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AN EVALUATION OF ECONOMIC DETERMINANTS OF
PROXIMATE DEFORESTATION CAUSES IN NIGERIA
BY
EZEMBA, AFOMA PHILOMENA
(PG/M.SC/11/59747)
M. SC THESIS SUBMITTED TO THE DEPARTMENT OF ECONOMICS
FACULTY OF SOCIAL SCIENCES
UNIVERSITY OF NIGERIA, NSUKKA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF
MASTER OF SCIENCE (M. Sc) DEGREE IN ECONOMICS
Supervisor: DR. W.F. FONTA
JANUARY, 2015
i
TITLE
An Evaluation of Economic Determinants of Proximate Deforestation Causes in Nigeria
ii
CERTIFICATION
Ezemba, Afoma Philomena, an M.Sc Student in the Department of Economics Faculty of the So-
cial Sciences, University of Nigeria, Nsukka with Registration Number: PG/M.Sc /11/59747 has
successfully completed the research requirement for the award of the Master of Science Degree in
Economics. The work in this project is original and has not been submitted in part or in full for
any other Certificate, Diploma or Degree of this or any other University.
15th January, 2015
…………………….…… ………………
DR. W. M. FONTA Date
(Supervisor)
……………………. ………………………
Prof. C. C. Agu Date
(Head of Department)
iii
APPROVAL
This research work, “AN EVALUATION OF ECONOMIC DETERMINANTS OF
PROXIMATE DEFORESTATION CAUSES IN NIGERIA”, has been read through and
approved to have met the minimum requirement for the award of the Master of Science (M. Sc) in
the Department of Economics of the above named institution.
………………………… …………..……………………
DR. W.M. Fonta Date
(Supervisor)
…………………............ …………….……………………
Prof. C. C. Agu Date
(Head of Department)
………………………. ...........…………….……………
Prof. I.A. Madu Date
(Dean of Faculty)
……………………………
External Examiner
iv
DEDICATION
To my Gals-
Mum- With warm thanks and appreciation
_________________________________________________
Babiee – Your love as a sister can only be equaled by your faithfulness as a treas-
ured friend
_________________________________________________
Sis Oby – For your love, assistance and for sharing your life experiences in other to
make me become a better me
_________________________________________________
You guys are just the best!
{{{HUGS!!!}}}
v
ACKNOWLEDGMENTS
With special thanks to:
Holy Spirit, who I owe my inspiration on the work.
My Supervisor, Dr.W.M. Fonta, for the support and training received during the elabora-
tion of this study. I appreciate your guidance, valuable comments, suggestions and above
all, your swift responses to me, throughout this work.
Dr. Enete, from department of Agricultural Economics UNN, for your considerable input
and valuable insights, which helped shape this research.
Dr. Nathaniel Urama, Dr. Ifelloni and Andy whose suggestions improved the quality of
the study.
Mr. Godstime Eigberomolem, for your insightfulness and intelligence and for a detailed,
thoughtful and constructive comments on almost every aspect of the work.
Dickson Emmanuel, the departmental reviewer of my proposal and a friend, for a com-
prehensive evaluation and constructive criticism of the work.
Ifeoma Ezenwile, for your good eye, and sharp mind in proof reading the work. For being
my lifelong pal- the best friend anyone could ever ask for.
All who have touched my life in different ways: Mr. Austin Akama – who believe in me,
Dr. Ukwueze (EzeB), Fr. Clement and Bro Emeka Ugwu- for your prayers and amazing
faith in God.
My dearest Ugoooo [Ugochukwu Anyanwu]- who started it all !!!
I alone accept all responsibility for the ideas expressed in this work and for errors or omissions.
Ezemba, Afoma Philomena
PG/M.Sc/11/59747
vi
TABLE OF CONTENTS
Title Page ……………………………………………………………………………… I
Certification …………………………………………………………………………… II
Approval Page …………………..…………………………………………………… III
Dedication ……………………………………………..……………………………… IV
Acknowledgement ………………………………………..…………………………… V
Table of Contents ……………………………………………..……………………… VI
List of Figures ……………………………………………….…………………………. IX
List of Tables ……………………………………………………………………........... X
Abstract ………………………………………………………………………………… XI
CHAPTER ONE
1.1 Background to The Study…………………….……………………………..…. 1
1.2 Problem Statement…..………………………………………………………… 3
1.3 Objectives of The Study……………………………………………………….… 7
1.4 Research Hypothesis ……………..….………………………………………… 8
1.5 Significance of The Study …...………………………….…….…….…………… 8
1.6 Scope and Delimitation of the Study………….……………………….………… 9
CHAPTER TWO
2.1 Conceptual Literature……………………………..…………………...…................. 10
2.2 Theoretical Literature …….………..…………..……………...……….……… 12
2.2.1 Economics of Land use……………………………………………… 12
2.2.2 Deforestation………………………………………………………… 12
2.2.3 Deforestation Theories….…………………………....……………… 13
2.2.4 Causative Pattern of Deforestation.................................................. 13
2.2.5 Drivers of deforestation……………………………………………………….. 14
2.2.5.1 Proximate Deforestation Causes………………………………..…… 14
2.2.5.2 Agents Decision Parameters…………………………………………. 16
vii
2.2.5.3 Underlying Deforestation Causes…………………………………… 16
2.2.6Theories or Urban Expansion…………………………………………………… 20
2.2.7Macroeconomic Policies, Deforestation and Agricultural land Expansion……. 21
2.2.7.1 Expenditure Switching Policies and Agricultural Sector…………… 22
2.7.7.2 Expenditure Switching Policies and Agricultural Land Expansion….. 23
2.2.8Exhaustible Resources………………………………………………….. 24
2.2.8.1 Theories of Resource Depletion…………………………………………. 24
2.2.8.2 Economics of Exhaustible Resources………………………………… 26
2.2.8.3 Criticism of Hotelling Rule…………………………………………… 28
2.3 Empirical Literature …………….………………………………………… .28
2.4 Limitations of the Previous Work………………………………………… 34
CHAPTER THREE
3.1 Theoretical Framework………….……………………………………........... 37
3.2 Theoretical Model.……………………………….………………………. 38
3.2.1 Basic Optima Control Model........................................................... 38
3.2.2 Model Specification. ………………………………………………... 39
3.3 The Empirical Model…………………………….…………………….……...... 45
3.3.1 Variable Description…………………………………………….……… 48
3.4 Apriori Information on the Model……………..………………………………… 58
3.5Estimation Procedure…………………………………………………………….. 59
3.6 Justification of the Model……………………………………………………… 60
3.7 Data and Data Sources……………………………………………………….. …. 61
viii
CHAPTER FOUR
4.1 The interpretation of the Analytical Model…………………………….. . 63
4.2 Descriptive Analysis…………… …..……………………….………….. 65
4.3 Stationarity Test Result…………………………………………………… 67
4.4 The Result of the effect of change in Trade Policy on Proximate Deforestation Caus-
es……………………………………………………………………… 69.
4.5 Theoretical and Statistical significance of the Recursive Model Result…… 76
4.5.1 First stage Estimation Result………………………………………. 76
4.5.2 Second Stage Estimation Result …….…………………...………… 84
CHAPTER FIVE
5.1 Summary of Findings………………………………………………………... 91
5.2 Policy Implication d the Findings……..…………………………..………… 94
5.3 Conclusion…………………………..………………………………………….… 95
References…………………………………………………………………. 97
Appendix…………………………………………………………………… 108
ix
LIST OF FIGURES
Figure 2.1 Recursive transmission of Trade and Exchange rate Policy Effect on Proximate
Deforestation Causes………………………………………………………………………………. 11
Figure 4.1 Agricultural Land Expansion and Nominal Protection Coefficients of Tradable
Crops............................................................................................... 67
x
LIST OF TABLES
Table 3.1 Apriori Expectation of the model………………………………………… 58
Table 3.2 Definition and Sources of Data…………………………………………… 62
Table 4.1 Stationarity Test……………………………………………………. 69
Table 4.2 OLS Result for Model 1…………………………………………….. 70
Table 4.3 First Stage OLS Estimation Result……………………………………. 75
Table 4.4 Second Stage Least Square Recursive Estimation Result ……… 85
xi
ABSTRACT
This study analyzes the economic determinants of proximate deforestation causes in varying trade
policy era in Nigeria, using an annual series for the period 1970-2012 from Central Bank of Nige-
ria Statistical Bulletin and Food and Agriculture statistics, FAOSTAT. The study aims to examine
how Agricultural land demand and relative prices of different crop categories respond to changes
in trade liberalization and exchange rate policies in Nigeria. A theoretical optimal control model
is developed to derive the socially optimal deforestation path of athree sector land use that will
optimize the use of forest land. The optimal conditions for choosing the level of forestland clearing
for competing economic land uses are derived. Employing simple linear regression, the empirical
analysis of the theoretical model indicates that increases in the relative returns of competing for-
estland uses increase proximate deforestation causes. Using a recursive model, an analytical
model of direct and indirect effect of trade policy on agricultural land demand through the rela-
tive price mechanism is empirically examined in two stages. The result obtained from this model
shows that trade liberalization directly increases the relative prices of tradable crops and thus,
increase the production incentives of exportable and mix-tradable crops relative to non-tradable
crops. The demand for agricultural land depends on the price elasticity and land requirement of
the crop category exported. To achieve a sustainable forest resource use and competitive agricul-
tural production with trade liberalization and exchange rate devaluation simultaneously, it is rec-
ommended that complimentary resource sustainable policies that will improve yield per hectare in
annual crops and equally internalize externalities resulting from these policies need to be imple-
mented.
Keywords: Optimal Control, Exhaustible Resources, Deforestation, Recursive Model, Relative
Prices, Trade Liberalization.
1
CHAPTER ONE
INTRODUCTION
1.1 THE BACKGROUND OF STUDY
Going by the current consumption and exploitation of tropical forest resources globally, there is an
increasing possibility that the wellbeing of this generation is at the expense of future generations.
Earlier researchers; Pinchot (1910), Hotelling (1931), Meadows et al. (1972) and Brooks and An-
drew (1974), however, anticipated this concern about the environmental degradation and social
cost associated with depletion of natural resources. For instance, Hotelling (1931) remarked that
the reason for the disappearance and exploitation of the world‟s supplies of minerals, forests and
exhaustible resources is because people feel that these common assets are now too cheap for the
good of future generations. However, sustainable development requires that the needs of the pre-
sent generation be met, without compromising the ability of the future ones to meet their own
need. Apparently, deforestation may provide current benefit from other economic uses, but it in-
volves future cost, in the sense that sustainability of the resource for future users is lost.
Although the optimistic school may put it that, as long as benefits arising from forest resource de-
pletion are invested elsewhere in the economy to increase social capital, then the adverse effect of
the current consumption on future generation can be compensated (Pearce and Warford, 1993 and
Toman, 1994).But then, primary tropical forests perform life support functions that cannot be pro-
vided by any social capital (Pearce and Turner,1990) and the re-conversion from alternative land
use back to forest may not be possible, because no effect of ecosystem rehabilitation, however so-
phisticated will ever recreate nature in its primeval state (Terborgh, 1991). Certainly, this genera-
tion, will have an unenviable task of explaining to the future generations what it was like to watch
the world‟s great rainforests disappear as remarked by Laurence (1999).
Despite the exhaustible nature of tropical rain forests and the irreversibility of land use decisions,
the degradation and exploitation of the primary forests are proceeding at a rate so rapid that it may
be difficult to maintain sustainable economic activities in the rainforest areas. Also, there is a pos-
sibility that, in few decades to come, the allocation of tropical land resources is going to become a
challenging situation, considering that the world (Nigeria in particular), may not afford the envi-
ronmental price of the present consumption of her tropical forest resources. The increase in defor-
estation has been attributed to pressure on forest products for exportation and consumption uses,
and pressure on forest by alternative land uses as a result of (1) - growing urbanization and indus-
2
trialization and (2) –Agricultural land expansion due to rising food demand as population increas-
es. This necessitates the need for optimal land use change that will provide a trade-off between
sustainable environment and economic welfare of the society.
Notwithstanding the global awareness and concern on the sustainability of forest land resources,
deforestation still persists in Africa, and in particular in Nigeria. The country currently has the
world‟s highest deforestation history. Over the last two decades, that is, from 1990 to 2000, and
2000 to 2010, Nigeria has been cited as among the first four countries out of nine key countries
which have had the highest rate of deforestation (FAO, 2010). Being a nation once covered by
360, 000 km2 of forest in 1975, now it presides over the reckless destruction of forest at the rate of
about 600, 000 hectares per year (Merem ,Tumasi, Richardson, Romorno, 2012). Between 1981
and 1985, Nigeria was losing her forest at annual rates of 4.0% (World Bank,1992). Between
2000 and 2005, the country lost 55.7% of its primary forest i.e. forest with no visible signs of past
or present human activities (Merem, Wesley, Twumasi, Richardson, Romorno, 2012). Conse-
quently, FAO (2010) reports that Nigeria has the highest deforestation rate in the world. The coun-
try recorded the third largest annual net forest loss (-3.7 %) for the period 2000 – 2010, losing
40,000 hectares annually over the period.
Contrary, to the widely held view that agricultural sector is the only competing use for forestland,
Geist and Lambin (2001), however, note that too much emphasis has been given to shifting culti-
vation and population growth as direct cause of deforestation at the decadal times scale. Thus, us-
ing 152 sub national case studies from different part of the world, they showed that proximate
causes of deforestation differ significantly across countries, following broader patterns of wood
extraction, agricultural and infrastructural expansion. These proximate causes refer to human ac-
tivities that directly affect the forest land change and thus constitute the direct sources of defor-
estation change. Although these proximate causes have the most immediate and visible impact on
deforestation; however, there has been a growing concern that macroeconomic policies oriented
towards stabilization in developing countries may have adverse underlying effects on sustainabil-
ity of forest resources.
Probably, the reason for the uncontrollable deforestation rate in Nigeria could be due to the failure
of the authorities to perceive it as a complex dynamic process. Whereby the decisions of defor-
estation agents could be shaped by macroeconomic policies which may have unintended environ-
mental effects when poorly designed or implemented. Empirical studies, for instance (Kaimowitz
and Angelsen, 1999) have shown that powerful macroeconomic tools such as exchange rate de-
3
valuation, trade liberalization subsidy removal have complex economy wide effects on forest land
use. Whereas, Barbier (2000) argues that, though trade liberalization has benefitted the agro-
industrial sectors by increasing their export-orientation and spurring economic development, how-
ever, there is a possibility that it may have indirect and/or direct effects on forest land depletion.
Sequel to inception of structural adjustment program (SAP), in Nigeria, the country has experi-
enced changes in trade and exchange rate policies which have affected domestic economy mainly
through changes in relative price and resource flows. The relative prices of agricultural products
have changed substantially. The average producer prices of agricultural output and timber prices
increased generally (Table A2, appendix), with the expectation of its‟ production incentives.
Whereas, the trend in agricultural land expansion generally increased over the period of trade lib-
eralization and exchange rate devaluation (see figure 4.1) in contrast to the limited expansion in
1970‟s and early 80‟s due to the strong bias against agricultural sector.
The role of agriculture in the reform has led to the concern as to whether these policy changes
have posed significant effect, particularly on pattern of agricultural land use and other proximate
deforestation causes. Although it is believed that these policies increase the economic welfare in
Nigeria, however the efficiency of these policies can be undermined by their ecological impacts
which could lead to sub-optimal resource allocation. However, empirical evidence has shown that
the overall effects of these policies are believed to be ambiguous and country specific, considering
that changes in trade and exchange rate policies can alleviate or worsen the deforestation agent‟s
decision parameters which through their influence on demand and supply could influence the net
benefits expected from these proximate deforestation causes. Depending on how the structure of
demand, the elasticity of the forest resources and competing economic uses, respond to wide var-
iation in the adjustment of these policies in different countries, the rate of the forest resource use,
as well as the forest land conversion varies.
1.2 STATEMENT OF PROBLEM
Deforestation of the tropical forest has been severe over the past several decades. Currently, it is
proceeding at an unprecedented rate. In 1998, the World Resources Institute [WRI], reports that
185 million hectares of tropical forests were destroyed from 1980 through 1995, as trees were cut
for timber and to clear land for agriculture and development. Although global awareness of the
consequences of deforestation is well known, the problem still persists in Nigeria. Report on veg-
etation and land use changes between 1976 and 1995 revealed that the area covered by undis-
turbed forest in Nigeria decreased by 53.5%,from 25,951 square kilometer (sq. km) in 1976 to just
4
12,114 sq.km in1991(Forest Management Evaluation and Coordinating Unit [FORMECU], 1996).
The rate of deforestation in Nigeria in the 1980s was of the order of 400,000 hectares yearly (Jai-
yeoba, 2002). The primary forest areas estimated at 1,556,000 hectares in 1990 fell to 736,000 and
326,000 hectares respectively in the period of 2000 through 2005 (FAO,2005). Going by the de-
forestation trend in the country, World Bank (1992) predicts that her remaining forest areas will
disappear within the next three decades.
Despite that, the deforestation rate continues to accelerate with mounting environmental, ecologi-
cal and economic impacts. Probably the most serious consequence is the loss of biodiversity. The
country‟s most bio diverse eco system, the old growth forests, is disappearing at a fast rate. Be-
tween 1990 and 2000, 79% of these forests were lost in Nigeria and since 2000 the country has
been losing on average, 11% of its primary forest each year (FAO, 2005). This is critical consider-
ing that species extinction has accelerated in the past decades and loss of biodiversity is likely to
be one of the most critical environmental problems of the century. Osemeobo (1993) reports that
over the past 15 years, the natural forests have witnessed declining supply of medicinal plant from
Nigeria forests. The concern over the decline stems partly from the role of these species in provid-
ing useful compounds for pharmaceutical and biotechnological research. For instance, Allen
(1992) reports that a compound derived from twigs and leaves from a tree in the Malaysian rain-
forest was found to stop the spread of one strain of HIV, but when the researchers returned for
more samples, they found that the strand of trees had been felled and no other trees in the vicinity
yielded the crucial ingredient. More so, the regulatory role of forest in maintaining co2, green-
house gas, is now worsening. The trend of land use change and deforestation shows that if the pre-
sent land use trend, particularly as affected by deforestation is not controlled, the potential of Ni-
geria becoming a net emitter of greenhouse gas in the future is high (Mo-
modu,Pelmo,Adesina,Siyabola,2011). Whereas, Siyanbola, Modu, Pelmo, Adesina (2003) report-
ed that the total carbon emission from land use change and deforestation is increasing annually.
This has been explained against the back drop of increasing deforestation in the country.
Anthropogenic factors which drive land use change in the country include: intensity of agricultural
activities, rate of forest logging and other wood product extraction activities, urbanization and
other major developmental activities (Momodet al., 2011). The intensity of logging and illegal ex-
ploitation of tree species has continued to pose serious threats to forest resources in Nigeria. Evi-
dence suggests that reasons for exploiting forest resources for fuel wood are rather complex. High
prices of imported petroleum products discourage the use of alternate fuel to firewood. Currently
5
in Nigeria, the main source of energy for cooking is kerosene, with about 90% of the population
depending on it for fuel. However, the supply has not always been regular and at best is prohibi-
tively expensive. As a result, approximately 60% of the population resorts to fuel wood on a daily
basis (Olatunbosun, 2010).
More so, the land available for in-situ conservation is being reduced on an annual basis for alter-
native uses (Osemeobo, 2012). Nigeria has been experiencing increasing urbanization over the last
five decades. Being the most populous country in Africa from UN forecasts, Nigeria is among the
eight nations projected to account for about 50% of the global population spike between 2005
through 2050 (FAOSTAT, 2008). This would further accentuate natural resources dependency
with pressures on the already stretched environment and forest resources. Angel, Sheppard and
Civco (2005) equally note that if average densities in the developing countries continue to decline
at the annual rate of 1.7 percent as they have, during the past decade, the built-up area of cities in
developing countries will increase to more than 600, 000 square kilometers by 2030. Thus, by
2030, these cities can be expected to triple their land area, with every new resident converting on
average, some 160 square meters of non-urban land in the coming years.
Apart from urbanization and infrastructural expansion, agricultural sector is equally competing
with forest land. Whereas expansion of agricultural land area was almost certainly an appropriate
agricultural development strategy in earlier decades, when there was abundant land, but the rate of
agricultural land expansion in the last few years has become a concern. Although, agriculture per-
ennially contributes the largest portion of Nigerian economic output (Akande 2003), however, ev-
idence from Falusi (1997) and UNDP (2009) shows that increases in agricultural output has been
accounted for by expansion in cultivated land rather than by increase in productivity. In addition,
indices shows that Nigeria, ranks among the lowest users of fertilizers in the world, with a rate of
10kg/hectare compared to the FAO recommendation of 200kg/hectare. Tellingly, the World Jour-
nal of Agriculture/Benson Idahosa University [WJA/BIU] (2009) study noted a drastic decline in
average yield per hectare, dropping from 14.9% in 1986-1990 to 2.5% in 1999, 1.33% in 2005 and
appalling .94% in 2008.
Some of the issues highlighted above point that deforestation in recent times is primarily driven by
land use changes as a result of forest frontier conversion to alternative uses. However, there is a
growing concern that the current pattern and rates of forest conversion to alternative use, in partic-
ular, agriculture, may not be optimal socially. That is, the external effects of land conversion are
largely ignored; the scarcity value of the forest resource to future generation is not fully captured.
6
This necessitates a need for an optimal land use change that will provide a trade-off between sus-
tainable environment and the economic welfare of the society.
Thus, given the global concern about the potential risk to sustainable forest land use posed by this
crisis to future generation, none of the studies based on literature reviewed in Nigeria, has consid-
ered the input nature of the forest land resource and the comparative returns of the competing eco-
nomic land uses as a problem that need to be examined. That is, how the functioning of deforesta-
tion agents‟ economic variables affect land use decision. Neither did these studies analyze the
relative price mechanism through which exchange rate and trade policies influence farmers‟ pro-
duction incentives. Recent studies by Oyekale and Yusuf (2008), investigated the economic de-
terminants of deforestation in Nigeria, while Oni, Oladele and Ajayi (2013) examined how specif-
ic macroeconomic factors influence the forest stock in the country. However, while determination
of macro-economic variables that affect deforestation has become a global concern, other issues,
such as land use decision at the margin between forest land and alternative economic uses, espe-
cially agriculture have become arguably more important.
Greater number of studies focusing on the inter-temporal nature of forest land allocation provided
qualitative understanding of the optimal land use. However, majority of these analytical analyses
have not often been followed by empirical analyses that will give a clearer idea of the quantitative
effect on land usage. Oyekale (2006), which is the only other study in Nigeria that analyzed the
inter-temporal nature of land use decision taken by deforestation agents, focused on dynamic op-
timal model of deforestation and agricultural production, but did not consider the dynamics of in-
frastructural expansion as a competing use for forest land in Nigeria. None of the very few empiri-
cal works that worked on this issue has considered how relative prices of different crop category
affect agricultural land expansion.
This is important considering that empirical evidence (Repetto, 1986) shows that overtime, farm-
ers respond strongly to differential incentives among crops than to overall discrimination against
agriculture. This is because it is easier to shift land and other resources from one crop to another
than to withdraw from agriculture altogether. Kaimowitz and Angelsen (1998) equally noted that
changes in relative prices within sectors may have a greater impact on deforestation than the over-
all sectoral terms of trade. They assert that pro-export policies designed to increase agricultural
and forest products export are likely to affect deforestation more than policies that promote pro-
duction for the domestic market. This effect as shown by Deacon (1995) will be stronger when
agricultural supply is more elastic. Thus, while the effect of trade liberalization and exchange rate
7
devaluation has drawn much attention in empirical studies on deforestation, little is known on the
relative price movement due to trade policy changes and how agricultural land responds to it.
In effect, the following questions arise:
1. How do deforestation agents‟ economic parameters influence the proximate deforestation caus-
es in varying trade policy era in Nigeria?
2. How do the relative prices of different crop category respond to changes in trade policy?
3. How does Agricultural Land Demand in Nigeria respond to changes in exchange rate policies in
Nigeria?
1.3 OBJECTIVES OF THE STUDY
The overall objective of the study is to explore the economic determinants of proximate deforesta-
tion causes in Nigeria, so as to gain insight on how competing economic land use decisions are
made and how forest land should be used. This shall be done by developing a theoretical model of
optimal deforestation path and empirically examine the economic factors that determine the choice
among a competing forest land uses (proximate deforestation causes) and the effect of relative
prices of different crop category on agricultural land expansion. Specifically, the objectives the
study intends to achieve are:
1. To analyze how deforestation agents‟ economic parameters influence the proximate deforesta-
tion causes in varying Trade policy era in Nigeria.
2. To analyze how the relative prices of different crop category responds to changes in trade poli-
cy.
3. To ascertain if changes in exchange rate policies affect Agricultural Land Demand in Nigeria.
8
1.4 HYPOTHESES
The following null hypotheses are put forward for the purpose of this study:
- The proximate deforestation causes in varying trade policy era in Nigeria is not affected by
deforestation agents‟ economic parameters.
-The relative prices of different crop category do not respond to changes in trade policy.
- Agricultural land demand in Nigeria does not respond to changes in exchange rate policies.
1.5 SIGNIFICANCE OF THE STUDY
Literature has shown that there is no universally accepted policy for controlling tropical deforesta-
tion; rather an understanding of the complex relationship between proximate causes and underly-
ing driving forces affecting forest degradation and conversion in a specified location is required
before policy intervention. However, in an economy like ours, where the private sector is free to
pursue its own interest, the outcome of the optimal model can provide policy relevant insights on
how the government can maximize social welfare subject to the available forest resources, despite
the limitation of data. Thus, the optimal model can be implemented across regions, despite the
complexity of addressing such issues in empirical context and also, empirical variations across
countries can equally be avoided too. In addition, by anticipating the likely outcome of certain
policy-mix, it is expected that this study will help the forest conservationist and land use planners
to explicitly identify the tradeoffs and alternative decisions that will simultaneously reduce forest
depletion without stagnating the economic development in other sectors that require land as input.
The study will equally assist the policymakers to be able to trace how macro level policy changes
affects incentive for efficient resource use at micro level. In effect, the interaction of micro and
macroeconomic forces will help the concerned in re- designing strategies on misguided policy in-
terventions that might have contributed to the present deforestation crisis in Nigeria, and equally
designed complimentary measures that will mitigate the negative environmental effect of these
polices. Finally, by not adopting the simplistic assumption that deforestation results from a single
dominant sector( i.e. agricultural sector), this study will equally appeal to researchers and resource
managers, by broadening their horizon of deforestation in Nigeria on the optimal use of forest land
among other competing economic uses.
9
1.6 SCOPE AND DELIMITATION OF THE STUDY
The analytical model adopted in this study is a deterministic optimal control approach. This im-
plies that the system is not affected by random variable and as such, stochastic effects are not in-
cluded in the optimal model. More so, the optimal control model is confined to derivation of the
demand function for optimal deforestation path involving Agriculture, forestry and infrastructural
(urban) sectors and thus, excluded the steady state analysis for optimal forest stock or forest land
at equilibrium.
The empirical model shall examine the developments in the demand for wood extraction, agricul-
tural land expansion and infrastructural (urban) extension in Nigeria covering the period, 1970
through 2012. However, the analysis shall be restricted to the interplay between changes in ex-
penditure switching policies (i.e. trade policy and exchange rate policy) and the following agent
decision variables: domestic relative price of tradable, relative foreign price of tradable, relative
domestic agricultural prices, agricultural output price and input prices, timber price, agricultural
value added, agricultural mechanization, agricultural productivity, industrial sector productivity,
export good price, transportation cost and kerosene prices on the three proximate deforestation
causes specified in the model.
