Reproducibility package for Designing Air Quality Measurement Systems in Data-Scarce Settings

While populations in low- and middle-income countries are exposed to some of the highest levels of air pollution and its consequences, the majority of economics research on the topic is focused on high-income settings where there is greater data availability. We compare and evaluate the three principal sources of air pollution data (regulatory-grade monitors, satellite, and low-cost monitors) in a sub-Saharan Africa context in terms of accuracy of PM2.5 measurement across different spatial and temporal frequencies and their performance when studying policy externalities. Satellite data is closely aligned with data from the regulatory-grade monitors at lower temporal frequencies. The low-cost monitors underestimate PM2.5 relative to the other data sources. Calibration, especially context-specific calibration, of the low-cost monitors’ data improves its alignment with other data sources. We use each data source to evaluate the air pollution externality of mobility-reduction policies using a difference-in-differences design and find similar results, especially in terms of percent reduction. We consider policymakers’ constraints to air pollution monitoring in low-income settings and demonstrate that co-locating one regulatory-grade monitor in a network of low-cost monitors can capture the spatial variation of pollution across an urban area and achieve better accuracy than either of these data sources alone. This provides a framework for policymakers to generate the data needed to evaluate environmental policies and externalities.