Conditional cash transfers to alleviate poverty also reduced deforestation in Indonesia  (Science Advances, 2020)

with Paul J. Ferraro

Solutions to poverty and ecosystem degradation are often framed as conflicting. We ask whether Indonesia’s national anti-poverty program, which transfers cash to hundreds of thousands of poor households, reduced deforestation as a side benefit. Although the program has no direct link to conservation, we estimate that it reduced tree cover loss in villages by 30% (95% confidence interval, 10 to 50%). About half of the avoided losses were in primary forests, and reductions were larger when participation density was higher. The economic value of the avoided carbon emissions alone compares favorably to program implementation costs. The program’s environmental impact appears to be mediated through channels that are widely available in developing nations: consumption smoothing, whereby cash substitutes for deforestation as a form of insurance, and consumption substitution, whereby market-purchased goods substitute for deforestation-sourced goods. The results imply that anti-poverty programs targeted at the very poor can help achieve global environmental goals under certain conditions. 

Code and Data

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Media coverage: The Guardian, VoxDev, ScienceNews, Bloomberg, Reuters

 Heat and observed economic activity in the rich urban tropics (The Economic Journal, 2024)

with  Alberto Salvo, Haoming Liu, and Eric Fesselmeyer

We use space-and-time resolved mobility data to assess how heat impacts Singapore, a rich city-state and arguably a harbinger of what is to come in the urbanizing tropics. Singapore’s offices, factories, malls, buses, and trains are widely air conditioned, its public schools less so. We document increased attendance and commuting to workplaces, malls, and the more air-conditioned schools on hotter relative to cooler days, particularly by low-income residents with limited use of adaptive technologies at home. Investment by rich cities may attenuate heat’s pervasive negative consequences on productive outcomes, yet this may worsen the climate emergency in the long run.

A Synthetic Control Evaluation of Brazil's Large-scale, Anti-deforestation Policy

with Paul J. Ferraro, Jonah Busch, and Jens Engelmann

In 2004, Brazil implemented a large-scale anti-deforestation policy in response to an alarming rate of deforestation in the Brazilian Legal Amazon (BLA). The policy, well known as the Plan for the Protection and Control of Deforestation in the Amazon (PPCDAm), has been lauded in the international media and by global organizations as an example of how concerted legal action can dramatically reduce deforestation rates in tropical nations. However, the extent to which the dramatic reduction in deforestation can be attributed to the PPCDAm, rather than other factors affecting deforestation in tropical nations more broadly, is not clear. To estimate the causal effect of this policy on deforestation rates, we compare deforestation trends in the BLA and the counterfactual of BLA constructed from the synthetic control design (Abadie, Diamond & Heinmueller, 2010; Abadie & Gardeazabal, 2003). Using a high-resollution satellite panel data set on global forest cover change, ecological biome spatial data, and panel data on factors of land use change in the BLA, this paper aims to control other factors affecting deforestation in the BLA in order to construct the counterfactual of BLA. 

This photo highlights both current burning (smoke in the hills on the left) and post-forest-burning (scorched area in the center). Taken in West Sumatera, Indonesia during August 2016 field survey.