.226 -0.328 -0.264 (46.62)** @.OS)** (30.57)** (5.55)** Pabna -0.242 -0.197 -0.309 -0.255 (13.84)** (7.41)** (19.5 l)** (5.52)** Dinajpur -0.252 -0.109 -0.321 -0.199 (25.40)** (3.40)** (35.93)** (4.24)** Born -0.248 -0.156 -0.316 -0.260 (26.83)** (6.68)** (30.85)** (5.98)** Numberof infants -0.202 -0.209 -0.421 -0.406 (3.59)** (4.16)** (3.27)** (3.00)** Numberof infantssquared 0.034 0.038 0.277 0.269 (0.66) (0.84) (2.32)* (2.13)* Numberof children -0.178 -0.177 -0.180 -0.178 (14.80)** (14.06)** (13.34)** (13.15)** Numberof childrensquared 0.014 0.013 0.012 0.011 (6.01)** (5.35)* * (4.79)** (4.62)** number of adult -0.104 -0.109 -0.142 -0.138 (7.77)** (7.87)** (6.93)** (7.05)** number ofadult squared 0.008 0.008 0.012 0.011 (5.75)** (5.56)** (5.64)** (5.31)** headfemale -0.015 -0.030 -0.148 -0.149 (0.34) (0.62) (3.02)** (3.09)** Head:manied,no spousepresent 0.097 0.100 0.350 0.345 (3.15)** (2.68)* (6.99)** (6.78)** Head:single,no spousepresent 0.108 0.090 0.240 0.186 (1.97) (1.71) (3.62)** (4.08)** Head:divorced, widowed, separated,nospousepresent -0.041 -0.033 0.160 0.166 (1.14) (0.84) (2.45)* (2.60) * Headage 0.0 16 0.015 0.020 0.020 (7.05)** (5.95)** (10.19)** (9.47)** Headage squared -0.000 -0.000 -0.000 -0.000 (6.77)** (5.45)** (8.39)** (7.34)** 129 Headnon-muslim -0.093 -0.065 -0.107 -0.093 (2.80)* (3.16)** (3.34)** (2.86)* Level of Head's edu: Below class 5 0.138 0.128 0.155 0.155 (4.33)** (4.69)** (4.70)** (4.19)** Level of Head's edu: Class 5 0.131 0.128 0.193 0.192 (8.91)** (7.65)** (11.19)** (10.17)** Levelo fHead's edu: Class 6 to 9 0.191 0.169 0.313 0.308 (10.28)** (10.44)** (10.78)** (11.01)** Levelof Head's edu: HigherLevel 0.305 0.273 0.467 0.458 (13.66)** (14.71)** (10.39)** (10.41)** Levelof Spouse's edu: Below class 5 0.066 0.060 0.143 0.140 (2.68)* (2.51)* (4.36)** (4.12)** Level of Spouse's edu: Class 5 0.045 0.046 0.114 0.117 (2.66)* (2.32)* (5.41)** (5.16)* * Levelof Spouse'sedu: Class 6 to 9 0.112 0.101 0.239 0.239 (4.17)** (3.53)** (9.86)** (9.30)** Levelof Spouse's edu: Higher Level 0.296 0.284 0.439 0.437 (6.65)** (6.53)** (9.18)** (9.52)** Differenceb/w headandmax edu: 1 level 0.088 0.076 0.111 0.110 (5.83)** (4.76)** (4.03)** (4.1 1)** Differenceb/w headandmax edu: 2 level 0.102 0.086 0.122 0.119 (5.57)** (5.56)** (4.75)** (4.15)** Differenceb/w head andmax edu: 3 level 0.135 0.120 0.226 0.216 (7.04)** (6.28)** (8.29)** (9.89)** Differenceb/w headand max edu: 4 level 0.159 0.145 0.341 0.315 (4.77)** (4.74)** (6.37)** (8.09)** FunctionallyLandless:0.05-0.49 0.072 0.082 0.008 0.006 (4.28)** (6.01)** (0.37) (0.22) Margina1:O.S to 1.5 0.148 0.173 0.082 0.100 (8.61)** (11.12)* * (3.21)** (4.64)** Small:1.5 to 2.5 0.269 0.299 0.190 0.206 (7.07)** (8.12)** (4.31)** (4.72)** Medium&Large:2.5 or more 0.419 0.476 0.319 0.327 (11.83)" (15.79)** (8.78)** (9.05)** Head'smajor activity: self-emp1oyment:non-agriculture 0.035 0.034 0.100 0.102 (1.66) (1.52) (2.26)* (2.27)* Head'smajor activity: Daily wage employment -0.058 -0.059 -0.023 -0.02 1 (3.60)** (3.8 8)** (0.80) (0.67) Head'smajor activity: Salary wage employment 0.015 0.004 0.038 0.036 (0.62) (0.18) (1.48) (1.29) Head'smajor activity: None 0.024 0.0 18 0.073 0.077 (1.14) (1.02) (1.73) (1.84) Number of non-farm enterprises 0.071 0.062 0.079 0.076 (3.79)** (3.22)** (2.83)* (2.79)* HHreceives domestic remittances-dummy 0.091 0.078 0.107 0.109 (2.45)* (2.94)* * (3.16)** (3.32)** HHreceives remittances from abroad-dummy 0.252 0.222 0.310 0.302 (5.13)** (6.10)** (4.88)** (4.47)* * number of cattle 0.004 0.005 (1.79) (1.86) 130 number o fchicken 0.001 0.001 (3.10)** (3.15)** Travel time to thana HQ (`00 mins) -0.032 (2.33)* Travel time to district HQ (`00 mins) -0.003 (2.25). Travel time to DhakaHQ (`00 mins) -0.036 (3.22)** %ofHHwith electric connection 0.001 0.000 (1.30) (0.54) %ofHHown agriculturalland -0.003 -0.000 (1.74) (0.06) Coverageo fmicro finance inThana in 2005 -0.001 -0.002 (1.10) (2.51)* Change inmicrofinance members between 03-05 0.002 0.001 (4.07)** (1.15) Constant 6.858 7.024 6.668 6.696 (94.25)** (47.45)** Observations 3660 3600 R-squared 0.48 0.50 0.56 0.56 2005: summary results Rural Urban endowments coefficients interaction endowments coefficients interaction Geographic dummies -0.002 0.032 0.006 -0.033 0.014 0.017 Household size variables 0.032 0.059 -0.003 0.031 0.012 0.000 Other demographic variables -0.002 0.220 0.002 -0.001 0.157 -0.004 Educationvariables 0.023 -0.019 -0.005 0.042 -0.089 -0.008 Landvariables 0.000 0.025 0.000 0.020 0.001 0.003 Occupationvariables 0.006 0.030 -0.008 -0.035 0.059 0.057 Number o fnon-farmenterprises -0.004 -0.003 0.000 -0.002 0.008 -0.001 Remittances 0.004 0.009 0.001 -0.001 0.036 0.000 Livestock 0.003 -0.021 -0.002 Constant 0 -0.275 0 0 -0.255 0 Total* 0.061 0.058 -0.008 0.022 -0.058 0.065 131 Annex 4: Laggingregions in Bangladesh:is there an East-Westeconomic divide? I. Explaininglocationeffects by includinglocationcharacteristicsin regressions' Columns (2) and (4) o f Table A-3.1 (Annex 3) list the coefficients for the "extended" regressions -adding location characteristics for the rural and urban sample respectively to the specifications in columns (1) and (3). These represent some o f the characteristics o f a geographic location likely to influence its economy, and for which data i s available from reliable sources. The variables added for the rural regression are the travel times to Dhaka, the district headquarter, and thana headquarter (from HIES community survey), percentage o f households in thana with electricity connection, and percentage owning agricultural land (from population Census, 2001). Since the Census was fielded in2001, these variables can be interpreted as indicators o fthe initial condition o f development ineach Thana. The urban regressions include all the Census variables but not travel times, since these were not available for urban