THE DETERMINANTS OF GLOBAL TROPICAL DEFORESTATION

Knishna Chandra Mahapatra

A Thesis Submitted in confonnity with the requirements for the degree of Master of Science in Forestry

Faculty of Forestry

University of Toronto

O Copyright by Krushna Chandra Mahapatra (2001)

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FACULTY OF FORESTRY University of Toronto

DEPARTMENTAL ORAL EXAMINATION FOR THE DEGREE OF MASTER OF SCIENCE IN FORESTRY

Examination of Mt. Kmshna Chandra Mihapatra

Examination Chair's Signature: &a w'

We approve this thesis and affirm that it meets the departmental oral examination requirements set dom for the degree of Master of Science in Forestry.

Examination Cornmittee:

THE DETERMINANTS OF GLOBAL TROPICAL DEFORESTATION

Master of Science in Forestry 200 1

Krushna Chandra Mahapatra Faculty of Forestry

University of Toronto

This thesis develops an econometric model of global tropical deforestation with special

attention to problems in earlier studies related to hypothesizing the effect of explanatory

variables, deforestation data, and estimation techniques. The issue of hypothesis testing is

addressed by hypothesizing that diffèrent underlying causes used to develop a theoretical

deforestation model may have both positive and negative effects on deforestation, the net

effect of which will Vary in different circumstances. The data problem is dealt with by

categonzing the deforestation data. Multinomial logistic regression results, used to address

some of the estimation issues, are compared with those fiom OLS and binary logistic

regression methods. Multinomial logistic regression was found to be more informative than

the other two approaches. While growth in population, agriculture, and road construction

caused deforestation in high deforesting countries, debt service growth replaces population

growth as the cause of deforestation in countnes experiencing medium deforestation levels.

This thesis was made possible thanks to the cooperation and support of a number

of individuals. 1 would like to express my sincere gratitude and appreciation to all. 1 am

fortunate and privileged to have Prof. Shashi Kant as my supervisor. Starting fkom the

day 1 reached at the university, he provided the necessary help, guidance, and financial

support in completing the thesis.

1 offer my thanks to Professors J.C. Nautiyal and D.K. Foot, the supervisory

cornmittee members, for their criticisms, suggestions, and giving me tlieir valuable time. 1

am also thankfùl to Professor D.N. Roy for his encouragement and moral support during

the study.

My successfbl accomplishment of the task at hand is owing to a great extent to the

loving, mernorable, and CO-operative Company of Mr. Sushi1 Kumar, Mr. Dinesh Mishra,

and Ayan Chakraborty. I also acknowledge with thanks Mr. Dave Pearce for editing the

thesis.

1 cannot forget the continued support and encouragement of my father, mother

and other family mernbers. Their dedication and inspiration has made me what 1 am

today.

Finally, 1 dedicate the thesis to Shree Maa and Shree Aurobindo for al1 this has

been possible because of their blessings.

TABLE OF CONTENTS

1 . Introduction ................................................................... ............................................... 1.1 Need for study on tropical forests

.................................................. 1.2 Extent of tropical deforestation ............................................. 1.3 Definition of tropical deforestation

................................. 1.4 Focus on level of tropical deforestation study 1.5 Need for this study ................................................................. 1.6 Organization of the study .........................................................

2 . Literature review .................................... ........................ 2.1 Micro level models ................................................................ 2.2 Regional level models ............................................................. 2.3 Macro level models ................................................................

................................................... 2.3.1 Descriptive models .................................................... 2.3.2 Analytical models ................................................... 2.3.3 Simulation models

2.3.3.1 Cornputable general equilibrium models ................. .................................... 2.3.3.2 System dynamic models

..................................................... 2.3.4 Empirical models ............ 2.3.4.1 Estimation techniques in global level studies

....... 2.3.4.2 Variables and hypotheses in global level studies ............................. 2.3.4.3 Resuits of global level studies

...................... 2.4 Comparative analysis of tropical deforestation models .................................... 2.5 Specific issues related to ernpincal models . . ...................................................... 2.5.1 Causation issues

......................................................... 2.5.2 Issue of data ....................................... 2.5.3 Issue of estimation problems

........................................ 3 . A Mode1 of tropical deforestation ......................................................... 3.1 Definition of deforestation

............................................................ 3.2 Causes of deforestation ............................................................. 3.2.1 Forest size

.................................................... 3.2.2 Population growth ..................................................... 3.2.3 Economic growth

.................................................. 3.2.4 Debt service growth .................................................. 3.2.5 Agricultural growth

................................................... 3.2.6 Road development .................................................. 3.2.7 Level of democracy

3.3 The mode1 ..........................................................................

4. Data and data sources ...................................................... 4.1 Deforestation data and sources .................................................. 4.2 Deforestation as a qualitative vanable .......................................... 4.3 Sources of data for the causes of deforestation ................................

..................................................... 5 . Estimation of the mode1 5.1 Logistic regressions ............................................................... . .

5.1.1 Tests of sigmficance ................................................. ........................................... 5 . 1.1.1 For a predictor set

5.1.1.2 For one predictor ............................................. 5.2 Ordinw Least Square regressions ..............................................

...................................................... 6 . Results and discussion 6.1 Effect of road as level vs . growth variable ...................................... 6.2 Estimation of the dcforestation mode1 ...........................................

6.2.1 Temporal stability of the models .................................... 6.2.2 Results of the multinomial logistic regression model

(without IDPERIOD). the Mode1 1 ................................. 6.2.3 Results of the binary logistic regression model

(without IDPERIOD). the Mode1 2 .................................. 6.2.4 Results of OLS regression without interactive dummies

(without IDPERIOD). the Mode1 3 ................................. 6.2.5 Results of OLS regression with interactive durnmies

(without IDPERIOD). the Mode14 ................................. 6.3 Comparative analysis of multinomial logistic. binary logistic.

................................................................ and OLS regressions 6.3.1 Comparison of results from multinomial and binary logistic

regression models (without IDPERIOD) .......................... 6.3.2 Comparison of results from multinomial logistic and OLS

regression models ..................................................... 6.3.3 Interpretation of results fiom multinomial logistic regression ..

6.3.3.1 Forest size ..................................................... 6.3.3.2 Population growth ............................................ 6.3.3.3 Economic growth ............................................. 6.3.3.4 Debt service growth .......................................... 6.3.3.5 Agricultural growth .......................................... 6.3.3.6 Road development ............................................ 6.3.3.7 Level of democracy .......................................... 6.3.3.8 Regional durnmies ............................................

6.4 Predictive efficacy of the mode1 ...................................................

7. Conclusion ..........................mm....mm..m.......~.................m....m.. References ................m.....m..m.....m.m.m....m.............................m..

Appendix . 1 Polity 98 project. Regime characteristic 1800- 1998 ....................... Appendix - 2 Average annual rate of natural forest loss for the penod 1990.95 ....... Appendix . 3 Total GDP of different countries (millions of national currency) at

constant pnce (1 990= 100) for different years ............................. Appendix . 4 Contribution of agicultural sector (millions of national currency)

towards Total GDP at constant price (1 990 = 100) ....................... Appendix . 5 Total GDP of different countries (millions of national currency) at

constant prices excluding contribution fkom agricultural sector, and their rate of growth during 1980-90 and 1990.95 ...................

Appendix - 6 Average annual growth of total debt service as a percentage of GNP during 1 980-90 and 1990.95 .........................................

Appendix . 7 Data on different variables of deforestation mode1 for different countries for the periods 1980-90 and 1990.95 ..........................

Appendix . 8 Data on different variables according to category of deforestation .... Appendix - 9 Multinomial logistic regressions (data set of 1995 to test the effect

of road as a growth vs . level variable) .................................... Appendix - 10 Multinomial logistic regression (Low is the baseline category) ........ . . Appendix . 1 1 Binary logrstic regression ................................................... Appendix . 12 OLS regression without interactive dummies ............................ Appendix . 13 Multinomial logistic regression (without penod dummy) .............. Appendix . 14 Binary logistic regression (without period dummy) ..................... Appendix - 15 OLS regression without interactive durnmies

(without period durnmy) ..................................................... Appendix - 16 OLS regression with interactive durnmies (without period dummy) .. Appendix - 17 OLS regression with interactive dummies after random changes in

the rates of deforestation ..................................................... Appendix - 18 Multinomial logistic regression after the cut-off points are reduced

by 7 percent ................................................................... Appendix - 19 Multinomial logistic regression after the cut-off points are increased

by 7 percent ................................................................... Appendix - 20 Calculation of probability of deforestation for different countries ...

LIST OF TABLES

Table 1 The strengths and weaknesses of various tropical deforestation models.. . . . . . . Table 2 Comparison of natural forest area (thousand ha.) of 1990 for some

countries fiom different sources.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . .. Table 3 Details of the data used for the estimation of deforestation model.. . . . .... . . . . . . Table 4 Results of Wald test for ROAD and ROADGR variables in . . multinomial logtstic regressions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . .. Table 5 Results of Wald tests in multinomial logistic and binary logistic regressions,

and t-test in OLS regression for IDPERlOD variable.. . . . . . . . . . . . . . . . .... . . . . .. . .. Table 6 Results of multinomial logistic regression (without IDPERIOD). . . . . ... . . . . . . ... Table 7 Results of binary logistic regression (without IDPERIOD). . . . . . . . . . . . . . . .. . . . . . . Table 8 Results of OLS regression without interactive dummies (without IDPERIûD) Table 9 Results of OLS regression with interactive dummies (without DPERIOD) .... Table 10 Significance of variables in LR tests of multinomial and . .

b i n q logistic regressions.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . .. Table 1 1 Cornparison of sign and significance of parameter estimates in

multinomial and binary logistic regression.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . Table 12 Comparison of sign and significance of parameter estimates in

multinomial logistic and OLS regression without interactive durnmies.. . . . . . . Table 13 Cornparison of sign and significance of parameter estimates in

multinomial logistic and OLS regression with interactive dummies.. . . . . . . . ... Table 14 Comparison of sign and significance of parameter estimates in OLS with

interactive dummies afler random changes in the rates of deforestation.. . . . . .. Table 15 Sign and significance of coefficient estimates in original multinomial logistic

regression model vs. those fiom new models after change in cut-off points.. . Table 16 Coefficient estimates, factor changes in the odds of High or medium

deforestation, and level of significance of the variables.. . . . . . . . . . . . . . . . . . . . . . . . . . Table 17 Comparison of effect of causes of deforestation on different categories

of deforestation (Le. High/Low and Med./Low). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 18 Classification of countries into different categories of deforestation.. . . . . . . . ...

Chapter 1

INTRODUCTION

1.1 Need for study on tropical forests

Tropical forests are valued for the direct economic benefits and for the host of

intangible benefits bestowed on society. These forests have a special role in the conservation

of biodiversity. It is well known that tropical forests, which occupy a mere 13.54 percent of

total land area (FAO, 1997), contain around 70 percent of al1 species (WRI, 1996). Many of

these species are threatened with extinction (Myers 1988). Tropical rain forests have as high

as 700 tree species per hectares (ha.) compared to 1 to 5 tree species in boreal forests (Botkin

and Talbot, 1992). The nch biodiversity of tropical forests is a source of genetic matenal for

genetic engineering. Also, around 500 million people, including 150 million indigenous

people, live in or at the edge of the tropical forests (Roper and Roberts, 1999). Most of them

are fully dependent on the forests not only for their livelihood, but also for their cultural and

spiritual traditions. Along with these benefits, annual production of industrial wood products

fiom tropical forests is worth US $ 100 billion, about 0.5 percent of global gross domestic

product (WCFSD, 1998). Besides timber production, a variety of non-wood forest products

(NWFP) are denved fiom the tropical forests. Over 150 NWFP are reported to be of

significance to international trade, the estimated value of which ranged between US$S and

US$lO billion per annurn during the 1990s, in addition to the value of NWFP traded at

national and local levels (Prebble, 1999). These forests also influence climate pattern, both

locally (e.g. watersheds and soi1 erosion protection) and globally (e.g. carbon sequestration).

Despite their continuing beneficial contributions to mankind, forests disappearance is

unabated, the consequences of which will be felt for generations to corne. In order to

understand this issue of deforestation, a number of studies have already been carried out.

However, there is lack of general agreement about the extent, causes, and consequences of

deforestation (Palo, 1994). More work is, therefore, necessary to overcome the disagreement.

1.2 Extent of tropical deforestation

Even though everyone is concerned about deforestation, no perfect estimate of the

forest area or its loss is available. Between 1923 and 1985, at least 26 calculations of closed

forestland were made; they ranged fkom 2400 million ha. to 6500 million ha. (Mathews et al.,

2000). Among them, assessments by Food and Agricultural Orgariization (FAO) are the most

widely cited data source. According to the 1990 forest resource assessrnent (revised in 1997),

there are 3,454 million ha. of forests in the world, representing 26.6 percent of the total land

area. Of this total forest area, 1756.3 million ha. (13.54 percent of the total land area) are

tropical forests and found mostly in developing countries (FAO, 1997). The F A 0 has

estimated the average annual rates of tropical deforestation to be 14.63 and 12.9 1 million ha.

for the penods 1980- 1990 and 1990- 1995 respectively. The total de forestation in developing

countries (which mostly have tropical forests) fiom 1980-1995 is about 200 million ha. in

which Latin American countties have the highest contribution, at 85 million ha. followed by

Asia and Afica with 60 and 55 million ha. respectively (FAO, 1997). Myers (1994) has

estimated that the average annual deforestation in humid tropics was about 13.2 million ha.

dwing late 1980s, while the FA0 estimate (sum of tropical rain forests and moist deciduous

forests) was 10.7 million ha. (FAO, 1997).

1.3 Definition of tropical deforestation

One of the main reasons behind the diverging estimations of deforestation is due to

varying definitions of deforestation itself. Wunder (2000) has divided the definitions into

'broad' and 'narrow' types. The broad version is inclusive in the sense that it highlights not

only forest conversion (the elimination of trees and shifts to other land uses), but also

di fferent types of degradation that reduce forest quality (densi ty and structure, ecological

services, biomass stocks, species diversity etc.). Myers (1 994) has used the broad version and

defines deforestation as the complete destruction of forest cover along with 'removal o f or

'unsurvivable injury to' the great majority of trees. The narrow version on the other hand

focuses on changing use of forestland by complete destruction of forest cover. The FA0 of

United Nations uses such a definition and defines deforestation as a "change in land use with

depletion of crown cover to less than 10 percent" (FAO, 1993). Changes within forest class

(fiom closed to open forest) which negatively affect the stand or site and, in particular, lower

the production capacity, are termed as forest degradation and are not included in the

deforestation estimates. The problem with this definition is that a loss of crown density fiom

a higher level such as 90 percent to a level just above 10 percent will be considered as

degradation, but it is an important aspect of deforestation (Saxena et al., 1997).

Obviously, degradation and deforestation tend to be intertwined phenomena in the

sense that the former oAen precedes the latter (Wunder, 2000). The broad definition is

powerful, as it informs public about the fact that large areas are affected by antluopogenic

changes. However, there are constraints in measuring forest degradation (Grainger, 1999),

which do not seem to be avoidable in the near hu re . Deforestation as defined by FA0

explains the extremes of forest loss that are permanent in nature. This definition will give

some insight about the deforestation process, though not a complete one. Henceforth, the

term deforestation used in this thesis refers to the definition adopted by FA0 (1993).

1.4 Focus on level of tropical deforestation study

While there is a need to study the tropical deforestation process, the unit of such study

is debatable. The main strength of micro or village level studies lies in their use of good

quality data. However, the conclusions fiom these studies are applicable to the specific area

studied and therefore, a generalization of policy recommendations is not possible. Moreover,

completing a large number of such studies for different areas may not be possible due to

resource constraints. Regional models cover a much larger area than microeconomic models,

but are limited to a region with distinctive characteristic. These models are better than

macroeconomic models at national or global level in the sense that they use reliable data on

land use patterns and other causal variables of tropical deforestation. However, these models

are not suitable to provide a picture of the whole country, nor for illustrating international

differences in the process of tropical deforestation. Resource constraints again are a major

factor prohibiting numerous such studies. The behavior of agents at a local level, e.g.

subsistence farmers, used to descnbe the deforestation process in microeconomic and

regional models is not ideal because these behaviors are the result of national and global

socioeconomic factors (Palo, 1999). Macroeconomic models at a national level using

simulation technique such as by Saxena (1997), and Saxena et al. (1997) are a better choice.

These models are usefùi to study the impact of various policy measures implemented by

national governments on deforestation. However, non-availability of data on a number of

variables limits the scope of these models. Cross-national studies are usehl in finding

reasons for international differences in the rate of deforestation, Le., between two countries

or groups of countries. However, Bilsborrow (1994) suggests that cross-national studies

invite imprecision by using a highly aggregated unit of analysis, the country. Acknowledging

the imprecision, Rudel and Roper (2997b) still argue that countries rernain relatively

cohesive units in socio-economic dimensions such as wealth, population growth, and

agricultural policies that appear to influence deforestation rates. Facts and figures support the

idea that deforestation rates Vary across the cohesive units or countries in suggestive ways.

Such cross-national studies are cost effective and use readily available data on broad

macroeconomic variables and therefore, are the best alternative given the resource constraint,

and limited area applicability of microeconomic and regional studies.

1.5 Need for this study

Though a number of cross-sectional empirical models have been developed by

researchers to find out the causes and process of deforestation, almost al1 of them suffer fiom

one or al1 of the problems pertaining to deforestation data, estimation techniques, and

contradictory hypothesis of deforestation. These problems have resulted in contradictory

findings about the causes of tropical deforestation. Further work is necessary to advance the

research in these types of models to determine the causes of tropical deforestation and

consequently suggest effective policy measures for conservation.

1.6 Organization of the study

This thesis first reviews and identifies the advantageddisadvantages of tropical

deforestation modeling approaches at the micro, regional, and rnacro level. The necessity for

a study on a cross-sectional empirical model is described in Chapter 2. In Chapter 3, a

tropical deforestation model that addresses the issue of causation is presented. It is foilowed

by a description of data and their sources in Chapter 4. Estimation of the model by logistic

regression techniques as well as the OLS regression method is discussed in Chapter 5. The

results of the estimated deforestation model are discussed and presented in Chapter 6.

Finally, conclusions fkom the study, and policy recommendations are outlined in Chapter 7.

Chapter 2

REVIEW OF LITERATURE

Based on the unit or scale of study, the vast literature describing the process of

tropical deforestation has been classified into microeconomic, regional, and macroeconomic

models by Kaimowitz and Angelson (1998). These studies at micro, regional, and macro

level use various modeling approaches in describing the deforestation process. They are

classified into: descriptive, theoretical and empirical models by Saxena (1997); analytical,

empirical, and simulation by Kaimowitz and Angelson (1998). A synthesis of these two

classification systems seems to be a better choice. Accordingly, the models at each level of

study are divided into four categories i.e., descriptive, theoretical, simulation, and empirical

models for identifjhg the strength and weakness of the contemporary literature'. As this

research work cornes under the category of macroeconomic studies, emphasis will be given

on this category while reviewing the literature.

' In descriptive analysis, perceptions of the causes of deforestation are presentcd without any hypothesis testing. The perceptions are based on an author's observation rather than on scientiîïcally collected observation.

Theoretical modeIs are based on, or are concerneci with, the ideas and abstract principles of deforestation, rather than practical aspects of deforestation. These models in mathematical form highlight the feedback mechanism that link specific sectors involved in deforestation, thus helps in deterrnining the logical implication of various assumptions.

Simulation models are the empirical verification of analytical models, but with some modifications. They use parameters based on stylized facts drawn fiom various sources to assess scenarios and impact of policy changes.

Ernpirical models quanti@ the relationship between dependent and exogenous variables using data on these variables and statistical methods. They are helpful in fmding the causal variables of deforestation.

2.1 Micro level models

These studies are based on characteristics of households at village level and f m e r s

Xe considered as the primary agents of de forestation. Models at micro-level include

analytical, simulation, and empirical models.

In analytical models, deforestation is descnbed through individual's (farmers)

behavior to allocate resources (land, labor and capital) in different production activities,

given the output (crop) prices, institutions, infrastructure development (e.g. road to transport

the output), and available technologies (Kaimowitz and Angelson, 1998). They are of two

types. Open economy models (Southgate, 1990; Angelson, 1999, Anderson and Hill, 1990)

assume that al1 pnces at the micro level are beyond the control of the household, Le.,

exogenous. With respect to wage rate, this implies that a perfect labor market with free

movement of labor exists (population level becomes endogenous) to satisQ the

predetermined wage rate. These models are based on the principle of maximizing the net

present value of benefits accrued from the production activities. Hence, anything that

increases pro fitabili ty of agricultural production will encourage f m e r s to devote more labor

(an increase in demand for labor is met by an increased supply, as wage rate is constant at

local level, a perfect labor market assumption) in clearing new forest areas, Le., more

deforestation. An increase in agricultural output price, agricultural productivity, or low

transportation cost increases profitability and therefore, results in hi& de forestation

(Southgate, 1990; Angelson, 1999). On the other hand, increases in wage rate and capital cost

reduce deforestation because less land is cleared due to a reduction in the level of profit. In

subsistence models (Angelson, 1999; Dvorak, 1992), the assumption of perfect labor market

is relaxed, Le., f m e r s are isolated fiom the market and allocate their time to production

activities based on their subjective preferences irrespective of wage rate. The objective in

subsistence models is to meet the subsistence level of consumption rather than profit making.

This category of farmers will clear the forests up to the point where income is sufficient to

meet that level of consumption. This implies that factors that increase profitability from

agricultural production will decrease deforestation because less agrîcultural area is needed to

meet the consumption level. Therefore, al1 the factors that increase deforestation in open

economy model have an opposite effect, Le., decrease deforestation in subsistence model

(Angelson, 1999a).

Simulation models by Angelson (1999) and Bluffstone (1995) are the empirical

venfication of analytical models mentioned earlier. Data are collected through surveys.

Results Vary according to the assumptions of the model. in the open economy model, where

price is considered as exogenous, low transportation cost, increased agricultural output pnce,

and low agricultural input price increase deforestation (Angelson, 1999; Bluffstone, 1995.

The opposite holds true in the subsistence model (Angelson, 1999).

In empincal models (Foster et al. 1997 quoted in Kaimowitz and Angelson, 1998;

Holden, 1997; Jones et al., 1995), multivariate regression analysis method is employed to

explore the causes of deforestation. These models use information on household as well as

village characteristics in estimation. Agricultural input prices, transportation cost, availability

of off-farm employment, wage rates, household size, Iand tenure security, and population

density are used as exogenous variables, and extent of forest area cleared around the village

as endogenous variable. Cross sectional data for the analysis is collected through sample

surveys fiom different villages. Al1 these empirical studies have similar conclusions

regarding the effect of a variable on deforestation, and they are same as in open economy

analytical models.

The main drawback of the microeconomic modeis lies with the contradictory results

fiom different models that are based on different assumptions. The assumptions of 'perfect

labor market' in the open economy model, and 'no production activity afier a certain

consumption level is met' in the subsistence model are rather unrealistic (Kaimowitz and

Angelson, 1998). Also, empirical and simulation models require high cost and expertise in

collection of data. These microeconomic models in general fail to diagnose the impact of

macroeconomic variables such as economic growth, taxes, subsidies, indebtedness etc. on

deforestation. In addition, conclusions are limited to areas with similar household

charactenstics.

2.2 Regional level models

These studies explore the deforestation process in areas such as districts, or

states/provinces, which are bigger than the area (village) covered in microeconomic models.

These regions differ fiom each other with respect to unique characteristics such as distinct

ecology, land use pattern, institutional and political history, trade networks, pattern of

settlement etc. (Lambin, 1994 quoted in Kaimowitz and Angelson, 1998). Models in this

category are of two types: spatial and non-spatial (Kaimowitz and Angelson, 1998). While

spatial models are either simulation or empirical (regression) models, non-spatial models are

empirical only.

The prime objective of spatial models is to project and display the likely 'hot spots'

of deforestation, that results fiom the continuation of current land use practices, in a

cartographie fonn (Lambin, 1997). GIS (Geographical Information System) technology is

widely used along with statistical techniques to develop such models.

In spatial simulation models landscape variables such as the distance of forests fiom

roads and markets, degree of fragmentation of existing forests, population density, soi1

quality, and a few socioeconomic variables (behavior of farmer at household level) are used

to identi@ the fiiture 'hot spots' and rates of deforestation in these locations (Lambin, 1997).

Data on these variables are for smail sample areas (number of such areas are in thousands) of

less than one square kilometer size that are selected randomly from a region under

investigation (Kaimowitz and Angelson 1998). While information on landscape variables is

collected fiom map layers of GIS, that for socioeconomic variables is collected fiom surveys.

