Download Lobbying, Trade and Resource Conversion

Lobbying, Trade and Resource Conversion
Edward B Barbier1,2
Richard Damania3
June 11, 2002
Acknowledgements: We are grateful for the comments and suggestions of Patrik Hultberg and
Linwood Pendleton, and for the research assistance of Lee Bailiff.
1
Department of Economics and Finance, University of Wyoming, PO Box 3985, Laramie, WY 82071-3985,
tel: 1 307 766 2178; fax: 1 307 766 5090; email: [email protected]
2
External Associate, Centre for Environment and Development Economics, University of York, Heslington, York,
YO1 5DD, tel: 44 1904 432 999; fax: 44 1904 432 998
3
School of Economics, University of Adelaide, Adelaide, South Australia. email: [email protected]
Abstract
Recent evidence suggests that the lobbying activities of special interest groups significantly
affect the forest conversion decisions of tropical countries. 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 the profit-maximizing economic
agent responsible for conversion. The relative weight given to social welfare considerations
relative to such contributions indicates the degree of government honesty or "corruption". We
investigate the effects of lobbying on rates of land conversion and examine how trade policy
influences the distortions created by political corruption. This results in five testable
propositions that we analyze through a panel analysis of agricultural land expansion over 196099 for low and middle-income economies. Our findings suggest that lobbying pressure and
political contributions by economic agents benefiting from resource conversion will affect
substantially the control of conversion by a corruptible government. Improvements in the terms
of trade and greater resource trade-dependency also provide additional incentives for resource
conversion, and a rise in the terms of trade of a country will tend to reinforce the negative
influence of corruption on the control of conversion by a government.
JEL classification: Q23, Q28, D78, F19
Keywords: corruption, developing countries, lobbying, open economy, political economy,
resource conversion, resource-trade dependency, terms of trade.
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Introduction
Concerns over the loss of tropical forests and global biodiversity have intensified in
recent years. 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 (FAO, 2001a). The continual loss
of tropical forests has important implications for biodiversity. It has been estimated that more
than four-fifths of many groups of plants and animals are found in tropical forests
(CIFOR/Government of Indonesia/UNESCO, 1999). Moderate estimates of future species
extinction rates due to tropical deforestation range from one to five percent per decade
(Matthews et al., 2000).
Across the tropics, the principal activity responsible for deforestation appears to be the
direct conversion of forests to permanent agriculture (FAO, 2001a). Stratified random sampling
of 10% of the world's tropical forests reveals that direct conversion by large-scale agriculture is
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%. Intensification of
agriculture in shifting cultivation areas comprises only 10% of tropical deforestation, and
expansion of shifting cultivation into undisturbed forests only 5%. However, there are important
regional differences. In Africa, the major process of deforestation (around 60%) is due to the
conversion of forest for the establishment of small-scale permanent agriculture, whereas direct
conversion of forest cover to large-scale agriculture, including livestock-raising, predominate in
Latin America and Asia (48% and 30%, respectively). Although agricultural conversion is the
principal cause of tropical deforestation, in many forested regions uncontrolled timber harvesting
is responsible for initially opening up previously inaccessible forested frontiers to permanent
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agricultural conversion and for causing widespread timber-related forest degradation and loss
(Ascher, 1999; Barbier et al., 1994; Matthews et al., 2000).
There is also growing recognition that tropical deforestation due to forestry activities and
agricultural conversion, especially for large-scale economic activity, is influenced by
government policy. In Central America and Amazonia, government support for cattle ranching,
forestry and large-scale agriculture has played a prominent role in deforestation (Ferraz and
Serôa da Motta, 1998; Kaimowitz, 1995; Mahar and Schneider, 1994; Young, 1998), while in
Asia, South America and Africa policy-induced expansion of large scale plantations, timber
harvesting and cash crops have been responsible for the destruction of large tracts of forested land
(Ascher, 1999; Broad, 1998; Hafner, 1998). Common to all these cases is the role of governments
in providing both tacit and overt support for the 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 that
determine land use decisions in these countries (Ascher, 1999; Desai, 1998; Broad, 1998; FAO,
2001b; Hafner, 1998). As argued by Ascher (1999, pp. 52-3), the result of such lobbying is that
governments in turn will deliberate create rent-seeking opportunities for those special interests that
benefit from favorable land use policies: "Imagine further that the government official is interested
in benefiting not so much a large segment of the population, but rather a set of specific common or
political actors whose support or cooperation is particularly valued. Government measures that
indulge specific producers or consumers can create rent-seeking opportunities. The government can
ration these privileges so that the recipient either benefits directly from the assignment of an asset at
low or zero cost (e.g., a land giveaway) or has an advantage over potential competitors in the same
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sphere of economic activity." As a consequence, government corruption – the use of public office
for private gain - is now seen to be an endemic problem dictating forest land use policies in
developing countries: "There exists broad evidence that in many countries corruption has been, or
still is, a driving force behind misappropriation of forest resources and tropical forest destruction"
(Hafner, 1998, p. 1).
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 special
interest lobbying pressures on agricultural land conversion. There is a varied and growing
literature that examines the effects of rent-seeking activities on environmental policy outcomes
in general. For instance, using the common agency framework Eliste and Fredriksson (2000)
investigate the effects of special interest lobbying on subsidies in the agricultural sector. They
find that more environmentally damaging sectors receive greater compensation for the costs
associated with environmental protection. Leidy and Hoeckman (1994) explore the interaction
between environmental policy efficiency and trade barriers, and demonstrate that polluters may
prefer inefficient policies since these increase the implicit level of protection. Rauscher (1994)
examines lobbying incentives in a polluting industry and finds that trade openness may have
ambiguous effects on lobbying intensity. López and Mitra (2000) investigate the impact of
corruption on the empirical relationship between income and pollution - the Environmental
Kuznets Curve (EKC). It is shown that corruption increases the income level at which the EKC
begins to decline.1
The contribution of this paper differs from previous work in several significant ways.
Most notably, and in contrast to existing work, we deal explicitly with resource dynamics. Thus
our model applies to resource-based environmental problems such as tropical deforestation and
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habitat loss, which have hitherto been ignored in the environmental political economy literature.
In addition, we extend the literature by investigating the interaction between corruption and trade
openness on policy outcomes. We are able to show this relationship specifically through an
economic model of resource conversion, trade and the effects of private lobbying on government
decision-making, which is consistent with previous literature on the political economy of
corruption (Bardhan, 1997; Grossman and Helpman, 1994; Schulze and Ursprung, 2001; Shleifer
and Vishny, 1993). Finally, from our theoretical model we derive five testable propositions
concerning lobbying, trade and resource conversion in developing countries. The empirical
model of the paper tests these propositions through employing a panel analysis of agricultural
land expansion over 1960-99 for low and 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 reallocation of global
resources, especially tropical forests and biodiversity. Thus, an understanding of the effects of
lobbying on land conversion and deforestation rates is of considerable economic relevance.
Secondly, tropical forests may confer significant cross-border external benefits, through their
role as stores of carbon, genetic material, habitat for endangered species, etc. This has prompted
calls for the use of various trade-based policies to coerce these nations to reduce the level of
resource exploitation. It is clearly important to determine whether trade policies strengthen or
weaken the distorting influence of lobby groups on domestic land use decisions. This paper
represents a first step in analyzing these issues in a political economy context.
Our approach is to develop a model in which the amount of land converted to agricultural
uses is determined by a self-interested government. The government is assumed to care about
the political contributions it receives from lobby groups and social welfare. The relative weight
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given to welfare considerations thus provides a measure of the degree of government honesty.
We investigate the effects of special interest lobbying on rates of land conversion and examine
whether trade policy reforms enhance or correct the distortions created by political lobbying.
The main propositions of our model appear to be supported by our empirical results. Lobbying
pressure and political contributions by economic agents benefiting from resource conversion will
affect substantially the control of conversion by a corruptible government. Improvements in the
terms of trade and greater resource trade-dependency also provide additional incentives for
resource conversion, and a rise in the terms of trade of a country will tend to reinforce the
negative influence of corruption on the control of conversion by a government
The remainder of the paper is organized as follows. The next section outlines the basic
model of the resource conversion and lobbying decisions of the private economic agent, while
the subsequent section examines the effects of trade openness and corruption on the resource
conversion decisions of the government. This analysis results in five basic propositions
concerning the influence of trade and lobbying on resource conversion. In the following section
we test these propositions through an empirical analysis of agricultural land expansion in
developing countries. The final section concludes the paper and discusses the wider implications
of the analysis.
Lobbying and Resource Conversion Decisions of Private Economic Agent
The focus of our model is a small open economy that contains a homogenous stock of a
finite, available natural resource, F(t). 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 an economic activity, such as agriculture or timber felling. Alternatively, F could
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also be a non-renewable stock, such as mineral ores or energy reserves, which is depleted
through mining, and so the model that follows could equally be applied to a conventional
exhaustible resource problem. However, in what follows, we will focus on resource conversion
as the principal economic activity and 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 make two additional assumptions. First, government is responsible for
management of the resource and determines the rate of conversion by issuing quotas. The quotas
define the maximum allowable conversion rate in any period. Second, the profit-maximizing
economic agent responsible for converting the resource seeks to influence the government’s
decisions through political contributions. We begin by considering the private lobbying decision
of the economic agent to convert the resource, and then include the political decision of the
government in response to lobbying, trade and other welfare effects of resource conversion.
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
t

