corruption, trade and resource conversion

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Journal of Environmental Economics and Management 50 (2005) 276–299

0095-0696/$ -

doi:10.1016/j.

$Paper pre

September 5–�Correspon

162, 1000 E. U

E-mail add

daniel.leonard

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Corruption, trade and resource conversion$

Edward B. Barbiera,b,�, Richard Damaniac, Daniel Leonardd

aDepartment of Economics and Finance, University of Wyoming, Department 3985, Room 162, 1000 E. University Avenue,

Laramie, WY 82071, USAbCentre for Environment and Development Economics, University of York, Heslington, York YO1 5DD, UK

cSchool of Economics, University of Adelaide, Adelaide, AustraliadSchool of Business Economics, Flinders University, Adelaide, Australia

Received 19 July 2004

Available online 5 April 2005

Abstract

Recent evidence suggests that special interest groups significantly affect tropical deforestation throughlobbying. We develop an open-economy model in which resource conversion is determined by a self-interested government that is susceptible to the influences of the political contributions it receives from theprofit-maximizing economic agent responsible for land conversion. We investigate the effects of lobbyingon the cumulative level of resource conversion and examine how trade policy influences the distortionscreated by political corruption. We derive testable predictions that are analyzed through a panel analysis ofcumulative agricultural land expansion over 1960–99 for low and middle-income tropical countries. Ourfindings suggest that increased corruption and resource dependency directly promote land conversion,whereas rising terms of trade reduce conversion.r 2005 Elsevier Inc. All rights reserved.

JEL classification: Q23; Q28; D78; F19

Keywords: Corruption; Developing countries; Lobbying; Open economy; Political economy; Resource conversion;

Resource-trade dependency; Terms of trade

see front matter r 2005 Elsevier Inc. All rights reserved.

jeem.2004.12.004

sented at the Workshop on ‘‘Trade, Biodiversity and Resources’’, University of Tilburg, The Netherlands,

6, 2002.

ding author. Department of Economics and Finance, University of Wyoming, Department 3985, Room

niversity Avenue, Laramie, WY 82071-3985, USA. Fax: +1307 766 5090.

resses: [email protected] (E.B. Barbier), [email protected] (R. Damania),

@flinders.edu.au (D. Leonard).

www.elsevier.com/locate/jeem

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E.B. Barbier et al. / Journal of Environmental Economics and Management 50 (2005) 276–299 277

1. Introduction

Concerns over the loss of tropical forests and global biodiversity have intensified in recentyears. Over the past decade, forest cover in the tropics has decreased by 12.3 million hectares (ha)annually, a deforestation rate of 0.7% per annum [15]. The continual loss of tropical forests hasimportant implications for biodiversity. It has been estimated that more than four-fifths of manygroups of plants and animals are found in tropical forests (CIFOR/Government of Indonesia/UNESCO [9]). Moderate estimates of future species extinction rates due to tropical deforestationrange from one to five percent per decade [27].Across the tropics, the principal activity responsible for deforestation appears to be

the direct conversion of forests to permanent agriculture [15]. Stratified random sampling of10% of the world’s tropical forests reveals that direct conversion by large-scale agriculture may bethe main source of deforestation, accounting for around 32% of total forest cover change,followed by conversion to small-scale agriculture, which accounts for 26%. Intensification ofagriculture in shifting cultivation areas comprises only 10% of tropical deforestation, andexpansion of shifting cultivation into undisturbed forests only 5%. However, there are importantregional differences. In Africa, the major process of deforestation (around 60%) is due to theconversion of forest for the establishment of small-scale permanent agriculture, whereas directconversion of forest cover to large-scale agriculture, including raising livestock, predominates inLatin America and Asia (48% and 30%, respectively). Although agricultural conversion is theprincipal cause of tropical deforestation, in many forested regions uncontrolled timberharvesting is responsible for initially opening up previously inaccessible forested frontiers topermanent agricultural conversion and for causing widespread timber-related forest degradationand loss [1,5,27].There is also growing recognition that tropical deforestation due to forestry activities and

agricultural conversion, especially for large-scale economic activity, is influenced by governmentpolicy. In Central America and Amazonia, government support for cattle ranching, forestry andlarge-scale agriculture has played a prominent role in deforestation [14,21,26,34], while in Asia,South America and Africa policy-induced expansion of large scale plantations, timber harvestingand cash crops have been responsible for the destruction of large tracts of forested land [1,8,19].Common to all these cases is the role of governments in providing both tacit and overt support forthe incursion of agricultural and logging activities into forests.Numerous studies suggest that the lobbying activities of special interest groups in many

developing countries have played a significant role in influencing key government policies thatdetermine land use decisions in these countries [1,8,11,16,19]. As argued by Ascher [1, pp. 52–53],the result of such lobbying is that governments in turn will deliberately create rent-seekingopportunities for those special interests that benefit from favorable land use policies: as aconsequence, government corruption—the use of public office for private gain—is now seen to bean endemic problem dictating forest land use policies in developing countries:Despite the prevalence of this link between rent-seeking, corruption and government land use

policy, the existing literature in economics has failed to examine the consequences of specialinterest lobbying pressures on agricultural land conversion. We develop a model in which theamount of land converted to agricultural uses is determined by a self-interested government. Thegovernment is assumed to care about the political contributions it receives from private economic

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E.B. Barbier et al. / Journal of Environmental Economics and Management 50 (2005) 276–299278

agents as well as social welfare. The relative weight given to welfare considerations thus provides ameasure of the degree of government honesty. The other player in the game is the land conversionagent. The players have different objectives and interact non-cooperatively. We investigate theeffects of bribes and corruption on rates of cumulative land conversion and examine whethertrade policy reforms enhance or correct these distortions. The main predictions of our modelappear to be supported by our empirical results. Our findings suggest that increased corruption,terms of trade (TOT) and resource dependency increase agricultural conversion directly.The contribution of this paper extends the varied and growing literature that examines the

effects of rent-seeking activities on environmental policy outcomes in general. For instance, usingthe common agency framework Eliste and Fredriksson [13] investigate the effects of specialinterest lobbying on subsidies in the agricultural sector. They find that more environmentallydamaging sectors receive greater compensation for the costs associated with environmentalprotection. Leidy and Hoekman [24] explore the interaction between environmental policyefficiency and trade barriers, and demonstrate that polluters may prefer inefficient policies sincethey increase the implicit level of protection. Rauscher [28] examines lobbying incentives in apolluting industry and finds that trade openness may have ambiguous effects on lobbyingintensity. Lopez and Mitra [25] investigate the impact of corruption on the empirical relationshipbetween income and pollution—the Environmental Kuznets Curve (EKC). It is shown thatcorruption increases the income level at which the EKC begins to decline.1

This paper differs from previous work in several significant ways. Most notably, and in contrastto existing work, we deal explicitly with resource dynamics. Thus our model applies to resource-based environmental problems such as tropical deforestation and habitat loss, which havehitherto been ignored in the environmental political economy literature. In addition, we extendthe literature by investigating the interaction between corruption and trade openness on policyoutcomes. Finally, from our theoretical model we derive testable predictions concerning lobbying,trade and resource conversion in developing countries. The empirical section of the paper teststhese predictions, employing a panel analysis of agricultural land expansion over 1960–99 for lowand middle-income economies.These issues are arguably of economic significance for at least two reasons. Firstly, as noted

above, political factors appear to be responsible for a major change in the use of global resources,especially tropical forests and biodiversity. Thus, an understanding of the effects of bribes andcorruption on land conversion and deforestation rates is of considerable economic relevance.Secondly, tropical forests may confer significant cross-border external benefits, through their roleas stores of carbon, genetic material, habitat for endangered species, etc. This has prompted callsfor the use of various trade-based policies to coerce these nations to reduce the level of resourceexploitation. It is clearly important to determine whether trade policies strengthen or weaken thedistorting influence of lobby groups on domestic land use decisions. This paper represents a firstbut significant step in analyzing these issues in a political economy context.

