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Causal Forces of Deforestation in the Brazilian Amazon:Does Size Matter?
Diana Weinhold
London School of Economics
Eustaquio ReisInstitute for Applied Economic Research (Ipea)
Danilo Igliori
University of Cambridge
III LBA Scientific ConferenceBrasilia, July 2004
Policy Problem
• Maintenance of biologically diverse ecosystems
• Land requirements and the opportunity costs of non-conversion
critical trade-off for developing countries
Motivation
• The economic literature on land use in developing countries has traditionally focused on efficiency and equity issues.
• Farm size is at central stage to discuss variation in the economic performance, and political and social issues in rural areas.
• Causes of deforestation have growing in interest recently in the land use literature.
• With few exceptions (Fearnside 1993, Walker et al 2000) the relationship between farm size and deforestation has not been properly addressed.
Objective
• In this paper we attempt to empirically investigate the existence of the relationship between farm size and deforestation.
• We use some traditional and some more recent econometric results for model evaluation in panel data and a comprehensive data set on land use in the Brazilian Amazon.
• We study the question of whether the size composition of agricultural establishments in the Amazon region plays a role in determining the rate of deforestation.
Data• The data used is part of a database (Desmat) managed by
IPEA/DIMAC (The Directorate of Macroeconomic Studies of the Institute of Applied Economic Research, Brazil).
• This is a data panel for all the municipalities of Brazilian Legal Amazon (AML) including thousands of variables on major economic, demographic and geo-ecological aspects.
• Desmat includes spatially detailed geo-ecological information available in GIS and socio-economic sources, in particular Demographic and Economic Census data observed in 5-year periods from 1970 to 2000.
Explaining Levels
• Is the size composition of establishments correlated with the level of land clearing?
• Dependent Variable: Log of Cleared Land, 1995
• Size composition: shares of private land within different size classes (7 classes, from tiny - < 10 ha, to Giant - >100,000 ha)
Explaining Levels
Control variables• Property rights: shares of private land within
different ownership status (owners, sharecroppers, renters, squatters)
• Area: municipality and establishments• Other: state dummies, distance to state and federal
capitals,percentage of good soil, km of navigable river, and natural vegetation variables.
• Omitted categories: Large establishment share (>1000 and <5000), and Owner-titled land share
Variables Model 1 Model 2 Model 3 Constant -0.8364
(-0.70) 0.1896 (0.14)
-0.8231 (-0.69)
Log (Municipality area) -0.1218 (-2.70)
-0.0264 (-0.69)
-0.1434 (-3.16)
Log (total establishment area, 1995) 1.0883 (22.57)
0.9053 (27.07)
1.1073 (22.95)
Tiny = share of private area in estabs < 10 ha
1.9427 (4.20)
2.1041 (4.68)
Small = share of private area in estabs >10 and <100 ha
0.9223 (3.27)
0.8571 (3.13)
Medium = share of private area in estabs >100 and <1000 ha
0.2492 (0.67)
0.3998 (1.06)
Exlarge = share of private area in estabs >5000 and <10000 ha
-0.3167 (-0.57)
-0.1880 (-0.35)
Super = share of private area In estabs >10000 and <100000 ha
-0.4189 (-1.03)
-0.3370 (-0.82)
Giant = share of private area In estabs >100000 ha
-0.6751 (-1.87)
-0.5911 (-1.61)
Renter share of private area in estabs rented
-1.2682 (-0.45)
-0.4782 (-0.22)
Sharecr share of private area in estabs w/sharecropping
-1.7473 (-0.99)
-2.7940 (-1.63)
Squatter share of private area in estabs with squatters
0.7702 (2.45)
0.4291 (1.25)
No obs. 257 257 257 R-squared 0.9432 0.9354 0.9448
Dependent Variable: Log of Cleared Land, 1995
Explaining Levels
Results
• In levels it is the small and tiny establishments and squatters that are clearing the most as a proportion of their land.
Explaining Changes
• Is the change of composition of establishments size correlated with changes in the extent of land clearing?
• We then model the change in cleared land from 1985 to 1995 and condition on levels in 1985.
• Dependent Variable = Growth of cleared land, 1985-1995
• Control variables for changes: total private land, shares of estab sizes and ownership.