0
0
F (t )  F (0)   h()d or F  h(t ), F (0)  F0 ,  h()d  F0
(1)
and
h(t )  h k , h(t )  0 .
(2)
In what follows, it is assumed that the constraint in (2) always binds so that actual resource
conversion depends on the government’s allocation decision.
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 of
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the current amount of resource conversion, h(t), as well as cumulative conversion over time,
t
 h()d  F
0
 F (t ) . The reason for this specification is twofold. First, current resource
0
conversion may influence output, as it is assumed that there is an immediate gain in production
from current conversion. For example, if the converting activity is agriculture, then the clearing
and burning that usually accompany land conversion will release nutrients that will boost current
agricultural productivity. Cumulative resource conversion also affects output of the conversion
activity, 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 and
other natural habitat means more arable land for agricultural production. A second implication
of the above specification is that helps to avoid corner solutions. Although it is possible that the
optimal rate of conversion, h*, may lead to complete depletion of the resource stock, F, over an
infinite planning horizon (0,∞). It is also possible that h* may also be zero as t. The
t
inclusion of cumulative resource conversion,
 h(t )dt = F0 - F(t), as an argument in any
0
production resulting from a resource converting activity suggests that this output does not
necessarily have to fall to zero if h* = 0.
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 as
t



Q  Q h(t ),  h()d   qF0  F (t ) h(t ),
0


q   0, q   0 .
(3)
Although the quota constraint (2) is binding, we now assume that the economic agent
engaged in resource conversion can influence the setting of this quota through expenditures, S(t),
on political contributions. Thus h = h(S), with S > 0, h' > 0 and h'' < 0. The private lobbying
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decision of the economic agent is therefore determined by maximizing the discounted profits, ,
received from resource conversion



Max   ( F , S ; p Q , )   e t p Q q( F0  F (t )) h( S )  S dt
S
(4)
0
subject to (1) and (2). Note that in (4) the time argument has been dropped to simplify notation,
δ is the discount rate and pQ is the price of the output produced from the resource conversion
activity. Denoting λ as the shadow price of the resource stock, the current value Hamiltonian is
H  p Q qh  S  h .
(5)
The corresponding first order conditions for an interior solution are (1) and


dH
1
 p Q q   h  1  0 or   p Q q  S h , S h 
dS
h

dH 
    or     p Q q h
dF
lim e t (t ) F (t )  0 .
t 
(6)
(7)
(8)
Given that h' > 0 for all feasible h and S, then Sh in (6) is derived by the inverse function
rule, and can be defined as the marginal cost of lobbying for greater conversion. Consequently,
equation (6) indicates that the economic agent will make political contributions each time period
until the marginal profits of the additional resource conversion resulting from such lobbying
efforts equal the marginal shadow cost of depleting the resource stock, λ. According to (7), any
change in the shadow value of the resource stock must equal the marginal cost of conserving that
stock, δλ, plus the marginal profits from an increase in cumulative resource conversion. As the
latter two expressions are positive, the value of the resource stock must be rising over time.
Intuitively, as profits from the economic activity rise with both current and cumulative resource
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conversion, the private agent will always have an incentive to lobby to increase conversion of the
resource stock. This implies that, if S > 0, then F is declining over time and thus the value to the
economic agent of finding additional stocks of the potentially convertible resource must be
increasing monotonically. Finally, (8) is the transversality condition for the economic agent's
infinite horizon profit-maximizing problem.
Taking the time derivative of (6) and substituting it into (7) yields
2
h 
Q
Q
  (h)  p Q q  S .

