Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation.
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Quasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts' selection of best case comparisons. The synthetic control method (SCM) offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal "blacklist" that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual) scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012). This demonstrates that SCM can yield helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and permutations on policies that are implemented in just a few locations.
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Sills, Erin O, Diego Herrera, A Justin Kirkpatrick, Amintas Brandão, Rebecca Dickson, Simon Hall, Subhrendu Pattanayak, David Shoch, et al. (2015). Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation. PLoS One, 10(7). p. e0132590. 10.1371/journal.pone.0132590 Retrieved from https://hdl.handle.net/10161/12709.
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Justin Kirkpatrick
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Justin is a PhD student at Duke University in the UPEP Environmental Economics program, a PhD Fellow at the Duke University Energy Initiative, and an NBER Pre-Doctoral Fellow in Energy Economics. Justin's research interests center on economic analysis surrounding environmental regulation, policy, and markets. His current applied areas of interest focus on the cost of intermittent renewables on the US electricity grid and the effectiveness of energy storage at mitigating those costs. His other research interests include household renewable energy decisions, consumer perceptions of risk in the presence of fracking, and other energy-environment issues. Previously, Justin served as an economist at NOAA Fisheries' Northeast Fisheries Science Center where he led a team modeling the economic impact of offshore wind development on Atlantic (US) fisheries. He has also worked for the State of Rio de Janeiro, and as an environmental consultant.
Primary Field: Environmental Economics
Secondary Fields: Energy Economics, Industrial Organization, Econometrics
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