Publication: Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning
Loading...
Published
2022-09
ISSN
Date
2022-09-15
Editor(s)
Abstract
Estimates of poverty are an important input into policy formulation in developing countries. The accurate measurement of poverty rates is therefore a first-order problem for development policy. This paper shows that combining satellite imagery with household surveys can improve the precision and accuracy of estimated poverty rates in Mexican municipalities, a level at which the survey is not considered representative. It also shows that a household-level model outperforms other common small area estimation methods. However, poverty estimates in 2015 derived from geospatial data remain less accurate than 2010 estimates derived from household census data. These results indicate that the incorporation of household survey data and widely available satellite imagery can improve on existing poverty estimates in developing countries when census data are old or when patterns of poverty are changing rapidly, even for small subgroups.
Link to Data Set
Citation
“Newhouse, David; Merfeld, Joshua; Ramakrishnan, Anusha Pudugramam; Swartz, Tom; Lahiri, Partha. 2022. Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning. Policy Research Working Papers;10175. © World Bank. http://hdl.handle.net/10986/38020 License: CC BY 3.0 IGO.”
Associated URLs
Associated content
Other publications in this report series
Publication Global Poverty Revisited Using 2021 PPPs and New Data on Consumption(Washington, DC: World Bank, 2025-06-05)Recent improvements in survey methodologies have increased measured consumption in many low- and lower-middle-income countries that now collect a more comprehensive measure of household consumption. Faced with such methodological changes, countries have frequently revised upward their national poverty lines to make them appropriate for the new measures of consumption. This in turn affects the World Bank’s global poverty lines when they are periodically revised. The international poverty line, which is based on the typical poverty line in low-income countries, increases by around 40 percent to $3.00 when the more recent national poverty lines as well as the 2021 purchasing power parities are incorporated. The net impact of the changes in international prices, the poverty line, and new survey data (including new data for India) is an increase in global extreme poverty by some 125 million people in 2022, and a significant shift of poverty away from South Asia and toward Sub-Saharan Africa. The changes at higher poverty lines, which are more relevant to middle-income countries, are mixed.Publication Intergenerational Income Mobility around the World(Washington, DC: World Bank, 2025-07-09)This paper introduces a new global database with estimates of intergenerational income mobility for 87 countries, covering 84 percent of the world’s population. This marks a notable expansion of the cross-country evidence base on income mobility, particularly among low- and middle-income countries. The estimates indicate that the negative association between income mobility and inequality (known as the Great Gatsby Curve) continues to hold across this wider range of countries. The database also reveals a positive association between income mobility and national income per capita, suggesting that countries achieve higher levels of intergenerational mobility as they grow richer.Publication The Macroeconomic Implications of Climate Change Impacts and Adaptation Options(Washington, DC: World Bank, 2025-05-29)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 Higher Education Expansion and Firm Organization(Washington, DC: World Bank, 2025-09-17)This paper investigates the impact of higher education expansion on firm performance in developing countries. It focuses on the significant expansion of higher education in Brazil between 2000 and 2012, which substantially increased higher education enrollment and graduation rates, thereby reducing the costs of hiring college-educated workers. Building on the theory of knowledge-based hierarchies and using a difference-in-differences approach and matching techniques, the paper finds that the surge in skilled labor supply led to a rise in the proportion of college-educated workers within firms in the treated microregions. This increase was accompanied by an increased prob-ability of firms adding knowledge hierarchies, followed by a rise in productivity and an increased likelihood of export. The findings suggest that policies affecting the cost and accessibility of hiring professionals and managers can significantly influence firms’ organizational structures, with implications for firm performance and productivity.Publication The Exposure of Workers to Artificial Intelligence in Low- and Middle-Income Countries(Washington, DC: World Bank, 2025-02-05)Research on the labor market implications of artificial intelligence has focused principally on high-income countries. This paper analyzes this issue using microdata from a large set of low- and middle-income countries, applying a measure of potential artificial intelligence occupational exposure to a harmonized set of labor force surveys for 25 countries, covering a population of 3.5 billion people. The approach advances work by using harmonized microdata at the level of individual workers, which allows for a multivariate analysis of factors associated with exposure. Additionally, unlike earlier papers, the paper uses highly detailed (4 digit) occupation codes, which provide a more reliable mapping of artificial intelligence exposure to occupation. Results within countries, show that artificial intelligence exposure is higher for women, urban workers, and those with higher education. Exposure decreases by country income level, with high exposure for just 12 percent of workers in low-income countries and 15 percent of workers in lower-middle-income countries. Furthermore, lack of access to electricity limits effective exposure in low-income countries. These results suggest that for developing countries, and in particular low-income countries, the labor market impacts of artificial intelligence will be more limited than in high-income countries. While greater exposure to artificial intelligence indicates larger potential for future changes in certain occupations, it does not equate to job loss, as it could result in augmentation of worker productivity, automation of some tasks, or both.
Journal
Journal Volume
Journal Issue
Collections
Related items
Showing items related by metadata.
