Publication:
Children in Monetary Poor Households: Global, Regional, and Select National Trends in the Progress against Child Poverty

Abstract
This paper presents the first estimates of extreme child poverty and child poverty using the World Bank’s recently revised international poverty lines. Using the international poverty line of $3.00 per day and the higher $8.30 per day poverty line (both expressed in 2021 purchasing power parity), the paper provides new results of the global and regional trends over 2014–24. The estimates show that 19.2 percent of children, approximately 412 million children, were living on less than $3.00 (2021 PPP) per day as of 2024, a reduction from 507 million children in 2014. This long-term decrease was slower than that for the general population. At the higher line of $8.30, the child poverty rate in 2024 was 65.9 percent, representing around 1.4 billion children, a drop from the 73.1 percent registered in 2014. At the regional level, the East Asia and Pacific and South Asia regions witnessed significant reductions in child poverty and extreme child poverty between 2014 and 2024, and the Europe and Central Asia and Latin America and the Caribbean regions showed reductions mostly in child poverty. In the same period, there was an increase in extreme child poverty in the Middle East and North Africa region. Sub-Saharan Africa experienced a “lost decade” of child poverty reduction between 2014 and 2024, increasing its concentration of global poverty. In 2024, Sub-Saharan Africa hosted more than three-quarters of children in extreme poor households (more than 311 million children), although its share of the global child population was around 23 percent. Country-level results show evidence of regional heterogeneity in progress against extreme child poverty.
Link to Data Set
Citation
Lara Ibarra, Gabriel; Salmeron Gomez, Daylan Alberto; Engilbertsdottir, Solrun; Diaz-Bonilla, Carolina; Delamonica, Enrique; Yablonski, Jennifer. 2025. Children in Monetary Poor Households: Global, Regional, and Select National Trends in the Progress against Child Poverty. Policy Research Working Paper; 11203. © World Bank. http://hdl.handle.net/10986/43680 License: CC BY 3.0 IGO.
Associated URLs
Associated content
Report Series
Report Series
Other publications in this report series
  • Publication
    The Economic Value of Weather Forecasts: A Quantitative Systematic Literature Review
    (Washington, DC: World Bank, 2025-09-10) Farkas, Hannah; Linsenmeier, Manuel; Talevi, Marta; Avner, Paolo; Jafino, Bramka Arga; Sidibe, Moussa
    This study systematically reviews the literature that quantifies the economic benefits of weather observations and forecasts in four weather-dependent economic sectors: agriculture, energy, transport, and disaster-risk management. The review covers 175 peer-reviewed journal articles and 15 policy reports. Findings show that the literature is concentrated in high-income countries and most studies use theoretical models, followed by observational and then experimental research designs. Forecast horizons studied, meteorological variables and services, and monetization techniques vary markedly by sector. Estimated benefits even within specific subsectors span several orders of magnitude and broad uncertainty ranges. An econometric meta-analysis suggests that theoretical studies and studies in richer countries tend to report significantly larger values. Barriers that hinder value realization are identified on both the provider and user sides, with inadequate relevance, weak dissemination, and limited ability to act recurring across sectors. Policy reports rely heavily on back-of-the-envelope or recursive benefit-transfer estimates, rather than on the methods and results of the peer-reviewed literature, revealing a science-to-policy gap. These findings suggest substantial socioeconomic potential of hydrometeorological services around the world, but also knowledge gaps that require more valuation studies focusing on low- and middle-income countries, addressing provider- and user-side barriers and employing rigorous empirical valuation methods to complement and validate theoretical models.
  • Publication
    Direct and Indirect Impacts of Transport Mobility on Access to Jobs: Evidence from South Africa
    (Washington, DC: World Bank, 2025-11-12) Iimi, Atsushi
    Access to jobs is essential for economic growth. In Africa, unemployment rates are notably high. This paper reexamines the relationship between transport mobility and labor market outcomes, with a particular focus on the direct and indirect effects of transport connectivity. As predicted by theory, wages are influenced by the level of commuting deterrence. Generally, higher earnings are associated with longer commute times and/or higher commuting costs. Local accessibility is also important, especially for individuals with time constraints. Both direct and indirect impacts are found to be significant in South Africa, where job accessibility has been challenging since the end of apartheid. For the direct impact, the wage elasticity associated with commuting costs is significant. Returns on commute are particularly high for women. Local accessibility to socioeconomic facilities, such as shops and health services, is also found to have a significant impact, consistent with the concept of mobility of care. To enhance employment, therefore, it is crucial to connect people not only to job locations but also to various socioeconomic points of interest, such as markets and hospitals, in an integrated manner. This integration will enable individuals to spend more time working and commuting longer distances.