areas. Two location-specific variables related to access to microfinance derived from the PKSF database are included: (i) the effective rate o f coverage o f microfinance at the thana level and (ii) the increase inmicrofinance coverage from 2003 to 2005 at the thana level. The coefficients o f these variables are subject to important caveats: (i)the likelihood o f measurement errors inthe indicators from the HIES community survey inparticular, (ii) possible multicollinearity between variables as variables may be interconnected, and (iii) the likely biases caused by the omission o f potentially important location attributes due to lack o f data. Additional caveats apply to the microfinance variables (see below). Given these caveats, and the natural limitations o f the cross-section data, the coefficients o f the regressions should be interpretedto represent correlates o f household consumption, rather thanits determinants. The variables related to travel times to different markets are significant correlates o f consumption for rural households. Electricity coverage and agricultural land ownership (as a proxy for the size o f the non-farm sector) have marginal effects on rural consumption and none for urban households. Perhaps most importantly, adding the location characteristics reduces the size o f the location effects represented by the coefficients o f the district dummies for most districts, inurban and rural samples alike (see Table A-3.1, Annex 3). While 15 out o f 16 districtshad significantly negative effects on household per capita consumption in the rural regressions in the absence o f location characteristics, nine districts had so after adding the location characteristics. For urban areas, location characteristics play a smaller role in explaining the district level location effects - the size and significance of most location effects become smaller, but are still significant for 11 out o f 16 districts, compared to fifteen originally. II. Spatialgapsinreturnstohouseholdaltributes-motivationfor theempiricalexercise" Existingliterature offers two broad explanations for the persistence o f spatial gaps inreturns to observed household attributes over the entire income distribution in the presence o f free factor mobility. First, in econometric estimation, return to same household attribute can be found to be significantly different across locations if heterogeneity across households and locations are not adequately controlled for. Existingliterature identifies at least three such sources o f unobserved household and location heterogeneity. According to standard location sorting model B la Roy (1951), households are sorted across regions in terms o f both observed and unobserved characteristics. For instance, while educational attainment is observed, ability o f an individual !,See Kotikula et a1(2007) for more detailed results. " From Shilpi (2008). See the paper for all details on the econometric estimation, results and their interpretations. 132 household member i s unobservable. Because o f selective migration o f workers with higher ability to urban areas, an individual in an urban area would earn higher wage compared to an observationally identical individual located in a rural area. In addition to ability sorting, agglomeration economies arising from increasing returns, labor market externalities, and knowledge spillovers can also cause wages in densely populated areas and in technologically advanced sectors to be higher (Fujita et al, 1999; Overman et al, 2007). Moreover, if public infrastructure has production externality, then workers in regions with better access to markets and better infrastructure could enjoy hgher wages relative to those located in other regions (Ravallion and Jalan, 1999; Jalan and Ravallion, 2002). The omitted variable biases resulting fiom the inability to control for spatial sorting o f unobserved household and locational characteristics do not however apply to all households and all locations equally. The ability sorting and agglomeration economies may affect wages in sectors which are technology and innovation intensive. Evidence from developing countries suggests that only a small fraction o f activities in urban centers fit such categorization (Fafchamps and Shilpi, 2005). Similarly, because o f predominance o f agricultural activities, the differences in rates o f returns between rural areas across locations are more likely to be due to differences inpublic capital and access to markets. The spatial differences in rates o f return to attributes can also be sustained in equilibrium if migration i s costly (Dahl, 2002; Bayer et al, 2007; Kanbur and Rapoport, 2005). The cost o f migration tends to vary across individuals and households as they face different level o f risks and costs. The migration costs are likely to be higher for poorer and middle income households who face credit constraints as well as higher opportunity costs o f disposing o f existing assets. Various costs associated with migration are likely to pose no serious hindrance to mobility o f members of well-off households. Similarly, short-term migration such commuting and temporary migration o f a member o f the household involves less costs than long-term and permanent migration o f the entire household. Proximity can also influence formation o f migration network and through it migration flows insubsequentperiods (Kanbur andRapoport, 2005). As a result, the difference in returns to household attributes will be smaller across areas inclose proximity to each other. Both locational sorting and migration literature thus suggests that returns to observed household attributes will vary across households depending on their position inthe welfare distribution and across regions depending on their relative proximity and locational characteristics. In Shilpi (2008), the returns