STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA UGANDA POVERTY ASSESSMENT OVERVIEW STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA 11 July 2022 1 © 2022 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. 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Photo credits: © Rachael Mabala Graphics design: Print Innovations and Publishers Limited STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Table of Contents List of figures ��������������������������������������������������������������������������������������������������������������������������������������������������������������������������������ii Acknowledgement �������������������������������������������������������������������������������������������������������������������������������������������������������������������� iv Acronnyms and Abbreviations������������������������������������������������������������������������������������������������������������������������������������������������v Introduction ���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 1 I. Poverty and Inequality: Facts ����������������������������������������������������������������������������������������������������������������������������������������� 3 Every third individual experienced transient poverty in the last five years, while every fifth remained chronically poor.������������������������������������������������������������������������������������������������������������������������������������������ 5 The Northern and Eastern regions continued having the highest poverty rates, as well as the biggest gaps in human capital outcomes and access to basic infrastructure������������������������������������ 6 Working in agriculture and lack of education are the strongest predictors of high poverty ���������������������� 8 Households with more children, larger household size, and with a single female income earner were more likely to have higher poverty rates ������������������������������������������������������������������������������������������������ 9 II. Why has there been limited progress in poverty reduction? ��������������������������������������������������������������������������� 12 Rural households and the poor experienced more frequent shocks and have limited safety nets ���������12 Half of the population was vulnerable to poverty in 2019/20 – education, asset ownership, and working status being key determinants ���������������������������������������������������������������������������������������������������������������������� 16 The change from subsistence agriculture to paid employment was at a higher pace among the wealthier and more educated ������������������������������������������������������������������������������������������������������������������������������������������ 19 Internal migration was not widely accessible for the poorest households living in lagging areas �����������21 COVID-19 slowed down the pace of structural change and increased vulnerability as many were pushed into agriculture��������������������������������������������������������������������������������������������������������������������������������23 Agricultural development, which is the key for poverty reduction, has not experienced substantial changes in production practices in recent years��������������������������������������������������������������������������������24 Inequality of opportunities in access to basic services persisted and was exacerbated by the COVID-19 pandemic ������������������������������������������������������������������������������������������������������������������������������������������������������������26 Increasing competition in the telecommunications sector could increase affordability and access, and reduce poverty������������������������������������������������������������������������������������������������������������������������������������������������29 III. Way forward: A policy agenda�������������������������������������������������������������������������������������������������������������������������������������� 33 References���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������� 36 Annex���������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������37 i UGANDA POVERTY ASSESSMENT OVERVIEW List of figures Figure O1. Headcount poverty rates across rural and urban areas during 2012/13–2019/20 (before and during COVID-19), %..........................................................................................................................................................3 Figure O2. Gini coefficient across rural and urban areas during 2012/13–2019/20 (before and during COVID-19)..........................................................................................................................................................3 Figure O3 Poverty transitions between 2015/16 and 2019/20 by head of household characteristics in 2015/16 year, %......................................................................................................................................................................................5 Figure O4. Headcount poverty rates in 2019/20 across areas, regions and subregions, %....................................................6 Figure O5. Poverty rate in 2016/17 and Human Capital Index circa 2016/17 by subregions....................................................7 Figure O6. Poverty rate in 2016/17 and access to electricity by subregions*.............................................................................7 Figure O7. Population density in Uganda in 2020**.......................................................................................................................................7 Figure O8. Night lights in Uganda in 2020***.................................................................................................................................................7 Figure O9. Headcount poverty rates in 2019/20, depending on head of household sector of employment, %.......................................................................................................................................................................................8 Figure O10. Headcount poverty rates depending on head of household education level in 2019/20, %......................9 Figure O11. Headcount poverty rates in households with different number of children in 2019/20, %..........................10 Figure O12. Headcount poverty rates depending on head of household marital and gender status in 2019/20, %................................................................................................................................................................................................10 Figure O13. Share of households that experienced at least one shock across survey years and rural and urban consumption quintiles, %.......................................................................................................................................................................13 Figure O14. Shocks and decline in different dimensions of wellbeing in 2019/20, % of households reporting decline....................................................................................................................................................13 Figure O15. Distribution of coping strategies during the last 12 months in 2019/20 across residence, head of household gender and rural/urban consumption quintiles, %.....................................................................13 Figure O16. Share of households with at least one shock during last 12 month across poverty transition status during 2015/16–2019/20, %...........................................................................................................................13 Figure O17. Incidence of shocks among Ugandans during March–June 2020, %.....................................................................14 Figure O18. Type of coping strategies used by households in Uganda during March–June 2020 across different dimensions, % of all strategies.......................................................................................................................................15 Figure O19. Incidence of beneficiaries of SCG in 2019/20 among individuals 60 years+ by consumption quintiles, %............................................................................................................................................................16 Figure O20. Incidence of beneficiaries of NUSAF in 2019/20 among individuals 15 years+ by consumption quintiles, %..............................................................................................................................................................16 Figure O21. Poverty and vulnerability rates in Uganda in 2019/20 across rural and urban areas, and regions, %...............................................................................................................................................................................................17 ii STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Figure O22. Vulnerability decomposition into poverty- and risk-induced components across rural and urban areas, and regions, %.............................................................................................................................................................................17 Figure O23. Changes in probability of being vulnerable to poverty with regards to non-vulnerable by different household characteristics..........................................................................................................................................18 Figure O24. Annualized growth rates of employment share in agriculture and GDP per capita, %.....................................19 Figure O25. Value added in agriculture in per capita terms in UGX constant prices and the share of dry months based on SPEI index..........................................................................................................................................20 Figure O26. Share of paid employment across different groups of population using LFS 14–64, %...................................21 Figure O27. Share of working age individuals employed in agriculture by consumption quintiles using UNPS 14–64, %...........................................................................................................................................................................................21 