1A growing number of related studies examine the link between special interest group lobbying and environmental

policy outcomes. Examples of papers that use the common agency framework include Fredriksson [17] and Damania

[10], and examples of studies that use the political competition framework include Hillman and Ursprung [20] and

Schulze and Ursprung [30].

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The remainder of the paper is organized as follows. The next two sections outline the basicmodel of resource conversion and the lobbying decisions of the private economic agent and thegovernment’s responses, while Section 4 examines the effects of trade openness and corruption onthe resource conversion decisions of the government and presents four basic predictionsconcerning the influence of trade and lobbying on resource conversion. In the following section wetest these predictions through an empirical analysis of agricultural land expansion in developingcountries. The final section concludes the paper and discusses the wider implications of theanalysis.

2. Lobbying and resource conversion decisions of the private economic agent

The focus of our model is a small open economy that contains a homogenous stock of a finite,available natural resource, FðtÞX0: In the following analysis, we treat the stock as a land resource,such as forests, wetlands or other natural habitat, which is subject to irreversible conversion by aneconomic activity, such as agriculture or timber felling. Alternatively, F could also be a non-renewable stock, such as mineral ores or energy reserves, which is depleted through mining, and sothe model that follows could equally be applied to a conventional exhaustible resource problem.2

However, in what follows, we will focus on resource conversion as the principal economic activityand thus consider F(t) to be a stock of natural habitat or land subject to agricultural conversion,or depletion through timber harvesting.To illustrate the potential influences of lobbying on the resource conversion (or depletion)

decision, we build in two distinct features. First, government is responsible for the management ofthe resource and determines the rate of conversion by issuing quotas. The quotas define themaximum allowable conversion rate in any period. Second, the profit-maximizing economic agentresponsible for converting the resource seeks to influence the government’s decisions throughpolitical contributions.Denoting h(t) as the amount of resource conversion and hk as the government quota, changes in

the resource stock over time F(t) are therefore determined by

FðtÞ � F ð0Þ ¼ �

Z t

0

hðtÞ dt or _F ¼ �hðtÞ; F ð0Þ ¼ F0; limt!1

FðtÞX0 (1)

and

hðtÞphk; hðtÞX0. (2)

In what follows, it is assumed that the constraint in (2) always binds so that actual resourceconversion depends on the government’s allocation decision. Attention is therefore focused on thepolitical decisions of the government.Irreversible resource conversion occurs as the result of some economic activity, such as

agriculture or timber felling. Output of this activity, Q, is therefore assumed to be a function ofthe current flow of resource conversion, h(t), as well as cumulated conversion over time,

2In fact our model can equally be applied to the case of a minerals resource where the quality of the output declines

with the amount extracted.

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R t

0 hðtÞ dt ¼ F0 � F ðtÞ: The reason for this specification is twofold. First, current resourceconversion may influence output, as it is assumed that there is an immediate gain in productionfrom current conversion. For example, if the converting activity is agriculture, then the clearingand burning that usually accompany land conversion will release nutrients that will boost currentagricultural productivity. Cumulative resource conversion also affects output of the conversionactivity, because it is generally assumed that production is dependent on the converted resource.Again returning to the example of agriculture, cumulative conversion of wetlands, forests andother natural habitat means more arable land for agricultural production. A second implication ofthe above specification is that it helps to avoid corner solutions.To focus the analysis further on the effects of lobbying on resource conversion, it will be

convenient to express the production function of the conversion activity as3

Q ¼ qðF0 � F ðtÞÞhðtÞ; q040; q00o0. (3)

The way the economic agent engaged in resource conversion can influence the setting of thequota is through expenditures, S(h), on political contributions or bribes. Specifically, followingGrossman and Helpman [18], we assume that in each period the resource-converting agent offersthe incumbent government a political contribution schedule (S(h)), which consists of a continuousfunction that maps resource conversion rates the government chooses, into a bribe.4 Givenknowledge of the bribe schedule, the government then proceeds to set its optimal policies. Thediscounted profits, P; received by the resource converting agent from resource conversion is givenby

PðF ; h; pQ; dÞ ¼Z 1

0

e�dt½pQqðF0 � F ðtÞÞh � SðhÞ dt; Sh40. (4)

Note that in (4) the time argument has been dropped for most variables to simplify notation, dis the discount rate and pQ is the price of the output produced from the resource conversionactivity. We also assume that the amount of political contributions or bribes required by thegovernment increases with the level of resource conversion, Sh40: In essence, Sh is the marginalcost to the private rent-seeking agent of ‘‘lobbying’’ the government for more resource conversion.

3. Political decision making by the government

We assume that the economy is small and open and trades some of its output from the resourceconverting activity in order to finance its imports. Let x(t) be the exports of output from theresource conversion activity, and m(t) is consumption of a composite imported good. It followsthat domestic consumption of the remaining output from the resource converting activity can bedefined as cðtÞ ¼ QðtÞ � xðtÞ: As the TOT, p, of the small open economy are exogenouslydetermined on international markets, and all the output of the resource converting activity is sold

3Ehui and Hertel [12] use a similar specification.4Following the common agency lobbying literature we assume that optimal bribe schedule is determined outside of

the model. However, this schedule is assumed to be contingent upon the amount of conversion. Observe that if the

assumption does not hold so that the private agent does not offer a bribe schedule and instead offers a single bribe

amount, then the payment can have no marginal incentive effects on the government’s decisions.

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at world prices, we can define the balance of trade condition of the economy as

px ¼ m; p ¼px

pm¼

pQ

pm. (5)

The political decision-making of the government in setting the optimal amount of resourceconversion in the economy will be influenced by the lobbying efforts of the economic agentengaged in resource conversion. However, the government may also have wider social welfareconcerns, such as maintaining domestic consumption and protecting the ecological and othervalues of the remaining stock of the natural habitat, F. Finally, the government is also responsiblefor managing the open economy, which involves ensuring that sufficient exports are generatedfrom the resource converting activity to allow an optimal flow of imported consumption goods.We follow Lopez and Mitra [25] and assume that the government’s utility function, G, is a

discounted weighted sum of political contributions and citizens’ welfare. To determine the optimalamount of resource conversion, exports and imports each period, the government chooses to

Maxh;x;m

G ¼

Z 1

0

e�dt½ð1� aÞSðhÞ þ aW dt; W ¼ W ðc;m;F Þ, (6)

subject to (1) and (5).The government’s objective function (6) is therefore a weighted sum of social welfare and

political contributions (or bribes).5 W is the aggregate social welfare function, which is assumed todepend on domestic consumption, c(t), imports, m(t) and the remaining natural resource stock,F(t). The inclusion of the resource stock in W indicates the presence of positive stock externalitiesassociated with F, which may consist of watershed protection, salinity prevention, soilconservation, wildlife habitat, tourism, carbon store, biodiversity preservation, and other possibleecological values. The function W is assumed to be additively separable and concave with respectto its arguments, i.e. qW=qi40; q2W=qi2o0; i ¼ c;m;F :Finally, the parameter 0pap1 is the weight given to aggregate social welfare, W, relative to

political contributions or bribes, S, in the government’s objective function. As lower values of aindicate the government’s willingness to set policies that diverge from the welfare-maximizinglevel of resource conversion in return for political contributions. The parameter ð1� aÞ can beinterpreted as an indicator of the level of corruption. The latter interpretation is similar to Schulzeand Ursprung [30], who note that in such models political contributions or bribes are given inorder to influence government policy, not the election outcome. The level of corruption in the

5Following Lopez and Mitra [25], we could assume that the government objective function (6) is

Maxh;x;m

G ¼

Z 1

0

e�dt½ð1� aÞSðhÞ þ ap dt; p ¼ pfW ðc;m;F Þg,

where W is the aggregate social welfare function as defined in (6) and p is the probability of the government being re-elected, which is assumed to be linear and increasing in W. This would make our model compatible with voting models

that take into account the motive of democratically elected government is to win elections. In Eq. (6), to sharpen the

analysis, we drop p but note that our model could easily be extended to include the re-election motive. In addition wealso exclude profits from W following Lopez and Mitra. Inclusion of this term complicates but does not alter the

analysis. Moreover as shown later in the paper by local truthfulness bribes reflect profits thus profits enter the

government’s objective function but with a different weight attached than other terms in the welfare function.