In order to develop the model, prior knowledge about the causes of deforestation and the

interrelated mechanism that leads to deforestation is necessary. Kaimowitz and Angelson

(1998) noted that prominent among them is the DELTA (Dynamic Ecological - Land Tenure

Analysis) model by Southworth et al. (1991) which describes the impact of different

immigration policies, land tenure practices, and road development scenarios on carbon loss

through deforestation in the Rondonia of Brazil. This model assumes that colonists occupy

new forest areas to settle and produce a variety of products. in the first stage, landscape

variables such as access to roads and markets, soil quality, type of vegetation, and land tenure

security detemine the attractiveness of new sites for settlement by potential colonists. The

distribution pattern of new areas is random among the colonists with better areas having

higher probability of being acquired. In the second stage, the amount of forest area cleared

and proportion dedicated to various land uses, which are dependent on farmer characteristics,

are explained stochastically based on probability distributions set by the researcher

(Kaimowitz and Angelson 1998). In the third stage, another probability distribution

determines how many of the colonists will abandon the area and occupy new areas vis-à-vis

permanent settlement at the end of the first year which is again determined by availability of

suitable new sites and productivity of current sites. The model is recursive with the

endogenous variables from each period determining the values of certain exogenous

variables in the following periods. For example, the decision to stay at the same plot and

cultivate certain crops wi 11 affect soil quality and pro fitability of agricultural production over

time (decreases) that in tum determines at what stage the f m e r will shift to a new area.

Similady, migration to new areas facilitates new road development, which in tum helps new

settlement. Data on landscape variables collected fiom GIS, and information on famer

characteristics collected through surveys are used to determine the mode1 parameters. These

parameters are used to assess fùture 'hot spots' along with the amount of deforestation that

might be the result of various immigration policies, land tenure practices, and road

deveIopment projec ts.

In spatial regression models, a binary variable forested/deforested is used as

endogenous variable, and landscape variables (no economic variables) similar to simulation

models collected from map layers of GIS are used as exogenous variables (Lambin, 1997).

Data on these variables are for small sample areas as in simulation models (Kaimowitz and

Angelson 1998). Logistic regression is used to determine the likelihood of areas to be

deforested in future given the independent landscape variables. Ludeko et al. (1990)

employing the logistic regression method found that areas that have better access and are

more fragmented are more likely to be cleared than otherwise. Another study using the sarne

rnethod concluded that high population density near the forest area increases forest clearing

(Brown et al., 1993). Since these models use information on specific locations in analysis,

they are usefiil in predicting the areas with greater chances of high deforestation, and the

proximate causes of such deforestation. They are, however, not suitable to predict the future

rate of deforestation.

Non-spatial models differ fiom spatial models in the sense that they use aggregate

variables at district or states/provinces level, i.e., administrative units instead of location

specific information. Since the soi1 quality and forest fragmentation varies to a wider degree

within these administrative units and these variables cannot be used as aggregate variables,

these models are not suitable to identify the future hot spots of deforestation (Lambin, 1997).

Nevertheless, these models can identiQ the underlying causes of deforestation. Also, these

models use a continuous endogenous variable in regression equations to analyze the

deforestation process; therefore, they can predict the hiture rates of deforestation. The

endogenous variable in these models varies fiom forest cover to amount of forest area

cIeared, and change in cropping area. Exogenous variables used are mostly economic rather

than geographic and include population growth, agricultural price, transportation cost,

income level, type of forestland (fertilelunfertile, hillylflat). Estimation techniques include

regression by OLS method (Lombardini, 1994), Maximum Likelihood method (Reis and

Guzmh, 1994), and Feasible Generalized Least Square method (Barbier and Burgess, 1996).

In these regional models, the effect of a variable on deforestation varies from one

model to the other and different studies in same type of model. For example, in spatial

regression model category, roads induce greater forest clearing in areas with good soils and

favorable climatic condition in Belize (Chomitz and Gray, 1996) and Carneroon (Mamingi et

al., 1996). On the other hand, another study in the sarne category reported that in southern

Mexico they diminish the detrimental effects by decreasing the poverty level, which is

assumed as one of the causes of deforestation (Deininger and Mienten, 1997 quoted in

Kaimowitz and Angelson 1998). in non-spatial models, higher road density and closeness of

forests to urban markets increase deforestation, for exarnple in Thailand (Cropper et al.,

1997) and in Brazil (Pfaff, 1997 quoted in Kaimowitz and Angelson 1998). Also, in non-

spatial models, Chakraborty (1994) reported that higher income increases deforestation in

India, which is opposite to the finding in Thailand (Panayotou and Sungsuwan, 1994).

Both the spatial and non-spatial models use better quality data on forest cover than

macroeconomic studies and each mode1 serves a different purpose in answering the causes,

location, and extent of dcfûrcstation. However, each of them in particular and regional

models in general have certain limitations, which cast doubts about the validity of the results.

While spatial regression models do not include farmer characteristics, prices and wage rates

that are vital in decision about land use planning, spatial simulation models have the

weakness of inability to separate correlation fiom causality, and non-inclusion of price

variables. Non-spatial models failed to incorporate variables such as fanner characteristics,

topography, soi1 quality, and data limitations render them ineffective in studying the effect of

price changes over time (Kaimowitz and Angelson, 1998). Along with the contradicting

results fiom different studies, regional models in general do not capture the effect of some of

the macroeconomic policy variables such as extemal debt, tax, devaluation etc. In addition,

they are often of not much help in suggesting major international differences that exist with

respect to extent of deforestation.

2.3 Macro level Models

Studies that treat a single country or group of countries as unit of analysis are referred

as macroeconomic modek. These models cover al1 the four modeling approaches, Le.,

descriptive, analytical, empirical, and simulation.

2.3.1 Descriptive models

In descriptive analysis, authors have tried to outline their thoughts on the broad

causes of deforestation in a country or globally supported by statistics on the status of forest

area and the outlined causes. The contributions of Mathur (1976), Tiwari (1983), Tucker and

Richard (1 983), WRI (1 994) and (Mathews et al., 2000) fa11 into this category.

Mathur (1 976) noted that diversion for forest area for developmental activities,

agricultural expansion, grazing, and forest fire are the causes of deforestation in India. While

forests are cleared for developmental needs such as mining on one hand, additional pressure

in the form of demand for forest products by the inhabitants, on the other hand, accelerates

the process. Similar is the case with agricultural expansion the effect of which is diversion of

forest area for crop production plus pressure on new forest areas for forest products that

would have been fulfilled by the diverted land. Tiwari (1983) pointed out that population

pressure is the underlying cause of deforestation in India. It increases the demand for fùel

wood that directly contributes to loss of forest area and, the rising food requirement increases

deforestation through agricultural expansion. Tucker and Richard (1983) emphasized that

demand for tropical products such as cotton, coffee, rice, and timber by developed countries

is the dominant factor of forest depletion. Cotton export is a cause of deforestation in

Western India. Coffee in Brazil, rïce in Burma and Thailand, and Timber in Indonesia and

Malaysia are also causes of deforestation. World Resources Institute (WRI, 1994) noted that

increasing human and livestock populations, poverty, demand for fuel wood and high

consurnption by industrialized nations are the causes of deforestation. Maintaining the s m e

line, Mathews et al. (2000) reported that population growth, economic growth, government

development programs (instead of the poverty reported in WRI, 1994), agricultural

expansion, and road construction are the causes of deforestation.

Along with identification of various causes of deforestation, these models often point

out many facets of the deforestation process, which have important implications in describing

deforestation through empirical models. One such contribution from descriptive studies is by

Rowe et al. (1992). They classified the causes of deforestation into two categories; while

agricultural expansion, overgrazing, fùelwood gathering, commercial logging, infrastructure

and industrial development, are placed in the category of 'direct causes', market and policy

failures, population growth and rural poverty, state of the economy (debt) are treated as

'underlying causes'. This classification has significant implications in econometric modeling

as will be clear in subsequent sections.

Though these models enhance the understanding about the causes of deforestation,

their validation through statistical testing procedures is necessary to reach any conclusion.

2.3.2 Analytical models

These models at macro level highlight the feedback mechanisms that link specific

sectors involved in deforestation to the broad economy through changes in prices, incomes,

taxes, and subsidies. These models differ fiom those at micro level due to their consideration

of some prices as endogenous, which makes it possible to analyze the effect of governrnent

policies through price changes. A maximum of three sectors of production are covered in

these models (Kaimowitz and Angelson, 1998). Jones and O'Neill (1992) used only the

agricultural sector; Deacon (1995), and Jones and O'Neill (1995) used the agricultural and

manufacturing sectors; Soest (1998) considered agricultural, forestry, and manufactunng

sectors to describe the deforestation process. Amsberg (1998) included the agticultural sector

with the forestry sector, which is divided into rnanaged and unrnanaged forest areas2. The

modeling approach is based on the pnnciple of profit maximization by fmers/loggers

subject to constraints of land and labor. It is assumed that forest area is the only source to

meet the increased demand for land. Clearance of land will continue up to the point where

marginal benefit fiom the additional land and labor is equal to the marginal cost of acquiring

them. Output price will determine the benefit fiom the cleared area through sale of timber or

agricultural production. Input prices such as wage rate, transportation cost etc. (these factors

are dependent on national policies on minimum wage rate, taxes, subsidies etc.), determine

the cost of acquiring an additional forest area. Policies that increase the cost and decrease the

benefits of additional forest clearing will reduce deforestation.

The results of these models vary according to the types of assumptions made. Jones

and O'Neill (1995), Deacon (1995), and Anderson (1996), assuming agricultural output

prices as exogenous, concluded that policies such as export taxes, and tariffs on the

agricultural sector will reduce deforestation. They also concluded that subsidizing agriculture

or logging through public road construction, protectionism, high guaranteed agricultural

prices, and low stumpage rates for tirnber will increase deforestation. However, if

agricultural output pnces are considered endogenous, the effects of policies that facilitate

agricultural export are different. Increased eaming from agricultural export will raise the

demand for food, which in turn will increase the pnce leading to less domestic consurnption.

The net effect of agricultural export on agricultural expansion (or deforestation) fiom export

demand and domestic demand is indeterminate (Jones and O'Neill, 1995). Rural road

Managed foresis are the plantation forests that are planted in order to be harvested regularly. Unmanaged forests in contrast are the mature natural forests that have zero net timber growth (Amsberg, 1998).

construction and higher agricultural productivity induce more forest clearing when

agricultural pnces are exogenous, since they increase the profit level. However, if the prices

are endogenous, increased production will decrease the price discouraging f m e r s to

encroach more forest area (Amsberg, 1998). When agriculture and forestry sectors are

considered separately, restriction on timber export has a negative effect on deforestation

since low export discourages tirnber production by lowering the pnce (Deacon, 1995);

however, if examined together, the result is the opposite. The low timber price due to export

restriction encourages agricultural production that in turn increases de forestation (Soest,

1998). Amsberg (1998) distinguished the effect of low timber prices on managed and

unmanaged forests. Low timber price will decrease deforestation in unmanaged forests due to

decreased profitability fiom harvesting remote areas; but deforestation will increase in

managed forests because plantation areas will be converted for agricultural purpose.

Assuming that output prices are exogenous, Hyde et al. (1996) concluded that deforestation

will be limited to a point away from market centers, where marginal benefits Erom the

additional land (fiom sale of timber output) equals to the marginal cost of acquiring that land.

Costs of acquiring land include those fiom harvesting and transporting the resources,

opportunity cost of labor, and securing property nghts (lands far away from markets are

considered as public lands). Al1 of these costs keep on increasing with increased distance

from the market centers, and at one point will surpass the exogenous output price. At this

point backstop technology such as new plantations, and development of substitutes of

currently used forest products etc. will operate to meet the demand gap and price level. So,

deforestation in public lands will stop afier a certain distance from market centres.

Analytical models are beneficial in obtaining general conclusions about the impact of

different policies on deforestation. They provide important insights into the potential indirect

effects of those policies through adjustments in factor markets and changes in demand

resulting from redistribution of income among different sectors (Kaimowitz and Angelson,

1998). However, these models cannot predict the effects of policies on deforestation without

making strong assurnptions about the elasticities of supply and demand of outputs and labor

(Kaimowitz and Angelson, 1998). In addition, these models have to be validated by empincal

analysis for their practical application.

2.3.3 Simulation Models

These models are of two types: (a) computable general equilibrium (CGE) models

and (b) system dynamic models.

2.3.3.1 Computable general equilibrium (CGE) models:

CGE models are the empincal application of analytical models, though with certain

modification. These models are either static, i.e., market equilibrium at a particular point of

time, or dynamic (time element is considered through inclusion of growth variables), where

equilibriurn in markets at future periods is deterrnined. These models consist of a series of

equations describing the feedback mechanisms that exist between variables of different

sectors of an economy. These interrelated equations are based on the detemination of the

equilibrium price level @rice is endogenous) in a market, where producers and consumers

make their decisions independently. Decisions by the players in a market are affected by

exogenous macroeconomic policies such as taxes, subsidies, export restrictions, interest rates

etc. Whereas static models are usefùl in determining the effect of exogenous variables at

present, dynamic models are usefbl over time. Al1 the CGE models of deforestation

mentioned in the following paragraph are static in nature.

Different approaches have been adopted to mode1 the deforestation process. In

conventional approach (Coxhead and Jayasuriya, 1994), agents (farmers or loggers) clear

forest up to the point where marginal profit or land rent is zero, i.e., maximize profit. So,

macroeconomic policy measures that reduce the profit level of the agents will decrease

deforestation. Policies such as export taxes and tariffs on timber and agricultural output will

glut the domestic market leading to a fa11 in price. This in him will reduce the profit level of

the producers, who will produce less of timber and agricultural output considered the main

causes of deforestation. In describing the profit maximizing behavior, no consideration is

given to property rights of the agents. However, fmers/loggers with well-defined property

rights over a stretch of forestland will decide the rate of clearance, not only based on profit

level from the sale of output, but also taking into account the value of forest conservation.

Models using the property right approach (Persson and Munasinghe, 1995) explicitly

consider the property rights to forests and their implications for forest use. Forest rotation

approach models (Thiel and Wiebelt, 1994) describe decision-making about the forest use as

an intertemporal allocation problem. These authors assume concession holders have clear

property rights over the forest, and calculate the rotation period for logging that rnaximizes

the net present value of retums based on timber prices, harvesting costs, interest rates, and

physical growth characteristics of trees (Faustrnann approach). High interest rate, high timber

price, and low harvesting cost lead to shorter rotation period preventing regeneration of the

logged areas, the result is deforestation.

With few exceptions, results fiom these models differ form one study to the other.

Wiebelt (1994) (quoted in Kaimowitz and Angelson 1998) following the conventional

approach found that real devaluation in Brazil would lead to deforestation in short-run due to

signifiant expansion of crop production and a small expansion in timber and livestock

output. But in long term, even though there will be increased export demand of timber, due to

low domestic demand of timber for construction, the net effect will be smaller. Devaluation

increases logging in Philippines in another conventional study (Cruz and Repetto, 1992).

With respect to subsidies tu industry the results are in the opposite direction. Persson

and Munasinghe's (1995) property right approach mode1 of Costa Rica revealed that

subsidies decrease deforestation by attracting resources (farmers and laborers) away fiom

rural activities. In contrast, results from a model based on conventional approach reported

that subsidies increase deforestation in Philippines (Cruz and Repetto, 1992).

Models concemed with logging taxes and log export bans also have contradictory

results. In Indonesia (Persson and Munasinghe, 1995) higher taxes on logging and log export

bans reduce logging, but they also increase agricultural area by shifiing resources fiom

forestry, with the net result being little change in forest clearing. However, Thiele and

Wiebelt (1994) concluded that these measures decrease deforestation in Cameroon, as they

assumed that there is no interaction between agriculture and forestry.

CGE models at the national level are appropriate for analyzing the interactions

between different sectors and markets. Since they take into account limited variables fiom

limited sectors, interactions between sectors is complex, and the models have substantial data

requirements, these models are best used when no alternative approach can be found to

analyze the deforestation scenario (Kaimowitz and Angelson, 1998).

2.3.3.2 System Dynamic model

A system dynarnic model is one that consists of a chain of interrelated equations

describing a phenornenon under study, such as deforestation. The dynamic nature of the

system comes from the inclusion of the feedback mechanism and time factor in describing

the relationship between variables. In a system model with feedback rnechanism, al1 of the

variables are endogenous and are detemined inside the system. So, once the functional form

and the parameter estimate of the relationships are determined, these models simulate the

effect of a change in the exogenous variable; thereby predicting the change in scenario. The

functional form (from a graphical representation of data on the variables) and the parameter

estimates (fiom regression analysis) that descnbe the relationship between the variables are

based on time series data.

The dynamic CGE models and System dynamic models are alrnost the sarne, except

that the former is based solely on price and airns at market equilibrium at every point of time,

while the later case includes price as one of the variables and may or may not reach

equilibrium at any penod. If a dynarnical system ever gets in an equilibrium state, it stays

there forever (Varian, 1992). The concept of equilibriurn makes CGE models

computationally restrictive in including more number of variables and still reaching

equilibrium. The exclusion of a number of variables fiom different sectors of an economy,

though they are relevant from a deforestation point of view, render these models less efficient

in assessing scenarios in totality. System dynamic models, in contrast, include more variables

fiom different sectors of a national economy, since they do not aim at equilibrium. Therefore,

these models are more applicable to reality.

There exist only two models in this category. Saxena et al. (1997) developed a system

dynamic model for India. This model consists of a set of interrelated equations involving

variables chosen fiom forestry, agriculture, livestock, energy, and socio-economic sectors of

Indian economy. The highlight of this model is the consideration of a broad definition of

deforestation. Both reduction in forest area and forest biomass are considered deforestation.

According to this study, "the causes of deforestation that could be explained by a set of

relationships shifts with the shift in dominance of the relationship of elements within and

arnong sectors. Hence, i t is too simplistic to point out the causation of deforestation at an

isolated level such as growth of population or growvth of trade" (Saxena et al., 1997). Time

series data starting fiom 1950 has been used to determine the parameter estimates. This study

claims that if the current trend continues, India will be completely deforested (in ternis of

forest biomass) within two/three decades. However, total forest area will grow up to 2025

due to plantations exceeding diversion of forest area for other purposes. After that, it will

start declining due to the dominance of diversion over plantations. Since the Ioss of forest

biomass is alarming, they have recommended restructuring the energy sector fiom one that

degrades the environment to a more environmental fnendly industry. Measures such as

biogasification, CO-generation, and increasing the end use efficiency of energy are the most

promising.

Considering the 'world' as a system, Jepma (1995) developed a system dynamic

model (IDIOM) of global tropical deforestation. This model is an integration of three

independent modules. A TROPFORM module focusing on the various aspects of tropical

timber production and trade, and their relationship with deforestation; SARUM describing

the main inter linkages of world economy; and a LAND USE module that deals with the

various aspects of agricultural land use in the tropics. Country level data are used for

initialization of the model. It is predicted that by 2025, around 50 percent of the commercial

timber reserves in the tropics would disappear. Similarly, increased demand for food

production will lead to a considerable loss of forests to agriculture. The author came to the

conclusion that a comprehensive range of development policies, which includes population

prograrns and improvements to agricultural productivity, as well as sustainable logging

practices, is the most effective way of conserving tropical forests. The cost of implementing

such policies is around US $32 billion per year (Jepma, 1995).

System dynamic models are quite complex and simultaneous implementation of a

nwnber of policy measures directed towards various factors of deforestation is ofien

daunting, although they depict the real life situation covenng many different sectors. Global

models (Jepma, 1995) included only major variables fiom agriculture, forestry, and socio-

economic sectors of an economy and failed to include the fine linkages with variables of

energy and livestock sector as in Saxena et ai. (1997). On the other hand, the scope of

replicating the model by Saxena et al. (1997) in a majonty of tropical countries is limited due

to data limitations.

2.3.4 Empirical models

These models attempt to explicitly identiQ the causes of deforestation along with

their quantitative estimates using multivariate analyses such as multiple linear regression

techniques. These models do not consider the feedback mechanisms involved in the

relationships between the different variables. Therefore, they are not usefül in analyzing or

assessing scenarios of different policy impacts on deforestation. Empirical models use data

on a few important macroeconomic variables, such as population, agricultural expansion,

national income, external debt, pnce level, extent of road etc., to make a generalization

regarding the major factors affecting tropical deforestation at a national or global level.

National level studies use time series data for a single country (Chakraborty, 1994) and in

global level studies cross sectional data for many countries are used, with some exceptions

that use panel data, for analysis (Capistrano, 1990). However, the scope of using time series

data is limited due to the non-availability of information on the extent of deforestation in

many countries. This problem has forced Chakraborty (1994) to use area of reserved forest,

instead of extent of deforestation, as an endogenous variable in his analysis. Similarly,

Capistrano (1990) used the forest area estimate from FA0 production yearbook as the

endogenous variable. The limitations of such data sources will be discussed in subsequent

paragraphs. Since cross sectional data for many countries for a particular period are readily

available, they are widely used in global level studies. Empirical models are thus the largest

category of deforestation models.

2.3.4.1 Estimation techniques in global level studies

Almost al1 multi-country empirical models are regression analyses (Palo, 1994;

Shafik 1994; Kant and Redantz, 1997; Rudel and Roper, 1997a), though with some

exceptions such as Boolean algebraic technique (Rudel and Roper, 1996). Kaimowitz and

Angelson (1998) noted that Boolean algebraic technique is not really a quantitative mode1

like regression models, but a qualitative comparative study to find cornmon characteristics

arnong countries with either high or low deforestation. Among the category of multi-country

regression analysis, almost ail studies have included the direct and underlying causes of

deforestation in a multiple regression equation estimated by OLS method. Only one study has

adopted a two-stage least square method approach (by maximum likelihood estimation

procedure) to differentiate the effect of direct and underlying causes of deforestation (Kant

and Redantz, 1 997).

2.3.4.2 Variables and hypotheses in global level studies

The selection of dependent (deforestation) and exogenous variables (causes of

deforestation) included in multi-country regression analysis is based on certain hypothesis

descnbing the relationship between the two types of variables. Endogenous variables in these

models Vary fiom one study to the other. They are; percentage of land area under forest cover

(Lugo et al., 1981; Palo, 1994), absolute forest area decline (Allen and Barnes, 1985;

Grainger, 1986; Rudel, 1994, Kant and Redantz, 1997), percentage decline in forest area

(Murali and Hedge, 1997; Tole, 1998), wood production (Gullison and Losos, 1993), and

expansion of agricultural land (Southgate, 1994).

The exogenous variables, included to test the hypothesis that they affect

deforestation, are related to population, national income, extemal debt, ternis of trade,

devaluation, road construction, and political factors such as stability and democracy. Apart

fiom these underlying causes, many studies have included direct causes of deforestation such

as agricultural expansion and logging as exogenous variables in their analysis.

Different models have used different indicators such as rural population, total

population (level variable), and their density or growth to capture the effect of population.

Some of the models have used a mix of these variables. Kant and Redantz (1 997), and Inman

(1993) used total population of the country in their analysis. Allen and Barnes (1985), Lugo

et al. (1981), and Pa10 (1994) used average population density of a country, and Gullison and

Losos (1993), Kimsey (1991), Pa10 (1994) etc. included average annual growth of population

as exogenous variable. Some authors used rural population density (Bawa and Dayanandan,

1997; Rudel and Roper, 1997a), while some others used rural population growth (Inman,

1993; Rudel and Roper, 199%). In general, the hypothesis is that rural population growth

increases deforestation for their need of agricultural land (Rudel and Roper, 1997a), and total

population does so due to their increased demand of agricultural and forest products (Kant

and Redantz, 1997). Others postulated that growth in population would have a negative effect

on deforestation through labor intensification in agriculture (Bilsborrow and Geores, 1994).

High national income increases demand for agricultural and forest products that in

turn increases deforestation through encroachment of agricultural area into forests, and

logging (Capistrano, 1990; Kant and Redantz, 1997). Higher national income also increases

availability of capital to be invested in rain forest region for logging (Rudel and Roper,

1997). On the other hand, some authors postulated that higher income is associated with less

fùelwood consumption, agricultural intensification through use of machineries and fertilizer,

and more off-farm employment opportunities, al1 considered to reduce deforestation (Pa10

and Lehto, 1996). Some authors hypothesized that deforestation increases at a faster rate at

low levels of income, slows down, and beyond a threshold level it starts decreasing with

increased national income (Stem et al., 1996). This relationship between deforestation and

level of national income is referred as 'Environmental Kuznets Curve'. The variables used to

test al1 these above hypotheses are level of national income (Capistrano, 1990; Bawa and

Dayanandan, 1997; Pa10 et al. 1996) andor its growth (Kant and Redantz, 1997; Pa10 et al.,

1996).

Regarding the effect of externai debt, it is hypothesized that high extemal debt

increases deforestation due to increase in export of agricultural and forest products (for debt

service), which is only possible by agricultural expansion and logging (Kahn and McDonald,

1994; Capistrano and Kiker, 1990). However, one of the authors speculated that high extemal

debt might be causing low deforestation. This is possible in cases where debt service is

problematic and national govemrnents stop developmental projects such as road construction

(considered to increase deforestation) in forested areas (Hansen, 1989). Variables used to test

these hypotheses are total extemal debt (Kant and Redantz 1997), extemal debt per capita

(Shafik, 1994), total external debt to GDP ratio (Kahn and McDonald, 1995), and total debt

service (Kahn and McDonald, 1994).

An improved term of trade (lower export prices) is expected to increase deforestation

through higher export of forest products such as timber (Kant and Redantz, 1997). Similar is

the case with higher real exchange rates (Shafik, 1994), or currency devaluation (Capistrano,

1990).

Few studies have tried to test the effect of access on deforestation (Rudel and Roper,

1997b; Tole, 1998). It is hypothesized that, with increased access deforestation will be the

result of dual effects of increased demand for land (speculation), and logging in areas that

were otherwise inaccessible. Rudel and Roper (1997b) included a durnrny variable to

represent a country with penetration road (data source is local reports), while Tole (1 998)

used percentage change in paved roads per hectare as an exogenous variable.