Figure O28. Individual consumption and its annual growth rate across household heads’ sector of employment in 2015/16 and 2019/20, UGX and %.........................................................................................................22 Figure O29. Individual consumption and its annualized growth rates across household heads’ migration status in 2015/16 and 2019/20, UGX and %.........................................................................................................................22 Figure O30. Working respondents using different rounds of the HFPS, % of respondents....................................................23 Figure O31. Work stoppages among those who worked in the previous round across rural/urban areas and economic sectors, %...............................................................................................................................................................................23 Figure O32. Share of working respondents among refugees and Ugandans before COVID-19 and during the first year of the pandemic, %...................................................................................................................................................24 Figure O33. Share of households in planting activities who used fertilizers at least once on any land plot during any of the agricultural seasons in 2013/14 and 2019/20, %..........................................................................25 Figure O34. Share of households in planting activities who used improved seeds on any land plot during either of the two agricultural seasons and who received any advice from extension services in 2013/14 and 2019/20, %.......................................................................................................................................................................25 Figure O35. Coverage rate, HOI and inequality for access to education and health among children in 2019/20, %................................................................................................................................................................................................27 Figure O36. Coverage rate, HOI and inequality for access to basic services among children in 2019/20, %...................27 Figure O37. Decomposing trends in the HOI between 2012/13 and 2019/20, percentage points....................................28 Figure O38. School attendance and participation in any type of schooling for children ages 3–18 years in March 2020 (before lockdown) and in March/April 2021 by area, region and pre–COVID-19 consumption quintiles, %......................................................................................................................................................................29 Figure O39. Access to mobile phones among individuals 16 years+ in 2019/20, %.....................................................................31 Figure O40. Effect of improved competition on Uganda’s poverty rate, change in percentage points............................31 iii UGANDA POVERTY ASSESSMENT OVERVIEW Acknowledgment This study was prepared by a team led by Aziz Atamanov (Senior Economist) and Nistha Sinha (Senior Economist). The contributing authors are listed in alphabetical order: Aziz Atamanov, Eduardo Alonso Malasquez Carbonel, Takaaki Masaki, Cara Ann Myers, Rogelio Granguillhome Ochoa and Nistha Sinha. The team is grateful for guidance and support from Keith E. Hansen (Country Director), Rosemary Mukami Kariuki (Country Manager), Pierella Paci (Practice Manager), Allen Dennis (Program Leader) and Nobuo Yoshida (Lead Economist). The team wants to acknowledge constant support and collaboration from Uganda government officials, in particular, from the Ministry of Finance, Planning and Economic Development and the Uganda Bureau of Statistics. The team has benefitted from excellent comments provided by peer-reviewers Carlos Rodriguez Castelan (Lead Economist, EAWPV), Ruth Hill (Lead Economist, EPVGE), Carolina Mejia-Mantilla (Senior Economist, ELCPV) and Dhiraj Sharma (Senior Economist, EAEPV). Useful comments and suggestions were also provided by Paul Corral (Senior Economist), Talip Kilic (Senior Economist), Eva Liselotte Lescrauwaet (Senior Operations Officer), Franklin Mutahakana (Senior Operations Officer), Fatima Naqvi (Senior Social Protection Specialist), Giulia Ponzini (Economist), Benjamin Christopher Reese (Senior Operations Officer), Rachel K. Sebudde (Senior Economist), and Richard Walker (Senior Economist). We are very thankful to Rural Livelihoods Information System team from FAO for providing harmonized income data for the Uganda National Panel Survey. Excellent administrative and operational support was provided by Esther Ampumuza (Team Assistant), Martin Buchara (Program Assistant), Clare Busingye (Senior Executive Assistant), Santosh Kumar Sahoo (Program Assistant), and Tsehaynesh H. Michael Seltan (Program Assistant). Finally, the team thanks Virginia Larby for proof-reading and editing the report.   iv STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Acronyms and Abbreviations CIAT International Center for Tropical LIC Low-Income Countries Agriculture LIPW Labor-Intensive Public Works CIDR CUNY Institute for Demographic MAI Market Accessibility Index Research ND-GAIN Notre Dame Global Adaptation Initiative CIESIN Center For International Earth Science Information Network NDP National Development Plan CUNY City University Of New York NUSAF The Northern Uganda Social Action Fund DCRM Displacement Crisis Response Mechanism PCO Partial Collusive Oligopoly Structure ECA Europe And Central Asia PDM Parish Development Model EM-DAT Emergency Events Database RHFPS Refugee High-Frequency Phone Survey FAO Food And Agriculture Organization RULIS Rural Livelihoods Information System GDP Gross Domestic Product SCG Senior Citizens Grant GNI Gross National Income SPEI Standardized Precipitation Evapotranspiration Index GRUMP Global Rural-Urban Mapping Project UBOS Uganda Bureau of Statistics GSMA Groupe Special Mobile Association UGX Ugandan Shilling HCI Human Capital Index UNESCO United Nations Educational, Scientific, HFPS High-Frequency Phone Survey and Cultural Organization HOI Human Opportunity Index UNHCR United Nations High Commissioner for IBRD International Bank for Reconstruction Refugees and Development UNHS Uganda National Household Survey ICT Information Communications UNPS Uganda National Panel Survey Technology VIIRS Visible Infrared Imaging Radiometer IDA International Development Association Suite IFPRI International Food Policy Research VNL VIIRS Nighttime Lights Institute WB World Bank ILO The International Labor Organization WDI World Development Indicators ITU International Telecommunications Union WELCOM Welfare And Competition LDC Least Developed Countries Microsimulation LFS Labor Force Survey v STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Introduction The share of Uganda’s population that lives below the poverty line has fluctuated over the last seven years, greatly influenced by shocks that have tested the resilience of the people. About 30 percent of the country’s population was poor in 2019/20, which is comparable to the poverty rate of 30.7 percent in 2012/13.1 The pattern of fluctuating poverty rates is largely driven by the experience of rural households. There was a surge in the poverty rate between 2012/13 and 2016/17 – linked to the drought in 2016/17 – followed by improvement in 2019/20 prior to the pandemic, when favorable weather conditions helped lift rural incomes. The COVID-19 pandemic pushed both urban and rural residents into poverty. Inequality, which reflects the extent to which different population groups benefit from Gross Domestic Product (GDP) growth, and affects the transmission of growth into poverty reduction, remained largely unchanged over this period and may even have worsened in urban areas. The findings of this report show that previously identified patterns and drivers of Uganda’s poverty changes persisted well into 2020 – shaped by low productivity and high vulnerability. Since 2012/13 there were emerging signs of structural change with workers moving from low to high productivity activities. Workers moved out of subsistence agriculture into paid work, and the share of workers engaged in the services sector has increased, which augurs well for income growth. Nevertheless, agricultural productivity was falling until recent years, and its increase after 2017 was mostly associated with good weather rather than improvement in production practices. The progress in structural change was negatively affected by COVID-19 pandemic when many people returned to agriculture following the job losses and closure of small businesses. 1 Household budget surveys in Uganda span across two years. Detailed informa- tion about data used is provided in Annex. UBOS revised its poverty line in 2021 using Uganda National Household Survey 2016/17. This updated poverty line is used throughout the report. 