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Improvements in Indigenous Land Rights and Deforestation:Evidence from the Brazilian Amazon
AidData and KfW
Geospatial Impact Evaluation
• Use spatial information on program activities• Merged with high-resolution geo-referenced outcomes
• Geo-referenced surveys• Remotely sensed (forest cover, nighttime lights)
• Causal attribution (identification) possible through matching, fixed effects, and discontinuity techniques
• Examples in growing number of fields/sectors• Land rights• Health• Governance• Post-conflict• Education
Does demarcating indigenous lands reduce deforestation?• Indigenous control, stewardship shown to be
associated with lower deforestation rates (Nelson et al. 2001, Nepstad et al 2006, Nelson and Chomitz2011, Nolte et al. 2013, Pfaff et al 2014, Vergara-Aseno and Potvin 2014)
• Most studies compare indigenous to other governance/rights Don’t consider time variation in protection status
• Given low rates of deforestation observed on indigenous lands, is demarcation likely to influence deforestation?
Does demarcating indigenous lands reduce deforestation?• Important to understand because alternative policies
show promise:• Monitoring technology and enforcement efforts (Hargrave and
Kis-Katos 2013, Arima et al, Assuncao et al 2014, Borner et al 2014, Borner et al 2015)
• Protected areas (e.g., Cropper et al 2001, Andam et al 2008, Borner et al 2010, Joppa and Pfaff 2011, Blackman et al 2011, Sims 2010)
• Payments for environmental services (e.g., Pfaff et al 2008, Robalino et al 2008, Honey-Roses et al 2011, Alix-Garcia et al 2012)
• Forest concessions (e.g., Mertens et al 2004) • Interventions in beef, soy supply chains (Nepstad et al 2014).
Project Description
• In 1988 constitution, Gov of Brazil committed to demarcating indigenous people’s territories
• Between 1995-2008, with funding and tech support from KfW and the World Bank, the PPTAL project identified, recognized, and studied 181 community lands.
• By 2008, 106 community lands demarcated, covering 38 million hectares (~35% of all indigenous lands in Amazon)
Project Description
• Demarcation: recognition by the Min of Justice
• Followed by regularization (entry into municipal, state and federal registries)
• Varied by community between 1995 and 2008• Median year is 2001
• Support for Boundary Enforcement
Data
• Treatment status• Boundaries of community lands
• Administrative data on demarcation dates
• Merged with satellite-based greenness measure• NASA Land Long Term Data Record (LTDR), 1982-2010
• Processed to Normalized Difference Vegetation Index (NDVI)
• Range is [0, 1] (0 = rocky, barren; 1 = dense forest)
• Annual NDVI max (and mean) measures
• Covariates• Climate (precip., temp.); topology (elevation, slope); distance
to rivers; gridded, interpolated population
Comparison Imagery from Manicoré Region, Brazil
Sample communities
Empirical Methodology
• Propensity Score Matching • Differences over time across matched treated/comparison
communities
• Match on baseline levels, pre-trends, & covariates
• Demarcated vs. not; “Early” (‘95-’01) vs “Late” (‘01-’08)
Δ𝑁𝐷𝑉𝐼𝑖𝑝= 𝛼 + 𝛽𝑇𝑖𝑝 + 𝜃𝑁𝐷𝑉𝐼𝑖𝑝1995 + 𝜃Δ𝑁𝐷𝑉𝐼𝑖𝑝 1982,1995 + Γ𝑋𝑖𝑝 + 𝐷𝑝 + 𝜖𝑖𝑝
Not Demarcated
Demarcated
Max
ND
VI
Year
NDVI Trends
Not Demarcated
Demarcated
Res
idua
l
NDVI Trends, Normalized by Year
NDVI Trends
Late Demarcation
Early Demarcation
Max
ND
VI
Year
NDVI Trends, Normalized by Year
Early Demarcation
Late Demarcation
Res
idua
l
Propensity Score Matching:1st Stage Results
Demarcation Year and NDVI Pre-Trends
Summary Statistics: Outcomes and Covariates
Cross-Section Results:Ever Demarcated
Differences-in-differences:
Demarcated vs. non-demarcated
Treatment = demarcated between ’95-’08
Outcome = Change in mean NDVI between ‘95 and ’10
Sample: 28 community pairs, matched on covariates
Cross-Section Results:Early Demarcation
Differences-in-differences:
“Early” vs. “Late”
Treatment = “Early” demarcation (‘95-’01)
Outcome = Change in max NDVI between ‘95 and ’01
Sample: 33 community pairs, matched on covariates
Cross-Section Results:Demarcation + Enforcement Support
Differences-in-Differences:
Treatment = Demarcation + Enforcement Support
Outcome = Change in max NDVI between ‘95 and ‘10
Sample: 44 community pairs, matched on covariates
Cell-year panel model
𝑁𝐷𝑉𝐼𝑖𝑐𝑡= 𝛼 + 𝛽1𝐷𝑒𝑚𝑎𝑟𝑐𝑎𝑡𝑒𝑑𝑖𝑐𝑡 + 𝛽2𝐸𝑛𝑓𝑜𝑟𝑐𝑒𝑚𝑒𝑛𝑡𝑖𝑐𝑡 + Γ𝐶𝑙𝑖𝑚𝑎𝑡𝑒𝑖𝑐𝑡 + 𝐷𝑐 + 𝐷𝑡 + 𝜖𝑖𝑐𝑡
• Treatment status at finer time intervals• Testing specific timing of effects (only after demarcation)
• Covariates available at finer spatial resolution – improved precision
• Fixed Effects• Control for time-invariant unobservables
Summary Statistics for Grid Cell Level Panel Dataset, Weighted by Community Size
Outcome = Level of max NDVI in year
Sample: 8,483 grid cells within demarcated communities with annual obs from 1982-2010
Standard errors clustered by community & year
Panel Results:Cell-Year Level
Panel Results: Cell Year Level
Post-2004: Satellite technology improves enforcement
Outcome = Level of max NDVI in year
Sample: 8,483 annual observations from 1982-2010 for grid cells within demarcated communities
Standard errors clustered by community & year
Robustness Checks
• Add community level trends as controls to cell-year panel model
• Limit propensity score estimation to 4 significant predictors, creating 37 pairs of communities; Use grid cells in this subsample of communities for cell-year panel model (n=404,405)
• Run cell-year panel model with full untrimmed, unmatched sample of cells (n=422,066)
• Run panel model using community-year data (n=1914)
Conclusions
• No clear, robust evidence of differences in deforestation attributable to the PPTAL project
• Project’s other aims included human rights protections
• Much lower rates of deforestation on indigenous lands in cross-section may not be related to land tenure status of these lands (or may be mediated through multiple, complex channels)
• Data deforestation rates in indigenous communities not necessarily available in early 90’s• Future programs may be able to target effectively
Next steps / future research
• Examining the impact on other outcomes – e.g. education outcomes using Brazil census data
• Identifying communities that have experienced conflicts or land disputes, where treatment effects may be larger
Extra Slides
Cell-year panel with community-level trends