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model is reflected by the government’s willingness to allow lobby groups to influence policies, e.g.,the propensity to sell policies for personal gains in the form of monetary transfers. This view ofcorruption is also consistent with Bardhan [6, p. 1321], who defines corruption as ‘the use of publicoffice for private gain’. Our formulation also closely follows the government’s objective function inGrossman and Helpman [18]. However, in contrast to all the existing work on the politicaleconomy of environmental policy, our model explicitly incorporates the dynamic characteristicsof the problem.In the game that forms the basis of this model the government selects the resource conversion

rate, h, while the private agent chooses the contribution schedule, S(h), that is offered to thegovernment (and taken as given). Thus, an equilibrium for this game is a contribution scheduleand a conversion rate for each period, such that: (i) the contribution is feasible;6 (ii) theconversion policy h maximizes the government’s welfare, G, taking the contribution as given [18].We do not discuss the well-known necessary conditions for equilibrium since these have beenextensively discussed in the political economy literature [7,18]. The necessary conditions for anequilibrium are defined by

ðMIÞ h maximizes G ¼R1

0 e�dt½ð1� aÞSðhÞ þ aW dt; W ¼ W ðc;m;F Þ; subject to

F ¼ �h; hX0 and px ¼ m:

ðMIIÞ h maximizes J ¼ G þP; subject to F ¼ �h; hX0 and px ¼ m

where P ¼R1

0 e�dt½pQqðF0 � FðtÞÞh � SðhÞ dt:

Condition (MI) asserts that the equilibrium conversion rate h must maximize the government’spayoff, given the contribution offered. Condition (MII) requires that h must also maximize thejoint payoffs of the private agent and the government. If this condition is not satisfied, agents havean incentive to alter their strategy to capture more of the surplus.7

The current-value Hamiltonian for the government’s payoff problem (MI) is

H ¼ ð1� aÞSðhÞ þ aW ð½qðF0 � FÞh � x; px;F Þ � mh, (7)

which is maximized with respect to choice of h and x. Note that m is the shadow value of theresource, defined in terms of its marginal contribution to the government’s objective function.The corresponding first-order conditions for an interior solution to (7) are (1) and8

qH

qh¼ ð1� aÞSh þ aW cq � m ¼ 0 or m ¼ aW cq þ ð1� aÞSh, (8)

qH

qx¼ a½pW m � W c ¼ 0 or W c ¼ pW m, (9)

�qH

qF¼ _m� dm or _m ¼ dm� a½W F � W chq0, (10)

6In this context this is taken to imply that the contribution must be non-negative. That is the private economic agent

cannot ‘‘tax’’ the government.7See [18], for an extensive discussion of this condition.8In what follows we show that h40 prevails.

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limt!1

e�dtmðtÞF ðtÞ ¼ 0. (11)

Condition (8) indicates that the marginal cost of additional resource conversion, m, must equal themarginal benefits. The latter consist of the weighted sum in the government’s utility calculation ofwelfare gain in the economy from the additional consumption resulting from conversion, aW cq;plus the additional political contributions that the government receives from the economic agentlobbying for more resource conversion, ð1� aÞSh: Eq. (9) is the open economy equilibriumcondition, which states that the marginal welfare contribution of domestic consumption fromresource conversion relative to the marginal welfare contribution of imported consumption, mustequal the TOT, p. Eq. (10) states that, from the government’s perspective, any change in theshadow value of the resource stock must equal the marginal cost of conserving that stock, dm; lessthe net marginal social benefits from conserving the resource. The latter include the stockexternality benefits of resource conservation, WF, minus the marginal welfare contribution of theadditional consumption from cumulative resource conversion, Wchq0. Finally, condition (11) isthe transversality condition corresponding to the government’s infinite horizon utility-maximizingproblem.Taking the time derivative of (8) and substituting it into (10) yields an expression for the change

over time in the optimal amount of resource conversion set by the government

½ð1� aÞShh þ aW ccq2 _h � a½W ccqh þ W cq

0 _F � aW ccq _x

¼ d½ð1� aÞSh þ aW cq � a½W F � W chq0,

_h ¼1

Zðd½ð1� aÞSh þ apW mq � a½W F þ W ccQq0h þ aW ccq _xÞ,

Z ¼ ð1� aÞShh þ aW ccq2o0. ð12Þ

Note that Zo0 follows from the necessary second order condition for maximizing (7) with respectto h.Similarly, the Hamiltonian for the problem MII is

H ¼ ð1� aÞSðhÞ þ aW ð½qðF0 � FÞh � x; px;F Þ þ pxqðF0 � F Þh � SðhÞ � lh, (13)

where l is the costate variable.The necessary conditions are Eq. (11) and:

qH

qh¼ ð1� aÞSh þ aW cq þ pxq � Sh � l ¼ 0, (14)

qH

qx¼ a½pW m � W c ¼ 0 or W c ¼ pW m, (15)

�qH

qF¼ _l� dl or _l ¼ dl� a½W F � W chq0 þ pxq0h. (16)

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Denoting the joint-payoff maximizing rate of resource conversion as he, we can again obtain anexpression for _h

e

_he¼1

Zðd½�aSh þ apW mq þ pxq � a½W F þ W ccQq0he

þ aW ccq _xÞ,

Z ¼ �aShh þ aW ccq2o0. ð17Þ

Since in the political equilibrium the optimal rate of resource conversion, he¼ h: Hence

combining Eqs. (8) and (14), yields: m ¼ lþ Sh � pxq: Differentiating with respect to time andsimplifying using (10) and (16): _hShh ¼ dðSh � pxqÞ; one solution which satisfies this condition is:Sh ¼ pxq and Shh ¼ 0: Moreover when he

¼ h this implies that he:

¼ h:must satisfy both Eqs. (12)

and (17), which holds if Sh ¼ pxq and Shh ¼ 0: By concavity of the objective function this is theunique solution to the problem. Furthermore, observe that from Eq. (4): Ph ¼ pxq and Phh ¼ 0:Hence, Sh ¼ Ph: That is, in equilibrium the change in contributions from the resource-convertingagent (Sh) truthfully reflect the profits obtained by the agent from greater access to the resourceðPhÞ: These conditions are the dynamic counterparts of the local truthfulness condition of thecommon agency lobbying model. Using these conditions in equation (12), resource conversionrates are given by

_h ¼1

aW ccq2ðd½ð1� aÞSh þ apW mq � a½W F þ W ccQq0h þ aW ccq _xÞ. (170)

Eq. (170) defines the political equilibrium level of resource conversion for h ¼ he: It suggests thatin equilibrium the government determines the level of resource conversion by comparing thepolitically relevant marginal benefits and marginal costs. The benefits from resource conversioninclude two components: the discounted welfare gains from increased imports, ðd½apW mqÞ; anddiscounted bribes paid to the government, ðd½ð1� aÞShÞ: The benefits of conversion are equatedto the net benefits from conservation. The latter are defined by the stock externality benefits of theresource, less the marginal utility from consumption of the resource: ða½W F þ W ccQq0hÞ; plus anadditional term representing the marginal welfare effects of resource-based exports, aW ccq _x: Inwhat follows this latter term is denoted as the ‘‘resource trade dependence’’ effect. Observe thatthe marginal benefits from bribes are given a weight of ð1� aÞ; while the marginal welfare effectsare given a weight of a: This suggests that in highly corrupt regimes (with a low a), bribes have agreater influence on the amount of resource conversion.

