Deforestation in the tropics - Summary
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The document discusses a study on the political economy of deforestation in the tropics. It provides background on literature on the topic from the 1990s and 2000s. It then summarizes the analysis, data, and model used in the study. The analysis suggests that logging firms fell trees to profit from wood sales, while district heads profit by selling logging permits, including illegally selling more permits than allowed. This illegal activity has increased with decentralization and the splitting of districts since the end of Suharto's regime in Indonesia. Satellite data on deforestation is used to measure changes in forest cover at the district level.
![Introduction
Literature
Analysis
Background
Data
The Model
Estimation
Results
Policy
Implications
Conclusions
References
Christoph Schulze - Masterseminar: Topics in Empirical Public Economics
The Political Economy of Deforestation in
the Tropics
Burgess, Hansen, Olken, Potapov, and Sieber [2012]
The Quarterly Journal of Economics (2012) 127 (4): 1707-1754
July 2, 2014](https://image.slidesharecdn.com/pres-170119114401/85/Deforestation-in-the-tropics-Summary-1-320.jpg)
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Deforestation in the tropics - Summary
- 1. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics The Political Economy of Deforestation in the Tropics Burgess, Hansen, Olken, Potapov, and Sieber [2012] The Quarterly Journal of Economics (2012) 127 (4): 1707-1754 July 2, 2014
- 2. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Overview Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions
- 3. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Introduction Stylized Facts ◮ counteracting climate change (Photosynthesis)
- 4. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Introduction Stylized Facts ◮ counteracting climate change (Photosynthesis) ◮ illegal logging one of major sources of deforestation
- 5. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Introduction Stylized Facts ◮ counteracting climate change (Photosynthesis) ◮ illegal logging one of major sources of deforestation ◮ annually about 20.000 km2 deforested in the tropics
- 6. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Introduction Stylized Facts ◮ counteracting climate change (Photosynthesis) ◮ illegal logging one of major sources of deforestation ◮ annually about 20.000 km2 deforested in the tropics Idea
- 7. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Introduction Stylized Facts ◮ counteracting climate change (Photosynthesis) ◮ illegal logging one of major sources of deforestation ◮ annually about 20.000 km2 deforested in the tropics Idea ◮ Development of deforestation
- 8. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Introduction Stylized Facts ◮ counteracting climate change (Photosynthesis) ◮ illegal logging one of major sources of deforestation ◮ annually about 20.000 km2 deforested in the tropics Idea ◮ Development of deforestation ◮ Role of corruption
- 9. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Introduction Stylized Facts ◮ counteracting climate change (Photosynthesis) ◮ illegal logging one of major sources of deforestation ◮ annually about 20.000 km2 deforested in the tropics Idea ◮ Development of deforestation ◮ Role of corruption ◮ Connection of changes in political system and deforestation
- 10. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Literature Many papers stem from 90’s or early 00’s
- 11. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Literature Many papers stem from 90’s or early 00’s Santilli et al. [2005] ◮ blaming ‘swidden’ agriculture on deforestation
- 12. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Literature Many papers stem from 90’s or early 00’s Santilli et al. [2005] ◮ blaming ‘swidden’ agriculture on deforestation Barbier et al. [1995] ◮ analyze different policies such as export ban and export tax
- 13. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Literature Many papers stem from 90’s or early 00’s Santilli et al. [2005] ◮ blaming ‘swidden’ agriculture on deforestation Barbier et al. [1995] ◮ analyze different policies such as export ban and export tax Dauvergne [1993] ◮ corruption from political point of view ◮ criticizes Suharto regime
- 14. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Literature Many papers stem from 90’s or early 00’s Santilli et al. [2005] ◮ blaming ‘swidden’ agriculture on deforestation Barbier et al. [1995] ◮ analyze different policies such as export ban and export tax Dauvergne [1993] ◮ corruption from political point of view ◮ criticizes Suharto regime Palmer [2001] ◮ analysis of corruption and market failures due to misplaced subsidies
- 15. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Literature Many papers stem from 90’s or early 00’s Santilli et al. [2005] ◮ blaming ‘swidden’ agriculture on deforestation Barbier et al. [1995] ◮ analyze different policies such as export ban and export tax Dauvergne [1993] ◮ corruption from political point of view ◮ criticizes Suharto regime Palmer [2001] ◮ analysis of corruption and market failures due to misplaced subsidies Olken [2006] ◮ empirical study to prove corruption
- 16. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Literature Many papers stem from 90’s or early 00’s Santilli et al. [2005] ◮ blaming ‘swidden’ agriculture on deforestation Barbier et al. [1995] ◮ analyze different policies such as export ban and export tax Dauvergne [1993] ◮ corruption from political point of view ◮ criticizes Suharto regime Palmer [2001] ◮ analysis of corruption and market failures due to misplaced subsidies Olken [2006] ◮ empirical study to prove corruption Fitrani et al. [2005] ◮ why do districts split?