 p Q q F 
S


p
q

S

p
q
h
or
S
h
h
h
(h) 2

Using S hh h  




(9)
h  
S , (9) can be re-written as
(h ) 2

1
h  
 pQq  Sh ,
S hh
S hh  
h
S  0.
h2 h
(10)
As h'' < 0, equation (9) suggests that changes over time in the political contributions
made by the economic agent are inversely related to the magnitude of current marginal profits
earned from lobbying for greater resource conversion. As these profits are always positive (see
(6)), political contributions will be declining over time, i.e. dS/dt < 0. From (10), it follows that
resource conversion will also decline over time, i.e. dh/dt < 0. How fast the conversion declines
will in turn depend on the magnitude of current profits earned from lobbying efforts. If these
profits are large, then both lobbying contributions and thus resource conversion will be greater in
the current period relative to the future. Equations (9) and (10) also indicate that the decision by
the economic agent as to how much to invest in political contributions to influence resource
conversion over time is also affected by the discount rate, . An increase in  means that the
future profits from lobbying for greater conversion are discounted more heavily. Current
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contributions and resource conversion will therefore be given greater weight than future lobbying
contributions. The result is that both S and h will again decline more sharply over time.
The optimal lobbying and resource conversion decisions of the economic agent over time
are determined jointly by the dynamic system comprising (1) and (9). In each time period,
resource conversion is influenced by the optimal lobbying effort, while political contributions are
in turn affected by current and cumulate resource conversion and other factors determining the
profits from lobbying for increased resource conversion.
Lobbying, Trade and the Political Economy of Resource Conversion
The political decision-making of the government in setting the optimal amount of
resource conversion in the economy each period will also be influenced by the lobbying efforts
of the economic agent engaged in resource conversion. However, the government may also have
wider social welfare concerns, such as maintaining domestic consumption and the ecological and
other values of the remaining stock of the natural habitat, F. Finally, the government is also
responsible for managing the open economy, which involves ensuring that sufficient exports are
generated from the resource converting activity to allow an optimal flow of imported
consumption goods. In this section, we develop our full political economy model to examine
how lobbying and trade interact to affect the resource conversion decision of the government.
From this analysis, we derive five propositions concerning trade, lobbying and resource
conversion.
We assume that the small open economy trades some of its output from the resource
converting activity in order to finance its imports. Let x(t) be the exports of output from the
resource conversion activity, and m(t) is consumption of a composite imported good. It follows
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that domestic consumption of the remaining output from the resource converting activity can be
defined as c(t) = Q(t) – x(t). As the terms of trade, p, of the small open economy are
exogenously determined on international markets, and that all the output of the resource
converting activity is sold at world prices, we can define the balance of trade condition of the
economy as
px  m,
p
px
pQ
.

pm
pm
(11)
The government of the small open economy is assumed to be self-interested and derives
utility from both the political contributions it receives from the private resource-converting
economic agent and aggregate social welfare in the economy. The government's utility function,
G, is a discounted weighted sum of political contributions and social welfare. To determine the
optimal amount of resource conversion, exports and imports each period, the government
chooses to

Max G   e t (1  ) S (h)  W dt , W  W (c, m, F ) .
h, x , m
(12)
0
subject to (1) and (11). W is the aggregate social welfare function, which is assumed to be
dependent on both 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
externalities associated with F, which may consist of watershed protection, salinity preservation,
soil conservation, wildlife habitat, tourism, carbon store, biodiversity preservation, and other
possible ecological values. The function W is assumed to be additively separable and concave
with respect to its arguments, i.e. W
i
2
 0,  W
i 2
 0, i  c, m, F . Finally, the parameter
0    1 is the weight given to aggregate social welfare, W, relative to political contributions, S,
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in the government's utility maximizing problem. As values of  indicate the government's
willingness to set policies that diverge from the welfare-maximizing level of resource conversion
in return for political contributions, this parameter can be interpreted as an indicator of the level
of corruption. This interpretation is similar to Schulze and Ursprung (2001), who note that in
models such as these political contributions or bribes are given in order to influence government
policy, not the election outcome. The level of corruption in the 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 of corruption is also
consistent with Bardhan (1997, p. 1321), who defines corruption as “the use of public office for
private gain”. Our formulation also closely follows the government’s objective function in
Grossman and Helpman (1995). However, in contrast to all the existing work on the political
economy of environmental policy, our model explicitly incorporates the dynamic characteristics
of the problem.
The current-value Hamiltonian for the open economy problem (12) is
H  (1  )S (h)  W q( F0  F )h  x, px, F   h
(13)
which is maximized with respect to choice of h and x. Note that  is the shadow value of the
resource, but now defined in terms of its marginal contribution to the government's utility
function.
The corresponding first order conditions for an interior solution to (13) are (1) and
dH
 (1  ) S h  Wc q    0
dh
or
  Wc q  (1  ) S h
H
  pWm  Wc   0 or Wc  pWm .
x
(14)
(15)
14

dH
    or     WF  Wc hq 
dF
(16)
lim e t (t ) F (t )  0 .
(17)
t 
Condition (14) indicates that the marginal cost of additional resource conversion, , must equal
the marginal benefits. The latter consist of the weighted sum in the government's utility
calculation of welfare gain in the economy from the additional consumption resulting from
conversion, Wcq, plus the additional political contributions that the government receives from
the economic agent lobbying for more resource conversion, (1-)Sh. Equation (15) is the open
economy equilibrium condition, which states that the marginal welfare contribution of domestic
consumption from resource conversion relative to the marginal welfare contribution of imported
consumption must equal the terms of trade, p. Equation (16) states that, from the government's
perspective, any change in the shadow value of the resource stock must equal the marginal cost
of conserving that stock, , less the net marginal social benefits from conserving the resource.
The latter include the stock externality benefits of resource conservation, WF, minus the marginal
welfare contribution of the additional consumption from cumulative resource conversion, Wchq'.
Finally, condition (17) is the transversality condition corresponding to the government's infinite
horizon utility-maximizing problem.
Taking the time derivative of (14) and substituting it into (16) yields an expression for the
change over time in the optimal amount of resource conversion set by the government
(1  )S
hh

