- (2002), the transfers were introduced within the time period we study. If transfers are made, the size and distribution of the pot varies depending on state law.
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- A. Propensity score < 0.1 Newly Protected 0.003 0.004** -0.007** Between 2001 and 2008 (0.004) (0.002) (0.003) N 253627 253627 253627 Mean of DV -.00251 -.00287 .00538 B. Propensity score < 0.01 Newly Protected 0.003 0.005** -0.008** Between 2001 and 2008 (0.006) (0.002) (0.004) N 198575 198575 198575 Mean of DV -.00322 -.00349 .00671 C. Propensity score < 0.0001 Newly Protected 0.003 0.005** -0.008* Between 2001 and 2008 (0.006) (0.002) (0.005) N 159660 159660 159660 Mean of DV -.00319 -.0042 .0074 Notes: Table reports results from OLS regressions relating changes in protected status to changes in land use sequence type.
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- For columns 4-6, coordinates which were protected between 2001 and 2010 are matched to observationally identical coordinates that were either never protected or always protected on the basis of propensity scores (see Online Appendix Table A9 for the matching regression). We retain only matched pairs with absolute differences in propensity score of less than 0.001. We match on changes between 2001 and 2010 for consistency with table 5. Standard errors are clustered at the municipality level. Stars indicate *** p < 0.01, ** p < 0.05, * p < 0.1. Spatial Economics Research Centre (SERC) London School of Economics Houghton Street London WC2A 2AE Web: www.spatialeconomics.ac.uk
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- In table A14 we explore other treatment timings. Panel A presents the baseline results. In Panel B, we narrow in by focusing on coordinates changing protection status between 2003 and 2008. Panel C focus on coordinate changing protection status between 2003 and 2010 and Panel D exploits the maximal variation by focusing coordinates changing status between 2001 (first year) and 2010 (last year).
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- Sequence Length and Clustering We perform clustering on slightly shorter sequences of length four, as well as clusters on sequences of length six. As in the main example for sequences of length five, we perform determine the optimal number of clusters using the test described in Tibshirani et al. (2001). Throughout, the statistically optimal number of clusters as per the gap statistic test is six. We transform the six resulting clusters into the three groups of sequences, forested, temporarily deforested, permanently deforested, and perform the same analysis as in the main table, relying on variation in the protection status coming from status changes between 2001 and 2009 for the four letter sequences and on status changes between 2001 and 2006 for the six letter sequences. The results are presented in Table A12 and are consistent with the results presented in our main analysis. Our results are robust to the choice of sequence length.
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- Stars indicate *** p < 0.01, ** p < 0.05, * p < 0.1. Online Appendices (Not necessarily for publication.) A Data Appendix A.1 Building the Municipality Level Conflict Panel We obtain data on land conflict from the Annual Reports of the Comissao Pastoral da Terra (CPT). The CPT was founded by the Catholic Church to highlight the plight of landless workers, small farmers and squatters. Since 1985, it has published an annual report on Conflitos no Campo (Violence in the Countryside). The report includes a multitude of conflict measures. We focus on the set of conflict measures that have been reported consistently between 1997 and 2010. The variables are ‘Disputes’, ‘Murders’, ‘Attempted Murders’ and ‘Death threats’. We discuss how each variable is constructed in turn.
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- The data is a random sample of 793,928 coordinates across the Brazilian Amazon. Two five year sequences are used for each coordinate, 2001-2005 and 2008-2012. The land use sequences are categorised into three groups, stable forest, temporarily deforested and permanently deforested, using the approach described in A.3. The dependent variable is a categorical variable which takes the difference between dummies indicating a particular classification. The independent variable indicates whether the coordinate was protected between 2001 and 2008 (the first year of each five year sequence). Coordinates which were protected between 2001 and 2010 are matched to observationally identical coordinates that were either never protected or always protected on the basis of propensity scores (see Online Appendix Table A9 for the matching regression). We retain only matched pairs with absolute differences in propensity score of less than indicated.
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- The independent variable indicates whether the coordinate was protected between 2001 and 2008 (the first year of each five year sequence). For columns 4-6, coordinates which were protected between 2001 and 2010 are matched to observationally identical coordinates that were either never protected or always protected on the basis of propensity scores (see Online Appendix Table A9 for the matching regression). We retain only matched pairs with absolute differences in propensity score of less than 0.001. We match on changes between 2001 and 2010 for consistency with table 5. Standard errors are clustered at the municipality level.
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Tibshirani, R., G. Walther, and T. Hastie (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63(2), 411–423.
- To calculate the area in a municipality under protection in say 2003, we simply calculate the total area in a municipality covered by one or more protected areas established up to (and including) 2003. For around 3% of reserves, data on the start date is missing. We assume these reserves came into existence prior to 1997 (i.e. before the start of our panel). For some of the reserves, we have been able to verify that this was indeed the case, but to the extent that some were established subsequently, this assumption introduces measurement error into our protected area data.
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- Treatment Timing As noted, we infer behavioural changes of settlers by studying patterns of land use focusing on longer time series of land use patterns. This creates a degree of arbitrariness in terms of the treatment timing. In the main table we assign a coordinate as being protected if in the initial year of each five letter sequence, it was classified as protected. Hence, the resulting variation in protection status is coming from coordinates whose protection status changed between 2001 and 2008.
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- Two five year sequences are used for each coordinate, 2001-2005 and 2008-2012. The land use sequences are categorised into three groups, stable forest, temporarily deforested and permanently deforested, using the approach described in A.3. The dependent variable is a categorical variable which takes the difference between dummies indicating a particular classification. The different panels use either the broad or narrow definitions of forested and temporary deforestation. The independent variable is a dummy indicating whether a coordinate was protected between 2001 and 2008 (the first year of each sequence). In columns 4-6, coordinates which were protected between 2001 and 2010 are matched to observationally identical coordinates that were either never protected or always protected on the basis of propensity scores (see Online Appendix Table A9 for the matching regression).
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- Two five year sequences are used for each coordinate, 2001-2005 and 2008-2012. The land use sequences are categorised into three groups, stable forest, temporarily deforested and permanently deforested, using the approach described in A.3. The dependent variable is a categorical variable which takes the difference between dummies indicating a particular classification. The independent variable indicates whether the coordinate was protected between the years indicated.
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- We match on changes between 2001 and 2010 for consistency with table 5. Standard errors are clustered at the municipality level. Stars indicate *** p < 0.01, ** p < 0.05, * p < 0.1. Table A12: Protection and Changes in Land Use Patterns: Robustness to the choice of sequence length Full Sample Matched Sample (1) (2) (3) (4) (5) (6) Forested Temp. Def. Perm Def. Forested Temp. Def. Perm Def.
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