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 The Economic Value of Weather Forecasts: A Quantitative Systematic Literature Review(Washington, DC: World Bank, 2025-09-10)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 The Marshall Plan: Then and Now(Washington, DC: World Bank, 2025-10-14)This paper is a product of the Development Policy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp.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 It’s Not (Just) the Tariffs: Rethinking Non-Tariff Measures in a Fragmented Global Economy(Washington, DC: World Bank, 2025-10-22)As tariffs have declined, non-tariff measures (NTMs) have become central to trade policy, especially in high-income countries and regulated sectors like food and green technologies. Although NTMs may serve legitimate goals, they could also sort countries and firms into or out of markets based on compliance capacity and differences in product mix. Documenting recent advances in the estimation of ad valorem equivalents (AVEs), this paper uncovers new patterns of use and exposure of NTMs. High-income countries rely more heavily on NTMs relative to tariffs, while low- and middle-income countries face steeper AVEs on their exports. Firm-level evidence shows that NTMs disproportionately affect smaller firms, leading to market exit and concentration. Poorly designed NTMs can harm productivity and welfare, while coordinated, capacity-aware use can deliver inclusive outcomes. Policy design, transparency, and diagnostics must evolve to reflect the growing role—and risks—of NTMs in a fragmented global trade landscape.
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 Morocco Economic Update, Winter 2025(Washington, DC: World Bank, 2025-04-03)Despite the drought causing a modest deceleration of overall GDP growth to 3.2 percent, the Moroccan economy has exhibited some encouraging trends in 2024. Non-agricultural growth has accelerated to an estimated 3.8 percent, driven by a revitalized industrial sector and a rebound in gross capital formation. Inflation has dropped below 1 percent, allowing Bank al-Maghrib to begin easing its monetary policy. While rural labor markets remain depressed, the economy has added close to 162,000 jobs in urban areas. Morocco’s external position remains strong overall, with a moderate current account deficit largely financed by growing foreign direct investment inflows, underpinned by solid investor confidence indicators. Despite significant spending pressures, the debt-to-GDP ratio is slowly declining.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 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 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 Digital Africa(Washington, DC: World Bank, 2023-03-13)All African countries need better and more jobs for their growing populations. "Digital Africa: Technological Transformation for Jobs" shows that broader use of productivity-enhancing, digital technologies by enterprises and households is imperative to generate such jobs, including for lower-skilled people. At the same time, it can support not only countries’ short-term objective of postpandemic economic recovery but also their vision of economic transformation with more inclusive growth. These outcomes are not automatic, however. Mobile internet availability has increased throughout the continent in recent years, but Africa’s uptake gap is the highest in the world. Areas with at least 3G mobile internet service now cover 84 percent of Africa’s population, but only 22 percent uses such services. And the average African business lags in the use of smartphones and computers as well as more sophisticated digital technologies that catalyze further productivity gains. Two issues explain the usage gap: affordability of these new technologies and willingness to use them. For the 40 percent of Africans below the extreme poverty line, mobile data plans alone would cost one-third of their incomes—in addition to the price of access devices, apps, and electricity. Data plans for small- and medium-size businesses are also more expensive than in other regions. Moreover, shortcomings in the quality of internet services—and in the supply of attractive, skills-appropriate apps that promote entrepreneurship and raise earnings—dampen people’s willingness to use them. For those countries already using these technologies, the development payoffs are significant. New empirical studies for this report add to the rapidly growing evidence that mobile internet availability directly raises enterprise productivity, increases jobs, and reduces poverty throughout Africa. To realize these and other benefits more widely, Africa’s countries must implement complementary and mutually reinforcing policies to strengthen both consumers’ ability to pay and willingness to use digital technologies. These interventions must prioritize productive use to generate large numbers of inclusive jobs in a region poised to benefit from a massive, youthful workforce—one projected to become the world’s largest by the end of this century.