1 UGANDA POVERTY ASSESSMENT OVERVIEW Identified inequality of economic opportunities and unequal accumulation of the human capital could hold back structural change in employment. The ability to change one’s economic sector of work appears to be unequal with the shift out of agriculture mainly taking place amongst men, older individuals, those with at least some level of formal education, and those from more well-off households. Unequal access to opportunities and unequal access to basic public services among young population identified in this report may therefore hold back the productivity and income gains that could come from structural change in employment sectors. Income generation strategies of households are also impacted by their resilience capabilities as the reported frequency of extreme weather shocks has increased in recent years. The pandemic further tested these capabilities. Accelerating poverty reduction in such a setting requires a two-pronged strategy. While at the macroeconomic level, policies addressing growth fundamentals are important for reducing poverty, from a microeconomic perspective, the report’s analysis shows that two strategies will be crucial. The first strategy is to lift the productivity and incomes of poor households in both rural and urban areas. While tackling agricultural productivity and job creation are at the top of the agenda here, making mobile phone services more widely accessible and affordable is a potential opportunity. The second strategy is to strengthen people’s resilience to shocks, particularly in rural areas. To have an impact, policies in both these areas will have to address the inequality in opportunities analyzed in the report. This document provides an overview of key report findings and identifies priority actions. The first part documents the facts on evolution, profile and poverty characteristics since 2012/13 and distinguishes between pre-COVID-19 and COVID-19 periods. The second part examines the reasons behind limited progress in poverty reduction starting from the analysis of shocks and vulnerability followed up by the analysis of structural changes, determinants of internal migration, agricultural development, inequality of opportunities and simulating distributional impact of increased competition in the telecommunication sector. The final section reviews the key policy points for action. The report’s analysis is based on three rounds of Uganda National Household Survey, three rounds of Uganda Panel Survey, seven rounds of Uganda High-Frequency Phone Survey, Uganda Refugee High-Frequency Phone Survey and AfroBarometer data (more detailed information about the surveys, concepts and definition used in this study can be found in the annex). The report uses geospatial data measuring night lights, precipitation, population density, distance to roads and so forth. Finally, the current document refers to published analytical reports such as the Systematic Country Diagnostic Update (World Bank; International Finance Corporation; Multilateral Investment Guarantee Agency 2021), the Country Economic Memorandum (World Bank 2022), and the previous Poverty Assessment (World Bank 2016). The full report with detailed technical analysis can be downloaded from here or from this page http://hdl.handle.net/10986/37752. 2 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA I. Poverty and Inequality: Facts2 Multiple shocks in 2012–2020 led to volatile poverty rates Most recently, the COVID-19 shock wiped out previous reductions in rural poverty rates and accelerated the increase in urban poverty rates. Disaggregation of the 2019/20 household survey between the pre-COVID-19 and COVID-19 periods, sheds some light on trends right before the pandemic, revealing a drop in rural poverty of six percentage points and very little change in urban poverty between 2016/17 and the pre-COVID period in 2019/20. However, poverty rates soared in the COVID-19 period of 2020, increasing by 7.2 percentage points in rural areas (wiping out previous gains), and by 3.1 percentage points in urban areas (Figure O1). In addition, the COVID-19 shock aggravated both the depth and severity of poverty, meaning that more money will be needed to lift the poor out of poverty and that inequality among the poor increased dramatically as well. Figure O1. Headcount poverty rates across Figure O2. Gini coefficient across rural rural and urban areas during 2012/13– and urban areas during 2012/13–2019/20 2019/20 (before and during COVID-19), % (before and during COVID-19) Source: UNHS, WB staff calculations. Note: A detailed explanation of poverty and inequality concepts and the data used to measure them is provided in the Annex. 2 For more detailed analysis check the Chapter 1 of the full report. 3 UGANDA POVERTY ASSESSMENT OVERVIEW At the national level, inequality in consumption did not change much between 2016/17 and 2019/20, however there were substantial changes within urban and rural areas. Inequality in consumption, measured by the Gini coefficient, has barely changed since 2016/17 in Uganda (Figure O2). However, inequality in urban areas has increased, mainly in the COVID-19 period, while rural inequality decreased significantly in the same period. The narrowing of rural inequality may be a reflection of a contraction in consumption amongst the better off rural households. Most of the change in poverty rates was due to average growth in household consumption rather than favorable shifts in the distribution of this growth. In rural areas between 2016/17 and the pre-COVID-19 period in 2019/20, the increase in the mean average household consumption (growth effect) accounted for more than 90 percent of all poverty reduction during the period. Meanwhile, the contribution of reduction in inequality (the distribution effect) was limited. This was reversed in 2019/20 between the pre-COVID and the COVID-19 period, when poverty increased in rural areas solely due to lower average household consumption (negative growth effect), and the reduction in inequality curbed the increase in poverty. The agricultural sector – where the majority of the poor were working – was affected by COVID-19-related mobility restrictions to a lesser extent after the first lockdown, which can explain the poverty reducing distribution impact in rural areas. In urban areas, both growth and inequality effects contributed to the increase in poverty observed in 2019/20 during both the pre-COVID-19 and COVID-19 periods. 4 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Every third individual experienced transient poverty in the last five years, while every fifth remained chronically poor. Poverty is a mix of persistent and transient – another reflection of the limited resilience of households. Figure O3. Poverty transitions between 2015/16 and 2019/20 by head of household characteristics in 2015/16 year, % Source: UNPS, WB staff calculations. Overall, about 20 percent of the population was persistently poor over the 2015/16 to 2019/20 period and about half of the population was never poor. The remaining 30 percent of the population experienced transient poverty: 18 percent of those who were poor in 2015/16 had moved out of poverty in 2019/20, while the remaining 12 percent moved from being non-poor in 2015/16 to being poor in 2019/20. The largest shares of persistent poor were observed among heads of household without formal education and who were either unemployed or out of the labor force in 2015/16 (Figure O3). 5 UGANDA POVERTY ASSESSMENT OVERVIEW The Northern and Eastern regions continued having the highest poverty rates, as well as the biggest gaps in human capital outcomes and access to basic infrastructure Location plays a key role in poverty status in Uganda, with rural areas and the Northern and Eastern regions exhibiting the highest poverty rates. In 2019/20, the poverty rate in rural areas was 33.8 percent – much higher than the urban rate of 19.8 percent (Figure O4). Indeed, more than 80 percent of all the poor in Uganda live in rural areas. Poverty rates also varied substantially across regions. Poverty in the Central region was about 15 percent, or almost three times lower than the poverty rate in the Eastern and Northern regions, which reached 42 percent and 40 percent, respectively. In particular, within the Eastern and Central regions, rural households were also poorer compared to urban households. Subregions also had stark differences in poverty rates; the Kampala subregion had the lowest poverty rate of just four percent, which was 20 times lower than that of the Karamoja subregion. Figure O4. Headcount poverty rates in 2019/20 across areas, regions and subregions, % Source: UNHS, WB staff calculations. 6 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Subregional spatial disparities in poverty are also reflected in gaps in human capital outcomes and access to basic infrastructure, which translates into low growth potential. Subregions with higher poverty rates also have lower Human Capital indices (HCI),3 (see Figure O5) and lower levels of infrastructure access, including access to electricity (Figure O6). In contrast, the combination of high population density, better access to infrastructure and markets, and higher human capital indices seem to contribute to subregional economic development, measured by night-time light (NTL). Thus, Kampala and its surrounding areas stand out in terms of night-time light and economic development (Figure O7 and Figure O8). Figure O5. Poverty rate in 2016/17 and Human Figure O6. Poverty rate in 2016/17 and access Capital Index circa 2016/17 by subregions to electricity by subregions* Figure O7. Population density in Uganda in Figure O8. Night lights in Uganda in 2020*** 2020** Kampala Kampala Sources: *World Bank 2020; UNHS 2016/17, World Bank staff calculations. ** WorldPop. *** Elvidge et al. 2021). 3 Human Capital Index encompasses stunting, mortality rates, years of schooling, and test scores in one indicator. 7 UGANDA POVERTY ASSESSMENT OVERVIEW Figure O9. Headcount poverty rates in 2019/20, depending on head of household sector of employment, % Source: UNHS, WB staff calculations. Working in agriculture and lack of education are the strongest predictors of high poverty Households working in agriculture were the most likely to be poor in Uganda. According to Figure O9, households whose head worked in agriculture had the highest headcount poverty rate (35.5 percent), which was seven percentage points higher than the average poverty rate among population from households with employed heads (28.7 percent). Put another way, 77 percent of poor households worked in agriculture. The education level of the head of household was another determinant of poverty and consumption levels in 2019/20. Across both rural and urban areas, there was a strong negative association between the level of education and poverty rates (Figure O10) – with much lower poverty rates for households whose head had a higher education level. For example, the poverty rate among households with uneducated heads reached about 48 percent in 2019/20 (this group accounted for 17 percent of all heads). 8 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Figure O10. Headcount poverty rates depending on head of household education level in 2019/20, % Source: UNHS 2019/20, WB staff calculations. This was almost twice as high as the poverty rate of households in which the household head finished primary education – 25.7 percent (also 17 percent of all heads). Even having primary incomplete education, after controlling for other household characteristics potentially affecting wellbeing, increases consumption by 18 percent compared to households where the head does not have any education at all. Importantly, spatial decomposition analysis also found that differences in education levels among heads of household were one of the biggest endowment factors accounting for the urban-rural consumption gap. Households with more children, larger household size, and with a single female income earner were more likely to have higher poverty rates Demography also plays an important role in poverty status, which is especially relevant given Uganda’s high population growth rates and burgeoning young population. Poor households were significantly larger with more children. Whereas the average household size among non-poor households in 2019/20 was 4.3 members, poor households averaged 5.5 members. In 2019/20, the headcount poverty rate in households with two or three children in 2019/20 was about 34 percent – 10 percentage points higher than among households with one or two children (Figure O11). Once controlled for other factors, having one additional child (ages 0–13 years) was associated with a reduction in consumption per individual by three percent. 