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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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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. |
format | Online Article Text |
id | pubmed-4501829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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