Political factors such as stability and democracy have been used to test their relevance

in the context of deforestation. Deacon (1994) used several measures of lawlessness and

government instability to test their effect on deforestation. Similarly, Didia (1997) used an

index of democracy to test whether democratic societies have less deforestation. These

studies hypothesized that the decision to conserve a forest and not to deforest is an

investxnent in an asset with long-term matunty. Investrnent will be less in risky environments

such as under political instability and authontarian government regimes. Shafik (1994)

considered it otherwise. According to the author, democratic societies will have higher

deforestation due to political pressures fiom different interest groups. Two different

indicators, political right index and civil liberty index, are used to test this hypothesis.

To test the effect of the agriculture sector on deforestation, variables such as the

change in agricultural area (Allen and Bames, 1985; Kant and Redantz, 1997 in second stage

of two-stage regression equation), index of per capita food production (Burgess, 1991), and

Agricultural export as a percentage of GNP (Rudel, 1989; Rudel and Roper, 1997a), are used

in regression analysis. It is believed that ag~icultural expansion, both to meet the domestic

food demand and for export to earn foreign currency is one of the main causes of

de forestation.

Total roundwood production (Bawa and Dayanandan, 1997; Mianardi, 1 W6), per

capita roundwood production (Allen and Barnes, 1985; Burgess, 199 1 ), and total roundwood

consumption (Kant and Redantz, 1997 in second stage of two-stage regression equation) are

used as proxies for logging. The idea is that increased demand for timber for domestic

consumption and export encourages logging, leading to deforestation.

2.3.4.3 Results of global level studies

Results from the above studies with respect to the effect of a variable on deforestation

differ fiom each other and are, therefore, confusing.

The effect of population on deforestation varies according to the nature of population

variable, i.e., level, growth or density of total or rural population. The result with respect to

level variable (Le., total population) is positive (increases deforestation) in Kant and Redantz

(1997), and negative (decreases deforestation) in Inman (1993). Al1 the studies (Allen and

Bames, 1985; Lugo et al., 198 1; Palo, 1994; Rock, 1996) that have included population

density as an exogenous variable (not rural), found the effect to be positive, Le., increases

deforestation. When used as a growth variable, in most cases the effect is positive (Gullison

and Losos, 1993; Kimsey, 1991; Pa10 et al., 1996; Rudel and Roper, 1996, Rock, 1996;

Southgate, 1994), although some of them reported no effect (Allen and Barnes, 1985; Palo,

1994), and one resulted in a negative effect (Burgess, 1991). Studies that included rural

population density as an exogenous variable found the effect to be positive (Bawa and

Dayanandan, 1997; Rudel and Roper, 1997a). The effect of nual population growth on

deforestation is found to be positive (Inman, 1 993).

Results regarding the effect of income on deforestation also Vary according to the

type of variable used, the levef or growth. A higher level of national income increases

deforestation in one group of studies (Burgess, 1992; Capistrano, 1990; Mainardi, 1996),

while some others reported that there is no effect (Bawa and Dayanandan, 1997; h a n ,

1993; Gullison and Losos, 1993), or decreases deforestation (Binswanger et al., 1987; Palo et

al., 1996). Some studies found the presence of Environmental Kuznets Curves (Cropper and

Grifiths, 1994; Rock, 1996). Similady, results h m use of national income as growth

variable, are mixed. A few studies concluded that growth and deforestation are generally

positively related (Kant and Redantz, 1997; Pa10 and Lehto, 1996), one concluded that

economic growth decreases deforestation (Palo et al., 1996), and some others find no

relationship (Allen and Bames, 1985; Deacon, 1994).

Many studies that included debt as an exogenous variable found the effect to be

positive (Bawa and Dayanandan, 1997; Kant and Redantz, 1997). Nevertheless, sorne other

studies concluded that there is no correlation between debt and deforestation (Gullison and

Losos, 1993; Kimsey, 199 1 ; Rudel and Roper, 1997a)

Few models that have included price variables such as agricultural and timber export

prices (Capistrano, 1 WO), total t ems of trade (Kant and Redantz, 1997) and real exchange

rates (Shafik, 1994), concluded that low export prices, high real exchange rate and

devaluation increase deforestation.

A Positive association has been found between deforestation and extent of road,

which is used as an indicator of access (Rudel and Roper, 1996; Tole, 1998).

With regard to political factors, the results are contradictory in nature. Deacon (1994)

and Didia (1997) found that politically stable democratic countries deforest less, but Shafik

(1 994) got the opposite result.

The effect of the agricultural sector on deforestation is different in different studies.

Allen and Barnes (1 98S), and Kant and Redantz (1 997) found that an increase in agricultural

area increases deforestation, while Mainardi (1996) (quoted in Kaimowitz and Angelson,

1998) found it to be insignificant. Using an index of per capita food production as an

indicator of agricultural expansion, Burgess (1991) found the effect as positive and Pa10 et al.

(1996) as no effect on deforestation. Some authors, using agricultural export as a percentage

of GNP as an exogenous variable found the effects on deforestation as positive (Rudel and

Roper, 1997a) and no effect (Rudel, 1989).

Per capita roundwood production, which is used as proxy for logging, has a positive

effect on deforestation (Allen and Barnes, 1985; Burgess, 199 1). When total roundwood

production is used as a proxy, the results are positive effect, i.e., an increase in roundwood

production increases deforestation, (Kant and Redantz, 1997; Burgess, 199 1 ; Rudel and

Roper, 1996) or no effect on deforestation (Mainardi, 1996, quoted in Kaimowitz and

Angelson, 1998).

These diverging results fiom the empincal studies, which are the main source of

disagreement about the causes of deforestation, c m be attributed to deforestation data and

estimation problems. Regarding the limitations of deforestation data, Kummer and Sham

(1 994) wrote, "any statistical results derived fiom these models can be dismissed as not being

based on a strong enough database". Fully supporting them, Kaimowitz and Angelson,

(1998) pointed out some estimation problems too. According to the authors, "models also

have problems arising form limited degrees of fieedom, multicollinearity, heteroscedasticity,

outliers, incorrect specification of causal relationship, and missing variables". However,

another source of these disagreements, which has not been realized, lies with hypothesizing

the nature of the deforestation process. Details of these issues will be discussed in section

2.5.

2.4 Comparative analysis of tropical deforestation models

It is apparent fkom the above discussion that al1 the different modeling approaches

adopted to describe the deforestation process have their own strengths and weaknesses. Table

1 presents those strengths and weaknesses in a concise format. In general, models at each

level, i.e., micro, regional, and macro, yield contradictory results about the effect of a

variable on deforestation. Microeconomic and regional models are not usehl because of their

non-inclusion of macroeconomic policies, financial constraints in data collection, and their

limited area applicability. In addition, these models are not suitable to find out an overall

picture of the whole country, and international differences in the process of tropical

deforestation. Macroeconomic models in contrast use aggregate data and therefore, obliterate

the effect of variables at the micro level. However, these models facilitate drawing inferences

about the causes of major international differences in the process of deforestation.

Among the macro level studies, descriptive and theoretical models are of limited use

in providing empirical evidence about the causes of deforestation because they are not based

on any statistical estimation technique. Although, simulation techniques such as by Saxena et

al. (1997) are better choices, non-availability of data on the number of variables, and the

complexity of estimation as well as applying the results in policy context limit the scope of

these models. Cross-national empirical models are usehl in finding causal variables of

international differences in the extent of deforestation, Le., between two countries or groups

of countries. These studies are less precise than the microeconomic studies as they use a

highly aggregated unit of analysis, the country (Bilsborrow , 1 994). Nevertheless, countries

remain relatively cohesive units in socio-economic dimensions such as wealth, population

growth, and agricultural policies that appear to influence deforestation rates (Rudel and

Roper, 1997b). Facts and figures support the premise that deforestation rates Vary across the

cohesive units or counties in suggestive ways. The results fiom cross-national studies may

be less precise, but are helpfùl in identimng the major causes of international differences in

the extent of deforestation. Moreover, these models are cost effective and use readily

available datasets on broad macroeconornic variables and, are therefore, the best alternative

given resource constraints and the limited area applicability of microeconomic and regional

studies.

Microeconomic

Table 1 : The strengths and weaknesses of varioui

Regional

Mode1

Macroeconornic 1. Descriptive

Strengths

2. Analytical

3. Simulation

4. Empirical

1. Use of good quality data fiom surveys

1. Less costly than Microeconomic studies

2. Better than Macroeconomic models in data quality

3. Uses GIS that helps in developing panel data

No data requirement Complementary to forma1 models

No data requirement Helps in analyzing policy options

Practical application of Analytical models Portrays real li fe situation

Easy to apply Lesser data requirement Helps find the causes of international differences in extent of de forestation

tro~ical deforestation models

1. Applicable to similar areas 2. Costly in ternis of data collection 3. No consideration of macroeconomic

variables 4. Do not suggest the reasons for regional

differences in extent of deforestation

1. Do not take into account farrner characteristics

2. No consideration for macroeconomic policy changes

3. Limitation in availability of data 4. Do not suggest the reasons for regional

differences in extent of deforestation

1. Based on authors observation without any scientific basis

1. Based on ideas or principles of deforestation without any practical application

1. Complex 2. High data requirement 3. Mostly suitable for national studies 4. Simultaneous application of number of policy

measures is often not given due attention

1. Data limitations 2. Use of aggregate data that obliterate the

actual scenario at micro level

2.5 Specific issues related to empirical models

As noted earlier, Kairnowitz and Angelson (1998) have considered the diverging

results fiorn multi-country regression analysis as a problem, and identitied the limitations of

deforestation data as well as estimation techniques as the root causes of such a problem.

However, the diverging results may be a reality rather than a problem. Even if sarne data and

estimation techniques are used, a causal variable may have different effects in different

regions and different time periods. There are possibilities that a variable has both positive

and negative effects on deforestation. The net effect may Vary over regions and tirne penods

due to changing dominance of one effect over the other. Descnbed below are the issues

related to effects of a causal variable on deforestation, data, and estimation techniques.

2.5.1 Causation issues

Two types of multi-country regression analyses exist with respect to the identification

of the causal variables of deforestation and their possible effects on deforestation. The first

category of models have hypothesized a 'single effect' causal mechanism such as positive or

negative effect of a variable on deforestation and tested the posited hypothesis (Rudel and

Roper, 1997a; Rudel and Roper, 1997b; Kant and Redantz, 1997; Didia, 1997; Deacon,

1994). These models used the one-tailed t-test procedure to test the significance of the

variables.

It, however, may be the case that a variable has multiple effects (positive or negative)

and one effect outweighs the other and thereby the net eflect varies in different situations.

The hypotheses of effects of higher income on deforestation, for exarnple, are as follows: (a)

higher income increases demand for agricultural and forest products, which in tum put

greater pressure on forests leading to deforestation (Kant and Redantz, 1997; Capistrano,

1990); (b) it decreases deforestation with more off-farm employment away from the

agricultural fiontier (agricultural expansion by farrners is often considered as main cause of

deforestation) and growing awareness for conservation of forests (Angelson, 1999). h fact,

both of these posited hypotheses might be operating simultaneousIy, the net effect of which

is captured in empirical estimates. Along with these general posited hypotheses, diverging

empirical results with respect to a relationship of a causal variable with deforestation

strengthen the argument that there indeed may be multiple effects of a variable on

deforestation. Hence, it camot be hypothesized in advance about the net effect of a variable

on deforestation, since it may Vary in different circumstances. Only the sign of the coefficient

estimate will determine the net effect of a variable. The second category of models used two-

tailed t-test to test the significance of the causal variables based on the estimation results

(Rudel, 1989; Inman, 1993; Shafik, 1994). Implicitly, the two-tailed t-test approach takes

into account the net effect phenornenon but authors have not mentioned these intricacies.

This issue is discussed in detail in Chapter 3.

2.5.2 Issue of data

While efforts are being made to develop a sound econometnc mode1 of tropical

deforestation, questions are raised about the validity of the deforestation data. Data on land

use, Le., cropland and pasture, and environmental conditions such as deforestation at the

country level are the least reliable (Bilsborrow and Geores, 1994; Palo, 1994; Kant and

Redantz, 1997). The suspicions about the reliability of deforestation data is rooted in the

problern of defining 'what is deforestation' as well as fiorn the varying methods o f obtaining

the deforestation data.

As discussed in section 1.3, two different types of definitions of deforestation exist at

present. While the broad definition includes different types of degradation along with forest

conversion to other land use practices in defining deforestation, the narrow version ignores

the degradation aspect. Accordingly, the estimated average annual deforestation in the humid

tropics was about 13.2 million ha. during 1980s by the broad version (Myers, 1994), while it

was 10.7 million ha. by the narrow version (FAO, 1993).

Whatever rnay be the definition, it is obvious that degradation and deforestation are

two interrelated phenornena where the former often leads to the latter. However, technical

and financial constraints of the developing countries are the limiting factors in measuring

forest degradation and it does not seem to be avoidable in the near füture. According to

Grainger (1 999), "monitoring degradation presents even more of a challenge, as conventional

change detection techniques are less able to measure small continuous changes in vegetation

attributes. Even with high resolution senson, classification thresholds and aggregation of

refiectances mask changes in tree density, which may be offset by increased biomass in the

herb layer". The broad definition is powerful enough in informing the public about the fact

that larger areas are affected by anthropogenic pressure. Deforestation as defined by FA0

explains the extremes of forest loss that is permanent in nature and therefore, use of this

definition gives some insight about the deforestation process, though not completely.

Even if the definition by FA0 (1993) is accepted as a standard one, the estimation of

extent of deforestation (or forest area) differ in different sources of FA0 due to non

uniformity in application of the definition, and varying methods of data collection. The

widely used data source, Forest Resource Assessrnent 1990 (FRA 1990), includes only

tropical closed, broad-leaved forests in estimating the forest loss, while the Production

Yearbooks include al1 forest and woodland, whether it is open or closed, coniferous or broad-

leaved Wmsey, 1991). For the first source, the changes in forest loss will be appreciable,

and in case of the second source, it will be marginal.

The F A 0 Production Yearbook compiles data on land use, especially forest area,

based on national governrnents' response to annual questionnaires without any empirical

basis. National govermnents' often have incentives to misreport actual forest area (Shafik,

1994). Hence, this data source is not reliable. On the other hand, FRA 1990 has forest cover

data of 90 countries in which 21 have two or more than two inventories, the rest have only

one (as old as 1965) or no inventory at all. In order to circumvent the problems of the lack of

forest cover data in some countries and the estimation at different time periods where such

data do exist, a different approach is used. The available data on forest resources, population

and socioeconomic variables are compiled fiom country statistics and stored in a database

called Forest Resources Information System (FORIS). The spatial information on vegetation

types, ecofloristic zones, and country and sub-national (statelprovince) boundaries are

collected fkom maps and integrated with statistical data in the form of geographical

information system (GIS). The sub-national units are stratified into a number of zones based

on ecological criteria. The Continuous Forest Inventory (CFI) concept is used in estimating

the extent of deforestation in each ecological zone. The forest cover data of these zones

contained in FORIS refer to di fferent time periods and are brought to standard years of 1 980

and 1990 with the help of a deforestation model. This model correlates forest cover change in

time with other variables including population density and population growth for the

corresponding period, initial forest cover area and ecological zone (the model parameters are

different for different ecological zones) under consideration (FAO, 1993). For developing the

deforestation model, only multi-date remote sensing data at the ecological zones are used and

the function expressed in the form of a differential fùnction. Hence, the deforestation model

is an ecological rather than an economic model. Mathematically,

Where,

Y is the percentage of non-forested area in a sub-national (statelprovince) unit

computed as Y = 100 * (total area - forest cover area)/total area

P = ln(1 + population density)

b,, b, and b, are the model parameters

The deforestation estimates for each ecological zone at a sub-national level is

aggregated at national level to find the total deforestation. In places where no reliable

estimates of forest cover area are available, they are extracted from calibrated vegetation

maps that exist in the FRA 1990 projects' GIS and used as input in the model.

The forest cover (or deforestation) data at the national level in FRA 1990 are

questionable as they are not the actual survey data covenng the whole country, but are

determined by a model.

To deal with the problem of deforestation data, Rudel and Roper, (1996) and Rudel

and Roper (1997b) used information kom FA0 (198 1, 1993) and local reports, to categorize

deforestation data into high and low groups with 1 percent as cut-off point, while the

deforestation rates vary from 0.3% in Rwanda to 7.2% in Jamaica (FAO, 1993). in the first

study, Boolean algebraic technique is used to create sets of countnes that are having similar

processes of deforestation. The second study has used logistic regression (instead of linear

regression) to estimate the probability of a country having high or low deforestation.

2.5.3 Issue of estimation problems

Multi - country regression analyses used to study the global tropical deforestation

process have suffered fiom many estimation problems. These are: distinction between direct

and underlying causes of deforestation, the problem of heteroscedasticity, and the use of

interactive regional dummy variables.

In a correct econometric model, direct and underlying causes cannot be used together

as explanatory variables. Underlying causes such as population, income level, extemal debt,

price level, etc. determine the direct causes such as agriculture/pasture area expansion, and

export of agriculturdforest products, which in tum determine the extent of de forestation.

Putting these causes together will mean econometric misspecification. Suppose,

Y = Extent of deforestation

X,= Average annual growth in area under cropland, the direct cause of

de forestation

X,= Average annual growth in population, the underlying causes of

de forestation

When the underlying cause and the direct cause are used together in the same regression

equation, the mathematical form of the relationship between deforestation and its causes will

be as foflows;

Y = + P I X I + P z X r + E ............ (1 )

The ceteris paribus effect of population growth on deforestation isp, which is the case in

studies such as Allen and Barnes (1985), Burgess (1991), Bawa and Dayanandan, (1997) and

Tole, (1 998). However, change in cropland area, a direct cause of deforestation, is influenced

by population growth, the indirect cause of deforestation. Mathematically,

............... XI =a, + a 1 X 2 + e l (2)

The estimation of Eq. (1) without considering Eq. (2) will lead to biased results since,

Y = p, + P , ( a , + q X 2 f ~ , ) + p z X z + ~

=(Po +P1%)+(Bt +P1a, )X, + (E+PIEI)

The coefficient of the underlying cause population growth is now modified by Pla,. The

coefficient estimate of the final mode1 can be calculated fiom the coefficient estimates

obtained fkom individual equations.

Only one study has used the two-stage least square estimation procedure to address

this problem (Kant and Redantz, 1997). In the first stage, underlying causes are used as

exogenous variables to predict the direct causes, and in the second stage, these predicted

direct causes are used as exogenous variables to estimate their effect on deforestation. The

effect of an underlying cause on deforestation is determined through its effects on different

direct causes. Hence, it is difficult to assess the actual explaining power of an underlying

cause. However, as explained above, deforestation cm be explained explicitly in ternis of the

underlying causes, which makes it possible in estimating their actual explaining power.

Another possible problem with this model is that it treats the direct causes as independent of

each other. This may not be correct, for the sarne underlying causes are used to explain the

different direct causes. In other words, there is problem of simultaneity in this model.

Heteroscedasticity is the violation of constant error variance assumption in

econometric modeling. This violation is most common in cross-sectional studies. Ignoring

tests and correction for heteroscedasticity wili result in unbiased but inefficient estimates

(Pyndick and Rubinfeld, 1992). There is often a possibility of rejecting a variable as

insignificant (because of low t-values), while it is not so. Capistran0 (1990) corrected for

heteroscedasticity between penods, resulting fiom the use of panel data, by dividing the

observations in each period by the square root of mean square error from the respective

penod regressions. Kant and Redantz (1997) used the White method to correct for

heteroscedasticity.

Regional durnmy variables are used to capture regional differences in the process of

deforestation. These differences across regions are captured through changes in the

intercepts. However, there is a need to know the regional differences in the effect of major

explanatory variables (variation in slope) such as population, on deforestation. interactive

dummies, which are the product of regional dumrnies and explanatory variables (e-g.

population), are used for this purpose. Absence of interactive durnmy variables in multi-

country regression deforestation models mean variables (causes of deforestation) included in

the model affect deforestation in the same manner across regions, which is not true in reality

(Kaimowitz and Angelson, 1998). This problem has been dealt with in Kant and Redantz

(1997) by including interactive dummies that involve various explanatory variables and

regional dummies for Asia, Afkica, and Latin Amenca regions. An interactive dummy found

to be significant in a particular region is retained as a separate variable, to distinguish its

effect fiom other regions. If the interactive durnmy involving an explanatory variable is

found to be insignificant in al1 the regions, the effect of that variable is treated to be the same

globally, which is captured by the coefficient estimate of the variable itself.

Another dimension of methodological problem that has not been looked into in most

of the studies might be the rather unrealistic assumption of linearity in the relationship

between the endogenous variable 'deforestation' and its causes, the exogenous variables.

Some evidence to this issue is provided by the results indicating presence of 'Environment

Kuznet Curve' phenomenon related to the relationship between extent of deforestation and

level of national income (Cropper and Grifiths, 1994; Rock, 1 996).

Out of the three issues described above, Rudel and Roper (1997b) attempted to

address the problem of deforestation data, and Kant and Redantz (1997) dealt with the

estimation problems. However, no study has attempted to solve these problems in totality.

Also, the issue of the hypothesis problem is not discussed at ail. In this research an attempt

has been made to address al1 these three issues together. in Chapter 3, a deforestation model

addressing the hypothesis issue is described. The problem of deforestation data is dealt with

in Chapter 4 followed by the estimation of the deforestation model in Chapter 5.

Chapter 3

A MODEL OF TROPICAL DEFORESTATION

Deforestation is a complex process where different factors (causes of deforestation)

involved are linked through a variety of pathways (Kahn and McDonald, 1994). These

factors have their roots in different sectors of an economy. Row et al. (1992) and Grainger

(1993) have divided the causes of deforestation in to direct and underlying causes. While it

seems that direct causes such as agriculture/pasture expansion, and forest products

consurnption/export are driving deforestation (Shafik, 1994), it is the underlying causes such

as population growth, road construction, econornic growth, debt etc., which influence the

direct causes to accelerate deforestation. Hence it is natural to consider the underlying causes

as the causal variables in describing the deforestation phenornena.

The empirical studies that exist at present have considered either positive (increases

deforestation) or negative (decreases deforestation) effects of the different causes of

deforestation. However, there are possibilities that these causes may have both positive and

negative effects simultaneously on deforestation. One effect may outweigh the other and,

therefore, the net effect need not be the same across countries. In some countries the net

effect will be positive while in some other it may be negative. The net effect might be zero if

the positive and negative effects neutralize each other. Hence, a realistic model of tropical

deforestation should be responsive to the possible dualistic effect of its causes. The proposed

model is presented in Fig. 1. The ellipse at the center is the endogenous variable

'deforestation' and the surrounding rectangles are its causes or exogenous variables. The '+'

and '-' signs denote the possible positive and negative effects of a variable on deforestation.

Details of each variable and the dual nature of causes of deforestation are discussed next.

Fig. 1 : Dual effects of the causes of deforestation

GROWTH 1 + Demand for forest produc ts

+ Timber export

+ Shelter. - subsistence POPULATION

GROY YTH 1- Labor intensification,\ 1

'urban migration, skills , and technology

+ Less fear of punishmeni

- Off-farm employment

DEFORESTATION

- Public pressure

ROAD - Better forest I management, patroIl ing

+ Free riding

7

+ Expansion

- Low per unit area pressure

PERCENTAGE FOREST AREA

Note: The '+' and '-' signs show whether a variable increases or decreases deforestation

3.1 Definition of deforestation (DE-

The endogenous variable, deforestation or its proxy, varies fiom one study to the

other in global deforestation models. As mentioned in section 2.3.4.2, the different variables

are; percentage of land area under forest cover, absolute forest area decline, percentage

decline in forest area, wood production, and expansion of agricultural land. Tole (1998) has

outlined the reasons for using "percentage change in forest cover" as a sensible endogenous

variable. "It is directly comparable across countries, does not discriminate between countries

on the basis of their rates of forest cover loss. A country like Brazil - with high absolute

forest area loss but relatively low percentage of annual deforestation should be given equal

weight in the analysis as Haiti - a small country with small forest area but high annual

percentage rate of forest cover loss. From the perspective of a country like Haiti, irrespective

of its standing vis-à-vis countries with vast reserves, this assumption is clearly warranted as it

suffers tkom serious deforestation problems". Hence, average annual rate of deforestation is

the endogenous variable in Our model. However, rate of deforestation is used as a qualitative

variable. Issues related to deforestation data are discussed in Chapter 4.

3.2 Causes of deforestation

As mentioned earlier, deforestation is an outcorne of interactions of many sectors

with the forest sector. In this thesis it is postulated that it is the interaction of different sectors

of an economy such as forest (e.g. extent of forest area), demographic (e.g. population

growth), macroeconomic (e.g. economic growth, and debt sentice growth), agricultural (e.g.

agricultural growth), infrastructure (e.g. developrnent of road), and political (e.g. level of

democracy) affect the extent of deforestation. It is also hypothesized that the effect of each

sector c m be captured by one variable. As the endogenous variable is the average annual

percentage change in area deforested, the average annual percentage change in the different

causes are used as exogenous variables. However, in some cases the level rather than the

growth variable is used and the reason for that is discussed when describing the individual

variables. Discussed below are the possible ceteris paribtcs effects of each variable on

deforestation, i.e., when al1 other variables are constant.

