# Micro-Estimates of Wealth for all Low- and Middle-Income Countries

arXiv: https://arxiv.org/abs/2104.07761 · HTML: https://mlsanity.com/paper/2104.07761/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 55 references that are in the map.
- First posted on arXiv 15 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [Mapping the world population one building at a time](https://mlsanity.com/paper/1712.05839/) (2017)

## Built on by (most cited first, 25 of 25)

- [GeoLLM: Extracting Geospatial Knowledge from Large Language Models](https://mlsanity.com/paper/2310.06213/) (2023)
- [Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access](https://mlsanity.com/paper/2109.02890/) (2021)
- [Evaluation Challenges for Geospatial ML](https://mlsanity.com/paper/2303.18087/) (2023)
- [Self-Supervised Representation Learning for Geospatial Objects: A Survey](https://mlsanity.com/paper/2408.12133/) (2024)
- [Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy](https://mlsanity.com/paper/2305.01783/) (2023)
- [Contrasting local and global modeling with machine learning and satellite data: A case study estimating tree canopy height in African savannas](https://mlsanity.com/paper/2411.14354/) (2024)
- [Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data](https://mlsanity.com/paper/2308.08656/) (2023)
- [Responsible AI for Earth Observation](https://mlsanity.com/paper/2405.20868/) (2024)
- [Revealing economic facts: LLMs know more than they say](https://mlsanity.com/paper/2505.08662/) (2025)
- [Debiasing Machine Learning Predictions for Causal Inference Without Additional Ground Truth Data: "One Map, Many Trials" in Satellite-Driven Poverty Analysis](https://mlsanity.com/paper/2508.01341/) (2025)
- [A satellite foundation model for improved wealth monitoring](https://mlsanity.com/paper/2604.23166/) (2026)
- [Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan](https://mlsanity.com/paper/2206.11400/) (2022)
- [Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?](https://mlsanity.com/paper/2211.08735/) (2022)
- [Interpreting wealth distribution via poverty map inference using multimodal data](https://mlsanity.com/paper/2302.10793/) (2023)
- [CausalImages: An R Package for Causal Inference with Earth Observation, Bio-medical, and Social Science Images](https://mlsanity.com/paper/2310.00233/) (2023)
- [Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach](https://mlsanity.com/paper/2411.02855/) (2024)
- [Seeing poverty from space, how much can it be tuned?](https://mlsanity.com/paper/2107.14700/) (2021)
- [Poverty rate prediction using multi-modal survey and earth observation data](https://mlsanity.com/paper/2307.11921/) (2023)
- [A Comparative Analysis of Wealth Index Predictions in Africa between three Multi-Source Inference Models](https://mlsanity.com/paper/2408.01631/) (2024)
- [Predictors of disease outbreaks at continentalscale in the African region: Insights and predictions with geospatial artificial intelligence using earth observations and routine disease surveillance data](https://mlsanity.com/paper/2411.06436/) (2024)
- [Predicting household socioeconomic position in Mozambique using satellite and household imagery](https://mlsanity.com/paper/2411.08934/) (2024)
- [Enhancing the KidSat Model: Integrating Geographical Encoding and Data Quality Assessment for Childhood Poverty Prediction](https://mlsanity.com/paper/2607.08281/) (2026)
- [Crowd-Sourced Geographies of Income: Using Google Maps Points of Interest as High-Frequency Proxies for Sub-Municipal Income Estimation in Sao Paulo, Brazil](https://mlsanity.com/paper/2608.07871/) (2026)
- [Beyond Point Predictions: Uncertainty-Aware Satellite Poverty Mapping for Public Policy](https://mlsanity.com/paper/2608.23322/) (2026)
- [Transfer Learning for Socioeconomic Estimation in Forced-Displacement Settings](https://mlsanity.com/paper/2609.15773/) (2026)

## Abstract (arXiv)

Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop the first micro-estimates of wealth and poverty that cover the populated surface of all 135 low and middle-income countries (LMICs) at 2.4km resolution. The estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Facebook. We train and calibrate the estimates using nationally-representative household survey data from 56 LMICs, then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each micro-estimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for new insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of the Sustainable Development Goals.