9 UGANDA POVERTY ASSESSMENT OVERVIEW Gender is also an important correlate of poverty. Female-headed households in rural areas, female- headed divorced and married households had higher poverty rates than their male counterparts (Figure O12). Furthermore, analysis of poverty rates by income earner composition showed a significantly higher poverty rate among households with one female earner in paid employment compared to households with one male earner in paid employment, potentially pointing to gender inequalities in the labor market. Figure O11. Headcount poverty rates in Figure O12. Headcount poverty rates households with different number of children depending on head of household marital and in 2019/20, % gender status in 2019/20, % Source: UNHS 2019/20, WB staff calculations. Note: *The difference between female and male heads of household is statistically significant. 10 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA 11 UGANDA POVERTY ASSESSMENT OVERVIEW II.4 Why has there been limited progress in poverty reduction? Rural households and the poor experienced more frequent shocks and have limited safety nets4 Experiencing shocks more frequently need not push vulnerable households into poverty; however, access to safety nets and other means to smooth incomes is limited. According to the UNPS data, the share of households that experienced shocks5 during the last decade before COVID-19 ranged from 30 percent to 40 percent in Uganda, with rural and the poorest households affected the most (Figure O13). These shocks frequently resulted in a decline in income and assets (Figure O14). To cope with the impact of shocks, households might employ a variety of strategies, including using savings, soliciting help from relatives and neighbors, and reducing consumption. The poor in Uganda were much more likely to use coping mechanisms such as an involuntary decline in food consumption (Figure O15). Furthermore, the UNPS data showed correlation between households who experienced shocks and the likelihood of being chronically poor or falling into poverty (Figure O16). 4 For more detailed analysis check the section I in Chapter 3 of the full report. 5 Respondents were asked if they experienced any shock during last 12 months from the wide list of distress events such as droughts, irregular rains, flooding, death of income earner(s) and so forth with a possibility to select shock not in the list. 12 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Figure O13. Share of households that experienced Figure O14. Shocks and decline in different at least one shock across survey years and rural and dimensions of wellbeing in 2019/20, % of households urban consumption quintiles, % reporting decline Source: UNPS 2019/20, WB staff calculations. Source: UNPS 2019/20, WB staff calculations. Figure O15. Distribution of coping strategies during Figure O16. Share of population with at least one the last 12 months in 2019/20 across residence, shock during last 12 month across poverty transition head of household gender and rural/urban status during 2015/16–2019/20, % consumption quintiles, % Source: UNPS 2019/20, World Bank staff calculations. Source: UNPS 2015/16 and 2019/20, WB staff calculations. Note: All coping strategies are taken into account regardless of their Note: The same households were considered during two rounds. rank. 13 UGANDA POVERTY ASSESSMENT OVERVIEW The majority of Ugandan households have experienced at least one shock during the COVID-19 pandemic, especially the poor and those living in the Northern region, who also used inferior coping mechanisms. The Uganda High-Frequency Phone Survey (HFPS) found that almost 60 percent of households in the first round experienced at least one shock after March 2020 (Figure O17).6 Figure O17. Incidence of shocks among Ugandans during March–June 2020, % Source: HFPS round 1, World Bank staff calculations. The incidence of shocks was higher among the poorest households from the first pre-COVID-19 consumption per adult equivalent quintile (65 percent) and those living in the Northern region (69 percent).7 Similar to the pre-COVID-19 period, the poorest households were less likely to rely on savings as a coping strategy and more likely to reduce food consumption when they experienced a shock (Figure O18).8 Shocks such as increased food prices, business failure and falling output prices had the highest incidence. 6 The incidence of shocks is not comparable across pre-COVID-19 UNPS data and HFPS. Both surveys used a different list of shocks. 7 According to the Uganda Refugee High-Frequency Phone Survey (RHFPS), almost 90 percent of refugee households experienced at least one shock between March and October/November 2020. In contrast to Ugandans, refugees did not have access to sav- ings to cope with shocks but relied mostly on aid and help from friends and relatives. 8 One important caveat here is that the recall period in the UHFPS is much shorter than in the UNPS and this may affect coping strategies as well. 14 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Figure O18. Type of coping strategies used by households in Uganda during March–June 2020 across different dimensions, % of all strategies Source: HFPS round 1, WB staff calculations. Note: All coping strategies used are included. Despite reasonable accuracy in targeting and effectiveness for the program beneficiaries, the current levels of expenditure on social protection in Uganda are rather low. According to the World Bank (2020a), the allocation to social development – which includes social protection expenditures – was only 0.7 percent of the overall government budget in FY19/20. Expenditure on the two largest direct income support programs, such as the Senior Citizens Grant (SCG) and The Northern Uganda Social Action Fund (NUSAF 3), was just 0.14 percent of GDP in FY17/18, which is lower than in neighboring countries like Kenya and Rwanda who spent 0.4 percent and 0.3 percent of GDP respectively, on direct income support. The most recent data confirms very low coverage of SCG and NUSAF programs, but also demonstrates their pro-poor incidence. As shown in Figure O19, about nine percent of all individuals aged 60 years and above benefited from SCG, and the coverage rate was twice as high among the poorest individuals from the bottom consumption quintiles. About 0.6 percent of individuals aged 15 years and above benefited from NUSAF and, similar to SCG, the coverage rate was higher among the poorest individuals (Figure O20). 15 UGANDA POVERTY ASSESSMENT OVERVIEW Figure O19. Incidence of beneficiaries of SCG Figure O20. Incidence of beneficiaries of in 2019/20 among individuals 60 years+ by NUSAF in 2019/20 among individuals 15 consumption quintiles, % years+ by consumption quintiles, % Source: UNHS 2019/20, WB staff calculations. Half of the population was vulnerable to poverty in 2019/20 – education, asset ownership, and working status being key determinants9 The high frequency of shocks and their correlation with poverty is significant because half of the population in Uganda was vulnerable to poverty in 2019/20. The vulnerability to poverty rate in Uganda was estimated to be 50 percent as of 2019/20 (Figure O21). This is much higher than the observed poverty rate of 30 percent in the same year. Rural areas are, as expected, characterized by both higher poverty and vulnerability rates (34 percent and 59 percent, respectively) compared to urban areas (20 percent and 26 percent, respectively). Among regions, the highest vulnerability rates were found in the poorest Northern and Eastern regions (66 percent and 64 percent, respectively). In relative terms, however, the largest difference between poverty and vulnerability rates was in the Western region, where the vulnerability rate was twice as high as the poverty rate. 9 For more detailed analysis check the section II in Chapter 3 of the full report. 16 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Risk-induced vulnerability prevailed in urban areas, while poverty-induced vulnerability prevailed in the poorest Eastern and Northern regions.10 Figure O22 shows that, at the national level, risk- induced vulnerability slightly prevails over poverty-induced vulnerability. The difference widens in urban areas, where high consumption volatility accounts for almost 70 percent of all vulnerability. By contrast, in rural areas vulnerability is equally driven by low consumption and its high volatility. At the regional level, vulnerability in the poorest Eastern and Northern regions is poverty-induced to a larger extent, while in the Central and Western regions vulnerability was more associated with high consumption fluctuations. Figure O21. Poverty and vulnerability rates Figure O22. Vulnerability decomposition into in Uganda in 2019/20 across rural and urban poverty- and risk-induced components across areas, and regions, % rural and urban areas, and regions, % Source: UNHS 2019/20, authors’ calculations. While idiosyncratic shocks are more prevalent on the national level, covariate shocks, such as weather and price shocks, are more important for rural residents.11 The ratios between the percentage of households that would fall below the poverty line from an idiosyncratic shock versus a covariate shock are higher than one at the country level, rural and urban areas and different regions. This shows that the impact of idiosyncratic shocks was consistently higher than the impact of covariate shocks. In relative terms, however, the role of idiosyncratic shocks is much more prevalent in urban areas and the Central and Western regions, compared to rural areas and the Northern and 10 Vulnerability can be driven either by permanent low consumption (poverty-induced) or high volatility of consumption (risk-in- duced). Poverty-induced vulnerability happens when the expected mean of consumption already lies below the poverty line. Households face risk-induced vulnerability when their expected consumption is higher than the poverty line, but high estimated variance leads to a probability higher than the established threshold of 29 percent for a given year. 11 Idiosyncratic shocks include household specific shocks such as health issues, job losses and so forth, which have a weak cor- relation across households living in the same community. In contrast, covariate shocks are correlated across households within communities or, in other words, households from the same community experience similar shocks. These may include price and weather shocks, political crises, and so forth. 