- 17. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Sketch of Analysis ◮ logging firms make profits by felling trees and selling wood
- 18. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Sketch of Analysis ◮ logging firms make profits by felling trees and selling wood ◮ head of district makes money by selling logging permits to firms
- 19. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Sketch of Analysis ◮ logging firms make profits by felling trees and selling wood ◮ head of district makes money by selling logging permits to firms ◮ national governments determine legal quotas for logging
- 20. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Sketch of Analysis ◮ logging firms make profits by felling trees and selling wood ◮ head of district makes money by selling logging permits to firms ◮ national governments determine legal quotas for logging ◮ head of districts might sell more than legal
- 21. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Sketch of Analysis ◮ logging firms make profits by felling trees and selling wood ◮ head of district makes money by selling logging permits to firms ◮ national governments determine legal quotas for logging ◮ head of districts might sell more than legal ◮ districts split due to decentralization → more heads of districts
- 22. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Sketch of Analysis ◮ logging firms make profits by felling trees and selling wood ◮ head of district makes money by selling logging permits to firms ◮ national governments determine legal quotas for logging ◮ head of districts might sell more than legal ◮ districts split due to decentralization → more heads of districts ◮ more illegally sold permits → more deforestation
- 23. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Background Asia crisis ended regime of dictator Suharto
- 24. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Background Asia crisis ended regime of dictator Suharto Suharto: ◮ former General who gained power by military putsch
- 25. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Background Asia crisis ended regime of dictator Suharto Suharto: ◮ former General who gained power by military putsch ◮ discrimination of certain ethnics and censorship of media
- 26. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Background Asia crisis ended regime of dictator Suharto Suharto: ◮ former General who gained power by military putsch ◮ discrimination of certain ethnics and censorship of media ◮ concentration of power
- 27. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Background Asia crisis ended regime of dictator Suharto Suharto: ◮ former General who gained power by military putsch ◮ discrimination of certain ethnics and censorship of media ◮ concentration of power ◮ economic growth under the cost of corruption
- 28. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Background Asia crisis ended regime of dictator Suharto Suharto: ◮ former General who gained power by military putsch ◮ discrimination of certain ethnics and censorship of media ◮ concentration of power ◮ economic growth under the cost of corruption Decentralization since fall of Suharto regime
- 29. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Background Asia crisis ended regime of dictator Suharto Suharto: ◮ former General who gained power by military putsch ◮ discrimination of certain ethnics and censorship of media ◮ concentration of power ◮ economic growth under the cost of corruption Decentralization since fall of Suharto regime ⇒ splits of districts
- 30. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area
- 31. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area ◮ Count deforested pixels (250m × 250m)
- 32. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area ◮ Count deforested pixels (250m × 250m) ◮ 438 images, equaling 34.6 million pixels (18.9 million pixels are forest)
- 33. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area ◮ Count deforested pixels (250m × 250m) ◮ 438 images, equaling 34.6 million pixels (18.9 million pixels are forest) ◮ red=deforested, green=forest, yellow=nonforest
- 34. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area ◮ Count deforested pixels (250m × 250m) ◮ 438 images, equaling 34.6 million pixels (18.9 million pixels are forest) ◮ red=deforested, green=forest, yellow=nonforest Different forest zones