4. Model predictions for the political equilibrium

Having defined the political equilibrium we now investigate the effects of corruption andvariations in the TOT and the degree of resource-dependence of the economy on resourceconversion levels. We also examine how corruption affects the influence of changes in the TOTand lobbying pressure on conversion. However, to derive more precise predictions about the

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interaction between resource conversion levels and the key parameters of economic significance itis necessary to specify the structure of the model in greater detail.9

Accordingly, it is assumed that Q ¼ qðF0 � FÞF; F40 and that the welfare function in the

government’s objective, G defined in (6), is given by10

W ¼ lnðcÞ þ b lnðmÞ � 2gðF0 � FÞFþ1. (18)

In Appendix A we demonstrate that using this welfare function equation (170) simplifies to:

_h

h¼ �d� _KðKFN þ FK�1Þ, (19)

where K ¼ ðF0 � F Þ is the cumulative level of resource conversion in the economy and

N ¼ E � gðFþ 1Þ; N ¼2N

1þ b; E ¼

d�px

2; � ¼

1� aa

and _K ¼ h.

Integrating (19):

ln h ¼ �dt �NKFþ1

Fþ 1þ F lnK

� �þ n,

where n is a constant. Thus (taking the exponential):

h ¼ e�dtK�F exp �NKFþ1

Fþ 1

� �M, (190)

where M is a positive constant (see the remark at the end of Appendix B).Note that since h ¼ _K ; Eq. (190) defines a separable differential equation. In Appendix B Eq.

(190) is solved for the equilibrium level of resource conversion. The solution is given by

expNKFþ1

Fþ 1

� �¼ exp

NFFþ10

Fþ 1

� �ð1� e�dtÞ þ e�dt. (20)

Eq. (20) implicitly defines the level of resource conversion in equilibrium as a function of theexogenous variables—the TOT, corruption, and taste parameters. The impact of each of these onresource conversion levels is complex and explored in the remainder of the paper. We use thesepredictions to form the basis of our empirical work in the next section. Since the aim of this paperis to examine resource conversion incentives in corrupt regimes and the empirical analysis in thefollowing section focuses on countries afflicted with high levels of corruption, hence we considerthe case where the government is sufficiently corrupt such that in Eq. (20) N40:However, we alsodiscuss the consequences of eschewing this assumption. All proofs are in Appendix C.

Prediction 1. In a political equilibrium, if the government is sufficiently corrupt, then an

improvement in the TOT leads to an increase in the equilibrium cumulative level of resourceconversion, i.e. dK=dpx40:

9Note that (170) defines a nonlinear differential equation which does not have an analytical solution. Hence we adopt

specific functional forms that yield closed form solutions to the problem. The empirical analysis presented in the next

section provides a test of the suitability of the model given the assumed specifications.10It is not possible to derive an explicit solution for h unless ðF0 � FÞ appears in the objective function W in a form

related to the production function Q ¼ qðF0 � F Þ:

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An improvement in the TOT could have two reinforcing effects that lead to higher levels ofcumulative resource conversion. Firstly, a rise in the TOT allows for a higher level of domesticconsumption, for any given level of resource conversion. Hence the marginal welfare gains fromresource conversion rise and the government responds by increasing the level of conversion. Inaddition, by the local truthfulness property of the equilibrium, an improvement in the TOTimplies that the government receives higher bribes and this too encourages further conversion. Anequivalent interpretation is that with an improvement in the TOT the private agent has more atstake and therefore increases the bribe offer.

Prediction 2. In a political equilibrium, greater corruption leads to an increase in the cumulative levelof resource conversion, i.e. dK=d�40:

Intuitively, an increase in corruption implies that the government places a greater weight onbribes, relative to social welfare. If the government releases more of the resource for production itreceives additional contributions from the private agent. As the weight given to bribes ð1� aÞrises, greater emphasis is placed on these additional contributions. Hence, a corrupt governmentchooses a higher level of resource conversion.Empirical evidence suggests that most low and middle-income countries tend to be highly

resource dependent in that they have a relatively high ratio of resource-based exports to totalexports or to GDP [4,29]. It is therefore of importance to determine the effects of resourcedependence on the level of resource conversion.

Prediction 3. In economies which are in a political equilibrium, greater dependency on resource

extraction for exports is not necessarily linked to a higher cumulative level of resource conversion.

In the model, x(t) represents the exports of output from the resource conversion activity, whichmust be aligned with the level of imports to meet the balance of trade condition. Consider achange in the taste parameter b such that the demand for imports rises. To satisfy the externalbalance condition, resource based exports must rise. A rise in x signals a growing resourcedependency of the small open economy, which induces higher levels of land conversion. However,since greater exports are accompanied by higher levels of imports, this lowers the demand fordomestically produced output and land conversion pressures are thus reduced. The impact istherefore ambiguous and the linkage becomes an empirical question which is investigated in thenext section.

Prediction 4. In a political equilibrium, greater lobbying pressure is accompanied by an increase inthe cumulative level of resource conversion.

Recall from our theoretical model that Sh is the marginal cost to the private rent-seeking agentof lobbying for more resource conversion. Thus, Sh also represents the degree of ‘‘lobbyingpressure’’ exerted by the agent on the government. Consider an exogenous increase in lobbyingpressure, perhaps caused by a decline in lobbying costs. A more corrupt government is likely to besusceptible to any increase in lobbying pressure and thus allows more resource conversion byeconomic agents to take place.The next section develops a panel analysis of cumulative agricultural land expansion over

1961–99 for tropical low and middle-income economies to explore further the four predictionsconcerning corruption, trade and resource conversion that we have derived from our model.

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5. Empirical analysis of agricultural land expansion in tropical developing countries

The predictions derived above stated in terms of changes in the cumulative level of resourceconversion in the economy, K ¼ ðF0 � FÞ: Assuming that conversion of forest habitat toagricultural land is the main resource conversion activity, then the appropriate dependent variablein an econometric analysis of the four theoretical predictions is the cumulative level of agriculturalland expansion, K ¼ ðA0 þ AÞ:11 This implies the following empirical model:

1þAit

Ai0¼ b0 þ b1ðterms of tradeÞ þ b2ðcontrol of corruptionÞ

þ b3ðresource trade dependenceÞ þ b4ðcontrol of corruption terms of tradeÞ

þ b5ðterms of trade resource trade dependenceÞ þ b6ðother variablesÞ

with the dependent variable being a (scaled) measure of the cumulative expansion of agriculturalland area, Ait, over a given time period, t, for each country i, relative to land area in some initialperiod, Ai0.