17 UGANDA POVERTY ASSESSMENT OVERVIEW Eastern regions. This suggests that covariate shocks such as weather, locust, and price shocks play a more important role for rural residents. Education, asset ownership, and working status were found to be the main determinants of vulnerability in Uganda. For households where the household head has an incomplete primary education the probability of being vulnerable drops by 15 percent relative to households where the head has no education. Ownership of selected assets such as TVs and motorcycles, used as a proxy of monetary wellbeing, were also associated with significantly lower chances of being vulnerable. Having the head of household out of the labor force or in subsistence farming increases the probability of being vulnerable compared to having the head of household in paid employment (Figure O23). Figure O23. Changes in probability of being vulnerable to poverty with regards to non-vulnerable by different household characteristics Source: UNHS 2019/20, authors’ calculations. Note: Base category is in parentheses. 18 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA The change from subsistence agriculture to paid employment was at a higher pace among the wealthier and more educated12 Figure O24. Annualized growth rates of employment share in agriculture and GDP per capita, % Source: LFS and WDI (modelled estimates). World Bank staff calculations. There was a noteworthy decline in the share of agricultural employment in Uganda between 2011/12 and 2018/19; however, this well-known marker of structural change was not accompanied by a robust GDP per capita growth as in most countries with similar economic development.13 The rate of decline of the share of agricultural employment in Uganda was faster than in most comparators – about two percent annually between 2011/12 and 2018/19 (Figure O24). Nevertheless, Uganda’s economic growth lagged behind, with the annualized growth rate of GDP per capita close to only one percent. 12 For more detailed analysis check Chapter 2 of the full report. 13 In order to compare the performance of Uganda in regard to structural change, we have selected countries with GDP per capita measured in constant international dollars in 2017 purchasing power parity similar to Uganda in 2011. 19 UGANDA POVERTY ASSESSMENT OVERVIEW Figure O25. Value added in agriculture in per capita terms in UGX constant prices and the share of dry months based on SPEI index Source: Global SPEI database accessed in Oct. 2021 and UBOS. Note: Value added was taken for fiscal year to account for lagged impact of weather. For example, the observation for the year of 2006 was based on value added from 2006/2007, while SPEI index was based on 2006 year. Agricultural productivity was falling before 2017 when it started to grow due to resumed economic growth and labor moving to services and industry sector; with performance impacted by weather shocks. At the same time, productivity in services and industry sectors, which absorbed excessive labor in agriculture, continued to decline across all years. In addition, given the importance of the agricultural sector in the economy, weather shocks affected Uganda’s economic performance in this period. There was a significant correlation in the country between the value added in agriculture in per capita terms and the precipitation during the last ten years (Figure O25). Thus, higher value added in agriculture happened in recent years with lower shares of dry months. A notable feature of structural change is that at this early stage it is also unequal – sectoral change in employment is mainly observed among men, older individuals, and those with at least some level of formal education. Overall, rural residents, females, individuals of 14–24 years of age, and those without education were less likely to have paid employment as their primary job in 2011/12 (Figure O26). The structural change during the 2011/12 to 2018/19 period – measured by the falling share of subsistence agriculture and the increasing share of paid employment – was also pronounced among groups that already had higher levels of paid employment, with one notable exception of rural areas. Wealthier individuals experienced faster rates of sectoral change in employment during the period from 2013/14 to 2019/20. As shown in Figure O27, the share of employment in agriculture among the working population was declining across all quintiles from 2013/14 to 2019/20, but the 20 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA rate of decline was higher among individuals from the wealthier quintiles. This may be related to constraints that the poorest face trying to enter the non-agriculture sectors, such as low levels of human capital, limited access to infrastructure and markets, and the up-front costs of transitioning from one sector to another. Figure O26. Share of paid employment across Figure O27. Share of working age individuals different groups of population using LFS employed in agriculture by consumption 14–64, % quintiles using UNPS 14–64, % Source: LFS 2011/12 and 2018/19, World Bank staff Source: UNPS, World Bank staff calculations. calculations. Note: Cross-section weights are used. Internal migration was not widely accessible for the poorest households living in lagging areas14 Internal migration may not have been widely accessible for the poorest households living in lagging agricultural regions, constraining their abilities to access better opportunities. About nine percent of household heads in Uganda in 2016/17 reported migrating at some point during the last five years. Rural to urban migration accounted for the smallest share in overall internal migration flows in 2016/17 (16 percent). Results from regression analysis revealed that, after controlling for other factors, households with less educated heads, households with more dependents, and households living in areas with predominantly subsistence agriculture and inadequate infrastructure were less likely to migrate. 14 For more detailed analysis check the section IV in Chapter 2 of the full report. 21 UGANDA POVERTY ASSESSMENT OVERVIEW On average, those who managed to move out of agriculture into other sectors – as well as those who migrated from rural to urban areas – benefited from gains in individual consumption between 2015/16 and 2019/20.15 Households in which the household head switched from the agricultural sector in 2015/16 to the non-agricultural sector in 2019/20 demonstrated the highest annualized consumption growth rate (Figure O28). With regards to internal migration, consumption grew in all groups during 2015/16 and 2019/20, but the annual growth rate was the same among those households who remained in rural areas and those who moved from rural to urban areas (Figure O29). Figure O28. Individual consumption and Figure O29. Individual consumption and its its annual growth rate across household annualized growth rates across household heads’ sector of employment in 2015/16 and heads’ migration status in 2015/16 and 2019/20, UGX and % 2019/20, UGX and % Source: UNPS 2015/16 and 2019/20, World Bank staff calculations. Note: The same households across years are used. Note: N=2,673 households. 15 A panel component from the UNPS 2015/16 and 2019/20 rounds was used to check consumption per adult equivalent changes in households depending on the household head’s sector of employment and internal migration status 22 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA COVID-19 slowed down the pace of structural change and increased vulnerability as many were pushed into agriculture COVID-19 may have slowed down the pace of structural change in Uganda, reversing momentum and pushing some individuals into the agricultural sector where they continue to be vulnerable to weather shocks. The share of agriculture in employment increased after the first lockdown when some individuals stopped working and some shifted to working in agriculture (Figure O30). This negatively affected the pace of structural change. The latest round of the HFPS – conducted after the second lockdown – showed a different pattern. Work stoppages were more universally distributed across areas and sectors and less related to COVID-19 restrictions (Figure O31). This may be related to droughts in most parts of the country in the first agricultural season of 2021. Figure O30. Working respondents Figure O31. Work stoppages among those using different rounds of the HFPS, % of who worked in the previous round across respondents rural/urban areas and economic sectors, % Source: HFPS, World Bank staff calculations. Note: Only the same respondents across all seven rounds are kept. 23 UGANDA POVERTY ASSESSMENT OVERVIEW Figure O32. Share of working respondents among refugees and Ugandans before COVID-19 and during the first year of the pandemic, % Source: RHFPS and HFPS, WB staff calculations. Work stoppages after COVID-19 were more pronounced among refugees than Ugandans, with slower income and employment recovery. For refugees, the share of working respondents dropped from 56 percent before COVID-19 to 36 percent in October/November 2020, and further to 32 percent in February/March 2021 (Figure O32). While work stoppages were also pronounced among Ugandans, employment fully returned to the pre-pandemic level after June 2020. In addition, lower shares of refugee households reported income levels in February/March 2021 being the same or higher than pre-COVID compared to numbers reported by Ugandans in February 2021. Agricultural development, which is the key for poverty reduction, has not experienced substantial changes in production practices in recent years16 Agricultural growth has largely been dependent on favorable weather as opposed to substantial changes in production practices. Poverty reduction depends on improved roved agricultural performance as the majority of poor households engage in subsistence agriculture and do not necessarily have access to non-farm opportunities. The median value of crop production per hectare among households engaged in growing crops increasing by eleven percent annually between 2013/14 16 For more detailed analysis check the section IV in Chapter 2 of the full report. 