  • Publication
    The Macroeconomic Implications of Climate Change Impacts and Adaptation Options
    (Washington, DC: World Bank, 2025-05-29) Abalo, Kodzovi; Boehlert, Brent; Bui, Thanh; Burns, Andrew; Castillo, Diego; Chewpreecha, Unnada; Haider, Alexander; Hallegatte, Stephane; Jooste, Charl; McIsaac, Florent; Ruberl, Heather; Smet, Kim; Strzepek, Ken
    Estimating the macroeconomic implications of climate change impacts and adaptation options is a topic of intense research. This paper presents a framework in the World Bank's macrostructural model to assess climate-related damages. This approach has been used in many Country Climate and Development Reports, a World Bank diagnostic that identifies priorities to ensure continued development in spite of climate change and climate policy objectives. The methodology captures a set of impact channels through which climate change affects the economy by (1) connecting a set of biophysical models to the macroeconomic model and (2) exploring a set of development and climate scenarios. The paper summarizes the results for five countries, highlighting the sources and magnitudes of their vulnerability --- with estimated gross domestic product losses in 2050 exceeding 10 percent of gross domestic product in some countries and scenarios, although only a small set of impact channels is included. The paper also presents estimates of the macroeconomic gains from sector-level adaptation interventions, considering their upfront costs and avoided climate impacts and finding significant net gross domestic product gains from adaptation opportunities identified in the Country Climate and Development Reports. Finally, the paper discusses the limits of current modeling approaches, and their complementarity with empirical approaches based on historical data series. The integrated modeling approach proposed in this paper can inform policymakers as they make proactive decisions on climate change adaptation and resilience.
  • Publication
    From Policy to Practice: Lessons from the Implementation of the Refugee Work Rights Policy in Ethiopia
    (Washington, DC: World Bank, 2025-11-10) Perez, Ana Maria; Rozo, Sandra V.
    This paper examines the early implementation of Ethiopia’s refugee work rights policy, with a focus on the issuance of permits that enable refugees to engage in economic activities. Building on significant legal and institutional advances under the 2019 Refugee Proclamation and subsequent directives, the analysis explores how these reforms are being operationalized in practice. Using a mixed-methods approach, combining document review, administrative data analysis, and semi-structured interviews, the paper identifies both progress and remaining challenges. Permit issuance has increased since the adoption of detailed operational guidance in 2024, reflecting the Government of Ethiopia’s commitment to operationalizing its progressive legal framework and ensuring that refugees can exercise their right to work. However, take-up remains modest, with about 5.2 percent of the working-age population holding a permit. Preliminary evidence suggests that coordination gaps, limited subnational capacity, low awareness among refugees and employers, and disincentives to formalize in a largely informal labor market are contributing to the low take-up. The paper offers policy suggestions, grounded in the Ethiopian context and emerging evidence, to help translate legal commitments into improved labor market outcomes for refugees.
  • Publication
    Monitoring Global Aid Flows: A Novel Approach Using Large Language Models
    (Washington, DC: World Bank, 2025-11-04) Luo, Xubei; Rajasekaran, Arvind Balaji; Scruggs, Andrew Conner
    Effective monitoring of development aid is the foundation for assessing the alignment of flows with their intended development objectives. Existing reporting systems, such as the Organisation for Economic Co-operation and Development’s Creditor Reporting System, provide standardized classification of aid activities but have limitations when it comes to capturing new areas like climate change, digitalization, and other cross-cutting themes. This paper proposes a bottom-up, unsupervised machine learning framework that leverages textual descriptions of aid projects to generate highly granular activity clusters. Using the 2021 Creditor Reporting System data set of nearly 400,000 records, the model produces 841 clusters, which are then grouped into 80 subsectors. These clusters reveal 36 emerging aid areas not tracked in the current Creditor Reporting System taxonomy, allow unpacking of “multi-sectoral” and “sector not specified” classifications, and enable estimation of flows to new themes, including World Bank Global Challenge Programs, International Development Association–20 Special Themes, and Cross-Cutting Issues. Validation against both Creditor Reporting System benchmarks and International Development Association commitment data demonstrates robustness. This approach illustrates how machine learning and the new advances in large language models can enhance the monitoring of global aid flows and inform future improvements in aid classification and reporting. It offers a useful tool that can support more responsive and evidence-based decision-making, helping to better align resources with evolving development priorities.