3.2.1 Forest sue (PRFORAR)

The rate of forest clearance tends to be related to the size of the forest area. Srnall

forests, being more accessible, tend to be destroyed at a faster rate than larger forests (Rudel,

1994). With a sarne road length, a country with higher percentage of' forest area will be less

accessible than a country with lesser forest area. In other words, the per unit pressure on

forest area will be less in countries with a higher percentage of land area under forests and,

therefore, expenence less deforestation. In contrat to these arguments, it is also true that

vastness of forest area creates an impression of 'fiee common good attitude' and thereby

results in free riding by the local population.

Percentage of land area under forests (PRFORAR) of a country at the beginning of

the penod is an ideal indicator of the forest size of a country. There c a ~ o t be a growth

variable for this factor because average annual rate of change in forest area is the average

annual rate of deforestation, the endogenous variable.

3.2.2 Population growth (POPGR)

Population growth is widely cited as the main cause of tropical deforestation. Two

centuries ago, Malthus introduced the idea that our planet has finite resources and increasing

human population will definitely put severe pressure on natural resources such as land and

forests (Palo, 1994). This view was reinforced in the 1972 UN Environment Conference in

Stockholm (Sayer, 1995 quoted in Wunder, 2000). As a growing population needs more

food, forest products, shelter, and income for subsistence, forests are cleared to meet these

demands. The demand for food and shelter is met by horizontal expansion of fmlands or

settled areas into forest areas. Therefore, deforestation occurs. Unsustainable collection of

fuelwood and timber satisQ the increasing demand for forest products. Low wage rates, due

to excess supply of labor, force the rural poor to continue unsustainable collection in order to

meet the subsistence level of income. Wunder (2000) has summarized the causes of a

positive effect of population on deforestation as follows;

(a) Higher labor supply decreases real wage rates and forest conversion costs,

encouraging farmers to clear forests

(b) Hi& demand for agricultural products raises their pnce and this production

incentive is sufficient to expand agricultural areas into forest areas

(c) High demand for agricultural land raises their pnce and encourage people to

supply more through conversion of forest area

The combined effect of these activities is deforestation. The Neo-Malthusian perspective is

somewhat strengthened given the positive correlations found between high population

growth and high deforestation (Gullison and Losos, 1993; Kimsey, 1991; Pa10 et al., 1996;

Rudel and Roper, 1996, Rock, 1996; Southgate, 1994).

At the other end of the spectrum, supporters of Boserup hypothesis argue that this

situation may not mise because of progress in technology and the high labor absorption

capacity of the agriculture sector (Bilsborrow and Geores, 1994). More people mean more

idea, a necessary ingredient for development of new technologies to cope with future

resource scarcities. In contrast to the Neo-Malthusian perspective, which assumes that

increased agricultural production is possible by expansion, the Boserup hypothesis argues

that increased food production is possible through labor intensification of agriculture. In

other words, a particular amount of food production is possible fiom a smaller area;

therefore, less agricultural expansion occurs. Hence, a growing population will decrease or

cease deforestation. Along with labor intensification, migration of rural people to urban areas

might also decrease pressure on forest areas (Bilsborrow and Geores, 1994). The premise is,

there are enough employment opportunities in cities and people do not have to depend on

forests for their livelihood. ui addition, there is less pressure on forests for shelter as demand

for shelter is met either by constructing high-rise buildings or by developing new townships

in fallow lands around the existing city. These above viewpoints have been strengthened by

the results from deforestation studies that show negative (Burgess, 1991 ; inman, 1993) or no

effect (Allen and Barnes, 1985; Palo, 1994) of population growth on deforestation.

Following the Neo-Malthusian proposition, at constant agricultural growth (ceteris

paribus condition in our model), a nse in population growth will reduce forest area because

of the demand of land for shelter, and illegal logging for income generation, but not for food

production. In contrast, an increase in population growth will have a negative or no effect on

deforestation due to labor-intensive agriculture (Boserup hypothesis), more skills and

technology, and out-migration fiom rural areas.

3.2.3 Ecooomic growth (GDP-AGR)

Two contradicting theones exist to explain the role of economic growth in tropical

deforestation. The 'irnmiserization theory', which goes back to Myrdal (1957), explains

natural resource exploitation by poor f m e r s through a vicious circle of reinforcing poverty.

Low levels of income force rural poor to carve small f m s out of forests (because use of

technology is beyond their reach), and extract forest products such as timber, fuelwood, and

NWFPs to meet the subsistence level of income during off-fami seasons. These activities put

severe pressure on tropical forests leading to deforestation. Resource scarcities due to

deforestation make fanners poorer, and push them to travel distant areas to create new

familands and collect forest products. This expands deforestation to new areas. This

proposition has been highlighted in the Brundtland report that says: "those who are poor and

hungry will often destroy their immediate environment in order to survive. They will cut

down forests; their livestock will overgraze lands; they will ovenise marginal lands; and their

growing nurnbers will crowd into the cities" (Brundtland, 1987 quoted in Wunder, 2000). A

low level of income and a lack of access to capital force people to be risk averse and adopt a

high discount rate in utilizing natural resources such as forests. This leads to deforestation

(Lumely, 1997). Empirical evidence to support this argument is provided by Rudel and

Roper (199%). An increase in income due to economic growth is, therefore, expected to

reduce deforestation. Economic growth creates ample off-fm employment opportunities

away fiom the fiontiers that divert the fanners frorn clearing the forests (Angelson, 1999).

Also, availability of capital helps in better forest management and creates awareness arnong

citizens for forest preservation (Capistrano, 1990).

In contrast, the rking economic growth can also have detrimental effects in

decreasing the forest area as well. As income increases in a country, the amount of local

capital available for investment in rain forest regions (for logging) increases leading to

deforestation (Rudel, 1996). Following loggers, peasants and land speculators encroach the

cleared forestland to enforce their property nghts. Pressures of these groups on the

surrounding forests along with the unsustainable logging practice of loggers exacerbate the

extent of deforestation. This is known as the 'frontier theory' of deforestation. Economic

growth also increases demand for agricultural and forest products, both for domestic

consumption and export (Kant and Redantz, 1997). Expansion of agriculhiral area, and

logging is necessary to meet these increased demands, thus deforestation occurs. Under the

ceteris paribus condition, agricultural production is held constant in Our mode1 and,

therefore, irrelevant to consider in conjunction with economic growth.

Because growth in agriculture is taken as an exogenous variable to study the effect of

the agricultural sector on deforestation, the growth rate of Gross Domestic Product excluding

the contribution of agriculture (GDP-AGR) is used as an explanatory variable to capture the

effect of economic growth on tropical deforestation.

3.2.4 Debt service growth (TDS/GNP)

Extemal debt arnong developing countries, which grew from about US $570 billion in

1980 to US $ 1.2 trillion in 1988, is one of the underlying factors driving tropical forest

conversion (World Bank, 1990). A basic hypothesis is that b a h c e of payment problems and

debt service obligations due to poor economic performance, combined with high extemal

debt push countries to take myopic decisions that ignore long-term natural resource concerns

(Leonard, 1985; Rowe et al., 1992 and Kahn and McDonald, 1994). Such decisions by the

policy makers involve expansion of agricultural areas into forest areas for more agricultural

production, and/or to intensify the exploitation of free timber with little investment. These

natural resources are exported to reduce the debt burden.

Some other countries, however, will use these exported timber, and the forest area of

these nations will be preserved. India, with zero percent rate of annual deforestation is an

example, which imports timber from Indonesia, Malaysia, Nigeria, and Burma. Developing

countries in general depend on credit for almost everything they import. Debt service for the

import of timber, therefore, will reduce deforestation. As well, some countries may invest the

debt in the altemate energy sector (e-g. solar energy, wind power, biogasification), improved

machines for wood processing (reducing wood waste), forestry activities (e.g. research on

sustainable forest management), and plantations. Al1 these activities reduce the burden on

forests.

Total debt service as a percentage of GNP (TDS/GNP) is an ideal indicator of the

pressure of debt on the national economy. Information on total debt service irrespective of

GNP does not indicate the burden on the national economy for debt service may increase

with an increase in national income. When both rise at the same rate, the pressure on national

income remains unaltered. However, if there is a rise in the debt service at constant national

income, it does increase pressure on the national economy and ultimately on natural

resources such as forests.

3.2.5 Agricultural growth (AGGR)

The two main sources of agricultural growth are the extension of agricultural area and

intensification of agricultural practices. While expansion increases deforestation,

intensification decreases the same. The two sources of the encroachrnent of agricultural areas

into forest areas are commercial agriculture and shifting cultivation. Commercial agriculture

such as sugarcane, tea, coffee, cocoa, palm oil, rubber and coca production in the nineteenth

and twentieth centuries was accomplished by clearing primary forests (Barraclough and

Ghimire, 1995). This expansion has resulted in the dispiacement of peasants fiom their land

to forested areas. World Bank (1992) asserts that new seulement for agriculture accounts for

60 percent of tropical deforestation. As well, cattle ranching for meat export have been a

major source of deforestation, particularly in Central and South America (Wunder, 2000).

About 85 percent of the deforestation in the Brazilian Arnazon is caused by some 5000

ranch-owners (Sponsel et al., 1996). Additionally, slash-and-burn agriculture, pnmarily for

self-provisioning by forest dwellers, migrants, or peasants, is fiequently blarned for

deforestation. In some Southeast Asian countries, shifiing cultivation accounts for up to 50

percent of natural forest conversion and 70 percent in tropical West Afncakemiarid Afica

(Shma , 1992).

Increased agricultural production can also be achieved by agricultural intensification

(Bilsbomw and Geores, 1994). Increased use of fertilizer, pesticides, inigation facility, and

new hybrid varieties has tremendous impacts on increasing agricultural production. Also,

agricultural area expansion is not always at the expense of forest area. Improved technology

often makes it possible to develop marginal lands for crop production that will reduce the

pressure on forest area.

It is true that growth in agriculture is necessary to meet the food demand of the

growing population. However, even if population growth remains constant (the ceteris

paribus condition), agricultural growth is needed to reduce the domestic food scarcity and

also for export to earn foreign exchange.

3.2.6 Road development (ROAD)

Road construction increases de forestation both directly and indirectly. The direct

cause is the conversion of forest area for road construction and the movement of machinery.

Indirectly, increased accessibility reduces transportation costs, raises land prices

(speculation), and makes feasible the extraction of forest, cattle and agricultural products in a

fringe around the road (Schneider, 1995). Al1 these factors attract developers and peasants to

forested hinterlands to exploit the natura! resources. This leads to deforestation. A positive

correlation has been found between deforestation and road variable (Rudel and Roper,

1997b; Tole, 1998) to strengthen these arguments.

Road construction, however, may reduce deforestation by better forest management

and patrolling in areas that could otherwise be illegally logged if there is the availability of

other means of transportation (such as waterways). Also, road constmctions around

townships away fiom forest areas will not have much detrimental effect in increasing

deforestation.

in this study, paved road as a percentage of the country's total road length (ROAD) is

used as an explanatory variable. The average annual growth rate would be the appropriate

variable to find the effect of road on tropical deforestation. The non-availability of data for

the penod 1980-903 restricts the use of the level variable (the end year data for a particular

period). However, the effect of road as a level (ROAD) vis-à-vis growth variable

(ROADGR) will be tested for the penod 1990-95 for which time series data is available. If

' Average annual change in each variable for two different periods, Le., 1980-90 and 1990-95 are used in the model. Therefore, for each country there will be two observations. For the ROAD variable, data prior to 1990 are not available. Therefore, percentage paved road in a country during 1990 and 1995 will be used as proxies for the average annual change in the percentage paved road during 1980-90 and 1990-95 respectively.

results of the variable in both the forms will be similar, road as a level variable will be used

as a proxy for road as growth variable in the final model.

3.2.7 Level of democracy (DEMOCRAC)

There are reasons to believe that democratic countries will have less deforestation. In

democratic societies there are checks and balances in the form of public protests, pressure

fiom environmental groups, the media and by opposition parties in legislative assembly

(Didia, 1997). Undemocratic or autocratic govements lack these pressures and therefore,

are expected to facilitate high deforestation. Deacon (1994) has found positive correlation

between deforestation and insecure property rights arising out of government instability and

an absence of government accountability. According to him, conserving a forest to yield a

Stream of benefits in future years rather than consuming it immediately is an act of

investment and given the volatile or predatory political environment such investments will be

low.

In contrast, prolonged judicial procedure and l e s fear of getting punishment may

increase illegal logging and, therefore, result in high deforestation in democratic countries.

Shafik (1994) concluded that democratic societies are likely to experience a more rapid loss

of forest area because they are subject to local pressures and are reluctant to enforce forest

protection. It is not uncommon in democratic societies to have deforestation due to a nexus

between officials in power and timber barons. In the words of Jacob (1988), "logging

companies are ofien willing to pay corrupt officiats for a concession to extract trees, or to pay

guards not to control or patrol a given area within a nature reserve". Also, undemocratic

countries such as those plagued with civil war, might have lesser deforestation if rebels

would be using forest as their hideouts.

Time series data on a democracy index variable for different countries is available in

Gurr and Jaggers (1999)~. The values range from O to 10 with O representing no democracy

and 10 a very high level of democracy. Because the level of democracy does not change each

year, but remains stable over many years until some major changes occur, it is not ideal to

use a growth variable. Therefore, for each country average democracy indices are calculated

for the periods 1980-90 and 1990-95.

3.3 The Model:

On the basis of the above description about the effect of various exogenous variables

on deforestation, the tropical deforestation mode1 can be specified as,

DEF = f (PRFORAR, POPGR, GDP-AGR, TDS/GNP, AGGR, ROAD,

DEMOCRAC) + E

The hypothesis is that these causal variables have both positive and negative effects

on deforestation, the net effect of which cannot be predicted in advance, but will be

detennined from the estimation results. This approach is similar to that of the two-tailed t-test

approach adopted by different researchers.

Mathematically, this proposition can be illustrated by considering a causation variable

'X' that has both a positive and a negative impact on endogenous variable 'Y' which

represents de forestation.

Y = a + p, X - p, X + other var idles+ E

Therefore, this can be expressed as

Y = a + (p, - P, ) X + other var idles+ E

The coefficient (Pi - P2) represents the net effect of the opposing effects of variable

'X' on deforestation. If Pi then the two effects offset and the net effect is zero. Under

4 For details of the variable refer Appendix - 1

these conditions it is difficult to determine whether or not variable 'X' is an important causal

factor with offsetting effects or is an unimportant causal factor. Standard empincal testing

techniques cannot distinguish between these two outcornes. In practice, however, the results

are the sarne and the policy implications are likely to be unaffected.

Chapter 4

DATA & DATA SOURCES

4.1 Deforestation data and its sources

The endogenous variable in our model, deforestation, is defined as a change in land

use with depletion of natural crown cover to less than 10 percent (FAO, 1993). Until

recentiy, deforestation estimates of F M 1990, published in F A 0 (1993) and in it's updated

version (State of the World's Forest report 1997 or SOFO 1997) of FA0 (1997), has been the

most comprehensive and widely used dataset. It mesures deforestation rates in a variety of

counîries, differing greatly in their social, demographic, and geological characteristics.

Though FRA 1990 estimates has been cnticized for using population density as an

exogenous variable in a deforestation model that generates the deforestation data, Pa10

(1999) claims that such criticisms are exaggerated. He asserts that the population variable

plays a minor role in the model where lagged forest cover and ecological zone variables

(rnodel parameters are different for different ecological zones) explained more than 90

percent of the variation. Moreover, validation of the model was performed by comparing the

deforestation estimates of the model with actual deforestation of 16 locations (at sub-national

level) for which two or more reliable estimates of forest cover at that time were available

(FAO, 1993). The mean difference between the observed and the predicted changes was 2

10.6 percent.

Recently, FRA 2000 (FAO, 2001) h s been released and it raised as many questions

as answers. The forest area information in FRA 2000 is based on expert opinion and satellite

imageries that were the basis for Forest Resource Assessment of 1980. Also, the 1990 forest

area estimates of this assessment, fiom which the deforestation rates for the period 1990-

2000 have been calculated, are quite different than those in SOFO 1997. A detailed

cornparison of forest area figures of some countries for the year 1990 reported in SOFO

1997, FRA 2000, and Production Yearbook (FAO, 2000) is presented in Table 2.

Table 2: Comparison of natural forest area (thousand ha.) of 1990 for some countries from differeo t sou rces

1 Forest Resource

- - --

Garnbia 435

Haiti 146

Kenya 17859

Mali 14159

Country

Angola

Bahamas

Congo

Costa Rica

Gabon

Mozarnbiaue 131198

Assessrnent 2000

70827

842

22182

2086

2 1897

24589

Zimbabwe 221 19

FA0 production yearbook figures are that of total forest area (forests and woodland)

State of the Worlds forests 1997

23214

180

19692

1415

18284

rather than of forest cover and thus expected to be greater than the forest resource assessment

figures. While the estimated figures of SOFO 1997 closely resemble the Production

Yearbook figures, the FRA 2000 figures are far higher. For some of the countries like Angola

and Zimbabwe the revised forest area estimates of 1990 in FRA 2000 are almost 3 times than

that of the estimates in SOFO 1997. According to Emily Matthews of World Resources

hstitute, "Given the different methodologies used in the FRA 1990 and FRA 2000, and the

very different 1990 baselines fiom which the rates of forest loss are calculated, the two

deforestation estimates should not be compared" (Matthews, 2001). As the FRA 2000 report

is not yet widely debated and serious concems have been raised about the reported figures,

FA0 Production Yearbook

23,194

324

19,902

1,569

19.900

the comparable dataset of FRA 1990 (FAO, 1993) for the deforestation data of 1980-90

penod, and SOFO 1997 (FAO, 1997) for 1990-95 penod are used.

Nevertheless, there is one problem in using these comparable datasets because the

former reports the natural forest area loss, while the latter reports the total forest area

(including plantations) loss. To recti@ this problem, the rate of natural forest loss for the

1990-95 period is calculated, and for this natural forest area estimates of countries during

1990~ and 1995 are required. Nahird forest area for the year 1995 is available in SOFO 1997.

For 1990, natural forest area is calculated by subtracting the plantation area reported in FRA

1990 from the total forest area in 1990 reported in SOFO 1 9976 (Appendix - 2).

4.2 Deforestation as a qualitative variable

Even if F A 0 dataset (FRA 1990) is the best choice among the available data sources,

it c m only be used with caution because of its questionable accuracy. Demaris (1992)

recomrnends that, "though there will be a loss of information, whenever measurement

adequacies prevent us from observing a variable precisely, it is better to categorize the data

and use as proxies for the underlying continuous variable in a logistic regression". Though

there is a loss of information by not using the continuous data, this loss is more preferable

than accepting the inaccurate results of OLS regression. Therefore, instead of using the

deforestation data as a continuous variable, it is being categorized into different categones

and used as a qualitative variable in logistic regression. As outlined earlier, these figures,

though not suitable for use as a continuous variable, are teasonable enough to be used as a

5 Natural forest area estirnates for the year 1990 are also available in FRA 1990, but we prefer not to use them. Since, total forest area estimates of SOFO 1997 are claimed to be more reliable (FAO, 1997) and plantation area estimates of 1990 in FRA 1990 are expected to be reliable, the calculated natural forest estimates of 1990 from these figures would be more reliable than those in FRA 1990. 6

For a few countries, the calculated rate of natural forest loss figures is negative, i.e., an increase in forest area in 1995. It may be because of an upward revision of the natural forest area estimates in SOFO 1997 for those countries. Since, it is unlikely to have increase in natural forest area in tropical countries, those negative estirnates are treated as zero.

categorical variable. Therefore, using the rate of deforestation data fiom FA0 (1 993, 1997),

countries are divided into three categories as low (c 0.70 percent per year), medium (0.70 to

4 . 4 0 percent per year), or high rate of deforestation ( 1.40 percent per year) and the

categorized deforestation data (DEF) is used as the endogenous variable. The basis for such a

classification is to keep approximately equal nurnber of observations in each category and

avoid persona1 bis .

The underlying idea of using deforestation data as a qualitative variable in logistic

regression is to report the effects of different factors of deforestation in probabilistic terms.

This avoids reporting estimation results fiom an OLS regression, which is based on relatively

unreliable deforestation data, as accurate. Besides, this approach will also somewhat rectiw

the problem of overlooking the extent of deforestation due to reduction in tree density. Even

if the rate of deforestation due to a loss of tree density is added to the current rate of

deforestation as defined by FA0 a country will remain in the sarne category except for those

at the margin of two categories.

4.3 Sources of data for the causes of deforestation

Among the exogenous variables, data on GDP-AGR, Le., growth rate of GDP

excluding agriculture, and TDS/GNP, Le., growth rate of ratio of debt service to GNP are

calculated. For GDP-AGR, first time series data from 1980 to 1995 on value added in

agriculture (national currency) is deducted from the corresponding time senes data on GDP

(national currency) at constant prices (1990 = 100). Then an exponential trend line is

incorporated in the calculated time series data fiom 1980-90 and 1990-95 to estimate the

average annual growth rate in GDP excluding the contribution of agriculture for those

penods. Data on total GDP and value added in agriculture are available in World Bank

(1999) and are presented in Appendix - 3 and Appendix - 4 respectively. The trend line

results are presented in Appendix - 5.

Average annual growth rate in debt seMce as a percentage of GNP for the periods

1980-90 and 1990-95 are calculated by incorporating an exponential trend line in the

available time series data for those penods. The time series data along with the trend line

results are reported in Appendix - 6.

The list of al1 the variables with their explanations, units of measurement and sources

are presented in Table 3. Appendix - 7 (according to continent) and Appendix - 8

(according to rate of deforestation, i.e., high, medium, and low) show the actual data of

different variables for various countries.

Table 3: Details of the data used for the estimation of deforestation model

Forest

Demographic

1 during 1980-90 and 1990-95 1 1 Jaggers, 1999 Note: '*' indicates calculated

Source Sector 1 Variables

Macro- economic

Agriculture

Infrastructure

Political

Data on rate of deforestation and causal variables (described in Chapter 3) for 64

Explanation 1 Unit

DEF

PRFORAR

POPGR

countries comprising of 33 African, 13 Asian, and 18 Latin American countries is collected

GDP-AGR*

TDS/GNP*

AGGR

ROAD

DEMOCRAC

for the periods 1980-90 and 1990-95. Therefore, a total of 128 observations are chosen to

Average annual rate of deforestation fiom 1980-90 and 1990-95 Percentage of land area of a country under forests in 1 980 and 1990 Average annual growth rate of population from 1980-90 and 1990-95

estimate the model. But, there are missing data in some variables for some countries for a

Average annual growth rate of GDP (excluding agriculture) during 1980-90 and 1990-95 Average annual growth rate of Total Debt service as a percentage of GNP during 1980- 1990 and 1990- 1995 Average annual growth rate of agricultural sector during 1980-90 and 1990-95 Percentage paved road of a country for the years 1990 and 1995 Average Democracy index of a country

particular penod, which reduces the number of effective observations to 1 17.

Percent

Percent

Percent

FAO, 1990 & FAO, 1997 FAO, 1990 & FAO, 1997 FAO, 1990 & FAO, 1997

Percent

Percent

Percent

Percent

hteger

World Bank, 1999

World Bank, 1999

World Bank, 1997 World Bank, 1999 Gurr and

Chapter 5

ESTIMATION OF THE MODEL

Multinomial logistic regression is used for estimation of the deforestation model. In

conjunction with tackling the deforestation data problem, it has the added advantage of

solving some of the estimation problems outlined earlier. First, Logistic regression models

are heteroscedasticity consistent (Aldrich and Nelson, 1984), therefore, parameter estimates

are not inefficient. Secondly, the differentiation of quantitative effect of a variable arnong

different categories of endogenous variable, i-e., hi& or medium deforestation, is inherent in

multinomial logistic regression and, therefore, there is no need to use interactive dumrnies.

Regional dumrnies, however, will be used to estimate the effect of regional differences (due

to unique character of a particular region) on deforestation. Thirdly, multinomial logistic

regression is non linear in nature (Aldrich and Nelson, 1984). Hence, the assurnption of

linearity in a relationship between dependent and exogenous variables is not imposed. In

addition to the advantages of the multinomial logistic regression, the theoretical deforestation

rnodel is based on underlying causes of deforestation and, therefore, there is no fear of biased

parameter estimates, which happen when direct and underlying causes are both used in the

same equation.

In addition to the multinomial logistic regression, the deforestation model is also

estimated by binary logistic regression and Ordinary Least Square regression (OLS)

procedures. The underlying idea is to compare the results of three different models and to

show the advantages of multinomial logistic model over the other two in dealing with the

problems outlined earlier. Further, it allows testing the sensitivity of the results pertaining to

the effect o f a variable on deforestation.

Compared to multinomial logistic regression, binary logistic regression does not have

the sophisticated feature of capturing the possible variation in the net effect of an exogenous

variable on deforestation across different categories of endogenous variable. It has only two

categories of endogenous variable and probability of observing one of the categories is one

minus the probability of observing another category. This means the effects of exogenous

variables are restricted to one category, while in multinomial logistic regression effects Vary

in different categories. It is, therefore, not surprising to see variation in the results between

these two models.