to observed household attributes are estimated using the Machado and Mata (2005) quantile regression based decomposition technique, with data from two rounds o f HIES (2000 and2005) o f Bangladesh. The regional gaps inwelfare inthe empirical analysis -measured by the difference in the distribution o f the log o f real per capita consumption expenditure -are decomposed into a "sorting" effect arising due to differences in observable household characteristics, and a returns effect resulting from differences in rate o f returns to those characteristics. Bangladesh provides an excellent case to study the roles o f different factors in explaining spatial differences in returns for several reasons. First, there are no administrative restrictions on migration in Bangladesh. As much o f the Bangladesh's population share the same ethnicity, religion and language, there exists no serious ethnic or cultural barriers to internal migration. Despite absence o f serious barriers to labor mobility, Ravallion and Wodon (1999) has shown that both sorting and returns effects are important in explaining average regional gaps in welfare inBangladesh. 133 III.Interpretingthe"returngaps"for householdsatdifferentpartsof thedistribution"' Migration would typically tend to equalize returns to endowments across regions, but also has associated costs that may deter the mobility o f the poor. As migration costs are unlikely to seriously impede the mobility o f better-off households, substantial IR-LIR gaps in returns for upper quantile households suggests "sorting" (across space) by unobserved attributes of households and economic activities. However, such sorting i s more likely to take place in urban areas o f IR, given that activities that require better individual attributes and are subject to agglomeration economies usually concentrate in urban areas. Given the predominance o f agricultural activities in rural areas, any differences in rates o f returns for better-off households across rural areas in IR and LIR are more likely due to differences inpublic capital and access to markets. For the upper quantiles o f rural IR andLIR, the return effects are statistically significant andexplain a thirdor more o fthe rural IR-LIRgap inexpenditures (Figure A-4.7). This suggests that differences in infrastructure, access to markets, and other public capital are important factors behind IR-LIRgaps inreturns to observed household attributes. At the same time, the differences inreturns between rural IR and rural LIRare smaller than those between IR and LIR for the upper quantiles, which indicates some degree o f sorting -of householdwith unobserved better characteristics and o f agglomeration forces - inthe urbanareas o f IR. This i s also suggested by significant urban-rural differences inreturns within each region for households inthe top 40 percent (Figures A-4.8 and 4.9). This gap has increased significantly for IR between 2000 and 2005 while remaining unchanged for LIR, which suggests that the main metropolitan areas in IR have experienced higher growth o f economic activities than the rest o f the country. The significant IR-LIR gaps in returns for poorer households on the other hand are consistent with the poor facing higher costs o f migration. There are no significant urban-rural differences in returns for poor households within each region (Figures A-4.8 and A-4.9), which seems to indicate no serious barrier to mobility o f the poor within a region. However, decomposition o f the gaps inper capita expenditures between the urban areas o f IR andrural areas o f LIR suggests substantial differences in returns for poorer households (Figure A-4.10). This seems to suggest substantial costs o f migration from LIR to IR- likely on account o f the mighty rivers separating these regions that make temporary migration and commuting difficult. iiiFromShilpi(2008). See the paper for moredetails. 134 ZV. Figures and tables FigureA-4.1 Mapsof PovertyReductioninBangladeshbetween2000 and 2005: Oldzilla level a. Poverty reduction between2000 and 2005 b. Current divisional boundaries- for comparison (percentagepoints) with the oldzilla boundaries Note:Numbers inmap indicate difference between 2000 and 2005 povertyrates; darker color denotes higher difference. FigureA-4.2: Comparing old and new district boundaries I Note: Grey lines show boundary of new zilla. 135 FigureA-4.3: Microfinance FigureA-4.4: Increaseinmicrofinance membershipin2003 by old districts membership(2003-2005) u e* ..%I Nofe:Increase inmembershiprefers to % change in members in (old) district between 2003 and 2005 Source PKSF data (2003,2005) FigureA-4.5: Concentrationof agro- FigureA-4.6: Employmentin manufacturing processingindustries(2006) (2006) CJ 1-2 la!& I - 5 % n 2-1U lakh 5-1096 2- *lUlakh * 10 % Note: Employment inall firms with 1O+ Total PersonsEmployed Note: Symbols show cities by population size in 2001. Only cities (TPE). 1dot represents2000 workers. Dots are randomly placed of more than 50,000 personsare shown. Darker areas show high within each upazilla. rate o femployment (comparedto total population). Source: Economic Census (2006); Population Census (2001) 136 FigureA-4.7: ReturnEffectsfor RuralIR-RuralLIR FigureA-4.8: ReturnEffectsfor Urban-RuralGapsin - Gaps(2000and2005) IR (2000& 2005) 0.35 0.3 0.25 Y 2 0.2 - -:0.1 < E 0.15 P gn0.05 0.05 - 0 4.05 '..I1 41 P w m l l h -m ...... -m ...... ...... FigureA-4.9: ReturnEffectsfor Urban-RuralGapsin FigureA-4.10: ReturnEffectsfor UrbanI RuralLI - - LIR(2000 & 2005) Gaps(2000 & 2005) 0 3 on I 1 02 0 Y p 0.15 2 :0.1 8- 0.05 n -0.05 -zow ...... -2w5 ...... ...... Source: Shilpi(2008) usingHIES(2000 and 2005) 1Table A-4.1: MaDDinglocationdummies (old districts) to new districts BansalPatuakhah Jhalokathi Bhola Patuakhali Barguna 137 Table A-4.2: ,ocation el :cts of (old) district dummies relal 