10
CHAPTER TWO
LITERATURE REVIEW
2.1 CONCEPTUAL LITERATURE
The empirical studies of deforestation have established three direct or proximate causes of defor-
estation as: Agricultural land expansion, wood extraction and infrastructural extension. Thus, de-
forestation in this model is explained by its proximate or direct causes. In the study, deforestation
has been attributed to microeconomic agents‟ decision making based on comparative returns
gained from converting forestland to competing economics uses. To understand the complex in-
teractions and factors involved in tropical deforestation, the study distinguishes between explana-
tions of deforestation at three different levels: proximate level, agents‟ decision level (local under-
lying level) and underlying macroeconomic level. The analysis starts from external level i.e. ex-
ternal macro-economic change, through sectorial production to land uses (proximate causes) and
down to forest land level. These proximate causes are expressed as a function of underlying
agent‟s decision parameters which are influenced by expenditure switching policies.
The variables at the proximate level illustrate the human activities (land uses) that directly affect
the environment and thus constitute proximate sources of change. These proximate causes which
are endogenous to the model, are those suggested by the analytical model in section (3.2) and di-
rectly impact upon forest cover. The proximate causes are grouped into two category based on- (1)
-pressure for forest products (for consumption and exports). The variable in this category include
wood extraction and (2)- pressure on forestland by alternative land uses. These variables agricul-
tural expansion and infrastructure extension which are determined by agents‟ decision level pa-
rameters. The agents‟ decision parameters in the model include different sectors output and input
prices as well as different returns from each competing land use sector.
Then at macroeconomic level, expenditure switching policies which can be seen as underlying
cause of deforestation, determines or influences the agents‟ decision about land usage through rel-
ative prices. The relative prices represents the channel through which trade and exchange rate pol-
icies affect land uses [in this particular case, agricultural land use]. These price changes in turn
influence the production decision of farmers. The expenditure switching policies are defined in
this study as various macroeconomic policies that affect composition of a country‟s expenditure
on foreign and domestic goods, such as tariffs, exchange rate policies, export taxes and subsidies,
which create a wedge between domestic price system and foreign prices. This is shown in figure
2.1:
11
11
Underlying Causes Relative Price mechanism
Agents Decision Parameters
Proximate Causes
Figure 2.1 Recursive Transmission of Trade and Exchange rate Policy Effect on Proximate Deforestation Causes
Source: Devised by the Author
DOMESTIC TRADE POLICY CHANGE-
[Direct effect of trade policy in form changes in tariff
and subsidies, Nominal Protection Coefficient...]
CHANGES IN AGRICULTURAL PRODUCTION
SCALE- depending on Price elastici-
ty/inelasticity of the crop category
CHANGES IN RELATIVE DOMESTIC
PRICES of tradable and non-tradable
Agricultural output prices [In form of
Depreciation and Appreciation]
CHANGES IN RELATIVE DOMESTIC
PRICES of agricultural and non -
tradable, non- tradable Agricultural
good
CHANGES IN AGRICULTURAL PRODUCTION SCALE-
depending on land requirement of the crop category
(Land intensiveness (Perennial]/ land extensiveness
[Annual]of the crop)
AGRICUTURAL LAND EXPANSION
[Comparative net Returns from com-
peting land use Activities,[Output and
input prices]
INFRASTUCTURAL LAND
EXPANSION
WOOD EXTRACTION
INTERNATIONAL TRADE POLICY
World market Price in form of External terms of Trade
Indirect effect of trade policy
via Exchange Rate
PRESSURE ON THE FOREST MARGIN
11
12
2.2 THEORETICAL LITERATURE
2.2.1 ECONOMICS OF LAND USE
The economics of land use assumes that land is allocated to the use with the highest land rent.
Many factors like crop prices, labor costs, location and accessibility determines the land rent for
different land uses. Thus according to Von Thủnen (1826), when applied to two land uses, agricul-
ture and forestry, anything that makes agriculture more profitable stimulates deforestation while
anything that makes forest more profitable has the opposite effect. According to Von Thủnen
model, farmers deforest because non forest uses are more profitable (i.e. have higher forest rent)
than forest uses, for a given land to expand. The agents must be able to bid away additional land
from forest to alternative land use. Thus, land conversion is basically guided by economic mecha-
nism, directing resources to their best uses. According to Repetto and Gills (1988), a widely ac-
cepted economic criterion for forest management is obtaining the maximum total benefit from all
the forest‟s various possible uses over the long run, discounting future benefits at an appropriate
interest rate. Under this criterion, there is a consideration that land uses are configured to ensure
that land is used in a way that maximizes the aggregate social net benefits that are reaped from the
land resource (Sergerson, Platinga, Irwin, 2006). This implies that land areas should be devoted to
the uses, forest or non-forest, that yield the greatest potential economic benefit whether or not the
benefits are reflected in market transactions (Repetto and Gills,1988).These net benefits are de-
termined primarily by prices of the respective output and price associated with forest resource.
2.2.2 DEFORESTATION
This results from complex socio-economic processes and thus, in many situations it is impossible
to isolate a single cause (Walker, 1987).According to Angelsen (1995), there is no clear definition
of deforestation, neither are there reliable estimates of its extent nor its primary causes, nor partly
based on this, there is no concession on the underlying causes. However, FAO (2001) defines de-
forestation as the conversion of forest to another land or long term reduction of tree canopy, while
Van Kooten and Bulte (2000) defines it as the conversion of forest to an alternative permanent
non-forested land use such as agriculture, grazing or urban settlement. Degradation on the other
hand occurs when the ecosystem functions of the forest are degraded but when the area remains
forested.
13
2.2.3 DEFORESTATION THEORIES
The main approaches to the deforestation phenomenon include:
-Impoverishment Approach (Neo-Malthusian approach) - Here, a combination of poverty and
demographics is seen as the main mechanism responsible for forest loss (Wunder and Verbist,
2003). Population growth plays a prominent role here and is seen as the underlying cause of de-
forestation. Thus, low labor absorption at the frontier and a low pace of technological innovation
mean that Malthusian scenarios dominate. The main factor responsible for deforestation is the
growing number of the poor. The more poor people have to live off a finite resource base, the
more they are left with no other alternative than to convert more forestland in order to survive.
Many researchers (Sandler, 1993; Vanclay, 1993 and Deacon, 1994) agree to this argument.
-Neo-Classical Approach (Micro Economic Approach) – This approach provides explanation
on how, under various forms of market failure, an agents‟ economic behavior leads to deforesta-
tion. The main factor responsible for deforestation is ill-defined or poorly defined property rights
and under valuation of forest benefits, either at the local, regional or global level (Wibowo and
Byron, 1993). Thus, an open or quasi-open access to forestland at the frontier encourages small
holders and large investors alike, to open up the forest and claim land rights afterwards. Here op-
timizing agents react to pull incentives, thus driving the dynamics of deforestation.
-Macroeconomic Approach- This attempts to establish the link between foreign debt and defor-
estation. The school hypothesizes that, faced with high level of indebtedness, developing countries
may adopt various debt servicing schemes that increase deforestation. The schemes include any
export – promotion and import – reduction programs related to the liquidation of forest (Wibowo
and Byron, 1993; Gullison and Losos, 1993). Yet other macroeconomic approach, e.g. work of
Capistrano and Kiker (1993), considered real exchange rate devaluation, per capita income, export
prices, etc.
2.2.4 CAUSATIVE PATTERN OF DEFORESTATION
-Thus, notwithstanding that numerous attempts have been made to explain the causative pattern of
tropical deforestation, two major and divergent pathways (mutually exclusive) of explanation that
emerged are, single factor causation and irreducible complexity (Geist and Lamb-
in,2002;2001).Proponents of --single factor causation suggest various primary causes such as
14
shifting cultivation (Amelung and Diehl, 1992;Myers,1993; Rerkasem,1996) and population
growth(Allen and Burnes,1985;Cropper and Griffiths,1994).
-Irreducible Complexity-This school believes that correlates of deforestation and causative vari-
ables are stated to be many and varied and thus revealing no distinct pattern(Rudel and Rop-
er,1996;Angelsen and Kaimowitz,1999;Bawa and Dayanandan,1997).According to this school,
the causes of deforestation vary from country to country and thus it may be difficult to generalize
that one or several factors are the most important(Murali and Hedge,1997). According to them,
deforestation results from complex socio-economic processes which in many situations, it is im-
possible to isolate a single cause (Walker, 1987). However, according to Lambin and Geist (2001),
deforestation is driven by identifiable regional variations of synergistic causes/ driver combina-
tions in which economic factors, institutions, national policies and remote influences are promi-
nent.
2.2.5 DRIVERS OF DEFORESTATION
The causes of deforestation vary from country to country (Rudel and Roper, 1997).It differs be-
tween countries depending on the socio-economic, political and physical structure of the country.
However, Pearce and Brown (1994) identified two main forces affecting deforestation(1)-
Competition between humans and other species for the remaining ecological niches on land. This
is substantially shown by conversion of forested land to other use such as agriculture, infrastruc-
ture and urban development. (2)-failure in the working of the economic systems to reflect the true
value of the environment and decision to convert tropical forests, encouraged by fiscal and other
incentives.
2.2.5.1 PROXIMATE DEFORESTATION CAUSES- These are seen to constitute final human
activities that directly affect the environment (Turner et al, 1990, 1993). Agents make decision
about these choice variables based on their own characteristics and exogenous decision parameters
to constitute immediate cause of deforestation (Kaimowitz and Angelsen, 1998).They connect the
changes in land cover (Biophysical attributes of the earth‟s surface) and land use (human purpose
or intent applied to human activities that directly alter the physical environments (Lambin and
Geist, 2001). In terms of scale, proximate causes are seen to operate at the local level. The com-
parative analysis of 152 case studies of tropical deforestation across the globe by Geist and Lamb-
in (2002), broadly identified the proximate causes of deforestation as agricultural expansion, wood
15
extraction and infrastructural extension (Mainardi, 1998; Lambin, 1994 and Kaimowitz and An-
gelsen, 1998).
-Wood Extraction- The extraction of wood or timber in combination with other proximate causes
has been reported to lead deforestation in 102 out of 152 cases i.e. 67% (Geist and Lambin, 2001).
The intensity of logging depends on the share of marketable species per hectare and the transpor-
tation cost. Logging damage can be evaluated in terms of biomass and in terms of biodiversity.
That is, the impact of logging activities on either the quantity or the quality of the vegetation. In
the felling process, biomass reduction consists of the trees extracted, plus the vegetation damaged
beyond recovery (Van Soest, 1998). Whitmore (1990) states that for every tree logged, generally,
one is damaged beyond recovery and another is damaged but will survive. In general, selective
logging does not cause much permanent deforestation and most reductions in biomass are reversed
quite quickly. However, damage inflicted upon forests increases substantially if the area is logged
over again so soon, that is if forest regeneration has not taken place sufficiently (Panayotou and
Ashton, 1992). Thus, although natural regeneration is generally sufficient in terms of biomass, re-
generation of commercial tree species are not always guaranteed (Grainger, 1993).
-Agricultural Expansion- The expansion of cropped land and pastures is by far, the leading prox-
imate cause of tropical deforestation. Agricultural encroachment by traditional small holder agri-
culture occupies a central position in the debate on tropical deforestation and shifting cultivators
have since long been viewed as the primary agents of deforestation in tropical developing coun-
tries (Geist and Lambin, 2001). The overall estimate of its share, based on cross-national statistical
analysis, ranges as high as 45% (UNEP, 1992) to 60% (Myers, 1992) with peak values ranking as
high as 7.9% (Amelung and Diehl, 1992). Thus, a broad consensus exists, that expansion of
cropped area and pasture constitute a major source of deforestation (Angelsen and Kaimowitz,
1999). However, from a review of 150 economic models of deforestation, Geist and Lambin
(2001), confirmed that agricultural expansion apparently is the most important proximate tropical
deforestation causes, operating in synergetic ways (cause connections) with other proximate fac-
tors such as wood extraction and infrastructure extension.
-Infrastructural Extension- The extension of infrastructure, in combination with other proximate
causes, explains 110 out of 152 cases of deforestation, i.e. 72%. These include transport infra-
structure, market and settlement expansion, private enterprise infrastructure e.g. oil exploration,
mining, etc. Settlement expansion (sprawl), together with other infrastructural improvement holds
16
the largest single share (Geist and Lambin, 2001). Urbanization has been identified as a significant
driver of forest loss in so many countries. Sprawl, from an economic point of view, occurs when
land uses in a particular area are inefficiently dispersed, rather than efficiently concentrated. This
happens when development cost are being subsidized by all tax payers in the metropolitan area.
Thus, both the developers and potential buyers of the newly developed property find living farther
out to be artificially cheap. This bias, known as public infrastructure problem, prevents developers
from efficient consideration of the trade-off between developing the land more densely within the
currently served areas and developing the land outside those areas, thereby promoting inefficient
levels of sprawl (Tietenberg and Lewis, 2010). The desirability of development farther from the
center of economic activity can also be promoted either by transportation subsidies or negative
externalities.
2.2.5.2 AGENT’S DECISION PARAMETERS (Local level causes)- Agents make decisions
about certain choice variables based on their own characteristics and given decision parameters
(Kaimowitz and Angelsen, 1998). The choice variables are the set of options available to allocate
the land for the agents, e.g. variables such as land allocation, labour allocation and migration,
capital allocation, consumption and other technological and management decisions. However,
agents decision with respect to the choice variables are influenced by agents‟ decision parameters.
The agents‟ decision parameters are external to individual agents and consist of output, labour and
other factor input prices, accessibility, available technology and information, risk, property re-
gimes, government restrictions and physical environmental factors. Thus, the characteristic and
decision parameters determine the set of permissible choices and constitute the local level causes
of deforestation (Kaimowitz and Angelsen, 1998).
2.2.5.3 UNDERLYING DEFORESTATION CAUSES-[Breaches of Efficiency in Forest
Management]
These are the fundamental forces that underpin the more obvious or proximate causes of tropical
deforestation. They are a complex of social, political, economic, technological and cultural varia-
bles that can constitute initial conditions in human-environmental relations that are structural in
nature(Lambin and Geist,2001).These factors determine the agent‟s characteristic and decision
parameter(Kaimowitz and Agelsen,1998).Consequently, tropical deforestation is determined by
different combinations of various proximate causes and underlying driving forces in varying geo-
graphical and historical context (Lambim and Geist,2002). The underlying causes are complex
17
combination of market failures, policy, institutional failures (Contreras-Hemosilla, 2000; Perrings,
2000), that are reasons associated with underestimation of forest resources (Benhin, 2006) .The
underlying causes are;
-Property Right-Economic theory of externalities holds that divergence between private and so-
cial costs and benefit is caused by the lack of appropriately defined (or enforced) property right
(Hanley, Shrogen and White, 2001).Institutional failures resulting from poor accountability of
state owned enterprises or inadequately defined property rights worsen equity and undermine the
incentives for sustainable forest management (Benhin, 2006). Property rights governing a resource
determine the manner the producers and consumers use a native‟s resource. An owner of a re-
source with a well-defined property right [one exhibiting exclusivity, transferability and enforcea-
bility], has a powerful incentive to use that resource efficiently, because a decline in the value of
that resource represents a personal loss (Tietenberg and Lewis, 2010)
The simple Hotelling rule assumes that property rights over resources are well-defined and pro-
tected. But in reality, resource endowed countries are often plagued by massive corruption and
weak institution. The concession rights to exploit these resources are often granted by government
hence leading to uncertainty. It‟s noted that under uncertain property rights, the resource owner
will have incentive to extract the resource more quickly than the social optimum would require.
This implies that uncertain property rights constitute a market failure which raises the speed of
resource extraction above the social optimum, which is detrimental to welfare.
Cornes and Sandler [1983] argued that common property leads to over exploitation of resources
when access is free, which leads to higher net benefits for conversion. Common access resources
are characterized by non-exclusivity and divisibility while under open access resources it is un-
profitable for a private party to invest in the protection or enhancement of the resource because of
the impossibility of recovering costs from other uses [Free riders]. There is no incentive for a user
to abstain from consumption since someone would step in instead [Non-exclusivity]. For exhaust-
ible resource, one person‟s use is at the expense of someone else‟s [divisibility].Thus, unrestricted
access destroys the incentives to conserve and promote an inefficient allocation resulting in exces-
sive exploitation of the resource. This is because in the presence of sufficient demand, unrestricted
access causes over exploitation and in addition to that, scarcity rent is dissipated because no one
appropriates the rent [Tietenberg and Lewis, 2010].
18
-Market Failure- Exclusivity, the chief characteristics of an efficient property right is frequently
violated in practice; when an agent making a decision does not bear all the consequences of his or
her action. One of the conditions of a perfect competitive market is that the cost or benefit born by
any individual from his own economic activity must match the cost and benefit born by the society
as a whole. When private cost and benefit of any economic activity diverge from the social costs
and benefits, it is said that externality exist. Free market forces are unable to deal with externali-
ties. Once the signals on the basis of which free markets will allocate resources are wrong, then
the market mechanism can hardly be expected to bring about an efficient allocation [Kanafani,
2000]
Much of the mismanagement and inefficient use of natural resources and environmental degrada-
tion can be explained in terms of market and policy failure. A well-functioning market signals the
relative scarcity of different resources through their prices and allocates them to their most highly
valued users. Prices generated by inefficient markets do not reflect the true social costs and bene-
fits of resource use, convey misleading information about resource scarcity and provide inade-
quate incentives for management efficient use and conservation of natural resources.
Bann (1997) noted that if too much of a resource is being consumed, it is a sign that the market is
failing to signal the growing scarcity of resources. From the supply side the same failure is evi-
dent. People are not investing in the environment [planting trees, conserving wildlife] because it is
not advantageous for them to do so. This implies that the market is failing to reward environmen-
tal conservers and investors. Many environmental assets valued by society are not bought and sold
in markets and as a result many environmental assets are underpriced. Individuals have no incen-
tives to reduce their use of these assets, still less to invest in their preservation and growth, unless
they are restrained by internalizing their externalities. The failure of the market to account for non-
priced benefit and costs may in various circumstances, be an important underlying source of forest
decline, shaping the actions of private agents in directions that are biased against the conservation
and protection of non-priced Benefits.
For instance, a situation where the forest landowner does not obtain the full value of social bene-
fits provided by forests, there will be less incentive to maintain lands under forests cover. Thus,
the market fails to generate the signals that would lead private operators in the direction of satis-
factory social objectives. This implies that if private agents are not compensated for the values of
forests that do not have a financial marketable dimension, they will be less interested in managing
19
forest resources and they are more likely to convert forested lands to the uses [Contreras-
Hermosilla, 2000]
-Mistaken Policy Intervention-This arises when government decisions or policies are responsible
for worsening allocation failures that lead to excessive forest loss .This actions are misdirected
policies that result in unintended deforestation and the inability of government institutions to pre-
clude preventable deforestation (Wibowo,1999).Many macroeconomic, monetary, fiscal and trade
policies designed for other purposes can have unintended side effects on the forest. Thus, policies
designed to improve the overall macroeconomic performance in an economy, but due to the pres-
ence of market failures such as the lack of prices for converted forest, would become an incentive
that worsen forest loss (Barbier and Burgess,1995). Rice, Marijnissen, Gregory (2000) noted that
policies that produce maximum economic growth are perceived as having little value if they also
generate unsustainable demands on the earth‟s resources. Special interest groups use the political
process to engage in what has become known as rent seeking, which increases the net benefits go-
ing to special interest groups, but lowers net benefits to society as a whole (Tietenberg and Lew-
is,2010).Government grant subsidies that encourage activities that are intrinsically uneconomic or
push alternative land uses beyond the limits of economic rationality. This effect is to shift the
margin of relative profitability between forest and the competing land use, encouraging more for-
est conversion than would otherwise take place.
It is interesting to note that one of the main advocates of Structural adjustment policies, the World
Bank (1994), states that the expansionary inputs of currency devaluation, tariff liberalization and
reduction of real interest rates may be most directly and adversely felt in the natural resources, es-
pecially in the forestry and fishery sectors. But the bank argues that these negative effects on for-
est resources are to a great extent due to the fact that government fails to implement mutually sup-
porting policies. The liberalization of agricultural markets may be expected to have implication for
both the level and stability of prices. It is generally expected that average producer prices will rise;
the presumption is that administered prices impose welfare losses that significantly outweigh any
welfare gains from stabilization. However, higher prices could encourage investments in resources
extraction but it could also enhance regulatory capacity, thus assisting the transition to more effec-
tive resource management. Government policies such as price intervention, taxes, subsidies, trade
liberalization are also likely to affect deforestation through influencing the comparative economic
returns to different economic activities on forest lands and stimulate the rent-seeking motivation
20
for activities that lead to forest decline (Benhin,2006).Many governments have deliberately adopt-
ed policies that accelerate the conversion of forest land to other uses through incentives that artifi-
cially lower the cost and increases the private profitability of the alternative land uses. While at
the further point are policies that appear at first glance to have few implications for forest use, but
which ultimately prove to be significant sources of policy –induced forest destruction.
2.2.6 THEORIES OF URBAN LAND EXPANSION
-Monocentirc Theory -The fundamental theory of urban land expansion is the monocentric urban
model. The model generates hypothesis linking the changes in urban land area to some of the fun-
damental building blocks of economies: income, population, agricultural land rent and transporta-
tion costs. The model, which is based on the works of Alonso (1964), Muth (1969) and Mills
(1972), is based on the assumption that urban spatial size is determined by an orderly market pro-
cess which correctly allocates land between competing uses. This theory provides the primary his-
torical cause of urban sprawl as community costs and land rents. It relies on the combined effect
of increasing income and lower transport as primary factors of expansion. However, while they
focus on the tradeoff between transportation costs and land rents, a more integrated approach built
upon recent advances by Denga, Huang, Uchida and Rozelle (2008) and Seto and Kaufman (2003)
extended the model to provide insight into increasingly dependent on manufacturing and rise of
service sector. The fundamental assumption of comparative static of Muth-Mills model as provid-
ed by Brueckner and Fansler (2001), is that housing is produced with capital and land under con-
stant returns. Developers maximize profit per acre of land, which equals: ( ) , where „r;
is the land rent, „i‟ is the rental price of capital, „s‟ is the structural density (capital per acre of
land). And „h‟ gives the square feet of housing per acre of land
-Containment Paradigm- The supporters of this paradigm assert that it is in the public interest to
contain unrestrained urban expansion to make cities compact. They are convinced that (1)- the
current density of urban land is low and needs to be increased, (2)- there is an excessive amount of
vacant land within the built up areas of cities that needs to be filled and (3)- The land in the urban
periphery needs to be left largely undisturbed.
-Making Room Paradigm- Offers an urban development strategy that aims to accommodate ur-
ban population grwth rate rather than constrict and constrain it. It is rounded on the conviction that
there is need to make at least minimal preparation for the sustainable growth and expansion of cit-
21
ies in urbanizing countries. Its‟ four main components include:(1)- Realistic Projections of urban
land needs, (2)-Generous metropolitan limits, (3)- Selective protection of open space, (4)-an arte-
rial grid of roads at one kilometer apart.
2.2.7 MACROECONOMIC POLICIES, DEFORESTATION AND AGRICULTURAL
LAND EXPANSION
The theory of comparative advantage, driven by differences in resource endowment is the key el-
ement in trade patterns. However, in standard trade theory, countries that have identical tastes, en-
dowments and technologies have no reason to trade. But introducing differences in the strength of
each country‟s property rights creates the basis for trade despite the countries being identical in all
other respects. This implies that property rights regime can serve as defacto basis of comparative
advantage (World Trade Organization [WTO], 2010, Chichilnisky, 1994).The principal argument
against unrestrictive trade is centered on the issues of sustainability and the distribution of the
benefits of trade. Thus, policies that produce maximum economic growth are perceived as having
little value if they also generate unsustainable demand on the earths‟ resources.(Rice, Ozing, Mri-
jinissen,Gregory, 2002). However, according to WTO (2010), overexploitation of natural resource
is affected by trade opening only when market failures such as rent-seeking or corruption are in-
volved. WTO (2010) further noted that international trade guarantees sustained growth by promot-
ing indirect and direct spillovers that offset the exhaustion of natural resources. However, unless
certain conditions, such as when property are poorly defined, trade may exacerbate the problem of
resource exploitation and eventual exhaustion thus making the resources exporting country wear
off and overturning the standard welfare effect from inter-nation trade theory.
Other macroeconomic factors with significant potential to impact upon deforestation include for-
eign exchange rate policy, external debt, governing sectors linked to deforestation. For instance,
devaluation or currency depreciation will stimulate exports, if agriculture and timber sectors are a
tropical country s‟ main non-oil trade exposed sectors, then making them more competitive
through sharp and repeated devaluation will expand production, which tends to accelerate defor-
estation (Wunder and Verbist, 2003). The appreciation of exchange rate coincides with the period
of oil boom (i.e export good boom). Thus according to Wunder and Verbist (2003), when oil and
mineral rich countries lose forests, this happens much quicker during oil bust period than during
boom periods. The core reason for this is the abundant influx of foreign exchange from mineral
export, which allows for higher government spending levels that attract people to cities. At the
22
same time, appreciated real exchange rate makes both agriculture and timber extraction less com-
petitive. In other words these countries become very expensive and highly urbanized. Conversely,
from research, opposite scenarios of foreign exchange scarcity and currency devaluation often lead
to an increased emphasis on land and forest based resources and a re-ruralizing of the economy,
which eventually also increase pressure on forest margins (Wunder and Verbist, 2003).
2.2.7.1 EXPENDITURE SWITCHING POLICIES AND AGRICULTURAL SECTOR
These are macroeconomic policies that affect the composition of a country‟s expenditure on for-
eign and domestic goods. They are categorized into trade policy and exchange rate policy (An-
yanwu, 1993).
-Trade policy- The direct effect on the prices of agricultural inputs and outputs make trade policy
a powerful instrument for bringing about desired changes in the agricultural sector (Oyejide,
1986). The effect of trade liberalization has two dimensions: (1)- The effect due to liberalization in
the domestic economy and (2)- The effect due to liberalization in the rest of the world (Chand,
1999). In domestic trade liberalization, some effects are direct and some indirect. (Kruegar et al,
1988), distinguished between direct and indirect trade policy measures affecting agricultural price
incentives. Direct trade policy measures were defined to include all measures which affected the
wedge between agricultural producer and border price directly. These measures include: domestic
agricultural taxes and subsidies, export taxes on cash crops, import tariff on food crops. Indirect
trade policy measures were defined as economy wide measures affecting the difference between
relative agricultural producer and border prices. These are broadly classified as: -Industrial protec-
tion and –Over valuation of exchange rate. Import restrictions directly affect the output prices of
traded goods. However, the direct effect of trade policy on output and input prices of traded goods
is likely to be greater than on non-traded goods (Oyejide, 1986).
-Exchange rate Policy-Over valued exchange rate have been blamed in many African countries
for the dramatic deterioration in agricultural sectors and trade balances. This serves as an impedi-
ment to producers of agricultural export crops and an implicit subsidy for imports of agricultural
and non-agricultural goods and services (Oyejide, 1986). Thus, a policy that keeps the real ex-
change rate low impedes growth of the tradable goods sector, particularly agriculture. This accord-
ing to Hopkins (1992), if agricultural goods are tradable, the effect of devaluation is straight for-
ward: domestic prices and output shift up and there is a reduction in the quantity of import. Thus,
23
if a substitution effect between tradable and non-tradable agricultural goods is more important
than the negative income effect, devaluation will have a positive impact on the price of non-
tradable (Minten and Kyle, 1995).
2.2.7.2 EXPENDITURE SWITCHING POLICIES AND AGRICULTURAL LAND EX-
PANSION
The price effect of agricultural trade liberalization has important environmental implication
through changes in the intensity and location of production as well as through their mix incentive
(Lankoski, 1997). A good starting point towards understanding the likely environmental effect of
trade liberalization is to consider how global volume and intervention distribution of agricultural
production changes with liberalization of agricultural trade reform. If the developed countries re-
duce their agricultural protection, the reduction in their food production would be offset by in-
creased output in the developing countries (Anderson, 1991).
