Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation

Abstract

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.

Limits of Brazil’s Forest Code as a means to end illegal deforestation

“…Including Pará's green municipalities program (38) and the number of fines at the municipal level issued by IBAMA (Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis) (13, 15) did not change the general conclusions described above and, in some cases, reduced the overall predictive power of the models. This suggests that these policies either (i) did not reduce deforestation beyond the CAR effect or (ii) covaried with CAR at the property level.…”

Compared with what? Estimating the effects of injury prevention policies using the synthetic control method

“…Robbins et al 27 also consider extensions to multiple outcome measures in high-dimensional data settings, and Klößner and Pfieffer28 develop multivariate synthetic control models that allow for time series predictors. Xu29 also merges the synthetic control method with the interactive fixed effects framework in a recent paper, and Sills et al 30 discuss and implement a bootstrapping method to produce CIs for the counterfactual. As we can see, there are many developments not covered here, and prospective analysts will likely benefit from studying these recent advances as well.…”

Does Certification Change the Trajectory of Tree Cover in Working Forests in The Tropics? An Application of the Synthetic Control Method of Impact Evaluation

“…It has been used to construct the counterfactual for single jurisdictions affected by anti-smoking legislation, minimum wages, terrorist conflict, and immigration controls [16,24,26,32]. Sills et al [17] illustrated the use of SCM for evaluating policy impacts on land use. SCM has also been suggested as a way to systematically choose comparison units for comparative case studies, thus bridging the quantitative/qualitative methodological divide [16].…”