 Wcc q 2 h  Wcc qh  Wc qF  Wcc qx  (1  )S h  Wc q  WF  Wc hq
1
h  (1  ) S h  pWm q  WF  Wcc Qq h  Wcc qx ,

  (1  ) S hh  Wcc q 2  0.
(18)
Note that β < 0 follows from the necessary second order condition for maximizing (12)
with respect to h. Interpretation of (18) is facilitated by examining the three possible cases faced
15
by the government: the situation in which the government is fully corrupt and cares only about
the political contributions it receives from the resource converting economic agent ( = 0); the
opposite case when the government is incorruptible and is concerned only with social welfare
maximization ( =1); and thirdly the general case in which the government's resource conversion
decision is influenced by both political contributions from the resource converting private agent
and social welfare (0 <  < 1).
If the government is completely corrupt and cares only about the resource conversion
lobbying interests ( = 0), then (18) reduces to
S
h  h  0.
S hh
(19)
However, increasing the amount of resource conversion over time is infeasible in an infinite
horizon problem, since eventually the resource stock must be completely converted. Therefore,
in our model, the government cannot be responding solely to the political contributions it
receives from the resource converting economic agent.
In the opposite case ( = 1), where the government is concerned only about maximizing
social welfare, W, condition (18) becomes

1
h 
Wc q  WF  Wcc Qq h *  Wcc qx *
2
Wcc q
*


and
h  0 if
*


WF  Wcc Qq h * Wcc qx *

 Wc q . (20)



An asterisk is used to denote the socially optimal amount of resource conversion chosen
by the government, h*. Whether resource conversion is increasing, decreasing or constant over
time will depend on the relative magnitude of the "conservation" versus the "conversion" motive.
W F  Wcc Qq h * Wcc qx *

The conservation motive,
, represents the net present value of holding


16
on to the resource stock. It is composed of the discounted stock externality benefits of the
resource less the effects on the marginal utility of consumption of cumulative resource
WF  Wcc Qq h *
conversion over time,
, plus an additional term representing the present value

marginal welfare effects of changes over time in resource-based exports. This additional term,

Wcc qx *
, we denote as the "resource-trade dependence" effect in the open economy. Finally,

the conversion motive, Wcq, is the welfare gains of increased consumption due to resource
conversion today. Condition (20) states that, if the conservation motive exceeds the conversion
motive, then h* will be increasing over time and we would expect current resource conversion to
be lower than future levels. Although it is possible that this may occur in earlier time periods, as
discussed above it is infeasible for resource conversion to increase indefinitely if F is a finite
stock. Alternatively, if the conversion motive exceeds the conservation motive, then resource
conversion today will exceed future conversion. For this problem, a socially optimal path in
which h* declines monotonically over time is a feasible solution.
Note that in (20) the impact of the resource-trade dependence effect, 
Wcc qx *
, on the

government's welfare-maximizing choice of how much to change resource conversion over time
is determined by whether resource-based exports in the economy are growing or declining. For
example, if resource-based exports are increasing over time (dx/dt > 0), then current exports will
be low relative to future levels. Domestic consumption of output from the resource conversion
activity will be falling, and thus the marginal welfare contribution of domestic consumption will
be high today but lower tomorrow. This suggests that an economy with growing resource-based
exports would seek to conserve more of the resource today. Consequently, under this scenario,
17
the resource-trade dependence effect would reinforce the conservation motive for ensuring that
the welfare-maximizing amount of resource conversion is lower today relative to future levels.
In contrast, if resource-based exports in the open economy are declining over time (dx/dt < 0),
then current exports will be high relative to future levels. The result is the opposite incentive
effect: An economy that is more resource-trade dependent today than in the future would have a
stronger motive to convert more of the resource today, and thus would prefer a higher current
amount of resource conversion relative to future levels.
Empirical evidence suggests that most low and lower middle-income small open
economies tend to be highly resource-trade dependent today, and if successful in their
development efforts, they are likely to be less dependent in the future (Sachs and Warner 1995).
Moreover, over an infinite time horizon, exports based on output produced from resource
conversion activity cannot expand indefinitely, as the natural resource will eventually be fully
converted. We would therefore expect that, under the conditions specified in our model,
resource-based exports would either decline over time, i.e. dx/dt < 0, or if these exports are
growing currently, at some point in the future resource-based exports must eventually decline as
the resource stock undergoes complete conversion. This stylized fact together with condition
(20) suggests the following proposition.
Proposition 1: A small open economy that is more resource-trade dependent today will
display a higher current amount of resource conversion relative to future levels.
In the general case, the government's choice of the amount of resource conversion in a
closed economy is influenced by both political contributions and social welfare (0 <  < 1), as
represented by condition (18) derived above. Denoting the level of resource conversion
satisfying (18) as h, it follows that
18


h  0

if

W F  Wcc Qq h Wcc qx 
(1  )

 Wc q 
Sh .




