deforestation in the tropics - summary

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Christoph Schulze - Masterseminar: Topics in Empirical Public Economics

The Political Economy of Deforestation inthe Tropics

Burgess, Hansen, Olken, Potapov, and Sieber [2012]The Quarterly Journal of Economics (2012) 127 (4): 1707-1754

July 2, 2014

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Overview

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Introduction

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Introduction

Stylized Facts

◮ counteracting climate change (Photosynthesis)

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Introduction

Stylized Facts


◮ illegal logging one of major sources of deforestation

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Introduction

Stylized Facts



◮ annually about 20.000 km2 deforested in the tropics

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Introduction

Stylized Facts




Idea

Introduction

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Introduction

Stylized Facts




Idea

◮ Development of deforestation

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Introduction

Stylized Facts




Idea


◮ Role of corruption

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Introduction

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Idea


◮ Role of corruption

◮ Connection of changes in political system anddeforestation

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Literature

Many papers stem from 90’s or early 00’s

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Literature

Many papers stem from 90’s or early 00’sSantilli et al. [2005]

◮ blaming ‘swidden’ agriculture on deforestation

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Literature



Barbier et al. [1995]

◮ analyze different policies such as export ban andexport tax

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Dauvergne [1993]

◮ corruption from political point of view

◮ criticizes Suharto regime

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Dauvergne [1993]



Palmer [2001]

◮ analysis of corruption and market failures due tomisplaced subsidies

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Dauvergne [1993]



Palmer [2001]


Olken [2006]

◮ empirical study to prove corruption

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Dauvergne [1993]



Palmer [2001]


Olken [2006]

◮ empirical study to prove corruption

Fitrani et al. [2005]

◮ why do districts split?

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Sketch of Analysis

◮ logging firms make profits by felling trees andselling wood

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Sketch of Analysis


◮ head of district makes money by selling loggingpermits to firms

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Sketch of Analysis



◮ national governments determine legal quotas forlogging

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Sketch of Analysis




◮ head of districts might sell more than legal

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Sketch of Analysis





◮ districts split due to decentralization → moreheads of districts

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Sketch of Analysis





◮ districts split due to decentralization → moreheads of districts

◮ more illegally sold permits → more deforestation

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Background

Asia crisis ended regime of dictator Suharto

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Background

Asia crisis ended regime of dictator SuhartoSuharto:

◮ former General who gained power by military putsch

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◮ discrimination of certain ethnics and censorship ofmedia

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◮ concentration of power

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◮ economic growth under the cost of corruption

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Decentralization since fall of Suharto regime

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Decentralization since fall of Suharto regime⇒ splits of districts

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Data

Moderate Resolution Imaging Spectroradiometer(MODIS) data set

◮ Satellite Data that count deforested area

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Data



◮ Count deforested pixels (250m × 250m)

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Data




◮ 438 images, equaling 34.6 million pixels (18.9 millionpixels are forest)

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Data





◮ red=deforested, green=forest, yellow=nonforest

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Different forest zones

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Data







◮ Production forest

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Data








◮ Conversion forest

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Data









◮ Conservation forest

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Data









◮ Conservation forest

◮ Protection forest

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Data

Figure : District level logging [Burgess et al., 2012]

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Data

Figure : Forest Cover Change Riau [Burgess et al., 2012]

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Data

First findings

◮ total deforestation 783,040 pixels (48,940 km2)

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Data

First findings


◮ Production forest 486,720 pixels

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◮ Conversion forest 179,360 pixels

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◮ Conservation forest 60,320 pixels

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◮ Protection forest 56,640 pixels

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Data

First findings





◮ Protection forest 56,640 pixels

On average 113 pixels per district (annually)

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Districts

Number of DistrictsProvince in 2000 in 2008NAD (Aceh) 13 23N. Sumatra 19 33W. Sumatra 15 19Riau 11 12Jambi 10 11S. Sumatra 7 15Bengkulu 4 10Lampung 10 14Bangka Belitung 3 7W. Kalimantan 9 14C. Kalimantan 6 14S. Kalimantan 11 13E. Kalimantan 12 14N. Sulawesi 5 15C. Sulawesi 8 11S. Sulawesi 21 24SE Sulawesi 5 12Gorontalo 3 6W. Sulawesi 3 5W. Papua 4 11Papua 10 29

