Where are All the Jobs? A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries

Abstract

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 9979Globally, both people and economic activity are increasingly concentrated in urban areas. Yet, for the vast majority of developing country cities, little is known about the granular spatial organization of such activity despite its key importance to policy and urban planning. This paper adapts a machine learning based algorithm to predict the spatial distribution of employment using input data from open access sources such as Open Street Map and Google Earth Engine. The algorithm is trained on 14 test cities, ranging from Buenos Aires in Argentina to Dakar in Senegal. A spatial adaptation of the random forest algorithm is used to predict within-city cells in the 14 test cities with extremely high accuracy (R-squared greater than 95 percent), and cells in out-of-sample "unseen" cities with high accuracy (mean R-squared of 63 percent). This approach uses open data to produce high resolution estimates of the distribution of urban employment for cities where such information does not exist, making evidence-based planning more accessible than ever before.

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“…However, there are two significant data constraints, common to developing country cities which make it difficult to capture access to economic opportunities, particularly for SSA cities: 1) lack of information on job locations, and 2) lack of information on transit routes and travel times (Peralta-Quiros et al, 2019). This study captures access to economic opportunities using the spatial density of employment developed in Barzin et al (2022). This measure is used to proxy for job density.…”

“…A job density proxy was measured based on spatial data of the economic activity distribution (Barzin et al, 2022) and added to the model to assess the relationship between rent and access to jobs. Better access to jobs tends to be associated with higher rents and housing costs so it is therefore assumed that α2 > 0 in Equation (1).…”