- 35. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area ◮ Count deforested pixels (250m × 250m) ◮ 438 images, equaling 34.6 million pixels (18.9 million pixels are forest) ◮ red=deforested, green=forest, yellow=nonforest Different forest zones ◮ Production forest
- 36. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area ◮ Count deforested pixels (250m × 250m) ◮ 438 images, equaling 34.6 million pixels (18.9 million pixels are forest) ◮ red=deforested, green=forest, yellow=nonforest Different forest zones ◮ Production forest ◮ Conversion forest
- 37. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area ◮ Count deforested pixels (250m × 250m) ◮ 438 images, equaling 34.6 million pixels (18.9 million pixels are forest) ◮ red=deforested, green=forest, yellow=nonforest Different forest zones ◮ Production forest ◮ Conversion forest ◮ Conservation forest
- 38. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Moderate Resolution Imaging Spectroradiometer (MODIS) data set ◮ Satellite Data that count deforested area ◮ Count deforested pixels (250m × 250m) ◮ 438 images, equaling 34.6 million pixels (18.9 million pixels are forest) ◮ red=deforested, green=forest, yellow=nonforest Different forest zones ◮ Production forest ◮ Conversion forest ◮ Conservation forest ◮ Protection forest
- 39. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Figure : District level logging [Burgess et al., 2012]
- 40. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data Figure : Forest Cover Change Riau [Burgess et al., 2012]
- 41. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data First findings ◮ total deforestation 783,040 pixels (48,940 km2)
- 42. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data First findings ◮ total deforestation 783,040 pixels (48,940 km2) ◮ Production forest 486,720 pixels
- 43. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data First findings ◮ total deforestation 783,040 pixels (48,940 km2) ◮ Production forest 486,720 pixels ◮ Conversion forest 179,360 pixels
- 44. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data First findings ◮ total deforestation 783,040 pixels (48,940 km2) ◮ Production forest 486,720 pixels ◮ Conversion forest 179,360 pixels ◮ Conservation forest 60,320 pixels
- 45. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data First findings ◮ total deforestation 783,040 pixels (48,940 km2) ◮ Production forest 486,720 pixels ◮ Conversion forest 179,360 pixels ◮ Conservation forest 60,320 pixels ◮ Protection forest 56,640 pixels
- 46. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Data First findings ◮ total deforestation 783,040 pixels (48,940 km2) ◮ Production forest 486,720 pixels ◮ Conversion forest 179,360 pixels ◮ Conservation forest 60,320 pixels ◮ Protection forest 56,640 pixels On average 113 pixels per district (annually)
- 47. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Districts Number of Districts Province in 2000 in 2008 NAD (Aceh) 13 23 N. Sumatra 19 33 W. Sumatra 15 19 Riau 11 12 Jambi 10 11 S. Sumatra 7 15 Bengkulu 4 10 Lampung 10 14 Bangka Belitung 3 7 W. Kalimantan 9 14 C. Kalimantan 6 14 S. Kalimantan 11 13 E. Kalimantan 12 14 N. Sulawesi 5 15 C. Sulawesi 8 11 S. Sulawesi 21 24 SE Sulawesi 5 12 Gorontalo 3 6 W. Sulawesi 3 5 W. Papua 4 11 Papua 10 29
- 48. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Parties involved Logging companies ◮ profit maximizing through selling wood ◮ have to buy permit with price b in order to sell logs later on
- 49. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Parties involved Logging companies ◮ profit maximizing through selling wood ◮ have to buy permit with price b in order to sell logs later on Heads of District ◮ profit maximizing through selling permits b ◮ there’s a probability π that they are caught selling too many permits
- 50. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Cournot Framework Profit maximization of logging firms maxqfd p(Q)qfd − cqfd − bdqfd (1)
- 51. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Cournot Framework Profit maximization of logging firms maxqfd p(Q)qfd − cqfd − bdqfd (1) Solving for the first order conditions, each firm is willing to pay a price for a permit up to bd = p(Q) − c (2) where Q is exogenous for the firms.