Maximum - Likelihood (ML) estimation procedure, which is used to estimate the

logistic regression model, is a visible alternative to OLS in nearly al1 situations to which the

latter applies. Asymptotically (for a sample size of around N-K = 100, where N = number of

observations and K = number of exogenous variables) the ML estimates exhibit the

properties of unbiasedness, efficiency, and normality, similar to that of OLS estimates

(Aldrich and Nelson, 1984). in such circurnstances if diverging results are obtained fiom

both the models, it is not possible to choose any one of them over the other.

Nevertheless, there is another way by which the better model cm be chosen. The

underlying idea of categorization of the deforestation data is due to their questionable

accuracy. Assuming that the data on deforestation are under-reported (since the deforestation

data in FRA 1990 are based on country statistics reported by national governments), a certain

arnount will be added to the existing estimates on rate of deforestation such that it does not

alter the categorization. Then the deforestation model will be re-estimated by multinomial

and OLS regression methods. Since the categorization remains unchanged, results fiom

multinomial logistic regression will be the sarne as before. However, results from OLS

regression may change as continuous data is used in estimation. If there is a change in the

results fkom OLS regression, it c m be accepted that multinomial logistic regression is more

stable and better than OLS in handling the relatively inaccurate deforestation data.

The above method will vaiidate the stability of the multinomial logistic regression

method over the OLS regression method with changes in the rates of deforestation. However,

it is essential to test whether the results from the multinomial logistic regression procedure

are sensitive to changes in the cut-off points (i.e. there is no persona1 bias in the selection of

the cut-off points). For this purpose, the cut-off points will be changed (increased and

decreased) by a certain percent and the deforestation model will be re-estimated by

multinomial regression procedure. Results fiom the mode1 afier such changes will be

compared with the results fiom the mode1 before such changes.

5.1 Logistic regressions

Logistic regression allows us to predict a discrete outcome frorn a set of exogenous

variables that may be continuous, discrete, or a combination of both (Pyndick and Rubinfeld,

1991). The Iogistic regression model is based on a cumulative logistic probability function.

The outcome variable, p i , is the probability of having one outcome or another based on a

nonlinear fùnction of the best linear combination of predictors; with two outcomes

(Tabachnick and Fidell, 1996):

Where,

e is the base of natural logarithrns, which is approximately equal to 2.7 18

pi is the probability of an observation to be in i th ( i = 1,2) category

X, is the exogenous variable

The logit or log of the odds is:

P. L=ln-= 1 - Pi

a+W,

That is, the linear regression equation is the (natural log of the) probability of being in one

group divided by the probability of being in another group. When the endogenous variable

has only two values, there is only one non-redundant logit that can be fomed. This is called

the binary logistic regression model. The procedure for estimating coefficients is Maximum

- Likelihood, and the goal is to find the best linear combination of predictors to maximize the

likelihood of obtaining the observed outcome kequencies. The coefficient estimates measure

the change in ' L ', the log of the odds ratio, for a unit change in ' X, '.

When there are more than two categories of outcomes, the analysis is called the

multinomial or polytornous logistic regression model and there is more than one logistic

regression equation. The number of equations is equal to the number of categories of

outcomes minus one. If the endogenous variable has ' J ' possible values, ' j-1 ' number of

non-redundant logits can be formed. The simplest type of logit for this situation is called as

the baseline category logit. It compares each category to a baseljne category (Aldrich and

Nelson, 1984).

If the baseline category7 is ' j ', for the ' ith y category, the rnodel is

Where i = 1 .,2, . . . j - 1

There will be a set of coefficients for each logit that is why each coefficient has two

subscnpts: the first identifies the logit and the second identifies the variable. For the base line

category ' J ', the coefficients are al1 zero (Norusis, 1999).

The above mentioned multinomial logistic regression procedure is used to estimate

the theoretical deforestation model descnbing the relationship between the rate of

deforestation and its causes. This is the Mode1 1, mathematical fonn of which is given below:

7 For j categories of endogenous variable, the j" category is treated as a baseline category. For example, with 3 categories of endogenous variable, the 3rd group is the baseline category.

Where

j = 3 (High, Medium, and Low deforestation)

i" category = High or Medium deforestation

j" category = Low deforestation

x, = PRFORAR x, = ROAD

X , = POPGR X , = DEMOCRAC

X, = GDP-AGR x, = IDASiA

x, = TDSIGNP x, = IDLAT

X, = AGGR X,, = DPERIOD

X, and X , are two regional durnrnies for countries representing Asia (IDASIA) and

Latin Arnenca (IDLAT) respectively. Along with the regional dumrnies, one more dummy

X,, (IDPERIOD) is used to differentiate the data for the two penods 1980-90 and 1990-95.

This allows us to test for temporal stability of the model.

As there are three categories of the endogenous variable - deforestation rate, there

will be two non-redundant logits, High/Low and Medium/Low.

In Mode1 2, the binary logistic regression procedure, which is a special case of

multinomial logistic regression with two outcornes only, is applied in estimating the

deforestation model. hstead of three categories, i.e., High, Medium, and Low, data on rate of

deforestation is divided into two categones (High and Low) of approximately equal number

of observations (to avoid personal bias). This categorized data is used as the endogenous

variable. The cut off point is 0.8 percent rate of deforestation, i.e., up to 0.8 percent is low

deforestation and beyond that point is high deforestation. The mathematical f o m of our

deforestation model when estimated by binary logistic regression is the same as that of

multinomial logistic regression except for ' i = High deforestation' and there will be one logit

instead of two, Le.,

Al1 statistical tests, and interpretation of results fiom binary logistic regression

model are similar to that of multinomial logistic regression.

5.1 -1 Tests of sigaificance

Various tests of a multinomial logistic model (also binary logistic model), as

mentioned in Demaris (1 992), are described below.

5.1.1.1 For a Predictor set

In OLS regression analysis, F - statistics is used to test the joint hypothesis that al1

coefficients except the intercept are zero. A corresponding test in logistic regression that

serves the same purpose is based on likelihood principle. The nul1 hypothesis is that al1

k0 -1) coefficients included in the 0'-1) equations are çimultaneously zero. The alternative

hypothesis is that at least one of these coefficients is nonzero. The test is a model chi-

squared statistics equal to - 2 log&) - [-2 log L, ] with kO' - 1) degrees of Creedom where

L, is the likelihood function with intercept only and L, is the likelihood function with al1 the

parameter estimates. The test result is shown under the heading 'model fitting information' in

SPSS software output.

5.1.1.2 For one Predictor

The two possible tests for the effect of a given predictor are:

(a) a global test for the impact of the predictor on the endogenous variable in general, and

(b) a test for the impact of the predictor on a given logit.

The nul1 hypothesis for the first, or global, test is that al1 ' J - 1' coefficients associated

with a particular predictor are simultaneously zero. In other words, the global test for the

variable ' Xk ' tests the nul1 hypothesis that

That is, Xk has no effect on any of the j-1 logits. The test is a chi-square test based on the

difference in chi-square statistics between the full model, with al1 predictors, and the reduced

model, with al1 predictors except X , . The test has J-1 degrees of freedorn; if it is

significant, then Xk has a significant impact on the endogenous variable. The result of the

test is shown as 'Likelihood Ratio (LR) test' in SPSS software output.

The second test is used to detemine which logits are significantfy affected by X , .

For large sarnple sizes, the test that a coefficient is zero can be based on the Wald statistics,

which has a chi-square distribution (NoruSis, 1999). A Wald test calculate a Z - statistic

which is

The Z value is then squared yielding a Wald statistics with chi-square distribution. The test

is based on the nul1 hypothesis that the coefficient estimatep,,-,,, is equal to zero in

( j - i ) a logit. Therefore, the test has 1 (number of restrictions) degree of fieedorn. The

SPSS software shows the result of the test under the heading 'Parameter Estimates'.

5.2 Ordinary Least Square regressioos

The OLS estimation procedure will be used to develop a multi-country regression

model using F A 0 deforestation data as a continuous variable, and this is Mode1 3. The model

includes al1 the exogenous variables as that of multinomial and binary logistic regression.

The mathematical form of the model without interactive dummies (as has been the case in

most of the cross-country regression analysis) is,

Y,, = p, + p,X,, + P Z X n 2 + ......... + &,Xnio + en ......( Mode1 3)

Where,

Y, = Rate of deforestation in n" country

Xnk= Exogenous variables as in multinomial logistic regression

Routine tests such as tests for heteroscedasticity, and multicollineariîy, comrnon in cross-

sectional regression analysis, will be carried out.

Although the above model provides a general idea about the deforestation process, it

is of little help in capturing the variation in the effects of the variables among different

categories of deforestation, i.e., high and medium, and thus, is of Iittle use in comparing the

results with the multinomial logistic model. Therefore, Model 4, a new OLS regression

model that is comparable with the multinomial logistic modei (Mode l), is developed. Model

4 includes al1 the variables as in Model 3, two dummy variables for three categories of

deforestation (high, and medium), and interactive dumrnies that are the product of these two

dummies and al1 the variables (such as HIGH*POPGR, MED*POPGR, HIGHSROAD,

MED'ROAD, HIGH*ASIA, MED*ASIA etc.) of Model 3. These interactive dummies are

represented with the same narne as in the original model, but with extension 'HI' and 'ME'

for high and medium category dummies (e.g. POPGRHI, and POPGRME for POPGR

variable in high and medium category) respectively. A significant coefficient estimate of the

interactive dummy involving any variable will suggest that the variable has a different effect

for a particular category of deforestation in contrast to the general category. For example, a

significant coefficient estimate of POPGRHI wiIl indicate that population growth has a

significantly different effect on deforestation in high deforesting countries.

The effect of a variable in High/Low and MedILow logit can be thought of in the

sarne way as the effect of the variable in high and medium category of deforestation in the

form of interactive dummies in OLS regression. Consider, for example, the effect of

variables in the HighlLow Iogi t of the Mode1 1:

Where,

i" category = High deforestation

j" category = Low deforestation

X , = Exogenous variables

A a n d P j k = Coefficient estimates of X,variable in explaining the

probability of high and low deforestation respectively

The coefficient estimate of a variable in High/Low logit is the coefficient estimate of

the variable in explaining the probability of high deforestation (&) minus the coefficient

estimate of the variable in explaining the probability of low deforestation (Pi,). The

coefficient estimates of the baseline category (P, ) are treated as zero (NoruSis, 1999). The

coefficient estimates of the interactive dummies in OLS regression are also the difference in

the effect of variables in a particular category (e-g. High) from the baseline category (Low)

for which the values of interactive dummies are zero. Therefore, the coefficient estimates of a

variable in HighlLow logit of Model 1 can be interpreted in the same way as the coefficient

estimates of interactive dummies with sufix 'HI' in Model 4. The case is similar with

coefficient estimates of the variables in MedlLow logit and interactive dummies with suffix

'ME' in OLS regression.

MS - EXCEL (for data entry), SPSS Version 10.0 (for logistic regressions) and SAS

Version 7 (for OLS regression) software are used for analysis of data.

Chapter 6

RESULTS & DISCUSSION

In the first step of data analysis, the effect of road on deforestation as a groowth

(ROADGR) vs. level (ROAD) variable is tested by estimating two multinomial logistic

regression equations. This is done to find a proxy for the average annual growth in paved

road for which data do not exist.

In the second step, the theoretical deforestation model described in Chapter 4 is

estimated by different estimation procedures. In Model 1, multinomial logistic regression

method is used in estimating the deforestation model. The deforestation model is also

estimated by binary logistic (Model 2), and OLS regression (Model 3) methods respectively.

The significance of the durnrny variable IDPERIOD (Le. temporal stability of the models) is

tested in al1 the models.

It is found that the IDPERIOD variable is not significant in any of the models.

Therefore, in the third step this variable is eliminated and Models 1 to 3 are re-estimated.

Model 3 (without IDPERIOD) does not include interactive dummies and, therefore,

the results are not comparable with the results obtained from Mode1 1 (without IDPERIOD).

Hence, in the fourth step, interactive dummies as mentioned in section 5.2 are included in the

Model 3 (without IDPERIOD). The estimated new model is Model 4 (without IDPERIOD).

In the fifth step, results fiom the Model 1 (without IDPERIOD) and Model 2 (without

IDPERIOD) are compared. Model 1 is chosen over Model 2 and the reasons for that are

outlined.

This is followed by cornpanson of Model 1 (multinornial logistic regression) and

Model 4 (OLS regression), both without the IDPERIOD variable in the sixth step. Diverging

results with respect to the effect of the variables are obtained from these models. Therefore,

stability test is done to select the preferred model. First, changing the rates of deforestation

estimates, stability of the OLS regression (Model 4 without IDPERIOD) is compared with

the multinomial logistic regression (Model 1 without IDPERIOD). After that changing the

cut-off points, the sensitivity of the results fiom the multinomial logistic regression model

(Model 1 without IDPERIOD) is tested. This model is found to be more stable than the OLS

regression model.

Therefore, Model 1 (without IDPERIOD) is the preferred model, the results of which

are discussed in the final step.

Al1 these above mentioned steps are discussed in the following sections. The model

results are interpreted at 1 O percent level of significance8.

6.1 Effect of mad as a level vs. growtb variable

The effect of road as a growth (ROADGR) vis-à-vis level (ROAD) variable in

rnultinomial logistic regression model is compared by estimating two multinomial logistic

regression equations, one with ROADGR and the other with ROAD variable using the

available data for the period 1990-95. The results of LR test indicate that the variable ROAD

is significant at a 10 percent level of significance but not the ROADGR variable. The chi-

square statistics at 2 degrees of fkeedom ( i-1 =2) for the ROADGR and ROAD variables

are 2.461 and 8.398 respectively, while the cntical value at 10 percent level of significance is

4.605.

The results of the Wald test @as a chi-square distribution with 1 degree of fkeedom)

for the significance of the ROADGR and ROAD variables along with their coefficient

estimates in different logits of multinomial logistic regressions are presented in Table 4.

Details of the results are given in Appendix - 9.

8 Bendel and Afifi (1977) quoted in Menard, (1995) even pointed out that it is beneficial to accept a 15 to 20 percent of level of significance, which increases the risk of rejecting the null hypothesis when it is crue (fmding a relationship that is not really there) but decreases the risk of failing to reject the null hypothesis when it is false (not fmding a relationship that is really there).

Table 4: Results of Wald test for ROAD and ROADGR variables in multinomial logistic regressions

ROADGR I ROAD

degree of freedom is 2.705)

At 10 percent level of significance, ROAD is significant in one of the logits

(Med/Low), but ROADGR is not significant in any of the logits. Nevertheless, the sign

pattern is same (positive) in both cases. This variation could be due to insufficient degrees of

freedom (N-K = 46) of the model. Accepting that road has the same effect when used as a

growth or level variable, the later measure (ROAD) is used for the full model (for the

combined periods of 1980-90 and 1990-95) to avoid the problem of non-availability of data

for the period 1980-90.

P 0.259

6.2 Estimation of the deforestation model

In the next step afier selection of the ROAD variable as a proxy for ROADGR

variable, multinomial logistic model (Model 1), binary logistic model (Model 2), and OLS

regression model (Model 3) are estimated. Al1 these three models include the causation

variables described in Chapter 4, two regional durnrnies for Asian (DASIA) and Latin

American (IDLAT) countries, and one dummy (IDPERIOD) to differentiate the observations

at two different time periods, 1980-90 and 1990-95.

Significant at 10 percent Ievel of significance (The critical value at 10 percent level of significance and 1

Wald

1.907

6.2.1 Temporal stability of the models

The pwpose of including the dummy variable IDPERIOD is to test whether there is

any change in the relationship between deforestation and its perceived causal factors over

P 0.024

P O. 105

Wald

0.051

P 0.038

Wald

6.366*

Wald

1.802

tirne. Tests of significance of the variable in different models will signify whether the

relationship is stable or not. An insignificant result denotes that there is no change in

relationship between deforestation and its causes over time. Therefore, it can be ascertained

that the results f?om the models are independent of time.

The chi-square statistics in LR tests for the IDPERIOD variable are 0.807 at 2

degrees of freedom in multinomial logistic regression, and 1.272 at one degree of freedom

(as there is only one logit) in binary logistic regression. The critical values at 10 percent level

of significance are 4.605 and 2.705 for 2 and 1 degree of ffeedom respectively. This suggests

that the variable IDPERIOD is not significant in both multinomial and b i n q logistic

regressions models at a 10 percent level of significance.

The results of Wald test for the variable IDPERIOD in multinomial and logistic

regression, and t-test9 in OLS regression are presented in Table 5. Detailed results of these

models can be found in Appendices - 10, 11 and 12. Results indicate that at 10 percent level

of significance the durnmy variable (IDPERIOD) is not significant in any of the logits of

both the models. The variable is also insignificant in OLS regression as evident fiom the t-

test. This suggests that there is no variation in data quality or relationship of dependent and

exogenous variables, between two time periods. In other words there is temporal stability in

the relationship between deforestation and its causal factors.

Table 5: Results of Wald tests in multinomial logistic and binary logistic regressions, and t-test in OLS regression for IDPERIOD variable

1 I 1 I 1 1 1 I

Note: The criticaI t-value at 10 percent level of significance is 1.67 1 for 60 degrees of tieedom and 1.658 for 120 degrees of fieedorn In our case, the OLS regression has 107 (N-K= 107) degrees of tieedom

9 At 5 percent level of significance, heteroscedasticity was found in al1 the OLS regressions. The t -sbtistics of different variables are the consistent t -values afier White's correction for heteroscedasticity.

OLS Multinomial logistic

P

-0.368

Binary logistic

wald

0.354

P

-0.442

wald

0.728

P 0.564

P

-0.1 12

wald

1.249

t - value

-0.78

The dummy variable IDPERIOD is found to be insignificant. Hence, it is eliminated

and Models 1, 2, and 3 are estimated again. Table 6 outlines the results of model 1, the

multinomial logistic regression. Table 7 presents the results fiom binary logistic regression,

the Model2. Table 8 demonstrates the results of OLS regression without interactive dummies

(Model 3). In Table 9, the results of OLS regression with interactive dummies (Model 4) are

shown.

6.2.2 Results of the multinomial logistic regression model (without IDPERIOD), the

The chi-square statistics for the overall model is 7 1 .O36 at 18 degrees of fieedom

[ k ( ~ - = 181, which is greater than the critical chi-square value of 25.989 at 10 percent

level of significance. Hence, the model is significant. Other results from the model are

outlined in Table 6, and the details are given in Appendix - 2 3.

Table 6: Results of multinomial logistic regression (without IDPERIOD) Variable

PRFORAR

POPGR

GDP-AGR

ROAD

DEMOCUC

IDASIA

I 1 I 1 1 1 1

* Significant at 10 percent level of sig~ficance (The critical values at I O percent level of significance is 2.705 for 1 degree of freedom and 4.605 for 2 degrees of freedom) + Significant at 1 1.6 percent level of significance (significance of this result is discussed in section 6.4.3)

Likelihood Ratio test Chi-square

4.3 15'

7.1 16*

2.660

IDLAT

7.991 *

1 .O85

25.352*

Wald test

-0.0299

1.420

-0.178

29.171*

0.04206

0.038 12

3.745

High/Low

3.696*

6.194*

2.016

4.5 1 1

MedLow P P Wald

-0.00375

0.79 1

0.02285

5.848*

1 .O55

2.907*

1

16.522"

Wald

0.082

2.345

0.055

4.348*

0.146

1 0.298*

0.05 16 1

0.09467

-2.039

0.0874 1 0.008

The LR test for significance of predictor shows that the chi-square statistics of al1

variables except GDP-AGR and DEMOCRAC are greater than the critical value at a 10

percent level of significance. Therefore, the variables except GDP-AGR and DEMOCRAC

are significant.

The Wald test for significance of coeficient estimates in different logit suggests that

the variables GDP-AGR, TDS/GNP and DEMOCRAC have chi-square statistics smaller

than the critical value in the High/Low logit at a 10 percent level of significance. Therefore,

these variables are insignificant, while the other variables are significant in the High/Low

logit. In Med/Low Logit, TDS/GNP, AGGR, ROAD, and IDASIA variables have chi-square

statistics greater than the critical value. Hence, these variables are significant in MediLow

iogi t .

6.2.3 Results of the binary logistic regression model (without IDPERIOD), the Mode1 2

The overall binary logistic model is significant as evident from the model chi-square

statistics of 5 1.194 at 9 degrees of &dom [ kb - 1) = 91, which is greater than the critical

chi-square value of 14.683 at 10 percent level of significance. Results of the LR test and the

Wald test are presented in Table 7. Appendix -14 shows the details of model results.

In LR test, at a 10 percent level of significance the variables POPGR, AGGR, ROAD,

IDASIA and IDLAT have chi-square statistics greater than the cntical value. Thus, these

variables are significant.

The Wald test for the coefficient estimates in the only possible logit,

Table 7: Results Variable

INTERCEPT

PRFOIUR

POPGR

GDP-AGR

TDS/GNP

AGGR

ROAD

DEMOCRAC

IDASIA

IDLAT

* Significant at 10

High/Low, indicate that the critical chi-square value is smaller than the calculated chi-square

values for the variables POPGR, AGGR, ROAD, IDASIA, and IDLAT at a 10 percent level

for 1 degree of freedom)

of binary logistic Likelihood Ratio test Chi-square

24.934*

1.236

18.418*

2.557

0.884

5.787*

9.667*

0.001

13.109*

27.629*

percent level of

of significance. Therefore, these variables are significant. The other variables are not

significant at the same level of significance.

regression (without IDPERIOD) Wald

Higti/Low

6.2.3 Results of OLS regression without interactive dummies (without IDPERIOD), the

Models 3

The F-test in OLS regression without interactive durnmies1° (model F - value is 5.80

at 9, 107 degrees of fieedom and significant at less than 0.01 percent level of significance)

for the significance of the predictor set shows that the model is significant. Results of t - test

P -8.320

-0.013

2.063

-0.146

0.020

0.270

0.054

0.003

3.219

4.187

--

'O Diognostic tests (eigenvalues and conditioning index) show absence of multicollinarity in al1 OLS regressions

Wald

17.385*

1.198

13.939*

2.488

0.874 f 5.398*

8.345*

0.00 1

11.3 15*

18.586*

significance (The critical values at 10 percent level of significance is 2.705

for the significance of each predictor are given in Table 8. Details can be found in Appendix

Table 8: Results of OLS regression without interactive dummies (without IDPEIUOD)

1 1

PRFORAR 1 -0.0069 1 -1.75*

INTERCEPT

1

Variable

-0.5002

POPGR

* Significant at 10 percent level of significance Note: The critical t-value at 10 percent level of significance is 1.67 1 for 60 degrees of freedom and 1.658 for 120 degrees of fieedom. In our case, the OLS regression has 108 (N-K= 108) degrees of freedorn.

P

-1.18

AGGR

ROAD

DEMOCRAC

DASIA

iDLAT

Arnong the predictors, only the variables PRFORAR, POPGR, IDASIA, and IDLAT

have t- values greater than the cntical value at a 10 percent level of significance. Therefore,

these variables are significant while the others are not significant.

t - value

0.4223

6.2.4 Results of OLS regression with interactive dummies (without IDPERIOD), the

Model 4

The Model 4, i.e., OLS regression with interactive dumrnies is significant at less than

0.01 percent level of significance with F - value of 27.92 at 29, 87 degrees of fieedom.

Results of the t - test are given in Table 9. Detail results cm be referred from Appendix - 16.

3 .50*

0.0340

0.0035

-0.0008

1.2443

1.2700

1.48

0.56

-0.03

3.46*

5.93*

The t- test shows that only the PRFORAR variable is significant in the base category

at a 10 percent level of significance. Arnong the interactive dummies with suffix 'HI' (for the

Table 9: Results Variable

INTERCEPT

PRFORAR

POPGR

GDP-AGR

TDS/GNP

AGGR

ROAD

DEMOCRAC

IDASIA

DLAT

* Significant at 10

category of high deforestation), PRFORAR, AGGR, ROAD, IDASIA, and DLAT are

Note: The critical t-value at 10 percent level of significance is 1.671 for 60 degrees of tieedom and 1.658 for 120 degrees of freedom. In o u case, the OLS regression has 88 (N-K= 88) degrees of freedom.

(without IDPERIOD) Durnrny for medium category (variables with sufix 'ME')

significant. in the category of medium deforestation, i.e., interactive dummies with 'ME'

P 0.0275

0.0025

O. 1347

-0.0 149

-0.00 1 5

0.0022

0.0023

-0.0073

O. 1904

0.3 175

extension, only the IDLAT variable is significant.

t - value

0.09

1.15

1.61

-0.99

-0.37

0.13

0.63

-0.54

1.23

2.20*

of OLS regression Base category

6.3 Comparative analysis of multinomial logistic, binary logistic and OLS regressions

witb interactive dummies ' Dummy for hi& category

(variables with suffix 'HI')

P 0.0244

0.0028

0.0727

0.0131

0.0009

0.003 1

0.0019

0.00 15

-0.1 155

-0.1021

In the first step, the results of the multinomial logistic regression mode1 will be

p 1.8 196

-0.0 160

O. 1966

0.05 16

0.0069

-0.1048

-0.0130

-0.0363

1.2789

0.8285

~cance

t-value

0.10

2.10n

1.07

1.11

0.25

0.23

0.69

0.14

-0.93

-1.02

compared with that of binary logistic regression followed by the cornparison of the former

t - value

3.30*

-3.80.

1.1 1

1 .23

1 .O2

-2.1 O*

-2.72*

- 1 .O6

3.204

2.24*

percent level of signiF

with OLS regression, both without and with interactive dummies.