'eto that of Dhaka district Rural Urban "Old" districts Divisions 2000 2005 Chow Test 2000 2005 Chow Test Mymensingh -0.305 -0.108 0.199 -0.208 -0.114 0.101 (43.33)** (12.96)** (32.01)** (13.75)** (11.83)** (12.28)** Faridpur Dhaka -0.357 -0.072 0.292 -0.323 -0.062 0.235 (56.43)** (8.24)** (31.96)** (26.57)** (7.16)* * (15.22)** TangaiVJamalpur -0.377 -0.236 0.126 -0.019 -0.269 -0.228 (50.11)** (30.45)** (24.45)** (1.32) (24.03)** (18.60)** Comilla -0.070 -0.069 0.015 -0.077 -0.130 -0.032 (15.39)** (10.77)* * (4.23)** (9.64)** (13.08)** (4.49)** Chittagong Chittagong -0.041 -0.045 -0.008 -0.104 -0.027 0.087 (5.65)** (3.72)* * (0.69) (18.02)** (2.93)** (14.00)** Noakhali -0.190 -0.274 -0.040 -0.305 -0.086 0.261 (19.71)** (6.22)** (1.66) (34.11)** (2.54)* (16.26)** Sylhet Sylhet -0.022 0.017 0.046 -0.151 -0.066 0.115 (3.77)** (1.70) (8.21)** (13.34)** (6.76)** (10.57)** Khulna -0.064 -0.276 -0.233 -0.3 15 -0.4 16 -0.098 (5.94)** (28.98)** (39.78)** (51.03)** (54.34)** (24.27)** Jessore Khulna -0.275 -0.281 -0.008 -0.365 -0.334 0.082 (33.97)** (24.38)** (1.45) (34.77)** (55.95)** (8.02)** Kushtia -0.242 -0.041 0.196 -0.378 0.135 0.535 (30.91)** (7.46)** (27.37)** (27.08)** (14.02)** (39.60) ** BarisaVPatuakhali Barisal -0.270 -0.358 -0.091 -0.141 -0.226 -0.066 (47.47)** (36.52)** (13.88)** (17.32)** (27.68)** (10.92)** Rajshahi -0.237 -0.287 -0.058 -0.267 -0.255 0.071 (26.96)** (19.99)** (5.35)** (33.29)** (16.00)** (4.49)** Rangpur -0.424 -0.3 18 0.096 -0.434 -0.328 0.119 (53.45)** (46.60)** (20.73)** (63.20)** (30.57)** (18.73)** Pabna Rajshahi -0.265 -0.242 0.015 -0.055 -0.309 -0.219 (39.97)** (13.85)** (1.21) (4.18)** (19.5 1)** (10.70)** Dinajpur -0.332 -0.252 0.060 -0.523 -0.321 0.243 (26.71)** (25.40)** (12.97)** (36.38)** (35.93)** (24.71)** Bogra -0.219 -0.248 -0.047 -0.097 -0.316 -0.21 1 (25.07)** (26.82)** (6.51)** (8.61)** (30.85)** (20.00)** Note; 1) Basic specifications o f rural and rban regressic 3 (Columns 1 Id 3, Table A. 1, Annex 3) E 2) **: significant at 1%level; used; 3) The coefficients corresponding to each (old) district in the Chow Test columns indicate the reduction in gap between Dhaka and the respective district from 2000 to 2005; these are coefficients o f the interaction terms between district dummies and dummy for 2005 in the model where both years are pooled.'" A positive and significant coefficient indicates reduction inthe location effect o f that district or reduction in its gap with Dhaka; and conversely for a negative and significant coefficient. 4) Shadedcells refer to districts for which the gap with Dhakadistrict has increased significantly. Source:HIES2000,2005 iv The regression model is: y =a+XPI+fl$b'ear =2005)+ zkp, +Db'ear = 2005)Zkp4 ,where Y islogofreal percapita consumption, X is other control variables, and Z is district dummies. p,is presented incolumn (3). 138 2003 2005 Numberofmembers: BRAC 3,341,325 4,289,969 Numberofmembers: ASA 2,071,486 4,180,157 Numberofmembers: Grameen 2,786,748 4,881,444 Number of members: Total 12,866,585 21,731,043 Shareof3 majorMFIs(%) 63.7 61.4 Effectiverateof coverage(% of 31.1 51.1 households) " The effectiverateof coverageis the estimatednumber ofmicrofinancehouseholdsdividedby the total number of households. Dueto multiple membershipswithin households,the number ofmicrofinancehouseholdsis estimatedto be 66 percentoftotal numberofmembers(see World Bank, 2006a). The totalnumberofhouseholdsisprojectedfrom Census2001, assumingan annualpopulationgrowthof 1.5 percent. 139 Annex 5: Creating humancapital: bridgingthe access and quality gap Table A-5.1: Malnutritionversus in( Sub-SaharanAfrican countries GDPper capita Malnutrition:weight for age of similar incomelevels (constant 2000 US%) (YOof childrenunder 5) Tanzania 335 16.7 Zambia 371 23.3 Comoros 379 25.0 Guinea 406 22.5 Banglad&: 1 1 d l e ' 419 .47.5 " Kenya 440 16.5 Nigeria 440 27.2 Mauritania 483 30.4 Lesotho 528 16.6 All 65.4 I 83.7 I 57.2 I 34.2 I 46.2 Source: DHS2004 Note: r-v3 12=Contraceptive prevalencerate; rmeasle=Vaccination rate against measles; rtt=TT Injectionrate for pregnant mothers; sanit=User of sanitary latrine (extended definition, inclusive of pit latrine); prenatal=Access to prenatal care from skilled health professionals; riron=User of iron tablet' injection. Table. 5.3: Determinants of health access and use of services Indicators/ Correlates (1) (2) (3) (4) (5) (6) rmeasle r v312 rtt sanit prenatal riron Leaveout mean for cluster 0.596 0.228 0.411 0.265 0.248 0.163 (12.26)** (4.01)** (7.00)** (3.22)** (3.98)** (2.20)* Whether poor -0.036 -0.03 -0.012 -0.067 -0.045 -0.046 (2.44)* -1.65 -0.99 (3.38)** (2.41)* (2.52)* Whether NGOmember 0.041 0.056 0.027 0.045 0.061 0.112 (2.68)** (2.91)** -1.84 (2.14)* (3.20)** (5.91)** Score o fnon-land asset 0.015 0.012 0.025 0.083 0.055 0.043 (2.93)** -1.76 (4.69)** (11.79)** (8.93)* * (6.39)* * Household head education primary 0.032 0.029 0.038 0.055 0.003 0.069 (2.01)* -1.34 (2.27)* (2.29)* -0.18 (3.11)** Household head education secondary 0.061 -0.021 0.048 0.137 0.126 0.148 140 (3.04)** -0.87 (2.57)* (5.73)** (5.44)** (6.12)* * Household headeducation higher 0.037 -0.004 0.052 0.219 0.312 0.328 -1.34 -0.12 (2.51)* (6.84)* * (9.36)** (9.32)** Household size -0.008 -0.001 -0.008 0.006 0 0.001 (3.03)* * -0.38 (3.46)** (2.09)* -0.12 -0.29 Woman's current age - respondent 0.055 0.035 -0.006 0.017 0.001 0.012 (7.75)** -0.13 -1.54 Square ofcurrent age - (3.94)** -0.84 (2.06)* respondent -0.001 -0.001 0 0 0 0 (6.83)** (3.74)** -0.05 (2.44)* -1 (2.15)* Region: Barisal 0.001 0.217 0.094 0.043 -0.103 -0.073 -0.03 (6.18)* * (2.92)** -0.74 (2.31)* -1.67 Region: Chittagong 0.004 0.117 0.057 0.024 -0.116 -0.067 -0.19 (3.85)** -1.77 -0.47 (2.62)** -1.7 Region: Dhaka 0.012 0.271 0.065 -0.024 -0.098 -0.085 -0.67 (7.69)** (2.12)* -0.52 (2.20)* -1.86 Region: Khulna 0.034 0.338 0.021 0.109 -0.067 -0.037 -1.76 (9.08)* * -0.68 (2.22)* -1.43 -0.88 Region: Rajshahi 0.025 0.35 0.033 -0.146 -0.081 -0.023 -1.2 (10.20)** -1.11 (3.36)** -1.74 -0.54 Constant -0.584 -0.447 0.624 -0.132 0.102 0.08 (5.58)* * (3.37)* * (5.98)** - 1 -0.92 -0.65 Observations 4482 4482 3521 4482 3521 3520 R-squared 0.09 0.09 0.10 0.22 0.14 0.12 Robust t statistics inparentheses (clustered standard error adjusted) * significantat 5%; ** significant at 1% Source: DHS2005 Data Note: r-v3 12=Contraceptiveprevalencerate; rmeasle=Vaccinationrate against measles; rtt=TT Injection rate for pregnantmothers; sanit=User of sanitary latrine (extended definition, inclusive of pit latrine); prenatal=Access to prenatal care from skilled health professionals;riron=User of irontablet' injection. TableA-5.4: The determinantsof educationalattainment,2005 Marginal and impacteffects Predictedhouseholdexpenditure 0.47** -0.113 -0.014 -0.022 0.028 0.038 0.083 (log) (0.18) Age -0.09** 0.021 0.003 0.004 -0.005 -0.007 -0.015 (0.02) Age squared 0.0003 -0.0001 -0.00001 -0.00002 0.00002 0.00003 0.0001 (0.0006) Femalet 0.05* -0.013 -0.002 -0.002 0.003 0.004 0.010 (0.02) Urban? -0.01 0.003 0.0003 0.001 -0.001 -0.001 -0.002 (0.03) Hindu' -0.12* * 0.031 0.004 0.005 -0.009 -0.010 -0.021 (0.04) 141 Other religion' -0.18+ 0.046 0.005 0.007 -0.014 -0.015 -0.029 (0. IO) Headyears ofeducation 0.07** -0.017 -0.002 -0.003 0.004 0.006 0.012 (0.01) Headsalary wage earner' 0.06 -0.014 -0.002 -0.003 0.003 0.005 0.010 (0.04) Headdaily wage worker' -0.30** 0.076 0.009 0.013 -0.023 -0.026 -0.049 (0.05) Headnot inthe labour force' 0.03 -0.008 -0.001 -0.002 0.002 0.003 0.006 (0.04) Femalehouseholdhead' 0.21** -0.048 -0.007 -0.011 0.009 0.017 0.040 (0.07) Educationof spouse of head 0.05** -0.011 -0.001 -0.002 0.003 0.004 0.008 (0.01) Spouse of headnot inhousehold' -0.14" 0.034 0.004 0.006 -0.010 -0.011 -0.023 (0.06) Birth order 0.04+ -0.009 -0.001 -0.002 0.002 0.003 0.007 (0.02) Numberof children -0.07** 0.018 0.002 0.003 -0.004 -0.006 -0.013 (0.02) Numberof adults 0.10** -0.023 -0.003 -0.004 0.006 0.008 0.017 (0.01) Total upazilaprimary schools -0.03 0.008 0.001 0.0022 -0.002 -0.003 -0.006 (00s) (0.02) Total upazila secondaryschools 0.18** -0.043 -0.006 -0.008 0.011 0.015 0.032 (00s) (0.06) Barishal' 0.20** -0.045 -0.006 -0.010 0.008 0.016 0.038 (0.06) Chittagong' 0.08* -0.020 -0.003 -0.004 0.004 0.007 0.015 (0.03) Khulna' 0.23** -0.051 -0.007 -0.012 0.009 0.018 0.043 (0.04) Rajshahi' 0.16** -0.037 -0.005 -0.008 0.008 0.013 0.029 (0.04) Sylhet' -0.18** 0.046 0.005 0.007 -0.014 -0.015 -0.030 (0.05) Observations 16,207 Endogeneitytest 1.27 Instrumentvalidity test 3.33 LR test statpooledv non-pooled 322** Notes: 1) Robuststandarderrors inparentheses. +significant at 10%; significantat 5%; * ** significantatl%.; 3) 'Variable is binaryandtherefore impactratherthanmargina Ffectsare calculated; 4) Endogeneitytest is basedon SmithandBlundell (1986) ;5.) Instrumentvaliditytest isbasedonatest ofthejoint significanceoftheinstkmentsinamodelwiththeoriginalhousehold expenditureper capitavariable. 142 Annex 6: Poverty,Vulnerability and the Role of Safety Nets I. The impact of recent riceprice increase on household werfare h c e i s the main staple food inBangladesh. HIES 2005 indicates that all varieties o f rice (coarse, medium and fine) accounted for about 24 percent o f total household expenditure and 39 percent o f total food expenditures. Poor households allocate about a third o f household expenditures to rice. The latter part o f 2007 saw a very sharp increase in rice prices following floods that damaged the Aus crop and cyclone Si& that affected the southern and southwestern districts. Increasing global prices hrther compounded the adversity and retail prices o f rice increased by around 38.8 percent in rural areas and 36.8 percent in urban areas from April 2007 to March 2008. Given the preeminence o f rice inhousehold diet and expenditures, a sharp increase inrice prices i s likely to have a substantial welfare and distributional impact. Households that are net rice producers would benefit from the improved terms o f trade; conversely, net consumers o f rice would be adversely affected. Exactly how increasedprices would affect poor households would depend on the distribution o f net buyers and sellers among them (Deaton, 1989; Ivanic and Martin,2008). Inadditionto the impact ofprice increases, householdwelfare would also depend on how responsive wages are to such increases (Ravallion, 1990); for example, if wages adjust sufficiently, they would mute the impacts o fprice increases on households that are net buyers. Deaton's (1989, 1997) approach is used to estimate the short-run impact o f rice price changes on household welfare. The first-order welfare effect o f rice price change is proportional to the net benefit ratio (NBR), which i s the difference between the production share o f rice and consumption share o f rice intotal expenditures. NBR can thus be interpreted as the elasticity o f expenditures (or real income) to rice price change, which also indicates the extent to which a household i s a net rice seller (buyer). MultiplyingNBR by the change in rice prices yields the instantaneous welfare impact on households. The longer run effects arising from induced wage responses to price changes can be estimated by combining Deaton's model with Ravallion's (1990) approach.