(21)
*
W qx
The resource-trade dependence term,  cc  , has clearly the same effect on changes

in resource conversion over time both in the general case of the full lobbying model and in the
case where government is concerned solely with social welfare maximization (compare
conditions (21) and (20)). Thus Proposition 1 holds for the general case as well. In fact, the only
difference between conditions (21) and (20) is that the former contains the additional conversion
motive term,
(1  )
S h . This expression represents the overall influence of additional political

contributions by the lobbyist on the government's resource conversion decision. If the
government increases the current amount of resource conversion, it knows it will receive Sh
additional contributions from the lobbyist, with (1–/) being the relative weight of the lobbying
influence in the government's utility function. Thus (1–/) may be interpreted as an indicator
of the degree of "corruptibility" or "dishonesty" of the government, and Sh represents the
"lobbying pressure" on the government from the resource-converting private agent. Note that the
extra term
(1  )
S h in (21) is always positive. This implies that the effects of corruption, (1–

/), on the resource conversion decision of a government is reinforced by greater lobbying
pressure, Sh. In other words, condition (21) states that, compared to the case in which the
government is solely concerned with social welfare maximization ( = 1), a corrupt government
that faces increased lobbying pressure will always have a greater conversion motive. It follows
that h  h * . If a corruptible government's choice of the amount of resource conversion is
influenced by the additional political contributions of a resource converting economic agent, the
19
government will allow more conversion today than in the future, in comparison to the socially
optimal conversion path, h*(t).
Proposition 2: The influence of corruption on the current amount of resource conversion
in the economy will be conditional on the degree of "lobbying pressure" exerted on the
government from the private rent-seeking agent who gains from such conversion.
Figure 1 indicates the contrasting outcomes suggested by (18) and (20) by depicting two
possible feasible paths for the socially optimal amount of resource conversion, h*(t), compared to
the amount of resource conversion influenced by lobbying, h(t). Although not shown in the
figure, it is clear from (18) that an increase in the relative influence of political contributions will
lead the government to choose a resource conversion path that declines even faster over time.
This is precisely the reason why a resource converting economic agent has a strong incentive to
influence the government through increased political contributions. As indicated earlier, this
incentive is self-reinforcing due to the influence of larger current profits from resource
conversion today on greater lobbying efforts by the economic agent (see equations (9) and (10),
above).
As noted above, we can denote the degree of corruptibility or dishonesty of the
government by the indicator (1–/) = ε. From (18), it is therefore possible to show how an
increase in corruption will influence the resource conversion decision of the government
dh S h S hh 
 ~  ~ h  0,
d



   (1  )  .


(22)

~
where   S hh  Wcc q 2 < 0, Sh > 0 and Shh > 0.2 Condition (22) states unambiguously that, if
the government is more corrupt, it will choose a higher current amount of resource conversion
relative to future levels.
20
Proposition 3: The current level of resource conversion will be higher relative to future
levels the more corrupt the government of the economy.
In a small open economy, changes in the terms of trade will also affect the resource
conversion path of the economy. Differentiating (18) with respect to p yields
dh 1 
x

 ~ 1  qWm  Wcc q   S hp ,
dp  
p


mWmm
 0, S hp  0 ,
Wm
(23)
where η is the elasticity of marginal welfare with respected to imported consumption, and Shp is
the terms of trade impact on the marginal contributions or payments that the government receives
from the resource-converting agent. Thus the term εShp can be defined as the marginal
propensity for bribe-taking by the government as a result of a change in the terms of trade of the
economy. It seems reasonable to assume that Shp is positive; i.e., an improvement in the terms of
trade of the resource-exporting economy will mean that the resource-converting agent will have
more funds with which to make additional payments to the government. The two remaining
terms of importance in condition (23) are the responsiveness of marginal welfare to a change in
imported commodities, (1  )qWm , and the impact of the change in p on the resource tradedependence effect, Wcc q
x 
. Signing these latter two terms requires further discussion.
p
If the absolute value of η is large, i.e.   1 , then marginal welfare in the economy is
highly responsive to a change in imported commodities. However, the situation where   1
occurs only when the export supply curve is backward bending. Hence,   1 would be
considered the normal case for an open economy. It follows from (23) that, given   1 , an
increase in the terms of trade will lead to a fall in resource conversion over time only under
certain conditions, i.e.
21
h
x
x
W q x 
 0, if   0 (i.e., m  0) or   0 (i.e., m  0) and 1  qWm  S hp   cc
 0. (24)
p
p
p
 p
However, as discussed above, it is infeasible that increases in resource-based exports can
be sustained indefinitely, i.e. eventually in the open economy dx/dt < 0. Differentiating the
balanced-trade condition (11) with respect to time and p, we obtain
x 
m
x
m
and
dp   2 dp,
p
p
p
(25)
which suggests that if exports are falling over time then imports must also be declining (dm/dt
<0), and as a consequence, the effect of a change in the terms of trade of the economy is to slow
down the rate of decline in exports over time, i.e.
x 
 0 . As a result of an increase in p the
p
economy will have an incentive to export more of its resource-based commodity in the future
relative to today. Under this scenario, condition (24) is now
h
 0, if
p
1  qWm  S hp  
Wcc q x 
0.
 p
(26)
Condition (26) can be interpreted as follows. An increase in the terms of trade will lead
to higher current levels of conversion relative to future levels if the marginal welfare gains from
additional imports, (1  )qWm , and the marginal propensity for bribe-taking by the government,
εShp, outweigh any change in the resource trade-dependence effect, 
Wcc q x 
. Under the
 p
assumption that dx/dt < 0, a rise in p will slow down this rate of decline in resource-based
exports in the economy. That should reduce the incentive for greater resource conversion today
at the expense of future conversion. Conversely, if the marginal gains from additional imports
22
and the marginal propensity for greater corruption are both large, an improvement in the terms of
trade increases the incentive for increased resource conversion today.
Proposition 4: An improvement in the terms of trade of the economy will influence the
current amount of resource conversion, but the direction of this effect is ambiguous. It will
depend on the relative impacts of the change in p on the marginal welfare gains from increased
imports and on the government's marginal propensity for higher bribes versus the impacts on the
resource trade-dependence effect.
Returning to condition (22), it is clear that any improvement in the terms of trade of the
economy will also affect how the degree of corruptibility of the government influences the rate
of resource conversion over time. Upon further differentiating (22) with respect to p
 2 h 1 
h 
 ~ S hp  S hh   .
p  
p 
(27)
From comparing (26) and (27), if an improvement in the terms of trade leads to an
increase in the current level of resource conversion,
h
 0 , then it will also reinforce the
p
impact of greater corruption on resource conversion, i.e.
 2 h
 0 unambiguously. However, if
p
a change in p decreases the current level of conversion relative to future levels,
h
 0 , then
p
the interaction with the impact of corruption on the rate of resource conversion over time is
ambiguous. In this case, an improvement in the terms of trade will reinforce the influence of
greater corruption on resource conversion only if S hp 
S hh h
.
 p
23
Proposition 5: If an improvement in the terms of trade leads to increased levels of
resource conversion today relative to future levels, then the change in p will also reinforce the
impact of greater corruption on resource conversion today. However, if an improvement in the
terms of trade decreases the current level of conversion, then the interaction with the effect of
corruption on resource conversion levels is ambiguous.
For completeness we now briefly outline the steady state properties of the model. In a
steady state, F  h  S  x      0 . Hence steady state levels of the resource stock, F, and
resource conversion, h, are given by the solution to the system:
F  h  0
(28a)
1
h  (1  ) S h  pWm q   WF  Wcc Qq h  0