6.3.1 Comparison of results from multinornial and binary logistic regression models

(without IDPERIOD)

As rnentioned earlier, both multinomial as well as binary logistic regression models

without the IDPERIOD variable are significant at a 10 percent level of significance.

Therefore, it is concluded that the predictor set significantly explains the endogenous

variable in both the models.

The results of LR test for significance of each predictor in multinornial and binary

logistic regression are compared in Table 10. Al1 variables have a similar significance pattern

in both the models except the TDS/GNP variable. This variable is significant in multinomial

logistic but not in binary logistic regression.

Table 10: Significance of variables in LR tests of multinomial and binary logistic

PRFORAR 1 N.S. 1 N S . I

re~ressions Variable

POPGR

1 TDSÏGNP 1 Sig. 1 N S . I

Multinomial logistic

GDP-AGR

Binary logistic

Sig.

I 1

ROAD 1 Sig. 1 Sig.

Sig.

N.S.

AGGR

N S .

Sig.

DEMOCRAC

Sig.

IDASIA

1 1 1 1

Note: Sig. means significant and N.S. means not significant at 10 percent level of significance

N.S.

IDLAT

The signs of the coefficients, and the results of the Wald test for the significance of

N S .

Sig.

different predictors in HighAow and MecULow logits of multinomial logistic regression, and

Sig.

Sig.

the High/Low logit of binary logistic regression are presented in Table 1 1.

Sig.

Table 11: Cornparison of sign and significance of parameter estimates in multinomial and binary logistic regression 1 Variable [ Multinornial Logistic 1 Binary Logistic

POPGR

GDP-AGR

TDS/GNP

AGGR

ROAD

Sig. +

DEMOCRAC

IDASIA

1 1 Sig. 1 N S - 1 Sig. Note: Sig. means significant and N.S. means not significant at 10 percent level of significance

Sig. - N.S. + N.S. + Sig. +

DLAT

A similar pattern in signs exists in both the models, but with fcw exceptions. The

N S +

Sig. + N.S. +

variables GDP-AGR and IDASIA have positive and negative signs respectively in Mednow

N.S. +

N.S. i-

N.S. + Sig. + Sig. +

Sig. +

logit of multinomial logistic regression, but opposite signs in High/Low logit of binary

Sig. - N.S. + N.S. + Sib. +

Sig. + N.S.

logistic regression. However, the sign pattern of al1 variables in Highnow logit of

Sig. + N.S. +

Sig. +

multinomial logistic regression is the same as in Hi-w logit of binary logistic regression.

Sig. +

Therefore, the conclusion is that the variation in signs of variables in different logits of

multinomial logistic regression is suppressed in binary logistic regression.

With respect to the significance of the coefficient estimates of the variables, AGGR,

ROAD and IDASIA, they are significant in b i n q and both logits of multinomial logistic

regression. GDP-AGR and DEMOCRAC are insignificant in every logit of both the models.

POPGR is significant in binary logistic regression indicating that it is a causal factor of

deforestation in hi& deforesting countnes. However, the POPGR variable is significant only

in one logit - HighILow - of the multinomial logistic regression. This suggests that POPGR

is the causal factor for a group of countries and not for al1 those countries that were treated as

high category in binary logistic regression1I. The significance pattern of IDLAT is the same

as that of POPGR. It is significant in the H i g h h w logit of binary logistic and the High/Low

logit of multinomial logistic regression, but not in the Med/Low logit of multinornial logistic

regression. The significance of the IDLAT variable in Highkow logit of binary logistic

regression indicates that Latin Amencan countries have a greater probability of having high

deforestation than the African countries (base category). The insignificant parameter estimate

of the variable in Med/Low logit of multinomial logistic regression, however, suggests that

not al1 countries that were included in the high deforesting category in binary logistic

regression have a greater probability of high deforestation. Similarly, TDSIGNP is significant

in MedKow logit of multinomial logistic regression, but not in binary logistic regression.

That is, debt service is a significant factor of deforestation in medium deforesting countries,

which otherwise is not decipherable from the binary logistic regression.

From the comparison of results of multinomial and binary logistic regression, it can

be concluded that multinomial logistic regression is more informative than the binary logistic

regression and therefore, the preferred model.

6.3.2 Compa&on of results from multinomial Iogistic and OLS regression models

After selecting multinomial logistic regression over binary logistic regression, the

next' step is to compare the results of the former with OLS regressions. A cornparison of

multinomial logistic regression with that of OLS regression without interactive dummies will

be followed by a comparison of multinomial logistic regression with OLS regression with

1 I In multinomial logistic regression the endogenous variable - average annual deforestation in different countries - is categorized into three categories high, medium and low. The countries in the medium category are redistributed among the high and low categories to reduce the number of categories to two (high and low), the endogenous variable in binary logistic regression. POPGR does not have any effect in those countries of the high category in binary logistic regression that are treated as medium category in multinomial logistic regression.

interactive dummies.

As mentioned earlier, overall the multinomial logistic regression is significant as

evident tiorn the global test for significance of the predictor set (chi-square statistics for the

model). Similarly, the OLS regression model without interactive dummies is also significant

as evident fiom the F - test. However, results of signs and significance of parameter

estimates indicate how best a model is informative. Table 12 presents sign and significance

of the parameter estimates in multinomial logistic and OLS regression without interactive

dummies.

Table 12: Cornparison of sign and significance of parameter estimates in multinomial

PRFORAR I - I - I -

logistic and OLS regression without interactive dummies OLS (without interactive dumrnies)

Variable

POPGR

Multinomial Logistic HighLow 1 Medliow

GDP-AGR

TDSIGW

AGGR

Sig. +

ROAD

Sig. - N S . + N.S. +

DEMOCRAC

N.S. +

Sig. +

IDASIA

1 Sig. 1 N S . 1 Sig. Note: Sig. means significant and N.S. rneans not significant at 10 percent level of significance

Sig. +

N S . 1 Sig.

Sig. [ N.S. + +

Sig. +

IDLAT

A cornparison between multinomial logistic regression with that of OLS regression

without interactive dummies shows that al1 the variables have same sign pattern except

GDP - AGR, IDASIA, and DEMOCRAC. The signs of GDP-AGR and IDASIA variables in

OLS regression are at least similar to the sign of these variables in Highi'ow logit of

multinomial logistic regression. However, the sign of DEMOCRAC variable in OLS

+ N S .

N S . +

N.S.

Sig. + N S . 1 N.S. - -+

Sig. +

N.S. -

4-

Sig.

Sig. +

+ N.S.

Sig. +

+ +

regression is completely different fiom those in different logits of multinomial logistic

regression but they are not significant. Tests of significance for those variables for which the

sign pattern is same indicate that, while AGGR and ROAD are significant in both logits of

multinomial logistic regression, they are not in OLS regression. Sirnilarly, TDSIGNP is

significant in at least one logit of multinomial logistic regression, but not in OLS regression.

Though POPGR and PRFORAR are significant in OLS regression, it is me for only one

logit (HighlLow) in multinomial logistic regression.

It might be the case that the variation in sign and significance of variables in OLS

regression fion1 that of multinomial logistic regression are due to non-inclusion of interactive

dummies in OLS regression. Inclusion of interactive dummies is necessary to make the

results of OLS regression comparable with that of multinomial logistic regression. Therefore,

the OLS regression is re-estimated after including the interactive dummies as explained in

section 5.2. The sign and significance of the interactive dummy variables are presented in

Table 13 and details are given in Appendix - 16.

Table 13: Comparison of sign and significance of parameter estimates in multinomial logistic and OLS regression with interactive dummies Variable

PRFORAR

Multinomial Logistic HighlLow 1 Med/Low

POPGR

OLS (with interactive dummies) Durnmy for High (HI 1 Dummy for Medium

-

GDP-AGR

TDS!GNP

Sig. +

AGGR

variables)

Sig.

N.S. +

ROAD

(ME variables) +

NS. +

N S . +

DEMOCRAC

1 Sig. 1 NS. 1 ~ i g . 1 Sig. 1 Note: Sig. means significant and N.S. means not significant at 10 percent level of significance

NS- + N S . +

Sig. +

IDASiA

WLAT

Sig. +

Sig. +

Sig. +

N.S. +

N.S. + N S . +

Sig. +

N.S. + Sig.

N.S.

N.S.

N.S.

Sig. +

NS. +

Sig.

N.S. - Sig.

N.S. +

Sig.

+ 1 +

NS.

N.S. + Sig.

NS. + N.S.

+ -+-

A comparison of coefficient signs of variables in HigWow logit of multinomial

logistic regression with those with 'HI' suffix in OLS regression with interactive dummies

indicate that PRFORAR, POPGR, TDSIGNP, IDASIA, and IDLAT variables have similar

sign pattems. But other variables have opposite signs. The tests of significance for variables

for which the sign patterns are same indicate that the variable POPGR is significant in

High/Low logit, but the sarne variable with 'HI' suffix in OLS regression is not significant at

10 percent level of significance.

Sirnilarly, a comparison between the coefficient signs of variables in MedLow logit

of multinomial logistic regression and the variables with 'ME' extension in OLS regression

indicate that POPGR, AGGR, ROAD, and IDLAT have a similar sign pattem, while for

others the sign pattem is different. The tests of significance of the variables, for which the

sign pattern is same, indicate that AGGR and ROAD variable are significant in Med/Low

logit, but not the corresponding variables in OLS regression. The IDLAT variable with 'ME'

extension is significant in OLS regression but not in Med/Low logit of multinomial logistic

regression.

It has already been mentioned that the coefficient estimates of multinomial logistic

have the same properties as that of in OLS regression. However, the results with respect to

sign and significance of the variables are found to be different in both the models. In these

circumstances, it is not possible to choose the results of one model over the other. Therefore,

the procedure of re-estimating the OLS regression model with interactive dummies after

random increase in the endogenous variable, Le., rate of deforestation as described in Chapter

5 is adopted.

The highest rate of deforestation in the categoiy of low deforestation is 0.65 percent

per year and the lowest rate of deforestation in the category of medium deforestation is 0.70

percent per year. Similady, the highest and lowest rate of deforestation in medium and high

deforestation category are 1.30 and 1.40 percent per year. Therefore, the existing data in low

WERCEPT

PRFORAR

POPGR

GDP-AGR

TDS/GNP

AGGR

ROAD

DEMOCRAC

IDASIA

IDLAT

for Hi h Medium 1

and medium categones can be increased by a maximum of 7 percent so that the observations

with highest rate of deforestation in those categories will not jump to the next category. The

curent rates of deforestation for different countries are increased randomly by O to 7 percent,

and the OLS regression with interactive dummies is re-estimatedI2. The results are presented

in Appendix - 17. The sign and significance of the variables afier such change are compared

in Table 14.

Table - 14: Cornparison of sign and significance of parameter estirnates in OLS with interactive dummies after random changes in the rates of deforestation

Sig. 1 N.S. I

Variable

Base category

+ N.S. + Sig. + N.S. + N.S.

N.S. + NS. + N.S. + N.S.

N.S. -

N.S.

N.S. NS.

N.S. 1 N.S. 1

OLS with interactive dummies before change in rate of deforestation

Durnrny for High + Sig.

Sig. + N.S. + N.S. + N.S. - Sig. - Sig.

N.S. + Sig. +

Sig. 1 NS. 1

OLS with interactive dummies after change in rate of deforestation

I 1

The signs of coefficient estimates of the variables TDS/GNP (in medium

Dummy for Medium + N.S. + NS. + N.S. - N.S. -

-- N.S. + N.S. -t

N S - - NS. + N.S. +

deforestation category), AGGR (base category), and DEMOCRAC (base category) have

Note: Sig. means significant and N.S. means not significant at 10 percent Icvel of significance N.S. 1

Base category + N.S. + Sig. + N S - + Sig. - NS. - N.S. + N.S. - N.S. - NS. -

'' Random numbers are generated for a range of 1 to 7 for the 1 17 observations. Therefore, each observation has a random number between O and 7. The rate of deforestation for each observation is normally under-reported, which is equal to the corresponding random nwnber. Random nurnbers are generated 4 times and hence, deforestation rates are varied for 4 times. The resulîs with respect to sign and significance of the variables remained same every t h e .

Sig. 1 Sig. 1 N S -

- Sig. 1 Sig. 1

changed in the re-estimated model. Also, the variables POPGR and IDASIA in medium

deforestation category become significant while they were not in the original model. Hence,

it is concluded that the multinomial logistic regression model (Model 1 without IDPERIOD)

is more stable than the OLS regression model (Model 4 without IDPERIOD) in explaining

the rate of deforestation with problems in deforestation data.

It is, however, yet to be established whether the results from the multinomial logistic

regression model are independent of the selection of cut-off points in categorization of the

deforestation data. This aspect is verified by changing (decreasing and increasing) the cut-off

points by 7 percent (the sarne rate by which the rates of deforestation are changed in the OLS

regression) and re-estimating the modeli3. The details of the results fiom the multinomial

logistic regression model after the cut-off points are reduced by 7 percent are presented in

Appendix - 18, and that of after a 7 percent rise are given in Appendix - 19. The sign and

significance of coefficient estimates in original rnultinomial logistic regression model is

compared with that from the new models in Table 15.

The comparison of sign and significance of coefficient estimates in Table - 15 reveals

that when the cut-off points are decreased by 7 percent the results remain unaltered. When

they are increased by 7 percent, the results are almost same as that of the original model, but

with two exceptions. The POPGR variable, which is insignificant in Med/Low logit of the

original model, becomes significant in the new model after the cut-off points are increased.

Similarly, the coefficient estimate of the IDASIA variable is significant and has a negative

sign in the MecüLuw logit of the original model. The coefficient estimate is insignificant with

a positive sign in the new model. These variations could be due to major changes in the

--

13 The lower cut-off point in medium category of deforestation is 0.7 percent per year, and in high deforestation category it is 1.4 percent per year. The number of observations in low category is 44, in medium 40, and in high category 33. When the cut-off points are decreased by 7 percent, the new cut-off points become 0.651 (0.7 - 0.049) and 1.302 (1.4 - 0.098) respectiveiy for medium and high category. The new observations in each category are 43, 40,and 34 for low, medium, and high respectively. Sirnilarly, afier 7 percent increase, the new cut-off points are 0.749 and 1.498 for medium and high deforestation category. The categorization has changed with 54 observations in low, 33 in medium, and 30 in high deforestation category.

number of observations in the low (fiom 44 to 54) and medium (Çom 40 to 33) deforestation

category.

Table 15: Sign and significance of coefficient estimates in original multinomial logistic regression model vs. those from new models after changes in the cut-off points Variable 1 Original mode1 1 AAer 7 percent decrease 1 AAer 7 percent increase in 1

PRFORAR

POPGR

GDP-AGR

TDS/GNP

AGGR

DEMOCRAC

-

ROAD I + I + I + I + 1 + I + I

Sig. - NS. + N.S. + Sig;.

IDASIA

] Sig. 1 N.S. 1 Sig. 1 N.S. 1 Sig. 1 N.S. Note: Sig. Means significant at 10 percent level of significance

HigidLow - Sig. +

Sig. +

IDLAT

From the above analysis it is concluded that results fiom multinomial logistic

in the cut-off points M e a o w

N.S. + N.S. + N.S. + Sig. + Sig.

N.S. +

regression model (Mode1 1 without IDPERIOD) do not change with changes (up to 7

HighlLow - Sig.

the cut-off points

Sig. +

Sig. +

percent) in the rates of deforestation, and remain relatively stable with minor changes in the

Med/Low

N.S. +

High/Low - Sig.

Sig. - N.S. + NS. + Sig

N.S. -

cut-off points. Hence, this is accepted as the preferred model. However, it should be kept in

MedILow

N.S.

Sig. +

Sig. +

mind that the results Crom rnultinomial logistic regression are liable to change with major

+ / + N.S. + N.S. + Sig.

N.S. +

changes in the cut-off points.

+

Sig. 1 Sig. + 1 +

Sig. +

6.3.3 Interpretation of results from multinomial logistic regression

Sig. - N.S. + N S .

Sig. +

N.S.

After selection of the preferred modei, the next task is to interpret the results both in

Sig. + N.S. + Sig.

Sig. + 1

qualitative and quantitative terms. The results of the multinomial logistic model presented in

+ Sig.

+ Sig.

NS . +

Table 6 and Appendix - 12 are discussed below.

+ NS.

N.S. +

Sig. +

N.S. +

As mentioned earlier the overall mode1 is highly significant (at less than 0.01 percent

level of significance), as evident from the chi-square test.

The global test (at 10 percent level of significance) for the significance of each

predictor indicates that al1 the variables except PRFORAR, GDPAGR, and DEMOCRAC

are significant. The Wald test for significance of the variables (at 10 percent level of

significance) in each logit suggests that PRFORAR, POPGR, AGGR, ROAD, IDASIA, and

IDLAT are significant in High/Low logit, and this is true for TDS/GNP, AGGR, and ROAD

variables in MediLow logit. The variable PWORAR though not significant in the Likelihood

Ratio test, it is significant in Wald test for High/Low logit. Menard (1995) and NoruSis

(1 999) have reported the disadvantage of using Wald test over Likelihood Ratio test.

According to them, for large coefficients, the estimated standard error becomes too large

resulting in failure to reject the nul1 hypothesis that the coefficient is zero, when in fact it

should not be. This means a variable significant in Likelihood Ratio test might not be in case

of Wald test. In Our case the results are reverse. In fact, the significance level in Likelihood

Ratio test is at 1 1.6 percent, which is not far away fiom the 10 percent level of significance

that we have chosen for our analysis. Therefore, the effect of the variable PRFORAR is

considered as significant, at least in the High/Low logit.

The equations for the two non-redundant logits, High/Low ( L, ) and Mednow ( LI ),

are given below:

L, = In[ p(High ) ] = -6.670 - 0.0299 * PRFORAR + 1.420 * POPGR - O. 178 GDP - AGR + 0.0 1 199 * TDS / GNP p(L0w)

+0.263 * AGGR t0.04206 * ROAD +0.03812 * DEMOCRACY + 3.745 * IDASfA +-4.511* IDLAT

L, = ~ n [ ~ ( ~ ~ ~ ) ~ = -4.086 - 0.00375 PRFORAR + 0.791 * POPGR - 0.02285 * GDP - AGR + 0.06 147 * TDS I GNP p(L0w)

+ 0.3 1 1 * AGGR + 0.05 16 1 * ROAD + 0.09467 DEMOCRACY - 2.039 IDASIA + 0.0874 1 * IDLAT

Qualitatively, the significance test shows the causal variables in different categories

of deforestation. However, interpreting the effect of a predictor in quantitative tems is not

simple. The logistic coefficient can be interpreted as the change in the log odds associated

with a one-unit change in the exogenous variable. A positive or negative parameter estimate

of a variable increases or decreases the log odds, when other predictors are constant. For

example, consider the coefficient of 2.420 for POPGR in High/Low logit. It suggests that

when population growth changes by 1 percent per year, the values of other variables remain

the sarne, the log odds of a country being in the category of high deforestation increase by a

factor of 1.420. However, it is easier to think of odds rather than log odds in interpreting the

parameter estimates. The base of natural logarithrn (approxirnately 2.718) raised to the

coefficient power denotes the factor by which the odds (rather than log odds) change when

the exogenous variable increases by one unit. For example, in High/Low logit, when

population growth changes from O to 1 percent, the odds in favor of a country being in high

deforestation increase by a factor of 4.138 (e'.420). To dari@ this statement, the probabilities

of a country having high deforestation with POPGR equal to O and 1 percent needs to be

calculated. The formula for such a calculation is given below:

Where,

. 2 = 1,2 = High, and Medium deforestation

J = 3 = Total nurnber of categories of endogenous variable

Calculation of probability of deforestation for Paraguay (IDLAT = 1) with

PRFORAR = 32.32, POPGR = O, GDPAGR = 3.46, TDS/GNP = -1 3.38, AGGR = 1.4,

ROAD = 9.4, DEMOCRACY = 5.4 for the penod 1990-95 is as follows:

Now,

- e - = 0.05 = Probability of high deforestation - 2.93 + e- 3.43 1 + e

- e P 2 - = 0.03 = Probability of medium deforestation

- 2.93 + e - 3.43 l + e

P3 = 1 - (0.05 + 0.03) = 0.92 = Probability of low deforestation

The odds of Paraguay having high deforestation with respect to low deforestation at O percent

POPGR are estimated as

Pl 0.05 - Odds = - - - = 0.05333 p3

0.92

The odds of having high deforestation with respect to low deforestation at 1 percent POPGR

can be calculated in the sarne way as above, which yields

0.17 - 0.220633 Odds = - -

0.77

Therefore, the odds in favor of high deforestation has increased by a factor of

The above-mentioned procedure for al1 variables is used to calculate the factor by which the

odds in favor of high or medium deforestation increase for 1 unit rise in the exogenous

variables. Table 16 presents the coefficient estimates ( p) , their level of significance (Sig.),

and the calculated factors (Exp ( P )) for different variables.

These results with respect to each variable are discussed in detail in the following

Table 16: Coeff~cient estimates, factor changes in the odds of High or medium deforestation, and level of significance of the variables

paragraphs with emphasis on variation in sign and significance (at 10 percent level of

Variable

PRFORAR

POPGR

GDPAGR

TDS/GNP

AGGR

ROAD

DEMOCRAC

IDASIA

IDLAT

significance) in different logits. The reasons for such variation are also presented in Table 17.

6.3.3.1 Forest size

The amount of land area under forests is an important determinant of deforestation.

An increase in forest area reduces the per unit pressure on forest area and, therefore, less

deforestation. In contrast, increase in forest area creates an impression of a fiee common

good attitude, which encourages fkee nding by the local people. The parameter estimate of

this variable has a negative sign both in High/Low and Med/Low logit. However, it is

significant in the first logit and insignificant in the next. An increase in arnount of forest area

significantly decreases the likelihood of high deforestation in cornparison to low

High/Law Med/Low i ~ v e l of significance

0.055

0.013

0.156

0.645

0.09 1

0.037

0.703

0.001

0.000

P -0.00375

0.79 1

0.02285

0.06 147

0.3 1 1

0.05 16 1

0.09467

-2.039

0.0874 1

P

-0.0299

1.420

-0.178

Exp ( P )

0.97 1

4.138

0.837

Exp ( P )

0.996

2.206

1 .O23

1 .O63

1.365

1 .O53

1 .O99

O. 130

1 .O9 1

Level of significance

0.774

O. 126

0.8 15

0.0 1 1

0.0 15

0.0 16

0.304

0.088

0.927

0.01 199

0.263

0.04206

0.038 12

3.745

4.5 1 1

1.012

1 .301

1 .O43

1 .O39

42.326

91.018

deforestation. The odds in favor of high deforestation decreases by a factor of 0.971 with 1

percent rise in the percentage of land area under forests. This is due to the fact that the effect

of decreased per unit area pressure dominates the eflect of €iee nding. However, in MedRow

logit, the effect of decreased pet unit area pressure is somewhat neutralized by the effect of

free nding and therefore, the parameter estimate is negative but not significant. The odds in

favor of medium deforestation remain almost constant (a factor of 0.996) with I percent

increase in the percentage of land area under forests.

6.3.3.2 Population growth

Population growth (POPGR) has a positive sign in both the logits. This means, ceteris

paribus, an increase in population growth increases the likelihood of a country being in high

and medium deforestation category than in low deforestation. The parameter estimate,

however, is significant in Hi-w logit and insignificant in Med/Low logit. This suggests

that an increase in population growth significantly increases the likelihood of a country being

in high deforestation category than in low deforestation. As mentioned in Table - 13 the

increase for 1 percent rise in POPGR is 4.138 times greater than with O percent nse. This

suggests that population growth is a significant factor of deforestation in countries that are in

high deforestation category. This result strengthens the Malthusian proposition that increased

population puts sever pressure on natural resources such as tropical forests. The effect of

increased demand for shelter and subsistence income that increases deforestation is dominant

over the Boserup effects of labor intensification and out-migration that decrease

deforestation. Therefore, the net effect increases the probability of deforestation in countnes

that are in the high deforestation category in cornparison to those in low category.

The positive and insignificant parameter estimate in M e d b w logit denotes that

population growth increases the likelihood of a country being in medium category than in

low deforestation category, but not significantly. The odds in favor of medium deforestation

increases by a factor of 2.206 with 1 percent rise in the rate of population growth. This

somewhat reinforces the Boserup hypothesis and out-migration phenomenon, which

neutralizes the Malthusian effect. The net effect of demand for shelter and subsistence

income (positive effects), and labor intensification plus out-migration (negative effects) is

positive but not strong enough in increasing deforestation. Since the significance level of this

parameter estimate is at 12.6 percent against the chosen 10 percent, it cannot be ruled out that

with rise in population, the harmful efiects on deforestation will dominate the neutralizing

effects, and therefore, deforestation.

The above results indicate that a growing population always has damaging effect on

natural resources. Though out-migration and labor intensification may reduce deforestation

in short run, in the long run, effect of a rising population growth will always have serious

consequences. With increased mechanization (less jobs) and limited space in cities to live in,

people will be forced to migrate towards forest areas. The increased pressure due to demand

for land and forest products will increase the Pace of deforestation.

