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Kazakhstan Poverty and Equity Assessment 2024 KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 4 Acknowledgements This poverty and equity assessment was prepared jointly by the Agency for Strategic Planning and Reform (ASPIR) of the Republic of Kazakhstan and the World Bank’s Poverty and Equity team in the Prosperity Practice Group. The report brings together a collection of poverty and inequality analytics produced in the past year, including evidence on the impact of the fiscal system in Kazakhstan, human capital accumulation and the quality of education, and the increased vulnerability due to climate change. The report was prepared by Metin Nebiler (Task Team Leader, World Bank) and Ramil Tokhtiyev (Director of the Social Sector Department, ASPIR), with a core team including Vladimir Kolchin (Economist), Ivan Torre (Senior Economist), Akmaral Zhakiyenova (Deputy Director of the Social Sector Department, ASPIR), Natalya Belonosova (Director of Labor Statistics and Living Standards Department, BNS ASPIR), Kyunglin Park (Consultant), Karina Margarita Acevedo Gonzalez (Consultant), and Arthur Hrast Essenfelder (Consultant). The team worked under the guidance of Zhandos Shaimardanov (Chairman, ASPIR), Syrymbet Kaskeyev (Deputy Chairman, ASPIR), Andrei Mikhnev (Country Manager, World Bank), Ambar Narayan (Practice Manager, World Bank), Obert Pimhidzai (Lead Economist), and David Knight (EFI Program Leader). An important element of the collaborative process in preparing this report involved having detailed discussions about the topics that are covered in the report. We would like to share our appreciation for the inputs from and discussions with colleagues from different ministries in Kazakhstan. We thank the team of Economic Research Institute (ERI) worked under the guidance of Adil Kusmanov (Deputy Chairman of the Management Board, JSC “Economic Research Institute”), Bauyrzhan Temirbayev (Director of the Center for Macroeconomic Analysis and Forecasting, JSC “Economic Research Institute”) and Meruert Zhumakhan (Senior Expert of the Center for Fiscal Policy, JSC “Economic Research Institute”) for insightful conversations on the “deep dive” chapters. In all of these areas, the report presents recommendations and conclusions that have been exclusively formulated by the World Bank. Numerous colleagues made this report possible. The team would like to thank peer reviewers Javier Baez, Moritz Meyer, Elizaveta Perova, Yeon Soo Kim, and Sjamsu Rahardja for their valuable suggestions that helped improve this report. We thank Natasha Sharma and Azamat Agaidarov for their comments and insightful discussions for the macroeconomic trends in Kazakhstan. The report was edited by Janine Thorne (Consultant). KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 5 Abbreviations and Acronyms D-index dissimilarity index GDP gross domestic product GE general entropy [index] HCI Human Capital Index HIES Household Income and Expenditure Survey HOI Human Opportunity Index LCU local currency units OECD Organisation for Economic Co-operation and Development PIAAC Programme for the International Assessment of Adult Competencies PISA Programme for International Student Assessment PIT personal income tax PPP purchasing power parity UNESCO United Nations Educational, Scientific and Cultural Organization KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 6 Contents Executive summary 9 Policy recommendations 12 Chapter 1. Poverty trends 13 Economic performance has been strong, but its pace has been declining over time 14 Poverty declined significantly, but the pace of the reduction is slowing 16 Poverty fell in all regions 18 Chapter 2. Poverty profile 21 The poverty profile has changed over time 22 Despite progress on human development and access to services, urban-rural disparities remain 22 Inequality is low but has been rising in recent years 24 Declining “shared prosperity” 26 Chapter 3. Drivers of poverty reduction 31 Economic growth driving poverty reduction 32 Understanding household income dynamics 32 Regional poverty dynamics 36 Chapter 4. Middle class, vulnerability, and chronic poverty 39 Chronic poverty patterns 40 Identifying vulnerable and middle-class households 44 Chapter 5. Fiscal policy 47 The fiscal impact on poverty and inequality is limited 48 Enhancing efficiency through better targeting 52 Chapter 6. Human capital 55 Quality of education 58 Human Opportunity Index and school performance 58 Quality of higher education 62 Preparing for digitalization 62 Chapter 7. Climate shocks 67 Vulnerability to climate change 68 High risk of floods 70 Drought in the northwest and southeast 70 Air pollution and heat waves 72 Chapter 8. Conclusion 73 Policy recommendations 74 References 77 List of figures Figure 1.1. Employment trends and sectoral distribution 15 Figure 1.2. Growth trends, 2000–22 15 Figure 1.3. Composition of GDP and productivity 15 Figure 1.4. GDP per capita and household consumption, 2000–22 17 Figure 1.5. Vulnerabilities in consumption growth 17 Figure 1.6. Poverty rate and gap, 2006–21 17 Figure 1.7. Poverty rates and distribution, 2006–21 19 KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 7 Figure 1.8. Regional convergence of poverty incidence 19 Figure 2.1. Demographic poverty profile, 2006 and 2021 23 Figure 2.2. Share of the poor population, by region and urban-rural distribution of poor, 23 Figure 2.3. Map of district-level per capita consumption, 2018 25 Figure 2.4. Distribution of access to services 25 Figure 2.5. Gini coefficient, 2006–21 25 Figure 2.6. Relative consumption patterns, 2006–21 27 Figure 2.7. Gini coefficient, peer comparison 27 Figure 2.8. Generalized entropy index 27 Figure 2.9. Shared prosperity and shared prosperity premium, 2006–21 29 Figure 3.1. Role of growth and redistribution in poverty reduction 33 Figure 3.2. Paes de Barros decomposition of changes in poverty 33 Figure 3.3. Income trends, 2006–21 33 Figure 3.4. Labor and self-employment income by decile, 2006–21 35 Figure 3.5. Agriculture income by decile, 2006–21 35 Figure 3.6. Social assistance and pensions by decile, 2006–21 35 Figure 3.7. Poverty reduction by area of residence, 2006–21 37 Figure 3.8. Poverty reduction by region, 2006–21 37 Figure 3.9. Poverty reduction by level of education, 2006–21 37 Figure 4.1. Lower chronic and transitory poverty, 2011–13 and 2019–21 41 Figure 4.2. Regional differences in chronic and transient poverty, 2011–13 and 2019–21 41 Figure 4.3. Stratification of poor, vulnerable, and middle-class people, 2006–21 43 Figure 5.1. Impact of fiscal policy on inequality, 2021 49 Figure 5.2. Impact of fiscal policy on Gini coefficient, across countries 49 Figure 5.3. Impact of fiscal policy on poverty, 2021 51 Figure 5.4. Net fiscal position, as a share of market income plus pensions 51 Figure 5.5. Marginal effect of fiscal policies on poverty 53 Figure 5.6. Benefit incidence of social assistance transfers by decile, 2021 53 Figure 6.1. Human capital accumulation in Kazakhstan 57 Figure 6.2. PISA scores, Kazakhstan and OECD average, 2009–22 57 Figure 6.3. PISA scores by income decile, 2009 and 2022 59 Figure 6.4. Average PISA test scores and poverty rates, by region 59 Figure 6.5. Relative PISA proficiency scores, 2009–22 59 Figure 6.6. Human Opportunity Index and coverage, 2012 and 2022 61 Figure 6.7. Variation explained by circumstance, 2022 61 Figure 6.8. Aggregate quality score of universities and country income level 61 Figure 6.9. Adult skill proficiency and quality of higher education 63 Figure 6.10. Gross graduation rate for tertiary education among peers 63 Figure 6.11. Self-reported digital skills, regional comparison 65 Figure 7.1. Climate risks, regional comparison 69 Figure 7.2. Impact of floods 71 Figure 7.3. Drought exposure of agricultural land 71 Figure 7.4. Impact of heat waves and air pollution 72 Figure B1.1.1. Poverty in peer countries, 2021 20 Figure B1.1.2. Poverty reduction among peer countries, 2006–21 20 Figure B2.1.1. Female labor force participation rate, Europe and Central Asia region, 2021 30 Figure B4.3.1. Changes in household size and composition and the risk of poverty 45 Figure B4.3.2. Characteristics of breadwinners and the risk of poverty 45 KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 8 List of tables Table 4.1. High dependency and lower human capital and employment among the chronically poor 43 Table 6.1. Determinants of the acquisition of digital skills 64 Table 6.2. Wage premiums for digital skills 66 Table 8.1. Proposed priority of policies 76 Table B3.1.1. Change in employment and real wages, 2014–16 34 List of boxes Box 1.1. International poverty comparison 20 Box 2.1. Gender gaps in Kazakhstan 29 Box 3.1. Reversal in the transmission channel of poverty between 2014 and 2016 34 Box 3.2. Stagnating urbanization 38 Box 4.1. Understanding poverty dynamics 42 Box 4.2. Identifying the middle class 44 Box 4.3. Why households fall into poverty 45 Box 5.1. How fiscal policy alleviates poverty and inequality 48 Box 6.1. The Human Opportunity Index 60 Box 7.1. The Disaster Risk Model 69 Methodological explanations This report uses a different methodology than the official poverty methodology calculated by the Bureau of National Statistics. In Kazakhstan, poverty rates declined irrespective of the methodology used to estimate them. For consistency with the government’s definition of poverty, this report uses poverty estimates derived from the national poverty measurement methodology, with two changes: 1. The methodology has been revised to obtain a constant absolute poverty line over time. The absolute poverty line is set at the 2021 national subsistence minimum in Kazakhstan. To preserve its real value at US$7.6 per person per day, the line is adjusted for inflation every year. The constant absolute poverty line provides a fixed standard for measuring poverty across different regions and time periods, allowing for direct comparisons of poverty levels over time. 2. The consumption aggregate used in this report does not include actual rent. The consumption aggregate should ideally include actual rent for tenants and imputed rent for homeowners. However, it is not possible to estimate imputed rent in Kazakhstan since the rent market is very small due to high homeownership. Therefore, rent is excluded from the consumption aggregate. 3. The consumption aggregate used in this report does not include durables. Durable goods are typically excluded from the consumption aggregate because their value is consumed over a long period rather than entirely in the period of purchase. Instead, an imputed value based on their annualized use or depreciation is often included to better reflect the flow of services derived from these goods in welfare analysis. However, the flow of services cannot be estimated due to absence of information on purchase history and the age of durable goods. The poverty rate based on the national poverty methodology is measured at 5.2 percent in 2021. The revised methodology for the national poverty rate is slightly higher, at 8.5 percent when using the same poverty line. The poverty rate according to the revised methodology remains significantly higher, mainly due to exclusion of expenditures on durables and actual rent. 9 EXECUTIVE SUMMARY KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 10 Executive summary Poverty reduction had been strong, but the pace has slowed since 2013. The economy of Kazakhstan has performed strongly since the turn of the century, growing at an annual rate of 4.7 percent from 2006 to 2021. Sustained economic and productivity growth brought higher incomes and a period of prosperity. Between 2006 and 2021, the per capita gross domestic product (GDP) (in constant LCU) rose from 548,912 to 791,285 tenge, and household consumption per capita (in constant LCU) rose from 279,891 to 500,529 tenge1. As poverty fell and living standards rose, the country transitioned from lower-middle-income to upper-middle-income status. The reduction in poverty over the past 15 years has been remarkable. About 5.9 million people were lifted out of poverty between 2006 and 2021, with the poverty rate declining from 49.5 to 8.5 percent. As living standards rose, the poverty gap index, which measures the average distance of poor people from the poverty line, also fell. The reduction in poverty occurred in three distinct phases: • Phase 1, rapid decline: From 2006 to 2013, poverty fell by 78 percent, as the poverty rate decreased from 49.5 to 11.1 percent. • Phase 2, reversal: From 2014 to 2016, an economic downturn caused the incidence of poverty to rise to 20.2 percent (an 82 percent increase). • Phase 3, slow decline: After 2016, poverty fell more slowly. It took Kazakhstan six years to undo the damage of the economic downturn and reduce poverty to its 2013 level (11.1 percent). Poverty continued to fall, reaching 8.5 percent in 2021. Disparities persist, although poverty fell significantly in all regions of Kazakhstan. Poverty rates in rural areas fell from 60.4 to 11.4 percent, whereas urban poverty decreased from 41.2 to 6.6 percent between 2006 and 2021. However, poverty remains relatively high in the Turkistan region, whose share of the poor population grew from 14.4 to 24 percent in this period. The demographic profile of poverty has also changed, as poor people are now more likely to be younger, less educated, and have larger families. All demographic groups benefited from the general decrease in poverty between 2006 and 2021. Child poverty rates dropped from 62 to 13 percent, yet the share of children in the poor population grew from 27 to 40 percent. Likewise, poverty among households with three or more children fell from 82 to 19 percent, but they now comprise about 44 percent of poor people. Chronic poverty, defined as consistent poverty over time, also decreased significantly, with rates of chronic poverty dropping by 37 percent (from 8.4 to 5.3 percent) between 2011–13 and 2019–21. There are regional variations in both chronic and transient poverty. Also, the risk of falling into poverty is higher for households that are growing larger, whereas those whose breadwinners are better educated have a lower risk of poverty. The main driver of poverty reduction has been consumption growth. This was due largely to higher labor incomes, as both wages and income from self-employment grew. Government transfers, especially pensions, were also vital: they doubled in value by 2021 in addition to expansion in the social assistance system during the pandemic. Income inequality has increased since 2016 but remains low relative to other upper-middle-income countries. The Gini 1 World Development Indicators. For household consumption, Households and NPISHs Final consumption expenditure (constant LCU) is used. In current prices, the GDP per capita increased from 667 211,6 tenge in 2006 to 5 284 726,7 tenge in 2022. EXECUTIVE SUMMARY 11 coefficient, which measures inequality, decreased from 27.0 in 2006 to 24.3 in 2015, and then rose to 26.4 in 20212. The welfare of high-income households increased relatively faster than that of other income groups, with the richest 10 percent of the consumption distribution having three times more than the poorest 10 percent in 2021 (up from 2.9 in 2015). Fiscal policy can help to build resilience and reduce vulnerabilities. Poverty trends in Kazakhstan have closely mirrored economic growth patterns, and fiscal policy can potentially build the resilience of poor and vulnerable people. Poverty levels decreased in periods of strong economic growth but stagnated or rose in times of weak or negative economic growth. It took six years for poverty levels to fall their pre-2014 levels after the economic downturn in 2015–16. Although fiscal policy reduces poverty (when pensions are considered as government transfers), it has the potential to address vulnerabilities more effectively. Currently, Kazakhstan’s social assistance programs3 are largely categorical, supporting specific groups rather than directly targeting the most vulnerable people. In 2021, only 31 percent of the social assistance transfers reached the poorest 10 percent of people. Fiscal intervention does reduce income inequality, causing a 19 percent reduction in the Gini coefficient. However, its redistributive efficiency can potentially be increased by focusing on more progressive tax collection and more targeted social transfers. The middle class expanded rapidly until 2013, but its growth has stagnated. The middle class, those who are neither poor nor vulnerable, grew to 67 percent of the population, up from 26 percent in 2006. This was driven by a reduction in poverty rates, but the proportion of vulnerable households was virtually unchanged. Since 2013, however, the share of the middle class has stagnated, as the structural transformation stalled and productivity failed to grow. The economy remains heavily dependent on commodity exports. Although efforts have been made to diversify the economy, progress has been limited. Human capital accumulation is key for accelerating growth and structural transformation to eradicate poverty and expand the middle class. Educational attainment significantly affects people’s economic mobility. Although access to education in Kazakhstan is universal, its quality and equity remain a concern. The Human Capital Index suggests that children in Kazakhstan achieve only 53–64 percent of their productivity potential, with significant disparities between income groups. Kazakh students score below the average for the Organization for Economic Co-operation and Development (OECD) in the Programme for International Student Assessment (PISA), which highlights the need to improve the quality of education. Moreover, household characteristics play a significant role in the quality of education, with household wealth and the language spoken at home being the most significant predictors of a student’s basic proficiency across all subjects. Climate-related shocks pose a growing risk, especially for poor and vulnerable people. Investments in enhancing the resilience of vulnerable households are crucial. According to the World Risk Index, Kazakhstan’s exposure to climate risks is below the average for the Europe and Central Asia region, but its vulnerability to climate risks is above the regional average. This vulnerability is exacerbated by the country’s underdeveloped coping and adaptive capacities. Climate change projections suggest that natural disasters will become more frequent and severe, disproportionately affecting rural people who rely on agriculture for their livelihoods. 2 Measured by using the methodology in the report and results may be different compared to the official statistics published by the Bureau of National Statistics due to different methodologies applied. 3 The definition of social assistance used in the report is as follow: Social assistance programs are non-contributory transfers in cash or in-kind and are usually targeted at the poor and vulnerable. Please see the Annex for more detailed categorization of government programs. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 12 Policy recommendations To sustain progress and achieve inclusive growth, Kazakhstan would need comprehensive policy measures to improve the effectiveness of fiscal system in protecting poor people, enhance human capital, improve the quality of education, and build resilience against economic and climate-related shocks. The redistributive performance of the fiscal system could be improved. To increase revenue collection, Kazakhstan could focus on progressive taxation, especially property and personal income taxes, rather than indirect taxes, which are regressive and disproportionately hurt low-income households. Tax policies could be used to raise revenue to deliver wider and more targeted social transfers to poorer households. Moreover, enhancing the efficiency of social spending by targeting transfers could considerably improve the effectiveness of its poverty alleviation efforts. Kazakhstan could invest in the quality of education to ensure that all children, regardless of their socioeconomic background, receive a quality education. Strengthening the accumulation of human capital could help the country achieve sustained, inclusive economic growth and prepare its workforce for the highly skilled, digitalization-driven jobs of the future. By reducing disparities in educational outcomes between rural and urban areas and across income levels, policymakers could help level the playfield for all children. They could also design targeted programs to improve literacy and numeracy skills, particularly in regions where educational attainment is lower. Building resilience against climate shocks is vital to protect people’s well-being and assets. As one of the most carbon- intensive economies in the world, Kazakhstan could promote renewable energy sources to mitigate its greenhouse gas emissions. In parallel, it could also strengthen infrastructure and community resilience and promote insurance against climate events. Implementing these policy recommendations could help Kazakhstan build on its progress, address current challenges, and ensure sustainable, inclusive growth. Enhanced human capital, inclusive economic policies, efficient public spending, and climate resilience are critical for achieving long-term development goals and improving the well-being of all people in Kazakhstan. 13 Chapter 1. POVERTY TRENDS KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 14 Chapter 1. Poverty trends Kazakhstan has made remarkable progress in reducing poverty and enhancing economic growth over the past two decades. However, growth and poverty reduction have slowed since 2013. Economic performance has been strong, but its pace has been declining over time The economy of Kazakhstan has grown strongly since 2000, at annual rate of 4.7 percent from 2000 to 2023. Between 2000 and 2007, growth was particularly rapid, at 9.7 percent and much faster than the global average. This period of economic prosperity was driven by a wave of market-oriented reforms, extensive extraction of resources, and substantial foreign direct investment. It facilitated the country’s transition from lower-middle-income to upper-middle-income status, with higher living standards and much lower poverty rates (World Bank 2018). Employment also grew rapidly, mirroring the increase in the labor force, as the population grew by 28 percent (Figure 1.1, panel a). Most employment gains were in the services sector, reinforcing the shift in employment from agriculture to services. Employment in the mining sector, construction, and industry saw little growth (Figure 1.1, panel b). Overall, about 857,000 jobs were created between 2010 and 2022, mainly in education (326,000), wholesale and trade (274,000), health (191,000), industry (172,000) and other service activities (228,000); in contrast, the agriculture sector lost around 1,186,000 jobs in the same period. The new employment opportunities were mainly in urban areas, which accounted for 85 percent of the overall jobs created since 2006. Despite this progress, the rate of growth has slowed gradually (Figure 1.2), as the country navigated economic setbacks. These shocks included the global financial crisis in 2008–09, the 2014–16 slowdown caused by falling commodity prices, and the COVID-19 pandemic in 2020. Each setback caused a drop in the growth of the per capita gross domestic product (GDP), from 9.7 percent in 2000–07 to 4.4 percent in 2010–14, and then to 2.9 percent in 2017–19 (Gill and others 2023). The COVID-19 pandemic had the largest negative impact, with the economy shrinking by 3.8 percent in 2020. Growth in GDP per capita rebounded in 2021 to 2.95 percent but declined again by 0.067 percent in 2022. Overall, the economy grew by 1.4 percent in 2021–22. The structural composition of the economy has remained relatively unchanged since the early 2000s (Figure 1.3, panel a). Mining has consistently contributed around 14 percent of GDP, with commodities comprising over 80 percent of total goods exports. Manufacturing’s share in the GDP has likewise been stable at 14 percent since 2004, whereas agriculture’s share decreased from 8.2 to 5.1 percent between 2000 and 2022. In contrast, the share of the services sector grew by five percentage points from 48 to 53 percent in 2022. Kazakhstan neared high-income status in 2014 but has been unable to sustain the progress of the early 2000s. Productivity growth, which was robust until 2008, has declined and even turned negative (Figure 1.3, panel b) (Agaidarov and others 2024) because of an overreliance on extractive industries, limited economic diversification, and the significant role of the state. To achieve sustainable growth and attain high-income status, Kazakhstan would need to overcome these structural challenges. Household consumption rose but remains volatile in line with the economic cycle. Robust economic growth brought significant increases in both per capita GDP and household consumption. GDP per CHAPTER 1. POVERTY TRENDS 15 Figure 1.1. Employment trends and sectoral distribution a. Labor force and employment, 2006–22 b. Employment by sector, 2010–22 10 000 100% 9 500 80% 9 000 60% 8 500 40% 8 000 20% 7 500 0% 7 000 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Agriculture Mining and quarrying Industry Labor force Employment Construction Services Source: Bureau of National Statistics of Kazakhstan. Figure 1.2. Growth trends, 2000–22 15 early 2000s 10 9,7 post - post-global economic post - 5 crisis downturn pandemic 4,4 2,9 0 1,4 -0,8 -0,3 15 -5 early 2000s 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 10 9,7 post - post-global economic post - 5 crisis downturn pandemic 4,4 2,9 0 1,4 Source: World Bank, World Development Indicators. -0,3 Note: GDP per capita growth rates are presented. -0,8 -5 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Figure 1.3. Composition of GDP and productivity 100% 12 10 80% a. Structure of GDP by the production method, 2000–22 8 b. Growth in total factor productivity, 1999–2021 60% 6 4 40% 2 100% 12 0 10 20% 80% -2 8 0% -4 6 60% -6 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 4 2011 1999 2012 2013 2015 2017 2021 2001 2016 2018 2019 2010 2014 2002 2003 2005 2006 2007 2009 2020 2000 2004 2008 40% -8 2 0 20% Agriculture Mining and quarrying -2 Industry Services Construction -4 Actual (TFP growth, y-o-y) Trend 0% -6 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2011 2021 1999 2012 2013 2015 2016 2017 2001 2019 2010 2014 2018 2002 2003 2005 2006 2007 2008 2009 2020 2000 2004 -8 Agriculture Mining and quarrying Industry Services Construction Actual (TFP growth, y-o-y) Trend Source: Bureau of National Statistics of Kazakhstan Note: GDP = gross domestic product; TFP = total factor productivity; y-o-y = year-on-year. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 16 capita in constant local currency units (LCU) increased significantly in real terms from 548,912to 791,000 tenge between 2006 and 2021, whereas household consumption rose from 279,891 to 500,529 tenge4 (Figure 1.4). Between 2006 and 2013 GDP per capita and household consumption increased by 31 and 63 percent respectively. However, progress has plateaued since 2013, with GDP per capita and household consumption realizing only a modest 10 percent overall increase between 2013 and 2021. The robust economic expansion brought strong increases in household consumption. Average consumption increased from 40,264 tenge to 60,960 tenge per month per capita in 2021 prices, with the highest growth (about 7 percent a year) between 2006 and 2013. This growth in consumption was significantly higher among the poorest 10 percent of people (Figure 1.5, panel a). However, consumption growth was reversed during the economic downturn in 2013–16. Although all households saw negative consumption growth, the reduction was smaller among the richest households. Consumption growth rebounded between 2016 and 2021 for all deciles but remained significantly lower than during the first period. The pattern of consumption growth among the poorest 40 percent of people highlights certain vulnerabilities. Consumption growth has fluctuated in line with the volatility in economic growth. The poorest 40 percent of people are very vulnerable to economic shocks, and their consumption growth rates fell to or even below zero during setbacks such as the 2009 financial crisis, the 2013–16 economic downturn, and the COVID-19 pandemic (Figure 1.5, panel b). Household consumption increased in times of strong economic growth but stagnated or fell in periods of weak or negative growth. Poverty declined significantly, but the pace of the reduction is slowing Poverty declined sharply from 2006 to 2013, driven by robust economic growth. However, poverty reduction has plateaued (and poverty even increased in 2013–16) as growth faltered. For continued progress in poverty reduction, Kazakhstan would need to protect poor and vulnerable households by building their resilience against economic shocks. The progress in reducing poverty over the last 15 years mirrored the trends in economic growth. As GDP per capita (in constant 2015 international dollars) increased, the incidence of poverty also declined rapidly, with the poverty rate dropping from 49.5 percent in 2006 to 8.5 percent in 2021. About 5.9 million people moved out of poverty, and the total number of poor people fell from 7.5 million to 1.6 million. This achievement is significant even by international standards (Box 1.1). Poverty rates declined irrespective of the methodology used to estimate them. For consistency with the government’s definition of poverty, this report uses poverty estimates derived from the national poverty measurement methodology, with two changes: i. The methodology has been revised to obtain a constant absolute poverty line over time. The absolute poverty line is set at the 2021 national subsistence minimum in Kazakhstan. To preserve its real value at US$7.6 per person per day, the line is adjusted for inflation every year. ii. The consumption aggregate used in this report does not include actual rent and durables. This reduction in poverty occurred in three distinct phases (Figure 1.6, panel a): Phase 1, rapid decline: From 2006 to 2013, poverty fell by 78 percent, as the poverty rate decreased from 49.5 to 11.1 percent. Phase 2, reversal: From 2013 to 2016, an economic downturn caused the incidence of poverty to rise to 20.2 percent (an 82 percent increase). 4 World Development Indicators. For household consumption, Households and NPISHs Final consumption expenditure (constant LCU) is used. In current prices, the GDP per capita increased from 667 211,6 tenge in 2006 to 5 284 726,7 tenge in 2022. CHAPTER 1. POVERTY TRENDS 17 Figure 1.4. GDP per capita and household consumption, 2000–22 900 000 800 000 718 864 791 285 700 000 600 000 548 912 500 000 457 552 500 529 400 000 300 000 311 409 200 000 279 891 100 000 162 615 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Households and NPISHs Final consumption expenditure (constant LCU) GDP per capita (constant LCU) Source: World Bank staff estimates based on World Bank national accounts data archives, OECD National Accounts, and the IMF WEO database. Note: GDP = gross domestic product; LCU = local currency units; NPISH = nonprofit institutions serving households. Figure 1.5. Vulnerabilities in consumption growth a. Consumption growth by percentile, 2006–21 b. Consumption growth, 2007–21 .1 .075 .15 .05 .1 growth rate .025 .05 0 0 -.025 -.05 -.05 -.1 0 20 40 60 80 100 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 percentile incidence_2006_2013 incidence_2016_2021 incidence_2013_2016 growth_bottom_40 growth_all Source: World Bank estimates, based on the Household Income and Expenditure Survey (HIES). Figure 1.6. Poverty rate and gap, 2006–21 a. Poverty rate b. Poverty gap index 60% 16,0% 49,5% 50% 14,0% 13,4% 12,0% 40% 10,0% 30% 8,0% 20,2% 20% 6,0% 3,6% 11,3% 4,0% 10% 11,1% 8,5% 1,8% 2,0% 1,3% 0% 0,0% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Source: World Bank estimates, based on the HIES. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 18 Phase 3, slow decline: After 2016, poverty fell more slowly. It took Kazakhstan six years to undo the damage of the economic downturn and reduce poverty to its 2013 level (11.1 percent). Poverty continued to fall, reaching 8.5 percent in 2021, despite the COVID-19 pandemic and the resultant (temporary) job losses. The poverty gap index fell dramatically from 11.9 to 1.1 percent from 2006 to 2021 (Figure 1.6, panel b). Under an ideal scenario of perfectly targeted assistance, the monetary equivalent required to lift all poor people over the poverty line was about 2.2 percent of GDP in 2006, or 225.1 billion tenge. By 2021 this had fallen to only 81 billion tenge, or 0.12 percent of GDP and 0.64 percent of central government revenue. This suggests that, under an optimal targeting scenario, elevating all poor people above the poverty threshold would have needed only minimal additional spending of 0.12 percent of GDP in 2021. The poverty gap index quantifies the average distance of poor people from the poverty line, reflecting both the depth of poverty and its incidence. It is calculated as the mean shortfall in income or consumption from the poverty line (counting nonpoor people as having zero shortfall), expressed as a percentage of the poverty line. Poverty fell in all regions All regions benefited from lower poverty, but the Turkistan region fares relatively worse. Poverty declined significantly in both urban and rural areas, and across all regions (Figure 1.7). By 2021 the urban poverty rate had fallen from 41.2 to 6.6 percent, and rural poverty had fallen from 60.4 to 11.4 percent. Thus, despite the sharp fall in poverty rates, higher poverty persisted in rural areas. Among regions, the largest reductions in poverty were in the Kyzylorda (from 74.4 to 13.1 percent), Atyrau (from 64.1 to 6.3 percent), and Jambyl regions (from 58.1 to 9.0 percent). The regions with the largest populations, Almaty and Turkistan, also saw the largest decline in poverty numbers between 2006 and 2021 (Figure 1.7, panels b and d). Turkistan is the most populous region, with 3.1 million people in 2021. The Turkistan region continued to host most of the poor population, with its share of poor people increasing from 14.4 percent in 2006 to 24 percent in 2021, despite the overall decrease in poverty numbers. In contrast, regions such as North Kazakhstan, Atyrau, West Kazakhstan, and Kostanay, whose populations range from 250,000 to 400,000, have fewer than 50,000 poor people each. Since poverty declined across the regions at a similar pace, the absolute gaps in poverty narrowed but the relative gaps remained unchanged. Some regional convergence in the incidence of poverty (the poverty rate) can be observed (Figure 1.8, panel a). For instance, Astana, which had the lowest poverty rate in 2006 at 19.8 percent, saw its poverty rate fall to 4.7 percent in 2021—a 15 percentage point decrease. In contrast, Kyzylorda, which had the highest initial poverty rate at 74.4 percent, saw the rate fall to 13.1 percent in 2021, a reduction of about 61 percentage points. However, the convergence is much weaker when the relative reduction in poverty incidence is assessed. Despite poverty rates across all regions falling by about 80 percent, this did little to narrow the regional disparities in poverty levels (Figure 1.8, panel b). Thus, although Kazakhstan has made significant strides in reducing poverty nationwide, the effect on regional poverty disparities has been minimal. CHAPTER 1. POVERTY TRENDS 19 Figure 1.7. Poverty rates and distribution, 2006–21 a. Poverty headcount rate, by area of residence b. Poverty headcount rate by region 70% 2006 2021 74,4% 64,1% 80% 58,3% 58,1% 56,8% 55,3% 60% 53,7% 60,4% 70% 52,5% 49,5% 48,8% 48,5% 54,3% 50% 60% 39,6% 37,7% 34,0% 50% 40% 41,3% 40% 19,8% 13,6% 30% 30% 13,1% 27,7% 12,4% 12,3% 12,0% 9,0% 8,2% 8,0% 7,5% 20% 6,6% 6,6% 6,3% 5,5% 5,6% 5,1% 4,7% 20% 15,9% 11,4% 10% 10% 14,6% 0% Mangystau 7,2% Karaganda Kostanay Pavlodar Astana_city West_Kaz Turkistan Aktobe North_Kaz 6,6% Atyrau Jambyl Almaty_city Kyzylorda Almaty East_Kaz Akmola 0% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 rural urban c. Poverty reduction, by area of residence d. Poverty reduction by region 7,5 1 200 1 083 8,0 Тысячи 911 7,0 1 000 796 Миллионы 5,9 696 6,0 800 661 582 566 5,0 549 480 470 600 460 457 441 436 3,9 397 387 369 361 353 348 3,6 337 335 308 4,0 303 293 261 400 110 259 253 3,1 2,9 203 49 203 86 117 109 3,0 115 112 110 110 107 102 100 200 54 55 44 44 42 41 2,0 1,6 0,9 0 0,7 Mangystau Karaganda Kostanay Pavlodar Astana_city West_Kaz Turkistan Aktobe North_Kaz Atyrau 1,0 Jambyl Almaty_city Kyzylorda Almaty East_Kaz Akmola 0,0 All rural urban 2006 2021 Poverty reduction 2006 2021 Poverty reduction Source: World Bank estimates, based on the HIES. Figure 1.8. Regional convergence of poverty incidence a. Absolute reduction in poverty b. Relative reduction in poverty Poverty rate in 2006 Poverty rate in 2006 0% 0% 0,0% 20,0% 40,0% 60,0% 80,0% -10%0% 20% 40% 60% 80% p er c en ta g e p o i n t r ed u c ti o n -10% -20% -20% -30% percent reduction -40% -30% -50% -40% -60% -50% -70% -80% -60% -90% -70% -100% Source: World Bank estimates, based on the HIES. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 20 Box 1.1. International poverty comparison The World Bank regularly produces internationally comparable estimates of poverty as part of its mandate. Measured in terms of a single global standard, the resulting country, regional, and global poverty statistics are used to monitor progress towards development goals set by the United Nations, the World Bank, and other partners. Poverty measured at the international poverty line of US$2.15 a day per capita in 2017 purchasing power parity (PPP) is used to track progress toward meeting the World Bank target of reducing the share of people living in extreme poverty to less than 3 percent by 2030. The Bank also reports global and regional poverty estimates at two other poverty lines, US$3.65/day per capita for lower-middle-income countries and US$6.85/day per capita for upper-middle-income countries (both in 2017 PPP). As an upper-middle-income country, the two lower poverty lines are not relevant for Kazakhstan; on both measures, its poverty rate is close to zero. The most relevant international poverty line for Kazakhstan is US$6.85 per person per day in 2017 PPP. After accounting for differences in the cost of living and inflation, this was equivalent to 33,563 tenge per person per month in 2021. Figure B1.1.1. Poverty in peer countries, 2021 Figure B1.1.2. Poverty reduction among peer countries, 2006–21 70% 180 55% Russian Federation (2006-2021) Миллионы 61% North Macedonia (2009-2019) Upper Middle Income High income 60% 160 140 39% 50% Kazakhstan (2006-2022) 33% 120 Costa Rica (2006-2021) 28% Indonesia (2006-2021) Colombia (2006-2021) Moldova (2012-2020) 40% Malaysia (2006-2018) Romania (2006-2020) Uruguay (2006-2021) 100 Georgia (2006-2021) Türkiye (2006-2019) Mexico (2006-2020) Croatia (2009-2020) 19% Serbia (2012-2020) 30% 80 Brazil (2006-2021) Chile (2006-2020) 14% 14% 13% 11% 10% 20% 60 8% 8% 7% 40 4% 3% 2% 10% 20 0% 0 North Macedonia Kazakhstan Russian Federation Moldova Costa Rica Indonesia Brazil Croatia Uruguay Georgia Colombia Romania Mexico Serbia Malaysia Türkiye Chile 0% -10% -20% -12% -30% -19% -24% -40% -31% -34% -50% -42% -60% -51% -53% Upper Middle Income High income -70% -59% - 59% -66% -80% - 70% -90% -78% -79% - 76% - 73% -85% Poverty rate 2022 Number of poor Source: Poverty and Inequality Platform Database, World Bank, Washington, DC (accessed May 2024), https://pip.worldbank.org/home. After its significant progress in reducing poverty, the rate of poverty in Kazakhstan was moderate relative to its upper-middle-income peers but higher than its high-income peers (Figure B1.1.1). Its 2021 poverty rate was lower than that of Indonesia (61 percent), Georgia (55 percent), Colombia (39 percent), and Mexico (33 percent), but significantly higher than in Malaysia (3 percent) and the Russian Federation (4 percent). Similarly, fewer people were poor, at 2 million people in 2021, which is relatively less than in other upper-middle-income countries. Moreover, the reduction in poverty in Kazakhstan was among the highest in upper-middle-income and high-income countries (Figure B1.1.2). Its 79 percent reduction in poverty between 2006 and 2021 was comparable to top performers like Malaysia and Russia. Source: World Bank. 21 Chapter 2. POVERTY PROFILE KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 22 Chapter 2. Poverty profile The poverty profile has changed over time Between 2006 and 2021, the demographics of poverty changed considerably. Poor people are more likely to be younger, with larger families and lower levels of education. Moreover, there are pockets of poverty, particularly in the Turkistan region. Poverty levels for all demographic groups fell between 2006 to 2021, regardless of their household and individual characteristics, but child poverty remains a significant risk. Kazakhstan’s demographic structure is changing, with the share of elderly people and young children increasing. This shift is mirrored in the poverty demographics. In 2006, poverty rates were highest among children and young adults. Despite a fall in child poverty rates from 62 percent in 2006 to 13 percent by 2021, the share of children among the poor population increased from 27 to 40 percent (Figure 2.1, panel a). Families with children are more likely to be in poverty (Figure 2.1, panel b). For instance, households with three or more children faced a poverty rate of 82 percent in 2006, which fell to 31 percent in 2013 and to 19 percent in 2021. However, despite this decline, these households constituted a larger share (about 44 percent) of the poor population by 2021. People with only primary or secondary education are consistently more likely to be poor than those with tertiary education (Figure 2.1, panel c). From 2006 to 2021, poverty among people with secondary education fell from 49 to 8 percent. Poverty rates remained lowest for people with tertiary education, dropping from 29 to 4 percent over the same period. However, the share of people with tertiary education in the poor population increased slightly by 2021, whereas the share of people with secondary education decreased. Regional poverty profiles also changed significantly (Figure 2.2). First, the share of poor people living in rural areas has increased marginally (by 2.1 percentage points) over the past 15 years. Second, some regions saw substantial increases in their relative shares of poor people. In 2006 most of the poor population (50 percent) was concentrated in five regions: Turkistan (14.4 percent), Almaty (12.1 percent), Karaganda (8.8 percent), Jambyl (7.7 percent), and East Kazakhstan (7.5 percent). By 2021, 24 percent of the poor population lived in the Turkistan region. Changes in the other regions were modest, with the relative share of the poor population rising in Aktobe, Mangystau, Astana city, and Almaty city. Consumption levels in Kazakhstan exhibit significant disparities across various districts, even within regions. A small area estimation suggests that Southern and Western Kazakhstan have lower average consumption levels, whereas consumption levels are higher in Eastern and Northern Kazakhstan. The model also shows substantial intraregional variation across districts. For instance, in the Almaty region, Raiymbekskiy district has the highest consumption levels in the country, whereas the levels in Panfilovskiy district are less than half of these. Some districts in the Mangystau region have particularly low consumption levels, with Tupkaraganskiy district having the lowest consumption level in the country. Despite progress on human development and access to services, urban-rural disparities remain Although access to services has increased, significant differences remain by income level and area of residence. Low-income and rural households are less likely to have access to basic services. Kazakhstan is classified as a country with high levels of human development, having made significant progress in recent years (United Nations, Kazakhstan 2023). It has attained universal enrollment in primary and secondary education. About CHAPTER 2. POVERTY PROFILE 23 Figure 2.1. Demographic poverty profile, 2006 and 2021 a. Age groups b. Household composition c. Education level 100% 100% 100% 9% 7% 7% 1% 4%4% 8% 11% 90% 3% 2% 90% 12% 10% 80% 16% 80% 80% 3% 70% 22% 70% 44% 47% 22% 25% 59% 60% 60% 60% 50% 20% 50% 40% 26% 40% 40% 2% 26% 23% 30% 30% 6% 20% 40% 20% 20% 40% 27% 26% 22% 10% 27% 10% 0% 0% 0% 2006 2021 2006 2021 2006 2021 0-14 15-29 other only pensioners Tertiary 30-44 45-59 only adults one adult Secondary or Professional 60 and above couple & 3+ children couple & 2 children Primary couple & 1 child Aged less than 15 Source: World Bank estimates, based on the HIES. Figure 2.2. Share of the poor population, by region and urban-rural distribution of poor, 2006 and 2021 60% 54,5% 52,4% 47,6% 45,5% 50% 2006 2021 40% 24,0% 30% 14,4% 20% 12,1% 8,8% 7,7% 7,5% 7,1% 7,0% 6,8% 6,8% 6,8% 6,6% 6,3% 6,2% 6,2% 6,1% 5,9% 5,8% 5,3% 4,9% 10% 4,6% 4,5% 4,0% 3,4% 3,4% 3,1% 2,7% 2,7% 2,7% 2,6% 2,5% 1,4% 0% rural urban Atyrau Jambyl Almaty Aktobe Akmola Pavlodar East_Kaz Turkistan Kostanay Kyzylorda West_Kaz Karaganda North_Kaz Mangystau Astana_city Almaty_city Source: World Bank estimates, based on the HIES. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 24 two-thirds of its people are educated to secondary level, and a third to tertiary level. Life expectancy at birth increased from 66 in 2006 to 73 in 2019, although it dropped to 70 after the pandemic but increased back to 75 in 2023. The under-five mortality rate has fallen from 2.9 to 1.0 percent in the last 15 years. Ensuring access to basic services is a challenge, as the population density is among the world’s lowest (7 persons per square kilometer) (World Bank and ASD 2021). About 42 percent of the population live in rural areas. Access to water and sanitation primarily depends on location: • In urban areas, access to centrally piped water is nearly universal (Figure 2.4, panel a). However, access is significantly lower in rural areas, where public wells and taps are also used. More affluent households are better able to mitigate water challenges; for example, richer households in rural areas are more likely to have access to piped water than their poorer counterparts. • Urban areas also generally have better access to improved sanitation services, including flush toilets (Figure 2.4, panel b). The disparity in access to sanitation between low- and high-income households is less pronounced in urban than in rural areas, where poorer people have significantly lower access to improved sanitation (such as flush toilets). Inequality is low but has been rising in recent years Inequality in Kazakhstan is relatively low, but it has increased since 2016 as the welfare of high-income households rose relatively faster. Income inequality in Kazakhstan is low relative to other upper-middle-income countries. The Gini coefficient, a measure of inequality, fell from 27.0 in 2006 to 24.3 in 2015 and then rose to 26.4 in 20215 (a U-shaped trend, Figure 2.5). Inequality declined until 2008, then stagnated until 2012, and declined again until 2015. However, after 2015 it rose significantly. 5 The Gini coefficient presented in this section differs from the national number by two reasons: i) regional deflator is used to harmonize welfare indicator across regions, ii) equivalence scale is used to account for household composition. CHAPTER 2. POVERTY PROFILE 25 Figure 2.3. Map of district-level per capita consumption, 2018 (24.04,25.60] (22.48,24.04] (22.92,22.48] (19.35,20.92] (17.79,19.35] (16.23,17.79] (14.67,16.23] (13.11,14.67] (11.54,13.11] (9.98,11.54] [8.42,9.98] Source: World Bank estimates, based on the HIES. Figure 2.4. Distribution of access to services a. Rural-urban access to water, quintiles 1 and 5 b. Rural-urban access to sanitation, quintiles 1 and 5 100 100 1,57 0,12 8,00 0,62 0,16 1,70 0,26 5,53 0,05 90 14,92 80 33,93 80 38,55 70 60 62,38 15,57 60 81,41 98,31 99,22 16,68 50 40 92,04 40 82,50 20 48,50 30 36,76 20 34,72 0 10 16,99 Urban Rural Urban Rural 0 Quntile 1 Quntile 5 Urban Rural Urban Rural From a spring, river, lake, pond Water is delivered by Quntile 1 Quntile 5 From public well or tap a water-carrier Central water pipied into dwelling From a public water pipe Flush toilet Composting toilet Other Source: World Bank estimates, based on the HIES. Note: Quintile 1 is the poorest 20 percent of households, and quintile 5 the richest 20 percent. Figure 2.5. Gini coefficient, 2006–21 28,0% 27,0% 27,0% 26,4% 26,0% 25,0% 24,0% 24,3% 23,0% 22,0% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Source: World Bank estimates, based on the HIES. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 26 The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality. Quantile ratios express the gap between rich and poor in terms of percentiles of the consumption distribution. QR (90/10), for instance, shows the income of people above the 90th percentile of the consumption distribution (the richest decile) as a multiple of those in the bottom 10 percent (the poorest decile). The welfare of high-income households increased relatively faster, as shown by the quantile ratios in Figure 2.6. The richest decile consumed 3.2 times more than the poorest in 2006, as per QR (90/10), but this decreased to 3.0 in 2021. The gap between the median and rich households, shown by QR (90/50), first fell but then rose significantly until 2021. At the bottom, the gap between the median and the poorest decile fell, as shown by QR (50/10). The main drivers of these changes are faster consumption growth among the richest decile, slower consumption growth among the poorest decile, and virtually stagnant median consumption between 2016 and 2021. Kazakhstan has one of the lowest levels of inequality among upper-middle-income and high-income countries, along with Moldova. At about 0.26, inequality is almost half that of the Latin American countries shown in Figure 2.7. There could be several reasons for this: • In Kazakhstan, the measure of welfare is consumption, whereas income is used in other countries. Income inequality is usually higher than consumption inequality, as richer households do not necessarily consume all their income (they save or invest). • The inclusion of top incomes in the datasets. Top incomes usually are not captured in the data if they differ systematically across countries; this may cause large differences in inequality indicators (Korinek and others 2006; Hlasny 2018; Lustig 2019; Blanchet and others 2022). The HIES is an intensive survey that requires significant commitment from participants. As a panel survey, it requires consumption data to be recorded on a quarterly basis. Households that agree to participate are interviewed every quarter over a three-year period, for a total of 12 interviews. This commitment means that especially high-income households tend to opt out of the survey. Inequality in Kazakhstan can mainly be explained by variations within regions rather than between them. Figure 2.8 shows the generalized entropy index, GE(0), which reveals important differences in welfare between and within regions. Panel a shows that between-regional inequality, though small, increased from 2006 to 2015 (and remained high), which suggests that the average consumption gaps between the regions increased in this period. Panel b shows that inequality in the Mangystau region was relatively low; it had the lowest index value of all the regions in 2021 (at 0.049). Almaty city had the highest index value (0.147), suggesting relatively high levels of inequality (or large welfare differences) within the region. Generalized entropy measures of inequality reach zero when all people have the same level of wealth. GE(0), the mean log deviation measure of generalized entropy, is used here because it is more sensitive to changes affecting the poorest segments of the population. Declining “shared prosperity” The growth in consumption by the poorest 40 percent of people has been slowing down. Shared prosperity in Kazakhstan displays large variations over time. The consumption growth of the poorest 40 percent of people exceeded the median consumption growth until 2013–18. In the next five-year period, the growth in the consumption of the poorest people declined. CHAPTER 2. POVERTY PROFILE 27 Figure 2.6. Relative consumption patterns, 2006–21 a. Percentile ratios b. Percentile of consumption 3,40 30 3,23 3,20 3,04 25 3,00 2,80 2,92 20 2,60 2,40 15 2,20 2,00 1,95 10 1,85 1,78 1,80 5 1,75 1,60 1,64 1,56 1,40 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 p90/p50 p50/p10 p90/p10 p50 p90 p10 Source: World Bank estimates, based on the HIES. Note: p = percentile of the consumption distribution. Figure 2.7. Gini coefficient, peer comparison 60% 52% 53% 49% 50% 45% 45% 41% 41% 34% 34% 35% 36% 38% 35% 40% 30% 26% 26% 30% 20% 10% 0% (2021) (2021) (2009-2019) (2021) (2020) (2022) (2018) Mexico (2020) (2021) (2021) (2021) (2020) (2020) (2021) (2020) (2020) Uruguay Indonesia Colombia Croatia Chile Moldova Kazakhstan North Macedonia Georgia Serbia Malaysia Costa Rica Brazil Romania Russian Federation Upper Middle Income High income Source: Poverty and Inequality Platform Database, World Bank, Washington, DC (accessed May 2024), https://pip.worldbank.org/home. Figure 2.8. Generalized entropy index a. Relative contribution to GE(0) index by region b. GE(0) inequality by region 100% 0,128 0,130 0,137 0,147 0,160 0,122 8% 12% 0,117 13% 0,116 90% 0,107 0,140 80% 0,120 0,080 0,079 0,079 0,077 0,076 70% 0,064 0,100 0,054 60% 0,049 0,080 50% 92% 87% 88% 0,060 40% 0,040 30% 20% 0,020 10% 0,000 0% 2006 2015 2021 Within Between Source: World Bank estimates, based on the HIES. Note: GE(0) = generalized entropy index. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 28 Shared prosperity measures the extent to which economic growth is inclusive by focusing on household consumption growth among the poorest people relative to the population as whole. Shared prosperity is the average annual growth rate in consumption by the bottom or poorest 40 percent of the population. The shared prosperity premium is the difference between the growth of consumption by the poorest 40 percent and the growth in consumption for the entire population. The trend in shared prosperity highlights the vulnerability of poorer people, with growth fluctuating and occasionally turning negative during economic downturns (Figure 2.9). For instance, in periods of strong economic growth, the growth in the consumption of the poorest 40 percent of people (shared prosperity) was around 1–2 percent. However, in 2013– 18 and 2016–21, shared prosperity premium was either zero or negative, signaling that poor people fared worse than the rest of the population. This highlights the need for targeted measures to bolster economic resilience and protect the most vulnerable people from economic shocks. CHAPTER 2. POVERTY PROFILE 29 Figure 2.9. Shared prosperity and shared prosperity premium, 2006–21 10 8 6 4 2 0 -2 2006-2010 2008-2013 2009-2013 2010-2015 2012-2017 2013-2018 2016-2021 Growth of the bottom 40% Shared Premium Median Income Growth Source: Poverty and Inequality Platform Database, World Bank, Washington, DC (accessed May 2024), https://pip.worldbank.org/home. Box 2.1. Gender gaps in Kazakhstan Achieving gender equality is crucial for development. Enacting legislation that protects women’s economic rights and opportunities marks a critical initial step towards creating societies that are inclusive, resilient, and robust. Evidence suggests that eliminating gender barriers can spur economic growth, alleviate poverty, foster social unity, and improve well-being and prosperity for both present and future generations. In Kazakhstan, gender disparities are relatively less stark; however, a concise overview of these disparities is provided in line with the goals and results of the World Bank’s Gender Strategy for 2024–30. Elevate human capital and end gender-based violence In Kazakhstan, women are more likely to attain higher education than men. In 2021 about 38.9 percent of adult women had earned a tertiary education degree, as against 31.1 percent of men. In academic performance, boys and girls showed comparable results in mathematics, but girls surpassed boys in reading by 27 points. Internationally, boys outperformed girls in mathematics in 40 countries, but girls led in 17 countries. In reading, Kazakhstani girls, like their counterparts in most other countries, largely scored higher than boys. Intimate partner violence remains the most widespread form of violence against women worldwide. In Kazakhstan, the share of women who have experienced intimate partner violence is below the global average of 27 percent. Notably, on April 15, 2024, Kazakhstan enacted a gender-based violence law to bolster protection against violence for women and children, including those who have survived domestic violence. This legislation is a step forward in promoting women’s rights and ensuring their safety, with a specific focus on safeguarding women and children from violence. Expand and enable economic opportunities Although the labor market outcomes for women in Kazakhstan are worse than those of men, these gender gaps are smaller than for its peer countries in the Europe and Central Asia region. In Kazakhstan, the labor force participation rate of women is among the highest in the region, at 64.4 percent, after Moldova, Iceland, and Azerbaijan. Unemployment rates are more even, with men at 4.3 percent and women at 5.5 percent in 2021. Vulnerable employment, which often lacks formal work arrangements and social safety nets (thus making poverty more likely), affected 23.3 percent of women and 25.6 percent of men in Kazakhstan in 2022. This is above the regional average for both genders in Europe and Central Asia. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 30 Figure B2.1.1. Female labor force participation rate, Europe and Central Asia region, 2021 80 64,4 70 60 50 40 30 20 10 0 Bosnia and… Uzbekistan Kazakhstan Montenegro Tajikistan North… Turkiye Italy Greece Turkmenistan Ukraine Serbia Belgium Albania Hungary Spain Kyrgyz… Slovenia Latvia Russian… Netherlands Germany Georgia Lithuania Belarus Switzerland Azerbaijan Iceland Moldova Croatia Bulgaria Poland France Portugal Romania Ireland United… Armenia Cyprus Source: World Bank. Wage inequality persists, with men earning 25 percent more than women in 2019, even after accounting for individual and household characteristics. The gender gap has declined over time as it was 36 percent in 2011. In terms of financial inclusion, 83.6 percent of women and 78.3 percent of men had a bank account in 2021. Although the share of women with bank accounts is below the average for Europe and Central Asia, it is comparable to that of upper-middle-income nations. Engage women as leaders The lack of gender parity in executive roles is a widespread issue, with women typically underrepresented in leadership across most nations. Still, female representation in Kazakhstan has been rising since 2010, although the numbers are not high. Currently, women hold 27.4 percent of parliamentary seats, on par with the average in upper-middle-income economies. They occupy 40.8 percent of managerial positions in Kazakhstan, which is high relative to peer countries. Source: World Bank and the Bureau of National Statistics. 