12

Prediction 1 implies that b140; i.e. an improvement in the TOT will lead to greater cumulativeagricultural expansion. Prediction 2 suggests b2o0; i.e. greater control of corruption will result inless agricultural land expansion. Prediction 3 queries the sign of b3, i.e. in a more resource trade-dependent economy the impact on cumulative land conversion is ambiguous. As noted in theprevious section, to derive these predictions about the direct effects of TOT, corruption andresource trade dependence on resource conversion required specifying the structure of thetheoretical model in greater detail. It is possible that a more general model may produceinteraction effects between these three explanatory variables. To explore this possibility, we haveincluded two additional interaction terms in the empirical model. However, we cannot specify apriori what the signs of the coefficients on these interaction terms, b4 and b5, might be, since these

11Ideally, to make the empirical model an exact test of the theoretical predictions would suggest expressing the

dependent variable in terms of changes in the cumulative level of resource conversion in the economy, K ¼ ðF0 � F Þ;rather in terms of the cumulative level of agricultural expansion, K ¼ ðA0 þ AÞ: However, as discussed in Barbier [3],since 1990 the United Nations Food and Agricultural ([32]FAO, 2001), which has been the international agency

responsible for compiling forest area data across all tropical developing countries has based its periodic global forest

resource assessment on population growth projections in order to overcome an inadequate forest data base for some

countries and regions. As the econometric literature on tropical deforestation has pointed out (see [3]), this means that

the FAO tropical forest cover data are inappropriate for cross-country analyses of deforestation, such as the one we are

conducting in this paper, that use demographic factors as explanatory variables. However, the assumption that

agricultural conversion is the main resource conversion activity responsible for deforestation in tropical countries is

supported by independent studies. Stratified random sampling of 10% of the world’s tropical forests reveals that direct

conversion by large-scale agriculture may be the main source of deforestation, accounting for around 32% of total

forest cover change, followed by conversion to small-scale agriculture, which accounts for 26% [15]. Intensification of

agriculture in shifting cultivation areas comprises 10% of tropical deforestation, and expansion of shifting cultivation

into undisturbed forests 5%.12The effect of the scaling is to eliminate the influence of cross-country differences in land endowment on the

dependent variable by effectively turning it into an index of cumulative agricultural land expansion in period t

compared to the initial period 0. Moreover, this index is easily translated into a percentage change in cumulative

agricultural land expansion, e.g. via ðK � 2Þ 100%: For example, in Table 1 across all countries, the average

cumulative expansion in agricultural land compared to the base year 1961 is 19.3%.

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interactions do not appear in the theoretical model. They are added as possible statistical controlsin the regressions, and as will discussed further below, they also have important intuitiveexplanations.A number of additional variables that explain cumulative agricultural land expansion are also

included in the model as exogenous controls in the regression model. Following Barbier [3], thecontrols chosen were growth in agricultural value added, cereal yield, rural population growth,real gross domestic product (GDP) per capita and the percentage share of arable plus permanentcropland in total land area.Finally, it is difficult to construct a reliable test of Prediction 4 of our theoretical model.

Although there are no direct indicators of lobbying pressure by the agricultural sector indeveloping countries, this variable could be proxied by two of the exogenous controls in ourempirical model, the growth in agricultural value added and the share of cropland in total landarea. Recall that in the political equilibrium of the theoretical model that lobbying pressure mustequal the gross payoffs to the private agent engaged in resource conversion, i.e. Sh ¼ pxq: It islikely that the returns to conversion are in turn positively related to the growth in agriculturalvalue added in the economy or to simply the scale of total agricultural activity in the economy.13

The above model was applied to a panel analysis of agricultural land expansion over 1961–99for tropical low and middle income countries in Africa, Asia and Latin America, with thedependent variable being the (scaled) measure of the cumulative expansion of agricultural landarea every year in each country relative to land area in the initial year 1961.14 The TOT for acountry is represented by an index of export to import prices ð1995 ¼ 100Þ; and resource-tradedependence is indicated by the share of agricultural and raw material exports as a percentage oftotal exports. The source of data used for these variables, plus the control variables of growth inagricultural value added, cereal yield, rural population growth, GDP per capita and thepercentage share of cropland in total land area, was the World Bank’s World DevelopmentIndicators, which has the most extensive data set for key land, agricultural and economic variablesfor developing countries over the period of analysis.The final variable required in the model is an indicator of control of corruption. The source

used for these data is a recent project on governance conducted by the World Bank, which putstogether a measure of the control of corruption and other governance indicators indexed on ascale of �2.5 (lowest value) to 2.5 (highest value) for 178 developing and advanced economies[22,23]. As the control of corruption indicator covers the broadest range of developing countries

13Alternative proxies for the lobbying pressure indicator were also employed in the regression analysis, such as the

percentage share of agricultural value added in the GDP of a country, agricultural value added per hectare and

agricultural value added per worker. However, none of these alternative proxy indicators for lobbying pressure

performed well in the regressions. In addition, for our sample the number of panel observations of the three alternative

proxy indicators was considerably smaller, which may explain why they performed so poorly in the regressions.14Annual data on permanent and arable cropland over 1960–1999 for each country is from the World Bank’s World

Development Indicators, which was used to calculate the annual percentage change dependent variable. Permanent

cropland is land cultivated with crops that occupy the land for long periods and need not be replanted after each

harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and

vines, but excludes land under trees grown for wood or timber. Arable land (in hectares) includes land defined by the

Food and Agricultural Organization of the World Bank as land under temporary crops (double-cropped areas are

counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land

temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.

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to date of any comparable indicator, it is ideal for our analysis. However, as this indicator is asingle point estimate in time (based on survey data corresponding for 1997–98 according to theauthors), including this time-invariant institutional index essentially amounts to incorporating a‘‘weighted’’ country-specific dummy variable in the panel regression [2, pp. 11–12].15

Table 1 provides a summary of the descriptive statistics of the variables, and Table 2 reports theregression results. Recall that the predictions of the theoretical model derived in the previoussection apply to countries that were considered ‘‘highly corrupt’’. In our cross-country data set,these appear to be countries that have a control of corruption indicator less that 0.1.16 Wetherefore applied our empirical model to both the full sample of all tropical developing countriesand to those countries that are designated as ‘‘highly corrupt’’. As a further check for robustness,we also examined the regression results for a key sub-sample, those countries with total land areagreater than 15,000 km2.17 For this sub-sample we again examined the results for all countries inthe sample as well as just the highly corrupt countries.Both one-way and two-way fixed and random effects models were applied, and the usual panel

tests for comparing these models against each other and ordinary least squares were conducted.Table 2 displays the results for the preferred models and the relevant statistics. The chi-squaredand F-tests for the pooled models as well as the LM test are significant, suggesting the presence ofindividual effects and thus rejection of the ordinary least squares model. Although the Hausmantest was significant for each regression, the random effects model is preferred over fixed effectsbecause the latter cannot estimate reliably the effects of the time-invariant control of corruptionvariable [2, pp. 11–12].18 The two-way random effects specification was chosen over the one-way

15As pointed out by Baltagi [2, pp. 11–12], including a time-invariant explanatory variable, such as the corruption

index we have used, can sometimes lead to collinear regressors in fixed effects estimation. As indicated in footnote 17,

this occurred when we applied our model to certain sub-samples of the data set. Also, as a time-invariant institutional

index is in itself a ‘‘weighted’’ country-specific dummy variable, including the corruption index in an OLS regression

will essentially imitate a fixed effects model. However, as reported in Table 2, our w-tests and F-tests for the pooled

model are highly significant, and therefore we cannot reject the hypothesis of the presence of individual effects. Given

these factors, it is therefore not surprising that the random effects model was the preferred specification to either the

OLS or fixed effects models.16As indicated above and in Table 1, the control of corruption indicator ranges from �2.5 (no control) to 2.5 (no

corruption). Although Table 1 shows that the average level of corruption across the tropical developing countries of our

sample is �0.35, the minimum value for a country is �1.57 and the maximum value is 1.95. The median for the entire

sample is �0.40. Given this distribution, designating the countries that have a control of corruption indicator less than