Publication Estimating Small Area Population Density Using Survey Data and Satellite Imagery(World Bank, Washington, DC, 2019-03)Country-level census data are typically collected once every 10 years. However, conflict, migration, urbanization, and natural disasters can cause rapid shifts in local population patterns. This study uses Sri Lankan data to demonstrate the feasibility of a bottom-up method that combines household survey data with contemporaneous satellite imagery to track frequent changes in local population density. A Poisson regression model based on indicators derived from satellite data, selected using the least absolute shrinkage and selection operator, accurately predicts village-level population density. The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey to obtain out-of-sample density predictions in the nonsurveyed villages. The predictions approximate the 2012 census density well and are more accurate than other bottom-up studies based on lower-resolution satellite data. The predictions are also more accurate than most publicly available population products, which rely on areal interpolation of census data to redistribute population at the local level. The accuracies are similar when estimated using a random forest model, and when density estimates are expressed in terms of population counts. The collective evidence suggests that combining surveys with satellite data is a cost-effective method to track local population changes at more frequent intervals.Publication Estimating Small Area Poverty and Welfare Indicators in Timor-Leste Using Satellite Imagery Data(World Bank, Washington, DC, 2020-09-28)This report is structured as follows: an in-depth explanation of the FHSAE method is presented in section two. Section three reviews the sub-district level data used in this study, which includes imprecise TL-SLS and DHS direct estimates, as well as satellite imagery data used in this study. The variable selection method used for the FHSAE model in this model is explained in section four. Section five provides the results of the FHSAE exercise on poverty estimates, average real per capita consumption and welfare index, presenting them in the graphical maps. Section six concludes.Publication Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes(World Bank, Washington, DC, 2022-06)Better understanding the geography of women’s labor market outcomes within countries is important to inform targeted efforts to increase women’s economic empowerment. This paper assesses the extent to which a method that combines simulated survey data from urban areas in Mexico with broadly available geospatial indicators from Google Earth Engine and OpenStreetMap can significantly improve estimates of labor force participation and unemployment rates. Incorporating geospatial information substantially increases the accuracy of male and female labor force participation and unemployment rates at the state level, reducing mean absolute deviation by 50 to 62 percent for labor force participation and 25 to 52 percent for unemployment. Small area estimation using a nested error conditional random effect model also greatly improves municipal estimates of labor force participation, as the mean absolute error falls by approximately half, while the mean squared error falls by almost 75 percent when holding coverage rates constant. In contrast, the results for municipal unemployment rate estimates are not reliable because values of unemployment rates are low and therefore poorly suited for linear models. The municipal results hold in repeated simulations of alternative samples. Models utilizing Basic Geo-Statistical Area (AGEB)–level auxiliary information generate more accurate predictions than area-level models specified using the same auxiliary data. Overall, integrating survey data and publicly available geospatial indicators is feasible and can greatly improve state-level estimates of male and female labor force participation and unemployment rates, as well as municipal estimates of male and female labor force participation.Publication Small Area Estimation of Poverty and Wealth Using Geospatial Data(World Bank, Washington, DC, 2023-07-18)This paper offers a nontechnical review of selected applications that combine survey and geospatial data to generate small area estimates of wealth or poverty. Publicly available data from satellites and phones predicts poverty and wealth accurately across space, when evaluated against census data, and their use in model-based estimates improve the accuracy and efficiency of direct survey estimates. Although the evidence is scant, models based on interpretable features appear to predict at least as well as estimates derived from Convolutional Neural Networks. Estimates for sampled areas are significantly more accurate than those for non-sampled areas due to informative sampling. In general, estimates benefit from using geospatial data at the most disaggregated level possible. Tree-based machine learning methods appear to generate more accurate estimates than linear mixed models. Small area estimates using geospatial data can improve the design of social assistance programs, particularly when the existing targeting system is poorly designed.Publication Improving Estimates of Mean Welfare and Uncertainty in Developing Countries(World Bank, Washington, DC, 2023-03)Reliable small-area estimates of economic welfare significantly inform the design and evaluation of development policies. This paper compares the accuracy of wealth estimates obtained from the empirical best predictor (EBP) of a linear nested error model, Cubist regression, extreme gradient boosting, and boosted regression forests. The evaluation draws two-stage samples from unit-level household census data in seven developing countries, combines them with publicly available geospatial indicators to generate small area estimates of assets for all seven countries and poverty for two, and evaluates these estimates against census-derived benchmarks. Extreme gradient boosting and Cubist regression generally produce more accurate predictions than traditional EBP models. A proposed two-stage residual bootstrap procedure slightly underestimates confidence intervals, but leads to higher coverage rates than the parametric bootstrap approach used for EBP predictions. These results demonstrate that, given a sufficiently large sample of enumeration areas, predictions from extreme gradient boosting or Cubist regression with a two-stage residual block bootstrap generally provide more accurate point and uncertainty estimates for generating small-area welfare estimates.