Journal
Journal Volume
Journal Issue

Related items

Showing items related by metadata.

  • Publication
    October 2025 Update to the Multidimensional Poverty Measure: What’s New
    (Washington, DC: World Bank, 2025-10-24) Lara Ibarra, Gabriel; Nguyen, Minh Cong; Salmeron Gomez, Daylan Alberto; Haddad, Cameron Nadim
    This note presents the 10th edition of the World Bank’s Multidimensional Poverty Measure (MPM) database, drawing on the latest country data from the Global Monitoring Database (GMD) as of October 2025. The MPM offers a broader view of poverty by examining deprivations along three dimensions of well-being: monetary poverty (measured using the international poverty line at $3.00 per person per day in 2021 PPP), education, and access to basic infrastructure services. This latest edition covers 113 economies for circa 2022, and notably includes India and Nigeria, which drastically increases the population coverage of the MPM globally.
  • Publication
    June 2025 Update to the Poverty and Inequality Platform (PIP)
    (Washington, DC: World Bank, 2025-06-11) Alfani, Federica; Aaron, Danielle V.; Atamanov, Aziz; Aguilar, R.Andres Castaneda; Diaz-Bonilla, Carolina; Devpura, Nancy P.; Dewina, Reno; Finn, Arden; Fujs, Tony; Gonzalez, Maria Fernanda; Krishnan, Nandini; Kochhar, Nishtha; Kumar, Naresh; Lakner, Christoph; Ibarra, Gabriel Lara; Lestani, Diego; Liniado, Julia; Lønborg, Jonas; Mahler, Daniel G.; Mejía-Mantilla, Carolina; Montalva, Veronica; Herrera, Laura L.; Nguyen, Minh C.; Rubiano, Eliana; Sajaia, Zurab; Castro, Diana M.; Seshan, Ganesh K.; Tetteh-Baah, Samuel K.; Mendoza, Martha C. Viveros; Wu, Haoyu; Yonzan, Nishant; Wambile, Ayago
    The June 2025 update to the Poverty and Inequality Platform (PIP) introduces several important changes to the data underlying the global poverty estimates. The most important change is the adoption of the 2021 Purchasing Power Parities (PPPs). In addition, new data for India has been incorporated and the existing series adjusted for comparability. This document details the changes to underlying data and the methodological reasons behind them. Depending on the availability of recent survey data, global and regional poverty estimates are reported up to 2023, together with nowcasts up to 2025. The PIP database now includes 74 new country-years, bringing the total number of surveys to over 2,400, for 172 economies.
  • Publication
    April 2022 Update to the Poverty and Inequality Platform (PIP)
    (World Bank, Washington, DC, 2022-04) Castaneda Aguilar, R. Andres; Dewina, Reno; Diaz-Bonilla, Carolina; Edochie, Ifeanyi N.; Fujs, Tony H. M. J.; Jolliffe, Dean; Lain, Jonathan; Lakner, Christoph; Ibarra, Gabriel Lara; Mahler, Daniel G.; Meyer, Moritz; Montes, Jose; Moreno Herrera, Laura L.; Mungai, Rose; Newhouse, David; Nguyen, Minh C.; Sanchez Castro, Diana; Schoch, Marta; Sousa, Liliana D.; Tetteh-Baah, Samuel K.; Uochi, Ikuko; Viveros Mendoza, Martha C.; Wu, Haoya; Yonzan, Nishant; Yoshida, Nobu
    The April 2022 update to the newly launched Poverty and Inequality Platform (PIP) involves several changes to the data underlying the global poverty estimates. Some welfare aggregates have been changed for improved harmonization, and the CPI, national accounts, and population input data have been updated. This document explains these changes in detail and the reasoning behind them. Moreover, a large number of new country-years have been added, bringing the total number of surveys to more than 2,000. These include new harmonized surveys for countries in West Africa, new imputed poverty estimates for Nigeria, and recent 2020 household survey data for several countries. Global poverty estimates are now reported up to 2018 and earlier years have been revised.