6.3.3.3 Economic growth

Economic growth increases deforestation through increased demand for forest

products and decreases through generation of o f f - f m employment. The parameter estimates

of this variable, though insignificant, have a negative sign in Highnow logit, and positive

sign in MedLow logit. This indeed is important in explaining the net effect phenomenon of a

variable. The negative sign in H i g h b w logit indicates that increase in econornic growth

decreases the probability of a country being in the category of high deforestation. A percent

rise in the rate of economic growth decreases the odds in favor of high deforestation by a

factor of 0.837, which is not significantly differentI4 nom 1. A rise in economic growth

generates ample employment for the rural poor away fkom the fiontiers who otherwise are

involved in cutting down the tress for subsistence income, and unsustainable shifting

cultivation practice for food. A better economic health in tum generates environmental

awareness among the citizens as well as government to protect the forests. These beneficial

effects nevertheless are somewhat neutralized by the detrimental effects through massive

investment in timber harvesting, and increased demand for forest products. The net effect of

economic growth is, therefore, negative but insignificant. in other words, economic growth is

not a causal factor of deforestation in countries having high deforestation.

An opposite scenario exists in countries that are in medium deforestation category.

The coefficient estimate in Mednow logit is insignificant with a positive sign. With 1

percent rise in economic growth, the odds in favor of medium deforestation increases by a

factor of 1 .OB, which is not signiticantly different fkom 1. This reflects that an increased

economic growth increases investment in rain forest region for tirnber harvesting, and also

the demand for forest products. These effects are mildly stronger than the effect through

increased off- farm emplo yment and environmental awareness.

The two different effects in two different groups of countries could be explained by

the percolation of beneficial effects of economic growth. It seems that in high deforesting

countries, people with low level of income reap some of the benefits of economic growth and

involve themselves with environment fiiendly activities away fiom forests. However, in

medium deforesting countries, the benefits become concentrated at the hands of rich people

who in tum invest them in rain forest region. In addition, the poor farmers continue to

involve in forest destruction. This aspect would be clear by incorporating an indicator of

incorne distribution (such as Gini index) in the analysis, but unfortunately information on this

variable is not available for a majority of tropical countries.

14 When the coefficient estimate is zero, EXP ( P ) will be 1.

Table 17: Cornparison of effect of causes of deforestation on differeot categories of

Percentage forest area

Population

growth

1 Service

itioo (Le. Higb/Low and Med./Low)

Sign & Signi- ficance - Sig.

+ Sig.

- N.S.

+ N.S.

+ Sig. + Sig.

Democracy + N.S.

- - -

ERect of low per unit area 1 - 1 Effect of low per unit pressure

High/Low Explanatioo

subsistence income stronger than / N.S. 1 labor intensification neutralizes

Med/Low

pressure is s&nger than fiee riding Demand for shelter &

urban oriented migration & labor 1 1 the effect of demand for shelter

Sigo & Signi- fîcance

Explanatioo

N.S.

+

lesser than but not significantly I N s S * I greater than but not significantly di fferent fiom off- faxm different from off-fann

is neutralized by the free riding e ffect Urban oriented migration &

intensification Demand for forest products is

timber is slightiy more than that 1 Sig. 1 deforestation, not for import of

+

employment Debt service from export of

& subsistence income Demand for forest products is

+

for import of timber Expansion stronger than

logging greater than better management and patrolling Less fear of punishent for illegal logging Rebels use forests as their

ernployment Debt service pnmarily fiom

intensification Encroachent and illegal

Sig. *a +

logging greater than better management and patrolling Less fear of punishrnent for illegal logging Rebels use forests as their

timber Expansion stronger than

Sig. +

intensification Encroachment and illegal

6.3.3.4 Debt service growth

Debt service that comes fiom harvesting of timber for export increases deforestation,

but when paid for import of wood it decreases deforestation. Also, if debts are used in

activities such as deveiopment of non-conventional energy sector, improving efficiency of

wood processing industries, developing plantations etc., then debt service will reduce

deforestation. If both types of effects are occurring simultaneously, the net effect might be

insignificant. In case of High/Low logit, the parameter estimate has a positive sign, but

insignificant. This means, high debt service favors high deforestation (the odds in favor of

hideouts 1 hideouts Zant and N.S. means not significant at 10 percent level of significance

high deforestation increases by a factor of 1.012), but not significantly. An increase in debt

service has the harmfiil effect slightly stronger than the beneficial effects. The net effect in

tum increases, but not significantly, the likelihood of observing a country with high

deforestation rather than one with low deforestation.

In case of MedLow logit, the parameter estimate is significant and has a positive

sign. With 1 percent rise in the growth rate of debt service, the odds in favor of medium

deforestation increases by a factor of 1.063. This shows that debt service mostly comes fiom

export of timber, not being paid for import of wood or being used for activities that reduce

pressure on forests.

It is clear that growth in debt service is one of the causal factors of deforestation in

countries with medium level of deforestation while it is not so in hi& deforesting countnes.

Statistics on forest products export and import (FAO, 1997; page 63 and 192-195)

corroborate these results. Tropical Latin Arnerican countries in general are both exporters as

well as importers with former greater than the later. Export of forest products is quite higher

than import in almost al1 tropical African countries except some in West Sahelian Afnca

(Mauritania, Niger, Senegal). Majority of tropical Asian countries import a substantial

arnount of forest products except for Malaysia and Indonesia. Most of the high deforesting

counhies are fiom Latin America with fewer representations fiom Asia and Afnca, where

forest product export is slightly greater than import. In fact some the countries are involved

in the business of importing fkom one country and exporting to other without doing any h m

to their own forest resource. Therefore, the effect on deforestation in these counhies is not

significant. Most of the countries in medium deforestation categow however, are fiom

Afnca where exports are far higher than imports. Therefore, the effect of debt service is

significant in this category. These countries are the poorest in the world with debt piling on

every year. With no other alternatives available, they use forest product export as a means of

meeting their debt obligation.

6.3.3.5 Agricultural growth

Agricultural growth, which constitutes both the crop and livestock sectors, is

necessary to reduce the gap between demand and supply of food. Also, they are an important

source of foreign income. Increase in crop production comes fiom encroachment of

agricultural land into forest area and intensification by means of irrigation, fertilizer, hybrid

seeds and mechanization. in the livestock sector, expansion is necessary to meet the growing

forage requirement. In Thailand for exarnple, the cultivation of cassava for fodder has

resulted in dramatic increase in deforestation over last decade (Tole, 1998). Intensification in

livestock sector is through efficient production of forage, use of growth hormones,

immunization etc.

The parameter estimates have positive signs and significant in both the logits. This

means an increase in agricultural growth significantly increases the likelihood of obsewing a

country with high or medium deforestation rather than low deforestation. The effect is almost

sarne in both category of deforestation. With 1 percent rise in agricultural growth, the

likelihood of a country being in the category of high deforestation increases by a factor of

1.301, and being in medium deforestation, the increase is by a factor of 1.365. The significant

effect of agricultural growth on deforestation comes mainly fiom expansion and effect of

intensification is negligible. Latin Arnerican countries are the leader in livestock expansion,

while AWcan and Asian countries have cropland expansion through shifting cultivation.

6.3.3.6 Road development

The parameter estimates for this variable are significant and have positive sign in

both the logits, H i g h h w and Med/Low. The odds in favor of high and medium

deforestation increase by a factor of 1 .O43 and 1.053 respectively for a percent rise in length

of paved road. This suggests that road construction consistently have detrimental effect on

forest conservation with a slightly stronger effect on MedlLow logit than on High/Low logit.

New road construction increases accessibility for logging and encroachment both, legally and

illegally, that in tuxn results in deforestation. Legally, national govemments allow logging

companies to develop new roads in order to harvest timber fiom dense forests. While logging

companies cut down part of the forests, the nearby area is cleared by f m e r s and land

speculators. Construction of national highways, though constmcted for communication

purpose, often used by poachers and illegal loggers to deforest the area that were not possible

without the road. The limited resource of forest departments is always scanty to apprehend

the culprits.

6.3.3.7 Level of democracy

The parameter estimates of this variable are insignificant in both the Iogits indicating

that the variable does not have an effect in describing the deforestation process. The positive

signs nevertheless indicate that increase in level of democracy increases deforestation. The

odds in favor of high and medium deforestation increase by a factor of 1 .O39 and 1 .O99 with

per unit rise in the democracy index. These results contradict the results reported by Didia

(1997) and Deacon (1994), who concluded that democratic societies would be having low

deforestation. Countries such as Ecuador, Trinidad and Tobago, Nicaragua, Costa Rica, and

Thailand are democratic in nature but have high deforestation, while most of the Afi-ican

countries plagued with civil war have low deforestation rate. These results conf rm that

democracy does not have any significant effect on forest conservation. In fact, they might

increase the forest area loss due to local pressures, corruption, and less fear of punishment

fiom violating rules and regulations directed towards conserving the forests.

6.3.3.8 Regional dummies

The coefficient estimates of IDASIA and IDLAT variables are significant with a

positive sign in High/Low logit. This means, being in Latin Arnenca or Asia rather than in

Afnca increases the probability of observing a country in the category of high with respect to

low deforestation. The odds ratio in favor of high deforestation increases by a factor of

42.326 for a country being in Asia rather than in Africa. Similarly, the odds in favor of high

deforestation increases by a factor of 91 .O1 8 for a country being in Latin Arnerica rather than

in Afiica. In other words, Latin Amencan and Asian countries have higher deforestation

compared to AWcan countries. The high EXP (P) values of IDASIA and IDLAT variables

denotes that apart form the variables included in the model, there are some other causal

variables that cause high deforestation in Asian and Latin Amencan countries.

The parameter estimate of DASIA in Med/Low logit is significant but having a

negative sign. The odds in favor of medium deforestation for a country being in Asia

decreases by a factor of 0.130. This means a country being in Asia (rather than in Afkica)

increases the likelihood of low deforestation. In other words a country being in Africa

increases the likelihood of medium deforestation. Therefore, it can be concluded that a group

of Asian counties have 1ow deforestation, and a group of Afiican countries have medium

deforestation.

The dummy variable DLAT has a positive sign but insignificant in Med/Low logit.

This suggests that being in Latin Arnenca does not increase the likelihood of observing a

country having medium deforestation. The odds ratio in favor of medium deforestation for a

country being in Latin Arnerica increases by a factor of 1 .O9 1, which is not significant at 10

percent level of significance.

In swnmery, Latin American countries have high deforestation, Asian countries have

both hi& and low deforestation, and Afncan countries have both medium and low

de fores tation.

6.4 Predictive efficacy of the model

Similar to that of linear regression, there are some mesures of fit in multinomial

logistic regression to detennine the predictive efficacy of the model. These R~ statistics are

called as pseudo - R~ and are of different types such as Cox and Snell, Nagelkerke and

McFadden R~. However, none of thern is universally accepted or employed due to one or

another problem associated with them (Aldrich and Nelson, 1984).

The efficiency of Our multinomial logistic model for tropical deforestation can be

tested by estimating the probabilities of a country to be in different categories of

deforestation and then cornparhg with the actual category to which it belongs. For examplr,

the probability of deforestation for Paraguay with POPGR = 2.8 is

e 1 .O4 - - 1.04 + e- 1.22 = 0.69 or 69 percent

l + e

- e

P 2 - 1.04 + e- 1.22 = 0.07 or 7 percent l + e

P3 = 1 - (0.69 + 0.08) = 0.24 or 24 percent

The calculated probabilities for al1 observations are presented in Appendix - 20. A

country is classified as belongs to a particular category of deforestation for which the

estimated probability is the highest. Ln the above example, Paraguay during 1990-95 is

predicted to be in the category of high deforestation, since this category has the highest

calculated probability. The predicted category is sarne as the observed category. The

predicted categorization is compared with the observed categonzation to determine the

predictive power of the model. A cross tabulation of observed and predicted categorization is

reported under the heading 'Classification table' in SPSS output. It denotes how many

observations are predicted accurately. Mentioned below is the classification table.

Table 18: Classification of countries into different categories of deforestation

Out of 33 observations in the category of hi& deforestation, 26 are predicted

Observed high rned low Overall Percentage

_1

correctly to be in high category, 7 of them are predicted inaccurately of which 3 belong to

medium category and 4 belong to low category. In the medium deforestation category, out of

Predicted

40 observations 23 are predicted accurately, while 17 of them are not. Sirnilarly, in the

category of low deforestation, 29 observations out of 44 are predicted accurately. Overall, the

high 26 7 5

32.5%

mode1 is able to correctly classi@ the observations in 66.7 percent cases.

low 4

10 29

35.9%

med 3 23 10

31.6%

Percent Correct

78.8% 57.5% 65.9% 66.7%

Chapter 7

CONCLUSION

Macroeconomic deforestation rnodels at the global level have been suffering from

one or other shortcomings, the prominent among which is the deforestation data problem.

Results of such models therefore, are contradictory in nature. This study addressed the data

problem by using a relatively sophisticated econometric method the multinomial logistic

regression model. In doing so it solved some of the issues related to hypothesizing the

deforestation process and estimation of models. The results of the study establish that

multinomial logistic regression is a better option in solving the problem of data while

describing the deforestation process efficiently. It is confirrned that the causes of

deforestation are not same globally. It is found that growth in population, agriculture and

road construction are the causes of deforestation both in high deforesting countnes. Growth

in debt service replaces population growth as a causal variable in medium deforesting

countries. Some of the causal variables such as economic growth, and democracy do not

significantly affect deforestation. The significant parameter estimates of IDLAT and IDASIA

suggests that apart fiom the variables that descnbe the high deforestation in a group of

countries, there are some other factors that describe the high deforestation rate in Latin

Arnerican and Asian countries particularly. Such factors could be income distribution, and

institutional arrangements at local level in forest protection, for which data are still not

available.

Though population growth is a significant factor of deforestation in high deforesting

countries, as evident from the level of significance in Med/Low logit its detrimental effect in

medium deforesting countries cannot be ruled out. Though supporters of Bosenip hypothesis

argue that it does not have any effect in the short run due to out-migration and labor

intensification, in the long run the increased demand for land and natural resources bound to

cause deforestation. Therefore, in tropical countries with high or medium rate of

deforestation, measures should be taken to curb population growth.

Though economic growth is an insignificant causal factor of deforestation, the

positive parameter estimate in MedlLow logit indicate that there is a need to address this

issue in medium deforesting countries. The harmful effects of economic growth on

deforestation completely overshadow the beneficial effects. It seems that the inequality in

incorne distribution is the shimbling block in rural poor not receiving the benefits of

economic growth, and therefore keeps on clearing forests for swival . The nch people on the

other hand invest vast resources with them to clear the forests. Mesures, therefore, should

be taken to reduce the income gap in these countries.

Growth in debt service is one of the causes of deforestation in medium deforesting

countnes. Though not significant the positive parameter estimate in High/Low logit indicate

that it can be a source of concem in high deforesting countries too. Therefore, measures

should be taken to ease the burden of debt in those countries, particularly those in medium

category.

Agricultural growth significantly increases deforestation in both hi& and medium

deforesting countries. It is due to the expansion of agicultural lands into forest areas.

Nevertheless, it is not wise to recomrnend a reduction in growth of agriculture. It should be

achieved by intensification rather than by expansion.

Policy measures to stop road construction, which consistently increases deforestation

in high and medium deforesting countries, is not recomrnendable due to their importance in

economic growth of a country. However, considerable reduction in forest area disturbed due

to road construction can be made by use of modem technology. The indirect harmfùl effects

of road construction such as illegal logging c m be minimized if forest departments use these

roads for patrolling the areas that were otherwise not easily accessible. In addition, roads can

be used for better forest management such as prevention of fire, and insect and Pest control.

Democratic societies are widely believed to have less deforestation, but no evidence

supporting this belief is found. In fact, though the results are not significant, the positive

parameter estimates indicate that democracy increases deforestation. Hence, public

awareness is necessary to prevent deforestation due to nexus between the politicians in power

and timber barons.

The results of the model definitely have some limitations. First among them is the

assumption that the variables that affect deforestation are exogenous (Le. independent of each

other), and deforestation does not affect the exogenous variables. Secondly, the results of the

model are sensitive to selection of cut-off points. With changes in cut-off points, results may

Vary. Finally, there is no perfect substitution for deficiency of data problem in an

econometnc model. Our method of solving this problem cannot therefore be claimed as final.

Availability of accurate deforestation data will definitely improve the scope such analysis in

future. Also, the availability of data on income distribution, and institutional arrangements

will help in explaining the high deforestation in most of the Latin Amencan and some of the

Asian counh-ies.

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shocks in Botswana: A cornputable general equilibrium approach. In Biodiversify

Conservation edited by C.A. Perrings and others, Kluwer Academic Publishers,

Amsterdam.

Wiebelt, M., 1994. Stopping deforestation in the Arnazon: trade-off between

ecological and economic targets? WeltwirtschaAliches Archive - Review of World

Economics, 13 1 : 542-568.

World Bank, 1 990. World Development Report, Washington D.C.

World Bank, 1992. World Development Report, Washington D.C.

World Bank, 1997. World Development hdicators, World Bank, New York.

World Bank, 1 999. CD-ROM on World Development hdicators, World Bank, New

York.

World Commission on Forests and Sustainable Development, 1998. Our

Forests . . .Our Future, March Report. WCFSD Secretariat, W imi peg .

WRI, 1994. The World Resources: A guide to the global environment 1994-95. World

Resources Institute, Oxford University Press, Oxford.

WRI, 1996. World Resources: A guide to the global environment 1996-97. World

Resources Institute, Oxford University Press, Oxford.

WRI, 1998. The World Resources: A guide to the global environment 1998-99. World

Resources Institute, Oxford University Press, Oxford.

Wunder, S., 2000. The economics of deforestation: the example of Ecuador, St.

Martin Press Inc., New York.

Polity98 Project

Regime Characteristics, 1800-1 998

Ted Robert Gurr and Keith Jaggers, Principal Investigators

Indicators of Democracv and Autocracv:

DmOC (3-nurneric) Range = 1-10 (O = low; 10 = high) Democracy Score: general openness of political institutions. The 10-point Democracy scale is constructed additively. The

operational indicator is derived from codings of authority characteristics according to the following criteria:

PARCOMP (5) Cornpetitive +3 (4) Transitional +2 (3) Factional +1

XRCOMP (3) Election +2 (2) Dual/transitional +1

XROPEN (onïy if XRCOMP=2 or 3) (3) Dual/election +1 (4) Election +1

XCONST (7) Executive parity or subordination +4 (6) Intermediate category +3 ( 5 ) Substantial limitations +2 (4 ) Intermediate category +1

AUTOC (3-numeric) Range = 1-10 (O = low; 10 = high) Autocracy Score: general closedness of political institutions. The 10-point Autocracy scale is constructed additively. The

operational indicator is derived from codings of authority characteristics according to the following criteria:

PARCOMP (1) Suppressed (2) Restricted

PARREG (4) Restricted (3) Factional/Restricted

XRCOMP (1) Selection

XROPEN (only if XRCOMP=l) (1) Closed (2 ) Dual/designation

XCONST (1) Unlimited authority (2) Intermediate category (3) Slight to moderate limitations

Autboritv Characteristics:

XRREG (3 -numericl Executive recruitment regulation: institutionalized

procedures regarding the transfer of executive power. 1 = Unrsgulated Changes in chief executive occur through

forceful seizures of power. 2 = Deaignation/tran6itional Chief executives are chosen

by designation within the political elite, without formal compet i t ion.

3 = Regulated Chief executives are determined by hereditary succession or in competitive elections.

XRCOWP (3 -numericl Executive recruitment cornpetition: extent to which executives

are chosen through competitive elections. O = Unrsgulated If power transfers are coded

(unregulated) on XRREG or involve a transition to/from unregulated.

1 = Sslection Chief executives are chosen by hereditary succession, designation, or by a combination of both, as in monarchies where whose chief minister is chosen by king or court.

2 = Dual/transitional Dual executives in which one is chosen by hereditary succession, the other by competitive election.

3 = Election Chief executives are typically chosen in or through competitive elections matching two or more major parties or candidates.

XROPEN (3 -numericl Executive recruitment openness: opportunity for non-elites to

attain executive office. O = Unragulatsd If power transfers are coded "1"

(unregulated) on XRRES or involve a transition to/from unregulated.

1 = Closed Chief executives are determined by hereditary succession.

2 = Dual/daaignation Hereditary succession plus executive or court selection of an effective chief minister.

3 = Dual/elsction Hexeditary succession plus electoral selection of an effective chief minister.

4 = Opon Chief executives are chosen by elite designation, competitive election, or transitional arrangements between designation and election.

XCONST (3 -numericl Executive constraints: operational (de facto) independence of

chief executive. 1 = Unlimitsd authority There are no regular limitations

on the executivels actions. 2 = Intermediate category 3 = Slight to moderata limitrtionr There are some real

but limited constraints on the executive. 4 = ~ntermediate category

5 = Subatantial limitations The executive has more effective authority than any accountability group but is subject to substantial constraints by them.

6 = Intermediate category 7 = E%scutive parity or iubotrdination Accountability groups

have effective authority equal to or greater than the chief executive in most areas of activity.

PARREG (3 -numeric) Regulation of participation: development of institutional

structures for political expression. 1 = Unrsgulated Political participation is fluid; there are

no enduring national political organizations and no systematic regime controls on political activity.

2 = Factional/transitional There are relatively stable and enduring political groups which compete for political influence at the national level but competition among them is intense, hostile, and frequently violent.

3 = Factional/restrfcted Polities which oscillate more or less regularly between intense factionalism and restriction: when one group secures power it restricts its opponentst political activities until it is displaced in turn .

4 = Restrictsd Some organized political participation is permitted without intense factionalism but significant groups, issues, and/or types of conventional participation are regularly excluded £rom the political process.

5 = Institutionalirsd ~elatively stable and enduring political groups regularly cornpete for political influence and positions with little use of coercion. No significant groups, issues, or types of political action are regularly excluded from the political process.

PARCOMP (3-numeric) Competitiveness of participation: extent to which non-elites

are able to access institutional structures for political expression. O = Unrsgulated Political competition implies a significant

degree of civil interaction, so polities which are coded "1" (unregulated) on PARREG are not coded for competitiveness.

1 = Supprsssed No significant oppositional activity is permitted outside the ranks of the regime and ruling Party.

2 = Restrictsd/traniitional Some organized, political competition occurs outside government, without serious factionalism, but the regime sharply limits its form, extent, or both in ways that exclude substantial groups from participation.

3 = Factioarl Polities with factional or factional/restricted patterns of competition.

4 = Transitional Any transitional arrangements from Restricted or Factional patterns to fully competitive patterns, or vice versa.

5 = Campstitive There are relatively stable and enduring political groups which regularly compete for political

influence at the national level; cornpetition among thern seldom causes widespread violence or diswuption.