(28b)
through using the first-order conditions, plus   0 and S h  p (q 
From (28a) it follows that
F
By (28b)
h
h 0
F
h
q 'h

).
 0 , as the F  0 isocline is defined by the horizontal axis.
F 0
 ( pq ' Wm pq ')  Wcc q '2 h 2
> 0, which indicates that the h  0 isocline is

 q ' 2Wcc qq ' h
positively sloped. The intersection of the isoclines defines the optimal steady state stock of Fs,
as shown in Figure 2. It is clear from the figure that of the two feasible paths for hα(t) indicated
in Figure 1, only one is optimal with respect to the steady state solution, which is the path in the
left-hand side diagram that shows resource conversion declining monotonically over time.
The comparative static properties of the steady state are identical to those described above.
Thus, for instance an increase in corruption, as measured by , shifts the h = 0 isocline upwards
24
and results in lower steady state stocks. Similarly for the case when the marginal welfare effects
of imports are high the h = 0 locus shifts upwards, thereby lowering steady state equilibrium.
However, there is little evidence that the rates of land conversion have stabilized in most
developing economies. It is unlikely that any of these economies have attained the steady-state
depicted in Figure 2. Hence, we do not explore the steady state properties of our model any
further. The next section develops a panel analysis of agricultural land expansion over 1960-99
for low and middle-income economies to explore further the five propositions concerning
lobbying, trade and resource conversion that we have derived from our model.
Empirical Analysis of Agricultural Land Expansion
The five propositions derived above suggest the following empirical model, assuming
that conversion of forest habitat to agricultural land is the main resource conversion activity:
Ait  A1t 1
 b0  b1 (control of corruption )  b2 (control of corruption * lobbying pressure )
Ait
 b3 (resource trade  dependence)  b4 (terms of trade)
 b5 (control of corruption * terms of trade)  b6 (other var iables )
with the dependent variable being the percentage change in agricultural land area, Ait, over a
given time period, t, for each country i. Proposition 3 implies that b1 < 0, i.e. greater control of
corruption will result in less agricultural land expansion. Proposition 2 suggests b2 > 0, i.e.
increased lobbying pressure will offset the influence of the control of corruption on reducing
agricultural expansion. Proposition 1 implies b3 > 0, i.e. a more resource trade-dependent
economy will have greater agricultural conversion. Proposition 4 suggests that the sign of b4 will
indicate the factors determining whether or not a terms of trade (TOT) improvement will lead to
increased agricultural conversion, i.e. b4 > 0 implies that a TOT improvement will result in more
25
agricultural land expansion due to the welfare gains of increased imports and the marginal
propensity for greater corruption versus the resource trade-dependence effects. Proposition 5
indicates that if b4 > 0 then b5 > 0; i.e., if an improvement in the terms of trade leads to an
increase in the current level of resource conversion, then it will also reinforce the impact of
greater corruption on resource conversion. Otherwise, the sign of b5 is indeterminate. Finally, a
number of additional variables that explain agricultural land expansion should also be included
in the model as exogenous controls in the regression model.
The above model was applied to a panel analysis of agricultural land expansion over
1960-99 for low and middle income economies in Africa, Asia and Latin America, with the
dependent variable being the percentage annual change in arable and permanent crop land area in
each country. Following Barbier (2001), the controls chosen were growth in agricultural value
added, cereal yield and rural population growth. The terms of trade for each country is
represented by an index of export to import prices (1995 =100), and resource-trade dependence
is indicated by the share of agricultural and raw material exports as a percentage of total exports
of each country. Although there are no direct indicators of lobbying pressure by the agricultural
sector in developing countries, this variable was proxied by the percentage share of arable plus
permanent cropland in total land area, under the assumption that this indicator of the overall
"scale" of agricultural activity in a country would reflect this sector's overall social importance
and thus potential political influence.3 The source of data used for these variables was the World
Bank’s World Development Indicators, which has the most extensive data set for key land,
agricultural and economic variables for 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
26
put together a measure of the control of corruption and other governance indicators for 178
developing and advanced economies (Kaufmann, Kraay and Zoido-Lobaton (1999a,b)). As the
control of corruption indicator covers the broadest range of developing countries to date of any
comparable indicator, it is ideal for our analysis. However, as this indicator is a single point
estimate in time (based on survey data corresponding for 1997-8 according to the authors),
including this time-variant institutional index essentially amounts to incorporating a "weighted"
country-specific dummy variable in the panel regression (Baltagi 1995).
Table 1 reports the regression results. The model was first applied to the sample of all
developing countries, and as a check for robustness, on key sub-samples such as tropical
developing countries, countries with GDP per capita (1995 US$) of less than $3,000 during any
time period of the analysis, Latin American and Asian developing countries only, and finally
developing countries with total land area greater than 100,000 sq. km. Both one-way and twoway 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 1 displays the
results for the preferred models and the relevant statistics. The chi-squared and F-tests for the
pooled models as well as the LM test are significant, suggesting the presence of individual
effects and thus rejection of the ordinary least squares model. The Hausman test was not
significant for each regression, which indicates that the random effects model is preferred over
fixed effects. The one-way versus two-way random effects specification was chosen based on
the significance of individual coefficients and the overall explanatory power of each
specification.
