Environmental policy’s new role in the
Brazilian Amazon
Jan Börner (UniBonn/CIFOR)
Jorge Hargrave (IPEA)
Krisztina Kis-Katos (Freiburg University)
Konstantin König (ICRAF)
Monique Sacardo Ferreira (IPEA)

Background
• Deforestation rates
peaked in 2004
• Command-and-
Control policy
considered largely
ineffective
• Broad changes in
governance system in
2004/5 (PPCdAM)
• Since then reductions
by over two thirds
0
5000
10000
15000
20000
25000
30000
2004200520062007 2008200920102011
years
Deforestation(sq.km) 0
1000
2000
3000
4000
5000
6000
7000
8000
9000
Numberoffines

Background
• Shift from large to smaller scale deforestation:
result of enforcement strategy (Rosa et al.
2012) or structural change (Pacheco 2011)?
Rosa et al. 2011

Research questions
1. Districts with high fine intensity were focus of
many complementary measures (e.g.
embargoes, exclusion from credit, etc.). What
is the deterrence effect of an individual fine
(field-based enforcement)?
2. Does targeting field operations towards large-
scale deforestation induce „avoidance
behavior“, e.g., an increase in small-scale
deforestation?

2010 fine locations and 2010/11
change in deforestation

Empirical strategy
• Grid-based approach
– Measurement of all covariates at 20x20km resolution
– Measurement of fine (location) and deforestation at
<=10x10 km resolution
• Matching analysis
– Pooling of 2010 and 2011 observation periods
– Exclusion of non-treated neighbors of treated grid
cells to reduce neighborhood effects
– Stratification by fine type (all fines vs only
deforestation fines)
– Analysis for small versus large-scale (>20ha*)
deforestation
– Problems: clouds and imprecise fines
* Minimum detection of DETER monitoring system

Cloud and/or Deforestation?
2010
2011
PRODES-Deforestation PRODES-Clouds PRODES + DETER -
Deforestation

Imprecisely measured fines
0
20
40
60
80
0.0
0.5
1.0
1.5
precise 44%
Imprecise (56%)

Matching full set of covariates
emb
Forest2007
acc_def
Clouds
Clouds_lagged
prodes_lag
prod_splagt1
DISTANCIA
DISTANCIAsq
MUNimprecise
TI_percent
UC
Assenta
smallsh
sh_high_lag
agrsh
pastsh
tractor_w
Prec_ann
Share.owners
-1 -0.5 0 0.5 1
unmatched
matched MHD
matched INV
Standardized difference in means

Matching (change in
deforestation)
ATT AI SE / AI p Number of
treated cells
All fine types / all cells -2.98 2.18 / 0.171 2848
All fine types / excluding
neighbors
-4.39 2.21 / 0.047 2848
Only deforestation fines -8.21 4.33 / 0.058 614
Only deforestation fines
no clouds
-8.42 5.31 / 0.113 410
Only deforestation fines
no clouds >20ha patches
-10.59 4.72 / 0.024 410
Only deforestation fines
no clouds <20ha patches
2.17 2.21 / 0.328 410

Matching (share of deforestation
patches > 20ha)
ATT AI SE / AI p Number of
treated cells
All fine types / excluding
neighbors
0.02 0.01 / 0.051 2848
Only deforestation fines 0.01 0.02 / 0.493 614
Only deforestation fines
no clouds
0.01 0.02 / 0.518 410

Key findings
• Significant deterrence effects of fines issued in
field-based enforcement campaigns
• On average a single additional fine reduces
deforestation by 10-20% in the subsequent year
• Effects vary across states (different dynamics of
confounding factors)
• Only limited evidence for “avoidance behavior”

Caveats
• Clouds, imprecisely measured fines, and
unobserved state-level action remain not
fully controlled confounding factors
• Balance on past deforestation cannot be
fully achieved with full set of covariates
• Further robustness tests pending

Conclusions
• After major reductions, field-based enforcement still
exerts significant and sizeable negative effects on
deforestation at the local scale
• Reason may not be the fine itself, but the host of
economic implications that arise from having
received one (embargo, etc.)
• Signs of “avoidance behavior”, i.e. small-scale
deforestation increasing in response to enforcement
that targets large-scale deforestation less
pronounced than suggested by previous analyses
• Average operational costs per fine are at about
R$4650 and thus probably still lower than the
environmental benefit of avoided deforestation
measured at carbon offset price values

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