- 52. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Cournot Framework Profit maximization of Heads of Districts maxqd b(qd)qd − π(qd, ¯q)rd (3)
- 53. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Cournot Framework Profit maximization of Heads of Districts maxqd b(qd)qd − π(qd, ¯q)rd (3) Plugging in the first order condition of the firms maximization problem yields: maxqd qdp D j=1 qj − cqd − π(qd, ¯q)rd (4)
- 54. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Cournot Framework Profit maximization of Heads of Districts maxqd b(qd)qd − π(qd, ¯q)rd (3) Plugging in the first order condition of the firms maximization problem yields: maxqd qdp D j=1 qj − cqd − π(qd, ¯q)rd (4) Derive with respect to q qdp′ + p − c − π′ (qd, ¯q)rd = 0 (5)
- 55. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Cournot Framework Assume functional form of inverse demand function p = a/qλ with constant elasticity of demand 1/λ
- 56. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Cournot Framework Assume functional form of inverse demand function p = a/qλ with constant elasticity of demand 1/λ Semi elasticity 1 Q dQ dn = 1 n2 − nλ (6) This will be the parameter estimated later on
- 57. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation Fixed effect Poisson quasi maximum likelihood (QML) count model
- 58. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation Fixed effect Poisson quasi maximum likelihood (QML) count model ⇒ Poisson regression standard tool for count models
- 59. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation Fixed effect Poisson quasi maximum likelihood (QML) count model ⇒ Poisson regression standard tool for count models ◮ Poisson assumes positive integer numbers
- 60. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation Fixed effect Poisson quasi maximum likelihood (QML) count model ⇒ Poisson regression standard tool for count models ◮ Poisson assumes positive integer numbers ◮ µ = exp(x′β) as mean specification ⇒ λ might vary across individuals according to specific function of x and β
- 61. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation Fixed effect Poisson quasi maximum likelihood (QML) count model ⇒ Poisson regression standard tool for count models ◮ Poisson assumes positive integer numbers ◮ µ = exp(x′β) as mean specification ⇒ λ might vary across individuals according to specific function of x and β ◮ in case of Poisson regression, QMLE may correctly identify certain features of reality (such as conditional mean although distribution misspecified)
- 62. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation Fixed effect Poisson quasi maximum likelihood (QML) count model ⇒ Poisson regression standard tool for count models ◮ Poisson assumes positive integer numbers ◮ µ = exp(x′β) as mean specification ⇒ λ might vary across individuals according to specific function of x and β ◮ in case of Poisson regression, QMLE may correctly identify certain features of reality (such as conditional mean although distribution misspecified) first order conditions of the general maximization problem of the Poisson QML estimator ˆβ: N i=1 (µ − exp(x′ iβ))xi = 0 (7) fulfilled in the case of E[µ|xi] = exp(x′ iβ)
- 63. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation As long as conditional mean is correctly specified the estimator will be consistent! ⇒ do not even require dependent variable to be Poisson distributed
- 64. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation As long as conditional mean is correctly specified the estimator will be consistent! ⇒ do not even require dependent variable to be Poisson distributed Interpretation of coefficient knowing that E(Q) = µpiexp(βn + ηit)):
- 65. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation As long as conditional mean is correctly specified the estimator will be consistent! ⇒ do not even require dependent variable to be Poisson distributed Interpretation of coefficient knowing that E(Q) = µpiexp(βn + ηit)): dQ dn =µpiexp(βn + ηit)β (8)
- 66. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation As long as conditional mean is correctly specified the estimator will be consistent! ⇒ do not even require dependent variable to be Poisson distributed Interpretation of coefficient knowing that E(Q) = µpiexp(βn + ηit)): dQ dn =µpiexp(βn + ηit)β (8) =Qβ (9) β = dQ dn 1 Q (10)
- 67. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Estimation As long as conditional mean is correctly specified the estimator will be consistent! ⇒ do not even require dependent variable to be Poisson distributed Interpretation of coefficient knowing that E(Q) = µpiexp(βn + ηit)): dQ dn =µpiexp(βn + ηit)β (8) =Qβ (9) β = dQ dn 1 Q (10) Semi elasticity
- 68. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Specification of quantity effect estimation E(deforestpit) = µpiexp(βNumDistrictsInProvpit + ηit) (11) with ◮ deforestpit as the dependent variable counting the pixels declared as deforested ◮ µpi as a province fixed effect ◮ NumDistrictsInProvpit as the number of districts in a province ◮ ηit as an island × year fixed effect