24 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA and 2019/20.17 However, this agricultural growth does not seem to be accompanied by commensurate changes in production practices and the use of improved inputs. For example, the share of households who used organic fertilizers increased from 11 to 14 percent, pesticides from 14 to 17 percent, and the use of inorganic fertilizers remained at about seven percent (Figure O33). Use of fertilizers remained unequal. Getting advice from extension services and using improved seeds fell between 2013/14 and 2019/20 (Figure O34). These findings suggest that positive external conditions, most notably favorable weather patterns, may have played the chief role in contributing to the sector’s robust growth. In particular, the years of 2018 and 2019 were considered to have very favorable weather conditions for agriculture. On the flip side, this also signals the sector’s vulnerability to poor weather conditions, such as the 2016/17 drought that caused a decline in agricultural incomes and an increase in the poverty rate. Figure O33. Share of households in planting Figure O34. Share of households in planting activities who used fertilizers at least once activities who used improved seeds on any on any land plot during any of the agricultural land plot during either of the two agricultural seasons in 2013/14 and 2019/20, % seasons and who received any advice from extension services in 2013/14 and 2019/20, % Source: UNPS 2013/14 and 2019/20, World Bank staff calculations. 17 To check the latest performance of the agricultural sector and the underlying factors behind agricultural production, we have used Rural Livelihood Information System (RULIS) harmonized datasets constructed from the UNPS for 2013/14 and 2019/20 years. 25 UGANDA POVERTY ASSESSMENT OVERVIEW Inequality of opportunities in access to basic services persisted and was exacerbated by the COVID-19 pandemic18 The Human Opportunity Index (HOI) was used to measure inequality of opportunities among children in Uganda. The HOI explores how the personal “circumstances” for which a child cannot be held accountable – like location, gender, household composition, or parental wealth – can affect the child’s probability of accessing basic services that are necessary to succeed in life, like timely education, clean water, electricity, and decent housing. In other words, the HOI measures access rates (coverage) to basic services (opportunities) adjusted by inequality and allows to measure progress toward universal equitable access to opportunities. The HOI also indicates which socio- demographic characteristics influence a child’s likelihood of access to a particular opportunity and identifies those with the largest contributions to inequality. The report focused the analysis on opportunities in education, access to basic services, and health. For education, inequality was found to be higher in opportunities that captured the quality of services and was largely explained by differences in location and monetary wellbeing. Figure O35 shows the coverage rates (dots) and HOI (bars) with the gap between the two reflecting the relative inequality of opportunity among children of different circumstances. None of the education opportunities had universal 100 percent coverage, which can be viewed as an aspirational goal for a society. Opportunities related to the quality of education such as starting and finishing school on time had the lowest coverage and highest inequality. If inequality is split by circumstances, location explained more than 40 percent of all inequality in school enrollment for primary age children and finishing school in time, with monetary wellbeing being the second largest contributor. Inequality in starting school on time was driven by monetary wellbeing and head of household education levels. Visits to health facilities and access to medication when needed were accessible for only 67 percent of children, with significant inequality across groups, especially regarding monetary wellbeing. As shown in Figure O35, the HOI in access to health services was close to 61 percent, which was 6 percentage points lower than the average coverage rate of 67 percent. The main contributing factor for inequality in access to health services was monetary wellbeing, measured by consumption per capita quintiles. It accounted for more than 55 percent of the inequality observed, compared to only 17 percent related to location. Only 48 percent of the poorest children from the bottom quintile were able to visit health facilities and access medication when ill, compared to 91 percent of children from the richest quintile. 18 For more detailed analysis check Chapter 4 of the full report 26 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Figure O35. Coverage rate, HOI and inequality Figure O36. Coverage rate, HOI and inequality for access to education and health among for access to basic services among children in children in 2019/20, % 2019/20, % Source: UNHS 2019/20, World Bank staff calculations. Note: Age of children is shown in brackets. Access to all infrastructure services in Uganda except drinking water was very limited and very unequal. For all services, except drinking water, the coverage rate in 2019/20 was very low (Figure O36). Access to electricity improves substantially if electricity off grid (mostly solar) is counted in, but the coverage was still below 60 percent. Access to sanitation and hand washing facilities was the lowest. The most unequal access was observed for the opportunity to receive electricity from the national grid. Getting access to electricity off-grid reduces inequality substantially. 27 UGANDA POVERTY ASSESSMENT OVERVIEW Figure O37. Decomposing trends in the HOI between 2012/13 and 2019/20, percentage points Source: UNHS 2019/20, World Bank staff calculations. Location, especially regional disparities, was responsible for the largest chunk of inequality in access to drinking water (78 percent), access to sanitation (41 percent), access to electricity from the national grid (55 percent), and access to electricity on and off-grid (44 percent). Inequality in access to a hand washing facility was an exception, with monetary wellbeing being the most important contributor to inequality (45 percent) and the head of household’s education level and location accounting for equal, but much smaller shares in inequality (25 percent each). Almost all of the improvements in the HOI indicators between 2012/13 and 2019/20 were due primarily to higher coverage rates for entire population without improving existing inequality, with access to off-grid electricity being the one noticeable exception where equitable distribution played a more important role. Figure O37 shows contribution of scale19 and distribution effects20 for the changes in 11 opportunities between 2012/13 and 2019/20. For most improved opportunities, distribution effect played a marginal role and any improvement observed was mostly due to a scale effect. The increase in the opportunity to access electricity (on- and off-grid) was the only one coming from both scale and distribution effects. 19 A change in the overall coverage for the entire population without any changes in inequality. 20 A change in the equality of access to the opportunity between the circumstance groups. 28 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Figure O38. School attendance and participation in any type of schooling for children ages 3–18 years in March 2020 (before lockdown) and in March/April 2021 by area, region and pre– COVID-19 consumption quintiles, % Source: HFPS, World Bank staff calculations. Note: Age range in the HFPS data was from 3–18 years and differs from the age range used for the HOI. The COVID-19 pandemic has stalled the progress Uganda had been making in improving human capital accumulation, in particular education. During the period March 2020–October 2021, schools in Uganda were fully or partially closed for 83 weeks – the longest closure in the world (UNESCO). Participation in schooling declined dramatically (Figure O38): about 90 percent of children aged 6–18 years participated in schooling before the lockdown was introduced in March 2020, but by March/ April 2021, this had dropped to only 46 percent of children participating in any learning activities. Increasing competition in the telecommunications sector could increase affordability and access, and reduce poverty21 The adoption of mobile telecommunication services has the potential to improve individual welfare. This may happen through multiple channels, including better labor market outcomes, more variety and better quality of goods and services, lower prices for buyers, improved student learning, expanded financial inclusion, and increased access to information and new markets through mobile broadband internet (Aker & Mbiti 2010, Aker, Ksoll & Lybbert 2012). Despite these benefits, there is a gap between service availability (i.e., coverage) and usage of mobile telecommunication services, especially regarding the adoption of mobile internet (Maudi & Dubus 2020, Granguillhome Ochoa et al. 2022). 21 For more detailed analysis check Chapter 5 of the full report. 