* The basic model i s as follows: (1) Awi =Ap[(PRi-CR,)+ 741,where AW = welfare effect expressedas percentage o f total expenditures o fhouseholdi &I = percentage changeof foodprice change; PR = food production ratio; CR = food expenditure ratio; 17 = wage rate elasticity with respect to food price change; and L = labor share inhousehold expenditures. The 38.8 percent rural and 36.8 percent urban increase in retail rice prices between April 2007 and March 2008 is used to calculate welfare impacts, with and without wage adjustments. For wage adjustments, in lieu o f using wage elasticity to price changes for which no consensus 'Weuse total expenditures as a proxy for income as expenditures data tend to be a more reliable indicator o f household welfare (Deaton 1989; Budd 1993; Barrett and Dorosh, 1996). * Such partial equilibrium analysis abstracts from economy-wide general equilibrium considerations which require modeling within a multi-market framework. While a CGE framework provides more analytical completeness, it also suffers from uncertainties arising from model parameters and distortions caused by the imposition o f substantial modeling structure on the problem (Barrett and Dorosh, 1996). We choose the simple partial equilibrium approach in the light o frice's clear dominance in the food consumption basket in Bangladesh and the inelastic nature o f its demand. 143 estimate i s available for Bangladesh, nominal wages are assumed to increase by 5 percent for all household^.^ This appears to be a reasonable assumption given recent history - BBS figures suggest that nominal wages increasedby 4.5 percent between FiscalYears 2006 and2007. Some important caveats to the analysis must be noted. Firstly, this estimation method does not take into account substitution o f other types o f food for rice in the consumption basket in response to rice price increases. While any substitution would dampen the adverse welfare impact o f rice price increase to consumers, it can be argued that because o f dietary and cultural reasons, the extent o f substitution out o f rice i s likely to be low in Bangladesh. The fact that wheat prices increasedby 30 percent inBangladesh duringthis period also reduces the likelihood o f substitution o f one staple grain for the other.4 Secondly, the welfare impacts estimated here consider a change inrice prices only and not in the prices o f other important food and nonfood items in the consumption basket like fuel, animal products and edible oil. Thirdly, the estimates indicate the welfare impact on households had they faced a similar a price shock and subsequent wage adjustment in the year 2005. Alternatively, the estimates can be interpreted as the welfare impact o f rice price increase on households inmid-2008, assumingthat the pre-rice price increase distribution o f expenditures was unchanged from what was seen in HIES 2005.5 Fourthly, the analysis does not take into account any supply response to rice price increases - an omission that i s however likely to be less relevant in the short-run but important in the long-run. Finally, any mitigating steps taken by the government, such as the direct sale o f limited quantities o f rice at lower prices, are not taken into account inthe analysis. The analysis indicates that, inthe short run, a majority o f households would be adversely affected by rice price increase (Table A). This is a direct result o f only 17 percent o f households in Bangladesh (including 6 percent o f urban households) being net sellers o f rice (HIES 2005). Eveninrural areas, only 22 percent o f households are net rice sellers, which is consistent with the fact that 65 percent of rural households are functionally landless. Inthe absence of nominal wage adjustments a 38.8 percent rural and 36.8 percent urbanrise in rice prices reduces real expenditures (income) by 4.8 percent for all households (Table A). The impact is more adverse for urban households than for rural households and for the poor than the nonpoor inboth urban and rural areas - since the incidence o f net sellers i s much lower and share o f rice in total expenditures higher among the poor than among the nonpoor. Average real income o f the bottom quintile declines by 10.5 percent, compared to less than 3 percent for the top two quintiles. Among occupation groups, only households headed by f m e r s - less than a fourth o f all households and about 30 percent o f rural households - benefit from rice price increase, since 52 percent of these households are net rice sellers. The adverse welfare impact i s highest among households headed by agricultural and non-apcultural day labor, and lowest among those Instead o f wage elasticity to rice prices, we simply use the change in nominal wage rates for two main reasons. Firstly,for Bangladesh the estimates o fwage elasticity are contentious. Ravallion (1990) estimates the short runwage elasticity for agricultural wages inBangladesh to be 22 percent and the long runwage elasticity to be 47 percent. Using more recent data from Bangladesh, Rashid (2002) argues that since the mid 1980s changes in rice prices have had a negligible impact on agricultural wages. Secondly, to measure the precise welfare impacts o f rice prices on households what matters is the change innominalwages, not the change innominalwages arising from rice price changes. Rice consumption is also quite inelastic to income. HIES 2000 indicates that households spend 24.7 percent o f their total expenditures on rice, roughly equivalent to figures from 2005 (24.3 percent); for poor households also the figures are very similar (33 percent in 2005 as opposed at 31.7 percent in 2000). This stability is noteworthy since during 2000-2005 real per capita expenditures rose at an annual rate o f 2.4 percent. Note that the levels o f expenditures (or income) or production do not matter in estimating the welfare changes; as long as the shares o fproduction and expenditure remain reasonably stable, the results would apply to households in 2008. 