Thus, the environmental effect of trade liberalization may be negative in developing countries due
to increased production and agricultural expansion. It has been assumed that if liberalization low-
ered the relative prices received by farmers in developed world as a result of expanded access to
their market and reduced subsidies and raised the relative prices in developing countries, the pres-
sure on environment would fall in the former but will rise with prices in the latter (Runge,1993).
The environmental effect of these production changes will depend on the adjustment in fertilizer
and pesticide use (inputs) in the short run and in the long run, on adjustment in capital and land
use in agriculture. However, it has been argued that some or all of the negative environmental ef-
fect in developing countries could be offset via the income effect of higher prices (Lankoski,
1997)
It has been expected that world market prices of most agricultural products will increase due to
trade liberalization. On the other hand, in countries where domestic prices were equal to or below
world market levels prior to trade liberalization, the price increases raise production incentives.
Thus, the production increases can occur at the intensive or extensive margin of agricultural pro-
duction and vice versa. However, agricultural input applications are highly correlated with pro-
ducer price incentive. Hence, given the increases in world market prices due to trade liberalization,
it has been expected that the use of these production inputs would decrease in developed countries
24
because of the reduction in relative producer prices and increases in developing countries (Lanko-
ski, 1997).
2.2.8 EXHAUSTIBLE RESOURCES
Exhaustible resources are those resources whose adjustment speed is so slow that they can be
made available once and only once by nature (Sweeny, 1992). Resources are exhaustible when
any use pattern makes their supply dwindle to zero; making the resources available in finite stocks
in nature (Kanafani, 2000). Crude oil or natural gas deposit are prototypical examples, but a virgin
wilderness, an endangered species, ozone layer or top soil can as well be viewed as depletable re-
sources, given that Old Growth forest have irreversible characteristics, such that once trees are cut
and the land is converted into other uses, it would be practically impossible to convert the same
into forest (Sultan and Benhin,2006). Their consumptive uses which can be allocated over time,
are gone forever or for a long time once they are used up. This means that the possibility of their
eventual renewal has no current economic significance. There are however different scenarios for
the consumptive use of the virgin forest; at one extreme of non-consumptive use, small groups can
backpack through the forest leaving little or no impact on the forest; or the forest can be clear cut
for timber or while at the other extreme of consumptive use, the forest can be converted to agricul-
tural use. Hence the greater the area that was used by these consumptive uses, the less the remain-
ing stock of virgin wilderness, and the more rapid the rate of stock decreases. The features of ex-
haustible resources include; (1)- The resource stock declines overtime whenever the resource is
being used.(2)- The stock never increases overtime; (3)-As the consumptive use increases, the
faster is the decrease in remaining stock. [i.e. the stock decrease is a monotonically increasing
function of the resource rate use];(4)-Finally, if the resource stock ever declines to zero, then no
use is possible without a positive stock.
2.2.8.1 THEORIES OF RESOURCE DEPLETION
The debate over the long run availability (sustainability) of natural resource remains polarized as it
was some years back. The egalitarian perspective adheres to the fixed stock paradigm whereas the
individualist supports the opportunity cost paradigm (Tilton, 2002).The debate centers on whether
the world as a whole can sustain the current rate of output growth in the face of declining stock of
25
exhaustible resources. The current theory of non-renewable resources derived from Hoteling work
considers user cost to be the rate of interest, thus taking the behavior of the entrepreneur to be
linked to changes in user costs over time. Though the pessimists have tried to relax the constraints
of the original model of Hoteling, each of the perspective can be criticized in terms of the risk in-
volved. The individualist scenario is considered to be a risky adventure from an egalitarian per-
spective, as it assumes that depletion occurs slowly. On the other hand, the egalitarian scenario
from the individualist perspective is based on a lack of courage resulting in more poverty and few-
er opportunities. Therisk in this scenario is whether the human population is willing to be able to
change its lifestyle (Vuuren,Strengers,Vries, 1999).
Whether the optimist or pessimists are right will likely depend on the shape of the cumulative
supply curve. The optimist cannot prove the pessimist wrong, nor can the pessimist prove the op-
timist wrong, so the debate continues. However, despite the shortcomings of the fixed stock para-
digm, its simple intuitive logic continues to attract adherents to its depletion perspective (Tilton,
2002).The two opposing schools to this argument are:
-Fixed Stock Paradigm (pessimistic school) -This is the concerned school that believes in the
exhaustible nature of natural resources. They contend that human societies are not self-adjusting
systems that will always ensure continuity and thus conscious effort should be made to readjust
and avoid paths that are not sustainable and may be catastrophic in the long run. Their fear being
that at the current rate of consumption, humankind will inevitably reduce the overall supply of
natural resources available to future generations, since the total quantity of such resource cannot
be modified. To them, because exhaustible resources require lengthy period of geographical time
to form, it implies that their supply at any particular point in time is a fixed stock that can only be
diminished with usage. And since demand on the other hand is a flow variable that continues year
after year, it will eventually exhaust a declining and non-renewable stock supply. Their concern
with the future is that, in a world of rising per capital income and growing population, resource
demand will expand rapidly, increasing the demand for exhaustible resources and accelerating
their depletion, making sustainable development harder to achieve (Tilton,1996)
Despite the fact that new discoveries and technological improvement have partly countered the
depletion process , the proponents of this view are still worried by two specific issues: (1)- Future
growth in demand could reach exceptional levels and thus seriously challenge the capacity to sat-
isfy requirements.(2)- Concern over the environmental impact of human activities. Thus they ar-
26
gue that the advent of new technology often produces such vast and rapid changes that existing
political and economic institutions are incapable of effectively coping with them. They thus main-
tain that failure to consider the social and environmental cost of such activities can lead to harmful
and excessive exploitation of resources.
-Opportunity Cost Paradigm (optimistic school)-The school believes in the inherent abilities or
self-correcting tendencies of societies to survive. It believes that even if the signals were true, that
self-adjusting mechanisms will take care of them; as long as appropriate public policies are in
place to assure these costs are internalized. Thus they do not consider the limited nature of overall
supply of natural resources to be a decisive factor. The central argument here is one of supply and
demand. They believe that before the last stock of any exhaustible resources could be extracted,
production costs would rise to such astronomical levels, that demand would cease to exist and the
remaining deposits of the resource will remain underground without having been completely ex-
hausted.
Hence, the cost increasing effects of depletion is countered by the cost-reducing effect of new
technology and new discoveries, since the rise in price accompanying any increase in resource
scarcity would set in motion an array of offsetting activities. Consequently, increasing levels of
exploitation would not automatically lead to scarcity of resource s .In addition, the opportunity
cost of extraction and transformation would have to include all external environmental factors.
Though proponents of this school built on Hoteling‟s work; they moved away from fixed stock
paradigm by relaxing Hoteling‟s assumptions ; allowing for new discoveries, technological
change, uncertainty and imperfect knowledge, oligopolistic and non-competitive market condi-
tion etc. (Tilton, 1996). Thus they believe that future generations are likely, in any case to be bet-
ter off than the current generations since the past has demonstrated that new technology can in-
crease the availability of exhaustible resource over time.
2.2.8.2 ECONOMICS OF EXHAUSTIBLE RESOURCES
Hotelling (1931), in his pioneering work on the economics of exhaustible resources, constructed a
framework in a closed-economy model to predict the behavior of prices and extraction paths in the
light of inter-temporal trade-offs or depletion user-costs (opportunity cost). The work tried to de-
termine how a resource should be extracted over time in order to maximize the welfare of current
27
and future generation and also to determine if economic completion can sustain the social opti-
mum level of exhaustion.
He noted that equilibrium between the flows of supply and demand of natural and finite resource
implies that resource owners must be indifferent as to when they extract and sell from the deposit;
if this were not the case, then the resource owner would extract either too much or too little rela-
tive to demand. He establishes the “Hoteling rule” which states that the price of an exhaustible
resource must grow at a rate of interest both along an efficient extraction path and in competitive
resource industry equilibrium; and is given by the equality:
Where Pt is the price in period t, Po, price in the initial period and r is the rate of interest. Thus the
equation describes the movement of the spot price of an exhaustible resource. If extraction cost is
zero, the present value of the price will remain constant. i.e., the price will compound at the rate r,
equal to the overall rate of interest in the economy. Inessence, the undiscounted value must be
growing at precisely the rate of interest, for the present value of price to be the same in all periods.
This implies that the equilibrium of the flows of demand and supply in resource markets require
the net price (i.e. price minus marginal extraction cost) to be the same at any point in time; if there
is to be no gain from shifting extraction among periods. Thus no other price time-path would se-
cure supply to regulate itself in order to clear the markets of finite resources. Thus, social optimum
is achieved when the price of the resource net of extraction costs grow at a rate equal to the rate of
interest which in turn, determines the efficient path of extraction of the natural resource. Hence,
when the present value of one unit extracted is equal in all periods, there is a social gain from in-
creasing or reducing the amount of the resource available in each period [Devarajan and Fisher,
1981].
What this means is that a social planner who chooses a resource extraction rate to maximize the
welfare of current and future generations, understands that due to the fixed supply of the resource,
any change in the rate of extraction in one period will trigger an opposite effect at some later peri-
od with negative consequences for the welfare of later generations (i.e., an increase in consump-
tion of the resource today may benefit the current generation, but it will reduce the consumption
possibilities of future generations(WTO, 2010).
28
But in a social framework, whereby the resource owner faces a scenario of whether to extract all
the resource today or tomorrow or split the extraction between the two periods; the decision will
depend on the price of the resource in the two periods; the higher the price tomorrow, the higher
the profit from future extractions and the lower the incentive to exploit the resource today. Hence
according to WTO (2010), in a competitive setting, price is usually equal to the marginal cost of
production, but in the above scenario, the price is higher because the resource owner takes into
account the depletion opportunity cost (in situ cost) in addition to the marginal cost of production.
Because if he did not take the depletion opportunity cost into account, current profits would come
at the expense of future profits, which is inconsistent with the profit maximizing behavior of a
competitive entrepreneur . Thus, to Hoteling, a competitive producer behaves like a social planner
taking into account the consequences of depleting resources by extracting less today.
2.2.8.3 CRITICISM OF HOTELLING RULE. -Despite the seemingly appeal of the basic Ho-
telling model, some economists believe that it is built on shaky assumptions. They noted that the
assumption of no technological change would pose a problem if the exhaustible resource is an es-
sential in production. To them the effect that the existence of an essential exhaustible resource will
have on the characteristic of an optimal plan will depend on the extent with which the reproduci-
ble inputs can be substituted for the exhaustible resources. Dasgupta and Heal (1974) and
Solow(1974) analyzed the optimal depletion of exhaustible natural resources in a growth model
context; where the resource is used as an input for production. They concluded that along the op-
timal path, the rate of consumption depends on the discount rate δ, on the elasticity of marginal
utility of consumption (n) and on the marginal productivity of reproducible capital. Dasgupta and
Heal (1974) further pointed that though elasticity of substitution is a vital parameter, that it would
be inappropriate to regard the technology of the economy as unchanging overtime. In their cri-
tiques, Maddox (1971) and Nordhaus (1973) on the other hand argue, as an essential exhaustible
resource is depleted; its market price will rise forcing entrepreneurs to search for cheaper substi-
tutes. These criticisms gave rise to two opposing school of resource depletion.
2.3 EMPIRICAL LITERATURE
Several approaches of deforestation modeling have been developed in empirical literature. Some
of these approaches involve theoretical exploration without empirical application. However, for
29
the purpose of this study, it is relevant to distinguish these studies into: analytical optimal control
deforestation models, models that combined analytical with empirical approach and those that
adopted only empirical approach.
However, among those that adopted only empirical approach;
In Mexico, Barbier and Burgess (1996), using panel analysis, estimated the relationship between
agricultural planted area and beef cattle on deforestation over the period 1970 to 1985. The analy-
sis showed that maize and fertilizer prices influenced expansion of planted area, while beef prices
and credit disbursement influenced cattle numbers.
Across sixty-five countries from different continents, Kant and Redantz (1997), conducted a cross
sectional deforestation analysis. Using a two-step regression method, they distinguished between
first-level and second-level deforestation causes for the period 1980 – 1990. Their result showed
that higher export prices, devaluation, per capita income and agricultural area increase the defor-
estation rate.
Scrieciu (2007), performed a regression analysis based on a panel dataset for fifty tropical coun-
tries from 1980- 1997 on the effect of specific underlying macroeconomic factors on tropical de-
forestation. The result shows that it may be in appropriate to provide a generalized macroeconom-
ic explanation of tropical forest depletion, but rather their relevance may be context -specific and
may not be empirically determined at the global level.
In Ghana, Yiridoe and Nanang (2001), using a two stage regression analysis, analyzed the effect
of some selected micro and macro-economic variables on deforestation. The study hypothesizes
that fuel wood consumption, forest product export, cocoa production and food crop production are
the direct causes of deforestation in Ghana. The result shows that forest products exported, fuel
wood consumption and food crop production directly affect deforestation.
For Nigeria, Oyekale and Yusuf (2008), used the error correction mechanism (ECM), to determine
the economic factors influencing deforestation for the period 1961 to 2000. The result shows that
increase in round wood production, human and livestock population, cereal and tuber crops as
well as increase in the land areas for other uses like housing and development has some significant
impact on deforestation.
30
For Nigeria, Oni.et al. (2013), examined the trend of forest stock and the effect of specific macro-
economic variables on tropical deforestation in Nigeria for the period 1970-2003 using error cor-
rection model (ECM). Their result shows that government expenditure, interest rate, exchange rate
policy, exports of sawn wood and population impact significantly on forest stock in Nigeria.
The optimal control models are grouped into two classes; those that explore the deforesta-
tion problem from the perspective of exhaustible resources (fixed stock paradigm) and
those that believe in the regenerative ability of tropical forests (optimist school). Among
the models that involve only theoretical or analytical explorations, there are those that clas-
sify tropical forest as renewable resources;
McAllister,Beard and Asafu-Ajaye (2000) developed an optimal control model of deforestation in
Laos which maximizes the discounted profit of the commercial contractors to log government
quotas and from illegal logging with the chance of being caught and fined for illegal logging as
expected cost of illegal harvesting. The model assumes that the forest stock,(X) increases with lo-
gistic growth,(f ) but decreases with level of illegal log harvesting,(h) and government quota lev-
els, (x).They concluded that a higher discount rate is associated with lower steady state level of
forest stock and greater levels of illegal logging.
For Sudan, Hassan and Hertzler (1988), developed an optimal control model of deforestation
which maximizes the discounted benefits of wood burning technology and forest stock from fuel
wood consumption (wood fuels and petroleum fuels) and agricultural yields. The model employ-
ing two state variables, assumes: ( 1)- That the stock of trees, (S) decreases with wood consump-
tion, (W) and increases, with afforestation (A) and natural growth, (g). (2)-that wood burning
technology, (K) is a composite measure of efficiency which increases with research(R) on im-
proved wood burning technology and forest stock. Thus: ( ) ( ) ( ).The model
concludes that for a socially optimal use of the forest, the price of heat from wood fuels should
exceed the marginal cost of harvesting and transport by the marginal cost to future generations;
and there can be no incentive for afforestation and research until wood resources are correctly
priced.
For Sudan, Barton, Hertzler and Hassan (1991), constructed a dynamic model of deforestation that
compares optimally managed forests with common property forests. The analytical model maxim-
izes the discounted net benefit of stock of trees from wood fuel consumption and agricultural
31
productivity. The study determined the extent, by which wood resources are underpriced and over-
exploited, as well as the user cost chargeable to obtain an optimal allocation of resources. It as-
sumes that the stock of trees (S) decreases with wood cutting and it increases with natural growth
(g) and afforestation (f), i.e. ( ) ( ) ( ) ( ). The result concludes that optimally
managed forest maintains the stock of trees whereas; the common property plan exhausts the stock
of trees; agricultural yield increases initially and then decreases with increasing deforestation.
Hassan et al. (2009) adopted a modified analytical model developed by Hassan and Hertzler
(1988) and Babu and Hassan (1995).The study adopted: (1)- a normative dynamic optimization,
that maximizes the sum of discounted social net benefit of converting two state variables; forest
stocks(S) and wood burning technology(k) from forest stock into agricultural land and, wood re-
sources into energy respectively.(2)-Market equilibrium simulation of current production and con-
sumption pattern in the agricultural market in Sudan. It assumes that the forest stocks increases
with natural growth, (G)(as a function of the stock itself) and with afforestation (F), decrease with
fuel wood consumption (which depends on the stock of technology for wood energy conversion)
Thus ( ) ( ( )) ( ( )) ( ).It equally assumes that the stock of technology for
wood energy conversion increases with research and dissemination of improved conversion tech-
nology(R). Thus: ( ) ( ).The control model is parametized and used to solve for optimal
timber extraction, afforestation and implicit timber prices, while the empirical model was solved
for stimulated static market clearing scenarios. The result showed that current rate of forest re-
source rent recovery and reforestation is not optimal, while optimal pricing, higher reforestation
efforts, promotion and availability of fuel substitute can curb deforestation rate in Sudan.
Among the models that classify tropical forest as exhaustible resources, some of the mod-
els explored the problem from the perspective of determining the optimal allocation of
land, for instance;
Ehui et al. (1990) proposed a two sector dynamic control model of socially optimal allocation of
land between forest and agriculture in the tropics by maximizing the discounted utility of forest
stock from agricultural yield and forest rental services(E).The model assumes a net deforestation
(i.e. classifying tropical forest as exhaustible resources), thus forest stock does not regenerate:
( ) ( ) . It also assumes that agricultural yields increase with tropical deforestation but
decreases with cumulative deforestation. Assuming a quadratic agricultural yield function, the im-
32
pact of certain variables on the optimal deforestation path was also examined. The result showed
that when returns to agriculture rise relative to the marginal benefits from forest, current period
deforestation increases relative to future rates.
Mcallister et al. (2000) developed a one sector optimal control model that maximizes community
profits generated from illegal timber harvesting and rental forest services in Laos. The model as-
sumes that forest stock, (X) decreases only with quantity of illegally harvested timber, (h), (i.e.
Forest stock is assumed to be an exhaustible resources); i.e. . The probability of making
profit is assumed to decrease, but at an increasing rate. The model concludes that increased de-
mand for timber has a negative impact on Laos forest; the negative impact of illegal harvesting is
exaggerated by the irreversibility of harvesting decisions,
Adu, Marbuah and Mensah (2012) proposed an optimal control model of deforestation that com-
pares the aggregate benefit of agricultural productivity of a profit maximizing farmer and that of
the society. The model focuses on the net deforestation (thus classifying tropical forest as exhaust-
ible resources) i.e. ( )
⁄ ( ).It assumes that agricultural productivity is a function of
labour, capital, purchased inputs, land and deforestation. The result of the model showed that the
path of deforestation taken by a social planner diverges from the path taken by a farmer.
Van Soest and Bulte (1996), analyzed the consequence of encroachment on logging in both prima-
ry and secondary forests. The logger maximizes the net present value of timber harvesting in both
primary (a) and secondary forests (b).The model assumes that net growth is negligible in primary
forest (i.e. primary forest is exhaustible). Thus fq (t) = -qa(t). It equally assume that the stock of
secondary forest increases with quantity of primary forest depleted and constant growth rate of
secondary forest biomass and decreases with quantity of biomass(secondary forest) harvested and
illegal agricultural conversion(.i.e.. it assumed that secondary forests can be renewed). The result
from the model shows that higher discount rates corresponds with enhanced depletion of both
primary and secondary forest and that initial logging effort in primary forests decreases with in-
creased encroachment, whereas the logging effort in secondary forests increase.
Van Soest (1998) simplified the optimal control model developed by Barbier and Rauscher (1994)
by introducing price uncertainty into the model. The model maximizes the present value of the
flow of utility of biomass harvested from the consumption of imported goods(c), and the size of
33
the forest area. The model assumes that forest stock increases with natural growth g(F) and de-
creases by mount of biomass harvested in each period; ( ) ( ( )) ( ) .The result
showed that declining terms of trade makes forest conservation cheaper compared to consumption
of imported goods.
For Cote d‟ivore, Ehui and Hertel (1989), used the two sector optimal control model developed by
Ehui et al. (1990) to estimate the impacts of social discount rate, relative agricultural and forestry
returns and expected technology on the optimal steady state forest stock using a quadratic yield
function. This is shown to decrease with increase in the social discount rate while the aggregate
yield function shows that forest stock is sensitive to changes in social discount rate. More so, the
rate of growth of deforestation is negative if the conservation motive is relatively weaker.
For Mauritius, Benhin and Sultan (2006), presents an optimal control model that maximizes the
aggregate profits of forest stock (f) from forestry and agricultural sector and the role of interest
groups on the optimal forest stock. The forest sector assumes timber production and non-timber
production while the forest stock changes over time with decrease in deforestation rate. Thus;
( ) ( ) The model equally maximizes the social utility of the forest stock from the per-
spective of the environmentalist (ƥ) and that of a group concerned with development (1-ƥ).The
result confirms that a rise in profitability of the agricultural sector leads to a lower optimum stock
of forest and if the party that places a relatively high weight on environmental services dominates
the decision making process, the optimum forest area will increase.
Across the tropics, Barbier and Burgess (1997), developed an optimal land use allocation rule be-
tween timber and agriculture at stand and forest level. Then at forest stand, the model for optimal
allocation of forest land to agriculture which assumes that forestland Ft decreases with increase in
agricultural land, Xt i.e. is used to derive a demand function that is estimated
across 53 tropical countries. The result shows that opportunity of forgone production benefit from
forestry is positively correlated with increasing forest conversions. Also increased population den-
sity increases forest clearance, whereas, rising income per capita and agricultural yield reduces the
demand for converted land.
On the other hand, in the school that combines the effect of micro and macroeconomic
forces and assumes that those forces determine the fate of the forest we have;
34
For Ghana, Benhin and Barbier (2001), developed a dynamic optimal control model of three sec-
tor forest resource exploitation. Using a recursive model of a three sector (maize, cocoa and tim-
ber extraction),developed from an optimal control approach, a piecewise linear regression was
used to distinguish the influence of post and pre structural adjustment program of changing com-
modity and input prices on forest land use for the period,1965 – 1995. The empirical result indi-
cated that cocoa land expansion and timber extraction are significant factors in deforestation;
though their impact on forest loss are reduced in the post adjustment period.
For Cameroon, Benhin and Barbier (1998), developed a dynamic control model to derive the op-
timal use of forest land for cash crop, food crop production and timber extraction. A piecewise
linear and switching regression approach was used to investigate the influence of pre and post oil
boom(1978-1988) and the post adjustment period (1989-1995) on cocoa, coffee, plantain, maize
lands and timber extraction. They conclude that along the optimal path that the stock of forest land
should be allocated up to the point where the marginal returns are equal across all uses; while the
value of marginal products of inputs used in production should be equal to their respective input
prices. However, the sum of the private cost of production and the social cost of timber related
forest loss should be equal to the value of marginal product of input. The empirical result indicates
that in the pre –oil boom and post adjustment period, the pressure on the forest was high; which
significantly reduced in the post oil-boom.
2.4 LIMITATIONS OF THE PREVIOUS WORK.
The empirical studies reviewed in the previous section indicated that overall, the dominance of
agricultural expansion is well perceived in modeling tropical deforestation, considering the theo-
retical underpinning of the relationship between forestry and agriculture. Thus, the entire optimal
control model reviewed emphasized only forestry and agricultural sector.
However, given that the proximate causes of tropical deforestation obtained from meta-analysis of
152 sub national cases of tropical deforestation by Geist and Lambin (2001) revealed that agricul-
tural expansion, wood extraction and infrastructural extension are the proximate causes of defor-
estation. Equally, considering that Adeoye et al. (2012), reported that primary forest were being
converted to agricultural use and housing estate in Nigeria, the emphasis given to agriculture and
forestry in tropical deforestation models as noted by Geist and Lambin (2001) certainly has to be
35
revised in favor of including the gradual expansion of infrastructural land as a proximate defor-
estation cause.
Whereas reasonable case studies of optimal control models of deforestation have been done in
West Africa; only one has been recorded for Nigeria: a work done by Oyekale (2006) despite the
high deforestation rate in the country. In addition to that, globally, except for the works of Benhin
and Barbier (1999 and 2001), much debate on the models that employ optimal control has focused
only on micro economic effect of forest decline. Most of the studies did not consider how the un-
derlying macroeconomic policies have affected or shaped the land use decisions at the proximate
level in Nigeria.
In addition, most of the empirical models focused on underlying variables, while those that con-
sidered proximate variables mixed it up with underlying variables. This implies that most of the
studies mixed up different levels of deforestation causes. For instance, in Ghana, Barbier and
Benhin (2001) and in Nigeria, Oyekale and Yusuf (2008)included population variable among
proximate level explanatory variables. While Barbier and Burgess(1997) mixed up population and
income in their proximate model. This misspecification, as noted by Kaimowitz and Angelsen
(1998), flaws the cause-effect relationship in the regression model.
In this study, the model includes, as a deviation from other optimal deforestation models, structur-
al density or improvement per acre of land, which is an index of height of buildings as an addi-
tional decision variable (control variable). It assumes that increasing investment in building per
acre of land will reduce urban land expansion. Thus, it argues that the relatively low density in
urban areas and agricultural land expansion result from under pricing of fringe infrastructure (i.e.
forest land) and distortionary public policies. Consequently, this study deviates from existing op-
timal deforestation models by forming a three sector forest land allocation model( instead of the
usual two sector models) that included infrastructural land expansion (urban land) as a proximate
deforestation cause and subsequently determines the empirical interaction of important economic
determinants of deforestation and the influence of change in trade policy and exchange rate policy
on deforestation in Nigeria. Equally, the model shall avoid the usual model misspecification in
deforestation models by excluding underlying explanatory macro variables like population, in-
come per capita, foreign debt etc. from the underlying agents‟ parameters at the second level, but
consider only the underlying effect of changes in trade and exchange rate policy at a different cau-
sation level. More so, there is a gap in the empirical literature concerning the quantitative relation-
36
ship between agricultural land demand and trade policy, especially through the price mechanism.
This study intends to address this gap.
37
CHAPTER THREE
METHODOLOGY
3.1 THE THEORETICAL FRAMEWORK
The deforestation framework established by Geist and Lambin (1998) shows that tropical defor-
estation is best explained by multiple factors and drivers acting synergistically (irreducible Com-
plexity) rather than by single- factor causation. Thus rather than providing support for the domi-
nant theories of global deforestation (i.e. neoclassical, impoverishment, macroeconomic), Geist
and Lambin‟s irreducible Complexity framework revealed that at proximate level, agricultural
land, wood extraction and infrastructure extension prevail in causing deforestation. These proxi-
mate causes are endogenous variables (first level deforestation causes), which are driven by eco-
nomic, demographic, policy, institutional and socio- political factors. Thus, the proximate varia-
bles in the optimal deforestation model (the theoretical model) are anchored on this framework,
while adopting the fixed stock model (i.e. that tropical forests are exhaustible resources). The
fixed stock paradigm is based on the neoclassical economic theory of optimal resource use. The
basic logic underlying this theory is maximization of the present value of stream of economic re-
turns.
Although, the theoretical application of optimal control theory in natural resource economics can
be traced back to Hotelling (1931), however, modeling of deforestation as an exhaustible resource
draws extensively from the works of Ehui et al. (1990).The theoretical model provides insights on
how a Social planner can choose a wide range of production options or decisions that influence
both present and future deforestation rate. The concept of a social planner is introduced as a rhe-
torical device that shows how decision should be made if all economic and social cost and benefit
are considered by a single decision maker. Thus, the greatest strength of an optimal control model
is its ability to provide useful insights on the inter-temporal nature of natural resources based on
the decisions taken by the planner. The dynamic nature of the model implies that any decision
made at any point in time, has future consequences on deforestation rate. Thus, it explains the
mechanism by which economic determinant of land use decision affect deforestation path. Conse-
quently, the first objective of the study is anchored on this framework, but modified to address Ni-
gerians‟ deforestation scenario.