0.1 as ‘‘highly corrupt’’ eliminates the top 15% ‘‘least corrupt’’ countries from the sample.17Attempts to apply the empirical model to other sub-samples, notably just Latin American and Asian countries and

countries with GDP per capita (1995$) of less than $3,000 during any time period of the analysis, led to problems of

collinear regressors in the fixed effects versions of the regressions. This particularly occurred when the sub-sample was

further reduced to ‘‘highly corrupt’’ countries only. As discussed in note 15, this problem can arise due to the

combination of including a time-invariant explanatory variable (the control of corruption indicator) in a regression

with a smaller number of observations, as suggested by Baltagi [2, pp. 11–12].18Although we report the Hausman test statistic for each regression in Table 2, we did not use this statistic in deciding

whether or not the fixed effects version is the preferred model for several reasons. First, as indicated in footnotes 15 and

17, Baltagi [2, pp. 11–12] points out that the fixed effects model ‘‘cannot estimate the effect of any time-invariant

variable like sex, race, religion, schooling or union participation’’. The fixed effects and random effects models and

relevant test statistics, including the Hausman test, were estimated using the LIMDEP 8 software package. In all

regression results the fixed effects model and the random effects model generated similar estimates and standard errors

for the coefficients of all explanatory variables except for the control of corruption indicator. The main difference

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Table 1

Summary statistics of variables

Variables Mean Standard deviation Number of observations

Cumulative agricultural land expansion 2.19 0.35 3773

(1+Ait/Ai0)

Terms of trade 113.07 38.73 2450

(1995 ¼ 100)

Control of corruption �0.35 0.60 2880

(no control ¼ �2.5; no corruption ¼ 2.5)

Agricultural export share 10.65 15.22 2503

(% of merchandise exports)

Control of corruption�Terms of trade �44.85 61.96 2078

Terms of trade�Agricultural export share 1185.78 1887.49 1776

Growth in agricultural value added 2.70 8.71 2672

(% annual change)

Cereal yield 1530.15 925.37 3532

(kg per hectare)

Rural population growth 1.42 1.43 3822

(% annual change)

Gross domestic product per capita 2035.30 3619.08 3167

($/person)

Cropland share 16.14 15.47 3774

(% of total land area)


model based on the significance of individual coefficients and the overall explanatory power ofeach specification.The results in Table 2 suggest that the model is strongly robust with respect to the influence of

the direct effects of control of corruption, the TOT and agricultural export share on agriculturalland expansion. The two interacted terms involving these three variables are also consistently

(footnote continued)

between the two versions is that the fixed effects model produced extremely large standard errors and widely fluctuating

coefficient estimates for the control of corruption indicator, confirming Baltagi’s contention that fixed effects is indeed

inappropriate for regressions incorporating this time-invariant variable. Baltagi [2, p. 70] states that ‘‘if either

heteroskedasticity or serial correlation is present, the variances of the Within and GLS estimators are not valid and the

corresponding Hausman test statistic is inappropriate’’. We found high serial correlation for the fixed effects

estimations corresponding to each of the four regressions reported in Table 2. For example, the estimated

autocorrelation of the fixed effects estimation for all countries of the full sample was 0.884, for highly corrupt countries

of the full sample was 0.875, for all countries with large land area was 0.879 and for highly corrupt countries with large

land area was 0.875. Thus, although a Hausman test is the standard procedure for determining whether a fixed effects

model should be preferred to a random effects model, it is a less reliable test for the panel analysis conducted in this

paper.

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Table 2

Panel analysis of corruption and tropical agricultural land expansion, 1960–99

Dependent variable: Cumulative agricultural land expansion (1+Ait/Ai0)

Cross-country estimationsa

Full sample Large land areab

All countries Highly corruptc All countries Highly corruptc

Explanatory variables (N ¼ 1363) (N ¼ 1204) (N ¼ 1267) (N ¼ 1149)

(Y ¼ 2:255)d (Y ¼ 2:262) (Y ¼ 2:271) (Y ¼ 2:271)Terms of trade/103 �1.207 �1.759 �1.415 �1.801

(1995 ¼ 100) (�4.246)** (�5.153)** (�4.331)** (�4.913)**

Control of corruption/101 �9.668 �9.339 �5.559 �8.881

(no control ¼ �2.5; no corruption ¼ 2.5) (�7.396)** (�4.348)** (�3.794)** (�4.400)**

Agricultural export share/103 3.758 4.766 3.767 4.708

(% of merchandise exports) (3.209)** (3.720)** (3.118)** (3.604)**

Control of corruption�Terms of trade/103 �2.191 �2.951 �2.475 �3.018

(�5.862)** (�6.591)** (�5.865)** (�6.364)**

Terms of trade�Agricultural export share/105 �1.698 �1.963 �1.569 �1.866

(�1.959)* (�2.109)* (�1.755)y (�2.109)*

Growth in agricultural value added/104 5.756 6.315 5.474 6.208

(% annual change) (1.144) (0.984) (1.010) (0.919)

Cereal yield/105 �5.094 �2.954 �5.238 �3.352

(kg per hectare) (�3.502)** (�1.659)y (�3.186)** (�1.791)y

Rural population growth/103 6.201 1.782 9.289 3.024

(% annual change) (0.759) (0.197) (1.091) (0.326)

Gross domestic product per capita/105 5.962 6.218 8.164 7.046

($/person) (4.165)** (3.324)** (5.134)** (3.643)**

Cropland share/102 3.957 3.443 3.646 3.358

(% of total land area) (17.853)** (14.518)** (15.724)** (13.634)**

w-test for pooled model 2252.185** 1974.509** 2046.968** 1833.741**

F-test for pooled model 57.165** 52.868** 52.900** 48.769**

Breusch–Pagan (LM) test 4693.85** 4171.62** 4398.08** 4061.77**

Hausman test 159.51** 147.16** 157.78** 132.78**

Preferred model Two-way random

effects

Two-way random

effects

Two-way random

effects

Two-way random

effects

**Significant at 1% level, *significant at 5% level, ysignificant at 10% level.at-ratios are indicated in parentheses.bCountries with total land area greater than 15,000 km2.cCountries with a control of corruption indicator of less than 0.1.dN ¼ number of observations. Y ¼ mean of dependent variable for the regression sample.


significant in all samples. Three of the exogenous controls are also highly significant: cereal yield,cropland share and GDP per capita.The regressions results confirm Prediction 2 of our theoretical model. For all countries—and

not just ‘‘highly corrupt’’ countries—increased corruption (more control of corruption) has adirect and positive (negative) effect on cumulative agricultural land expansion. This resultprovides empirical support of the main result derived from our theoretical model. A morecorruptible government places a greater weight on bribes relative to social welfare, and as aconsequence, will allow private agents paying these bribes to engage in more land conversion.