144 headed by salaried workers (Table A). This i s primarily because salaried workers, who tend to be better off, have a lower share o f rice intheir total expenditures. Land ownership matters: inrural areas, only households with more than 1.5 acres o f cultivable land(roughly 17percent o f all rural households) benefit from rice price increase as a group. With the assumption o f a 5 percent nominalwage increase, the overall adverse impact is reduced: a 38.8 percent rural and 36.8 percent urban rise in rice prices reduces expenditures (income) by 2.9 percent for all households. Poor households suffer severely even after wage adjustments. For example, the expenditures o f the bottom quintile group fall by 7.7 percent despite a 5 percent adjustment in nominal wages. The differential impact o f rice price increase among different groups is also evident from the wage increases needed for households to be welfare neutral to the rice price increase. While households belonging to the top quintile would on average require around 5.3 percent increase in nominal wages to be as well off as before the price increase, households belonging in the lowest quintile would need a 19.9 percent increase. The wages o f agncultural day labor, non-agnculturalday labor and salaried workers would needto rise by 19.4, 14.4 and 7.1 percent respectively to counteract the impact o f the rice price increase between April 2007 andMarch2008. Table A: Welfare impact of a rice price increase across different householdcategories Household Share o ftotal Rice Rice Net Net EstimatedWelfare Wage category number o f consumption production Sellers o f Benefit Impact increase households as % o f total as '30o ftotal rice Ratio Without With (%I (%) expenditures expenditures (NBRN) (NJ3R) wage wage neededfor (CR) (PR) response response welfare (5%) neutrality All 24.3 11.7 17.2 -12.7 -4.8 -2.9 13.6 Rural 74.6 26.7 14.8 21.6 -11.9 -4.6 -2.8 14.4 Urban 25.4 17.2 2.3 4.2 -14.9 -5.5 -3.1 11.5 By expenditure defile Quintile 1 36.1 8.8 8.8 -27.3 -10.5 -7.7 19.9 Quintile 2 30.4 13.1 15.3 -17.3 -6.6 -4.4 17.4 Quintile 3 25.3 13.6 19.4 -11.7 -4.5 -2.5 13.8 Quintile4 20.2 13.6 22.1 -6.5 -2.5 -0.8 8.6 Quintile 5 12.1 9.0 19.1 -3.1 -1.1 0.3 5.3 By povertycategory Bottom 10YO 10 38.1 7.3 6.5 -30.8 -11.8 -8.9 20.7 Extremepoor 25.1 35.6 10.0 10.3 -25.5 -9.8 -7.1 19.6 Moderate poor 14.9 29.3 12.6 15.1 -16.7 -6.4 -4.2 17.0 Non-Poor 60 19.1 12.0 20.2 -7.0 -2.7 -1.0 9.5 By net buyer/ seller category Net buyer 82.8 24.4 3.5 0.0 -20.9 -8.0 -5.8 18.5 Net seller 17.2 23.9 50.7 100.0 26.9 10.4 11.2 -33.8 By main occupationof householdhead Agri day labor 18.1 32.7 7.6 7.2 -25.1 -9.7 -6.7 19.4 Fanner 23.8 26.1 34.2 51.8 8.0 3.1 3.7 1.5 Non-agriday labor 16.8 28.0 3.7 4.4 -24.3 -9.3 -5.6 14.4 Non-agriself- employed 22.9 21.5 6.6 11.2 -14.9 -5.7 -5.0 23.5 Salaried 18.4 16.9 4.8 7.5 -12.1 -4.5 ..- -0.8 7.1 ... Source: Own calculations from HIES 2005. Note: Estimatedwelfare impact calculatedby assuminga 38.8 percent rural and 36.8 percent urban increase inrice prices and by assuming wage adjustment to be 5 percent. 145 Table B indicates how the adverse impact on real expenditures translates to changes in poverty estimates. Notably, these estimates simulate the impact on the "baseline" poverty situation in 2005, and do not take into account the poverty reduction that would have taken place during 2005-2008 due to the economic growth during that period. Notwithstanding this caveat, a few clear messages appear from these estimates. on poverty indicatorscan bequite large, Table B: Impact of a rice price increase on national due to the large real expenditurehncome poverty indicators impact and the clustering o f the Without With wage With wage wage response response population around the poverty line. response (5%) (10%) With the baseline poverty and extreme poverty^^^' 4.6 2.6 0.9 headcount rate (HCR) of 2005, a 38.8 ExtremepovertyHCR' 5.7 4.0 2.5 percent rural and a 36.8 percent urban Giniindex' 5.2 4.4 3.7 increase in rice prices would increase Source: Own calculationsfromHIES2005 povertyHCR and extremepovertyHCR (1): Percentagepoint increase;(2): "LOincrease II. TheIFPlUpanel study Design of the survey The study involved re-surveying a sample o f households in 102 villages located in 14 districts who were interviewed as part o f a baseline survey between 1994 and 2000. The baseline survey comprised o f three separate surveys - since they were designed and implemented as separate studies to study the impacts o f three different policy interventions occurring at different sites, namely microfinance (henceforth known as MF sites), agnculture/fishing technologies (AT sites) and educational transfers (ET sites). Thus the timing o f the baseline round differs across study sites: June-July 1994 for the MF sites, June-September 1996 for the AT sites, and September- October 2000 for the ET sites. These districts and villages were selected to spanthe range o f agro-ecological conditions found in rural Bangladesh and, while the sample cannot be described as representative of rural Bangladesh in a statistical sense, it broadly characterizes the variability o f livelihoods found in rural Bangladesh. The most recent follow-up survey, conducted in 2006-07, was done on a sample o f 1,787 households who were core households from the original survey plus 365 households who were "splits" from the original household. The survey was complemented by a qualitative study designed to examine perceptions o f changes from women and men in a sub- sample o f the survey communities. 