Users also downloaded
Showing related downloaded files
Publication Classroom Assessment to Support Foundational Literacy(Washington, DC: World Bank, 2025-03-21)This document focuses primarily on how classroom assessment activities can measure students’ literacy skills as they progress along a learning trajectory towards reading fluently and with comprehension by the end of primary school grades. The document addresses considerations regarding the design and implementation of early grade reading classroom assessment, provides examples of assessment activities from a variety of countries and contexts, and discusses the importance of incorporating classroom assessment practices into teacher training and professional development opportunities for teachers. The structure of the document is as follows. The first section presents definitions and addresses basic questions on classroom assessment. Section 2 covers the intersection between assessment and early grade reading by discussing how learning assessment can measure early grade reading skills following the reading learning trajectory. Section 3 compares some of the most common early grade literacy assessment tools with respect to the early grade reading skills and developmental phases. Section 4 of the document addresses teacher training considerations in developing, scoring, and using early grade reading assessment. Additional issues in assessing reading skills in the classroom and using assessment results to improve teaching and learning are reviewed in section 5. Throughout the document, country cases are presented to demonstrate how assessment activities can be implemented in the classroom in different contexts.Publication The Journey Ahead(Washington, DC: World Bank, 2024-10-31)The Journey Ahead: Supporting Successful Migration in Europe and Central Asia provides an in-depth analysis of international migration in Europe and Central Asia (ECA) and the implications for policy making. By identifying challenges and opportunities associated with migration in the region, it aims to inform a more nuanced, evidencebased debate on the costs and benefits of cross-border mobility. Using data-driven insights and new analysis, the report shows that migration has been an engine of prosperity and has helped address some of ECA’s demographic and socioeconomic disparities. Yet, migration’s full economic potential remains untapped. The report identifies multiple barriers keeping migration from achieving its full potential. Crucially, it argues that policies in both origin and destination countries can help maximize the development impacts of migration and effectively manage the economic, social, and political costs. Drawing from a wide range of literature, country experiences, and novel analysis, The Journey Ahead presents actionable policy options to enhance the benefits of migration for destination and origin countries and migrants themselves. Some measures can be taken unilaterally by countries, whereas others require close bilateral or regional coordination. The recommendations are tailored to different types of migration— forced displacement as well as high-skilled and low-skilled economic migration—and from the perspectives of both sending and receiving countries. This report serves as a comprehensive resource for governments, development partners, and other stakeholders throughout Europe and Central Asia, where the richness and diversity of migration experiences provide valuable insights for policy makers in other regions of the world.Publication Argentina Country Climate and Development Report(World Bank, Washington, DC, 2022-11)The Argentina Country Climate and Development Report (CCDR) explores opportunities and identifies trade-offs for aligning Argentina’s growth and poverty reduction policies with its commitments on, and its ability to withstand, climate change. It assesses how the country can: reduce its vulnerability to climate shocks through targeted public and private investments and adequation of social protection. The report also shows how Argentina can seize the benefits of a global decarbonization path to sustain a more robust economic growth through further development of Argentina’s potential for renewable energy, energy efficiency actions, the lithium value chain, as well as climate-smart agriculture (and land use) options. Given Argentina’s context, this CCDR focuses on win-win policies and investments, which have large co-benefits or can contribute to raising the country’s growth while helping to adapt the economy, also considering how human capital actions can accompany a just transition.Publication World Development Report 2006(Washington, DC, 2005)This year’s Word Development Report (WDR), the twenty-eighth, looks at the role of equity in the development process. It defines equity in terms of two basic principles. The first is equal opportunities: that a person’s chances in life should be determined by his or her talents and efforts, rather than by pre-determined circumstances such as race, gender, social or family background. The second principle is the avoidance of extreme deprivation in outcomes, particularly in health, education and consumption levels. This principle thus includes the objective of poverty reduction. The report’s main message is that, in the long run, the pursuit of equity and the pursuit of economic prosperity are complementary. In addition to detailed chapters exploring these and related issues, the Report contains selected data from the World Development Indicators 2005‹an appendix of economic and social data for over 200 countries. This Report offers practical insights for policymakers, executives, scholars, and all those with an interest in economic development.Publication Lebanon Economic Monitor, Fall 2022(Washington, DC, 2022-11)The economy continues to contract, albeit at a somewhat slower pace. Public finances improved in 2021, but only because spending collapsed faster than revenue generation. Testament to the continued atrophy of Lebanon’s economy, the Lebanese Pound continues to depreciate sharply. The sharp deterioration in the currency continues to drive surging inflation, in triple digits since July 2020, impacting the poor and vulnerable the most. An unprecedented institutional vacuum will likely further delay any agreement on crisis resolution and much needed reforms; this includes prior actions as part of the April 2022 International Monetary Fund (IMF) staff-level agreement (SLA). Divergent views among key stakeholders on how to distribute the financial losses remains the main bottleneck for reaching an agreement on a comprehensive reform agenda. Lebanon needs to urgently adopt a domestic, equitable, and comprehensive solution that is predicated on: (i) addressing upfront the balance sheet impairments, (ii) restoring liquidity, and (iii) adhering to sound global practices of bail-in solutions based on a hierarchy of creditors (starting with banks’ shareholders) that protects small depositors.