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Parties involved

Logging companies

◮ profit maximizing through selling wood

◮ have to buy permit with price b in order to sell logslater on

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Parties involved

Logging companies

◮ profit maximizing through selling wood

◮ have to buy permit with price b in order to sell logslater on

Heads of District

◮ profit maximizing through selling permits b

◮ there’s a probability π that they are caught sellingtoo many permits

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Cournot Framework

Profit maximization of logging firms

maxqfdp(Q)qfd − cqfd − bdqfd (1)

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Cournot Framework

Profit maximization of logging firms

maxqfdp(Q)qfd − cqfd − bdqfd (1)

Solving for the first order conditions, each firm is willingto pay a price for a permit up to

bd = p(Q)− c (2)

where Q is exogenous for the firms.

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Cournot Framework

Profit maximization of Heads of Districts

maxqdb(qd)qd − π(qd, q)rd (3)

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Cournot Framework



Plugging in the first order condition of the firmsmaximization problem yields:

maxqdqdp

D∑

j=1

qj

− cqd − π(qd, q)rd (4)

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Cournot Framework



Plugging in the first order condition of the firmsmaximization problem yields:

maxqdqdp

D∑

j=1

qj

− cqd − π(qd, q)rd (4)

Derive with respect to q

qdp′ + p− c− π′(qd, q)rd = 0 (5)

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Cournot Framework

Assume functional form of inverse demand functionp = a/qλ with constant elasticity of demand 1/λ

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Cournot Framework

Assume functional form of inverse demand functionp = 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

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Estimation

Fixed effect Poisson quasi maximum likelihood (QML)count model

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Estimation

Fixed effect Poisson quasi maximum likelihood (QML)count model⇒ Poisson regression standard tool for count models

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Estimation


◮ Poisson assumes positive integer numbers

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Estimation



◮ µ = exp(x′β) as mean specification⇒ λ might vary across individuals according tospecific function of x and β

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Estimation




◮ in case of Poisson regression, QMLE may correctlyidentify certain features of reality (such asconditional mean although distribution misspecified)

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Estimation




◮ in case of Poisson regression, QMLE may correctlyidentify certain features of reality (such asconditional mean although distribution misspecified)

first order conditions of the general maximization problemof the Poisson QML estimator β:

N∑

i=1

(µ− exp(x′iβ))xi = 0 (7)

fulfilled in the case of E[µ|xi] = exp(x′iβ)

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Estimation

As long as conditional mean is correctly specified theestimator will be consistent!⇒ do not even require dependent variable to be Poissondistributed

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Estimation


Interpretation of coefficient knowing thatE(Q) = µpiexp(βn+ ηit)):

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Estimation



dQ

dn=µpiexp(βn+ ηit)β (8)

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Estimation



dQ


=Qβ (9)

β =dQ

dn

1

Q(10)

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Estimation



dQ


=Qβ (9)

β =dQ

dn

1

Q(10)

Semi elasticity

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Specification of quantity effect estimation

E(deforestpit) = µpiexp(βNumDistrictsInProvpit + ηit)

(11)

with

◮ deforestpit as the dependent variable counting thepixels declared as deforested

◮ µpi as a province fixed effect

◮ NumDistrictsInProvpit as the number of districts in aprovince

◮ ηit as an island × year fixed effect

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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 aprovince

◮ ηwit as the wood type by island × year fixed effect

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Results (MODIS Data)

All ForestNumber of districts in province 0.039**

(0.016)Observations 608Number 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

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Results (Official Production Data)

All wood observationsLog price Log quantity

Number of districts in province -0.017 0.084*(0.012) (0.044)

Observations 1003 1003Number 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

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Further Specifications: Substitutes

E(deforestdit) = µdiexp

(

βPCOilandGasdit+γNumDistrictsdit + ηit

)

(13)

◮ PCOilandGasdit per-capita oil and gas revenuereceived by the district

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Further Specifications: Substitutes

E(deforestdit) = µdiexp

(

βPCOilandGasdit+γNumDistrictsdit + ηit

)