146 Identifying"shocks"inthe IFPRIlongitudinalstudy The "shocks" module in the IFPRI panel i s modified for the Bangladeshi context from a similar module developed in Hoddinott and Quisumbing (2003). The module asks households to consider a list o f adverse events and indicate whether the household was adversely affected by them. Agroclimatic shocks include flooding, and erosion and pestilence affecting crops or livestock. Economic shocks include asset or property losses (not due to theft), but owing to river erosion, eviction, fires, or other reasons. PoliticalhocialAegal shocks include extortion by mastans (organized crime syndicates), court cases and bribery, as well as long duration hartals (general strikes) and political unrest. Crime shocks include the theft andor destruction o f crops, livestock, housing, tools or household durables as well as crimes against persons. Health shocks includeboth death and illnesshjury - distinguishingbetween death o f the primary income earner and death o f other household members, and disaggregating illness shock into loss of income foregone and the medical expenses resulting from illnesshnjury. Life cycle shocks include dowry payments, wedding-related expenses, and property division (usually upon the death o f the father in a cross-generational household). In addition to these specific shocks, households were also asked to enumerate the three most important adverse shocks that they had experienced since the last survey. III. Afew lessonsfrom existingpublicworksprograms Experiences with existing public works programs suggest some principles o f success, some o f which outlined below. Wagerates should be determined by the local market wagefor unskilled labor. While higher wages can have positive effect on transfer benefits, and sometimes on the market wage, these mustbe weighed against potentialadverse effects. Higher wages canreduce the self-selection feature o f a program - namely the feature that better-off households will not have the incentive to participate in the program - and deter employment generation by driving up wages inthe private sector labor market. Wage rates should also be consistent with budgetary resources. For example, in Maharashtra's (India) long-standing Employment Guarantee Scheme (EGS), the feature o f employment guarantee may have been undermined by the increase in wage rates in 1988, when the average EGS wage became higher than market wages and were not accompaniedby commensurate expansion inbudget. Ravallion et a1found that average monthly expenditures on EGS actually fell after the increase in wages, and employment fell by one-third, suggesting some rationing in employment. Wage schedules should also be gender neutral (not discriminate against women). Certain kinds o f wage structures (like piece rates) can facilitate the participation o f women, as can features like local work sites and childcare facilities.6 Labor intensity for public works projects should be higher than the local norm for similar projects. In this context, minimizing possible conflict between objectives of workfare programs would be important. Such conflicts may engender tradeoffs between high labor intensity and efficient infrastructure investments, which need to be taken into account for program design. Risk mitigation benefits are high when there is credible availability of the program during times of need. Making the program available at all times, expanding automatically during For discussions on women's participation inworkfare programs, see Deolalikar and Gaiha (1993b) 147 crisis when demand is high(as i s the case with EGS) will maximize risk mitigation. In case o f programs that operate mostly in lean seasons, a history o f successful operation can create the credibility among the poor necessary to mitigate risk. Delivery of beneJits, as well as cost-eflectiveness can be improved by effective organization at the local level. Briefly, this would involve strengthening local governments, buildingtheir capacity to implement the project efficiently and increasing their accountability to local communities. Many o f the shortcomings o f programs like Bangladesh's own FFW and India's NREG, for example, can be traced to inadequacies inlocal implementingauthorities. Closely related to the above is the need to encourage the creation and participation of coalitions of the poor that empower them, like labor unions or community-based organizations, which can improve accountability o f implementing agencies and reduce leakage from corruption and administrative malpractice. Delivery o f benefits from EGS i s seen to have improved markedly with the participation o f voluntary organizations, as Deshpande (1988) shows inhis study on Jawahar Taluka o f Thane. Geographic targeting could also enhance a worvare program 's impact, whereby regions with large concentrationso f vulnerable groups canbe identifiedfor programallocations. A statutory guarantee of employmentcan be beneJicia1, an example for which is EGS. Such a feature can empower the poor by creating a sense o f entitlement among them, enhances insurance benefits to the poor, often allows scarce resources to go to the poorest first (albeit only to those able to work), and reduces some o f the possibilities o f corruption (Ravallion et al, 1993). Public works programs are important policy measures to reduce vulnerability, especially in the context o f South Asian countries with large informal sectors, where the level o f institutional development limits the outreach o f formal safety nets. It i s also important, however, to remember that public works programs do not reduce vulnerability from all sources, nor are they able to reach such vulnerable groups as the old, the infirm and children. Other programs that target vulnerability o f the poor are thus needed as complements. National Rural Employment Bank having initiated a study 'This was a more limited intervention than the NREGA, focusing on municipal areas, though it covered a wider range o f activities and has since beenredesignedto focus on more narrow objectives. 148 These statistics are reportedby Mathur(2007). See this paperfor moredetailedfigures. See, for example, findings from social audits referredto in "NREGA: Dismantlingthe contractorraj" by Jean Drkze (The Hindu, November 20 2007); "Long road to employment guarantee" by Jean Dreze and Sowmya Kidambi (The Hindu, August 7 2007) lo The survey results are reported briefly in a newspaper article by Pamela Philipose (Indian Express)