1. 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

2. 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?

3. 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

4. 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 Program requires technicians to map properties
Pará: introduced in 2004 for some types of properties, redesigned and made obligatory for all properties in Program requires landowners to enter boundary points (unsupervised) online
Pará’s program model for national expansion
Neither program is equivalent to tenure

5. 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 ( ), keep only points forested in 2005
Measurements
Forest cover from 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…

6. Land and Poverty Conference, 2017

7. 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

8. 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
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

9. 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 ( )
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

10. 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.

11. CAR effectiveness varied over time
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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.

12. 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).