• other control variables and omitted variables as in the analysis for levels
Variables Model 1 Model 2 Model 3 constant 0.8603
(0.10) 2.1286 (0.25)
-1.3654 (-0.17)
Log(cleared land,1985) -4.5913 (-6.38)
-3.7828 (-6.20)
-4.1020 (-5.89)
Log (Municipality area) 0.3755 (0.74)
0.4657 (1.18)
0.1429 (0.33)
Growth of total establishment Area, 1985 – 1995
8.7006 (12.97)
7.0566 (14.79)
9.0469 (12.90)
Log(total establishment area in 1985)
4.1865 (4.03)
3.2624 (4.94)
3.9812 (4.12)
Change in Tiny share, 1985 - 1995
8.2549 (2.27)
10.5373 (2.71)
Change in Small share, 1985 - 1995
2.4569 (0.90)
1.4786 (0.57)
Change in Medium share 1985 - 1995
-2.3446 (-0.67)
-1.0732 (-0.31)
Change in Exlarge share 1985 - 1995
-3.4477 (-0.76)
-2.1218 (-0.49)
Change in Super share 1985 - 1995
-3.7032 (-0.97)
-3.2415 (-0.91)
Change in Giant share 1985 - 1995
-10.1914 (-2.25)
-11.4055 (-2.30)
Tiny share, 1985 3.5362 (0.74)
1.9054 (0.40)
Small share, 1985 5.0644 (2.05)
4.0580 (1.67)
Medium share, 1985 -0.6826 (-0.22)
-0.8366 (-0.26)
Exlarge share, 1985 -3.7399 (-0.67)
-2.4220 (-0.43)
Super share, 1985 -2.0576 (-0.65)
-0.9762 (-0.32)
Giant share, 1985 -4.9973 (-1.18)
-5.0556 (-1.05)
Change in Renter share 1985-1995
3.7721 (0.18)
6.7894 (0.35)
Change in Sharecropper Share, 1985-1995
-16.0937 (-1.36)
-19.6620 (-1.45)
Change in Squatter share, 1985-1995
9.2743 (3.52)
7.4037 (2.81)
Renter share, 1985 10.4524 (0.51)
19.8466 (1.04)
Sharecropper share, 1985 -21.2954 (-1.11)
-23.0401 (-1.13)
Squatter share, 1985 8.0450 (2.50)
7.9108 (2.28)
No obs. 257 257 257 R-squared 0.7522 0.7487 0.7785
Dependent Variable = Growth of cleared land,
1985-1995
Explaining Changes
Results
• This more or less confirms the first result,
• Caution: there is evidence that the reason squatters are associated with more clearing has to do with the fact that they operate on very small scales.
Causality
• The model for changes has the additional benefit that, controlling for the initial levels of the variables, many of the hidden omitted-variable problems so common in levels will be eliminated from the analysis.
• This does not mean that omitted variable problems have been eliminated entirely, however, for again, a correlation in changes could be due to other, unmeasured and/or omitted variables.
Causality 2
• we adopt some recent econometric techniques for panel model evaluation to test for a type of Granger-causality.
• Essentially we shall test whether knowledge about the size composition of establishments will help us predict, out-of-sample, the future rate of deforestation (and vice versa).
• We use panel model evaluation techniques suggested by Granger, C.W. and L. Huang (1997). Evaluation of Panel-data models: Some Suggestions from Time-series, unpublished manuscript, UC San Diego.
Causality 3Applications for the Amazon
• Andersen, L., C. Granger, E. Reis, D. Weinhold, and S. Wunder (2003). The Dynamics of Deforestation and Economic Growth in the Brazilian Amazon, Cambridge University Press: Cambridge.
• Weinhold and Reis (2003) . Land Use and Transportation Costs in the Brazilian Amazon, Working paper, AAE, UW-Madison.
Causality 4
• In our first evaluation attempts we find no evidence of causality.
• This suggests that the correlations found are just that and we should be careful before making policy decisions with the hope of altering the rate of land clearing.
Conclusions
• Our cross-section modelling using regression analysis for levels and changes in cleared area suggests correlation between size and deforestation.
• Tiny establishments apparently clear more land proportionally.
• These results don’t pass a more rigorous causality test.
• We should be cautious and extra attention should be paid to trying to figure out the underlying causal mechanisms between establishment size and deforestation.
• There is large scope for further research on the matter.