29 UGANDA POVERTY ASSESSMENT OVERVIEW In Uganda, affordability remains one of the main constraints for the country to realize the full potential of digital transformation and its associated welfare benefits. The Information Communication Technology (ICT) sector is highly concentrated, which affects the level of competition for telecom operators and subsequently the prices they charge customers. According to the International Telecommunications Unit (ITU), which monitors affordability of selected standardized baskets of ICT measured in prices expressed as a percentage of Gross National Income (GNI) per capita, for four out of five standardized ICT baskets, the price in Uganda was higher than the median for African and least developed countries. While Uganda has relatively high levels of ICT penetration, access to mobile phones varies across groups, with the poorest individuals having lower ownership rates. Overall, 59 percent of individuals ages 16 years and above had access to mobile phones in 2019/20. However, access to mobile phones among the poorest consumption quintile was half the level of the top quintile: 36 percent versus 79 percent, accordingly (Figure O39). About 70 percent of all owned mobile phones were basic ones with text and calling functions only. The poorest individuals from the bottom consumption quintile were more likely to own basic phones compared to the richest individuals from the top quintile: 83 percent versus 58 percent, accordingly. As a direct result of increased competition in the telecommunications sector, the reduction in average ICT prices, and the entrance of new users of telecommunication services, poverty is expected to decline by about 0.8 percentage points, without significant changes in inequality. Simulation shows that improvement in competition is associated with an average total increase in individual consumption by 1.7 percent, with 0.9 percent coming from current users and 0.8 percent coming from new users. Among current users, the positive impact increases by consumption quintile, given their larger expenditures on ICT services and higher coverage rates. In contrast, the positive impact of improving competition for new users is pro-poor, with larger consumption growth observed among the population from the bottom wealth quintiles. Consistent with increased consumption, poverty is expected to fall by about 0.8 percentage points (Figure O40). 30 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Figure O39. Access to mobile phones among Figure O40. Effect of improved competition individuals 16 years+ in 2019/20, % on Uganda’s poverty rate, change in percentage points Source: UNHS, World Bank staff calculations. Source: UNHS 2019/20, World Bank staff calculations. Note: Poverty is measured using the updated poverty line, with the poverty rate of about 30 percent in 2019/20. 31 UGANDA POVERTY ASSESSMENT OVERVIEW 32 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA III. Way forward: A policy agenda Raise productivity and income earning opportunities Investing in human capital development is a critical pathway for poverty reduction in Uganda. In 2019/20, the education level of the head of household was found to be the key determinant of poverty and consumption levels, highlighting the imperative to invest in educating young population. Furthermore, policies and investments focusing on expanding access to basic services like health and education should also focus on factors that improve the quality of those services to deliver more significant gains in outcomes and ultimately reduce poverty. This is particularly important in the wake of the COVID-19 related school closures, during which 60 percent of children were not engaged in any learning activities. Ensuring that these students return to school and are able to recover the lost learning will be a critical part of the human capital recovery process. As access to public services gradually expands, targeting lagging regions can improve opportunities for the poorest. Location and monetary wellbeing are the most significant factors explaining the inequality in most human-capital related indicators for Ugandan children. Given the strong correlations between education level, poverty status and economic mobility, increasing opportunities for children in poorer and more vulnerable regions could be an important pathway for breaking the cycle of poverty. In addition, more attention will need to be placed on distribution to ensure that expansion is equitable and continues to increase opportunities for those who need it the most, including refugees. Economic transformation will require a fundamental shift in the nature of production – from low investment, informal activities – to higher-capital, more productive employment. In Uganda, the agricultural sector – in which the majority of the population currently works – has the lowest productivity level compared to services and industry. Structural change is integral to economic growth and poverty reduction in Uganda, involving the reallocation of labor from less productive (especially subsistence agriculture) to more productive 33 UGANDA POVERTY ASSESSMENT OVERVIEW sectors. In particular, accelerating wage job creation is key for faster economic transformation and poverty reduction. Households that transition out of agriculture into other sectors and those who migrate from rural to urban areas tend to benefit from increases in consumption, demonstrating the poverty reducing effects of structural change. Therefore, reducing the barriers and costs to migration could enable more vulnerable households and individuals in Uganda to take advantage of opportunities in other sectors and in more economically vibrant places. COVID-19 may have slowed down the pace of structural change in Uganda, reversing the momentum of economic transformation, and calling for policies to revitalize the industry and services sectors. The lockdowns and mobility restrictions imposed to limit the spread of COVID-19 had a disproportionally higher impact on urban areas and those working in the services sector. Therefore, restoring growth in the industry and service sectors will be an important part of the COVID-19 recovery strategy. In addition, providing targeted support to refugee and host community businesses and the self-employed can contribute to the mitigation of shocks while fostering job creation in refugee hosting districts. Finally, Uganda has identified ‘digital transformation’ as one of the key drivers of growth and increasing access to ICT can have poverty reducing benefits as well. The digital sector represents one of the fastest growing sectors in Uganda, with positive spillover effects on other sectors of the economy, the combination of which can play a key role in the post-COVID-19 recovery (World Bank 2020c). Providing affordable and ubiquitous telecommunication services is particularly important to promote technology-based empowerment, which is especially relevant for Uganda’s fast growing, young population. Currently, affordability remains one of the main constraints for Uganda to realize the full potential of digital transformation. However, simulations show that increased competition in the telecommunications sector – and the associated reduction in average ICT prices – as well as the entrance of new users can generate welfare gains with a limited impact on equality. Strengthen household resilience A key component of sustainable poverty and vulnerability reduction in Uganda is addressing the country’s high susceptibility to climate, health, and forced displacement-related shocks and the exposure of the poor and vulnerable population to these shocks. The significant fluctuations in Uganda’s poverty story over the last decade highlights the extent to which ongoing shocks have limited the sustained reduction of poverty in the country. A large portion of Ugandans are clustered around the poverty line and are therefore sensitive to sudden changes in external conditions. As such, mitigating the severity of shocks, increasing the coverage of social protection programs for Ugandans and refugees, and developing better coping mechanisms are essential for buffering against the negative impact of shocks. Expanding safety nets for both Ugandans and refugees is an important strategy to help the vulnerable face shocks without falling (deeper) into poverty. Efforts should be made to ensure that households do not have to resort to coping mechanisms that jeopardize their long-term wellbeing and future prospects, such as reducing food consumption and forgoing investments in the health and education of children. Given the limited fiscal space, it is critical to ensure that the sector’s resources, including the potential expansion of some of the existing programs, are channeled to the 34 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA poor and vulnerable in the neediest geographical areas. This allocation should consider the potential for social protection programs to both address deficiencies in human capital and help mitigate the vulnerability of households to shocks. For example, the Disaster Risk Financing (DRF) pilot – under the Uganda Social Action Fund (NUSAF) 3 project – is an example of a successful adaptive social protection system that could be expanded under this framework. Furthermore, policies and programs should aim to foster inclusion and cohesion between refugees and host communities by maintaining public service provision and investments for both groups, such as the Displacement Crisis Response Mechanism (DCRM).22 Introducing the monitoring refugees in the national statistical systems is an important way to facilitate their inclusion. While social protection programs can play an important role, policies to promote insurance and savings schemes can also contribute to mitigating the negative impacts of shocks. Relying on savings is considered one of the more preferable coping mechanisms for dealing with a shock (as opposed to involuntary reduction in food, for example). Given that the vast majority of Uganda’s labor force (especially the poor and vulnerable) work in the informal sector, traditional social insurance schemes may not be viable and alternative schemes must be designed. For example, policies can provide fiscal incentives to improve the take-up of voluntary savings schemes by informal sector workers. Financing options for farmers should also be more accessible, including the development of insurance schemes for farmers to protect against climate change. That said, limited access and the lack of an enabling environment are key reasons why agriculture finance and insurance remain at sub-optimal levels in Uganda. Increasing the resilience of the agricultural sector is essential to address the higher poverty and vulnerability rates in rural areas. Almost 60 percent of rural Ugandans are vulnerable to poverty compared to just 26 percent of urban Ugandans. Furthermore, the Northern and Eastern regions, which depend mainly on agriculture, have particularly high vulnerability rates at almost two-thirds of the population. Importantly, while idiosyncratic shocks are more prevalent on the national level, covariate shocks such as weather and price shocks, are more important for rural residents who depend on agriculture. Therefore, increasing the resiliency of the agricultural sector could go a long way to reducing the overall levels of vulnerability for rural Ugandans. More broadly speaking, diversifying household incomes and increasing access to non-farm jobs are fundamental to improving resilience and reducing vulnerability to shocks. In order to achieve this, investing more in human capital accumulation, particularly education, is essential. Households with higher levels of education are less likely to work in agriculture, are less vulnerable to shocks, and have better coping mechanisms for dealing with shocks. Investments in education take time to pay off, and thus more immediate strategies should also be employed to help households better cope with shocks in the near term. Nevertheless, for Uganda’s long-term future, economic transformation and increasing education levels are vital for the country to successfully reduce poverty and vulnerability. 