The results in Table 1 suggest that the model is strongly robust with respect to the
influence of the control of corruption and the terms of trade effect on agricultural land expansion.
27
In the full sample, all variables are highly significant except for cereal yield. In the latter sample,
the signs of the coefficients of the three control variables are as expected; agricultural expansion
in developing countries increases with rural population and agricultural value added growth and
declines with increasing cereal yield. Finally, the coefficients of the remaining variables are all
of the correct signs as predicted by the above five propositions.
Further insights of the regression results with respect to the five propositions can be
gained by Table 2, which indicates the various components comprising the total effects of
control of corruption, terms of trade and resource trade-dependency on agricultural land
expansion.4 The direction of these various effects confirms the predictions suggested by all five
propositions derived from our theoretical model. For all samples of countries, increased
corruption (i.e. less control of corruption) has a direct and positive (negative) effect on
agricultural land expansion (Proposition 3). As greater lobbying pressure occurs, the impact of
corruption on agricultural conversion is increased further (Proposition 2). A rise in the terms of
trade of a country also has an unambiguous, direct and positive impact on agricultural land
expansion (Proposition 4). Increased resource trade-dependency is also unambiguously
associated with greater resource conversion (Proposition 1). Finally, a rise in the terms of trade
of a country also reinforces the impact of corruption on agricultural expansion. That is, an
improvement in the TOT not only has a direct impact on increasing agricultural land expansion,
but also ensures that the effect of corruption on agricultural land expansion is greater
(Proposition 5). In essence, increased terms of trade means that there will be greater rents
available to the private agents responsible for resource conversion, as well as a larger potential
share of these rents in the form of bribes to potentially corruptible government officials that are
responsible for controlling conversion.
28
Note, however, that Proposition 5 and the regression results do not imply that increased
corruption in an economy will necessarily reinforce the TOT effects on agricultural land
expansion. As Table 2 shows, as a country becomes more corrupt, i.e. the control of corruption
indicator falls, the total impact of any TOT improvement on agricultural land expansion is
reduced. Greater corruption tends to dissipate, rather than amplify, the TOT effects on resource
conversion. In the context of our theoretical model, a heuristic explanation of this result might
be that, because increased corruption perhaps implies that the government requires higher
payoffs for any given level of resource conversion, then additional rents accruing to private
agents involved in resource converting activities as a result to a TOT improvement may actually
be spent on these additional payoffs rather than on increased resource conversion.
Conclusion
The theoretical and empirical findings of the paper suggest that lobbying pressure and
political contributions by economic agents that benefit from resource converting activities will
have a substantial effect on the control of conversion by a government that is corruptible. We
also find that improvements in the terms of trade and greater resource trade-dependency also
provide additional incentives for resource conversion, and a rise in the TOT of a country will
tend to reinforce the negative influence of corruption on the control of conversion by a
government. Overall, these results support the many studies suggesting that special interest
groups have a played a significant role in determining land use decisions in developing countries,
particularly the conversion of forests and other natural habitat to agriculture.
It is also tempting to conclude from our findings that an important policy mechanism by
which the rest of the world can reduce resource conversion in resource trade-dependent
29
developing economies is through sanctions, taxation and other trade interventions that reduce the
terms of trade of these economies. Our analysis suggests that such a decline in the TOT would
reduce agricultural land expansion directly and indirectly through mitigating the impact of
corruption on resource conversion. Perhaps this might be the case. However, the reduction in
TOT is likely to have additional economic consequences not captured in our model, such as the
loss of foreign exchange earnings that could be employed to import advanced technology and
capital, or to be invested in human capital, to put the developing economy on a path that reduces
its dependence on resource-based exports. Thus trade interventions may have a short-term effect
of reducing resource conversion today in developing economies but the long run consequences
may be that these economies will have little opportunities to diversify away from a resourcedependent pattern of growth and trade.5
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 developing
economy, other than continue to point out the economic losses that the economy is likely to incur
as a result of such linkages. Whether such negative publicity is likely to change domestic
policies within the developing economy is doubtful. As our model has shown, if a government is
corruptible, a resource-converting agent producing a tradable good has considerable incentives to
influence the resource conversion decisions of that government.
30
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32
Figure 1. Feasible Paths for the Rate of Resource Conservation
h(t)
h(t)
h*(t)
hα(t)
h*(t)
time (t → ∞)
hα(t)
time (t → ∞)
Two possible feasible paths for the socially optimal conversion path, h*(t), compared to the
conversion path influenced by lobbying, hα(t).
Figure 2. Long Run Equilibrium and Phase Diagram
h(t)
h  0
Fs
F(t)
Table 1. Panel Analysis of Developing Country Agricultural Land Expansion, 1960-99
Dependent Variable: Arable and permanent cropland expansion (% annual change)a
All
Countries
(N = 1,694)
Cross-Country Estimationsb
Tropical
Poorer
Latin America
c
Countries
Countries
and Asia
(N = 1,294)
(N = 1,438)
(N = 1,018)
Large Land
Aread
(N = 1,269)
Growth in agricultural
value added
(% annual change)
0.144 x 10-1
(2.139)*
0.179 x 10-1
(1.765†
0.142 x 10-1
(1.849)†
0.120 x 10-1
(1.402)