38
In addition, a framework defined by Kaimowitz and Angelsen (1998), specifies the exogenous de-
forestation determinants (i.e .deforestation agents' decision parameters) that influences the demand
for agricultural land and wood extraction. These decision parameters are external to individual
agent, and thus include those factors that immediately affect the decisions of agents to deforest
such as output and input prices, productivity in that sector, available technology, physical envi-
ronmental factors, etc...Whereas, the framework for urban land expansion will be from a modified
Monocentric urban land model approach specified by Setoand Kaufman (2003); Denga, (2008).
According to the monocentric model, the demand for urban land and its expansion arises from the
trade-off between commuting cost (transport cost) and land rents. However, a more integrated ap-
proach built upon recent advances by Denga et al. (2008) and Seto and Kaufman (2003),which
extended the framework to provide insights on the increasing dependence on industrialization and
the rise of service sector on demand for urban land shall be adopted in this study. Then, Dorns-
busch „s (1974) two sector model for small open economy shall be modified to assess how farm-
ers‟ production incentives affect agricultural land expansion through the relative price mchanism.
3.2 THE THEORETICAL MODEL
3.2.1 BASIC OPTIMAL CONTROL MODEL
In a basic optimal control model, the objective of a decision maker is to find the optimal trade-off
between current and future use of a resource. This is done by determining the controls that maxim-
ize the value of the objective function subject to the equation of motion, given the initial value of
the state variables. A general mathematical expression of the optimal control model of a non-
renewable resource as proposed by Hotelling can be represented as:
( ) ∫ , ( ( ) ( )-
Subject to the constraints that:
( ) ( ).... (i), ( ) .......(ii), ( ) ...... (iii), ( ) ......(iv),
( ) .....(v)
39
whereby, S is the stock of natural resource and q is the rate of use of the natural resource. Equa-
tion (i) is the equation of motion for the natural resource, S. Equation (ii) gives the initial endow-
ments of the resource, while (iii), (iv) and (v) are the constraints imposed on the state variable, S.
3.2.2 MODEL SPECIFICATION– [Analytical Model for objective 1]
FAO (2005) estimated that Nigeria has been losing on average 11% of its primary forests annual-
ly. Consequently, the model presented below focuses on deforestation in primary forests, so as to
model Nigeria‟s situation. In effect, the optimal model adopted the fixed stock paradigm (i.e. pri-
mary forests are assumed to be exhaustible). Hence, this analytical model addresses the first objec-
tive of the study.
The Social Planner’s Problem
The above basic model can be modified to accommodate a social planner faced with a decision of
whether to keep land in a sustainable forested state or to convert the forest land to agricultural land
or infrastructural (urban) land use. The model adopted the McConnell (1989) basic approach for
the optimal land use and a modified Barbier and Burgess (1997) model, which shall be extended
or modified to address the first objective of the study.
Following McConnell (1989), the problem of optimal forest land use can be viewed as one of
maximizing the social returns to land in its different uses. Thus defining the social benefits as a
separable function of forestland in each use, where by the total benefits are the sum of the individ-
ual land use benefits:
B = ………(1)
Here, Bf is the total (discounted) net benefits of forest management. Bais the (discounted) net ben-
efit of agriculture and Bv is the (discounted) net benefit of urban use.
Forest Sector: Following Barbier and Burgess (1997), the net benefit from sustainable forest
management includes the timber benefit (w) and environmental benefits of the forest (e), (i.e. the
amenity value of the forest).
Thus the total (discounted) net social benefits of forest management are:
40
…… (2)
However, forest land resource contributes to society‟s welfare in two ways: (1) it is utilized as an
input factor for production (2)- it provides valuable services in preserved states (the amenity bene-
fit). Thus, the amenity benefit, E(t), of the forest depends on the stock of forest land, F(t), and on
the extent of deforestation caused by over logging, Dw,:
( ) * ( )+ ……….(3)
The forest produces timber for commercial use and fuel wood for household consumption. Fol-
lowing Benhin and Barbier (2001), the production for the forest sector assumes timber production,
W(t) to depend on the stock of forest land, F(t) and other inputs used in Timber extraction, Zw(t)
(e.g. Investment in efficient wood processing and recycling of pulp). However this model will ex-
tend Benhin and Barbier (2001) approach by assuming further that timber production equally de-
pends (inversely)on the quantity of forest land degraded beyond recovery , Dw, by over logging :
( ) ( ( ) ( ) ( ) ……….(4)
Since land is a productive input in this model, it is assumed that increasing stock of land, increases
forest net returns from timber production and environmental benefit:
i.e.
, showing a positive marginal benefit from the forest sector and
, showing an
increasing marginal benefit from forest stock as the forest land declines.
Agricultural Sector: Modifying the approach of Ehui and Hertel (1989), the agricultural produc-
tivity function in this model, A(t) is assumed to depend on the amount of land devoted to agricul-
tural production,(La) and vector of other purchased inputs, (Za),such as fertilizer, improved seed-
ling and machinery used in agricultural production:
( ) , ( ) ( ) ………(5)
Increases in agricultural land are equally assumed to increase the net return from agriculture at a
diminishing rate:
.
41
Infrastructural Sector: The urban land productivity builds on the Muth-Mills model of urban
sprawl as specified by Brueckner and Fansler (2001), whereby housing is produced with capital
and land. The producers maximize profit per acre of land, which equal to ( ) . Where
r is the land rent, i is the rental price of capital, S is the structural density (capital per acre of land
or improvements per acre of land, which is an index of height of buildings) and p is the housing
rent at price per square foot. Consequently, the urban land productivity in this model depends on
the amount of land devoted to urban sprawl (Lv), which is the square feet of housing, housing rent
(R), and the vector of capital input per stock of land (Zv) i.e. cost of capital, (i) and other inputs
used in the structural density. In this model, cost of land, both forest land and converted land is
assumed to be zero. Thus, r in the Muth-Mills model is equal to zero:
( ) , ( ) ( ) ( ) ………(6)
Likewise, concave benefit function is assumed, so that returns to increasing urban land use are
positive but diminishing:
,
Given that the total amount of land, in the economy at any given time, t is:
( ) ( ) ( ) ……..(7)
This implies that land is fixed but can be allocated.
Thus given the above framework, the objective of the optimal control model is to maximize social
welfare. Adopting a utilitarian approach, the social welfare function maximizes the sum of dis-
counted flow of net benefit:
∫ * ,
( ) ( ) * ( * +) ( ) , ( ) * * ( )+ ( ) + ( ) ( )-
( ) , ( ) * ( )+ ( ) ( )- ( ) , ( ) * ( )+ ( ) ( )-+ ( )
42
Subject to the constraints that:
( ) ( ) ( ) * ( )+ ….…….(9)
( ) ……….(10)
( ) ..……..(11)
( ) ( ) ( ) ….…..(12)
( ) ( ) ( ) ..…….(13)
( ) ( ) ( ) ( ) ( ) ………..(14)
Where, W measures the present value of the society‟s welfare, which maximizes the following
control variables: denotes the discount factor. Revenues in
each period are discounted at the rate r, which is assumed to be constant in order to simplify the
analysis. Equation (8) indicates that revenues are derived from forestry, agricultural and urban
sectors. The net benefit is the sum of the net returns from the three sectors. Periodic net returns
from urban sector, forest sector and agricultural production are equal to the total revenue in that
period minus the costs of inputs incurred in the period.
Thus, the net returns from urban land use increases with increase in the quantity of land allocated
to the urban sector and the demand for houses. More so, the model assumes that the demand for
shelter in urban areas is met either by constructing high rise building (high density buildings) or
by developing new township areas through deforestation. While, demand for food production in
agriculture is met either by agricultural intensification (using fertilizers, improved seedlings) or by
agricultural land expansion through deforestation .However, the cost of forest land (both convert-
ed and unconverted form) is not part of the profit function because there is no market price for
forestland (Benhin and Barbier, 2001) and deforestation cost is assumed to be zero (Ehui and Her-
tel, 1989).F(t) gives the time path of the forest stock, which has an initial value of F(0) = F0 in
equation (12).
The state variables in the model are the stock of forest land (F), lands devoted to agricultural pro-
duction (La) and urban land Use (Lv). The variables; Cw, and Ca are the respective per unit cost of
purchased inputs used in timber and agricultural production and i is the rental price of capital or
43
other inputs used in house production. Pe is per unit returns to environmental value of the forest
resources, while the per unit output price of urban sector, agriculture and timber at time t are de-
noted respectively by the variables R, Pw and Pa.
Equation (9) defines the equation of motion that controls the forest stock. (t)gives the change in
forest land over time, i.e. the deforestation rate. [The dot over the variable shows a change over
time]. The negative signs indicate that deforestation reduces the level of the stock, thus, the right-
hand sides of the equations of motion shows the amount of forest stock used. In the model, defor-
estation is defined as: (1) the conversion of forestland to other land uses (FAO,2001;Van Kooten
and Bulte,2000), with the assumption that forest vegetation is not expected to re-grow naturally in
that area(Kissinger, Herold, De Sy,2012). This implies that once a forestland is converted to urban
or agricultural land, the lands receive urban or agricultural rents forever (Capozza and Hes-
ley,1990) and thus decision to re-convert is irreversible.(2)- Deforestation also occurs as a result
of over logging or clear felling (Meyers, 1991), consequently, it is further assumed that during
timber harvesting, when over logging or clear felling is applied that deforestation occurs, resulting
in a loss of fixed amount of forest land,(Dw), that is damaged beyond recovery.
From (9), forest land depletes by over logging and conversion to urban land, Dv, and to agricultur-
al land, Da. Equation (10) & (11) are the equations of motion for agricultural and urban use re-
spectively. This implies that increases in total land area devoted to agricultural and urban use at a
specified time (t) is assumed to be due to conversion of forestland to agricultural and urban lands.
At time zero, equation (12) gives the initial endowments of the state variables, while constraints
(13) and (14) specify the non- negativity constraints imposed on the state and control variables
respectively to make them economically meaningful. Thus, the problem in equation (14) is solved
by using Maximum principle of Pontryagin et al (1950).
Hence, given that benefits from forestland use, Bf, agricultural land benefits, Ba, and urban land
benefits, BVare concave functions of land, the necessary and sufficient conditions for an interior
solution for the problem specified by equations (14) – (21) is obtained by maximizing the current
value Hamiltonian, :
( ( ) , * * + + - , * + -
, * + -) , * + - …..(15)
44
Where ( ) ( * +)
The current value Hamiltonian measures the dynamic benefit expressed in terms of current value,
obtained by normalizing the Hamiltonian. It gives the total increase in the value of the stock of
forest land. The co-state variables, , are the current value multipliers that give the re-
spective shadow values of sustainable forest preservation for wood production and converted for-
estland to agricultural and urban lands at time (t). These variables give the dynamic cost to future
generations of maintaining and converting from forest land to alternative use
The function,( ), gives the flow of net returns at time, t. The term, , * + -
, which is the total dynamic cost of forest land, gives the increase in the value of the
stock of forest land at time t, while and are the respective increase in the value of agri-
cultural and urban land.
However, the derivation and the solution of the socially optimal allocation of land among
agriculture, urban and forest use is shown in the Appendix, while the interpretation of the
optimal model solution shall be shown in chapter 4.
Consequently, from equation (43) in appendix, we have:
……(43)
(Where Gt is equal to Bf, which is the opportunity cost of a unit of converted forest land in any
given period)
Thus, equation (43) is an inverse demand function for converted forest land, since it represents the
equilibrium shadow price of forest land conversion as:
[
] ...........(44)
Thus, from the inverse demand function in (44), the direct demand function for converting forest
land to either agriculture or urban use is given by:
, - ………(45)
, - ..........(46)
45
From (45) and (46), the demand for converting forest land to urban land or agricultural land is
negatively related to its opportunity cost, Gt and also other additional agent‟s decision parameters,
Y such as input and output prices of timber ( Cw, Pw), input and output price of agricultural prod-
ucts (Pa, Ca), Urban output/rent (i, R).. et cetra, which collectively determine the net benefits from
the three sectors as specified in equation (15). Consequently, the demand for forest land for com-
peting activities, j in compatible form is:
, - ……(47)
3.3 THE EMPIRICAL MODEL
MODEL 1- [Measure for objective 1]
The demand expression in equations 45 and 46 for the forest land conversion to agricultural and
urban land derived from the optimal control model serve as a theoretical basis to analyze objective
1 of the study. Whereby the demand for forest land for competing activities is given by:
, - ……(46)
Where Gt, which is the opportunity cost of choosing a particular land option, is the foregone the
net benefits of competing land uses. For instance, when the shadow price of forest, (λ) falls, there
will be an increase in demand for competing land uses, as implied by equation (44), which shows
the inverse relationship between the marginal opportunity cost of forest and the reconversion rate
of alternative land use (i.e .agriculture or urban use). However there is no available data on forest ,
agriculture and urban land values in Nigeria to proxy foregone benefit of alternative land uses.
Thus, from the optimal control model, the economic impact of proximate deforestation causes will
depend on – (1) The shadow price of forest land and –(2) The relative return of the forest sector
and its competing land sectors. This implies that, if urban expansion and agricultural land expan-
sion are competing activities on the forest frontier, then an increase in the return from urban sec-
tor, will negatively affect agricultural land expansion and the forest land, and vice versa.
46
Thus tropical deforestation is the result of three major distinct demands:
, - ……(47)
, - . .......(48)
, - ……(49)
Where, are the demand for agricultural land, demand for infrastructural land and de-
mand for wood extraction. Yi is a vector, comprising of the price of output produced by a particu-
lar land activity, the price of input used by that activity, and other exogenous factors that may in-
fluence the demand for that land use. Thus Ki is a vector comprising of different rents of different
land activities in the model. This can be proxied by productivity in these sectors, given that Deng
et al. (2008) noted that if a land market exist, the higher return or productivity should be captured
onto land rental or land value. In the model, it is also assumed that an increase in comparative re-
turn to infrastructural sector on forest sector, for instance, could lead to infrastructural expansion
at the expense of forest and equally substitution of agricultural expansion for infrastructural ex-
pansion. Therefore the model shall control for the comparative returns or productivities of differ-
ent land activities on each of the demand for specific land use.
Whereas most of the deforestation models in empirical literature mix proximate and underlying
causes as explanatory variables, in this study, the analysis will be limited to agents‟ decision vari-
able (local level causes). This will prevent the problem of misspecification that results from dis-
torted causal relationship when variables at different levels are mixed up in regression analysis as
noted by Angelsen and Kaimowitz (1999).In addition, three major turning points marked Nigerian
trade policy context in the last few decades. Thus, in other to examine how changes in trade poli-
cy as a third level deforestation cause have affected proximate deforestation causes in Nigeria
through the agents‟ decision parameters, a differential intercepts are introduced. Thus the changes
in trade policy in Nigeria as broadly divided into three era in this study: (1)- The pre reform era (
1972- 1986, i.e. years of trade restriction), (2)- During reform (1987-1994) and (3)- The post re-
form era (1995 till date.
47
The econometric specification of the model is as follows:
( ) ( ) ( ) , ( )-
, ( )- ( ) ( ) ) …..(50)
( ) ( ) , ( )- , ( )-
( ) , ( )- , ( )- ( )
( ( ) ( ) ) ….(51)
( ) ( ) , ( )- , ( )-
( ) ( ) …..(52)
, for observation in 1987-1994, and 0, otherwise
, for observation in 1995-2012, and 0, otherwise
Whereby the benchmark category = Years of trade restriction
Differential effects of trade policy in the reform period, 1987-1994
Differential effects of trade policy in the reform period, 1995-2012
Wf = Demand for wood extraction, LA = Demand for Agricultural Land
AGP = Agricultural Return IND = Industrial sector productivity
FOR = Forest rent PW = Timber prices
MEC = Agricultural mechanization AVA = Agricultural value added
EXP = Export good price TC = Transportation cost KER = Kerosene Price
LV = Demand for infrastructural land CAC = cash crop price SAC = Staple crop price
48
, are the error terms in respective equations presumed to be uncorrelated with
the respective explanatory variables. All the variables are expressed in natural logs, except the
dummy variables, so as to normalize the dependent variables, whereas the explanatory variables
are transformed in other to linearize the relationship between them and the dependent variables
and equally correct for heteroscedasticity. The choice of lagging the agricultural staple crop price
(SAC) is informed by the fact that, in order to maximize their expected profits, deforestation
agents (farmers) use prices observed during the previous year to decide the quantity of land to be
cultivated that period. Thus, a one-period lag may allow the time involved in supply responses to
change in price. But that of cash crops is exogenously determined, and thus current values shall be
used.
In the urban land model, other important variables like: per capita income, population and foreign
direct investment are excluded in the second level variables, because they are underlying macro
variables influencing the other variables. This will prevent model misspecification. Also, the theo-
retical model adopted suggested the inclusion of credit facilities to farmers as one of the agents‟
decision variable. But, research in Nigeria, (Akande, 2003), has shown that though the annual
loans to agriculture have actually increased, but to a large extent, credit allocation failed to comply
with policy stipulations. Thus, it is believed that in Nigeria, such credit facilities are not put to in-
tended use, and therefore is excluded in this model.
3.3.1 -VARIABLE DESCRIPTION
Demand for agricultural land (La), to be proxied by annual change in agricultural land.(2)-
Demand for infrastructural (Urban) land, (Lv). However in Nigeria, there is no data on annual
urban land change. Thus, it shall be proxied by industrial production index. The rationale for this
is that infrastructural land expansion is manifested in sizeable industrial index. This seems to be a
more reliable proxy (3)- Quantity of wood demanded (WD), shall be proxied by Sawn wood
production, as done by Oyekale and Yusuf (2008).
-Agricultural out prices: Considering that different crops have their own land intensity require-
ment and also based on how sensitive market prices are to changes in supply (demand elasticity),
the model shall consider how changes in prices of cash and food crops can alter the balance be-
tween land uses. Consequently, agricultural output price for subsistence crops- (1)-Staple crops
price (SAC), shall be distinguished from (2) -Cash crops price (CAC) sold in international mar-
49
kets. The rationale for this is that changes in prices of the two main types of agricultural outputs
may affect forest depletion differently than changes in the general price of agricultural crops, be-
cause of the differences in land intensity and technologies involved. These prices shall be proxied
by average real producer prices of the crops categories in Nigeria in naira/tonne.
-Timber Prices (PW): The output price of forest sector product activities will be proxied by real
average price of exported sawn wood in Naira per kilogram. This approach is adopted from
Benhin and Barbier (2001).
-Technological progress: The effect of technological change on deforestation depends on its‟ ef-
fect on the choice variable (land) and output market. Thus the model shall distinguish between
land extensive technological progress and land intensive technological progress :-(1) -
Agricultural Mechanization (MEC). This is land extensive technology, involving agricultural
mechanization. It shall be proxied by annual agricultural machinery per 100 square kilometer of
arable land.(2)-Agricultural Value Added (AVA): This measures the land intensive technologi-
cal progress (i.e.the investment allocated to agriculture inform of higher yield, e.g. fertilizer, high
yielding seedling). The rationale for including this variable is that investment in agriculture, could
lead to higher productivity per hectare, thus decreasing the chances of expanding agricultural land.
Its‟ increase indicates intensive agriculture and thus it is the agricultural output per unit of the
scarce factor (i.e. land). This shall be proxied by total value added in agriculture per hectare of
cultivated land (i.e. output per scarce factor, land).
-Transportation cost (TC): This indicates the impact of transportation on the cultivated land
change. It shall be proxied by pump price of fuel in Naira per liter. This approach is adopted from
Sheppard, (2011).When transportation cost is low; the urban core is expected to expand relatively
more.
-Export good price (EXP) – This captures the effect of an increase in the world price of an export
good on the industrial land demand. The rationale for including this variable is that companies and
industries who trade and produce in the cities by combining input of capital and land to produce
an export/local goods for external and local market, provide a separate commercial demand for
urban land. Thus, this determines the impact of changes in the productivity of land in export good
production and the impact of an increase in the world demand for export good on land use. This
50
shall be proxied by export price of crude oil/ barrel, which is the main export good produced in
Nigeria.
CONTROL VARIABLES: To improve the predictive power of the model and the precision of
the estimates, the study intends to control for other relevant parameters that may have determinis-
tic effect on the demand for forest land conversion. The productivities in the competing sectors,
determines the effect of economic construct on the land use changes:
-Industrial sector return (IND): This shall be calculated as the ratio of real industrial output
(GDP in industrial sector) to the total GDP (Deng et al., 2006). This is included, to account for
urban land rent, based on the rationale that expansion of industry and services sector will have an
effect on the size of urban area. For instance, when the government acquires forest land for urban
use, the acquisition price is based on land productivity of alternative use. The industrial sector that
shall be used include: manufacturing, building and construction, crude petroleum/natural gas, min-
ing. This shall be calculated as the total value of GDP created.
-Agricultural productivity (AGP): In urban monocentric model, agricultural rent is an important
variable determining urban land demand, but, since there is no data on land rent in Nigeria, how-
ever, as noted by Deng et al. (2008), if a land market exists, the higher productivity should be cap-
italized into land rental. Thus, agricultural rent in monocentric model is equivalent to agricultural
productivity in Kaimowitz and Angelsen frame work. Thus, this shall be proxied by real agricul-
tural output (GDP in agricultural sector).
--Kerosene price (KER):Panayotou and Sungsuwan (1994) reported a negative relationship be-
tween kerosene prices and forest cover, while Hassan et al. (2009) suggested the inclusion of fuel
wood substitute in deforestation models. The rationale for including this variable is that in Nigeria,
kerosene is the main alternative for energy beside fuel wood. This is because the empirical litera-
ture suggests that aggregate consumption of fuel wood depends on the price of its‟ substitute
among other things (Yiridoe and Nanang, 2001; Panayotou, 1994). More so, there is no reliable
data on fuel wood consumption in Nigeria. This shall be proxied by pump price of dual purpose
kerosene in naira per liter.
-Forest Rent – this captures the value or productivity in forest sector.
51
MODEL 2
The rationale for estimating Agricultural expansion is that empirical evidence shows that it is the
main cause of deforestation in tropical countries. Therefore, changes in agricultural production
and land demand can be estimated from changes in prices due to the effect of trade and exchange
rate policies. Thus, from the conceptual framework, the relationship linking expenditure switching
policies to agricultural land expansion shall be modeled by a structural equation framework using
a two stage recursive model proposed by Kant and Redantz (1997). But, whereas their framework
used cross sectional data in their analysis, here the model will use time series data. The study first,
establishes the link between first order explanation, direct effect of trade policy on domestic rela-
tive prices and then, the indirect effect of real exchange rate on agricultural land area. The relative
prices among these crop categories indicate the relative incentives to farmers. The basic channel
through which trade policy ( in the form of tariff changes) impact on factor market is through its
effect on relative prices. Hence, a starting point for the complex interaction between changes in
agricultural land demand and expenditure switching policies (trade and exchange rate policies), is
to examine the extent trade policy has influenced agricultural land expansion through changes in
agricultural relative prices. This shall be extended to how changes in relative prices alter the pro-
duction decisions of farmers through induced effect of real exchange rate, thus deciding on how
much land to clear.
First –Stage Estimation Model (Direct Trade policy effect – Measure for objective 2)
The analytical framework for assessing the effect of trade policy on relative prices can be done
using modified Dornbusch‟s(1974) framework for a small open economy producing three goods:
Tradable crops (Exportable and Importable) which are traded at exogenously given world prices
and non-tradable crop with a price flexibility moving to equalize domestic supply and demand.
Since the economy is a price taker, the domestic price of traded goods ( ), the nominal ex-
change rate, R, and agricultural export tax te , and import tariff, ti :
( ) …….(1)
( ) …..(2)
52
Dividing (1) by (2) gives:
⁄
⁄ ( )
( )⁄ ....(3)
Equation (3) implies that the domestic price of importable relative to the exportable is a function
of foreign prices and trade policy variables such as tariffs, subsidies. Whereby, the
term ( )
( )⁄ gives the overall trade bias, T. However, the trade policy exerts its‟ influ-
ence on the entire structure of relative prices essentially through the real exchange rate mecha-
nism. Thus, the impact that trade policy would have on domestic prices of tradable can be exam-
ined based on their relative price vis-a –vis non tradable crops assumed to be substitutable to trad-
able crops in domestic agricultural production. This will determine the long run resource alloca-
tion, and would allow the crop specific price effects of domestic policy to be compared with each
crop category:
⁄
⁄ ( ) ..……..(4)
⁄
⁄ ( ) ………(5)
From (4) and (5), the real exchange rate (
⁄ ) is an important factor in both export oriented
and import -competing farm and non farm production. This implies that trade policy directly af-
fects the domestic price of exportable relative to importable, which in turn affect the domestic
price of exportable relative to non-tradable crops. Thus the domestic price ratios among the three
categories of crops, in turn determines the production and investment decisions of a farmer (a de-
forestation agent). One of these decisions is on how much land to clear, considering that each crop
category has its own land demand and can alter the balance between land uses. Thus, the excess
supply of exportable, demand for importable for non-traded crops depend on the relative prices:
[
⁄
⁄ ] ………..(6)
53
Where Qjis the demand or supply of non-tradable good demand,Pn is the domestic price of non-
tradable good, and Z represents other exogenous factors influencing demand and supply of non-
tradable goods, such as income ,productive capacity of the economy etc.
Differentiating (6), with Z held constant gives:
[
] [ ] ………..(7)
Where and are respectivedemand elasticities. From the comparative analysis of demand and
supply, when the equilibrium is displaced, the necessary relationship between tradable and non-
tradable is given by:
[ ] ……..(8)
Integrating and taking the logarithm of equation(8) gives:
⁄ *
⁄ + ……..(9)
Thus from equation 9, any change in Pe and Pidue to changes in trade policy, give the induced
change in the domestic price of exportable relative to non-tradable. Equation (9) captures the ex-
tent of substitutability in production decisions between non tradable and tradable crops. Distin-
guishing between different categories of export crops , equation (9) can be modified from equation
(3) to express Pn as a weighted average of the proportionate changes in the foreign prices of dif-
ferent tradable crop categories, such that the proportionate change in the domestic price of an ex-
port crop j can be expressed as:
[
⁄ ] *
⁄ + ∑ *
⁄ + ……..(10)
Where and
are the respective foreign price of export crops j and i. „T‟ from equation (3)
gives the overall trade bias, which can be proxied by trade restrictive/ liberalization variables. „Z‟
controls for other exogenous factors influencing domestic relative price such as government ex-
penditure, etc. The tradable crop prices expressed in foreign currency controls for the effect of the
real exchange rate. In addition, the exportable crop category is further subdivided into (1)- export-
54
able (Pe) and (2)- mixed tradable crops (Pe). The direct effect of trade policy, which occur in form
of taxes , subsidies on trade and quantitative restriction are captured by nominal protection coeffi-
cient (NPC), trade openness (TOP) and the external terms of trade (
⁄ ) which controls for the
liberalization in the rest of the world. Thus the estimable equation for the impact of trade policy
on relative price of the three classes of tradable relative to non-tradable crop is given as:
(
⁄ )
( ) ( ) (
⁄ )
(
⁄ )
( ) ( ) ( ) ( )
,( ( )- , ( )- …(11)
(
⁄ )
( ) ( ) (
⁄ )
(
⁄ )
( ) ( ) ( ) ( )
,( ( )- , ( )- ……(12)
Each crop is selected based on different consideration: Price elasticity and land intensiveness.
Maize is the most important non-tradable crop in Nigeria. It is grown for domestic consumption.
The chosen export crop is cocoa, the major export crop in Nigeria, with domestic production high-
er than domestic consumption. It is a perennial crop with price inelasticity. Rice is chosen as an
import competing crop, because the domestic consumption is higher than domestic production.
The mixed tradable crop is cassava, considering that it was a limited tradable crop from 1974 -
1989, while it becomes tradable from 1990 till date (EUROSTAT, 2000). It equally has high do-
mestic consumption as well as high domestic production. It is an Annual crop and price elastic,
because Nigeria is the world‟s highest producer of this crop.
