From a new working paper on “The Political Economy of Deforestation in the Tropics” by Robin Burgess, Matthew Hansen, Benjamin Olken, Peter Potapov, and Stefanie Sieber (link),
Logging of tropical forests accounts for almost one-fi fth of greenhouse gas emissions worldwide, significantly degrades rural livelihoods and threatens some of the worlds most diverse ecosystems. This paper demonstrates that local-level political economy substantially affects the rate of tropical deforestation in Indonesia. Using a novel MODIS satellite-based dataset that tracks annual changes in forest cover over an 8-year period, we fi nd three main results. First, we show that local governments engage in Cournot competition with one another in determining how much wood to extract from their forests, so that increasing numbers of political jurisdictions leads to increased logging. Second, we demonstrate the existence of “political logging cycles,” where illegal logging increases dramatically in the years leading up to local elections. Third, we show that, for local government officials, logging and other sources of rents are short-run substitutes, but that this a¤ect disappears over time as the political equilibrium shifts. The results document substantial deviations from optimal logging practices and demonstrate how the economics of corruption can drive natural resource extraction.
There’s lots to like about the paper, including a well-identified causal story. (They were lucky that others had already done most of the leg-work needed to demonstrate this.) It is also a timely contribution, as Indonesia is one of the pilot cases for the new global REDD initiative to deal with green house gas build up through forest protection “carbon credits” (link). This kind of “diagnostic” research can determine intervention points that should be targeted by future programs aiming to promote forest conservation. It’s already a long paper, but their case would be strengthened if they provided some narrative accounts that demonstrated the plausibility of their interpretation of the data.
