Poverty maps provide a granular representation of the spatial distribution of poverty within a country or geographical area. They are increasingly useful tools for evaluative analysis, enabling a more refined assessment of the relevance and effectiveness of development interventions by leveraging new data sources that can serve as informative proxies for subnational poverty levels. Traditionally, poverty maps have relied on survey or census data to derive poverty estimates. The emergence of nontraditional data sources such as satellite imagery, call detail records (CDRs), smartphone metadata, and Wi-Fi connectivity opens new possibilities for achieving more timely and accurate poverty estimates. The high level of disaggregation provided by these sources allows for the visualization of poverty estimates at the household or village level, generating a more nuanced estimation of poverty than many conventional approaches do.1 Furthermore, comparing poverty estimates at different points in time permits examination of temporal changes in poverty at very high levels of spatial disaggregation.

Poverty maps enable various stakeholders (government officials, program managers, the media, and others) to deepen their understanding of poverty and its determinants, allowing them to target development policies and programs in a more informed manner. They are also particularly helpful in the context of evaluation, allowing evaluators to examine the effects of interventions on the incidence and magnitude of poverty, including changes over time.

As noted above, poverty maps have traditionally relied on survey or census data to derive poverty estimates. This was only feasible where recent and accurate data existed. Data can be outdated or missing, and large-scale data collection efforts can be time-consuming and expensive. For instance, a 2015 World Bank study on the availability of traditional poverty data concluded that there was no meaningful way of monitoring poverty using conventional sources (such as census or survey data) for over a third of the world’s low- or middle-income countries (Serajuddin et al. 2015). This study revealed that among the 155 countries for which the World Bank monitors poverty data, 29 had no poverty data points and 28 had only one poverty data point during 2002–11.

There is therefore an urgent need for cost-efficient rapid tools that can develop up-to-date poverty estimates. The emergence of nontraditional data sources, recent advances in data science and artificial intelligence, and increased computational capacity open new possibilities for using more indirect proxies of poverty to derive accurate and timely poverty maps. Specifically, poverty maps could play a critical role in assessing the relevance of targeting and evaluating program effectiveness by using poverty proxies for spatial counterfactual analysis.

This paper aims to provide guidance on methods to create poverty maps based on different data sources. The methods explored can be categorized in two main groups:

1. Methods based on either or both household surveys and census data on assets, consumption, expenditures, and access to services. This category includes methods (for example, small-area estimation and some of its variants) that use data sources such as large-scale surveys. Such surveys include the Living Standards Measurement Study (LSMS) and national household surveys. These approaches primarily apply multivariate statistical techniques to derive poverty estimates.
2. Methods based on more indirect proxies for poverty estimation, such as remote sensing data, CDRs, and the Global System for Mobile Communications or smartphone subscriptions. These approaches mostly rely on the application of various machine learning techniques, remote sensing, and geospatial analysis for poverty estimation.

Five data sources and corresponding methods are considered for their potential usefulness for poverty estimation and mapping. Each section includes a brief overview of the data requirements, methodology, applicability considerations, limitations, examples, and helpful references. The purpose of this paper is to show evaluators and other stakeholders how to leverage different poverty proxies to estimate poverty rates in the context of evaluation.2 Through greater knowledge and use of nontraditional data sources, more temporally and spatially disaggregated estimations of poverty can be produced in a timely and cost-efficient manner. These estimates provide a critical complement to traditional statistics, filling some existing data gaps and improving the understanding of coverage or outreach of policy interventions targeted toward poor and vulnerable groups, and the poverty alleviation effects of these interventions.