55
In addition, Valdes (1986) argues that long term investment in agriculture is a function of the rela-
tive price of agriculture to non- agriculture (i.e. the domestic terms of trade). Therefore, the price
competitiveness of agriculture at most aggregate level shall be estimated too:
(
⁄ ) ( ) ( )
( ) , ( )- , ( )-
(
⁄ ) …….(13).
Equation (13) controls for the sectoral and economy-wide price intervention on agriculture.
Where
⁄ = the relative domestic price of an export crop and a non-tradable crop (a proxy for
exchange rate)
⁄ = the relative domestic price of a mix-tradable crop and a non-tradable crop (a proxy for
exchange rate)
⁄ = domestic relative price of agricultural crop and non-tradable agricultural good
⁄ = the external terms of trade
⁄ = relative world price of the exportable to
mix-tradable crop
=World price of the tradable crop(in US. Dollar)
= World price of an import competing
crop (in US. Dollar)
= World price of the mix-tradable crop (in US. Dollar) TOP = Tradeintensity ratio
NPCe = Nominal Protection coefficient of the export crop NEX = Nominal exchange rate
NPCm = Nominal Protection coefficient of the mix-tradable crop CAP = Capital inflow
BOT = External terms of trade in agriculture GEX = Government Expenditure
56
DU2 , for observation in 1987-1994, and 0, otherwise
DU3 , for observation in 1995-2012, and 0, otherwise
Whereby the benchmark category = Years of trade restriction (1970-1986)
Equation (11) and (12) give the price effect of trade policy on different classes of tradable crops
relative to non-tradable. To avoid multicollinerity with real exchange rate, which is reflected in the
dependent variables and the nominal protection coefficients, the world prices of the tradable crops,
, rather than the border prices shall be used. Mores so, to distinguish the episode ef-
fect of major progressive changes as trade liberalization unfolds, dummy variables are included.
The interaction terms capture how specific trade openness affects the relative price movements
during trade liberalization episodes. Other relative price determinants are controlled for.
Variable Description- Trade intensity ratio (TOP), as a measure of trade openness is included.
This variable is assumed to be directly related overtime to degree of trade liberalization (though it
has a limitation of giving a higher value even when trade is distorted), but then it is the closest
proxy Nigeria data can provide. It is proxied as total import plus export as a ratio of GDP. The ex-
ternal terms of trade, (
⁄ ) is an index of national income in developed countries. This variable
affect the relative price of tradable to non-tradable movement through both income and substitu-
tion effect (Edwards, 1989). Nominal protection coefficient (NPC) is a measure of trade bias[ i.e.
it indicates the level of restrictiveness. It is a simple estimate of the extent to which the price of a
particular product has been affected by government intervention. It is defined as the ratio of the
product‟s domestic price to its international price (Pursell an Gupta, 1998). Government expendi-
ture (GEX) captures the substitution effect of government spending and the productive capacity of
the economy. Balance of trade (BOT) is defined as the ratio of (export-import) to export. This is
included because exogenous changes in export price associated with a booming sector affects the
real exchange rate. Capital inflow (CAP) is proxied by foreign direct investment. It is included
considering that any change in capital inflow would affect inter-temporal consumption and real
exchange rate. The relative price of agriculture to non-agriculture (
⁄ )is measured as the ra-
tio of implicit price of gross output in Agriculture to implicit price of non-traded sector, which is
service sector, as done by Garcia (1883).
57
Second Stage Estimation (measure for objective 3)
Indirect trade policy effect
The indirect (induced) effect of trade policy results from (1)- the effect on the real exchange rate
and (2)- reforms from other sectors (i.e. the economy wide effect of trade policy). Thus, the rela-
tive price of tradable to non-tradables (
⁄
), (
⁄ )
and the external terms of agriculture
(
⁄
)capture the indirect effect of trade policy) from first stage equations. These variables
play an intermediary role in transmitting the price incentive effect of trade policy on agricultural
land and hence they are endogenously determined in the model. In the second stage estimation, the
different endogenous variables in equations (11)-(13shall be regressed on agricultural land. In the
second stage, change in agricultural land variable shall now be regressed on the four endogenous
variables: Predicted relative prices of export and non-tradable, predicted relative price of mix-
importable and non-tradable, predicted agricultural terms of trade :
( ) .
⁄ /
.
⁄ /
.
⁄ /
(
⁄ )
, .
⁄ /-
, .
⁄ /-
, .
⁄ /-
, (
⁄ )- ….(18)
To distinguish the movement of the real exchange rate (the different relative price of tradable and
non-tradable) and the agricultural terms of trade at different exchange rate policy regime, dummy
variable is introduced in the model and fully interacted.
58
3.4APRIORI INFORMATION ON THE MODEL
Table 3.1 Apriori Expectation of the Model
Effect of an increase
in:
Expectation on
Wood Extraction
Expectation on urban
land
Demand
Expectation on Agricul-
tural land Demand
Cash crop price-
-/+
NA
-/+
Staple crop price-
-
NA
-
Agricultural input
price
Ambiguous NA +
Timber price -/+ NA NA
Agricultural mechani-
zation
+ NA +
Agricultural value
added
- NA -
Transportation cost - - NA
Industrial sector
productivity
+ + -
Service sector produc-
tivity
+ + -
Export Good Price Ambiguous + _
Agricultural produc-
tivity
+ _ +
Kerosene price + NA NA
Exchange rate dummy +/- Ambiguous +/-
Trade dummy +/- Ambiguous +/-
Source: Computed by the author
59
3.5 ESTIMATION PROCEDURE
-Diagnostic Estimation: First, Augmented Dickey-Fuller (ADF) test shall be applied on the indi-
vidual series to correct for autocorrelation and equally test for stationarity of the series and the or-
der of integration of the variables under consideration. ADF eliminates the autocorrelation in the
error term that will bias the test. Thus, the individual series shall first be tested in the level form
for stationarity, if they are not stationary, then the series shall be tested at the first difference, or
second differences…etc to check if subsequent differencing of the series removes the non-
stationary at 5% significance level. This shall be done with trend option to ensure that the sto-
chastic properties are time invariant. This shall be augmented with Phillip-Perron (PP) (Phillip,
1987; Perron, 1998), since the presence of structural break will tend to bias ADF test towards non
rejection of the null hypothesis.
As is usually common in time series analysis, there is a high probability of errors being correlated.
Thus, in general, the test of detection of autocorrelation, Breusch Godfrey (BG) test on the residu-
als shall be conducted. The model specification shall be verified-
Model Estimation: Though in a recursive model, with errors uncorrelated across two equations,
i.e., disturbances across equations exhibit zero contemporaneous correlation, ordinary least square
(OLS) is the recommended estimator (Greene, 2002, Gujarati, 2007). However, given that most
regression equations are probably mis-specified to some extent, i.e., in reality, there are reasons to
doubt the strict assumptions required for a recursive model, unless factors constituting the error
terms in the model are fundamentally different for each equation (i.e. factor affecting the endoge-
nous explanatory variable that are not explicitly specified in the model are uncorrelated with de-
forestation). The consequence of such misspecification is that OLS estimation, will in general be
biased, and thus the t and F statistics will be invalid. To control for possible specification error due
to simultaneity in recursive model 2SL estimates as suggested by Bollen et al. (2007) shall be
used. In effect, to avoid the bias, a two stage least square estimator shall be used as the estimator.
Thus, the recursive model shall be estimated using two stage method. In the first stage, conceptu-
ally, 2SLS procedure involves the creation of an instrumental variable from the original specifica-
tion. These new variables are free of confounding effects from correlated disturbances. They are
then employed in a second regression generation analysis to estimate the structural coefficient.
60
Thus, all the three endogenous variables,(
⁄ )
(
⁄ )
and(
⁄ )shall be regressed on
their respective explanatory variables using ordinary least square to obtain the estimated values. In
the second stage, the estimated values now act as instrumental variables to be used in the least
square regression of the final endogenous variables.Although, stata package does 2sls, as one step
procedure using „ivregress‟, however, when a structural equation is recursive (triangular) the ad-
visable option is to perform the two step computation for the instrumental variable estimate in-
stead of using ivregress. To obtain an adjusted standard errors, the residuals from the second stage
equation shall be computed by using the parameter estimates obtained with regress but substitut-
ing the instrumented variable (the predicted value of the endogenous variables) for the original
value of that variable (Green, 2012). A stepwise adjustment to the covariance matrix shall be
done. This will correct the variance –covariance by applying the correct mean squared error as
specified by Wiggins (2013) and Sanchez (2011).
3.6 JUSTIFICATION OF THE MODEL
Optimal Control Model: According to Sergerson et al (2006), when irreversibility or stock ef-
fects results from current land use decision, then the optimal land use decision is based on a dy-
namic inter temporal land allocation problem. Thus, the preference for optimal control approach is
based on the inter-temporal nature of optimal land use decision, whereby current forest land use
decisions affect future return. In effect, the optimal control model considers the entire planning
horizon as an entity, by taking into consideration, the effect of subsequent period decisions, unlike
in static models. More so, whereas Maximum Principle and Dynamic programming methods are
equivalent method for optimization over time, maximum principle of Pontryagin et al(1950),
which is less restrictive in form of controls and constraints, is generally better when there is no
uncertainty. i.e. when random effects are not considered in the model(Dixit, 1990).
Empirical model: The main limitation of most deforestation models is the combined use of dif-
ferent level deforestation causes as explanatory variables, which results in misspecification of the
model. However, Kant and Redantz (1997) have used the two- stage recursive estimation proce-
dure to address this problem. Thus, in line with the work of Kant and Redantz, the complex nature
of effect of trade policy on agricultural land demand is best modeled using structural equation.
61
Whereby the indirect effect of trade policy on real exchange rate ( proxied by relative prices of
export and mix-tradable to non-tradable, which are endogenous to the system, is best determined
by a structural equation. In effect, the recursive model allows the effect of the mediating variables:
, (
⁄ )
(
⁄ )
and(
⁄ ), which are the endogenous variables in the middle of the
causal chain to be captured. Thus, since the conceptual framework of the study is such that, there
are no reciprocal directed paths, i.e. each equation exhibits unilateral causal dependence and ex-
hibits zero contemporaneous correlation within the disturbance terms, the model is recursive or
hierarchical and best estimated by ordinary least square. In model 1, preference for dummy model
approach is based on its‟ flexibility in classifying data set into mutually exclusive categories.
Thus, the use of dummy variable captures the probable differential effect that trade policy in Nige-
ria may have on outcomes of proximate deforestation causes, as against a pooled regression that
disregards the possible difference in the two periods. In interactive form, the model assumes that
conditional relationships, if they exist are not immediately modeled in the additive model. How-
ever, interactive model apart from providing an accurate and detailed description of the relation-
ship in the data set equally increases the explanatory power of the model. This implies that failure
to include a multiplicative term when interaction does exist in the data constitutes a specification
error, because omitted interactive terms will be correlated with included variables, thus giving a
biased estimate in additive model specification. More so, although in a recursive model, ML or
2SLSgive identical result, ML propagates error throughout the system of equations, whereas 2SLS
isolates misspecification in the equations that are mis-specified. Also, aside that 2SLS is quite ro-
bust against multicollinearity and specification problem, it has small sample properties superior to
other estimators.
3.7DATA AND DATA SOURCES
The data sources include: Central Bank of Nigeria (CBN), Food and Agricultural Organization
Statistics (FAOSTAT), World Development Indicator (WDI), Nigeria National Petroleum Corpo-
ration (NNPC) Annual Bulletin), Econ Statistics (ECONSTAT). The full description of data
sources and unit of measurement is given in table 3.2. Stata 12 shall be used for the analysis.
62
Table 3.2 Definition and Sources of Data
Variable Acronyms Sources
Agricultural land Demand (sq km) LA ECONSTAT
Demand for Wood extraction WD FAOSTAT
Infrastructural Extension LV CBN
Staple Crop Price (Naira/tonne) SAC FAOSTAT
Cash Crop Price (naira/tonne) CAC FAOSTAT
Agricultural Value Added (kg/hectares) AVA ECONSTAT
Agricultural Mechanization (tractor/100 sq km) MEC ECONSTAT
Transport Cost (naira/ liter) TC CBN, NNPC Annual Bulletin
Industrial Productivity IND CBN
Export Good price EXP NNPC Annual Bulletin
Timber price PW FAOSTAT
Agricultural Productivity AGP CBN
Forest Rent FOR WDI
Relative price of exportable to non-tradable crop ⁄ FAOSTAT
Relative price of mix-tradable to non-tradable crop ⁄ FAOSTAT
Domestic terms of trade in Agriculture ⁄ CBN
Trade intensity Ratio TOP CBN
Nominal Protection Coefficient of tradable crops NPC FAOSTAT
Nominal exchange rate NEX CBN
External terms of Trade
⁄ CBN
Share of Government Expenditure (naira million) GEX CBN
Balance of Trade (naira million) BOT CBN
Capital inflow (US. Dollar) CAP WDI
63
CHAPTER FOUR
EVALUATION OF RESULT
4.1 THE INTERPRETATION OF THE ANALYTICAL MODEL
Along the optimal deforestation path, for total net benefit to be maximized, the planner must bal-
ance the net benefits obtained by depletion of forestland with the opportunity cost of decreasing
the forest land stock.
From equation (15), λ is the shadow value of preserving the forest land for sustainable forest pro-
duction and services. It gives the dynamic cost to future generations of reduced stocks of forest
land today rather than in the future.
The term [ ( ) ( ) * ( )+- represents the equation of motion of the forest stock. It
shows the amount of forestland that is used up. Thus the total dynamic cost of using the forest
land is given by λ[ ( ) ( ) * ( )+], where λ is the dynamic price of forest land.
While are the respective increases in the dynamic value of agricultural and urban
land.
Equation (20) [see appendix] describes the optimal deforestation rate obtained by balancing short
term benefits against long term benefits (i.e. optimal balance between current welfare and future
consequences). The equation implies that at optimum, the marginal benefit of current deforestation
rate associated with wood extraction should be equal to the marginal cost of current use in terms
of the future benefits forgone ( i.e. its shadow value of forest land).
Equations (19) and (21) [as restated in equation (34)] implies that along the optimal paths, land
should be allocated among agriculture, forestry and urban use up to a point where the shadow val-
ues of all uses are equal. i.e., where the shadow value of forestry λ, equals the shadow value of
agricultural land, and also equal to the shadow value of urban land, .
Equations (25)-(27) determine the respective adjustment in the shadow values of the forestry, ag-
ricultural and urban land along the optimal paths. Equations (25)-(27) implies that forest land shall
be utilized up to the point where the respective values of marginal product of land in forestry, ag-
riculture and urban sector equals the respective social cost of this capital, Equation (25)
gives the cost of utilizing the services of a unit of landthe forest at any given time.
64
[ - , - the marginal net
returns of wood production [ * * + + ( * +)] and the social cost of
deforestation associated with wood extraction, [ ( * +)-.
Equations (28)-(30) gives the expression for the shadow value of λ, λ(t)
gives the change in net return caused by a marginal reduction in forestland at any point in time for
all future periods. The equations (28) – (30) has two effects: - The scarcity effect, given
by ( * + . The scarcity effect at any given time is the terminal scarcity value discounted to the
current time, t. The remaining term within the integral, [ ∫ , ( ) * * + +
- ( * + ] is the cost effect. It gives the present value of the cost saving associated
with the marginal unit of the forest land stock. Likewise, ( ∫ [ * +
] )and ( ∫ [
* + ]
) in equations (29) and (30) measure the re-
spective impact of the marginal units of land devoted to agricultural and urban land upon future
deforestation costs.
Constraints (31) – (33), which means F is exhausted or λ = 0, implies that the stock of forest land,
agricultural land and urban land must be either depleted or their respective present value shadow
prices should equal zero. This implies that the present value of the resource far off in the future
cannot be positive. The term λ = 0 means exhaustion of the forestland because deforestation can-
not jump to zero until F is zero.
Equation (38) shows the trade-off between land devoted to forest benefit (sustainable wood pro-
duction and amenity services) as against agriculture and urban use. From the equation, the optimal
allocation of forest land is obtained at the point where the marginal returns are equal across all us-
es. This means that forest land is reserved for forest production services up to the point where the
marginal net returns of wood production and marginal environmental benefit, as well as the social
cost of deforestation associated with wood extraction equals the marginal net benefits of agricul-
tural production from converted land and also equal to the marginal net benefits of urban land
production from converted land. This implies that the cost of utilizing the services of one unit of
forest land at any given time for agricultural production or urban land use is the foregone benefits
of marginal wood production and amenity benefit that would have been obtained from that unit of
forest land.
65
In equation (38a), the terms, [ * + - ,
* + - measure
the respective net cost of not allocating an additional unit of land in agriculture and urban use,
which is added to the direct marginal contribution of forest benefit to give the social cost of utiliz-
ing the services of one unit of forest land at any point in time.
4.2 Descriptive Analysis
In order to compare the variations in the explanatory variables in pre-reform, during reform and
post reform periods, summary statistics for the sub periods are reported in tables A1 and A2 (see
Appendix). The agricultural land mean shows an upward trend from an average mean value of
8.7e +06 hectares in pre-reform era (1970-1986) to 1.56e + 07 during the reform era, recording a
44.2% increment. Whereas the average mean increased further to 1.84 + 07 in post reform era, in-
creasing further by 15.2%. The overall upward trend indicates increasing expansion of agricultural
land with trade liberalization episodes. Figure 4.1 shows that the adoption of liberalization poli-
cies following the turning points coincides with sharp subsequent increases in Agricultural land
Expansion.
Table A1 (see appendix) indicates that the average real producer prices of staple crop (SAC) and
cash crop (CAC) during the two sub period of trade liberalization episodes generally increased
from reform era; however, the cash crop (CAC) recorded a much higher increment relatively. The
mean value of trade openness (TOP) in pre reform era, which is 0.12, increased by 18.2% and
88.8% respectively in reform and post reform trade episodes (table A.2). The reform era and post
reform era, witnessed general increases in relative price of exportable crop to non-tradable crop
(
⁄ ), 2.9 and 4.7 respectively against 1.2 in pre-reform to a lesser extent, increase in relative
prices of the mix-tradable crop to non-tradable crop,(
⁄ ), 0.36 and 0.57 respectively as against
0.23 during the reform era). This indicates depreciation of real exchange rate of
⁄ and
⁄
(Table A2).
66
The prices of mix-tradable crop, pm relative to non-tradable crop followed a similar pattern. Their
movement has been closely linked to variations in the external terms of trade, which in turn has
been increasing. These price increases could be attributed to the direct export incentives as well as
the indirect incentives from higher real exchange rate resulting from trade liberalization policies.
The nominal protection coefficients of exportable (NPCe) and mix-tradable (NPCm) in the sub
periods of trade liberalization episodes, generally declined, except for (NPCe) which declined
from an average value of 0.46 in pre reform era to 0.23 and further rose to 0.31in the post reform
era. NPC for mix-tradable crop, cassava, recorded a very high value of 4.5 in the reform era. This
indicates that the crop is highly protected against international prices during the era (Table A.2).
As seen in figure 4.1, the annual agricultural land expansion was highly correlated with variations
in NPCm and NPCe, particularly in the pre reform and reform era. There is an inverse relationship
between this variable and agricultural land demand. NPC of mix-tradable cassava crop during the
pre-reform era is > 1. This shows that the crop was highly protected against the international pric-
es. This points to a policy-induced bias favoring domestic production of the crop over internation-
al substitution. However, the ratio, (NPC) has started falling during the reform and post reform
era. On the other hand, the NPC of exportable cocoa crop < 1 during the pre-reform era. This in-
dicates, the relative higher price o international cocoa over domestic prices. The convergence be-
tween the two prices, as is seen in reform and post reform, may indicate liberalization of taxes and
tariff. One important conclusion that can be drawn from the result in the table and the graph is that
given the low NPC of cocoa, relative to NPC of cassava, cocoa producers are more adversely af-
fected by the policy incentives. Thus, it is possible that the reduced export tax on cassava stimu-
lated a switch from cocoa to mix-tradable cassava.
67
Fig 4.1 Agricultural Land Expansion and Nominal Protection Coefficients of Tradable Crops
Source: Devised by the Author using Stata 12
4.3 Stationarity Test Result
Prior to the estimation, the augmented Dickey fuller (ADF) and Phillips-Perron (PP) tests with
trend option are used to check the order of integration in each of the time series. Both analyses test
the null hypotheses that the series contain a unit root. A time series is stationary if the test statis-
tics is greater than the critical value in absolute terms. Table 4.1 shows that except for MEC,
which is stationary at level form, all the variables in model 2 are not stationary at data level.
Therefore it is necessary to carry a co-integration test. First differences of the variables were taken
to determine the order of integration of the variables. This indicates that all the variables are sta-
tionary at first difference and thus integrated of order one, before testing for co-integration rela-
tionship. The analyses indicate that all the series are stationary at first difference of the variable at
5% critical value in absolute terms.
15.5
16
16.5
17
lnla
-3-2
-10
12
1970 1980 1990 2000 2010year
lnnpce lnnpcm
lnla
The Vertical Dotted Lines..............Signifies Trade Policy Turning Points
68
Considering that differencing leads to loss of long run properties of the series, to determine
whether the non-stationary variables are cointegrated, Engel Grangers‟ (1987) two step procedure
was used. An OLS regression and a unit root test, the EG-ADF test. This was done by generating
residuals from long run equations of non-stationary variables using DF tests. These residuals were
found to be stationary for the models, thus confirming that the variables were co-integrated (Table
4.1).
69
Table 4.1 Stationarity Test
ADF PP ADF PP
Data level (-4.224) Level 1 (-4.233)
Variable
lnLa -2.237 -2.332 -6.851 -6.839
lnWD -2.672 -2.552 -7.158 -7.400
lnLV -6.992 -6.957
lnWPR -1.274 -1.606 -4.475 -4.395
lnSAC -3.476 -3.578 -6.201 -6.212
lnCAC -1.561 -2.038 -5.152 -5.082
lnMEC -5.965 -6.698
lnAVA -2.672 -2.552 -7.158 -7.400
lnTC -2.64 -2.538 -7.896 -8.135
lnFOR -0.75 -0.720 -7.476 -7.372
lnAGDP -1.959 -1.857 -6.270 -6.385
lnIND -3268 -3.292 -5.870 -5.878
Lnpepi -1.929 -1.900 -6.521 -6.575
Lnpmpe -2.911 -2.817 -5.846 -5.848
Lnpepn -2.555 -2.788 -6.000 -5.993
Lnpmpn -2.579 -2.744 -7.530 -7.462
Lnpapna -2.818 -2.655 -7.760 -8.837
lnTOP -1.918 -1.97 -5.881 -5.874
lnNPCe -2.063 -1.870 -6.737 -7.051
lnNPCm -3.005 -2.968 -5.713 -5.733
lnGEX -0.41 -0.553 -3.717 -3.89
lnBOT -3.443 -3.330 -6.070 -6.118
lnCAP -4.056 -4.171 -12.836 -13.113
lnNEX -1.667 -1.695 -7.644 -7.560
Note: figures within parenthesis indicate critical values, Mackinnon (1991) at significance at 5% level
Source: Authors‟ estimation using stata 12.
70
4.4 The Result of Effect of Change in Trade Policies on Proximate Deforestation Causes
Table 4.2 OLS Result for Model1
(1) (2) (3)
VARIABLES lnLA lnWD lnLV
DU2 -2.135* 0.579** -0.543**
lnCAC 0.372***
DU2.lnCAC -0.321***
DU3 -3.514*** -0.0595 -0.235
DU3.lnCAC -0.115
lnMEC 0.611**
ln AVA -0.933***
ln SAC -0.659***
DU2.lnSAC 0.657***
DU3.lnSAC 0.550***
ln AGP 0.171*** 0.146***
ln IND -0.197*** 0.156***
ln KER -0.00112
ln PW 0.0748***
DU2.lnPW -0.115***
DU3.lnpw -0.0441
Lntc -0.0231 0.608***
DU3.lnTC -0.746***
ln EXP 0.121
ln FOR -0.370**
Constant 23.59*** 3.880*** 4.218***
Observations 43 43 43
R-squared 0.965 0.909 0.779
*** p<0.01, ** p<0.05, * p<0.1
Note: columns 1, 2 and 3 show the estimate of agricultural land result, wood extraction result and infra-
structural land result.
Source: Authors‟ estimation using stata 12.
71
-Agricultural Land Result-
Agricultural value added (AVA), which is the land intensive technological variable, has the largest
impact on Agricultural land demand and is significant at 5% level. The elasticity of agricultural
land area with respect to AVA is 0.93. A I percent increase in AVA leads to a 0.93 percent reduc-
tion in demand for agricultural land. This implies that technological investments involving high
yields per hectare (such as fertilizer, high yielding seedling) will shift resources away from the
extensive practice. The magnitude of the coefficient suggests that AVA is more important in de-
termining agricultural land than others. Thus, it should be considered in enacting policies that will
discourage agricultural land expansion.
On the other hand, agricultural land demand responds positively to agricultural mechanization
(MEC), which captures the effect of land extensive technological progress. This is statistically
significant at 5% level, with an estimated elasticity of 0.24 (Table 4.2, column [1]). This conforms
to the apriori expectation that farmers will generally prefer technology that saves labor than land.
Labor-saving and extensive technologies stimulate land expansion by improving the profitability
of agriculture (Wunder, 2004).
The elasticity of agricultural land demand with respect to lagged agricultural staple crop price dur-
ing the era of trade restriction (SAC) is about 0.66 percent. It is significant at 5% level with a neg-
ative sign. This is another variable that has high statistical and economic impact on agricultural
land demand given the size of the coefficient. The result shows that a 1 percent increase in the
previous year‟s price of staple crop during the era of trade restriction, translates to 0.66 percent
decline in demand for agricultural land. As expected, the staple crop price has a negative sign dur-
ing this period. This is because when agricultural output prices are sensitive to changes in supply
(as the case is in staple crop) and the production is for domestic market, prices are endogenous and
demand for the product is inelastic, small increases in output may lead to large decline in agricul-
tural prices. This, could make agricultural production less, rather than more profitable and thus
reduce land expansion (Angelsen et al, 1998).
The coefficient of the interaction variable [ ] which captures the differential effect of
lagged staple crop price during the policy reform era is positive and significant at 5% level. This
implies that during the policy reform era, for each additional unit increase in lagged staple crop
price, the agricultural land area is estimated to increase by 0.65 percent compared to the period of
72
trade restriction (the base category). Similarly, the positive insignificant coefficient of
[ ] indicates that during the post reform era [1995-2012], the staple crop price elas-
ticity is estimated to increase by an additional 0.55 percent for each additional unit increase com-
pared to the era of trade restriction. The implication of this is that strong negative effect of staple
crop price on agricultural land area reduces as trade liberalization and exchange rate devaluation
unfolds. This confirms the empirical study by Anglsen et al (1999) that states that policies such as
trade liberalization and exchange rate devaluation which improves terms of trade for agriculture,
increases deforestation.
The cash crop price (CAC) elasticity of agricultural land demand was estimated to be about 0.37
percent. The estimate is significant at 5% level with the expected positive sign. This is consistent
with the literature that considering that supply increases do not depress prices much because it has
higher demand elasticity, the aggregate effect will not be large enough to influence world prices.
Thus, the net effect will be higher pressure on forest margin (Von Amsberg, 1994).
The coefficient of the cash crop interaction term ( ) is negative and significant at 5%
level. The value of the coefficient indicates that a shift from trade restriction era to policy reform
era (1984-1994), due to liberalization of trade, reduces agricultural land demand elasticity by 0.11
percent. This implies that the elasticity of cash crop price during the liberalization episode (1987-
1994) is 0.26 (i.e. 0.371-0.115) percent. However, the coefficient of the interaction
term( ) is not significant at % level, but is still positive. This implies that the elasticity
of agricultural land demand with respect to cash crop price, during the post reform trade liberaliza-
tion episode (1995-2012), though positive, is the same effect with that of the base category. This is
consistent with the works of Angelsen et al, 1999 and Lamb (2000). They are of the view that, ex-
ports crop production and its effect on agricultural land is mildly affected by the current period
prices. The weak statistical effect of this variable, support the evidence by Briggs (2007) that there
have been sharp trade policy reversals in the post reform period in Nigeria. This policy reversal,
most likely adversely affected the export sector.
