J. Alix-Garcia, L. Rausch, J. L’Roe, H. K. Gibbs, and J. Munger Land and Poverty Conference, 2017 Avoided deforestation linked to environmental registration in the Brazilian Amazon J. Alix-Garcia, L. Rausch, J. L’Roe, H. K. Gibbs, and J. Munger

Land and Poverty Conference, 2017 Motivation Deforestation is a major contributor to and avoided deforestation an important mitigation strategy for climate change Brazil: second largest deforester in world Efforts to conserve forest on private lands require knowing boundaries Brazil engage in major mapping effort starting in early to mid-2000s Question: Does mapping of private lands affect land use change?

What we do, what we find Do: Find: Land and Poverty Conference, 2017 What we do, what we find Do: Use a random set of points in Mato Grosso and Pará to examine avoided deforestation impacts of land mapping between 2005 and 2014 Examine summary statistics and pre-trends to understand selection Use later-registered points as counterfactuals for earlier-registered points Find: More potentially productive properties enroll in program Program reduced deforestation on registered lands by 62.5% -- a total avoided deforestation of 10% across the two states There is no variation in impact across states Impacts are higher for early registrants

The policy: Rural Environmental Registry (CAR) Land and Poverty Conference, 2017 The policy: Rural Environmental Registry (CAR) Purpose of registration: allow policymakers to monitor deforestation Program implementation differed across states Mato Grosso: land registration began in early 2000s, accelerated when transferred to CAR in 2009. Program requires technicians to map properties Pará: introduced in 2004 for some types of properties, redesigned and made obligatory for all properties in 2008. Program requires landowners to enter boundary points (unsupervised) online Pará’s program model for national expansion Neither program is equivalent to tenure

Land and Poverty Conference, 2017 Data Analyze stratified random sample of points in Mato Grosso and Pará in Amazon Biome 10,000 random points generated in areas of each state with and without CAR: total of 40,000 points Areas not eligible for CAR excluded For analysis period (2005-2014), keep only points forested in 2005 Measurements Forest cover from 2001-2014 measured using PRODES CAR/non-CAR variable for points that applied between 2006 and 2013 Drop pre-2006 applications due to confusion Assign each point first date of application Other geographic covariates: slope, elevation, agricultural aptitude, distances from road/city, etc…

Land and Poverty Conference, 2017

Land and Poverty Conference, 2017 Estimation strategy Use point level FE (γp) and time effects (γt) in linear probability model Forestpmt = α+βCpt+γp+ΣωxtγtX+εpmt Cpt=1 after first year applied for CAR Time effects interacted with state dummies and agricultural suitability Sample limited to those points that eventually registered for CAR Also test for heterogeneity by state using interaction of state with Cpt Drop points after deforested (eliminate “false” variation – like discrete time duration model) Cluster SE at municipal level Counterfactual: later registrants serve as counterfactual for early registrants

Registering properties on land with higher potential productivity Land and Poverty Conference, 2017 Registering properties on land with higher potential productivity Characteristic CAR by 2014 Never-CAR T-test for difference Proportion forested points, 2005 0.530 0.582 -2.135*** Municipal defor 2001-2004 0.019 0.015 3.757*** % municipality forested, 2001 0.487 0.480 0.156 Normalized defor risk -0.056 -0.198 2.293*** Km to nearest highway 83.272 73.675 1.222 Km to nearest city 49.474 47.661 0.481 Agriculturally apt (0/1) 0.275 0.190 4.463*** Pasture, 2012 0.194 4.753*** Secondary vegetation, 2012 0.087 0.076   Forest, 2012 0.458 0.443 0.322 Crop, 2012 0.035 0.017 3.948*** Observations 13721 16263 29984 Lesson: can’t use non-registrants as counterfactual

Land and Poverty Conference, 2017 Pre-trend test suggests that later registrants are fine counterfactual for early ones Dependent variable Forested at year’s end (2001-2005) 2002 x registered pre-2010 0.010 (0.007) 2003 x registered pre-2010 2004 x registered pre-2010 0.008 (0.008) 2005 x registered pre-2010 -0.002 (0.011) 2006 x registered pre-2010 Time effects yes Time x Para Time x soy Observations 41911 Adjusted R^2 0.091

Land and Poverty Conference, 2017 CAR reduces deforestation by 10%; no differential effects across states Dep. Var: forest =1 Points that eventually registered All points Change in probability of forest from CAR registration 0.008*** 0.005* 0.005*** 0.004 0.003** 0.006*** (0.002) (0.003) Additional CAR effect on forest in Pará 0.005 0.002 -0.004 (0.004) Time trend of forest cover -0.004*** -0.003*** -0.002*** (0.001) (0.000) Additional time trend in Pará Time effects yes Time x Pará no Time x soy apt Observations 58977 126275 The first 4 columns show that CAR reduces defor by 10% across both states. Reduction of defor among subsample is a 62% impact. The last two columns illustrate selection – the inclusion of non-CAR points here means that the comparison group has a low counterfactual deforestation, hence less impact. Standard errors clustered at municipality level. FE estimator.

CAR effectiveness varied over time Land and Poverty Conference, 2017 CAR effectiveness varied over time This shows effectiveness by cohort. Highest effectiveness is for earliest years (perhaps an enthusiastic bunch) and for registrants from 2012 forward (maybe due to forest law interaction). Estimated impacts from table S5. Bars show the sum of coefficients and 95% confidence intervals for baseline plus all years up to year of effect.

Conclusion We find: We conclude: Land and Poverty Conference, 2017 Conclusion We find: More potentially productive properties enroll in program Program reduced deforestation on registered lands by 62.5% -- a total avoided deforestation of 10% across the two states There is no variation in impact across states Impacts are higher for early registrants We conclude: Land mapping has been helpful in inducing avoided deforestation It probably would not have had much effect on its own – complementary policies gave it teeth It seems the low cost program (Pará) worked as well as the more technically intense one (Mato Grosso).