31 Chapter 3. DRIVERS OF POVERTY REDUCTION KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 32 Chapter 3. Drivers of poverty reduction Consumption growth has significantly benefited poor people, with substantial poverty reduction across all regions, in both urban and rural areas. Economic growth driving poverty reduction The sharp reduction in poverty from 2006 to 2021 was driven primarily by economic growth, rather than by the redistribution of resources (Figure 3.1). In 2006–13, for example, economic growth contributed to the reduction of poverty by 5.2 percentage points, whereas redistribution played a minimal role. The economic downturn in 2014–16 reversed some of these gains, and poverty began to rise. However, since 2016, economic growth has been the sole driver of poverty reduction. Labor income played a crucial role in poverty reduction between 2006 and 2021 (Figure 3.2). Labor income, which includes earnings from both employment and self-employment, was the primary force behind the decrease in poverty levels in this period, with an especially large role from 2006 to 2013. However, its impact was reversed during the 2014– 16 economic downturn. Nonlabor incomes also contributed substantially to poverty reduction (as discussed below). The contribution of nonlabor income was significantly smaller in 2006–13 and 2014–16 than in 2016–21. During the latter period, the growth in nonlabor income played a significant role in reducing poverty, similar to labor income. Understanding household income dynamics Higher labor incomes and government transfers have been the mainstay of household income growth, whereas the share of income from agriculture has been declining. The share of various income sources has remained relatively constant, except for a slight increase in employment and pension income (Figure 3.3, panel a). Income from wage employment constituted the largest share of total income in all periods, with its share increasing from 61 percent in 2006 to 65 percent in 2021. Self-employment income has remained stable, whereas the share of agricultural income, which had already been low, continued to decline to just 2 percent in 2021. Conversely, government transfers, particularly pensions, have increased. Pension income constituted about 11 percent of household income in 2006 and 2013, rising to 16 percent by 2021. Household income has increased significantly for all income groups since 2006. However, after an initial increase between 2006 and 2013, household incomes have stagnated. Household income levels for each decile were almost similar across 2013, 2016, and 2021. The average daily per capita household income rose from US$6.8 in 2006 to US$10.7 in 2017 purchasing power parity (PPP) in 2013, reflecting a 57 percent surge in household earnings. Average income levels decreased to US$10.0 per capita per day in 2017 PPP in 2016 and almost returned to its 2013 levels by 2021, at US$10.5. Rapid growth in wages substantially contributed to poverty reduction until 2013, when the real value of average salaries peaked (Figure 3.4, panel a). The economic downturn of 2014–16 saw wages declining, but their real value stabilized between 2016 and 2021. One exception to this decline was the poorest 10 percent of people. The wages of the poorest decile had grown from US$3.3 per day in 2006 to US$5.2 per day in 2013 (in 2017 PPP). They maintained this level in 2016 despite the downturn, and real wages then grew to US$5.7 per day by 2021. CHAPTER 3. DRIVERS OF POVERTY REDUCTION 33 Figure 3.1. Role of growth and redistribution in poverty reduction 4 Annualized contributions of growth and redistribution to poverty reduction 3 0,1 2 2,9 1 0 -1 -2,3 -2,7 -2 0,0 0,0 -5,2 -3 -4 -5 -0,3 -6 2006-2021 2006-2013 2013-2016 2016-2021 Growth Distribution Source: World Bank estimates, based on the HIES. Note: Decomposition based on method by Ravallion and Datt (1991). Figure 3.2. Paes de Barros decomposition of changes in poverty 0,641 0,632 0,8 0,455 0,444 0,6 0,36 0,309 0,4 0,166 0,113 0,108 0,079 0,2 0,023 0,001 0 -0,013 -0,01 -0,039 -0,085 -0,2 -0,186 -0,245 -0,266 -0,4 -0,487 -0,6 2006_2013 2013_2016 2016_2021 2006_2021 Propensity Share adults Share employed Labor income Non-labor income Source: World Bank estimates, based on the HIES. Note: Decomposition based on method by Barros and others (2006) and its extension by Azevedo and others (2012). Figure 3.3. Income trends, 2006–21 a. Share of sources of income b. Income levels by decile 35 per cpaita per day in 2017 PPP US$ 2021 65% 8% 5% 16% 1% 5% 30 25 2016 64% 6%3% 12%3% 12% 20 15 2013 64% 8%3%11%2%12% 10 2006 61% 8%3% 11%3% 14% 5 0 0% 20% 40% 60% 80% 100% Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Decile 10 Labor - wage Labor - self employed Government - social assistance Government - pension Agriculture income Other income 2006 2013 2016 2021 Source: World Bank estimates, based on the HIES. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 34 Self-employment income followed a similar trajectory, but the impact of the economic downturn was more pronounced (Figure 3.4, panel b). Households have reported higher self-employment income every year since 2006. Income levels in 2013, 2016, and 2021 were comparable and relatively stable despite economic shocks such as the 2014–16 downturn, the COVID-19 pandemic, and conflicts in Russia, a significant trading partner. Box 3.1. Reversal in the transmission channel of poverty between 2014 and 2016 After a period of rapid poverty reduction between 2006 and 2013, the poverty rate in Kazakhstan increased significantly from 11.1 percent to 20.2 percent in 2016, mainly because of the economic downturn. Gross domestic product (GDP) per capita declined by 0.27 percent in 2015 and 0.33 percent in 2016. Household budgets were adversely affected, primarily through lower real wages rather than job losses during the downturn. On average, employment decreased by 2.2 percent, according to cross-sectional data. However, real wages failed to maintain their purchasing power, declining by 5 percent. All sectors experienced real wage declines, with the most substantial reductions in manufacturing, commerce, transport, construction, and mining. The construction sector also saw the largest drop in employment. An analysis of panel data from the Household Income and Expenditure Survey (HIES) gives even more pronounced results. Employment loss was significantly lower, around 0.8 percent between 2014 and 2016. The financial services sector experienced the highest employment loss, at 8.3 percent, whereas other sectors saw much lower declines. Conversely, the overall decline in real wages was significantly higher, at 11.8 percent. In sectors with relatively low employment losses, real wages contracted, whereas in sectors with high employment losses, real wages remained relatively stable. Table B3.1.1 Change in employment and real wages, 2014–16 Percentage Cross-section Panel data Sector Real wage Employment Real wage Employment Agriculture, hunting, and fishing 4.0 -4.4 -4.0 -0.3 Mining -5.4 -4.0 -16.2 2.2 Manufacturing -6.9 1.2 -10.3 -1.1 Public utility services -4.4 3.1 -11.5 -4.3 Construction -5.8 -8.6 -14.5 0.9 Commerce -6.3 -1.6 -14.1 -2.6 Transport and communications -5.9 -2.1 -11.4 -2.0 Financial and business services -3.8 -5.4 -0.9 -8.3 Public administration -3.1 -7.8 -10.7 0.1 Other services, unspecified -3.8 7.3 -11.7 3.2 Total -5.0 -2.2 -11.8 -0.8 Source: World Bank estimates, based on the HIES. Agricultural income has declined (Figure 3.5). Income from agricultural activities remained steady in 2006–13 but started to decrease for low-income households by 2016; it remained consistent for higher deciles. By 2021 agricultural income had fallen sharply, as fewer households engaged in agriculture. Government transfers increased significantly, especially during the pandemic when benefits were increased. Social assistance payments have risen since 2006, with low-income households benefiting relatively more (Figure 3.6, panel a). By 2013, social assistance payments were slightly higher in real terms than in 2006, but the most significant surge CHAPTER 3. DRIVERS OF POVERTY REDUCTION 35 Figure 3.4. Labor and self-employment income by decile, 2006–21 a. Wage income b. Self-employment income 20 3,0 per capita per day in 2017 PPP US$ per capita per day in 2017 PPP US$ 18 16 2,5 14 2,0 12 10 1,5 8 6 1,0 4 0,5 2 0 0,0 Decile 10 Decile 10 Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 2006 2013 2016 2021 2006 2013 2016 2021 Source: World Bank estimates, based on the HIES. Figure 3.5. Agriculture income by decile, 2006–21 0,7 per capita per day in 2017 PPP US$ 0,6 0,5 0,4 0,3 0,2 0,1 0,0 Decile 10 Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 2006 2013 2016 2021 Source: World Bank estimates, based on the HIES. Figure 3.6. Social assistance and pensions by decile, 2006–21 a. Social assistance 6 b. Government pensions per capita per day in 2017 PPP US$ per capita per day in 2017 PPP US$ 1,0 5 0,8 4 0,6 3 0,4 2 0,2 1 0,0 0 Decile 10 Decile 10 Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Decile 1 Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 2006 2013 2016 2021 2006 2013 2016 2021 Source: World Bank estimates, based on the HIES. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 36 occurred by 2021, when transfers doubled relative to 2016. This was mainly due to sizeable increases in social assistance during the COVID-19 pandemic. Government pensions also rose significantly, more so for richer households (Figure 3.6, panel b). Pension payments to low-income households have remained relatively stable, but higher-income households received increasingly larger pensions. In real terms (2017 PPP), government pensions to high-income households rose from US$1.5 per capita per day in 2006 to US$5.7 per capita per day in 2021. Regional poverty dynamics Poverty fell in both urban and rural settings, and the urban-rural poverty gap has narrowed slightly. In all periods, poverty reduction can be explained by improvements in household welfare in both urban and rural areas. About 54 percent of the overall poverty reduction from 2006 to 2021 occurred in rural areas, with urban poverty reduction accounting for nearly all the rest (Figure 3.7). However, the interaction effect, which measures the contribution of migration from rural to urban areas, was zero, indicating limited internal migration dynamics in Kazakhstan. Poverty fell in all the regions in Kazakhstan, especially in the Almaty and Turkistan regions. The most notable decreases occurred in 2006–13 in four regions, with subsequent reversals during the economic downturn of 2014–16, particularly in the Turkistan and Jambyl regions. In 2016–21, poverty reduction efforts were most successful in the Turkistan region (Figure 3.8). The educational attainment of the household head was a significant determinant of poverty reduction, with the most substantial reductions in families where the head had achieved a secondary education (Figure 3.9). Households whose breadwinners were less educated were the ones moving out of poverty in times of positive economic growth, but they were also the ones who were pushed into poverty during economic downturns. CHAPTER 3. DRIVERS OF POVERTY REDUCTION 37 Figure 3.7. Poverty reduction by area of residence, 2006–21 20,0 9,1 5,3 4,1 10,0 0,4 0,3 0,2 0,0 -0,1 -0,2 -0,2 -0,3 -0,4 -10,0 -4,5 -7,0 -11,6 -20,0 -19,1 -19,5 -19,8 -21,0 -30,0 -40,0 -38,4 -40,9 -50,0 2006-2013 2013-2016 2016-2021 2006-2021 Interaction effect Population shift Urban Rural Absolute change in poverty Source: World Bank estimates, based on the HIES. Note: Decomposition based on method by Ravallion and Huppi (1991). Figure 3.8. Poverty reduction by region, 2006–21 2,0 1,68 1,77 1,0 0,0 -1,0 -2,0 -3,0 -4,0 -3,69 -3,55 -5,0 -6,0 -5,43 -5,40 -5,29 2006-2013 2013-2016 2016-2021 2006-2021 Interaction effect Population shift Akmola Aktobe Almaty Atyrau West Kazakhstan Jambyl Karaganda Kostanay Kyzylorda Mangystau Pavlodar North Kazakhstan Turkistan East Kazakhstan Astana Almaty Source: World Bank estimates, based on the HIES. Note: Decomposition based on method by Ravallion and Huppi (1991). Figure 3.9. Poverty reduction by level of education, 2006–21 7,2 9,1 10,0 0,0 -10,0 -9,3 -11,7 -20,0 -30,0 -40,0 -32,3 -34,8 -38,4 -40,9 -50,0 2006-2013 2013-2016 2016-2021 2006-2021 Interaction effect Population shift Secondary Tertiary Absolute change in poverty Source: World Bank estimates, based on the HIES. Note: Decomposition based on method by Ravallion and Huppi (1991). KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 38 Box 3.2. Stagnating urbanization Relatively low levels of domestic migration were the main reason for the slowing growth of urbanization in Kazakhstan. Administrative sources suggested that nearly 5 percent of the population migrated within Kazakhstan over the five-year period before 2019 (World Bank, 2022d). Survey data indicated that rural-to-urban migration was responsible for only one-fifth of urban population growth between 2010 and 2015 (World Bank, 2022e). This is corroborated by recent evidence from the Listening to Kazakhstan survey, which showed that around 96 percent of people had no intention to migrate in 2022–24. According to the Kazakhstan Country Economic Memorandum (CEM), high housing costs and very high home ownership by regional standards—a legacy of the command economy—contributed to high moving costs and impeded people’s mobility. This led to relatively higher spatial wage differences. For example, despite having the highest average incomes in the country, over two-thirds of residents of Astana and Almaty live in housing they would have been unable to afford had they not owned it. The estimated housing cost, based on imputed rent, exceeded the national average by 310 percent in Almaty city and by 460 percent in Astana. People who migrate within Kazakhstan are relatively better educated. The CEM reported that over half of domestic migrants had more than a secondary education (considerably above the national share of 30 percent of people with a secondary education). Furthermore, the top 10 destinations of the domestic migrants attracted 77 percent of highly educated migrants in 2019, compared with 46 percent of less-educated peers. Source: World Bank, 2022d. 39 Chapter 4. MIDDLE CLASS, VULNERABILITY, AND CHRONIC POVERTY KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 40 Chapter 4. Middle class, vulnerability, and chronic poverty Poverty trends in Kazakhstan have closely mirrored economic growth patterns. Poverty decreased in periods of strong economic growth and stagnated or increased in times of weak or negative economic growth. Although the middle class has grown, the share of people vulnerable to poverty has remained the same. Chronic poverty has also declined, but there is significant variation across regions. Chronic poverty patterns Chronic poverty fell by 37 percent, from 8.4 to 5.3 percent, between the 2011–13 and 2019–21 periods, and transient poverty declined by 31 percent, from 17.8 to 12.4 percent (Figure 4.1; see also Box 4.1). Both chronic and transient poverty rates fell slightly during the COVID-19 pandemic. The patterns of chronic and transient poverty changed after the economic crisis of 2014–16, when the economy experienced a protracted U-shaped recovery (rather than the V-shaped recovery after COVID). Chronic poverty rose to 12.4 percent in 2015–17 (a 67 percent increase), up from 7.4 percent in the precrisis years (2013–15), whereas transient poverty increased from 14.7 to 17 percent. The share of chronic poverty reached its highest value (42 percent) in 2015–17, as the prolonged economic slowdown caused a disproportionate increase of the number of chronically poor households. The downturn also resulted in a significant increase in the share of households in poverty for three years (that is, it increased the length of poverty spells). Chronic poverty is defined as being consistently poor, that is, households whose average per capita income or consumption is at or below the poverty line for several years (Jalan and Ravallion 1998). Although there is no specified number of years that constitutes chronic poverty, five years are often used; this report uses three years because of data constraints. Total poverty is the percentage of households who are poor at least in one period. The difference between total and chronic poverty is transient poverty (see Box 4.1 for details). At a regional level, the picture of chronic and transient poverty is mixed. The total poverty rate varies significantly across regions. As the level of economic development and the state of labor markets varied across the regions, so did their share of chronic poverty in both 2011–13 and 2019–21. The chronic poverty rate more than doubled in three regions (Figure 4.2). In the Almaty region, chronic poverty increased from 1 percent in 2011–13 to 3.6 percent in 2019–21, whereas it grew from 2.4 and 3.6 percent to 7.9 and 7.2 percent in Aktobe and Jambyl, respectively. In contrast, five regions— Atyrau, West Kazakhstan, Mangystau, Pavlodar, and Turkistan—saw a substantial decline in chronic poverty (by more than 50 percent) between these two periods. CHAPTER 4. MIDDLE CLASS, VULNERABILITY, AND CHRONIC POVERTY 41 Figure 4.1. Lower chronic and transitory poverty, 2011–13 and 2019–21 45% 42,2% 40% 34,5% 36,8% 35,6% 35% 34,9% 31,2% 33,6% 29,9% 32,1% 30% 25% 8,4% 12,4% 10,0% 9,8% 20% 8,0% 7,4% 7,9% 15% 6,1% 5,3% 10% 17,8% 18,2% 15,3% 14,7% 16,8% 17,0% 14,7% 13,4% 12,4% 5% 0% 2011-2013 2012-2014 2013-2015 2014-2016 2015-2017 2016-2018 2017-2019 2018-2020 2019-2021 Transient poverty Chronic poverty Share of chronic poverty Source: World Bank calculations using constructed three-year panels based on data from the 2011–21 HEIS. Figure 4.2. Regional differences in chronic and transient poverty, 2011–13 and 2019–21 a. 2011–13 b. 2019–21 50% 50% 40% 40% 36% 25% 30% 30% 24% 22% 20% 20% 19% 16% 19% 19% 24% 19% 19% 15% 16% 13% 15% 15% 10% 15% 14% 11% 16% 22% 17% 10% 19% 10% 10% 9%14%7% 10% 12% 10% 12% 13% 9% 10% 9% 7% 8%10%6% 8% 7% 6% 5% 5% 7% 6% 8% 5% 7% 4% 4% 6% 2% 1% 4% 3% 2% 4% 2% 2% 2% 0% 0% Almaty_city Jambyl Almaty_city Jambyl Akmola Aktobe Almaty Atyrau West_Kaz Karaganda Kostanay Kyzylorda Mangystau Pavlodar North_Kaz Turkistan East_Kaz Astana_city Akmola Aktobe Almaty Atyrau West_Kaz Karaganda Kostanay Kyzylorda Mangystau Pavlodar North_Kaz Turkistan East_Kaz Astana_city Chronic poverty Chronic poverty Transient poverty Transient poverty Share of chronic poverty Share of chronic poverty Source: World Bank calculations using constructed three-year panels based on data from the 2011–21 HEIS. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 42 Box 4.1. Understanding poverty dynamics Poverty dynamics inform policymakers about the share of poverty and who are at the risk of permanent poverty, allowing them to design better policies. By tracking the welfare of the same households for an extended period, researchers can learn whether it is mostly the same households that are trapped in poverty for long time or whether poverty in a particular area is more transient. The analysis also helps to identify people who are at risk of falling into poverty. The analysis in this section builds on a background paper, “Examining Chronic Poverty, Vulnerability and Middle class in Kazakhstan”, which uses newly constructed panel datasets. If poor households are observed over time, it becomes clear whether they are in long-term (chronic) or short-term (transient) poverty. A household is classified as chronically poor if its average per capita income or consumption is at or below the poverty line over several years. Total poverty is the percentage of households that are observed to be poor at least in one period. The difference between total and chronic poverty constitutes transient poverty, or the share of households in poverty for at least one year, but not for a much longer period. Households in transient poverty could achieve nonpoor status across all periods if their consumption could feasibly have been smoothed. More importantly, their transient poverty usually stems from either a business cycle or a household-level shock. Source: World Bank. Chronically poor people have limited opportunities to escape from poverty, given their high dependency ratios and below-average human capital. In 2019–21, the average household size for chronically poor people was 6.3, as against 3.9 for the never-poor group ( Table 4.1). Among the chronically poor group, 62 percent of households consisted of two adults plus three or more children. Higher education does not guarantee an escape from chronic poverty: 16 percent of breadwinners of chronically poor households had a tertiary education. The employment ratio of adults in chronic poverty is respectively 10 and 13 percentage points lower than among transient and never-poor households; this contributes to persistently low incomes. CHAPTER 4. MIDDLE CLASS, VULNERABILITY, AND CHRONIC POVERTY 43 Figure 4.3. Stratification of poor, vulnerable, and middle-class people, 2006–21 100% 90% 26 80% 37 38 39 43 70% 58 53 58 59 64 60 63 63 62 66 67 60% 24 50% 27 27 26 40% 26 30% 27 24 24 26 26 49 23 24 25 25 26 20% 37 24 35 35 30 10% 18 17 15 20 16 11 12 11 12 11 9 0% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Poor Vulnerable Middle class Source: World Bank calculations using constructed three-year panels based on data from the 2011–21 HEIS. Table 4.1. High dependency and lower human capital and employment among the chronically poor 2011–13 2019–21 Never poor Never poor Population Population Transient Transient Chronic Chronic Share of population 100% 8% 18% 74% 100% 5% 12% 82% Household size 4.1 5.8 4.8 3.7 4.2 6.5 5.5 3.9 Urban 55% 34% 46% 60% 59% 40% 48% 62% Rural 45% 66% 54% 40% 41% 60% 52% 38% Household composition Couple and 1 child 25% 15% 26% 26% 19% 9% 15% 20% Couple and 2 children 21% 27% 27% 19% 22% 24% 28% 21% Couple and 3+ children 14% 49% 24% 7% 22% 62% 45% 16% Lone parents 3% 3% 3% 3% 2% 1% 1% 2% Only adults 24% 4% 14% 29% 15% 2% 6% 17% Only pensioners 4% 0% 1% 5% 8% 0% 1% 10% Mixed 9% 2% 6% 10% 12% 2% 3% 14% Income and education Household annual income per 591 299 429 664 639 313 426 692 capita (US$, 2017 PPP) Number of employed 2.0 1.9 2.1 2.0 1.6 1.6 1.7 1.6 Ratio of employed adults 0.69 0.60 0.66 0.70 0.56 0.44 0.54 0.57 Breadwinner with tertiary 31% 15% 24% 34% 37% 16% 32% 39% education Source: World Bank calculations, based on the HIES. Note: PPP = purchasing power parity. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 44 Identifying vulnerable and middle-class households Since 2006, the middle class has increased 2.5-fold to reach 67 percent of the population in 2021, up from 26 percent in 2006 (Figure 4.3; see also Box 4.2 for the calculation of the figures). This represents an annualized growth rate of 6.4 percent. However, the growth of the middle class was reversed in the aftermath of the economic crisis of 2014–16, when its share temporarily fell to 53 percent. A key finding is that the middle class grew solely because of the lower poverty rate, whereas the share of people vulnerable to poverty was largely stagnant, with small increases in 2007–10 and 2016. Box 4.2. Identifying the middle class With the share of poor people falling below 10 percent of the population, the question arises whether Kazakhstan has succeeded in reducing the share of people vulnerable to poverty, thereby increasing the proportion of the middle class. Policymakers are often interested in measuring the middle class, given its importance in fostering economic development and stability. As Kazakhstan lacks an official definition of the middle class, and the middle-class lines estimated in other countries are less applicable, this report uses an empirical estimate of the threshold for the middle class, based on the most recent microlevel data. The lower bound for the middle class is the vulnerability threshold, estimated in 2017 PPP terms at US$10.3 per adult equivalence consumption. The analysis uses a set of two-year panels constructed from Kazakhstan HEIS cross- sections from 2011 to 2021 to estimate the probability of nonpoor households falling into poverty in the next period. It uses US$7.6 a day, in 2017 PPP terms, as the adopted national poverty line. Following the methodology developed by López-Calva and Ortiz-Juarez (2014), households with a low probability (less than 10 percent) of becoming poor are categorized as middle class. The predicted income associated with this probability is defined as the vulnerability threshold, drawing the line between economic stability attained by the middle class and a state of vulnerability. Source: World Bank. CHAPTER 4. MIDDLE CLASS, VULNERABILITY, AND CHRONIC POVERTY 45 Box 4.3. Why households fall into poverty The probability of a household falling into poverty depends largely on household-level shocks and characteristics, along with spatial and time factors. Household shocks are divided into three broad categories: demographic, nonlabor market, and labor market events. • Demographic events are a primary determinant of the probability of becoming poor. If household size increases, the probability of falling into poverty increases by 4.5 percentage points. Conversely, if a household becomes smaller, the poverty risk falls by 3 percentage points (Figure B4.3.1). • Nonlabor market events also have pronounced effects, with private transfers and income from agriculture having the largest impact, followed by social assistance.1 The risk of falling into poverty declines by 0.3 percentage points if a household’s share of private transfers or income from agriculture increases by 10 percent. It declines by 0.23 percentage points for social assistance. • The effect of a labor market event appears to be marginal. If the share of employed people among adults increases by 10 percentage points, the risk of falling into poverty declines by 0.3 percentage points. Figure B4.3.1. Changes in household size and composition and the risk of poverty a. Household events b. Household types Source: World Bank calculations using constructed two-year panels based on data from the 2011–21 HEIS. Note: Coefficients of nonlabor market events should be interpreted as a change in the share of an income source in total household income between year 1 and year 2 (in percentage points). Figure B4.3.2. Characteristics of breadwinners and the risk of poverty Source: World Bank calculations using constructed two-year panels based on data from the 2011–21 HEIS. Note: The reference age group is breadwinners younger than 30 years. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 46 The composition of households is likewise an important determinant of poverty. Rather than simply considering the size of the households, the regression model used in this analysis considers seven types of households, because households of the same size face different poverty risks depending on their composition. In the figures, the reference group is households with two adults plus one child, and the poverty risk is expressed as being higher or lower than the risks facing this reference group. For example, for households consisting only of adults of nonworking age, the risk of falling into poverty is 10 percentage points lower than for the reference group. The probability is more than twice as low, at -4 percentage points, for households consisting of only adults. Having an additional child is associated with a higher risk of falling into poverty—2.1 and 4.1 percentage points for households with two adults plus two children and two adults plus three or more children, respectively. Finally, single adult households have a similar risk of becoming poor as the reference group. Another important determinant is the characteristics of the breadwinner. For breadwinners with a higher level of education, the probability of falling into poverty is 2.1 percentage points lower (Figure B4.3.2). If a breadwinner becomes unemployed or drops out of the labor market, the risk is 4.0 and 1.4 percentage points higher, respectively. Households with male breadwinners are marginally better off. The risk of falling into poverty decreases with the age of breadwinners. For example, it is lower by 0.9 percentage points for breadwinners ages 30–44 years old than for those under 30. Source: World Bank. Note: 1. The relative change in household income from pensions appears to have no statistically significant effect, probably because it is already captured in the variable “household type: only pensioners”, which has a large effect. 47 Chapter 5. FISCAL POLICY KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 48 Chapter 5. Fiscal policy Poverty reduction was mainly driven by economic growth, and fiscal policy played a limited role in redistributing the gains of the economic growth. Fiscal policy can play a crucial role in providing safety nets for poor and vulnerable households, particularly during periods of low economic growth. Poor and vulnerable people are more likely to suffer disproportionately from economic slowdowns, which affect various areas of their lives. The country could achieve more effective outcomes by better targeting social transfers and subsidies to poor and vulnerable households. About 1.6 million people in Kazakhstan were considered poor by the end of 2021. As the pace of poverty reduction is slowing, it is vital for policymakers to increase efforts both to stimulate growth and ensure that the benefits of such growth are shared by all income groups. This section analyses fiscal policy in Kazakhstan, focusing on social spending. It discusses the impact of taxes, transfers, and subsidies on poverty and inequality, providing important insights into the impact of fiscal policy. Box 5.1. How fiscal policy alleviates poverty and inequality Fiscal policy can play a key role in reshaping the income distribution. It is one of a government’s main instruments for both addressing immediate needs and promoting long-term growth, with wide-ranging impacts on poverty and inequality. Governments use fiscal policy to generate revenue to finance public spending. Policy decisions on direct and indirect taxes, subsidies, pensions, and other direct transfers, along with public spending on education and health, have an individual and combined impact on the income distribution and people’s standard of living. Different combinations of these policies across countries generate different distributional outcomes. Fiscal policy can also shield poor and vulnerable households from the adverse impacts of income shocks and help avert negative health and education outcomes. Without fiscal interventions, early setbacks in these areas could have a lifelong impact on people’s ability to earn a living. The fiscal choices made by a government during an economic downturn can at best deliver essential support to vulnerable populations or at worst aggravate poverty and increase economic inequality. Countries heavily reliant on commodity revenues are particularly susceptible to significant and unpredictable fluctuations in commodity prices. This vulnerability limits their capacity to effectively respond to adverse economic shocks, especially in the absence of adequate fiscal buffers accumulated during periods of high oil prices. When governments reduce expenditures in response to declining oil revenues, the most vulnerable groups suffer the most if safety nets are insufficient. Furthermore, long-term economic growth may be hindered if governments divert resources away from productive projects. Source: World Bank 2022b; 2022d. The fiscal impact on poverty and inequality is limited The analysis in this section uses income as a welfare aggregate, unlike the previous sections where consumption is used to identify the trends in poverty and inequality. Kazakhstan’s fiscal system helped to reduce income inequality, as shown in Figure 5.1. Before any fiscal policy is considered (labeled “market income” in the figure), the Gini coefficient is 0.406. Once pensions are added, the coefficient drops to 0.337, which shows that pensions reduce income inequality. The same CHAPTER 5. FISCAL POLICY 49 Figure 5.1. Impact of fiscal policy on inequality, 2021 0,45 0,4055 0,4 0,35 0,315 0,337 0,3 0,273 0,306 0,25 0,2 Market Income plus Disposable Income Consumable Income Final Income Pensions (M.I. P.) Old-age pension as a government transfer Old-age pensions as deferred income Source: World Bank estimates, based on the 2021 HIES. Note: Market income = income before any fiscal policy is considered. Disposable income = market income + pensions – direct taxes + transfers. Consumable income = disposable income – indirect taxes + subsidies. Final income includes in-kind spending on education and health. Figure 5.2. Impact of fiscal policy on Gini coefficient, across countries 25 20 19,1 17,2 16,9 14,8 14,3 13,7 13,5 12,8 12,8 15 12,5 12,1 11,6 11,4 10,6 10,5 10,4 10,3 10,2 10,1 10,0 10,0 9,0 8,8 8,4 10 8,0 7,5 7,1 7,1 7,0 6,8 6,7 6,7 6,5 6,4 5,6 5,4 5,2 4,9 4,8 4,4 4,4 4,2 3,7 3,6 3,5 3,5 3,4 2,8 5 2,3 2,3 1,6 1,2 0,7 0 CRI CIV IRN TJK NIC SLV ESP BLR IND IDN PER ALB JOR PRY LKA LSO ZAF TZA BFA RUS POL ETH EGY KAZ CHL BRA USA BOL TUR ECU COL URY KEN UKR VEN PAN ARG HRV TUN TGO UGA ROU MEX GHA ZMB SWZ ARM GTM HND BWA NAM COM DOM Source: World Bank calculations, based on the HIES for Kazakhstan, other country results from the CEQ Data Center on Fiscal Redistribution: Visualization Tools, CEQ (Commitment to Equity) Institute, Tulane University, New Orleans, https://commitmentoequity.org/datavisualization/. Figure 5.2. Impact of fiscal policy on Gini coefficient, across countries KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 50 is true for direct taxes and transfers: when these are included, the coefficient declines to 0.306 (labeled “disposable income”). Indirect taxes and subsidies cause a slight rise in inequality (to 0.315, labeled “consumable income”). The largest equalizing effect is from in-kind spending on education and health, which causes the Gini coefficient to fall to 0.273. Thus, in total, fiscal interventions cause the Gini coefficient to drop from 0.337 to 0.273, a reduction of around 0.064 of a Gini point. The redistributive effect of fiscal policy increases to 0.132 when pensions are considered as government transfers. This analysis shows that the reduction in inequality stems mainly from the strong equalizing effect of social spending on education and health. Direct taxes and transfers are also equalizing and help mitigate the inequality-increasing impact of indirect taxes. However, the distributive impact of fiscal policy in Kazakhstan is low relative to other upper-middle-income countries where similar analysis has been undertaken. The significant impacts in countries such as Spain, the United States, Mexico, and Poland demonstrate the potential of fiscal policy to redistribute resources and promote inclusive growth. The highest absolute decrease in the Gini coefficient from fiscal policy is observed in Spain (0.172 of a Gini point), the United States (0.148), and Poland (0.121), as against only 0.064 of a Gini point in Kazakhstan. Overall, fiscal policy in Kazakhstan does reduce poverty. When contributory pensions are considered as government transfers, fiscal policy significantly reduces poverty, from 22.5 to 18.6 percent after taxes and transfers (Figure 5.3). If, however, pensions are deemed deferred income, fiscal policy causes an increase in the incidence of poverty from 13.9 to 18.3 percent. The highest increase in the incidence of poverty is from regressive indirect taxes, a common pattern among countries. Although the poverty rate for consumable income is higher than for disposable income in peer countries, the impact of indirect taxes on poverty is relatively moderate. Direct transfers mean that households in the bottom decile are net receivers of the fiscal system, as is shown by an analysis of the incidence of fiscal interventions across the deciles of the distribution (as a share of market income plus pensions) (Figure 5.4). Moreover, education and health transfers are strongly progressive, and their benefits are relatively more concentrated in the bottom deciles. These in-kind transfers lift the impact of fiscal policy in the bottom of the distribution and explain the sharp reduction in the inequality of final income. In-cash transfers on their own would not be sufficient to offset the burden of taxes for households in the second and higher income deciles. But when combined with in-kind transfers, they boost the final position of low-income households, making the households from first five deciles net receivers of fiscal resources. From decile 6 and above, households are net payers to the fiscal system. However, the variations are large for individual fiscal policies (Figure 5.5): • Direct transfers reduce the poverty headcount ratio by 6.4 percentage points when old-age contributory pensions are treated as deferred income. Their impact rises to about 10.5 percentage points when old-age contributory pensions are treated as transfers. • The largest impacts on poverty are from the contributory pensions, the base pensions, the child benefit, and the state benefit, which reduce the poverty headcount by 4.4, 4.1, 1.6, and 1.4 percentage points, respectively. • The individual impact of each of the targeted social assistance program6 is relatively small—even though they are targeted to low-income households, their budgets are far smaller than the budget for pensions. Indirect taxes lead to higher overall increases in poverty than do direct taxes (3.7 percentage point increase in poverty). Value added tax increases poverty by about 3.5 percentage points. Others, like medical insurance and social tax, have a smaller impact. Personal income taxes and property taxes have a very limited impact on poverty. 6 The definition of social assistance used in the report is as follow: Social assistance programs are non-contributory transfers in cash or in-kind and are usually targeted at the poor and vulnerable. Please see the Annex for more detailed categorization of government programs. CHAPTER 5. FISCAL POLICY 51 Figure 5.3. Impact of fiscal policy on poverty, 2021 25 22,5 20 18,6 15 13,9 10 12,1 5 0 Market Income (M.I.) Disposable Income Consumable Income Old age pensions as government transfer Old-age pensions as deferred income Source: World Bank estimates, based on the 2021 HIES. Note: Market income = income before any fiscal policy is considered. Disposable income = market income + pensions – direct taxes + transfers. Consumable income = disposable income – indirect taxes + subsidies. Figure 5.4. Net fiscal position, as a share of market income plus pensions 300 250 200 150 100 50 0 -50 -100 poorest 2 3 4 5 6 7 8 9 richest Direct Taxes Direct Transfers Indirect Taxes Indirect Subsidies Inkind Net Total Net Cash Source: World Bank estimates, based on the HIES. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 52 Several areas of improvement can help increase the redistributive effect of the fiscal system: • Revenue collection is low relative to countries in the Organisation for Economic Co-operation and Development (OECD). Whereas on average, taxes comprised 34.1 percent of GDP in the OECD in 2021, the ratio was only 14.1 percent in Kazakhstan. This affects the government’s spending options, including allocations for health, education, and social transfers. • Personal income tax, which constitutes a significant share of the revenue collection, is a flat tax. In many best-performing countries, it is a progressive tax, with rates rising in line with incomes. • Indirect taxes comprise a relatively large share of tax revenue. Such regressive taxes impose a higher burden on low- income households, thus reducing the redistributive capacity of the fiscal system. Social assistance programs are mainly categorical, targeting specific groups such as households with disabled or elderly members, or those with multiple children, which are typically associated with a higher risk of poverty. Although categorical targeting may well be easier to implement than means-tested programs, exclusion and inclusion errors can be larger. Overall, the objective of the social assistance system in Kazakhstan is equality (giving everyone the same resources and opportunities, regardless of their circumstances or needs) rather than equity (recognizing that people start from different places and providing additional resources to those in need). The targeted social assistance program is the most effective, but its coverage is very limited: only households with incomes below 70 percent of the subsistence minimum are eligible. This means the program targets only the poorest 3 percent of people. Exclusion errors occur when the intended beneficiaries or people who need it cannot access support, making the system inefficient. Inclusion errors or leakages occur when unintended beneficiaries or those who do not need it receive support, making the system wasteful. Finally, fuel and utility subsidies, which are usually regressive, remain large. Kazakhstan uses significant resources to subsidize fossil fuels, leading to inefficient allocation of resources. In 2021 the size of fossil fuel subsidies was estimated at 2.8 percent of GDP (World Bank 2022c); these subsidies tend to benefit richer households. Fiscal policy can play an important role in redirecting the regressive fuel subsidies to protect households instead. Enhancing efficiency through better targeting The effectiveness of social spending can be significantly enhanced. Kazakhstan spends 2.8 percent of GDP on social assistance, as against only 1.8 percent among its upper-middle-income peers. Yet its social assistance system was relatively less effective than those in upper-middle-income and high-income countries. Most high-income economies can offset the impact of taxes on poverty through more targeted social assistance programs and more effective delivery systems. Restructuring social assistance transfers from categorical to means-tested eligibility criteria could help eradicate poverty. Although the existing system is diverse and generous, it mainly supports selected categories of people rather than directly targeting the most vulnerable people. In 2021, only 16 percent of the social assistance transfers are delivered to the poorest 10 percent (31 percent if base pension is not considered). The introduction of the digital family card offers significant potential to accurately identify poor and vulnerable people and support low-income households. Integrating benefits such as disability, multiple children, and elderly universal base pension into a unified, targeted social assistance program could enhance efficiency and provide more substantial support to people in need. CHAPTER 5. FISCAL POLICY 53 Figure 5.5. Marginal effect of fiscal policies on poverty Direct Transfers - Total 6,4% Base pension (social assistance) 4,4% Contributory pensions 4,1% Child benefit 1,6% State benefit 1,4% Indirect subsidies - Total 0,5% Utility subsidy 0,4% Social State benefit 0,4% Scholarship 0,2% Targeted social assistance 0,1% Fuel subsidy 0,1% Other transfers 0,0% Housing assistance 0,0% Alcohol exices 0,0% Tobacco excises -0,1% Fuel excises -0,1% Property taxes -0,1% Import tariffs -0,4% Social Tax -0,5% Contributions - social -0,6% Contributions - medical insurance -0,7% Contributions - pensions -0,8% Persona Income Tax -0,8% Direct Taxes - Total -1,9% VAT -3,5% Indirect taxes - Total -3,7% -6,0% -4,0% -2,0% 0,0% 2,0% 4,0% 6,0% 8,0% Source: World Bank estimates, based on the 2021 HIES. Note: PIT = personal income tax; SA = social assistance; VAT = value added tax. Figure 5.6. Benefit incidence of social assistance transfers by decile, 2021 90% 80% 80% 70% 60% 50% 40% 29% 31% 30% 25% 23% 16% 20% 13% 11% 7% 10% 0% Targeted Child State Benefit Social State Scholarship Housing Base Total Total Social Benefit Benefit assistance pension without base Assistance pension decile 1 decile 2 decile 3 decile 4 decile 5 decile 6 decile 7 decile 8 decile 9 decile 10 Source: World Bank estimates, based on the HIES. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 54 The Digital Family Card enhances government efforts to evaluate family vulnerability autonomously, thereby facilitating the automatic implementation of support measures, including for people who lack access to government internet platforms. The mechanism is simple: potential beneficiaries are contacted by text message to give their consent for the service. Once consent is obtained, social benefits, payments, or other forms of support are systematically allocated and directly transferred to the beneficiaries’ bank accounts. This initiative establishes a comprehensive, gender-sensitive, and efficient framework for social support across the population. With its incorporation into the Social Code of Kazakhstan, the project has successfully integrated information pertaining to over 6 million families into the card’s database. This integration significantly enhances the effectiveness of the allocation of government funds and the precision of social assistance delivery, leveraging real-time data. The poverty gap in consumable income in Kazakhstan is about 4.8 percent, requiring 0.4 percent of the GDP in 2021 to close the gap with perfect targeting. In 2021, Kazakhstan allocated 2 percent of its GDP to three social assistance programs: the base pension, the child benefit, and the state social benefit for disability. These programs are categorical and universal in nature. By consolidating these social assistance transfers and targeting low-income households rather than providing universal transfers, Kazakhstan can potentially eliminate poverty. Although managing this operationally may be challenging, alternative approaches involving uniform transfer amounts can significantly reduce the incidence of poverty. For instance, simulation results suggest that poverty could be reduced from 18 percent (at the consumable income) to 5 percent by redirecting the social assistance transfers currently received by the top 60 percent of the income distribution. 55 Chapter 6. HUMAN CAPITAL KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 56 Chapter 6. Human capital Human capital accumulation is key for accelerating growth and structural transformation to eradicate poverty and expand the middle class. Educational attainment significantly affects people’s economic mobility (Narayan and others 2018). Although access to education in Kazakhstan is universal, its quality and equity remain a concern. The Human Capital Index suggests that children in Kazakhstan achieve only 53–64 percent of their productivity potential, with significant disparities between income groups. Economic literature indicates that without strengthening human capital, countries can neither achieve sustained and inclusive economic growth nor can they adequately prepare their workforce for the highly skilled jobs of the future, driven by digitalization. Kazakhstan’s robust economic growth in the 2000s resulted in significant welfare improvements, evidenced by higher real wages and household incomes, along with lower levels of inequality and poverty. However, since 2014, economic growth has decelerated, with annual productivity turning negative, declining by 2–3 percentage points per year. Economic transition is necessary to mitigate the country’s dependence on its fossil fuel-based economic model (Agaidarov and others 2024). This transition, supported by improved skills and strengthened human capital, has the potential to stimulate economic growth (World Bank 2022a). Although all aspects of human capital are important for escaping from poverty, this section emphasizes the quality of education and the equality of access to such quality educational opportunities in Kazakhstan. Human capital is defined as people’s accumulated experience and skills, including education, health, training, and abilities. It helps determine their productivity and earning capacity, which in turn influences their contribution to economic growth and their ability to escape from poverty. Economic literature shows a strong correlation between an individual’s educational attainment and their earning potential, with higher educational levels generally leading to better job prospects, higher income, and more stable employment, thereby reducing their risk of poverty. In Kazakhstan, as in many other economies, educational outcomes are significantly correlated with economic security. Regression analyses suggest that households with breadwinners holding tertiary education degrees are 6 percentage points less likely to be poor and 11 percentage points more likely to belong to the middle class, and therefore to have more economic security (Figure 6.1, panel b). Developing human capital can play a vital role in increasing productivity, boosting growth, promoting mobility, and ultimately building resilience to reduce poverty and inequality. Human capital accumulation, measured by having a tertiary education degree, is positively associated with higher productivity in Kazakhstan (Figure 6.1, panel a). However, only 13 percent of new employment opportunities created since 2010 were in sectors with high labor productivity, such as the manufacturing, finance, scientific, and information sectors. In contrast, around one-third of new employment opportunities were in sectors with low productivity, including education, water, accommodation, healthcare, and public administration. Although Kazakhstan has made significant progress in building human capital, there is room for improvement in several areas. Despite both the secondary school enrollment rate and the literacy rate approaching 100 percent, education remains a significant determinant of poverty status. Poverty rates were the highest among people with lower levels of education, and this pattern has persisted over time. In 2006 the poverty rate was 65 percent for individuals with at most a primary education, but only 30 percent for those with tertiary education. This disparity has decreased, dropping from 65 to 16 percent for people with lower levels of education and from 30 to 6 percent for those with high levels of education. CHAPTER 6. HUMAN CAPITAL 57 Figure 6.1. Human capital accumulation in Kazakhstan a. Labor productivity and education b. Determinants of being poor and middle-class 80% Share of tertiary graduates Finance and insurance 70% Professional and Public administration scientific activities Education Information and 60% communication Middle-class 10,8% Arts and entertainment 50% Real estate operations Healthcare Administrative and support services 40% Other types of Electricity and energy supply services Manufacturing Trading Water supply Construction industry 30% Accommodation and Transportation and food warehousing 20% Poor -6,2% Agricultural industry 10% 0% 0 5 000 10 000 15 000 20 000 25 000 -10,0% -5,0% 0,0% 5,0% 10,0% 15,0% Labor productivity Sources: Panel a: Workforce Development Center 2022. Panel b: World Bank estimates, based on the HIES. Note: Panel b. Probit regression results are presented in the table with standard errors in parentheses. The data are pooled between 2011 and 2021. The coefficients are significant at the 1% level. The regression includes the following control variables: gender, age, income sources, number of employed people on the family, change in household size, family type, breadwinner labor force status, year, and region fixed effects. Figure 6.2. PISA scores, Kazakhstan and OECD average, 2009–22 a. Mathematics b. Reading c. Science 510 510 510 489 487 498 498 489485 490 490 493 490 492 490 487 491 490 476 491 472 470 470 470 450 432 450 450 423 425 425 423 430 430 430 405 410 410 393 410 400 397 390 387 386 390 390 390 370 370 370 350 350 350 2009 2012 2015 2018 2022 2009 2012 2015 2018 2022 2009 2012 2015 2018 2022 Kazakhstan OECD Kazakhstan OECD Kazakhstan OECD Source: World Bank estimates, based on the 2022 PISA. Note: OECD = Organisation for Economic Co-operation and Development; PISA = Programme for International Student Assessment. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 58 Quality of education There are significant disparities in the quality of education across socioeconomic groups. According to the Human Capital Index (HCI) developed by the World Bank, children in Kazakhstan are expected to complete 13.7 years of schooling, more than the OECD average. However, when adjusted for learning outcomes, the expected years of schooling decrease to 9.1 years. A socioeconomic analysis of the HCI shows a marked disparity in harmonized test scores between children from the lowest and highest income deciles. Whereas children from the wealthiest quintile are projected to achieve 64 percent of their productivity potential, those from the poorest quintile might reach only 53 percent. Moreover, substantial regional disparities in human capital persist, with HCI scores varying from 0.57 in Atyrau to 0.69 in Astana city. The performance of Kazakh students on the OECD’s Programme for International Student Assessment (PISA) suggests significant potential for improving educational outcomes. In 2022 students achieved an average score of 425 in mathematics, well below the maximum score of 625 and the OECD average of 472 (Figure 6.2). The disparity is even more pronounced in reading, where they scored 386, markedly below the OECD average of 476. Tests scores in all subjects appear to have stagnated since 2012. Average test scores still differ considerably between children from the poorest and richest households, although equity has improved (Figure 6.3). Substantial inequalities persist in the learning outcomes of 15-year-olds, as determined by their socioeconomic background, gender, and school location. Students from the highest-income group consistently achieve higher scores than those from the poorest group. Female students typically outscore their male counterparts, and students attending urban schools generally perform better than those in rural areas. Although disparities in PISA scores by location decreased from 2009 to 2018, those related to gender and socioeconomic status widened. Regional differences in the quality of education persist. Students attending urban schools generally perform better than those in rural areas (Figure 6.4). Tests scores in each subject are negatively associated with regional poverty rates—as the share of poor people in a region increases, average test scores fall. Moreover, students in the two cities, Almaty and Astana, outperform those the rest of the country. In contrast, students in Turkistan, where a quarter of poor people lived in 2021, fared the worst in all subjects. Around half of Kazakh 15-year-olds do not meet the benchmark for functional literacy (PISA Level 2, as shown in Figure 6.5). The quality of education displays the same pattern as the poverty statistics: significant improvements up to 2012, followed by a period of stagnation, and then a decline until 2022. Across all subjects, proficiency remains low. In mathematics, 50 percent of students do not demonstrate proficiency; in science and reading, the figures are 45 and 64 percent, respectively. Notably, only 2 percent of students achieved high proficiency in mathematics in 2022, a minimal increase from 2009, and the share of high-performing students in science and reading is negligible. Socioeconomic background remains a significant determinant of basic proficiency levels across all subjects. For students in the lowest income decile, basic proficiency rates in mathematics are 41 percent, with no students achieving high proficiency. Conversely, students in the highest income decile exhibit a basic proficiency rate of 65 percent, with 5 percent achieving high proficiency. The disparities are even more pronounced in reading and science, where proficiency rates range from 22 to 54 percent and 43 to 70 percent respectively between the lowest and highest deciles. A similar divide exists between urban and rural students. In urban areas, basic proficiency in mathematics is about 52 percent, as against only 43 percent in rural areas. In reading, the proficiency rates are 42 percent for urban students and 24 percent for rural students, whereas in science, they are 58 and 46 percent, respectively. Human Opportunity Index and school performance Although basic proficiency in all subjects in Kazakhstan below the OECD average, the estimated Human Opportunity Index (HOI) is even lower, highlighting substantial disparities among groups in achieving desired educational outcomes (Figure 6.6). For instance, in mathematics, 52 percent of the students achieved basic proficiency in 2022. However, the basic proficiency rate varied significantly depending on individual and household characteristics. The D-index (discussed CHAPTER 6. HUMAN CAPITAL 59 Figure 6.3. PISA scores by income decile, 2009 and 2022 a. Mathematics b. Reading c. Science 600 554 600 600 559 545 550 550 550 500 548 500 535 500 549 450 450 450 400 400 400 350 319 350 350 305 300 300 263 300 250 250 250 276 245 262 200 200 200 5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 75th 90th 95th 5th 10th 25th 50th 75th 90th 95th 2009 2012 2009 2022 2009 2022 Source: World Bank estimates, based on data from the 2022 PISA. Note: PISA = Programme for International Student Assessment. Figure 6.4. Average PISA test scores and poverty rates, by region a. Mathematics b. Reading c. Science 14% 14% 14% 12% 12% 12% 10% 10% 10% Poverty rates 8% 8% 8% 6% 6% 6% 4% 4% 4% 2% 2% 2% 0% 0% 0% 350 400 450 500 300 350 400 450 380 400 420 440 460 480 Test scores Test scores Test scores Source: World Bank estimates, based on data from the 2022 PISA. Note: PISA = Programme for International Student Assessment. Figure 6.5. Relative PISA proficiency scores, 2009–22 Share of PISA students scoring below Level 2 (functional literacy), Kazakhstan a. Mathematics b. Reading c. Science 100 1 1 2 2 100 100 80 40 80 39 80 41 43 35 36 54 49 49 44 54 percent 58 percent percent 60 60 60 40 40 40 59 60 59 57 64 64 45 49 50 55 45 20 20 20 42 0 0 0 2009 2012 2018 2022 2009 2012 2018 2022 2009 2012 2018 2022 High proficiency High proficiency High proficiency Basic proficiency Basic proficiency Basic proficiency Below basic proficiency Below basic proficiency Below basic proficiency Source: World Bank estimates, based on data from the 2022 PISA. Note: OECD = Organisation for Economic Co-operation and Development; PISA = Programme for International Student Assessment. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 60 in Box 6.1) was estimated at 9.7 percent; this rate indicates the difference in proficiency rates among children to achieve the same rate across all demographic and socioeconomic groups. Finally, the HOI, where the coverage rate is adjusted for these differences, drops to 46.7 percent in Kazakhstan. The highest dissimilarity index is observed in the basic literacy outcomes for reading. In 2022, the basic proficiency rate for basic reading literacy was 36.4 percent, significantly lower than the OECD average. Considering variations due to individual and family characteristics, the HOI decreases to 27.5, indicating a substantial D-index of 24.5 in this subject. Similarly, basic literacy in science is affected by family background. The D-index for achieving basic literacy in science was 11.3 percent, which lowers the overall coverage from 55.9 percent to 49.6 percent. The D-index can be decomposed into different sets of circumstances that may affect a child’s outcome. To identify the impact of individual and family characteristics on basic proficiency, eight circumstances are analyzed (Box 6.1). The analysis of learning outcomes across different geographic and socioeconomic groups reveals important inequalities. The variation in outcomes among these groups is analyzed and attributed to factors such as geographic location, household characteristics, and income quintiles. Box 6.1. The Human Opportunity Index An equality-of-opportunity profile can help policymakers design effective public policies to level the playing field for all children. The dissimilarity index (D-index) measures whether existing opportunities (access to services) are allocated equitably, comparing the probability of access to a given opportunity among groups with different circumstances. The D-index ranges from 0 to 1: • A value of 0 implies that access to an opportunity is the same among the general population, regardless of people’s situation (such as whether they live in urban or rural areas or are male or female). • A value of 1 indicates that a group is completely excluded from access. The D-index effectively measures the share of opportunities that would have to be reallocated across different groups of children for all groups to have equal access, or more precisely, the fraction of people who would need to have a good or service reassigned as a percentage of all people who have access to the good or service. Accordingly (1 − D) would be the percentage of available opportunities that were properly allocated. The Human Opportunity Index (HOI) can be shown as: HOI = C × (1 – D) where C is the coverage rate, and D is the dissimilarity index. Hence, the HOI can be seen as the average coverage rate, discounted by one minus the inequality index D. Should all possible groups have the same access, the D-index would be zero, and the HOI would equal the coverage rate. In the other extreme, should one group have full access while another has none, the D-index would equal one and the HOI zero. Source: World Bank Comprehensive analysis, incorporating both family and individual characteristics, indicates that household wealth and the language spoken at home are the most significant predictors of a student’s basic proficiency across all subjects (Figure 6.7). In particular, the language spoken at home has a significant impact on reading outcomes, highlighting substantial disparities between groups. Household wealth, as indicated by possessions in the home, is consistently a key factor in differences of basic literacy in mathematics. The correlation between a student’s basic literacy and household characteristics is a robust empirical finding, documented extensively in both developed and developing nations. In CHAPTER 6. HUMAN CAPITAL 61 Figure 6.6. Human Opportunity Index and coverage, 2012 and 2022 60 50 40 30 20 10 0 2012 2022 2012 2022 2012 2022 Math Reading Science Coverage HOI Source: World Bank analysis, based on the 2022 PISA. Note: HOI = Human Opportunity Index; PISA = Programme for International Student Assessment. Figure 6.7. Variation explained by circumstance, 2022 100 20,1 24,9 Male Student 80 35,3 Father’s educational attainment: tertiary education Rural school 60 41,0 35,2 Language at home: Russian 20,1 Mother's educational attainment: tertiary education % 40 14,5 Father’s occupational status (isei) (bottom 40%) 6,9 10,6 Mother’s occupational status (isei) (bottom 40%) 13,9 13,5 12,9 20 Home possesions at home (B40%) 7,1 10,2 13,1 9,8 4,9 0 0,5 Math Reading Science Source: World Bank analysis, based on the 2022 PISA. Note: PISA = Programme for International Student Assessment. Figure 6.8. Aggregate quality score of universities and country income level Source: Demirgüc-Kunt and Torre 2022. Note: This graph plots, for every country with available data, the aggregate university quality score (vertical axis) and the log GDP per capita at PPP in 2019 (horizontal axis). Only countries present in at least one of the six rankings are included. Orange points indicate countries in Europe and Central Asia. The black line indicates the nonparametric regression. ECA = Europe and Central Asia; GDP = gross domestic product; PPP = purchasing power parity. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 62 addition, the urban/rural divide is significant in explaining differences in learning outcomes, as it affects access to services. Parental occupation status also emerges as a critical familial characteristic that influences children’s learning outcomes in all subjects. Quality of higher education The quality of universities in Kazakhstan is lower than those of its peers, and it is particularly low for the country’s income level (Figure 6.8). In the analysis of Demirgüc-Kunt and Torre (2022), the aggregate university quality score for Kazakhstan is 9.3 points (in a theoretical range of 0 to 100), well below the average of 15 points for Europe and Central Asia and 20.7 points for countries of a similar income level. Other former republics of the Soviet Union show similar levels of comparable underperformance. The relatively low quality of universities is mirrored in relatively lower skills proficiency among tertiary education graduates in literacy, numeracy, and problem solving, according to data from the Programme for the International Assessment of Adult Competencies (PIAAC) (Figure 6.9). This reflects the correlation between “input quality” and “output quality” in higher education: poor-quality universities result in graduates with poor skill proficiency. Kazakhstan seems to face a low-quality–high-enrollment equilibrium (Figure 6.10). Its gross graduation rate of higher education is particularly high—the number of graduates of first-degree tertiary programs is equivalent to more than 56 percent of the population in the theoretical age of graduation, according to data from the United Nations Educational, Scientific and Cultural Organization (UNESCO). In this sense, the benefits from improving the quality of the higher education system could be significant, as the coverage of the system is already very wide. Small improvements in university quality may improve the skill proficiency and academic outcomes of a significant share of the population, eventually leading to higher productivity. Preparing for digitalization Digital skills are increasingly vital for labor market outcomes and individual earnings; however, substantial disparities persist in the acquisition of and returns on digital skills in the labor market. Digitalization is profoundly influencing people’s lives, employment prospects, and the developmental trajectories of countries by transforming various sectors. It has introduced new paths to innovation, efficiency, and inclusivity, offering significant benefits and opportunities for individuals, organizations, and countries. With digital skills becoming increasingly important for labor market outcomes and broader social inclusion, ensuring that people have these skills is essential. As countries transition to digital service delivery and a digital economy, a lack of digital skills can increase disparities, potentially widening the gap between low- and high-income households. In Kazakhstan, self-reported digital skills are above average for the Europe and Central Asia region (Figure 6.11). About two-thirds of the population report having basic digital skills, such as managing files and sending emails. About 41 percent report having intermediate skills, which include installing software and setting up devices. However, only 16 percent of people possess advanced digital skills, such as programming capabilities. The acquisition of digital skills depends significantly on individual and family characteristics. Children born to educated parents or living in households with higher income are more likely to develop digital skills than their low-income or less- advantaged counterparts. In both Kazakhstan and the Europe and Central Asia region, females are less likely to possess digital skills than males. Also, whereas rural residency and educational attainment are closely linked to digital skills in the Europe and Central Asia region, these associations are not as pronounced in Kazakhstan (Table 6.1.). Ensuring equality of opportunity in developing digital skills will be crucial to develop an equitable human capital in Kazakhstan. CHAPTER 6. HUMAN CAPITAL 63 Figure 6.9. Adult skill proficiency and quality of higher education Sources: World Bank, based on PIAAC data; Demirgüç-Kunt and Torre 2022. Note: This graph plots the country-level average skills proficiency scores in PIAAC for tertiary graduates (vertical axis) against the country-level quality of higher education (horizontal axis). The quality of higher education is proxied by the aggregate university quality score as calculated by Demirgüç-Kunt and Torre (2022). Red points indicate EMDEs in Europe and Central Asia. PIAAC = Programme for the International Assessment of Adult Competencies. Figure 6.10. Gross graduation rate for tertiary education among peers Sources: Demirgüç-Kunt and Torre 2022; UNESCO; World Bank. Note: This graph plots the gross graduation rate for tertiary education (vertical axis) and the aggregate university quality score (horizontal axis). The black line indicates the linear fit between the variables. The dashed horizontal line indicates the average value of the gross graduation rate, whereas the dashed vertical line indicates the average value of the university quality score. The gross graduation rate is defined as the ratio between the annual number of graduates from first degree tertiary programs (International Standard Classification of Education (ISCED) 6 and 7, bachelor’s and master’s degrees respectively) and the population in the theoretical age of the most common first-degree program. The aggregate university quality score is sourced from Demirgüç-Kunt and Torre (2022). KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 64 Table 6.1. Determinants of the acquisition of digital skills dependent variable: Europe and Central Asia Kazakhstan having advanced skills Age -0.0214*** -0.0357** (0.0037) (0.0151) Age-squared 7.28e-05*** 0.0001 (1.69e-05) (0.0002) Top 50 0.2583*** 0.2052*** (0.0173) (0.0623) female -0.1555*** -0.0274 (0.0142) (0.0593) urban 0.1409*** 0.0239 (0.0157) (0.0588) Secondary education 0.2262*** 0.0574 (0.0230) (0.2032) Higher education 0.7708*** 0.5221** (0.0272) (0.2092) Father’s education 0.1108*** 0.3040*** (0.0158) (0.0722) Mother’s education 0.1061*** 0.1072 (0.0162) (0.0726) Source: Life in Transition Survey. Note: **p < .01 ***p < .001. Digital skills are crucial for accessing quality employment opportunities. The returns on digital skills are significantly higher in Kazakhstan than in the Europe and Central Asia region (Table 6.2). In the Europe and Central Asia, the wage premium for basic digital skills is minimal and statistically insignificant; in contrast, in Kazakhstan, people with basic digital skills earn about 25 percent more than those without any such skills. The wage premium increases for people with medium and advanced digital skills: workers with medium digital skills earn 46 percent higher wages than those without any such skills, and those with advanced digital skills earn 50 percent more. In the Europe and Central Asia region, the wage premium for advanced digital skills is only 14 percent. Investments in human capital affect and are affected by the structure of the economy. For instance, in the absence of competitive markets, education is less likely to contribute to economic development. In the former Soviet Union and other countries with planned economies, extensive educational systems, though impressive on paper, often failed to translate into economic progress because the inefficient economic systems underutilized educated people (Becker 1995). Another pivotal concept is agglomeration economies, suggesting potential areas for improvement in Kazakhstan’s utilization of its human capital investments. The digital transformation is a critical component for the enhancement of public service provision. The Republic of Kazakhstan has allocated substantial resources to enable citizens to access public services digitally. This is particularly vital for a nation that covers an expanse of 3 million square kilometers and is characterized by one of the world’s lowest population densities. According to the Ministry of Digital Development, Innovation and Aerospace Industry, Kazakhstan has made 92% of all governmental services available online, with 85% accessible via mobile devices. Additionally, the populace can utilize the Telemedicine program to access health services. Nonetheless, the widespread utilization of these services by the citizenry necessitates investments in digital competencies and internet connectivity throughout the nation. CHAPTER 6. HUMAN CAPITAL 65 Figure 6.11. Self-reported digital skills, regional comparison 90 Manage files and send e-mails 80 Install new software or devices 68,49 Write a computer program using a specialized programming language 70 60 41,30 50 40 30 15,92 20 10 0 TJK KGZ MDA HRV ARM UZB AZE GEO MKD TUR ECA MNE KOS BGR BIH ROU KAZ RUS POL BLR DEU ALB SRB Source: World Bank estimates by using the Life in Transition Survey 2023. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 66 Table 6.2. Wage premiums for digital skills dependent variable: log(wage) Europe and Central Asia Kazakhstan basic digital skills 0.0283 0.240**   (0.0336) (0.114) medium digital skills 0.111*** 0.464***   (0.0353) (0.150) advanced digital skills 0.144*** 0.511***   (0.0415) (0.146) female -0.115*** -0.0767   (0.0224) (0.0854) secondary education -0.0219 -0.192   (0.0421) (0.285) higher education 0.162*** 0.211   (0.0502) (0.288) experience 0.0319* 0.0453   (0.0181) (0.0835) parent’s education 0.0517*** -0.0127   (0.0165) (0.0569) Source: Life in Transition Survey. Note: **p < .01 ***p < .001. 67 Chapter 7. CLIMATE SHOCKS KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 68 Chapter 7. Climate shocks Disasters and climate change disproportionately hurt poor and low-income communities. To mitigate the effects of climate shocks on vulnerable people, Kazakhstan could prioritize investments in enhancing their resilience. Vulnerability to climate change Kazakhstan’s vulnerability to climate change is higher than the average for the Europe and Central Asia region. According to the World Risk Index, its exposure to climate risks is below the average for the region, but its vulnerability to climate risks is above the regional average. This vulnerability is exacerbated by Kazakhstan’s underdeveloped coping and adaptive capacities (Figure 7.1). Moreover, with rising average temperatures and increasingly variable rainfall, the country remains susceptible to natural disasters. Such disasters are increasing in frequency and intensity, exposing vulnerable populations to climate risks. Climate change projections suggest risks to Kazakhstan’s developmental trajectory. Climate shocks are expected to have a substantial impact on the economy, especially in sectors such as agriculture and forestry. Such shocks are likely to disrupt lives and livelihoods, especially among poor communities. The expected changes in temperature and precipitation patterns could lead to more frequent and intense droughts, floods, landslides, and heat stress events, which would disproportionately affect rural livelihoods and vulnerable communities who rely heavily on the land. Poor and low-income communities are more exposed and vulnerable to climate change and disasters. People who lack adequate income and human development opportunities are not only resource-poor but also frequently live in hazardous areas or in poorly constructed homes that are prone to damage or collapse. Wealthier households typically have better access to coping mechanisms, such as financial savings, insurance, and even early warning systems. These socioeconomic disparities not only intensify the immediate impacts of shocks but also extend the duration of recovery and reconstruction efforts. Disasters can have long-term effects, pushing affected households into difficult choices between regular expenses (such as food, education, and health care) and the longer-term costs of asset replacement or reconstruction. Thus, it is vital to understand the relationship between poverty and climate risk in the country. With its large land territory, Kazakhstan is exposed to various climate change-related shocks. To assess and quantify the impact of these hazards, whether extreme events or long-term climatic changes, the rest of this chapter discusses three types of exposure: (a) population, (b) built-up assets, and (c) agricultural land, in terms of the relationship between exposure to hazards and consumption levels. The hazards discussed here are floods, heat stress, droughts, and landslides. Although not a climate hazard as such, air pollution is also discussed, as it exacerbates vulnerability through its effects on health, educational attainment, and labor productivity. CHAPTER 7. CLIMATE SHOCKS 69 Figure 7.1. Climate risks, regional comparison 20 60 18 50 16 Vulnerability Index 14 40 Exposure Index 12 10 30 8 20 6 4 10 2 0 0 France Serbia Slovenia Bulgaria Slovak Republic Kazakhstan Russia N. Macedonia Lithuania Belarus Azerbaijan Germany Ukraine Austria Poland Spain Moldova Turkey Armenia Georgia Romania Tajikistan Montenegro Kyrgyz… Albania Croatia Czech Rep. Bosnia and… Italy Uzbekistan Turkmenistan Source: World Risk Index. Box 7.1. The Disaster Risk Model The Intergovernmental Panel on Climate Change defines a natural hazard as the potential occurrence of a natural or human-induced physical event or trend that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, ecosystems, and environmental resources (IPCC and others 2019). Exposure describes the location of people and assets in an environment where they may be threatened by these natural hazards. People and assets may be exposed, yet not adversely affected if they are not vulnerable. Vulnerability summarizes the propensity or predisposition to be adversely affected when exposed, measured by characteristics that favor a negative impact of a hazard if exposed to it. Disaster risk is then the probability of a negative impact in the future caused by a natural hazard. Together, hazard (H), exposure (E), and vulnerability (V) drive disaster risk (R) (IPCC 2012): R=f (H, E, V) Sources: IPCC and others 2019; IPCC 2012. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 70 High risk of floods Floods are particularly threatening for poor and vulnerable households. The main climate-related risk in Kazakhstan is flooding, particularly in terms of damage to economic assets. In April 2024 the country experienced one of the worst floods in its history, with devastating consequences and damages estimated at 200 billion tenge (US$446 million). This followed six major flooding events between 1985 and 2013, whose economic impacts were most strongly felt in the western Atyrau region and the southern Kyzylorda region. These regions respectively lose an average of 11 and 5 percent of GDP to flooding every year, contributing to the countrywide loss from flooding of US$480 million per year. Intermediate scenarios of climate change suggest that, even without any changes in the population, extreme river flooding may rise by 72 percent. The agricultural land most vulnerable to annual flooding is primarily in the northern and southern regions of Kazakhstan. Here, most districts experience flooding depths exceeding 0.5 meters across areas greater than 100 hectares (Figure 7.2, panel a). Such deep flooding can have profound impacts on crop yields, food prices, and overall food security. In the northern, eastern, and southeastern regions, however, people have relatively higher rates of consumption (a proxy measure of income or welfare, as discussed in earlier), which suggests that local populations may have more resources to take preventative and adaptive measures, despite the considerable impact of flooding. People’s vulnerability, or their capacity to mitigate or prevent adverse effects from their high exposure to floods, is primarily driven by their socioeconomic status. To create an integrated picture of hazard, exposure, and vulnerability, Figure 7.2, panels b–d overlay the impact of river flooding with per capita rates of consumption at a district level. In these figures, a three-by-three matrix shows quantiles of decreasing per capita consumption in the reds, expected annual impact classes for the population (panel d) and built-up assets (panel c), and expected annual exposure classes for agricultural land (panel b). The correlation between per capita consumption and the anticipated impact of floods on communities, their infrastructure, and their lands give important insights on vulnerability. The potential impact of floods on mortality, built- up assets, and agricultural lands could be extensive throughout the country. Districts with lower per capita consumption, such as South Kazakhstan and Kyzylorda in the south, Atyrau and West Kazakhstan in the west, and Kostanay in the north, particularly for agricultural concerns, are most susceptible to flooding. South Kazakhstan’s population is exceptionally vulnerable to the compounded impacts of flooding across the three exposure categories. This puts lives and livelihoods at risk and diminishes the adaptive capacity of communities, and especially poor households, to floods. Poorer households have limited mobility and may well have little option but to live in flood-prone areas; they are also less likely to be able to afford insurance. This is a critical concern in areas that are susceptible to severe flooding. Drought in the northwest and southeast Droughts occur more frequently and are becoming more severe. Drought is a significant concern in the northwestern regions, and the southeastern part of the country also experiences extensive and recurrent droughts (Figure 7.3, panel a). The western regions of Mangystau, Atyrau, and West Kazakhstan, along with specific areas in Kyzylorda, Almaty, Kostanay, and Akmola, experience the most frequent and severe droughts. Over 30 percent of the cropland in these regions is subjected to drought stress at least once every four to five years, potentially leading to substantial crop failures that could critically undermine food security. Should climate change cause a 3°C increase in temperature, droughts are likely to become four to ten times more frequent. This would exacerbate land degradation, desertification, and dust storms, particularly in the Kyzylorda and Mangystau regions, which would have an 80 percent probability of severe droughts every year by the end of the century. The impact of such droughts could potentially include a 33 percent reduction in wheat yields by 2030 and a 10 percent decrease in livestock productivity, underscoring the urgency of mitigating these challenges. CHAPTER 7. CLIMATE SHOCKS 71 Figure 7.2. Impact of floods a. Agricultural land b. Agricultural land and consumption c. Built-up assets and consumption d. Mortality and morbidity Source: World Bank estimates, based on the HIES. Note: EAI = expected annual impact. Figure 7.3. Drought exposure of agricultural land a. Agricultural stress index b. Frequency of exposure to drought Source: World Bank estimates, based on the HIES. Note: FAO = Food and Agriculture Organization. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 72 The increasing frequency and extent of droughts affecting agricultural land in the western half of Kazakhstan coincide with the growing socioeconomic vulnerability of the people in these areas, as indicated by the 2018 per capita consumption rates (Figure 7.3, panel b). In these regions, poor communities who depend on agriculture are especially susceptible to lower crop yields due to droughts, which could lead to price increases and food insecurity. These areas could be prioritized for interventions to mitigate the potential detrimental effects of droughts on rural livelihoods. Air pollution and heat waves Air pollution and heatwaves exert a comparatively moderate impact in Kazakhstan. Heatwaves mainly affect the southwestern part of the country, where wet bulb globe temperatures commonly reach around 35 degrees Celsius during episodes of heat stress, with a 20-year return period (Figure 7.4, panel a). The hazard and expected annual impact of air pollution follow a similar spatial distribution pattern. The risk patterns are similar when people’s consumption patterns are considered (Figure 7.4, panel b). In the southern and western districts of the South Kazakhstan and Mangystau regions, people are relatively poor, with low levels of consumption, and annual mortality from pollution is relatively high. Figure 7.4. Impact of heat waves and air pollution a. Heat stress exposure b. Air pollution exposure Source: World Bank estimates, based on the HIES. Note: EAE = expected annual exposure. 73 Chapter 8. CONCLUSION KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 74 Chapter 8. Conclusion Kazakhstan has made remarkable progress in reducing poverty and enhancing economic growth over the past two decades. The country transitioned from lower-middle-income to upper-middle-income status, driven largely by market- oriented reforms, extensive resource extraction, and substantial foreign direct investment. This economic transformation has significantly improved living standards and reduced poverty rates from 49.5 percent in 2006 to 8.5 percent in 2021. However, economic growth has slowed since 2014, and productivity has started to decline. Income inequality has increased, and regional disparities in poverty persist, particularly in rural areas and the Turkistan region. The COVID-19 pandemic exacerbated these challenges, highlighting the need for resilient and inclusive economic strategies. Fiscal policies and social assistance programs have had a modest impact on poverty and inequality. Although the fiscal system has helped to reduce income inequality, there is room for improvement in targeting social assistance and increasing the efficiency of public spending. The country also faces growing risks from climate-related shocks, which disproportionately affect poor and vulnerable people. Educational attainment plays a crucial role in people’s economic advancement and helps to prevent them falling into poverty. Although access to education in Kazakhstan is widespread, issues with the quality and fairness of education persist. According to the Human Capital Index, children in Kazakhstan only reach 53–64 percent of their potential productivity, with notable differences across income levels. To sustain progress and achieve inclusive growth, Kazakhstan could address these challenges through comprehensive policy measures to enhance human capital, improve the quality of education, and build resilience against economic and climate-related shocks. Policy recommendations Kazakhstan could invest in the quality of education to ensure that all children, regardless of their socioeconomic background, receive a quality education. Strengthening the accumulation of human capital could help the country achieve sustained, inclusive economic growth and prepare its workforce for the highly skilled, digitalization-driven jobs of the future. By reducing disparities in educational outcomes between rural and urban areas and across income levels, policymakers could help level the playfield for all children. They could also design targeted programs to improve literacy and numeracy skills, particularly in regions where educational attainment is lower. International experience shows that focused and sustained reform efforts can yield impressive improvements in a country’s human capital: 1. Implement education reforms for equal access to quality education. Develop and enforce education policies to enhance the quality and effectiveness of the education system. These policies should ensure equal educational opportunities for all students and strive to raise overall educational attainment levels. Unlike school enrollment, which is universal in Kazakhstan, learning deficiency is often unobserved. Improvements in measuring learning and acting on evidence can improve equity by revealing hidden barriers. The absence of robust evaluation implies that educational frameworks may operate without clear direction or a consensus on the ultimate goals. 2. Enhance the pre-tertiary education system. Focus on improving the quality of pre-tertiary education. Basic education reforms should promote innovative teaching and learning methods, emphasize basic skills development, upgrade learning environments and facilities, and improve the management of educational institutions. 3. Improve higher education. Enhance the relevance, sustainability, and quality of higher education. According to the Listening to Kazakhstan Survey, 47 percent of people believe that universities do not provide a high standard of education. Moreover, Kazakhstan currently faces a low-quality–high-enrollment equilibrium. Significant investment in higher education is essential to increase the quality of education, which is crucial for improving productivity. Higher education graduates play a vital role in this productivity enhancement. CHAPTER 8. CONCLUSION 75 4. Invest in digital skills. Recognize the importance of digital skills in the future labor market and support skills development in this area. Only one-third of people report that schools in Kazakhstan effectively prepare students for the labor market (Listening to Kazakhstan 2024). Integrate digital skills development into the school curriculum at every level. Provide lifelong learning opportunities for individuals of all ages to improve their digital skills, ensuring they remain competitive in a rapidly digitalizing world. The redistributive performance of the fiscal system could be improved. To increase revenue collection, Kazakhstan could focus on progressive taxation, especially property and personal income taxes, rather than indirect taxes, which are regressive and disproportionately hurt low-income households. Tax policies could be accompanied by targeted social transfers to poorer households. Moreover, the fiscal system could help to mitigate the impact of economic shocks, and countercyclical fiscal policies could be implemented to support the economy during downturns. 1. Revise eligibility requirements from categorical to means-tested criteria. Transitioning from categorical to means- tested criteria can increase the effectiveness of the social assistance system. Categorical systems often have high leakage levels, benefiting high-income households. Enhancing the existing targeted social assistance program could serve as a model for refining all the social assistance schemes. For more effective poverty eradication, the budget of the targeted social assistance program could be increased by consolidating other programs, and its target group could be expanded beyond the current 3 percent of the population. 2. Redirect government spending from broadly applied subsidies to targeted support for poor and vulnerable people. Currently, only 30 percent of the fuel subsidies directly support the poorest 40 percent of people. Targeted transfers are relatively more effective in supporting low-income households: around 80 percent of the benefits of targeted social assistance support the poorest 10 percent of the population. 3. Increase revenue collection through progressive taxes. Revenue collection is low, and efforts to increase revenue through regressive taxes can hurt vulnerable people. Revenue could be enhanced through property and carbon taxes and making personal income tax more progressive. About 53 percent of people believe the tax rate for high- income earners should be higher, whereas only 1 percent hold that the rate for low-income earners should increase (Listening to Kazakhstan 2024). Similarly, only 9 percent agree that value added tax should be higher than its current level of 12 percent. If indirect taxes need to be raised, these should be accompanied cash transfers to mitigate adverse effects on the most vulnerable people. Building resilience against climate shocks is vital to protect people’s well-being and assets. As one of the most carbon- intensive economies in the world, Kazakhstan could promote renewable energy sources to mitigate its greenhouse gas emissions. In parallel, it could also strengthen infrastructure and community resilience and promote insurance against climate events. 1. Invest in risk insurance programs. Risk insurance programs are crucial for managing the financial impacts of climate shocks. They provide compensation for losses, incentivize risk reduction, promote economic resilience, and support access to credit. The government could ensure universal coverage of these programs by providing financial support for low-income households. 2. Support quick recovery. Financial inclusion, such as access to emergency borrowing, and robust social protection systems are essential for aiding firms and individuals in recovering from disasters. Adaptive social protection systems, which can be rapidly scaled up to cover more people and provide greater support after a disaster, are particularly effective. 3. Invest in resilient infrastructure and develop monitoring and early warning systems for climate events. The government could develop strategies to mitigate the risks and impacts of climate events, especially as the frequency of these events is expected to increase. It is essential to ensure that the risks are mitigated particularly for low- income households, as they have fewer resources for these investments. Preparations such as early warning system, and effective emergency management can significantly reduce physical damage and economic losses. In the long run, the country could invest in climate resilient infrastructure for all families by providing support for low-income households. KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 76 Implementing these policy recommendations could help Kazakhstan build on its progress, address current challenges, and ensure sustainable, inclusive growth. Enhanced human capital, inclusive economic policies, efficient public spending, and climate resilience are critical for achieving long-term development goals and improving the well-being of all people in Kazakhstan. Table 8.1. Proposed priority of policies Policy area Short-term Long-term -Evaluate learning outcomes to measure -Allow systematic tracking of learning outcomes over educational quality across regions and time and promote innovative teaching and learning establish serious targets. methods. -Invest in teacher training to manage student -Ensure quality school management in all regions. learning. -Enhance vocational training aligned with industry -Provide instructional support to students needs. Human who are lagging in outcomes. -Upgrade educational infrastructure equitably. capital -Prioritize early childhood education and -Implement spatially blind social policies and ensure literacy programs. high quality of schools, hospital, and social services -Invest in digital skills in national curriculum. across all regions. Provide financial support to impoverished -Accelerate the universal coverage and usage of families to have access to computers. digital infrastructure. -Revise eligibility requirements from -Monitor the performance of social assistance categorical to means-tested criteria. delivery system; evaluate targeting, coverage, and -Integrate Digital Family Card and social adequacy. assistance systems to improve inclusion and -Redirect government spending from broadly applied exclusion errors. subsidies to targeted support for poor and vulnerable Fiscal Policy people. -Extend the coverage of the Targeted Social and Social Assistance program by revising the eligibility -Introduce active labor market programs to address Protection criteria and providing fair assistance for poor. major challenges in the labor market including -Improve program design with labor market productivity and mobility. incentives by providing more productive -Improve shock-responsiveness and the adaptability jobs, introducing job search support and (re-) of the social assistance system. training courses for the beneficiaries. -Identify barriers in tax compliance including -Increase the progressivity of personal income behavioral, tax declaration process, etc. tax. -Invest in national climate insurance -Invest in resilient infrastructure. programs. Provide financial support to those -Develop monitoring and early warning systems for who cannot afford. climate shocks. Climate shock resilience -Support quick recovery from climate shocks. -Adopt comprehensive disaster risk management Adapt cash support programs after a climate strategy encompassing risk identification, disaster for low-income households. management, and adaptation. -Strengthen institutional coordination for -Promote climate-smart agriculture practices. effective climate risk management. -Improve data collection and analysis to identify the most vulnerable to climate impacts. Source: World Bank. 77 REFERENCES KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 78 References Agaidarov, Azamat, David Stephen Knight, and Natasha Sharma. 2024. Kazakhstan Economic Update: Shaping Tomorrow: Reforms for Lasting Prosperity. Kazakhstan Economic Update. Washington, DC: World Bank. http://documents.worldbank. org/curated/en/099759502082435630/IDU133466db918b7c14af01903b1ab7f20dfb809. 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KAZAKHSTAN POVERTY AND EQUITY ASSESSMENT 80 ANNEX Old age pension (all schemes, national, civil servants, veterans, other special) Contributory pensions Survivors pension (all schemes, national, civil servants, veterans, other special) Disability pension (all schemes, national, civil servants, veterans, SOCIAL other special) INSURANCE Occupational injuries benefits Paid sickness leave benefits Other social insurance Health Maternity/Paternity benefits Training (vocational, life skills, cash for training) Employment incentives/wage subsidies Employment measures for disabled Labor market policy measures (active LM programs) Entrepreneurship support /startup incentives (cash and in kind grant, microcredit) LABOR MARKET Labor Market services and intermediation through PES Other Active Labor Market Programs Out-of-work income maintenance (Unemployment benefits, Labor market policy support contributory (passive LM programs) Out-of-work income maintenance (Unemployment benefits, non- contributory) REFERENCES 81 Poverty targeted cash transfers and last resort programs Family/ children/orphan allowance (including orphan and vulnerable children benefits) Unconditional cash transfers Non-contributory funeral grants, burial allowances Emergency cash support (including support to refugees/returning migrants) Public charity, including zakat Conditional cash transfers Conditional cash transfers Old age social pensions Social pensions Disability benefits/war victims noncontributory related benefits (non-contributory) Survivorship Food stamps, rations and vouchers Food distribution programs Food and in-kind transfers Nutritional programs (therapeutic, supplementary feeding and SOCIAL PLHIV) ASSISTANCE In kind/non-food support (education supplies, free texts and uniforms) School feeding School feeding Public works, workfare and Cash for work direct job creation Food for work (including food for training, food for assets etc.) Health insurance exemptions and reduced medical fees Education fee waivers Food subsidies Fee waivers and subsidies Housing subsidies and allowances (and "privileges") Utility and electricity subsidies and allowances Agricultural inputs subsidies Scholarships/education benefits Other social assistance Social care services, transfers for care givers What is left out from above categories Domestic transfers, inter-family in kind gifts and monetary transfers Domestic private transfers Alimony (divorce and food) PRIVATE TRANSFERS Income and support from charity/ private zakat, support for churches and NGOs* International private transfers Remittances from abroad