The estimated elasticity of industrial sector productivity (IND) is approximately 0.2 percent. The
estimate is statistically different from zero at 5% level, and correctly signed (negative). This
shows that increasing sector productivity decreases agricultural land area. This is in line with the
73
result of the study by Deng et al (2008), who report that increased productivity of industrial land
reduces demand for agricultural land due to substitution of agricultural land for urban land.
The agricultural productivity variable is significant (5%) with the expected positive sign. The elas-
ticity is 0.17 percent. This implies that a 1 percent increase in agricultural productivity increases
agricultural land area by0.17%. This is because, this raises the opportunity cost of forest land and
makes agricultural land expansion attractive. And according to Wunder and Verbist (2004), farm-
ers react positively to the opportunity of more profitable cultivation, compared with other alterna-
tives and this equally attracts new comers.
-Model fit and Diagnostic Check Result- Overall, the model fit is satisfactory considering the
statistical significant level of the estimated variables and high R2 (95.2%). The result of the Dur-
bin-Watson statistics reported a value of 1.93 (Table A3, see Appendix). The result indicates that
the null hypothesis indicating the presence of a serial correlation has been rejected. As a model
robustness check, the Linktest records a non-significant Hat2 (t value of -0.98). Thus, the test fails
to reject the null hypothesis that the model does not have omitted variable bias. The Bgofrey test
for serial correlation with a high P-value of 0.83 (Table A3 ) indicate the failure to reject the null
hypothesis of no serial correlation.
-Wood Extraction Result
The coefficients of the dummy variables, DU3 and DU2 express the respective differential effects
of policy reform era and post reform era on agricultural land demand relative to the era of trade
restriction. The coefficient of DU2is positive and significant at 5 level, while that of DU3 is not
significant (Table 4.2, column [2]). Hence, the demand for wood extraction in the reform era is
significantly higher by ( , -) percent than what it is in the era of trade re-
striction. On the other hand, the coefficient of DU3 is not significant but positively signed. The
insignificance of this variable could be explained by changes in the trade liberalization episode in
effect during the period. This shows also that trade liberalization episode during the reform era
significantly favours the exportation and extraction of timber in Nigeria.
Industrial Productivity (IND) has the largest impact on wood extraction. The coefficient has the
expected positive sign and statistically significant at 5% level. A 1 percent increase in productivity
in industrial sector, translates to 0.16 percent increment in wood extraction. This shows that in-
74
crease in the opportunity cost of forest competing land use (e.g. due to expansion of industries or
an increase in productivity of land in production) increases forest degradation from increased
wood demand.
The elasticity of wood extraction with respect to demand for agricultural productivity is 0.15 per-
cent. The coefficient is significant at 5% level with an expected positive sign. This also indicates
the effect of increasing opportunity cost of forest competing sector on forest degradation. Thus,
increasing agricultural productivity will increase the demand for wood extraction.
The estimated elasticity of timber price during the years of trade restriction (WPR) is approxi-
mately 0.08 percent. The result shows that during the period of trade restriction, that a percent in-
crease in timber price increases the demand for wood extraction. Contrastingly, the elasticity of
demand for wood extraction with respect to timber prices during the era of policy reform
(DU2.lnWPR) is 0.14 (i.e 0.075 - 0.115) percent and negatively signed. This implies that for each
additional percent increase in timber price during the reform trade liberalization episode that the
demand for wood extraction decreases by 0.12%. However, the a priori expectation on timber
prices in the empirical studies has been mixed. Positive and negative effects have been reported.
This result support the works of Sonhgen et al. (1999), that higher timber prices actually lead to
increase in forest plantation during trade liberalization.
The estimated coefficient of kerosene price (KER) is not significant and the result contrasted from
the a priori expectation. This failure to explain the variation in wood extraction demand probably
is due to the fact that during the period of the study, kerosene price is relatively subsidized in the
country and as such, the impact of the variable cannot be felt.
-Infrastructural Extension (LV) Result
The dummy variables (DU2 and DU3) results indicate that the elasticity of infrastructural exten-
sion in different trade liberalization episodes are significantly associated with decreases in infra-
structural extension (at 5% level), though the dummy variable for the post reform era is not signif-
icant.
The elasticity of infrastructural extension with respect to various independent variables was high-
est (in absolute terms) for transport cost. This implies that the transport price explains a good per-
centage of variation in infrastructural extension. The elasticity of infrastructural extension with
75
respect to transport price during the era of trade restriction (lnTC) is 0.61 (Table 4.2, column [3).
The coefficient is positive and significant at 5% level. This implies that during the era of trade re-
striction, transport cost significantly contributes to the increase in LV. However, during the era of
trade restriction, the coefficient of transport cost records a positive sign. The elasticity of infra-
structural extension with respect to transport cost during the reform era and post reform era is
( ) percent and ( ) percent respectively. The result implies
that though increasing infrastructural extension is associated with higher transport cost (approxi-
mately, 0.61%) during the era of trade restriction, however for each additional percent increase in
transport cost during the reform and post reform era, it is estimated to decline by 0.57 and 0.24
percent respectively compared to the era of trade restriction. This supports the a priori expectation
and is equally confirming earlier position maintained by Brueckner and Fansler (2001) that an in-
crease in transport or commuting cost lowers disposable income at all locations, reducing the de-
mand for urban house at urban fringes and leading to lower infrastructural expansion.
The forest rent (FOR) elasticity, which captures the effect of productivity in forest sector was es-
timated to be about 0.31. It is significant at 5% level with negative (expected) sign. The result im-
plies that an increase in the opportunity cost of forest land which competes with infrastructural
land will naturally reduce the demand for land for infrastructural use.
The elasticity of infrastructural extension with respect to an industrial export good price is 0.12
percent, but not significant at 5% level. However, it has the expected positive sign, indicating that
an increase in the world price of an export good will increase infrastructural expansion. This is
because the opportunity cost of conserving forest land increases and there will be a switch to pro-
duction of the industrial export good.
76
4.5 Theoretical and Statistical Significance of the Recursive Model Result
4.5.1 First Stage Estimation Result
Table 4.3 First stage OLS Estimation Result
(1) (2) (3)
VARIABLES lnPe/Pn lnPm/Pn lnPa/Pna
Lnpe*/pi
* 0.642*** 0.185**
Lnpm*/pe
* 0.129 0.181*
LnNEX -0.202** -0.142**
LnTOP -0.0544 -0.153** -0.239**
DU2 2.891*** 3.412*** 0.900*
DU2.lnTOP 1.105*** 1.320*** 0.236
DU3 1.698*** 1.869*** 1.027**
DU3.lnTOP 0.102 0.0800 0.387***
LnNPCe 0.621***
LnGEX -0.122* -0.147**
Lnpm*/pi
* 0.585***
LnNPCm 0.561***
LnBOT -0.145***
LnCAP -0.119
Observations 43 43 37
R-squared 0.918 0.921 0.864
*** p<0.01, ** p<0.05, * p<0.1
Notes: column (1), (2) and (3) of table 4.3 indicate the respective result for the relative prices of exportable
to non-tradable, mix-tradable to non-tradable and agriculture to non-agriculture equations.
Source: Authors‟ estimation using stata 12.
77
-The Result of Relative Price of Exportable to non-tradable (
⁄
) Equation
DU2 and DU3, the dummy variables that capture the differential effect of different episodes of
trade liberalization, reform and post reform era episodes, on
⁄ are significant and positive
respectively. This indicates that trade liberalization in the reform (1987-1994) and post reform era
(1995-2012) lead to the depreciation of the relative prices of exportable to non-tradable
⁄
inreal terms compared to the era of trade restriction (1970-1986), which is the base category. This
finding is consistent with the notion that when a small country liberalizes its trade, demand for
importable increases and demand for non-tradable decreases in response to the relative price
changes. Assuming that Marshall-Lerner conditions hold, a real depreciation is necessary to main-
tain internal and external balance (Edwards, 1989).
The estimates indicates that a 1 percent increase in the external terms of trade (i.e. world price ex-
port relative to import,[
⁄ ])would increase the domestic price of exportable relative to non-
tradable (
⁄ ) by 0.64 percent (see Table 4.3). The positive significant coefficient of the terms
of trade indicates that an improvement in terms of trade (e.g. due to increase in the world price of
exportable relative to import) reduces the domestic price of importable and thus decreases the de-
mand for non-tradable crops. This will cause the depreciation of the exchange rate (domestic rela-
tive price of exportable to importable,
⁄ ). This is consistent with the „income’ effect that
comes as the improvement in the trade balance raises income of the domestic economy and higher
demand for the non-tradable goods emerges. To restore the internal equilibrium the real exchange
rate is required to depreciate (Borgev et al, 2008).
The estimated coefficient for nominal exchange rate (NEX) which captures the relative difference
between domestic inflation with that of its main trading partner is significant and negative. The
elasticity of
⁄ with respect to NEX is 0.2 percent. This means that 1 percent increase in
78
nominal exchange rate leads to 0.2 percent decrease in the domestic price of export crop compared
to that of non-tradable crop.
The coefficient of government expenditure is negative and significant at 10% level. This indicates
that I percent increase in the share of government expenditure would lead to approximately 0.12
percent decrease (appreciation) in the real exchange rate of exportable crop and non-tradable crop
⁄ . The expected negative sign shows that government propensity to spend on non-tradable
crops is higher than its propensity to spend on traded crops. This is not surprising, considering that
government expenditure comprises mainly of non-tradable goods. Thus, an increase in govern-
ment expenditure will lead to a rise in demand for non-tradable (Edwards, 1989). This is a substi-
tution effect, which leads to real exchange rate of
⁄ appreciating.
The coefficient of the relative foreign price of mix-tradable to exportable (
⁄ ), though, it is
not significant, recorded a positive relationship between (
⁄ and
⁄ . This indicate that an
increase in relative world price of
⁄ (e.g. due to increase in world export price) leads to an
increase in domestic price of export crop relative to non-tradable domestic price (depreciation ef-
fect).
Nominal protection coefficient of exportable crop (NPCe) is significant and has positive effect on
real exchange rate of
⁄ . The elasticity of an export crop NPC is 0.62 percent. In response to a
1 percentage point increase in NPC of an export crop, the real exchange rate of
⁄ increases
(depreciates) by 0.63 percent. The size of the coefficient (0.62) shows that NPCe has important
impact on the real exchange rate of
⁄ . NPCe, which is the ratio of domestic price of the ex-
port crop to the border price of the crop, measures the extent of government intervention on the
export crop. The positive sign indicates that an increase in the relative price of domestic and bor-
der price (e.g. due to decrease in border price of the export crop) will increase the domestic price
79
of export crop, and thus lead to a depreciation of
⁄ . This improves the price competiveness
of exportable crop relative to non-tradable, encouraging their production relative to non-tradable.
The coefficient of trade openness (TOP) measures the elasticity of real exchange rate of
⁄
during the period of trade restriction (the base category) with respect to trade openness. The coef-
ficient is negative and not significant. The non-significant effect shows that during the era of trade
restriction, there was no significant increase in the ratio of export and import to that of GDP, thus
confirming the restrictiveness of trade in that era. The negative sign indicates that an increase in
trade openness index during the trade restriction era is associated with decrease in the real ex-
change rate of
⁄ . The result lends support to the fact that if trade openness reflects trade lib-
eralization, then an increase in openness should lead to a deterioration of the current account posi-
tion and a real depreciation should follow as reported by Bogoev et al (2008).Whereas, the coeffi-
cients of the interactive variables ( ) and ( ) which measure the differential
elasticities of trade openness in different trade liberalization episodes are positive (depreciation
effect) the coefficient of ( ) which represent the differential impact of TOP during the
reform era (1987-1994) is significant. The elasticity is 1.06( ) percent. This im-
plies that though, trade openness increases (i.e. depreciates) the real exchange rate of
⁄ , how-
ever, for each additional unit increase in trade openness index during the reform era, the real ex-
change rate of
⁄ increases(i.e. depreciates) more by 1.06 percent compared to the era of trade
restriction(the base category). However, ( ), the post reform era is not significant. The
positive but insignificant result implies that, compared to era of trade restriction, for every addi-
tional unit increase in TOP, the
⁄ in real terms increases (i.e. depreciates) more during the
post reform era (1995-2012). The insignificant result compared to the significant result recorded
for( ) in the reform era provides support that there have been sharp policy reversals in
trade openness since the end of SAP in Nigeria as reported by Briggs (2007)
80
- The Result of Relative price of mix-tradable to non-tradable crop (
⁄ ) Equation
As table 4.3 indicates, the coefficients of nominal protection coefficients for a mix-tradable
crop(NPCm), Government Expenditure (GEX), external terms of trade(
⁄ ), the nominal ex-
change rate (NEX), the two structural dummy variables, DU2 and DU3, as well as the interaction
variable ( ) are all significant at 5% level. However, the coefficient of the trade policy
interaction variable ( ), for the post reform trade episode is not significant.
The dummy variables DU2 and DU3 positive and significant results indicate that the elasticity of
real exchange rate of
⁄ during different trade liberalization episodes (trade reform era, post
reform era) are associated with a real depreciation of
⁄ compared to the period of trade re-
striction. This equally conforms with the theoretical (krueger, 1978) and empirical literature (Li,
2004), that credible trade liberalization has been associated with real depreciation.
The relative world price of mix-tradable to exportable (
⁄ ) has the largest impact on the real
exchange rate of a mix-tradable to non-tradable crop (
⁄ ) The estimated coefficient of
⁄ is positive with elasticity of about 0.59. The implication is that the domestic price of a mix-
tradable crop is positively and significantly affected by changes in the world price of mix-tradable
crop compared to the world price of exportable , thus leading to depreciation of real exchange rate
of
⁄ .
This is closely followed by the nominal protection coefficient (NPCm), with an elasticity of 0.56
percent. Thus, a one percent increase in NPCm translates to a 0.52 percent depreciation of the real
exchange rate of mix-tradable to non-tradable crop. The NPCm measures the extent to which the
domestic prices differ from world prices and hence is a measure of trade bias. The positive sign
(real depreciation) of the NPCm implies that a relative increase in domestic price of the crop leads
to increase in price of the mix-tradable crop and hence its real depreciation.
81
The external terms of trade (
⁄ ), expressed as the ratio of world price of export crop to im-
port crop, equally has a large effect on the real exchange rate of
⁄ . The elasticity of
⁄
is 0.47 percent with the expected positive sign. This indicates that a 1 percent improvement in
terms of trade will cause a 0.47 percent depreciation of the real exchange rate of
⁄ . This
implies that the intra-temporal income effect of terms of trade dominates the substitution effect in
this case. This is because Bogoev et al (2008) argues that, to restore the internal equilibrium the
real exchange rate is required to depreciate, as the improvement in the trade balance raises income
of the domestic economy and higher demand for the non-tradable goods emerges.
The nominal exchange rate, which is significant at 5 % level has an elasticity of 0.14 percent. This
leads to appreciation (negative effect) of real exchange rate of
⁄ . The coefficient of govern-
ment expenditure (GEX) bears a significant negative sign, with an elasticity of 0.15 percent. The
negative sign indicates that government spends more on non-tradable crops than tradable sector.
This in turn has appreciated
⁄ in real terms via the current account deficit. This result sup-
ports previous literature (Elbadwin, 1994) that reported that government expenditure usually falls
more on non-tradable compared to tradable. More so, rising tradable prices (due to reduced export
tax) reduces government expenditure, considering that taxes on agricultural export contribute sig-
nificantly to government budget. Since government spends more on non-tradable, the income loss
reduces prices and supply of the non-tradable due to reduced demand. This contracts the sector
and leads to appreciation of real exchange rate of
⁄ .
The elasticity of
⁄ in real terms with respect to trade openness during the era of trade re-
striction is approximately 0.15 percent. This implies that although increased trade openness is as-
sociated with lower (overvaluation of) real exchange rate of
⁄ (approximately 0.15 percent)
during this era, however, during the trade policy era indicated by the interaction variable
( ) the elasticity is 1.22 ( ) percent. Whereas the post reform trade
82
liberalization episode ( ) is not significant; however it also recorded a positive effect
too. The positive signs of these variables indicate that for each additional unit increase in trade
openness index during the post reform and reform liberalization episodes,
⁄ is estimated to
gain an additional 0.08 and 1.32 percent unit increase (depreciation) respectively compared to
that of trade restriction period. This corroborates with previous findings (Bogoev et al, 2008) that
trade depreciates the relative price of tradable to non-tradable. Thus, the increased trade openness
leads to a higher relative price of tradable, thereby resulting in depreciation of
⁄ in real terms.
Again, the insignificance of the post reform interactive variable, ( ), confirms a differ-
ent trade policy episode in Nigeria and reversal of some of the policies in the reform era.
- The Result of Relative price of Agriculture to Non-Agriculture(
⁄ ) Equation
As can be seen in table (4.3), the coefficient of the dummy variable for the post reform era (DU3)
is positive and significant at 5% level, whereas the coefficient for the reform era (DU2) is only
significant at 10 % level. This implies that the different trade liberalization episodes (reform and
post reform episodes) led to significant increases in agricultural terms of trade compared to trade
restriction era. This implies that the structural break caused by trade policy in these two eras fa-
voured more of the agricultural price index than that of non-agricultural price. This is not surpris-
ing, considering that liberalization of trade favours agriculture more than services (the non-
agricultural) because agricultural products are reasonably traded internationally unlike services.
The coefficient of external terms of trade(
⁄ ) was found to be positive and significant, with an
elasticity of 0.19 percent. This implies that an improvement in external terms of trade via a reduc-
tion in world price of importable or increase in price of exportable increases agricultural prices by
0.19 percent relative to that of non-agricultural sector. This is the substitution effect of external
terms of trade, which occurs when the domestic sector shifts the production towards the tradable
(exportable) sector due to improvement in terms of trade. This results in higher prices in the trada-
ble sector (agriculture) relative to the non-tradable sector.
83
The estimated coefficient of inflow of capital (CAP) has negative effect on agricultural terms of
trade. Though not significant, this shows that proceeds of capital inflow increase the demand for
non-agricultural goods, which is mostly non-tradable. This has decreased the agricultural terms of
trade. The result supports the work of Valdes (1986), who reported that large capital inflows se-
verely constrained the growth of agricultural production by reducing the competitiveness of agri-
cultural tradable sector, given that, a large net inflow of capital will induce a lower real exchange
rate and reduce the surplus in the current account and adversely affect agriculture.
The elasticity of agricultural terms of trade with respect to balance of trade (BOT) is 0.15 percent.
A 1 percentage point increase in balance of trade (due to increased margin of export over import)
would lead to approximately 0.14 percent decrease in agricultural terms of trade. This shows that
balance of trade appears to favour non-agricultural than agricultural sector.
The estimated coefficient of trade openness (TOP), which measures the elasticity of agricultural
terms of trade during the trade restriction era, was found to be negative and significant at 5% lev-
el. This shows that 1 percent increase in trade openness during this era leads to about 0.24 percent
decrease in agricultural price relative to that of non-agricultural sector. This suggests that the trade
favours non-agricultural sector more than agricultural sector. This could be attributed to the ne-
glect of agricultural activities during this era and the booming of oil prices which contracts the ag-
ricultural sector.
Whereas, the coefficient of the interactive variables ( ) and ( ) are posi-
tive, with coefficient of ( ) being significant while that of ( ) is not. This
indicates that during the post reform trade liberalization episode, the elasticity of agricultural
terms of trade with respect to TOP is 0.15 ( ). This shows that, for every addi-
tional unit increase in trade openness, the agricultural term of trade is estimated to increase by
0.24 during reform era and 0.39 during the post reform episode, compared to that of the era of
trade restriction. This implies that government trade and pricing policies during the post reform
era (1995-2012) strongly favored agricultural price over non-agricultural sector.
-Model fit and Diagnostic Check Result- The explanatory power of the endogenous models (i.e.
the first stage estimation equations) ranges from 92% - 83% (Table A3, see Appendix), indicating
good fits of the estimated endogenous variables. The Durbin-Watson statistics for real exchange
rate of pe/pn, Pm/pn and external terms of trade Pa/pna are 1.62, 1.83, and 1.6 respectively. The P-
84
value of the Bgodfrey statistics in that order are 0.27, 0.99 and 0.30. Judging from the test result,
the high p- value more than the 95% significance threshold, the test fails to reject null hypothesis
that there is no serial correlation. Equally, the Linktest model specification tests in that same other.
The Hat2 t- statistics are 0.88, 0.58 and 0.12 (Table A3) the insignificance of the Hat2 signifies
failure to reject the null hypothesis that there is no specification error. This is equally confirmed
by the p-values from the Ramsey RESET test.
4.5.2 Second Stage Estimation Result
-Agricultural Land Demand Result
Given that when a structural equation system is recursive (triangular), the two stage computation
is preferable, and predicted instruments from the first stage were used for the second stage analy-
sis. However, considering that this method of indirect least square is used for the estimation, ad-
justment was performed to the covariance matrix after the second stage estimation in order to
compute the correct standard error. Then the result of the regression is re-run.
85
Table 4.4 Second Stage Least Square Recursive Estimation Result
(1) (2)
VARIABLES MODEL A MODEL B (Std-Error corrected mod-
el)
( ) 0.303 0.303
(0.261) (0.281)
( ) -0.334 -0.334
(0.282) (0.303)
( ) -0.453*** -0.453***
(0.151) (0.163)
( ) 0.58*** 0.58**
(0.175) (0.189)
Dum 0.842*** 0.842***
(0.332) (0.358)
( ) 0.960*** 0.960***
(0.206) (0.221)
( ) -1.147*** -1.147***
(.229) (0.246)
L(npe*/pi)
* -0.426*** -0.426***
(0.0854) (0.092)
Dum.(lnpe*/pi
*) 0.441*** 0.441***
(0.113) (0.122)
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: Authors‟ estimation using stata 12.
86
On Table 4.4 column (1), Model A gives the estimates for the regression corresponding to the in-
strumental variable estimation. However, the standard errors do not take into account that
⁄ ,
⁄ and
⁄ were predicted in the previous regressions. The estimated result of the adjusted
covariance matrix of the standard error is given by model B, column (2). The coefficients of the
variables are the same with that of the model in table but with lower standard error and t-statistics
(see Tables A.7 and A8 in Appendix for details).
The „dum‟ variable represents dummy for the flexible exchange rate regime for the period, 1980-
2012, which represents the period of exchange rate devaluation in Nigeria. The value of the coef-
ficient indicates that a shift from fixed to flexible exchange rate regime (i.e period of exchange
rate devaluation), increases agricultural land demand elasticity by 84 percent(i.e. multiplied by
100). This corroborates with previous findings of Aune et al ( ) in Tanzania and Wunder (2003),
that if agriculture is a tropical country‟s main trade exposed sectors, then making it more competi-
tive through sharp and repeated devaluations will accelerate deforestation. This implies that real
devaluation prompt agricultural land expansion, both through increasing output prices and equally
by having land substituted for agricultural inputs in response to input price increases.
As can be seen from the result in table 4.4, agricultural terms of trade, (
⁄ ), i.e the relative
price of agriculture to non-agriculture has the largest relative price effect on agricultural land in
absolute terms. This measure captures the effect of sectorial and economy wide price incentive on
agricultural land demand. The elasticityduring fixed exchange rate
⁄
)is estimated to be about
0.96 and positive, indicating the importance of agricultural terms of trade in explaining agricultur-
al land area. The elasticity during the flexible exchange rate is -0.19 (i.e. 0.96-1.15). This implies
that during the period of flexible exchange rate regime, agricultural land elasticity with respect to
(
⁄ ) is lower by 1.15 percent. This shows that the elasticity is positive during fixed exchange
rate and negative during flexible exchange rate. This result indicates that, during the period of
fixed exchange rate, (1970-1986), exchange rate policy favoured non-agricultural products in Ni-
geria. This reduces the profitability of agriculture by lowering the output price for agricultural
products. This could be attributed to the spending and resource movements of Dutch diseases dur-
87
ing the period and the oil boom which drew resources out of agriculture sub sector to non-tradable
sector. Thus, depending on the income effect, the economically rational response of farmers to
depressed prices is either to increase total output to reach comparable export earnings (Convention
on Biological diversity CBD, 2002) or to reduce farm sector activities and switch to other eco-
nomic sectors, for instance service or manufacturing. However, given the scarcity of alternative
economic options outside of agriculture that exist in Nigeria, farmers are likely to expand produc-
tion and thus increase pressure on marginal areas.
Contrastingly, the predicted expansion in agricultural land due to devaluation of exchange rate
during the flexible exchange rate, may have actually lowered incentives to expand production and
thus contributed to decreased pressure on marginal lands as the negative coefficient of the interac-
tive dummy during the years of devaluation (
⁄
) shows.
The external terms of trade elasticity during the fixed exchange rate regime(
⁄ )was estimated
to be about 0.43 percent and is statistically significant at 5% level whereas, the elasticity during t
period of flexible exchange rate is 0.01 (i.e. -0.43+0.44). As the result shows, an increase of I per-
cent in the external terms of trade (e.g due to a sharp rise in the world price of exportable), will
lead to about 0.43 percent decrease in demand for agricultural land during the fixed exchange rate
regime due to overvaluation of exchange rate. The negative effect of external terms of trade shock
signifies that the income effect of this variable dominates the substitution effect during the period.
Conversely, a 1 percent increase in the external terms of trade during the flexible exchange rate
regime (
⁄ ), will actually increase agricultural land area by approximately 0.01 per-
cent. This suggests that during the period of flexible exchange rate regime, agricultural land de-
mand elasticity with respect to (
⁄ ), is higher by about 0.44 percent. The estimated elastici-
ties are plausible in the light of existing literature, given that Valdes (1986) reported that large ex-
ogenous increase in export price associated with a booming sector appreciates the exchange rate
due to the spending effect of the additional income from „dutch disease‟ adversely affects the non-
booming tradable sector. Obviously, in Nigerian situation, the decreased demand in agricultural
land during the fixed exchange rate era is as a result of large influx of foreign exchange resulting
88
from world oil export prices in the 1970s‟. The induced resource movement effect of new reve-
nues led to higher employment and government expenditure in the booming sector, neglecting ag-
riculture and consequently decreasing agricultural production and land demand.
The coefficient of the real exchange rate of exportable crop to non-tradable crop during the period
of trade restriction
⁄
) was found to be statistically insignificant in both period of exchange
rate regime. Despite the insignificance of the coefficient of
⁄
), which represents the elasticity
during fixed exchange rate regime, the result records 0.38 percent increase in agricultural land in
this period compared to the ( ) percent decrease in agricultural land during the
period of flexible exchange rate. From the analysis, the elasticity of agricultural land with respect
to the real exchange rate of
⁄
) is lower by approximately 0.40 percent during the period of
flexible exchange rate regime. Although, devaluation is usually thought to increase exportable
crop price, and thus, the incentive to deforest for agricultural purpose would increase with devalu-
ation as argued by most empirical studies. However, in Nigerian scenario, this does not hold, con-
sidering that the coefficient of the interactive dummy (
⁄
) is negative and not signifi-
cant. This indicates that increases in domestic price of exportable perennial crop (cocoa) or de-
crease in non-tradable crop (maize) due to the devaluation of the exchange rate decreases agricul-
tural land. Thus, there is a direct relationship between the price of non-tradable (annual) crop and
agricultural land. Consequently, the result does not follow the usual a priori expectation of some
empirical literature; however, it is consistent with the findings of Angelsen et al (1999) and
Gbetnkom (2003). These studies equally reported negative insignificant effect of coefficient of
perennial export crop. Thus, as Angelsen et al (1999) noted, this could be because perennial ex-
port crops are less erosive than annual crops and do not deplete soil fertility fast. More so, with
increasing prices from devaluation, farmers of perennial export crop adjust their production incen-
tive by rehabilitating existing plantation rather than expanding to new fertile lands. This seems to
be the case in Nigeria, considering that a study done by UNEP (2001) in Nigeria reported that in-
centives created by liberalization policies, including higher output prices was more of rehabilita-
tion of existing cocoa farms rather than a further expansion of cultivated land.