- 69. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Specification of price effect estimation Here they use official production data log(ywpit) = βNumDistrictsInProvpit + µwpi + ηwit + ǫwpit (12) with ◮ log(ywpit) as the price or quantity of wood type w harvested in province p and year t ◮ µwpi as a wood type by province fixed effect ◮ NumDistrictsInProvpit as the number of districts in a province ◮ ηwit as the wood type by island × year fixed effect
- 70. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Results (MODIS Data) All Forest Number of districts in province 0.039** (0.016) Observations 608 Number of districts in province 0.082** (sum of L0-L3) (0.020) Observations 608 * p < 0.1; ** p < 0.05; *** p < 0.01 Table : Effects on quantities
- 71. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Results (Official Production Data) All wood observations Log price Log quantity Number of districts in province -0.017 0.084* (0.012) (0.044) Observations 1003 1003 Number of districts in province -0.034** 0.135** (sum of L0-L3) (0.013) (0.056) Observations 1003 1003 * p < 0.1; ** p < 0.05; *** p < 0.01 Table : Effects on prices and quantities
- 72. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Further Specifications: Substitutes E(deforestdit) = µdiexp βPCOilandGasdit +γNumDistrictsdit + ηit (13) ◮ PCOilandGasdit per-capita oil and gas revenue received by the district
- 73. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Further Specifications: Substitutes E(deforestdit) = µdiexp βPCOilandGasdit +γNumDistrictsdit + ηit (13) ◮ PCOilandGasdit per-capita oil and gas revenue received by the district All forest Oil and gas revenue -0.003** per capita (0.002) Observations 6464 * p < 0.1; ** p < 0.05; *** p < 0.01 Table : Substitutes
- 74. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model interpretation of coefficients of OLS regression dlnQ dn = 1 Q dQ dn and dlnP dn = 1 P dP dn (14)
- 75. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model interpretation of coefficients of OLS regression dlnQ dn = 1 Q dQ dn and dlnP dn = 1 P dP dn (14) = semi elasticities
- 76. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model interpretation of coefficients of OLS regression dlnQ dn = 1 Q dQ dn and dlnP dn = 1 P dP dn (14) = semi elasticities 1 Q dQ dn 1 P dP dn = dQ Q dP P = dQ dP · P Q (15)
- 77. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model interpretation of coefficients of OLS regression dlnQ dn = 1 Q dQ dn and dlnP dn = 1 P dP dn (14) = semi elasticities 1 Q dQ dn 1 P dP dn = dQ Q dP P = dQ dP · P Q (15) = price elasticity of demand
- 78. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model interpretation of coefficients of OLS regression dlnQ dn = 1 Q dQ dn and dlnP dn = 1 P dP dn (14) = semi elasticities 1 Q dQ dn 1 P dP dn = dQ Q dP P = dQ dP · P Q (15) = price elasticity of demand official production data: -5.24
- 79. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model interpretation of coefficients of OLS regression dlnQ dn = 1 Q dQ dn and dlnP dn = 1 P dP dn (14) = semi elasticities 1 Q dQ dn 1 P dP dn = dQ Q dP P = dQ dP · P Q (15) = price elasticity of demand official production data: -5.24 MODIS data: -2.27
- 80. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model Recalling: 1 Q dQ dn = 1 n2 − nλ from theoretical framework (16) = 1 n2 − n1 ǫ (17)
- 81. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model Recalling: 1 Q dQ dn = 1 n2 − nλ from theoretical framework (16) = 1 n2 − n1 ǫ (17) on average 5.5 districts per province (our n)
- 82. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model Recalling: 1 Q dQ dn = 1 n2 − nλ from theoretical framework (16) = 1 n2 − n1 ǫ (17) on average 5.5 districts per province (our n) ⇒ MODIS data set: 0.034
- 83. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Goodness of the model Recalling: 1 Q dQ dn = 1 n2 − nλ from theoretical framework (16) = 1 n2 − n1 ǫ (17) on average 5.5 districts per province (our n) ⇒ MODIS data set: 0.034 ⇒ Model gives exact short run predictions of semi elasticities
- 84. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π
- 85. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π ◮ increase top down monitoring
- 86. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π ◮ increase top down monitoring ◮ creation of monitoring institutions
- 87. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π ◮ increase top down monitoring ◮ creation of monitoring institutions ◮ harder punishments
- 88. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π ◮ increase top down monitoring ◮ creation of monitoring institutions ◮ harder punishments Policy strategies
- 89. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π ◮ increase top down monitoring ◮ creation of monitoring institutions ◮ harder punishments Policy strategies ◮ export ban of raw logs