(13)

◮ PCOilandGasdit per-capita oil and gas revenuereceived by the district

All forestOil and gas revenue -0.003**per capita (0.002)Observations 6464

* p < 0.1; ** p < 0.05; *** p < 0.01

Table : Substitutes

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Goodness of the model

interpretation of coefficients of OLS regression

dlnQ

dn=

1

Q

dQ

dnand

dlnP

dn=

1

P

dP

dn(14)

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dlnQ

dn=

1

Q

dQ

dnand

dlnP

dn=

1

P

dP

dn(14)

= semi elasticities

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dlnQ

dn=

1

Q

dQ

dnand

dlnP

dn=

1

P

dP

dn(14)

= semi elasticities

1Q

dQdn

1P

dPdn

=

dQQ

dPP

=dQ

dP·P

Q(15)

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dlnQ

dn=

1

Q

dQ

dnand

dlnP

dn=

1

P

dP

dn(14)

= semi elasticities

1Q

dQdn

1P

dPdn

=

dQQ

dPP

=dQ

dP·P

Q(15)

= price elasticity of demand

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dlnQ

dn=

1

Q

dQ

dnand

dlnP

dn=

1

P

dP

dn(14)

= semi elasticities

1Q

dQdn

1P

dPdn

=

dQQ

dPP

=dQ

dP·P

Q(15)


official production data: -5.24

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dlnQ

dn=

1

Q

dQ

dnand

dlnP

dn=

1

P

dP

dn(14)

= semi elasticities

1Q

dQdn

1P

dPdn

=

dQQ

dPP

=dQ

dP·P

Q(15)


official production data: -5.24MODIS data: -2.27

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Recalling:

1

Q

dQ

dn=

1

n2 − nλfrom theoretical framework (16)

=1

n2 − n1ǫ

(17)

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Recalling:

1

Q

dQ

dn=

1


=1

n2 − n1ǫ

(17)

on average 5.5 districts per province (our n)

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Recalling:

1

Q

dQ

dn=

1


=1

n2 − n1ǫ

(17)


⇒ MODIS data set: 0.034

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Recalling:

1

Q

dQ

dn=

1


=1

n2 − n1ǫ

(17)


⇒ MODIS data set: 0.034

⇒ Model gives exact short run predictions of semielasticities

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Policy Implications

Increase π

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Policy Implications

Increase π

◮ increase top down monitoring

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Policy Implications

Increase π


◮ creation of monitoring institutions

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Policy Implications

Increase π



◮ harder punishments

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Policy Implications

Increase π




Policy strategies

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Policy Implications

Increase π




Policy strategies

◮ export ban of raw logs

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Policy Implications

Increase π




Policy strategies


◮ permit for trees cut and not trees transported→ tell firms where to log

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Policy Implications

Increase π




Policy strategies



Other approaches

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Policy Implications

Increase π




Policy strategies



Other approaches

◮ educate people about consequences

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Conclusions

1. Satellite data do have additional explanatory power

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Conclusions


2. decentralization even increases corruption

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Conclusions



3. subdividing jurisdictions can lead to moredeforestation

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Conclusions




4. standard economics models help to explain illegalbehavior

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Conclusions




4. standard economics models help to explain illegalbehavior

5. infer actions to counteract corruption from model

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Questions

◮ why does deforestation increase in particular illegalzones?→ role of roads and infrastructure in general

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Questions


◮ measurement: legal logging taking place via fellingindividual trees

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Questions



Doubts and other explanations of deforestation

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Questions




◮ decline in enforcement

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Questions




◮ decline in enforcement

◮ changes in legal logging quotas

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References


Edward B Barbier, Nancy Bockstael, Joanne C Burgess,and Ivar Strand. The linkages between the timbertrade and tropical deforestation-indonesia. The World

Economy, 18(3):411–442, 1995.

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Introduction

Literature

Analysis

Background

Data

The Model

Estimation

Results

Policy

Implications

Conclusions

References


Charles Palmer. The extent and causes of illegal logging:An analysis of a major cause of tropical deforestationin indonesia. 2001.

Marcio 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.

deforestation in the tropics - summary

Economy & Finance