89
The elasticity of agricultural land demand with respect to relative price of mix-tradable (annual)
crop to non-tradable crop during the fixed exchange rate(
⁄
) is 0.45 percent. This indicates
that a 1 percent increase in the output price of a mix-tradable annual crop (cassava) relative to a
non-tradable crop(maize) is associated with a 0.45 percent increase in the demand for agricultural
land during the flexible exchange rate regime due to exchange rate devaluation. Whereas, the elas-
ticity during the flexible exchange rate(
⁄
)is ( ) percent. As the
result shows, the elasticity of agricultural land demand during the flexible exchange rate is higher
by about 0.58 percent compared to what is obtainable during fixed exchange rate. Thus from the
result, it can be deduced that real devaluation effect on the real exchange rate of
⁄
), which
increase the relative output price of Pm, an annual tradable crop provided an incentive for in-
creased agricultural land demand. This result lends support to the works of Angelsen et al (1999),
who reported that increasing output price for annual crops has in part been responsible for conver-
sion of forest land into crop land. As the result indicates, with devaluation favouring exports, this
may have prompted a shift from a mix-tradable annual crop with a greater portion of the crop be-
ing consumed domestically. More so, given that it takes less than a year for cassava to mature
compared to cocoa, it will be more profitable for export farmers to switch to cassava production.
The positive effect of increase in(
⁄
) on agricultural land is not surprising considering that
Nigeria has comparative resource advantage in the production of cassava and currently is the
worlds‟ leading producer, with 33 million metric tons of fresh tubers per annum (FAO, 2008).
Hence the price is perfectly elastic. In addition, an increase in annual exportable crop is expected
to have a negative environmental impact (increased Agricultural land demand), since annual crops
deplete soil faster than perennials. They require more new lands to boost productivity, especially
under low-input extensive farming system that is prevalent in Nigeria.
Model Fit and Diagnostic check Result- The model fit is satisfactory, considering that almost all
the variables are significant. The adjusted R2 shows that the explanatory variables explain 89% of
the variation in Agricultural land demand (Table A3, see Appendix). The Durbin-Watson statistics
(Table A3) is 2.4. This indicates that the null hypothesis indicating the presence of serial correla-
90
tion has been rejected. In the Linktest for model specification, the Hat2 is not significant (Table
A3). Thus, Linktest has failed to reject the assumption that the model is specified correctly. This is
equally confirmed by The Ramsey RESET test with a P- value (0.94) that is higher than the
threshold of 95% significance. So the test fails to reject the null and thus conclude that no more
variables are needed.
91
CHAPTER FIVE
SUMMARY, POLICY IMPLICATIONS AND CONCLUSION
5.1 Summary of Findings
The analysis of the models yielded interesting results. The solution of the optimal control model
shows that the decision to convert forest land either to agricultural or infrastructural/ urban uses
depends on the discounted marginal net returns of various use of land. The solution to the optimal
model shows that the optimal allocation of forest land is obtained at the point where the marginal
net return for wood production, marginal environmental benefit and social cost of deforestation
associated with wood extraction, equals the marginal benefit of agricultural production from con-
verted forestland and marginal benefit of urban land. The theoretical analysis indicate that the
economic impact of proximate deforestation cause will depend on (1)- The relative return of forest
sector and its competing land sectors which is determined by the output and input prices of respec-
tive land uses. (2)- The shadow price of forest land. This suggests an inverse relationship between
relative returns of competing land uses.
The major result from the proximate deforestation equations (the first model) indicate that in-
creases in productivity of competing land sectors, i.e industrial productivity (IND) and agricultural
productivity (AGP) significantly increase the proximate deforestation causes, thus raising the op-
portunity cost of competing forest land uses. Whereas, the forest rent (FOR), which proxies the
shadow value of forest is negatively related to demand for infrastructural extension. This indicates
that higher return or profitability in competing land use sector is among the main economic factors
driving proximate deforestation causes in Nigeria. In the agricultural land equation, the agricultur-
al value added has the highest statistical and economic impact on agricultural land demand with
elasticity of 0.93 percent. Thus, it is expected that AVA should be a strong policy variable that
policy makers can use to discourage deforestation in Nigeria. The staple crop price during the
trade liberalization episodes increases agricultural land expansion whereas; the cash crop price
leads to decline in agricultural land demand.
The demand for wood extraction is significantly higher during the reform era compared to the pe-
riod of trade restriction and post reform trade liberalization episode. The estimated elasticity of
demand for wood extraction with respect to timber prices is significantly positive during the post
restriction era whereas, increasing timber prices during the reform and post reform trade liberali-
92
zation era decreases the demand for wood extraction. The elasticity of transport price in different
trade policy era has the greatest effect on infrastructural extension.
Generally, the weak statistical evidence from the interactions of specific variables with post re-
form dummy (DU3) suggest that there were trade policy reversals in the reform era. The result
from the proximate deforestation causes indicates that increases in productivity in competing land
during exchange rate and trade liberalization increases the proximate deforestation causes. Conse-
quently, the null hypotheses that the proximate deforestation causes in Nigeria in varying trade
policy era is not affected by deforestation agent‟s decision parameters is rejected.
In model 2, first stag estimation, the result shows that trade liberalization will stimulate export by
devaluation of the real exchange rate of
⁄ and
⁄ . This evidence is provided by the trade
restriction episode dummy variables (DU2 and DU3) which reflect the differential positive effect
of different trade liberalization episodes on the relative prices of
⁄
⁄ . These posi-
tive effects imply a depreciation of real exchange rate of these variables. The devaluation effect of
trade policy on the real exchange rate of these variables (DU2.lnTOP) and (DU3.lnTOP) on the
real exchange rate of
⁄
⁄ . The elasticities of these variables in both equations show
that trade openness index increases (i.e. depreciates) the real exchange rate of the relative prices
during the reform and post reform era trade episodes. This contrasted with the significant negative
effect (i.e over-valuation) of trade openness during the era of trade restriction. Thus, liberalization
of trade policy created strong production incentive in favor of exportable crops relative to non-
tradable crops.
The estimated elasticities of external terms of trade (
⁄ ) on all the equations in the first stage
estimation are positive and significant. This implies that increase in the world price of exportable
relative to importable, reduces the domestic price of importable and thus decreases the demand for
non-tradable crops. This will cause the depreciation of relative prices in real terms. Whereas in the
the agricultural terms of trade equation, (
⁄ ) the substitution effect of external terms trade
results in higher price of agricultural relative to non-tradable sector.
93
The magnitude of the elasticity of nominal protection coefficient (NPC) in both real exchange rate
equations, (0.62 and 0.56), shows that NPC has an important depreciation (positive) effect on the
relative prices. It follows that government expenditure appreciates the relative prices in both equa-
tions in real terms. A 1 percent increase in government expenditure leads to .14 and 0.12 percent
decrease in the real exchange rate of
⁄ and
⁄ respectively.
The findings from the first stage estimation model clearly show that trade liberalization episodes
initiated in Nigeria during the reform and post reform era have positive effect on exportable and
mix-tradable good production. Thus trade liberalization increased substantially, the incentive to
produce exportable related to non-tradable, whereas, trade restriction discriminated against export.
It equally led to significant increases in agricultural terms of trade. Therefore, the null hypotheses
that relative price of different crop category do not respond to changes in trade policy is rejected.
In the second stage estimation model, the estimates show that an increase in the relative price of
export crop (perennial) to non-tradable crop (annual) during exchange rate devaluation decreases
agricultural land area. Although, the elasticities are not significant in both fixed and exchange rate
regime, this suggests that increasing agricultural land area during exchange rate devaluation is not
caused by increasing producer price of export crop, cocoa.
There is however, strong evidence that an increasing relative price of mix-tradable (annual) crop
to non-tradable crop during the period of exchange rate devaluation contributed significantly
(0.13) percent of agricultural land area expansion. This result indicates that with exchange rate
devaluation and an increase in relative price of mix-tradable, annual crop increases agricultural
land demand compared to the period of exchange rate devaluation. This suggests that increasing
producer price of cassava relative to non-tradable crop (maize) increases the demand for agricul-
tural land. This implies that with exchange rate devaluation, agricultural land area in Nigeria in-
creases with increase in annual exportable crops but declines with increase in perennial exportable
crop.
The result also provides evidence that a 1 percent increase in the external terms of trade during the
exchange rate overvaluation decreases the demand for agricultural land area. In the exchange rate
devaluation period, the demand for agricultural land area significantly increases. The findings in
this model indicate the strong intermediary role of the real exchange rate through the relative out-
94
put prices movement on agricultural land. Thus, the null hypothesis that agricultural land area
does not respond to changes in exchange rate policies is rejected.
5.2 Policy Implication of the Findings
The reform process in Nigeria did not recognize the importance of complementary policies
that will address the adverse environmental effect of the expenditure switching policies on
sustainability of resource use. There aren‟t policies designed to analyze the effect of these
expenditure switching policies on environment to protect vulnerable sectors that will be af-
fected by the reform.
Although, a suboptimal response from forest resources could be achieved by devaluation
of exchange rate as a means of enhancing export production, however, a socially optimal
resource use can be achieved by imposing a pigouvian tax. This will internalize the envi-
ronmental opportunity cost that is not considered in converting forest land to other uses.
If a sustainable forest resource is a targetable policy in Nigeria, then the result from the
study reinforces the need to simultaneously combine trade liberalization and exchange rate
devaluation policies with appropriate public policies that will mitigate the adverse envi-
ronmental effect from market failure.
If Nigerian agricultural sector is to maintain a sustainable competitiveness in international
trade, then it must invest in intensive agriculture that will boost productivity per hectare
especially for annual crops and also provide peasant farmers with easy access to subsidized
input, considering that trade liberalization equally increases input prices, and acreage is the
main determinant of level of output in rural agricultural production.
Land use laws in Nigeria need to be re-designed to encourage sustainable environmental
consideration and economic objective. This can be achieved by enacting legal and institu-
tional framework that will regulate the activities of forest use against open access exploita-
tion.
Considering that demand for shelter in urban areas can be met either by constructing high
rise buildings or by expanding land through deforestation, increasing investment per hec-
tare of infrastructural land will therefore, reduce urban land expansion. More so, Policy
makers can place high tax rate on low buildings.
95
5.3 Conclusion
This study uses a theoretical and empirical model to explore the economic determinants of proxi-
mate deforestation causes in Nigeria. In the theoretical model, an optimal control analysis allows a
social planner to choose deforestation paths that will maximize net social benefits among three
competing economic land uses: wood extraction, agricultural land expansion and infrastructural
extension (urban land). The solution of the optimal model shows that along the optimal deforesta-
tion path, the economic impact of proximate deforestation cause depends on the relative discount-
ed net marginal benefits of the forest land. The optimal model provided the theoretical framework
to empirically examine how deforestation agents‟ decision parameters influence the proximate de-
forestation causes in different trade policy era in Nigeria, spanning the period 1970-2012. The re-
sult indicates that relative productivity from competing land uses raises the opportunity cost of
forest land and makes conversion to an alternative land use attractive. It equally shows that not all
price increases due to trade liberalization and exchange rate devaluation will lead to increase in the
proximate deforestation causes. Trade liberalization and exchange rate devaluation increases the
producer prices of staple and cash crops, however, agricultural land area increases with increasing
staple crop price and declines with increasing cash crop price.
An analytical framework for a small open economy producing four crops: An exportable, import-
able, mix-tradable and non-tradable crop was developed. Then using two stage recursive model,
the analytical model was empirically analyzed to examine how direct and indirect effect (through
the induced effect of the exchange rate) of changes in domestic trade policies influence agricultur-
al land expansion. This is done through the relative price mechanism of different crop category.
The insight from the model indicates that although domestic trade liberalization creates strong
production incentive in favour of exportable and mix-tradable crop relative to non-tradable crop,
the resulting environmental effect on agricultural land use depends on (1)- the nature of export
crop grown and(2)- The price elasticity of the crop. Thus, increase in price due to exchange rate
devaluation could only increase production incentive and agricultural land area demand signifi-
cantly, when the export or mix-tradable crop is price elastic. However, if it is price inelastic, a de-
preciating exchange rate though increases export production incentive, will not significantly in-
crease agricultural land area expansion. Given a set of category of crops and specific elasticities to
prices, future research can study the comparative impact of relative prices movements due to in-
96
ternational trade liberalization on agricultural land demand between developing and developed
countries.
97
REFERENCES
Adeoye, N.O., Abegunde, A., Adeyinka, S. (2012). "Geospatial Analysis of Deforestation and Land Use
Dynamics in a Region of South Western Nigeria" in Deforestation around the World; Moutinho, P.
(Eds); In Tech
Adu, G., Marbuah, G. and Mensah, J.T. (2012). "Contribution of Agriculture to Deforestation in the Trop-
ics: A Theoretical Investigation." African Review of Economics and Finance, 3(2), 1-11.
Akande, T. (2003). "Nigerian Macro Report: A Report to the Afrint Project." Unpublished paper. The Ni-
gerian Institute of Social and Economic Research (NISER) United States Department of Agricul-
ture: International Database (USPSD)
Allen, J. and Burnes, D.F. (1985). "The Causes of Deforestation in Developing Countries." Annals of the
Association of American Geographers, 75(2), 163-184.
Allen, S.C. (1992). "Loss of a Tree Means Loss of an HIV Blocker." Boston Globe.
Alonso, W. (1964). Location and land use; Cambridge: Harvard University Press.
Amelung, T. and Diehl, M. (1992)."Deforestation of Tropical Rain Forests. Economic Causes and Impact
on Development". KielerStudien, 241.-Mohr, J.C., Tὔbingen.
Anderson, K. (1992). "Agricultural Trade Liberalization and Environment: A Global Perspective." World
Economy, 5:153-171.
Angel, S., Sheppard, S.C. and Civco, D. L. (2008). "Dynamics of Global Urban expansion, Transport and
Urban Development." The World Bank, 3(4), 21-32.
Angelsen A. and Kaimowitz, D. (1999). "Rethinking the Causes of Deforestation: Lessons from Economic
Models." The World Bank Observer, 14 (1), 73-98.
Angelsen, A. (1995). "Shifting Cultivation and Deforestation: A Study from Indonesia." World Develop-
ment, 23(10), 1713-1729.
Angelsen, A. (1999). "Agricultural Expansion and Deforestation: Modeling: The Impact of Population,
Market Forces and property Rights." Journal of Development Economics, 58 (1), 185-218.
Angelsen, A. and Kaimowitz, D. (1998). "Rethinking the Causes of Deforestation; Lessons from Economic
Models." The World Bank Research Observer, 14(1), 73-98.
Angelsen, A., Shitndi,E. and Aarrestad, J.C. (1999). "Why do Farmers expand their Land into Forests?
Theories and Evidence from Tanzanian." Environment and Development Economics. 4, 313-331.
Asheim, G. (1994). "Net National Product as an Indicator of Sustainability." Scandinavian Journal of
Economics, 96(2), 257-265.
Babu, S.C. and Hassan, R. (1995). "International Migration and Environmental Degradation: The case of
Mozambican Refugees and Forest Resources in Malawi." Journal of Environmental Management,
43, 233-247.
98
Bann, C. (1997). "An Economic Assessment of Tropical Forest Land Use Options." Ratanaakiri, Province,
Camodia EPSEA Research Report Series, Economy and Environment Program for Southeast, Asia,
Singapore.
Barbier, E. B. (2000). "Links between Economic Liberalization and Rural Resource Degradation in the De-
velop ing Rgions." Agricultural Economics, 23, 299-310.
Barbier, E.B. and Burgess, J.E. (1997). "The Economics of Tropical Forests Land Use Options." .Land
Economics, 73(2), 174-195.
Barbier, E.B. and Rauscher, M. (1994). "Trade, Tropical Deforestation and Policy Interventions." Envi-
ronmental and Reserve Economics, 4, 75-90.
Barbier, E.J. and Burgess, J.C. (1996). "The Economic Analysis of Deforestation in Mexico." Environmen-
tal and Development Economics, 1: 203-239.
Barton, J., Hertzler, G., Hassan, R. (1991). "Deforestation and the External Costs Imposed on Agriculture
in Sudan." Paper presented at the 35th Annual Conference of the Australian Agricultural Econom-
ics Society, Armidale, New South Wales, 11-14 February.
Bawa, K.S. and Dayanandian, S. (1997). "Socioeconomic Factors and Tropical Deforestation." Nature,
386(6625), 562-563.
Benhin, J.K. and Barbier, E.B. (1999). " A Case Study Analysis of the Effects of Structural Adjustment on
Agriculture and on Forest covers in Cameroon." A Report for Center for International Forest Re-
search (CIFOR), University of York.
Benhin, J.K. and Barbier, E.B. (2001). "The Effects of the Structural Adjustment Program on Deforestation
in Ghana." Agricultural and Resource Economics Review, 30(1), 66-80.
Benhin, J.K.A. (2006). "Agriculture and Deforestation in the Tropics; A Critical Theoretical and Empirical
Review." Ambio, 35, 9-16.
Benhin, K.A. and Sultan, R. (2006). "Agricultural Expansion, Interest Groups and Deforestation in Mauri-
tius: A Dynamic Approach." A Proposed Paper for the Third World Congress of Environmental
and Resource Economists. Kyoto, Japan.
Bogoev, J.; Terzijan, S.; Egert, B and Petrovska, M. (2008). "Real exchange rate Dynamics in Macedonia:
Old Wisdom and new Insights." Economics- The open Access open Assessment E-Journal, 2 (18),
1-19.
Bollen, K.A., Kirby, J.B., Curran, P.J., Paxton, P.M. and Chen, F. (2007). "Latent Variable Models under
Misspecification: Two Stage Least Squares (2SLS) and Maximum Likelihood (ML) Estimators."
Sociological Methods and Research, 38: 46-86.
Briggs, I. N. (2005). "Nigeria: Protecting Domestic Productions in Times of Liberalization and the Short,
Medium and Long Term Implications for Regional Integration." Paper Presented at the ECOWAS
Regional Workshop on Multilateral Trade Agreements, Parliament of Ghana and The Friedrich
Ebert Stiftung Ghana, Accra.
99
Brooks, D, B. and Andrews, P.W. (19740. "Mineral Resources, Economic Growth and World Population."
Science, 185, 13-19.
Brueckner, J.K. and Fansler, D.A. (2001). "The Economics of Urban Sprawl; Theory and Evidence on the
Spatial Size of Cities." The Review of Economics and Statistics, 479-482.
Bulte and Van Soest, D. P. (1996). "A Note on High Discount rates and Depletion of Primary Forest." Ag-
ricultural and Resource Economics, 21(2), 341-350.
Capistrano, A.D. and Kiker, C.F. (1995). "Macro-Scale Economic Influences on Tropical Forest Deple-
tion." Ecological Economics, 14(1), 21-29.
Capozza, D.R., Helsley, R.W. (1990). "The Stochastic City." Urban Economics, 28, 187-203.
Center for Biological Diversity, CBD. (2002). "Assessing the impact of trade liberalization on the conser-
vation and sustainable use of Agricultural Biological Diversity." Peer Review.
Chand, R. (1999). "Effects of Trade Liberalization on Agriculture in India: Commodity Aspects." The
Center for research and Development of Coarse, the GGPRT Centre, Working Paper, 1-82.
Chichilnisky, G. (1994). "North-South Trade and the Global Environment." American Economic Review,
84(3), 851-874.
Clark, C.W. (1990). Mathematical Bioeconomics: The Optimal Management of Renewable Resources.
(Eds) New York: Wiley, 91-95.
Contreras-Hermosilla, A. (2000). "The Underlying Causes of Forest Decline." (=CIFOR Occasional Paper,
30) – Center For International Forestry Research, Bogor.
Cornes, R. and Sandler,T. (1983). "On Commons and Tragedies." American Economic Review, 73, 806-
8144
Cropper, M. and Griffiths, C. (1994). "The Interaction of Population Growth and Environmental Quality."
American Economic Review, 85(2), 250-254.
Dasgupta, P. and Heal, G. (1974). "The Optimal Depletion of Natural Resources." Review of Economic
Studies, (Symposium issue), 3-28.
Deacon, R. T. (1995). "Assessing the Relationship Between Government Policy and deforestation." Jour-
nal of Environmental Economics and Management, 28, 1-18
Deacon, R.T. (1994). "Deforestation and Rule of Law in a Cross – Section of Countries." Land Economics,
70(4), 414-430.
Deng, X., Huang, J., Rozelle, S. and Uchida, E. (2008). "Growth, Population and Industrialization and Ur-
ban Land Expansion of china." Urban Economics, 63(1), 96-115.
Devarajan, S. and Fisher, A.C. (1981). "Hotelling's Economics of Exhaustible Resources: Fifty Years Lat-
er." Economic Literature, XIX, 65-73.
Dixt, A.K. (1990). Optimization in Economic Theory (2nd
Edition.). Oxford University Press, New York.
100
Dornbusch, R. (1974) "Tariffs and Non-traded Goods." Journal of international Economics, 4: 177-185.
Edwards, S. (1988). Exchange Rate Misalignment in Developing Countries (Eds); Baltimore and London:
John Hopkins University Press.
Edwards, S. (1989). Real Exchange Rates Devaluation and Adjustment(Eds): Cambridge Massachusetts:
MIT Press.
Ehui, S. K. and Hertel, T.W. (1989). "Deforestation and Agricultural Productivity in the Cote d' Ivore."
American Journal of Agricultural Economics, 71(3), 703-711
Ehui, s.K., Hertel, T.W. and Preckel, P.V. (1990). "Forest Resource Depletion, Soil Dynamics and Agricul-
tural Development in The Tropics." Economics and Environmental Management, 18(2), 136-154.
Elbadawi, I. A. (1988). "Foreign Trade, Exchange Rate and Macroeconomic Policies and the Growth Pro-
spects for Sudanese Agriculture." New Haven, Conn: Economic growth Center, Yale University.
Falusi, A.O. (1997). "Agricultural Development and Food Production in Nigerian: Problem and Pro-
spects," In: Shaib, B., Adedipe, A., Aliyu, A. and Jir, M.M. (Eds). Integrated Agricultural Produc-
tion in Nigeria: Strategies and Mechanisms for Food Security, National Agricultural Research Pro-
ject, 1997.
FAO. (2001). "Global Forest Resources Assessment 2000: Main-Report." FAO Forestry Paper, 140-
Rome.
FAO. (2005). "Global Forest Resources Assessment." FAO Forestry Paper 147, Rome.
FAO.(2001). "Global Forest Resources Assessment 2000-Main Report." FAO Forestry Paper 140, Rome,
Italy.
FAO.(2010). "Global Forest Resources Assessment 2010 Main Report." FAO Forestry Paper 163, Rome.
FAOSTAT (2008). Available online at http://faostat.fao.org/site/5501.(Accessed on September, 2013).
FORMECU.(1996). "Forest Resources Study Nigeria. Revised National Report Volume." Overview pre-
pared by Beak Consultants LTD, GEOMATICS International. Inc. Geomatics Nigeria LTD.
Gbetnkom, D. (2003). "Forest Management and Climate Change in Africa: Evidence from Cameroun."
Unpublished Work.
Geist, H.J. and Lambin, E.F. (2002). "Proximate Causes and Underlying Driving Forces of Tropical Defor-
estation." Bio Science, 52(2), 143-150
Giest, H.J. and Lambin, E.F. (2001). "What Drives Tropical Deforestation? A meta-Analysis of Proximate
and Underlying Causes of Deforestation Based on Sub national Case Study Evidence." LUCC Re-
port Series (4), CIACO, Louvain-La-Neuve.
Grainger, A. (1993). Controlling Tropical Deforestation (Eds.). Earthscan, London.
Green W. H. (2012). Econometric Analysis (7th Edition); Upper Saddle River: NJ. Prentice Hall.
101
Greene,W.H. (2002). Econometric Analysis (5th Edition); Prentice Hall, upper Saddle River, Jersey.
Gujarati, D.N., Sangeetha. (2007). Basic Econometrics (5th Edition); Tata McGraw-Hill, New Delhi.
Gullison, R.E., Losos, E.C. (1993). "The Role of Foreign Debt in Deforestation in Latin America." Conser-
vation Biology, 791), 140-147.
Hanley, N., Shrogen, J.F., white, B. (2001). Introduction to Environmental Economics (Eds); Oxford: Ox-
ford university Press, Oxford.
Hassan, R. and Hertzler, G. (1988). "Deforestation from the Overexploitation of Wood Resources as a
Cooking Fuel: A Dynamic Approach to Pricing Energy resources in Sudan." Energy Economics,
10(2), 163-168.
Hassan, R., Hertzler, G. and Benhin, J. (2009). "Depletion of Forest Resources in Sudan: Intervention op-
tions for Optimal Control." Energy Policy, 37, 1195-1203.
Hotelling, H. (1931).„The Economics of Exhaustible Resources.‟Journal of Political Economy, 39, 137-
173.
Jaiyeoba, I.A. (2002). Environment Africa Atlases: Atlas of Nigeria( Les Eds). JA, 122-123, Paris
Kaimowitz, D. and Angelsen, A. (1998). "Economic Models of Tropical Deforestation .A Review." Centre
for International Forestry Research, 1-128, Jakarta.
Kanafani, N. (2000). "Resource Economics economics and the Theory of Exhaustible Resources." A Paper
Presented at Policy Reform and Natural Resource Management.
Kant, S. and Redant, A. (1997). "An Econometric Model of Tropical Deforestation." Forestry Economics,
3(1), 51-86
Kissinger, G., Herold, M., De Sy, V. (2012). "Drivers of Deforestation and Forest Degradation." A Synthe-
sis Report for REDD + Policymakers.
Krueger, A., Schiff, M. and Valdes, A. (1998). "Agricultural Incentives in Developing Countries: Measur-
ing the effects of Sectorial and Economic Wide Policies." World Bank Economic Review, 2:225-
271.
Krueger, A.O. (1978). Foreign Trade Regimes and Economic Development: Liberalization Attempts and
Consequences (Eds); Lexington Press: Mass, Ballinger.
Lamb R. L. (2000). "Food Crops, Export and Short Run Policy Response of Agriculture in Africa." Agri-
cultural Economics, 22, 271-298.
Lambin, E. F. (1994). "Modeling Deforestation Process.AReview." TREES Series B: Research Report,( 1) -
Office of Official Publications of the European Community, Luxembourg.
Lanloski, J. (1997). "Environmental Effects of Agricultural trade Liberalization and Domestic Agricultural
Policy Reforms." United Nations Conference on Trade and development, UNCTAD Discussion
Papers, Palais, des Nations, 1211 Geneva, Switzerland.
102
Laurence, W.F. (1999). "Reflections on the Tropical Deforestation Crises." Biological Conservation. 91,
109-117.
Lyon, K.S. (1999). "The Costate Variable in Natural Resource Optimal Control Problems." Natural Re-
source Modeling,12(4), 413-426.
Maddox, J.(1971). The Doomsday Syndrome London (Eds): Macmillian.
Mainardi, S. (1998). "An econometric Analysis Factors Affecting Tropical and Subtropical Deforestation."
Agrekon37(1), 23-62.
McAllister, R., Beard, R., Asufu-Adjaye, J. (2000). "An optimal Control Model of Deforestation in the
PDR of Laos." A Paper presented at 4th Annual Conference of the Australian Agricultural and re-
source Economics Society, Australia.
McConnell, E.K, (1989). "Optimal Quantity of Land in Agriculture." Northeastern Journal of Agricultural
Resource Economics, 18 (Oct.), 63-72.