- 90. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π ◮ increase top down monitoring ◮ creation of monitoring institutions ◮ harder punishments Policy strategies ◮ export ban of raw logs ◮ permit for trees cut and not trees transported → tell firms where to log
- 91. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π ◮ increase top down monitoring ◮ creation of monitoring institutions ◮ harder punishments Policy strategies ◮ export ban of raw logs ◮ permit for trees cut and not trees transported → tell firms where to log Other approaches
- 92. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Policy Implications Increase π ◮ increase top down monitoring ◮ creation of monitoring institutions ◮ harder punishments Policy strategies ◮ export ban of raw logs ◮ permit for trees cut and not trees transported → tell firms where to log Other approaches ◮ educate people about consequences
- 93. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Conclusions 1. Satellite data do have additional explanatory power
- 94. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Conclusions 1. Satellite data do have additional explanatory power 2. decentralization even increases corruption
- 95. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Conclusions 1. Satellite data do have additional explanatory power 2. decentralization even increases corruption 3. subdividing jurisdictions can lead to more deforestation
- 96. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Conclusions 1. Satellite data do have additional explanatory power 2. decentralization even increases corruption 3. subdividing jurisdictions can lead to more deforestation 4. standard economics models help to explain illegal behavior
- 97. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Conclusions 1. Satellite data do have additional explanatory power 2. decentralization even increases corruption 3. subdividing jurisdictions can lead to more deforestation 4. standard economics models help to explain illegal behavior 5. infer actions to counteract corruption from model
- 98. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Questions ◮ why does deforestation increase in particular illegal zones? → role of roads and infrastructure in general
- 99. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Questions ◮ why does deforestation increase in particular illegal zones? → role of roads and infrastructure in general ◮ measurement: legal logging taking place via felling individual trees
- 100. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Questions ◮ why does deforestation increase in particular illegal zones? → role of roads and infrastructure in general ◮ measurement: legal logging taking place via felling individual trees Doubts and other explanations of deforestation
- 101. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Questions ◮ why does deforestation increase in particular illegal zones? → role of roads and infrastructure in general ◮ measurement: legal logging taking place via felling individual trees Doubts and other explanations of deforestation ◮ decline in enforcement
- 102. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Questions ◮ why does deforestation increase in particular illegal zones? → role of roads and infrastructure in general ◮ measurement: legal logging taking place via felling individual trees Doubts and other explanations of deforestation ◮ decline in enforcement ◮ changes in legal logging quotas
- 103. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Edward B Barbier, Nancy Bockstael, Joanne C Burgess, and Ivar Strand. The linkages between the timber trade and tropical deforestation-indonesia. The World Economy, 18(3):411–442, 1995. Robin Burgess, Matthew Hansen, Benjamin A Olken, Peter Potapov, and Stefanie Sieber. The political economy of deforestation in the tropics*. The Quarterly Journal of Economics, 127(4):1707–1754, 2012. Peter Dauvergne. The politics of deforestation in indonesia. Pacific Affairs, pages 497–518, 1993. Fitria Fitrani, Bert Hofman, and Kai Kaiser*. Unity in diversity? the creation of new local governments in a decentralising indonesia. Bulletin of Indonesian Economic Studies, 41(1):57–79, 2005. Benjamin A Olken. Corruption and the costs of redistribution: Micro evidence from indonesia. Journal of public economics, 90(4):853–870, 2006.
- 104. Introduction Literature Analysis Background Data The Model Estimation Results Policy Implications Conclusions References Christoph Schulze - Masterseminar: Topics in Empirical Public Economics Charles Palmer. The extent and causes of illegal logging: An analysis of a major cause of tropical deforestation in indonesia. 2001. M´arcio Santilli, Paulo Moutinho, Stephan Schwartzman, Daniel Nepstad, Lisa Curran, and Carlos Nobre. Tropical deforestation and the kyoto protocol. Climatic Change, 71(3):267–276, 2005.