  • Publication
    The World Bank’s New Inequality Indicator
    (Washington, DC: World Bank, 2024-06-11) Haddad, Cameron Nadim; Mahler, Daniel Gerszon; Diaz-Bonilla, Carolina; Hill, Ruth; Lakner, Christoph; Lara Ibarra, Gabriel
    The World Bank recently introduced a new key indicator to guide its work: the number of countries with high inequality, defined as a Gini index above 40. The new indicator was introduced as part of the new World Bank vision of ending poverty on a livable planet. This paper reviews why reducing inequality matters for ending poverty on a livable planet, summarizes the advantages and disadvantages of using the Gini index to track inequality, outlines challenges in measuring inequality, and discusses what a Gini threshold of 40 implies. Using the most recent data for every country, 52 countries of a total of 169 countries are classified as high inequality countries, which represents a decline from 74 countries at the beginning of the millennium.
  • Publication
    March 2024 Update to the Poverty and Inequality Platform (PIP)
    (Washington, DC: World Bank, 2024-04-01) Castaneda Aguilar, R. Andres; Castillo, Adriana; Devpura, Nancy P.; Dewina, Reno; Diaz-Bonilla, Carolina; Edochie, Ifeanyi; Farfan Bertran, Maria G.; Fernandez Romero, Jaime; Foster, Elizabeth; Fujs, Tony H. M. J.; Gonzalez Icaza, Maria F.; Jolliffe, Dean; Knippenberg, Erwin W.; Krishnan, Nandini; Lakner, Christopher; Lara Ibarra, Gabriel; Lestani, Diego G.; Mahler, Daniel G.; Montalvo Talledo, Veronica S.; Montes, Jose; Nguyen, Minh C.; Olivieri, Sergio; Paffhausen, Anna Luisa; Redaelli, Silvia; Saavedra, Trinidad B.; Sanchez Castro, Diana M.; Tetteh-Baah, Samuel K.; Viveros Mendoza, Martha C.; Wu, Haoyu; Yonzan, Nishant; Yoshida, Nobuo
    The March 2024 update to the Poverty and Inequality Platform (PIP) involves several changes to the data underlying the global poverty estimates. In particular, some welfare aggregates have been revised, and the CPI, national accounts, and population input data have been updated. This document explains these changes in detail and the reasoning behind them. Moreover, 101 new country-years have been added, bringing the total number of surveys to more than 2,300. Depending on the availability of recent survey data, global and regional poverty estimates are reported up to 2022. This is the first time PIP is reporting global poverty estimates post-2019, covering the period of the COVID-19 pandemic.

Users also downloaded

Showing related downloaded files

  • Publication
    VAT Cashback Programs in Practice: The Case of Devolve-ICMS in Rio Grande do Sul, Brazil
    (Washington, DC: World Bank, 2025-08-28) Bachas, Pierre; Flores, Tatiana; Lara Ibarra, Gabriel; Mantovani, Anderson Aparecido; Oliveira, Evandro Souza de; Padilha, Giovanni; Scot, Thiago
    This note describes the Devolve-ICMS program, an innovative VAT cashback initiative aimed at refunding taxes to low-income households in the state of Rio Grande do Sul, Brazil. Using administrative records, the authors document several stylized facts about the workings of the program. First, even the poorest households sometimes consume in formal stores. Second, as a share of reported income, lower income families consume relatively more on average. Third, 60 percent of households only receive a flat cash transfer, unrelated to their formal consumption–this highlights the trade-offs in generosity vs. incentivizing formal consumption. Finally, formal consumption is substantially higher for female headed households and for participants in a concurrent lottery program that also incentivizes formal consumption.
  • Publication
    The Burden of Road Traffic Injuries in Brazil: Evidence for Policy
    (Washington, DC: World Bank, 2025-09-08) Lamas, C.B.; Caldeira, G.P.; Obelheiro, M.R.; Bastos, J.T.
    In 2023, Brazil recorded more than 34,000 fatalities due to road traffic injuries (RTIs). Most of the victims were traveling by motorcycle. This assessment of the burden of RTIs, disabilities, and deaths consists of: (1) a road safety analysis using available official data in Brazil; (2) a computation of globally recognized metrics for assessing the burden of disease, including years of life lost (YLL), years lived with disability (YLD), and disability-adjusted life years (DALYs); (3) a comprehensive cost analysis of traffic crashes in Brazil to estimate expenses related to medical care, hospital stays, production losses, human and administrative costs, property damage, and other traffic-related costs; and (4) the collection and analysis of primary data on RTIs from hospital surveillance and the monitoring of crash victims after hospital discharge—as assessed one and three months after discharge. Key findings from assessing the burden of RTIs in Brazil using global metrics found that, in 2021–22, RTIs and associated deaths resulted in 1.7 million YLL, 637,000 YLD, and 2.34 million DALYs in Brazil. Males accounted for the majority of the total DALYs at 83.7 percent. Motorcyclists contributed to 55.5 percent of total male DALYs, and 39.0 percent of DALYs among women. An estimate of the total costs of road traffic crashes in Brazil—factoring in medical expenses, hospital care, production losses, human costs, administrative expenses, property damage, and other costs—reveals that the total cost of traffic crashes in Brazil is an estimated US$61.3 billion per year, representing 3.8 percent of GDP. Intangible human costs represent 57 percent of the total cost, while production losses make up 17 percent of the total cost. Based on these and other findings from hospital surveys, the study recommends that Brazilian policymakers: (1) embrace road safety as a high political commitment; (2) shift road design culture and practice; (3) implement evidence-based policy from data-driven analysis; and (4) manage exposure through safer modal split.