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However, the regressions contradict Prediction 1. In contrast to the prediction, a rise in the TOTof a country has a direct and negative impact on agricultural land expansion. While Prediction 3 isambiguous, the empirical results indicate that increased resource-trade dependency leads togreater agricultural land expansion in a tropical developing economy. Finally, with regard toPrediction 4, if we accept cropland share as a proxy for lobbying pressure, then there is strongsupport for this prediction from the regression results as cumulative land conversion increaseswith the share of cropland to total land area. However, if we consider growth in agricultural valueadded as the appropriate proxy for lobbying pressure, then there is little support for Prediction 4as this variable is not significant in all regressions.Our regression results also indicate that an improvement in the TOT not only has a direct

impact on increasing agricultural land expansion but also interacts with the influence ofcorruption and agricultural export share on agricultural land expansion. The sign of theinteraction term between the TOT and control of corruption is negative, suggesting that theimpact of a rise in the TOT on reducing cumulative land conversion is dissipated if the country ismore corrupt and amplified if there is less corruption. The interaction term between the TOT andagricultural export share is also negative, indicating that the impact of a TOT increase onlowering cumulative land conversion is enhanced if the country is more dependent on resource-based exports and reduced if the country is less resource-dependent in its exports.19

Further insights from the regression results can be gained by Table 3, which indicates the effectsof the significant variables influencing tropical land conversion, including any interacting effects.The latter effects are particularly important in determining the various components comprisingthe total effects of control of corruption, TOT and resource trade-dependency on agriculturalland expansion. Note that the effects reported in Table 3 are in terms of elasticities, or percentagechanges, which are evaluated at the sample regression means for the relevant variables.A 1% rise in the TOT has a direct impact on decreasing cumulative agricultural land expansion

ranging from 0.06% to 0.09%. This elasticity effect will be reinforced by any increase inagricultural export share and greater control of corruption. The result is that the total elasticityeffect of 1% rise in the TOT is a reduction in agricultural land conversion of between 0.11% and0.19%, depending on the type of country.The direct effect of greater control of corruption appears to be a reduction in cumulative

agricultural land expansion of between 0.11% and 0.22%. The interaction between the TOT andgreater control of corruption reinforces the latter’s direct influence on limiting land conversion, sothat a 1% reduction in corruption may decrease cumulative land conversion by 0.17–0.30% intropical developing countries.A 1% rise in the agricultural export share of a country may have a direct impact of raising

agricultural land conversion by around 0.02%. However, the moderating effect of the level of the

19According to Wunder [33], this phenomenon is particularly relevant for oil producing tropical countries. For

example, when a tropical country experiences an oil-price boom or discovers new oil reserves, there is a real

appreciation of the booming country’s currency. This is also equivalent to a price shift in favor of non-traded goods.

The result is a ‘‘Dutch disease’’ effect, in that the oil and non-traded sectors of the economy expand at the expense of

those facing declining competitiveness. In most tropical developing countries, the latter non-oil trade sectors are

typically agriculture, fisheries, forestry and non-oil mining, which are also the sectors most associated with forest

conversion. As a consequence, a country with a large non-oil primary product export sector is likely to experience a

greater slow down in forest land conversion as a result of the rising TOT from an oil price boom.

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Table 3

Total elasticity effects

Effectsa Full sample Large land area

All countries Highly corrupt All countries Highly corrupt

1. Terms of trade

Terms of trade only �0.061 �0.089 �0.071 �0.091

Agricultural export share effect �0.009 �0.011 �0.009 �0.011

Control of corruption effect �0.045 �0.079 �0.056 �0.084

Total effects �0.114 �0.179 �0.136 �0.186

2. Agricultural export share

Agricultural export share only 0.018 0.024 0.019 0.024

Terms of trade effect �0.009 �0.011 �0.009 �0.011

Total effects 0.009 0.012 0.010 0.013

3. Control of corruption

Control of corruption only �0.175 �0.217 �0.110 �0.214

Terms of trade effect �0.045 �0.079 �0.056 �0.084

Total effects �0.220 �0.296 �0.166 �0.298

4. Cereal yield �0.036 �0.020 �0.036 �0.022

5. Cropland share 0.311 0.262 0.270 0.252

6. GDP per capita 0.032 0.029 0.041 0.032

aOnly effects significant at 10% level or better are indicated. All effects are indicated as elasticities evaluated at the

means of the respective regression samples.


TOT on agricultural export share suggests that the total effects of a 1% rise in resource tradedependency on land conversion may be only 0.01%.Finally, a 1% increase in GDP per capita and cropland share raises cumulative land conversion

by 0.03–0.04% and 0.25–0.311%, respectively. In contrast, a 1% rise in crop yields, reduces landexpansion by 0.02–0.04%.

6. Conclusion

The theoretical and empirical findings of the paper confirm that ‘‘bribes’’ by economic agentsthat benefit from resource converting activities will have a substantial effect on the control ofconversion by a government that is corruptible. We specifically find that improvements in theTOT, greater resource trade-dependency and increased ‘‘lobbying pressure’’ also provideadditional incentives for cumulative resource conversion in corrupt countries. Overall, theseresults support the many studies suggesting that special interest groups have played a significant

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role in determining land use decisions in developing countries, particularly the conversion offorests and other natural habitat to agriculture.Our empirical results confirm Prediction 2 from our theoretical model that corruption appears

to be associated with cumulative land expansion in tropical developing economies. The directeffect is as expected; increased corruption leads to greater land conversion. In this regard, ourempirical analysis conforms not only with the theoretical prediction of this paper but also with thepropositions of others, notably Lopez and Mitra [25], who suggest that greater corruption isassociated with increased environmental degradation. Of course the latter authors were concernedwith the influence of corruption on pollution flows, whereas we have confirmed this relationshipboth theoretically and empirically for a decline in an environmental stock (cumulative resourceconversion) in developing countries.Although our empirical analysis suggests opposite TOT effects on cumulative tropical land

expansion compared to Prediction 1 from the theoretical model of resource conversion, this ispossibly due to the necessary simplification of the model’s structure to obtain the latter prediction.In a more general version of our model, Prediction 1 may be ambiguous, and as a result the directeffect of a change in the TOT, i.e. the sign of the coefficient b1 in our regression, could be positiveor negative empirically. Our estimation results suggest that this effect is negative, i.e. acrosstropical countries an improvement in the TOT reduces cumulative land conversion.The direct influence of the agricultural export share also suggests that greater resource

dependency is associated with land conversion. Developing countries that are more structurallydependent on non-oil primary products for their exports are more susceptible to processes ofagricultural land expansion. However, these resource dependency effects on land conversion maydepend on what happens to a country’s TOT. There is some statistical evidence from theregression analysis that countries with higher TOT may reduce the resource dependency effects onland expansion.20 In addition, we find that the level of corruption also asserts an indirect effect onland conversion in tropical countries via interaction with the TOT. The impact of a rise in theTOT in reducing cumulative land conversion is dissipated if the country is more corrupt andamplified if there is less corruption. One possible reason is that increasingly corrupt governmentofficials may siphon off any additional foreign exchange earnings, thus reducing the incentives ofprivate agents to slow down cumulative conversion in response to a TOT increase.21

Finally, the presence of these significant interaction effects between the TOT and corruptionand primary product export dependency suggest caution in assuming that an important policy

20Evidence of this phenomenon is found in a study of eight tropical oil producing countries, which found that these

countries faced fewer pressures for conversion or degradation of their forests than non-oil producing countries, and that

forest loss was particularly less pronounced during periods of oil booms [33]. As the author points out (p. 30), the most

important causal factor was ‘‘a real appreciation of the booming country’s currency’’, and the decline in land

conversion is likely to be greater given the importance of the traded non-oil primary product sector of the economy. The

eight countries studied by Wunder [33] were Cameroon, Ecuador, Gabon, Indonesia, Mexico, Nigeria, Papua New

Guinea and Venezuela.21Wunder [33] suggests that there may be another explanation of this interaction effect for some oil-producing

tropical countries. For example, if the TOT appreciation is due to an oil boom, then one consequence is higher rents in

the oil and non-trade good sectors. Corruptible officials will therefore be able to enrich themselves by diverting more

resources away from non-oil primary product sectors, including agriculture, that are mainly responsible for

deforestation. The result is again a slowing down of cumulative agricultural land expansion and forest conversion.

