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<p>This paper offers a novel and robust approach to measure the distributional impact of crises at a sub-national level, in settings where conventional economic data are delayed, incomplete, or unavailable. Using the COVID-19 pandemic as an illustrative case study, we first employ satellite-derived nighttime light data to construct a monthly indicator of spatial inequality for all African countries from 2015 to 2021. This measure captures disparities within sub-national areas by examining the distribution of light per person across uniform one-kilometre cells. We show that national measures of inequality mask substantial heterogeneity at sub-national level. We then apply a Causal-ARIMA (Causal-AutoRegressive Integrated Moving Average) approach, which generates counterfactual forecasts based solely on pre-pandemic dynamics, to identify the distributional effects of the COVID-19 shock. The results indicate that wealthier and more industrialized areas implementing more stringent containment measures experienced more pronounced reductions in inequality. The study underscores the potential of our empirical strategy for tracking inequality dynamics during times of crisis, offering a valuable tool for policymakers when canonical data sources are inaccessible.</p>

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Beyond data gaps: tracking spatial inequality in Africa via nighttime lights

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月20日

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  • Bruno Martorano,
  • Elena Perra,
  • Marco Tiberti

摘要

This paper offers a novel and robust approach to measure the distributional impact of crises at a sub-national level, in settings where conventional economic data are delayed, incomplete, or unavailable. Using the COVID-19 pandemic as an illustrative case study, we first employ satellite-derived nighttime light data to construct a monthly indicator of spatial inequality for all African countries from 2015 to 2021. This measure captures disparities within sub-national areas by examining the distribution of light per person across uniform one-kilometre cells. We show that national measures of inequality mask substantial heterogeneity at sub-national level. We then apply a Causal-ARIMA (Causal-AutoRegressive Integrated Moving Average) approach, which generates counterfactual forecasts based solely on pre-pandemic dynamics, to identify the distributional effects of the COVID-19 shock. The results indicate that wealthier and more industrialized areas implementing more stringent containment measures experienced more pronounced reductions in inequality. The study underscores the potential of our empirical strategy for tracking inequality dynamics during times of crisis, offering a valuable tool for policymakers when canonical data sources are inaccessible.