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

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 im...

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Autores principales: Sills, Erin O., Herrera, Diego, Kirkpatrick, A. Justin, Brandão, Amintas, Dickson, Rebecca, Hall, Simon, Pattanayak, Subhrendu, Shoch, David, Vedoveto, Mariana, Young, Luisa, Pfaff, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4501829/
https://www.ncbi.nlm.nih.gov/pubmed/26173108
http://dx.doi.org/10.1371/journal.pone.0132590
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author Sills, Erin O.
Herrera, Diego
Kirkpatrick, A. Justin
Brandão, Amintas
Dickson, Rebecca
Hall, Simon
Pattanayak, Subhrendu
Shoch, David
Vedoveto, Mariana
Young, Luisa
Pfaff, Alexander
author_facet Sills, Erin O.
Herrera, Diego
Kirkpatrick, A. Justin
Brandão, Amintas
Dickson, Rebecca
Hall, Simon
Pattanayak, Subhrendu
Shoch, David
Vedoveto, Mariana
Young, Luisa
Pfaff, Alexander
author_sort Sills, Erin O.
collection PubMed
description 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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spelling pubmed-45018292015-07-17 Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation Sills, Erin O. Herrera, Diego Kirkpatrick, A. Justin Brandão, Amintas Dickson, Rebecca Hall, Simon Pattanayak, Subhrendu Shoch, David Vedoveto, Mariana Young, Luisa Pfaff, Alexander PLoS One Research Article 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. Public Library of Science 2015-07-14 /pmc/articles/PMC4501829/ /pubmed/26173108 http://dx.doi.org/10.1371/journal.pone.0132590 Text en © 2015 Sills et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Sills, Erin O.
Herrera, Diego
Kirkpatrick, A. Justin
Brandão, Amintas
Dickson, Rebecca
Hall, Simon
Pattanayak, Subhrendu
Shoch, David
Vedoveto, Mariana
Young, Luisa
Pfaff, Alexander
Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation
title Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation
title_full Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation
title_fullStr Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation
title_full_unstemmed Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation
title_short Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation
title_sort estimating the impacts of local policy innovation: the synthetic control method applied to tropical deforestation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4501829/
https://www.ncbi.nlm.nih.gov/pubmed/26173108
http://dx.doi.org/10.1371/journal.pone.0132590
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