22 The DCRM is a new framework to handle displacement-related shocks. It is a pre-planned and pre-financed mechanism for shock response. The process of disbursing resources for public service provision from the DCRM is agreed upon in advance. The government then selects indicators regarding public service provision and monitors them over time. If the indicators drop below threshold levels, the DCRM rapidly and automatically disburses resources. The public service investments financed by DCRM resources are pre-agreed and include the development of schools, water supplies and health care facilities. 35 UGANDA POVERTY ASSESSMENT OVERVIEW References Aker, J. C.; & Mbiti, I. M. (2010). “Mobile Phones and Economic Development in Africa.” Journal of Economic Perspectives 24 (3): 207-232. doi:10.1257/jep.24.3.207. Aker, J. C.; Ksoll, C. & Lybbert, T. J. (2012). “Can Mobile Phones Improve Learning? Evidence from a Field Experiment in Niger.” American Economic Journal: Applied Economics 4 (4): 94-120. doi:10.1257/ app.4.4.94. Elvidge, C.D.; Zhizhin, M.; Ghosh T.; Hsu F.C.; & Taneja J. (2021). Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sensing 13(5), p.922, doi:10.3390/rs13050922. Granguillhome Ochoa, R.; Lach, S.; Masaki, T.; & Rodríguez-Castelán, C. (2022). “Mobile internet adoption in West Africa.” Technology in Society 68: 101845. doi:doi.org/10.1016/j.techsoc.2021.101845. Hasbi, M. & Dubus, A. (2020). “Determinants of mobile broadband use in developing economies: Evidence from Sub-Saharan Africa.” Telecommunications Policy 44 (5). doi:doi.org/10.1016/j. telpol.2020.101944. International Telecommunications Unit (2022). https://www.itu.int/itu-d/sites/statistics/. UNESCO (United Nations Educational, Scientific and Cultural Organisation). Dashboards on the Global Monitoring of School Closures Caused by the COVID-19 Pandemic. World Bank (2016). The Uganda Poverty Assessment Report 2016: Farms, Cities and Good Fortune - Assessing Poverty Reduction in Uganda from 2006 to 2013. World Bank, Washington, DC. World Bank (2020). Uganda Economic Update, 14th Edition, February 2020: Strengthening Social Protection to Reduce Vulnerability and Promote Inclusive Growth. World Bank, Washington, DC. World Bank (2022). Growth, trade, and transformation. Country Economic Memorandum for the Republic of Uganda. World Bank, Washington, DC. World Bank; International Finance Corporation; Multilateral Investment Guarantee Agency (2021). Uganda Systematic Country Diagnostic Update. World Bank, Washington, DC. World Bank and UNHCR. Uganda Refugee High-Frequency Phone Survey on COVID-19 (RHFPS). Uganda Bureau of Statistics. Uganda National Panel Survey (UNPS). Uganda Bureau of Statistics. Uganda National Household Survey (UNHS). Uganda Bureau of Statistics. Uganda High-Frequency Phone Survey on COVID-19 (HFPS). 36 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Annex Main data The Uganda National Household Survey (UNHS) is the main source of data for the calculation of official poverty rates in the country. The UNHS was conducted in 1999/00, 2002/03, 2005/06, 2009/10, 2012/13, 2016/17 and 2019/20. The most recent UNHS samples were designed to allow for the generation of representative estimates at the national level, for urban and rural areas and for the 15 sub-regions of Uganda. The poverty assessment relies on the UNHS to report official poverty and inequality numbers, analyze internal migration, measure vulnerability to poverty, construct the human opportunity index, and simulate the impact of competition in the telecommunications sector on poverty. The latest round of the UNHS in 2019/20 was affected by the COVID-19 pandemic. The field work was interrupted during the lockdown, which effectively fully omitted the second quarter (April–June 2020). The implications of this are discussed in the main text of the report. In particular, the report applied post-stratified population weights in the UNHS 2019/20 to ensure subregional and rural/urban population shares in two subsamples (pre and COVID-19) have the same distribution as a nationally representative yearly survey. The Uganda National Panel Survey (UNPS) is another annual multi-topic household survey that commenced in 2009/10. The UNPS 2009/10 was followed by additional rounds of data collection in 2010/11, 2011/12 and 2013/14, 2015/16, 2017/18, 2018/19 and 2019/20 rounds. Similar to the UNHS, the UNPS collects detailed consumption data. In addition, the UNPS collects detailed and high- quality information on income and agricultural activities. It provides representative estimates at the national, rural-urban and regional levels. The poverty assessment relies on this survey to measure chronic and transient poverty, explore the impact of shocks and the distribution of coping strategies, and finally to analyze the determinants of agricultural development in the country by measuring utilization of fertilizers, improved seeds, and extensions services. The annual Labor Force Survey (LFS) is specifically designed to construct representative labor market indicators. Stand-alone labor force surveys were conducted in 2012, 2016/17, 2017/18 and 2018/19. The sample was nationally representative with three main reporting domains: national, rural/urban, and disaggregated by sex. Where particular indicators had sufficient data, further disaggregation was made by age groups and other characteristics. The poverty assessment relies on the LFS to analyze employment trends and structural changes in the economy. In June 2020, the Uganda Bureau of Statistics (UBOS), with the support from the World Bank, officially launched the High Frequency Phone Survey (HFPS) to track the impacts of the COVID-19 pandemic monthly for a period of 12 months. The survey aimed to recontact the entire sample of households that had been interviewed during the Uganda National Panel Survey (UNPS) 2019/20 round and that had phone numbers for at least one household member or reference individual. The 37 UGANDA POVERTY ASSESSMENT OVERVIEW first round (baseline) of the survey was conducted in June 2020 and interviewed 2,227 households. A subsequent six rounds attempted to reach the same households with the sample size in the latest seventh round of 1,950 observations. The poverty assessment relies on the HFPS to measure impact of COVID-19 on Ugandan population. The World Bank (WB) in collaboration with the Uganda Bureau of Statistics (UBOS) and the United Nations High Commissioner for Refugees (UNHCR) launched and conducted the Refugee High Frequency Phone Survey (RHFPS). The RHFPS tracked the impacts of the pandemic between October 2020 and March 2021. The survey sample includes respondents with active phone numbers that were selected randomly from the Profile Global Registration System (ProGres) of UNHCR, and the refugee household survey carried out by UBOS and the World Bank in 2018. The sample was representative at seven strata constructed as a combination of regions and different countries of origin. In order to reduce the bias related to only interviewing households with phone numbers and non-response, the data from the 2018 representative refugee household survey was used to produce and calibrate the weights for all three rounds of the phone survey. 38 STRENGTHENING RESILIENCE TO ACCELERATE POVERTY REDUCTION IN UGANDA Main concepts and definitions Poverty is measured by comparing welfare measure with some defined threshold called poverty line below which an individual is considered poor. The headcount ratio is the proportion of the population that is classified as poor. The poverty line indicates the minimum level of welfare required for healthy living. Healthy living is usually defined as being able to afford the food covering the minimum required caloric intake and additional non-food items. Updated poverty line is used in this poverty assessment based on the UNHS 2016/17. Depth of poverty or poverty gap indicates how far off an average poor householdis from the poverty line. It captures the mean consumption shortfall relative to the poverty line across the whole population. Poverty severity or squared poverty gap considers the distance separating the poor from the poverty line (the poverty gap) as well as the inequality among the poor. Conceptually, poverty severity puts a higher weight on households/individuals, who are further below the poverty line. The Gini coefficient is a measure of inequality. A Gini coefficient of 0 indicates perfect equality while 1 signifies complete inequality. The welfare measure or welfare aggerate in this study is measured by consumption per adult equivalent as calculated by the Uganda Bureau of Statistics unless stated otherwise. For inequality measures and growth rates, consumption and income aggregates were spatially deflated by regional poverty lines to account for differences in prices across different areas unless stated otherwise. Quintiles are based on consumption per adult equivalent, which was spatially deflated by regional poverty lines unless stated otherwise. Improved sanitation includes access to a flush toilet, a VIP latrine, a covered pit latrine with a slab, an uncovered pit latrine with a slab, and a compost toilet. All access should be unshared with other households. Improved water includes access to piped water, borehole, protected well/spring, gravity flow scheme, rain and bottled water. 39 UGANDA POVERTY ASSESSMENT OVERVIEW For more information please visit www.worldbank.org/uganda Join discussion on ugandainfo@worldbank.org http://www.facebook.com/worldbankafrica http://www.twitter.com/worldbankafrica http://www.youtube.com/worldbank 40