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mechanism by which the rest of the world can reduce resource conversion in developingeconomies is through sanctions, taxation and other trade interventions that reduce the TOT ofthese economies. First, our empirical analysis suggests that such a decline in the TOT would havea direct impact of increasing, rather than reducing cumulative agricultural land expansion intropical countries. Second, as Table 3 shows, the direct and some interactive effects of a TOTchange can work in opposite directions. In addition, for low- and middle-income economiesespecially, any reduction in TOT is likely to have additional economic consequences not capturedin our model, such as the loss of foreign exchange earnings that could be employed to importadvanced technology and capital, or to be invested in human capital to put the developingeconomy on a path that reduces its dependence on resource-based exports. Thus the long runconsequences of trade interventions may be that these economies will have little opportunities todiversify away from a resource-dependent pattern of growth and trade.At the end of the day, there may be very little that other countries can do to influence the

linkages between special interest lobbying, trade and resource conversion within a developingeconomy, other than continue to point out the economic losses that the economy is likely to incuras a result of such linkages. Whether such negative publicity is likely to change domestic policieswithin the developing economy is doubtful. As our model has shown, if a government iscorruptible, a resource-converting agent producing a tradable good has considerable incentives toinfluence the resource conversion decisions of that government.

Acknowledgments

We are grateful for the comments and suggestions of Chris Costello, Joe Herriges, AnthonyHeyes, Patrik Hultberg, Carol McAusland, Wallace Oates, Linwood Pendleton, Cees Withagenand two anonymous reviewers, and for the research assistance of Lee Bailiff.

Appendix A. Derivation of Eq. (19)

The FOCs for the problem in MI simplify as follows:

qH

qh¼ ð1� aÞSh þ

aq

c� m ¼ 0, (A.1)

qH

qx¼ 0) x ¼ bc; c ¼

qh

1þ b; x ¼

bqh

1þ b. (A.2)

Substituting (A.2) into (A.1) to eliminate c:

ð1� aÞSh þðbþ 1Þa

h¼ m. (A.3)

Differentiating (A.3) with respect to time:

ð1� aÞShh_h �

ðbþ 1Þa

h2_h ¼ _m. (A.4)

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Using (10)

_m ¼ dm� a 2gðFþ 1ÞKF �ðbþ 1Þq0

q

� �t, (A.5)

where K ¼ ðF0 � F Þ:Using Shh ¼ 0 and Sh ¼ pxq we get:

_h

h¼ �d� _KðKFN þ FK�1Þ, (19)

where K ¼ ðF0 � F Þ; _K ¼ h; N ¼ 2N=ð1þ bÞ; E ¼ ðdð1� aÞpxÞ=2a; N ¼ E � gðFþ 1Þ:

Appendix B. Solution to Eq. (20)

Substituting h ¼ _K in (190) yields the separable equation:

KF expNKFþ1

Fþ 1

� �dK ¼ e�dtM dt, (B.1)

where M is a positive constant.Integrating (B.1) with respect to time:

expNKFþ1

Fþ 1

� �¼ N u�

e�dtM

d

� �, (B.2)

where u is a constant. The boundary conditions are:At t ¼ 0; F ¼ F0 and K ¼ 0 hence in (B.2) 1 ¼ Nðð�M=dÞ þ uÞ ) 1þ NðM=dÞ ¼ Nu: At t ¼

1; F ¼ 0 and K ¼ F0 hence in (B.2) expðNFFþ10 =ðFþ 1ÞÞ ¼ Nu: The specific solution is then:

expNKFþ1

Fþ 1

� �¼ exp

NFFþ10

Fþ 1

� �ð1� e�dtÞ þ e�dt. (20)

Remark. Taking the total derivative of (20) with respect to time we can obtain the exact form ofEq. (190):

h ¼ e�dtk�F exp �NKFþ1

Fþ 1

� �dN

exp NFFþ10

Fþ 1

� �� 1

� �� ,

where the square bracket is the constant M which depends on N:

Appendix C

Taking the total derivative of (20) wrt N and t, respectively, yields:

AKFþ1

Fþ 1þ NKF dK

dN

� �¼ B

FFþ10

Fþ 1ð1� e�dtÞ (C.1)

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where A ¼ expðNKFþ1=ðFþ 1ÞÞ; B ¼ expðNFFþ10 =ðFþ 1ÞÞ

and ANKFh ¼ de�dtðB � 1Þ (C.2)

Rearranging (C.1):

AðFþ 1ÞNKF dK

dN¼ BFFþ1

0 ð1� e�dtÞ � AKFþ1 � OðtÞ. (C.3)

To evaluate the sign of this expression it is necessary to determine the sign of O for all t 2 ½0;1:Note that at t ¼ 0; Kð0Þ ¼ 0 and hence Oð0Þ ¼ 0 and at t ¼ 1; K ¼ F0; A ¼ B and Oð1Þ ¼ 0:Next we must evaluate the sign of OðtÞ over t 2 ð0;1Þ: Differentiating:

dOðtÞdt

¼ BFFþ10 de�dt � AhKFðNKFþ1 þ ðFþ 1ÞÞ. (C.4)

Thus

dOð0Þdt

¼ BFFþ10 d40, (C.5)

dOð1Þ

dt¼ �AhFF

0 ðNFFþ10 þ ðFþ 1ÞÞo0 if N40. (C.6)

Using (C.2) to simplify (C.4):

dOðtÞdt

¼ 0)BFFþ1

0

B � 1¼ KFþ1 þ

Fþ 1

N. (C.7)

Since K is monotone then (C.7) can only hold once. Evaluating d2OðtÞ=dt2 when dOðtÞ=dt ¼ 0 wehave �de�dtðFþ 1ÞKFho0: Hence (C.7) defines the maximum point. It follows that OðtÞ40 overt 2 ð0;1Þ hence dK=dN40 8t 2 ð0;1Þ if N40:

Prediction 1. Recall that N ¼ ðd�px=2Þ � gðFþ 1Þ and N ¼ 2N=ð1þ bÞ: Thus dN=dpx ¼ d�=ð1þbÞ40 and dK=dpx ¼ ðdK=dNÞðdN=dpxÞ40 when N40. The assumption that N40 summarizes thecondition that the government is sufficiently corrupt.

Prediction 2. By a similar procedure:

dK

d�¼

dK

dN

dN

d�40

when N40.

Prediction 3. Since x is endogenously determined in the model, it is not possible to directly determinethe causality relation from x to K. Instead we infer the impact of greater resource dependency

indirectly by examining the correlation between cleared land and exports when b increases. Note thatby the balance of trade condition, a rise in the taste parameter b induces higher imports and an

equivalent rise in exports.

Define the level of cumulative exports as

X ðtÞ ¼

Z t

0

xðtÞ dt,

ARTICLE IN PRESS


where x ¼ KFh=ð1þ b�1Þ: Then using h ¼ _K :

_X ¼1

ð1þ FÞð1þ b�1Þ

dKFþ1

dt.

Equilibrium levels of cumulative exports are:

X ¼KFþ1

ð1þ FÞð1þ b�1Þ.

Observe that when b increases then N ¼ 2N=ð1þ bÞ decreases as does K (Prediction 1), but thedenominator of X also increases, hence the outcome is ambiguous.

Prediction 4. The proof is similar to that of Prediction 1: consider an exogenous lobbyingefficiency parameter—denoted as r: The political equilibrium requires that rSh ¼ pxq: It followsthat

N ¼d�pxq

2� gðFþ 1Þ ¼

d�rSh

2q� gðFþ 1Þ.

Thus, sign ðdK=drÞ ¼ sign ðdK=dpxÞ40:

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corruption, trade and resource conversion

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