Urban areas concentrate a disproportionate share of economic opportunities in developing countries, yet residing in a city does not guarantee access to its labor market. This note presents a diagnostic of urban job accessibility among the poor and explores a methodology for extending this analysis to data-scarce environments using geospatial data. Drawing on various traditional data, such as population and economic censuses, as well as geospatial data sources, we present various measures of job accessibility and apply it across seven cities in Colombia, Ghana, the Philippines, and South Africa. The analysis finds that job accessibility is systematically lower for households in the poorest welfare quintiles, a pattern that seems to persist regardless of job type or sector and that holds consistently across cities, even as its magnitude varies. These disparities reflect structural features of urban form, housing markets, and transport systems that push lower-income populations toward the periphery and away from where the jobs are. Beyond the analyzed cities, the note takes an initial step in testing the use of satellite-derived features to identify neighborhoods that are jointly deprived in both the welfare and job access dimensions, in cities where conventional data are unavailable; this approach, while still emerging, shows promise for helping fill data and analytical gaps. Overall, by identifying the poor’s access to jobs from a spatial perspective, the findings can carry direct implications for transport investment, land-use planning, and economic development policy, and are accompanied by a replication package designed to support adaptation across urban contexts.
| Repository name | URI |
|---|---|
| Reproducible Research Repository (World Bank) | https://reproducibility.worldbank.org |
Paper exhibits were reproduced on a computer with the following specifications:
• OS: Windows 11 Enterprise
• Processor: INTEL(R) XEON(R) PLATINUM 8562Y+ (2.80 GHz) (2 processors)
• Memory available: 128.0 GB
Run time: ~ 1 hour
To reproduce the findings in this paper, a replicator must:
Job_Accessibility_Index.Rproj00.master.R and run the code.Since not all the data is included, the package includes the results produced by replicators. These files can be used to review the results presented in the paper.
Some data is restricted and has not been included in the reproducibility package. For more details, please refer to the README file.
| Author | Affiliation | |
|---|---|---|
| Maria Davalos | World Bank | mdavalos@worldbank.org |
| Henry Stemmler | World Bank | hstemmler@worldbank.org |
| Varun Kshirsagar | World Bank | varun.kshirsagar@gmail.com |
2026-08-12
| Location | Code |
|---|---|
| World | WLD |
The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.
| Name | URI |
|---|---|
| MIT License | https://opensource.org/license/mit |
| World Bank IGO Rider | https://github.com/worldbank/metadata-editor/blob/main/WB-IGO-RIDER.md |
| Name | Affiliation | |
|---|---|---|
| Maria Davalos | World Bank | mdavalos@worldbank.org |
| Reproducibility WBG | World Bank | reproducibility@worldbank.org |
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Reproducibility WBG | DECDI | World Bank - Development Impact Department | Verification and preparation of metadata |
2026-08-12
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