  • Publication
    Poverty Mapping in the Age of Machine Learning
    (World Bank, Washington, DC, 2023-05-04) Henderson, Heath; Corral, Paul; Segovia, Sandra
    Recent years have witnessed considerable methodological advances in poverty mapping, much of which has focused on the application of modern machine-learning approaches to remotely sensed data. Poverty maps produced with these methods generally share a common validation procedure, which assesses model performance by comparing subnational machine-learning-based poverty estimates with survey-based, direct estimates. Although unbiased, survey-based estimates at a granular level can be imprecise measures of true poverty rates, meaning that it is unclear whether the validation procedures used in machine-learning approaches are informative of actual model performance. This paper examines the credibility of existing approaches to model validation by constructing a pseudo-census from the Mexican Intercensal Survey of 2015, which is used to conduct several design-based simulation experiments. The findings show that the validation procedure often used for machine-learning approaches can be misleading in terms of model assessment since it yields incorrect information for choosing what may be the best set of estimates across different methods and scenarios. Using alternative validation methods, the paper shows that machine-learning-based estimates can rival traditional, more data intensive poverty mapping approaches. Further, the closest approximation to existing machine-learning approaches, using publicly available geo-referenced data, performs poorly when evaluated against “true” poverty rates and fails to outperform traditional poverty mapping methods in targeting simulations.
  • Publication
    South Asia Development Update, April 2025: Taxing Times
    (Washington, DC: World Bank, 2025-04-23) World Bank
    Growth prospects for South Asia have dimmed. The global economic environment has become more challenging and is a source of heightened downside risks. After a decade of repeated disruptions, South Asia’s buffers to cushion new shocks are slim. Tackling some of its greatest inefficiencies and vulnerabilities could help South Asia navigate this unusually uncertain outlook: unproductive agricultural sectors, dependence on energy imports, pressures from rising global temperatures, and fragile fiscal positions. For most South Asian countries, increased revenue mobilization is a prerequisite for strengthening fiscal positions. Even taking into account the particular challenges of collecting taxes in South Asian economies—such as widespread informal economic activity and large agriculture sectors—South Asian economies face larger tax gaps than the average emerging market and developing economy (EMDE). This suggests the need for improved tax policy and administration. Until fiscal positions have strengthened, the burden of climate adaptation will disproportionately fall on the private sector. If allowed sufficient flexibility, private sector adaptation could offset about one-third of the likely climate damage by 2050. This may, however, require governments to remove obstacles that prevent workers and firms from moving across locations and activities. As growth prospects dim, the challenge grows to create jobs for South Asia’s rapidly expanding working-age population. South Asia’s large diasporas could become a source of strength if their knowledge, networks, and other resources can be better tapped for investment and trade.
  • Publication
    Algorithms and Bureaucrats: Evidence from Tax Audit Selection in Senegal
    (Washington, DC: World Bank, 2025-09-05) Bachas, Pierre; Brockmeyer, Anne; Ferreira, Alipio; Sarr, Bassirou
    Can algorithms enhance bureaucrats’ work in developing countries? In data-poor environments, bureaucrats often exercise discretion over key decisions, such as audit selection. Exploiting newly digitized micro-data, this study conducted an at-scale field experiment whereby half of Senegal’s annual audit program was selected by tax inspectors and the other half by a transparent risk-scoring algorithm. The algorithm-selected audits were 18 percentage points less likely to be conducted, detected 89% less evasion, were less cost-effective, and did not reduce corruption. Moreover, even a machine-learning algorithm would only have moderately raised detected evasion. These results are consistent with bureaucrats’ expertise, the task complexity, and inherent data limitations.