0.169 x 10-1
(2.072)*
Cereal yield
(kg per hectare)
-0.138 x 10-3
(-1.886)†
-0.142 x 10-3
(-1.096)
-0.463 x 10-4
(-0.290)
-0.223 x 10-3
(-1.979)*
-0.830 x 10-4
(-0.919)
0.115
(1.982)*
0.842 x 10-1
(0.928)
0.477 x 10-1
(0.464)
0.703 x 10-1
(0.814)
0.132
(1.833)†
Control of corruption
(no control = -2.5
no corruption = 2.5)
-2.191
(-5.510)**
-1.791
(-3.035)**
-1.772
(-2.686)**
-2.677
(-4.888)**
-2.384
(-5.284)**
Control of
corruption*Lobbying
pressure
0.275 x 10-1
(2.629)**
0.196 x 10-1
(1.498)
0.149 x 10-1
(0.597)
Explanatory
Variables
Rural population growth
(% annual change)
0.316 x 10-1
(1.697)†
0.372 x 10-1
(3.067)**
Terms of trade
(1995 = 100)
0.886 x 10-2
(3.596)**
0.968 x 10-2
(2.596)**
0.943 x 10-2
(2.767)**
0.137 x 10-1
(4.605)**
0.916 x 10-2
(3.174)**
Agricultural export share
(% of merchandise exports)
0.126 x 10-1
(2.601)**
0.157 x 10-1
(2.629)**
0.611 x 10-2
(0.803)
0.987 x 10-2
(1.117)
0.150 x 10-1
(2.886)**
Control of
corruption*Terms of trade
0.159 x 10-1
(4.642)**
0.156 x 10-1
(2.929)**
0.141 x 10-1
(3.036)**
0.189 x 10-1
(4.642)**
0.164 x 10-1
(4307)**
χ-test for pooled model
F-test for pooled model
Breusch-Pagan (LM) test
Hausman test
Preferred model
159.905**
2.284**
49.80**
7.98
One way
random
effects
159.231**
1.740**
27.64**
11.07
Two way
random
effects
152.603**
1.489**
14.31**
6.67
Two way
random
effects
156.600**
4.369**
126.62**
15.10†
Two way
random
effectse
131.274**
2.482**
44.89**
8.54
One way
random
effects
Notes:
a
Sample mean for all developing countries is 0.96%, for tropical countries 1.03%, for poorer countries
0.96%, for Latin America and Asia 0.84%, and for large land area countries 1.07%.
b
t-ratios are indicated in parentheses.
c
Countries with GDP per capita (1995 US$) < $3,000.
d
Countries with land area > 100,000 sq. km.
e
Also corrected for significant autocorrelation.
** Significant at 1% level, * significant at 5% level, † significant at 10% level.
Table 2. Total Effects of Control of Corruption, Terms of Trade and Resource Dependence
Effectsa
1. Control of Corruption
Control of corruption only
Lobbying pressure effectb
Terms of trade effectc
Total Effect
All
Countries
Tropical
Poorer
Countries Countries
Latin America
and Asia
Large
Land Area
-2.191
0.372
1.825
0.006
-1.791
-1.772
-2.677
1.778
-0.013
1.621
-0.151
2.025
-0.652
-2.384
0.430
1.915
-0.038
2. Terms of Trade
Terms of trade only
Control of corruption effectd
Total Effect
0.009
-0.006
0.003
0.010
-0.006
0.003
0.009
-0.006
0.003
0.014
-0.005
0.009
0.009
-0.008
0.001
3. Resource-Trade Dependence
0.013
0.016
Notes:
a
0.015
Only effects significant at 5% level or better are indicated.
Sample mean of lobbying pressure indicator is 13.50 for all developing countries and 11.56 for large land
area countries.
c
Sample mean of terms of trade index is 114.82 for all developing countries, 114.05 for tropical countries,
115.29 for poorer countries, 107.35 for Latin America and Asia and 116.75 for large land area countries.
d
Sample mean of control of corruption indicator is – 0.37 for all developing countries, –0.41 for tropical
countries, –0.43 for poorer countries, –0.26 for Latin America and Asia and –0.47 for large land area
countries.
b
Notes
1
There are a growing number of related studies that examine the link between special interest group lobbying and
environmental policy outcomes. Examples of papers that use the common agency framework include Fredriksson
(1999) and Damania (2001), and examples of studies that use the political competition framework include Hillman
and Urpsrung (1994) and Schulze and Urpsrung (2000).
2
If h  0 then it follows immediately that condition (22) is less than zero. However, as Figure 1 indicates it is
possible that h  0 for at least some initial period. In this case condition (22) is still negative zero if
S hh
Sh
 h .
From (19), the left-hand side of this expression is the change in the rate of resource conversion over time chosen by
a completely corrupt government. The latter change in the conversion rate will always be higher than for the general
case depicted in (18). Thus (22) is unambiguously negative.
3
Note from equations (9) and (18), that the influence of lobbying on resource conversion rates is positively related
to the scale of agricultural activity. In the absence of direct measures of political lobbying we therefore use the scale
of agricultural production as a proxy for lobbying pressures. Alternative 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 as well in the regressions as share of arable and
permanent cropland of total land area.
4
In Table 2, the additional influences of lobbying pressure and terms of trade on the effect of the control of
corruption on agricultural land expansion are all evaluated at the sample means for these respective indicators for
each of the five samples. The additional influence of control of corruption on the impact of the terms of trade on
agricultural expansion is also evaluated at the sample mean for control of corruption for each of the five samples.
For example, in the sample of all developing countries in Table 2 the total effects of the control of corruption are
calculated as –2.191 + (0.0275*13.50) + (0.0159*114.82) = 0.006.
5
To clarify these short-run and long-run impacts, we stated that in our model we expect that for most developing
economies resource-based exports are either declining, or would hopefully decline as development proceeds, over
time, i.e. dx/dt <0. We also concluded that the likely effect of an improvement in the terms of trade would be to
x 
 0 . The result is that any trade intervention that
reduce the rate of decline in resource-based exports, i.e.
p
reduces the terms of trade, p, of the economy will produce the opposite effect, and the economy will export more of
its resource-based commodity today relative to the future. Thus such an intervention would actually force the
economy to increase its resource trade-dependency today. This is the short-term effect. However, supposing the fall
in export price is greater than the expansion in exports. The result would be a loss in foreign exchange earnings that
the economy could otherwise invest in economic resource for developing alternative, non-resource based industries.
The failure to develop the latter industries would mean that the economy would be unable to find substitutes for
resource-based exports, which in our model would eventually shift the entire x(t) path down at some future time.
This is the long-term consequence of trade sanctions against the resource-based economy.