Meadows, D.H., Meadows, D.L., Randler, J. and Behrans, W. (1972). The Limits to Growth: A report for
the club of Rome's Project on the Predicament of Mankind (Eds.). New York ,Signet, NY.
Merem, E.C., Wesley, J., Tumasi, Y.A., Richardson, C. and Romorno, C. (2012). "Tropical Forest land-
scape Change and the Role of Agro forestry Systems in Southern Nigeria." British Journal of En-
vironment and Climate Change, 2(3), 288-317.
Mills, E.S. (1972). Urban Economics (Eds); Glenville 111, Scott Forestman.
Minten, B., and Kyle, S. (1995). "The Impact of international Prices and exchange Rates on Domestic Food
Prices in Zaire", An unpublished Thesis.
Momodu, A.S., Siyanbola, W.O., Pelemo, D.A., Obioh, I.R., Adebina, F.A. (2011). "Carbon Flow Pattern
in the Forest Zones of Nigeria as Influenced by land-Use Change." African Journal of Environmen-
tal Science and Technology, 5(9), 700-709.
Murali, K.S. and Hedge, R. (1997). "Patterns of Tropical Deforestation." Journal of Tropical Moist Forest
Science, 9(4), 465-476.
Muth, R.F. (1969). Cities and Housing. Chicago: University of Chicago Press, Chicago.
Myers, N. (1980). "The Present Status and Future Prospects of Tropical Moist Forests." Enviromental Con-
servation, 7(2), 101-114.
Myers, N. (1991). "Tropical Forests: Present Status and Future Outlook." Journal of Climatic Change,
19(3), 3-32.
Myers, N. (1992) "Tropical Forests: The policy Challenge." Environmentalist, 12(1), 15-27.
Myers, N. (1993). "Tropical Forests: The main Deforestation Fronts." Environmental Conservation, 20(1),
9-16.
103
National Population Commission, (2004). "National policy on Population for Sustainable Development."
2004 NPC Report Series No 1, Abuja.
Nordhaus, W.D. (1973). "World Dynamics: Measurement without Data." The Economic Journal, 15(7),
1156-1183.
Olabatunbosun, A. (2010). "The Need for a Legal Regime for a Sustainable Forest Management in Nige-
ria." IUCN Academy of Environmental Law e-Journal Issue, (1), 1-9.
Oni, O.A., Oladele, O.I. and Ajayi, O.M. (2013). "The Economics of Tropical deforestation in Nigeria."
Life Science Journal, 10(2), 1048-1055.
Osemeobo, G. J. (2012). "Back to Tradition: Taboo in Bio conservation in Nigeria. "International Journal
of Agricultural Sciences, 3, 351-356.
Osemeobo, J.G. (1993). "The Hazards of Rural poverty; Decline in Common Property Resources in Nigeri-
an Rainforest Ecosystems." Environmental Management, 38(3): 201-212.
Oyejide, T. A. (1986). "The Effects of Trade and Exchange Rate Policies on Agriculture in Nigeria." Re-
search report No 55, International Food Policy Research Institute.
Oyekale, A.S. and Yusuf, S.A. (2008). "An Error correction modeling (ECM) of the Determinants of De-
forestation in Nigeria." Sustainable Forestry, 23(1), 19 -36.
Panayotou, T. (1994). "Empirical Tests and Policy Analysis of Environmental Degradation at different
Stages of Economic Development." Working Paper 238.International labour Office, Technology
and Employment Pogramme, Geneva.
Panayotou, T.H. and Ashton, P.S. (1992). Not by Timber alone: Economics and Ecology for Sustaining
Tropical Forests (Eds.); Island Press: Washington D.C.
Panayoyou, T.H. and Sungsuwan, S. (1994). "An Econometric Analysis of causes of Tropical Deforesta-
tion: The Eases of Northwest Thailand." In Causesof Tropical Deforestation, The Economic and
Statistical analysis of Factors Giving Rise to the Loss of Tropical Forests. Brown, K., Pearce,
D.W. (eds). University College London Press, London, 192-210.
Pearce, D.W. and Brown, K. (1994). "Saving the World's Tropical Forests." In Brown, K. and Pearce, D.W.
(Eds). The Causes of Tropical Deforestation: The Economic and Statistical Analysis of Factors
Giving Rise to the Loss of the Tropical Forests, UCL Press, London.
Pearce, D.W. and Turner, R. K. (1990). Economics of Natural Resources and the Environment (Eds); Har-
vester Wheatshef Press, Hemel Hempstead: London.
Pearce, D.W. and Warford, J.J. (1993). World Without End: Economics, Environment and Sustainable De-
velopment (EEds); World Bank/ Oxford University Press, Oxford.
Perrings, C. (2000). "The Biodiversity Convention and Biodiversity Loss in Sub-Saharan Africa." In :The
Economics of Biodiversity Conservation in Sub-Saharan Africa: Mending the ARIC. Perrings, C.
(Eds) Edward Elgar, Cheltenham, U.K., 1-37.
104
Phillips, P.C. and Perron, P. (1988). "Testing for a Unit Root in Time Series Regression." Biometrica, 75,
335-346.
Pinchot, G. (1910). The fight for Conservation Doubleday (Eds); Page and Company, New York, NY.
Pontryagin, L.S., boltyanskii, R.V., Gamkrelidze, R.V., Mishchenko, E.F. (1962). The Mathematical Theo-
ry of Optimal Pocessse ; Trirogoff, New york.
Pursell, G.and Gaputa, A. (1997). "Trade Policies and Incentives in Indian Agriculture: Methodology,
Background Statistics, and Protection and incentive indicators Sugar and Sugarcane." Background
Paper No1, The World Bank.
Repetto, R. (1986). "Economic Incentives for Sustainable Production: A Paper Prepared for the Economic
Development Institute Seminar on Land and Water Management." World Resources Institute.
Repetto, R. and Gillis, M. (1988). Public Policies and the Misuse of Forest Resources (Eds); Cambridge
University Press: London.
Rerkasem, B. (1996). "Montane Mainland Southeast Asia in Transition." Chiang Mai University Consorti-
um: Chiang Mai.
Rice, T., Ozing, S., Marijnssen, C., Gregory, M. (2000). "Trade Liberalization and its' Impacts on Forest."
An Overview of the Most Relevant Issues, (Fern) Forest for Life Campaign and the Trade and In-
vestment Unit of WWF International.
Rudel, T. and Roper, J. (1996). "Regional Patterns and Historical Trends in Tropical Deforestation: 1976-
1990. A qualitative Comparative Analysis." Ambio, 25(3), 160-166.
Rudel, T. and Roper, J. (1997). "The paths to Rain Forest Destruction. Cross-national Patterns of tropical
Deforestation, 1975-1990." World Development, 25(1), 53-65.
Runge, C.F. (1993) "Trade Liberalization and Environmental Quality in Agriculture." International Envi-
ronmental Affairs, 5 (2) 95-128.
Sanchez, G. (2011). "Instrumental Variable for Triangular/Recursive System with Correlated Disturb-
ances." STATA Corp.
Sandler, T. (1993). "Tropical Deforestation, Markets and Market Failures." Land Economics, 69(3), 225-
233.
Scrieciu, S. S. (2001). "Can Economic Causes of Tropical Deforestation be identified at a Global Level?"
Ecological Economics 62 (34), 603-612.
Sergerson, K., Platinga, A.J., Irwin, E.G. (2006). "Theoretical Background" in: Economics of Land use
Change. Bell, K.P., Boyle, K.J. and Rubin, J. (eds). Ashgate: Aldershot, U.K. 79-111.
Seto, K.C., Kaufman, R.K. (2003). "Modeling the drivers of Urban Land Use change in the Pearl River
Delta, China.Integrating remote Sensing with Socioeconomic data." Land Economics, 79, 106-
121..
105
Sheppard, S. (2011). "Measuring and Modeling Global Expansion." In Global Urbanization .Wachter, S.
and Birch, E. (Eds). Philadelphia,: University of Pennsylvanian Press, Philadelphia.
Siyanbola, W.O, Oketola, F.O., Pelemo, D.A., Momodu S.A., Adegbuligbuligbe, A.O., Adesina, F.A., Ojo-
lo, Ijalana, S.A. (1996). "Green House Gases Emission Reduction in Nigeria: Least-Cost Reduction
Strategies and Macroeconomic Impacts." Forestry for sector Report, 3(b), US Country study Pro-
gram.
Siyanbola, W.O., Momodu, A.S., Pelemo, D.A., Adesina, F.A. (2003)."„Green Gas Mitigation Options As-
sessment for Nigeria:2000-2004." Forestry and land Use Change Sector Reports Submitted to
Nest/ Global change Strategies International Inc, 11
Solow, R. (1974). "Richard Ely Lecture; the Economics of Resources or the Resources of Economics."
American Economic Review, 46(2), 1-14.
Solow, R. (1974a). "Intergenerational Equity and Exhaustible Resources." Review of Economic Studies, 42,
29-45.
Sonhgen,B., Mendelson, R. and Sedjo, R.A. (1999). "Forest Management, Conservation and Global Timber
Markets." American Journal of Agricultural Economics, 81 (1),1-13.
Southgate, D. (1994). "Tropical Deforestation and Agricultural Development in South America." In: The
Causes of Tropical Deforestation, The Economic and Statistical Analysis of Factors Giving Rise to
the Loss of the Tropical Forest( Eds.). Brown, K. and Pearce, D., Oxford:134-143, UCL
Press,Oxford.
Sweeney, J.L. (1992). Economic Theory of Depletable Resources: An Introduction in Handbook of Natural
Resources and Resource and Energy Economics (3rd
Ed.): Allen, V.K. and James, L.S.
Terborgh, J. (1992). Diversity and the Tropical Rainforest (Eds); Scientific American library: New York.
Tiebout, C. (1956). "A Pure of Local Expenditures." The Journal of Political Economy, 64(5), 416-424.
Tietenberg, T, and Lewis, L. (2010). Environmental Economics and Policy (6th ed.); Pearson Press: Inc
Tilton, J.E. (1992). "Mineral Endowment, Public Policy and Competiveness; A Survey of Issues." Re-
sources Policy, 18(4), 237-249.
Tilton, J.E. (1996). "Exhaustible Resources and Sustainable Development: Two Different Paradigms." Re-
source Policy, 22(1/2), 91-97.
Toman, M.A. (1994). "Economics and Sustainability: Balancing Trade-Offs and Imperatives." Land Eco-
nomics, 70(4), 399-413.
Turner, B.L., Clark, W.C., Kates, R.W., Richards, J.F., Mathew,J.T. and Meyer, W.B. (1990) (eds). The
Earth as Transformed by Human Action. Global and Regional Changs in the Biosphere over the
Past 300 years. Cambridge University Press: Cambridge, New York, Port Chester, Melbourne and
Sydney.
106
Turner, B.L., Moss, R.h. and skole, D.L. (1993)."Relating Land Use and Global Land Cover Changes." A
Proposal for IGBP-HDP Core Project- (=IGBP Report; 24/HDP Report;5).
UNEP (1992)."The World Environment 1972-1992." The United Nations Environment Programme, Nairo-
bi.
United Nations Environmental Program ,UNEP (2002). Global Environmental Outlook 3, Nairobi, Kenya.
United Nations Environmental Programme (UNEP) (2001). "Economic Reform, Trade liberalization and
the Environment." A Synthsis of UNEP Country Projects: A Synthesis Report. Unpublished.
United Nations Environmental Programme [UNEP].(2005). "Integrated Assessment of the Impact of Trade
Liberalization on the Rice Sector." A Synthesis of UNEP Country Projects: A Synthesis Report
(Unpublished).
Valdes, A. (1986). "Impact of trade and Macroeconomic Policies on Agricultural Growth; The South
American Experience." Reprinted from Economic and Social Progress in Latin America, Wash-
ington DC., International food Policy Research Institute.
Van Kooten, G.C. and Bulte, E.H. (2000). The Economics of Nature: Managing Biological Assets (Eds).
Blackwells Press.
Van Soest, D. P (1998) Tropical Deforestation and Economic Perspective: Thesis on Systems, Organiza-
tion and Management (Eds); Labyrinth Publications, Netherlands.
Van Soest, D.P. and Bulte, E. (1996). "Tropical Deforestation, Timber Concessions and Slash and Burn
Agriculture- Why Encroachment may Promote Conservation of Primary Forests." Forest Econom-
ics, 2: 55-66.
Van Vuuren, D. P., Strengers, B. J. and DeVries, H. J. (1999). "Long Term Perspectives on World Metal
Use- A System Dynamics Model." Resources Policy, 25 (4), 239-255.
Vanclay, J.K. (1993). "Saving the Tropical Forest needs and Prognosis." Ambio, 22(40), 225-231.
Von amsberg, J. (1994). "Economic Parameters of Deforestation." Policy Research Working Paper No
1550, the World Bank, Washington.
Von Thủnen, J.H. (1826). Der IsolieteStaut in Beziehung auf landschaft und NationlỏKonomie. Trans by
Wartenberg, C.M. (1966) Von Thủnen‟s isolated State. Oxford; Pergamon Press.
Walker, R.T. (1987). "Land Use Transition and Deforestation in Developing Countries." Geographical
Analysis, 19(1), 18-30.
Whitmore, T.C. (1990). An Introduction to Tropical Rain Forests (Eds); Oxford: Claredon Press, Oxford.
Wibowo, D. H. and Byron, R. N. (1997). "Deforestation Mechanisms: A Survey." Working Papers on Eco-
nomics, Ecology and the Environment, No 19.
Wibowo, D.H (1999). "Deforestation Mechanisms: A Survey." International Journal of Social Economics,
26, 455-474
107
Wiggins, V. (2013). "Must I Use All my Exogenous Variables as Instruments when Estimating Instrumen-
tal Variable Regression?" STATA Corp.
WJA/BIU (2009) "BGL Agriculture Report." BP, Statistics Review/BGL Research, USA.
World Bank (1994). "Economy-Wide policies and the Environment." Washington D.C.
World Bank, (1992). "World Development Report Development and the Environment." Washington D.C.
World Bank.
World Resources Institute [WRI] (1998). World Resources (Eds); New York: Oxford University Press:
185, New York.
World Trade Organization (2010)."Trade Theory and Natural Resources." World Trade Report 2010, Ge-
neva.72-111.
Wunder, S. (2004). "The Impact of Trade and Macroeconomic Policies on Frontier Deforestation.' ASB
Consortium. World Agroforestry Centre, ICRAF.
Wunder, S. and Verbist, B. (2003)."The Impact of Trade and Macroeconomic Policies on Frontier Defor-
estation." ASB Lecture Notes 13, World Agro-Forestry Center.
Yiridoe, E., Nanang, D. (2001). "An Econometric Analysis of the Causes of Tropical deforestation in Gha-
na." A Paper presented at the American Agricultural Economics association Conference, Chicago,
IL.
108
APPENDIX
APPENDIX A. THE MAXIMUM PRINCIPLE SOLUTION:
The current value multipliers, ( ) , ( ) (t) or the current shadow prices (Clark, 1990) are de-
fined as :
( ), ( ) (t) = ( ) ……..(16)
Where i = . represents the respective discount shadow prices or present value
multipliers.
, -
……… (17)
[i.e. normalizingthe Hamiltonian function gives current-value Hamiltonian]
…….. (18)
[The same holds for -.The resulting first order conditions are given below:
……….(19)
( ) [
* +] ………..(20)
………(21)
[
* +] ………(22)
,
* +- ……...(23)
[
* }- …….(24)
From equation (15), the equations of the motion of the co-state variables are given by:
( ) * * + + ( * +) ………(25)
* + …….(26)
* + ……..(27)
Equations (25) – (27) determine the adjustment in the shadow values, , along the opti-
mal paths.
109
Thus, the expression for the shadow values, , can be derived from the above equations-
(25) – (27). Solving (25) by integrating and taking the inverse exponential function of both sides,
yields the following expression for the current value multiplier, :
( ) ( * + ∫ , ( ) * * + + - ( * +
…(28)
Also doing the same for equations (26) and (27) respectively gives:
( ) ∫ , * + -
…….(29)
( ) ∫ , * + -
……..(30)
The shadow values ration the use of the resource between time periods, by ensuring that at the
margin, the resource has the same discounted value in each time period (Lyon, 1999). Thus,
give the respective rate of converting forest land into agricultural and urban lands as reflected by
the changes in shadow value of the lands over time, denotes the capital gains from the forest land
which represents the rate of conversion of forest land.
The equations of the transversality condition are given by:
( ) , ..……..(31)
( ) , ………(32)
( ) , …..…..(33)
Constraints (31)-(33) state that the present value of the resource leftover at infinity must be zero.
From equation (19) and (20) we have:
……..(34)
From equations, (25) - (27), equation (34), also implies that, ….(35)
Using the above condition in equations (34) and (35), equations (25),(26) and (27) are combined
to give:
( ) * * + + , * +- r
* + * + ………(36)
This is equal to:
( ) * * + + , * +- * +
* + … (36a)
Substituting (20) for into (36a):
110
( ) * * + + * + , ( ) ( * * + +)-
* +
* + ..….. (37)
Therefore:
( ) * * + + * + [ ( ) ( * * + +)]
* +
* + r ……(38)
( ) * * + + * + [ ( ) ( * * + +)]
* +
* + r ..(38a)
Equations (38) and (38a) give the trade-off between land devoted to forest benefit (timber and en-
vironmental) as against agriculture and urban use. is the cost of utilizing the services of
one unit of forest capital at any point in time. are the respective social costs of
capital for forestry, agriculture and urban land, which are the costs of converting a unit of forest
land to agriculture or urban use.
Thus in line with Barbier and Burgess (1997), equation (38a) can be interpreted as the opportunity
cost of the stock of forest land, which is the forgone benefits of other economic use, and thus rep-
resented as Gt:
( ) * * + + * + , ( ) , * * + +- ...(39)
* + …….(40)
* + ........(41)
However, equation (38a) equally implies that:
…..(42)
Thus, equation 42 can be expressed as:
……(43)
Where ( ) * * + + * + , ( )
, * * + +-,
* + ,
* + and
(Where Gt is equal to Bf, which is the opportunity cost of a unit of converted forest land in any
given period)
111
Table A.1 Descriptive analysis
1970-1986
VARIABLE MEAN STANDARD DEVIA-
TION
MIN MAX
LA 87705235 2610221 4864500 1.27e+07
WD 1709235 978842.1 566000 3271000
LV .0002 .0001 .00001 .0005
WPR 1093.5 1303 108 3435
SAC 875.8 494.3 189 1600
CAC 1070.6 777.8 288 3500
MEC 2.61 .958 1.06 4.06
AVA 1053.4 326.9 654.9 1655.4
TC .129 .073 .095 .395
FOR 1.35 .513 .811 2.96
AGP 23899.4 27296 1690.8 69572.4
IND 3027.7 2319.6 221 8377
1987-1994
LA 1.56e+07 2008271 1.27e+07 1.80e+07
WD 2800125 176847.3 2533000 3000000
LV .0008 .0005 .0002 .002
WPR 9563 6998.3 442 2098
SAC 7111.5 2834.4 3318 11000
CAC 18307.6 18196.3 7500 61180
MEC 4.78 .386 4.23 5.34
AVA 1181.5 68.9 1094 1305.4
TC 1.84 3.70 .325 11
FOR 3.32 .682 2.66 4.49
AGP 81667.6 7957.3 67015.7 90463.2
IND 4416.2 497.1 3610.3 5084.4
112
1995-2012
LA 1.84e+07 948581.2 1.63e+07 1.98e+07
WD 1463624 535371 602030.3 2356000
LV .009 .004 .003 .016
WPR 13271.9 8763 1494 33383
SAC 48561.9 34095.6 5580 123
CAC 196562.3 116186.2 73402 396470.4
MEC 6.66 .510 5.48 7.42
AVA 1348.3 138.3 1171.4 1598.4
TC 42.3 23.0 11 71
FOR 1.58 .814 .683 3.39
AGP 202977.2 89381.4 93798.8 347653.3
IND 9567.2 4294.8 5221.7 18906.5
113
Table A.2 Descriptive analysis for the Second Model
1970-1986
VARIABLE MEAN STANDARD
DEVIATION
MIN MAX
LA 8705235 2610221 4864500 1.27e+07
PEPI 8.14 4.70 1.77 20.95
PMPE .0232 .016 .009 .059
PEPN 1.22 .346 1 2.19
PMPN .228 .082 .107 .407
TOP .119 .082 .018 .238
NPCe .458 .201 .172 .879
NPCm 4.52 2.37 .814 8.47
GEX 0.195 .119 .0512 .474
BOT 1133.8 2344.5 -2564.1 5091.1
CAP 2742724 2829217 -7388700 5423273
NEX 100.5 13.5 51.89 113.2
PAPNA .990 .335 .541 1.554
1987-1994
LA 1.56e+07 2008271 1.27+07 1.80e+07
PEPI 13.9 8.70 7.19 34.5
PMPE .037 .030 .010 .092
PEPN 2.89 2.55 .842 8.20
PMPN .359 .304 .105 .842
TOP .130 .096 048 .288
NPCe .233 .143 .097 .455
NPCm 1.30 1.30 .195 4.01
GEX .313 0.217 .108 .696
BOT 38055.5 21188.4 9747 64168
CAP 1.05e+07 6096596 3786671 1.96e+07
NOEXCH 7.54 4.48 2.958333 14.7
PAPNA 1.023 .335 .541 1.55
114
1995-2012
LA 1.84e+07 948581.2 1.63e+07 1.98e+7
PEPI 16.2 10.2 5.66 34.3
PMPE .080 .040 .015 .148
PEPN 4.71 2.25 3.22 13.2
PMPN .586 .205 .384 1.19
TOP 1.17 .488 .301 1.74
NPCe .312 .201 .073 .758
NPCm .503 .174 .242 .946
GEX 3.18 1.53 .884 5.96
BOT 2332291 203 -85562 5900000
CAP 3.83e+07 2.95e+07 1.00e+07 8.84e+07
NEX 78.2 33.1 .742 107
PAPNA 1.25 .209 ..855 1.65
Table A.3 Diagnostic Estimation Results
MODEL DWSTAT BGODFREY LINKTEST OVTEST
Chi2 P- value HAT2 F statistics P- value
LA 2.44 -0.17 0.12 0.94
PePn 1.63 1.80 .278 -0.88 3.30 0.034
PmPn 1.83 0.00 .996 0.58 1.28 0.302
PaPna 1.60 1.051 .305 0.12 0.45 0.71
LA 1.93 0.042 0.84 -0.98 1.17 .28
WD 1.59 4.56 0.02 1.19 1.82 0.167
LV .59 8.4 0.01 -1.98 -2.75 -0.012
115
Table A.4 First Stage Recursive Estimates, (lnpe/pn)
Adj R-squared = 0.8927
Root MSE = .24083
Lnpe/pn Coef. Std. Err. t P>t [95% Conf. Interval]
Lnpe*/pi
* .6415978 .1752782 3.66 0.001 .2845678 . 9986277
Lnpm*/pe
* .129422 .1226143 1.06 0.299 -.1203352 . 3791793
lnNEX -.2016549 .0764308 -2.64 0.013 -.3573394 -.0459704
LnTOP -.0543618 .0828723 -0.66 0.517 -.2231672 .1144435
DU2 2.891438 .7875601 3.67 0.001 1.28723 4.495645
DU2(lnTOP) 1.104591 .2327694 4.75 0.000 .6304554 1.578727
DU3 1.697856 .5199009 3.27 0.003 .6388529 2.75686
DU3(lnTOP) .1022661 .1952652 0.52 0.604 -.2954762 .5000084
lnNPCe .6205958 .1499283 4.14 0.000 .3152018 . 9259898
lnGEX -.1222697 .0650193 -1.88 0.069 -.2547097 0101703
_cons 1.835957 .5637661 3.26 0.003 .6876026 2.984311
Table A.5 First Stage Recursive Estimates, (lnpm/pn)
Adj R-squared = 0.8959
Root MSE = .21065
Lnpm/pn Coef. Std. Err. t P>t [95% Conf. Interval]
Lnpm*/pe
* .1808617 .0955893 1.89 0.068 -.0138473 .3755708
Lnpm*/pi
* .5848195 .1369068 4.27 0.000 .3059494 .8636896
LnNEX -.1418133 .0646184 -2.19 0.036 -.2734367 -.0101898
LnTOP -.1525145 .0685337 -2.23 0.033 -.2921132 -.0129159
DU2 3.4123 .6264431 5.45 0.000 2.136277 4.688323
Du2lnTOP 1.320051 .185537 7.11 0.000 .942124 1.697977
DU3 1.869498 .4470973 4.18 0.000 .95879052.780206
DU3lnTOP .0799927 .1669737 0.48 0.635 -.2601216 .4201069
LnNPCm .5613639 .1028193 5.46 0.000 .3519279 .7708
LnGEX -.1468716 .0546423 -2.69 0.011 -.2581743 -.0355688
_cons 1.128703 .5357995 2.11 0.043 .0373151 2.220091
116
Table A.6 First Stage Recursive Estimates, (lnpa/pna)
Adj R-squared = 0.8245
Root MSE = .19123
Lnpa/pna Coef. Std. Err. t P>t [95% Conf. Interval]
DU2 .8999352 .4973281 1.81 0.081 -.1187952 1.918666
LnTOP -.2392571 .0892519 -2.68 0.012 -.4220813 -.056433
DU2lnTOP .2358818 .1593208 1.48 0.150 -.0904721 . 5622356
LnBOT -.145121 .046034 -3.15 0.004 -.2394173 -.0508247
Lnpe*/pi .1849415 .0901163 2.05 0.050 .0003466 .3695365
DU3 1.027254 .4718694 2.18 0.038 .0606731 1.993834
DU3lnTOP .3871296 .1222776 3.17 0.004 .1366553 .637604
lnCAP -.1186758 .0731343 -1.62 0.116 -.2684846 .0311331
_cons 2.088734 1.009195 2.07 0.048 . 0214926 4.155975
Table A.7 Second Stage Recursive Estimates,( )
Adj R-squared=
0.8904
Root MSE = .10651
Lnla Coef. Std. Err. t P>t [95% Conf. Interval]
( ) .3037845 .26099 1.16 0.255 -.232688 .8402571
Dum. ( ) -.3343942 .2816294 -1.19 0.246 -.9132918 .2445034
-.4533822 .151451 -2.99 0.006 -.7646942 -.1420702
Dum ( ) .5830951 .1752735 3.33 0.003 .2228152 . 9433749
Dum .8419979 .3322026 2.53 0.018 .1591458 1.52485
( ) .9598588 .2057135 4.67 0.000 .5370086 1.382709
Dum ) -1.147077 .2287621 -5.01 0.000 -1.617304 -.6768498
lnpe*/pi
* -.4261398 .0854487 -4.99 0.000 -.6017822 -.2504975
Dum lnpe*/pi
* .4407741 .1132982 3.89 0.001 .2078863 .6736618
_cons 15.88045 .2292184 69.28 0.000 15.40929 16.35162
117
Table A.8 Adjusted covariance matrix of the standard error, ( )
Coef. Std. Err. T P>t [95% Conf. Interval]
( ) .3037845 .2810983 1.08 0.290 -.2740213 .8815904
Dum. ( ) -.3343942 .303328 -1.10 0.280 -.9578938 .2891054
-.4533822 .1631198 -2.78 0.010 -.7886797 -.1180847
Dum ( ) .5830951 .1887777 3.09 0.005 .1950569 .9711332
Dum .8419979 .3577976 2.35 0.026 .1065344 1.577461
( ) .9598588 .221563 4.33 0.000 .5044295 1.415288
Dum ) -1.147077 .2463874 -4.66 0.000 -1.653534 -.6406204
lnpe*/pi
* -.4261398 .0920322 -4.63 0.000 -.6153148 -.2369649
Dum lnpe*/pi
* .4407741 .1220274 3.61 0.001 .1899431 .691605
_cons 15.88045 .2468789 64.32 0.000 15.37298 16.38792