Polity Hyperlinks and Related Web Sites

Hvperlink to Politv at the University of Colorado, where earlier versions of Polity data and codebooks are available (including Polity III,

Polity IIId, and Polity mu)

Hvperlink to the Inter-University Consortium for Political and Social Research, where earlier versions of Polity data and codebooks are deposited (including Polity,

Polity II, and Polity m)

Hyperlink to the Center for International Development and Conflict Mananement -

fm9 where the Polity Project is housed at the University of Maryland, College Park

Hvperlink to the Center for Systemic Peace (CSP), where global trends are presented (including Polity Democracy and Autocracy trends)

and related data resources are available

Ap~endix - 2 Average annual rate of natural forest loss for the period 1990-95

Country

Angola Benin Botswana Burkina Faso Burundi

Total forest area (SOFO 1997)

(a) 23385 4923

14271 4431 324

Plantation area (FRA 1990)

(b) 171 2C

1 28

1 32

1990 Natural forest area

(a - b) 23214 490a

1995 Natural forest area

(SOFO 1997) 22080 461 1

% Deforestation

1 .O0 1.22 0.50 0.7q

-3.22

14270( 13916 4403 425 1

1 Sa 225

Asia

Country

Cambodia

Pakistan

Philippines

'~o ta l brest area Plantation area 1990 Natural 1995 % (SOFO 1997) (FRA 1990) forest area Natural forest area Deforestation

(a) (b) (a - b) (SOFO 1997) 1054 335 719 700 0.53

Latin America

-

Sri Lanka Thailand Vietnam

Country

189j 13277 9793,

Total forest area (SOFO 1997)

1657 11101 7647

19d 1699 756/ 1252 1

210d 7693,

(a) I (b)

0.54 2.38 0.12,

Plantation area (FRA 1990)

(a - b) Bahamas Belize Bolivia

1990 Natural forest area

--

Brazil Colombia

18d d 18C

Costa Rica Dominican Repu blic Ecuador

56391 1

El Salvador Grenade

1 992 1995

1455 1714

12082

Guadeloupe Guatemala Guyana

Jamaica 1 2 4 211 233

3

7000

4d 141 5 id 1 704

12018 124

L

Haiti Honduras

51217 55691 1

54299 18C

d 118 4

87 4253

18620

40 51 171

541 19

2d 12 462d 4

Mexico Nicaragua Panama

- - - -

Venezuela 46514 364 461 50

O 4C 12

13 4622

-

Paraguay Peru Trinidad and Tobago

1995 / % Natural forest area Deforestation

87 421 3

18608

57927 631 4 31 18

(SOFO 1997) 1

--

13160 68646

1 74

155 20

57772 6294

13 263

9i 3109 13147 68383

1 8 156

g s g $ B 8 s e 8 ô q $ 8 $ T + + + + +

5 & W S w W w w Y w w w w w w . r ? r = ? * q ~ q + o q e 9 0 v - v - a C D - b - - - - F - N c u N N

O

p ~ e a s s s z s e s + + + + + + + + + e g s a ~ w u W w w u ~ u u w w W W Y W " " " " L g _ S - ~ d - Q) w (D '=!=!ZS

I

CV N i n x s o ? q g $ e s u s + + o + + + s e s e s ! % w s e y y e L G g g + F a j u j 6 F 6 & & ~ ~ a 1 . - . - N -

g g S Z 8 8 Z Z 8 g 8 8 z S 8 X n a g g i g ; ~ + + g ~ g g g + - Q ~ c c ~ G & - G F F ~ I - - N F

N 8 g g 3 S Z Z Z 8 g 8 S Z $ B W W & Y : & & ; & & & W W A W W l c b - W Q b - ? - C D ? q q + & & h t h 4 < & 4 F F - I F F N Y

t - m m - " H 8 $ $ , o * , $ $ ? 8 ,$ ~ w " w W W W W W y W W W W W ~ b ~ ? * o q m r * - 2 2 Z 2 2 q - C c i n < D . - & - ' - I I

S S g + g $ g F g $ g g + + 5 $ 9 W W U ~ W W W W W W W W W Q q e q ~ q Q W W U

* ~ c & n - c - - - r + Z 9

Asia

Bhutan Cambodia lndia Indonesia Malaysia Nepal Pakistan Papua New Gi Philippines Sri Lanka Thailand Viet Nam

Countryname Bangladesh

Bolivia Brazil Colornbia Costa Rica Dominican Rep Ecuador El Salvador Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Paraguay Peru Trinidad and Tc Venezuela

isso issi 1982 1983 1984 1985 1986 1987 is8û 1989 isso issi 1992 1993 1994 199s

6.2~+05 6.9€+05 7.1€+05 7.5€+05 7.8E+05 8.1~+05 8.5~105 8.8E+05 9.1€+05 9.3€+05 9.9€+05 1 .OE+06 1,1€+06 1.1€+06 1.2€+06 1.2€+06

Latin America

s X 8 8 g + + + + L w w w w * T * 9 i ' a h a w N

g e l n r n O 7 O + + W W W W * m b O

+ + + + W W W W = ' 7 o q

p ~ $ a ~ ~ ? g q ~ a W W W W W W W W W W W W ~~X~~~~t~?~X

8 8 8 + + + W W W ..h - 0 4 -

Country Bangladesh Bhutan Cambodia lndia Indonesia Malaysia Nepal Pakistan Papua New G Philippines Sri Lanka Thailand Viet Nam

Brazil Colombia Costa Rica Dominican R Ecuador El Salvador Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Paraguay Peru Trinidad and Venezuela

Latin America 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 199d

Angola F Benin Botswana Burkina Faso Burundi Cameroon Central Afnca Chad Congo Cote d'Ivoire Gabon Gambia Ghana Guinea Guinea-Bissa Kenya Liberia Madagascar Malawi Mali Mauritania Mozambique Niger Nigeria Rwanda Senegal Sierra Leone Somalia Tanzania Togo Uganda Zambia

A~wndix - 5 Total GDP of different countries (million of national currency) at constant prices excludlng contribution from agricultural sector, and their rate of growth during 1980-90 and 1990.95

Country Bangladesh Bhutan Cambodia lndia lndonesia Malaysia Nepal Pakistan Papua New G Philippines Sri Lanka

Brazil Colombia Costa Rica Dominican Re Ecuador Et Salvador Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Paraguay Peru Trinidad and ' Venezuela

Amendix - 6 Total debt service as a percentage of GNP and its average annual growth during 1980-90 and 1990-95

Country 1- 1981 19û2 1983 1964 1985 1986 1987 198û 19m IW 1991 1982 1993 1~ 1995 1 9 ~ ~ 19~045 Angola - 2.48 2.76 2.51 3.51 3.41 3.96 3.94 6.99 4.73 13.27 20.11 9.49 32.49 Benin Botswana Burkina Fa Burundi Cameroon Central Afr Chad Congo, Re Cote d'Ivoi Gabon Gambia, T Ghana Guinea - Guinea-Bis Kenya Liberia Madagasc; Malawi Mali Mauritania Mozambiqr - Niger Nigeria Rwanda Senegal Sierra Leoi Somalia Tanzania - Togo Uganda Zambia

Country Bangtades Bhutan Cambodia lndia lndonesia Malaysia Nepal Pakistan Papua Ne\ Philippine! Sri Lanka Thailand Vietnam

Countw Bolivia Brazil Colom bia Costa Ria Dominican Ecuador El Salvado Guatemal; Guyana Haiti Honduras Mexico Nicaragua Panama Paraguay Peru Trinidad ai Venezuela

Latin America

Guyana: ln 1990 the govemment paid back US $299 million (GNP = US $289 million) wilh the help of IMF to gel fresh fundings from creditors to support the economic remvefy program started in mid - 1988

Nicaragua: In 1991, Nicaragua made significant progress in aîtracting foreign resources. Wilh the help of the countries in ils consultative group, the govemment paid the arrears on its mu!Ulateral debt (US $533 h i l e GNP was US S 1288), which enableû it, in the later half of the year to obtain more fundin~s

Ap~endix - 7 Data on different variables of deforestation model for different countries for the periods 198040 and 1990-95

COUNTRY Angola Benin Botswana Burkina Fas Burundi Carneroon Cen AfrRep Chad Congo CBte d'Ivoi Gabon Gambia Ghana Guinea Guinea-Biss Kenya Liberia Madagascar Malawi Mali Mauritania Mozambique Niger Nigeria Rwanda Senegal SierraLeone Somalia Tanzania, Ur Togo Uganda Zambia Zimbabwe

i 30

Africa DEF PRFORAR POPGR GDP-AGR TDSlGNP AGGR ROAD DEMOCRAC 0.70 20.0 0.5 25.0 0.0

0.70 24.3 -. . . 2.4 14.0 2.41

contd .....

Asia COUNTRY

Bangladesh Bhutan Cambodia lndia Indonesia Malaysia Ne pal Pakistan Papua New C Philippines Sri Lanka Thailand Vietnam

1 COUNTRY Bolivia Brazil Colombia Costa Rica Dominican R Ecuador El Salvador Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Paraguay Peru Trinidad an Venezuela

- ---- DEF PRFORAR POPGR GOP-AGR TOSlGNP AGGR ROAD DEMOCRAC 3.90 7.4 2.7 5.0 6.2 2.7 7.0 0.0

Lat. America DEF PRFORAR POPGR GDP AGR TDSIGNP AGGR ROAD DEMOCRAC~

contd.. . . . .

COUNTRV Angola Benin Botswana Burkina Fas Burundi Camermn Cen AfrRep Chad Congo C8te d'Ivoi Gabon Gambia Ghana Guinea Guinea-Biss Kenya Liberia Madagascar Malawi Mali Mauritania Mozambique Niger Nigeria Rwanda Senegal SierraLeone Somalia Tanzania, U Togo Uganda Zambia Zimbabwe

t990-95 Africa

DEF PRFORAR POPGR GDF-AGR TDSIGNP AGGR ROAD DEMOCRAC 1 .O0

Asia

Bhutan Cambodia lndia lndonesia Malaysia Nepal Pakistan Papua New ( Philippines Sri Lanka Thailand Vietnam

- - - --

COUNTRY Bolivia Srazil Colombia Costa Rica Dominican R Ecuador El Salvador Guatemala Guyana Haiti Honduras Mexico Nicaragua Panama Paraguay Peru Trinidad an Venezuela

COUNTRY Bangladesh

Lat. America DEF PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAO DEMOCRACI 1.16 46.6 2.4 - -5.7 3.4 5.5 9.01 0.40 65.2 1.7 3.2 11.4 3.7 9.2 8.0 0.50 47.5 1.7 6.6 -10.5 1.4 11.9 9.0 2.90 27.7 2.4 5.3 -5.3 3.6 16.7 10.0 1.60 35.0 1.9 4.6 2.6 2.5 49.3 2.8 1.60 42.4 2.2 3.5 -7.9 2.5 12.7 9.0 3.10 5.6 2.2 7.1 -6.2 1.2 20.0 9.0 2.00 38.7 2.9 4.7 -6.5 2.5 27.5 4.0 0.00 94.5 0.9 6.8 -31.9 12.2 7.3 4.8 0.00 0.5 2 .O -4.5 -4.8 -2.6 24.2 2.1 2.30 41.2 3.0 3.7 1.3 2.9 20.2 7.0 0.90 29.5 2.1 2 .O 13.2 0.4 37.3 2.8 2.50 48.4 3.7 0.5 27.8 0.3 10.0 6.4 2.1 O 41.2 1 -9 5.6 -1 2.4 4.4 33.5 8.4 2.60 32.3 2.8 3.5 -1 3.4 1.4 9.4 5.4 0.30 53.2 1.9 6.1 3.3 5.1 10.9 3.6 1 .O0 30.4 1.1 0.0 2 .O 1.3 51 .O 9.0 1 . I O 50.6 2.3 2.8 -6.3 1.9 39.3 8.2

DEF PRFORAR POPGR GDP-AGR TDSlGNP AGGR ROAO DEMOCRAC 0.50 5.0 2.2 5.4 -4.5 1.1 7.9 6.0

ZOUNTRY Bangladesh Bhutan Botswana Botswana Brazil Brazil Burundi Burundi Cameroon Cameroon CenAfrRep CenAfrRep Colombia Congo Congo Côte d'Ivoire Gabon Gabon Guinea-Bissau Guyana Guyana Haiti

NOTES:

Aowndix - 8 Data on different variables according to category of deforestation

Low deforesting countries

DEF PRFORAR POPGR GDP-AGR TDSIGNF AGGR ROAD DEMOCRAC JDASIA IDLAT IDPERlOD

1. African countries are represented by 0, and 1 represents countries in Asia and latin America in IDASIA and IDLAT variables respectively 2. In the variable IDPERIOD, O and 1 represent data for the period 1980-90 and 1990-95 respectively

Low deforesting countries (Contd .....)

>OUNT RY lndia lndia lndonesia Kenya Kenya Liberia Mauritania Mauritania Mozambique Niger Niger Papua New GL Papua New GL Peru Peru Rwanda Rwanda Senegal SierraLeone Somalia Vietnam Zimbabwe

DEF PRFORAR POPGR GDP AGR TDSIGNFAGGR ROAD DEMOCRAC IDASlA IDLAT IDPERIOD -1.81 14.0 1.9 6.2 6.2 3.1 45.7 8.0 1 O 1

Medium deforesitng countries

:OUNTRY Angola Angola Benin Benin Bolivia Burkina Faso Burkina Faso Chad Chad Colombia C6te d'Ivoire Gambia Gambia Ghana Ghana Guinea Guinea-Bissai lndonesia Madagascar Madagascar Malawi Mali Mali

IEF PRFORAR POPGR GDP AGR TDSIGNF AGGR ROAD DEMOCRAC IDASIA 1DLAT IDPERIOU 0.70 20.0 2.6 4.1 9.5 0.5 25.0 0.0 O O (1

1 .O0 18.6 3.7 -4.8 32.5 -1.8 25.0 0.0 O O 1 1.22 43.5 3.1 3.6 6.7 4.9 20.0 9.0 O O 1 1.30 49.6 3.1 1.3 1.2 5.1 20.0 0.0 O O (1

1.20 52.6 2.8 6.2 -14.0 2.0 4.3 7.8 O 1 a 0.70 17.2 2.6 3.8 2.3 3.1 16.6 0.0 O O O 0.70 16.1 2.8 1.9 10.6 4.6 16.0 0.0 O O 1 0.70 9.6 2.4 7.6 7.4 2.7 0.8 0.0 O O (3

0.83 8.9 2.7 -1.5 16.1 6.9 0.8 0.0 O O 1 0.70 51 .O 2.1 3.7 13.9 2.9 11.9 8.0 O 1 (3 1 .O0 19.0 4.2 0.9 -4.2 -0.5 8.7 0.0 O O (3

0.80 9.0 3.0 4.8 14.9 0.4 32.0 7.6 O O O 0.87 8.3 3.8 2.8 -10.4 2.6 35.3 2.4 O O 1 1.21 40.0 3.0 5.7 3.6 2.4 24.9 3.0 O O 1 1.30 45.6 3.4 4.9 12.3 1 .O 19.6 0.0 O O (1 1.13 27.4 3.0 3.6 -7.9 4.5 16.4 0.1 O O 1 0.80 70.8 1.7 3.4 4.1 6.7 8.3 0.0 O O (3

1 .O0 61.8 1.8 6.7 11.1 3.4 46.0 0.0 1 O C 0.73 26.3 3.2 -0.6 -29.2 1.6 11.5 9.0 O O 1 0.80 28.5 3.2 0.5 15.2 2.5 15.4 8.8 O O C 1.30 29.0 3.5 0.8 1.5 1.7 20.0 3.2 O O 1 0.80 10.6 2.9 -0.9 11.8 4.3 10.9 0.0 O O C 0.95 9.8 3.2 2.5 13.0 3.1 12.0 7.0 O O 1

Medium daforesitng countries (Contd .....)

Mexico Mozambique Ne pal Nepal Nigeria Nigeria Senegal Tanzania, Uni1 Trinidad and 1 Uganda Uganda Venezuela Venezuela Zambia Zambia

1 Zimbabwe

COUNTRY Mexico

DEF PRFORAR POPGR GDP-AGR TDSIGNF AGGR ROAD DEMOCRAC IDASIA IDLAT IDPERIOU 0.88 29.5 2.1 2.0 13.2 0.4 37.3 2.8 O 1 1

ZOUNTRY Bangladesh Cambodia Costa Rica Costa Rica Dominican Re( Dominican Re1 Ecuador Ecuador El Salvador El Salvador Guatemala Guatemala Honduras Honduras Malawi Malaysia Malaysia Nicaragua Pakistan Pakistan Panama Panama Paraguay Paraguay Philippines Philippines SierraLeone Sri Lanka Thailand Thailand Togo T w o f rinidad and T

High deforesting countrles

IEF PRFORAR POPGR GDP AGR TDSIGNFAGGR ROAD DEMOCRAC IDASIA I D U T JDPERIOU r) 7 7 n 0.0 O

Multinomial Logistic Regressions (dataset of 1995 to test the effect of road as growth vs. level variable)

As a erowth variable (ROADGR)

Case Processing Summary

Model Fitting Information

DEF high med low

Valid Missing Total

N 16 16 23 55

O 55

Pseudo RSquare

Nagel kerke McFadden

Likelihood Ratio Tests

Sig.

.O02

L

Model lntercept Only Final

Effect lntercept PRFORAR POPGR GDP-AGR TDSIGN P AGGR ROADGR DEMOCRAC IDASIA IDLAT

-2 Log Likelihood of

Reduced

-2 Log Likelihaod

1 19.749 78.826

Model 90.142

Chi-Square 11.316 2.885 9.491 1.310 3.61 9 4.601 2.461 4.905 9.772

Chi-Square

40.923

Sig. .O03 236 .O09 .SI9 .164 .l O0 .292 .O86 .O08 .O01

d f

18

The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced moâei is fomed by omitting an effect from the final model. The nuIl hypothesis is that al1 parameters of that effect are 0.

DEF high lnterœpt

PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROADGR DEMOCRAC IDASlA IDLAT

med lntercept PRFORAR POPGR GOP-AGR TDSIGNP AGGR ROADGR DEMOCRAC IDASIA l DLAT

-

Std. Emr 3.672 .O27 .979 .186 .O43 .279 .188 .168

1.988 1.801 3.192 .O24 .956 .169 .O37 .246 .IO8 -164

1.870 1.524

Classification

-- -

Wald 7.607

.923 6.829 1.265 .618

2.082 1.907 1.428 4.191 6.891 2.514 2.402 2.782

.127 3.147 2.775 .O5 1

3.868 582 .O10

Sig. .O06 .337 .O09 -26 1 .432 -149 -167 .232 .O41 -009 -113 -121 .O95 -722 .O76 .O96 -82 1 -049 -445 -92 1

Predicted I I 1 Percent

Overall Percentage 1 32.7% 1 25.5% 1 41.8% 1 67.3% 1

Observeci high med low

- -

95% Confidence Interval for

Lower Bound

high 11 3 4

B ) Upper Bound

f .O28 87.867

1.168 1.126 2.587 1.871 1.696

2881 .l76 3858.916

Contd....

med O

11 3

low 5 3

15

Correct 68.8% 64.7% 68.2%

As a level variable (ROAD)

Case Processing Summary

Model Fitting Information

DEF higti med low

Valid Missing Total

Final 72.889

N 16 16 23 55

O 55

Pseudo R-Square

Nagelkerke McFadden

Likelihood Ratio Tests

Effect lntercept PRFORAR POPGR GDF-AGR TDSIGNP AGGR ROAD DEMOCRAC IDASIA IDLAT

-2 Log Likelihood of

Reduced Model

84.475 Chi-Square

11.585 Sig.

.O03

.311 ,021 .337 .O71 .O23 .O1 5 .O49 .O0 1 .O0 1

The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect fram the final model. The nuIl hypothesis is that all parameters of that effect are 0.

OEF high lnterœpt

PRFORAR POPGR GDP-AGR TDS/GNP AGGR ROAD DEMOCRAC IDASlA IDiAT

med lnterœpt PRFORAR POPGR GDP-AGR TOSlGNP AGGR ROAD DEMOCRAC IDASlA IDLAT

Classification

Wald 6.883 1.612 5 .O22 2.094 -328

2.991 1.802 1.664 3.941 6.156 4.214 1.328 2.757

-383 4.064 4.172 6.366 4.615 2.429 -396

Sig. -009 -204 .O25 -148 -567 .O84 -179 -197 .O47 .O13 .O40 .249 .O97 .536 .O44 .O41 .O12 .O32 . I l 9 529

Predicted

I I 1 percent O bserved high

95% Confidence Interval for

meâ low Overall Percentage

>

Ex Lower &und

.920 1.329 .SI9 .946 .94 f .982 .896

1 .O49 2.374

high 11

Upper Bound

1.018 69.973

1.104 1.106 2.660 1 .O99 1.708

1641 .O89 1584.61 2

3 2

29.1 %

med 2

12 2

29.1 %

low 3

Correct 68.8%

2 18

41.8%

70.6% 81 -8% 74.5%

Multiaomial Logistk Regression (Low is the baseline category)

Case Processing Summary

Model Fitting lnforrnaîion

Pseudo RSquam

Nagel kerke McFadden

_I

Likelihood Ratio Tests

Effect lntercept PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAD DEMOCRAC IDASlA IDLAT IDPERIOD

Sin. ,

.O00

-- - --

-2 Log Likelihood of

Red uced Model

194.779 188.235 190.266 186.098 190.866 191.369 191 -946 185.236 208.41 9 21 1.691

d f

20

Model lntercept Only Final

Chi-Square 11.019 4.475 6.506 2.337 7.1 O6 7.608 8.185 1.476

24.658 27.931

Sig. .O04 -107 .O39 .311 .O29 .O22 .O1 7 -478 .O00 .O00 .668

-2 Log Likelihood

255.603 183.760

The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The nuIl hypothesis is that al1 parameters of that effect are O.

Chi-Square

71.843

DEF high htercept

PRFORAR POPGR GDP-AGR TDSIGNP AGGR Rom OEMOCRAC IDASIA IOLAT

med lntercept PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAO DEMOCRAC IOASlA IDLAT IOPERIOD

Std. Error 2.112

.O16 575 .128 ,026 .158 .O20 .IO2

1.183 1.116 .618

1.888 .O1 3 .519 .O98 .O24 .128 .O21 .O96

1.201 .957 .51 8

Classification

Wald 9.092 3.856 5.749 1.815 .138

2.833 4.442

.252 9.600

15.715 .354

3.852 .122

1.995 .O38

6.101 5.869 6.008 1.422 3.038 ,000 .728

Sig. .O03 .O50 .O16 .178 .?IO .O92 .O35 .616 .O02 .O00 .552 . O 5 0 .727 .lS8 .a45 .O14 .O15 .O14 .233 .O81 .987 .393

Predicted

I I 1 Percent Observed high

--

9 5 % Confidence lnterval for

med low Overall Percentage

_I

Ex Lower Bound

-940 1.286 -655 -959 -957

1.003 .862

3.846 9.363

-206

high 26

Upper Bound

1 .O00 12.234 1,081 1 .O63 1.777 1 .O86 1.285

397.458 743.722

2.325

1 .O21 5.761 1.234 1.113 1.755 1 .O99 1.354 1.298 6.423 1.773

8 5

33.3%

med 2

25 9

30.8%

low 5

Conect 78.8%

8 29

35.9%

61 .O% 67.4% 68.4%

Binary Logistic Regression

Case Processing Summary

Valid Missing

lT0M 1 ::il high=upto0.8 and high low=>0.8 low

Mode! Fming Information

N 56 61

Pseudo R-Square

Nagelkerke McFadden .324

Model lntercept Only Final

_r

Likelihood Ratio Tests

Effect lntercept PRFORAR POPGR GDP-AGR TDS/GNP AGGR ROAD DEMOCRAC IDASIA IDLAT IDPERIOD

-2 Log Likelihood

161 -983 109.517

-

-2 Log Likelihood of

Reduced Model

135.509

Chi-Square

52.466

Chi-Square 25.992

-836 19.574 2.664 1 .î3 t 5.738 9.676 .O1 9

1 3.918 28.786

1.272

The chi-square statistic is the difference in -2 log-likelihoods between the final mode1 and a reduced model. The reduced model is fomied by omitting an effect from the final model. The nul1 hypothesis is that al1 parameters of that effect are 0.

d f

10

Sig.

.O00

high=upto0.8 and W > 0 . 8 h W lntenept

PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAD DEMOCRAC IDASIA IDLAT IDPERIOD

Classification

Std. Enor 2.177

.Of2

.584

.O94

.O23

. I l 5

.O19

.O79

.972 999 .504

Wald 17.351

.a16 14.257 2 .5n 1 .2l4 5.312 8.327

.O19 12.003 19.043

1.249

Predicted 1 1 percent

Observed high

95% Confidence Interval for

low Overall Percentage

Ex1 Lower Bound

365 2.888

.716 -981

1 .a40 1.018 -846

4.316 11 .O49

.654

high 41 11

44.4%

low 15

Correct 73.2%

50 55.6%

82.0% 77.8%

Source

Aopendix - 12

OLS Regression without interactive dummies

Dependent Var iable: def

Analys is o f Variance

Sum o f Mean DF Squares Square

Mode1 10 31 .92195 3.19219 E r r o r 1 06 64.42621 0.60779 Corrected T o t a l 116 96.3481 5

Va r iab le INTERCEPT PRFORAR POPGR GDP-AGR TDS/ GNP AGGR ROAD DMOCRAC IDASIA IDLAT IDPERIOD

Root MSE 0.77961 Dependent Mean 1.11187 Coef f Var 70.11688

Parameter Estimates

Parameter Est imate

- O . 38494 - 0.00728

O . 404 -0.01126

O . 00695 0.03264 0.00361 0.00272 1.22651 1.24453

-0.11207

Standard E r ro r 0.49147 0.00373 O. 1 3837 0.02836 O. 00654 O. 03332 0.00538 O. 02542 0.26809 O . 23544 0.15121

t Value -0.78 -1.95 2.93

-0.40 1 .O6 0.98 0.67 0.11 4.58 5.29

-0.74

Test of F i r s t and Second Moment Spec i f i ca t ion

DF Chi-Square Pr > Chi Sq

F Value P r > F

5.25 <. O001

Fi-Square 0.3313 Adj R-Sq O. 2682

consis tent Pr > I t l t value

0.4352 -0.92 0.0538 -1.85 0.0042 3.53 0.6921 -0.43 0.2905 1.08 0.3295 1.45 0.5040 0.56 0.9149 0.10 <.O001 3.43 <.O001 5.80 0.4602 -0.78

Multinomial Logistic Regression (without period dummy)

Case Processing Summary

Model Fitting lnfonnation

DEF high med low

Valid Missing Total

1 Mode1 lntercept nly

N 33 40 44

117 O

117

1 Final

-2 Log

Pseudo RSquare

ox and nell Nagel kerke McFadden

Likelihood Ratio Tests

Effect lntercept PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAD DEMOCRAC l DASlA IDLAT

-2 Log Likelihood of

Reduced Model

198.160 188.883 191.684 187.228 192.131 192.369 1 92.559 185.653 209.920 2 13.739

- --

Chi-Square 13.592 4.315 7.1 16 2.660 7.564 7.801 7.991 1 .O85

25.352 29.1 71

- -

Sig. .O01 . i l 6 .O28 264 .O23 .O20 .O18 .581 .O00 .O00

The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The reduced model is fonned by omiîting an effect from the final model. The nuIl hypothesis is that al1 parameters of that effect are 0.

DEF high lntercept

PRFORAR POPGR GDP-AGR TDSlGNP AGGR ROAD DEMOCRAC IOASlA l DlAT

med lntercept PRFORAR POPGR GDP-AGR TOSlGNP AGGR ROAD DEMOCRAC IDASlA IDLAT

Std. Error 2.040

.O16

.571

.126

.O26

.î56 -020 .1w

1 .l67 1.110 1.847 .O1 3 -51 7 .O97 .O24 .128 .O21 .O92

1.196 .950

Wald 10.695 3.696 6.194 2.016 .212

2.855 4.348

.146 10.298 16.522 4.893

.O82 2.345 .O55

6.508 5.947 5.848 1 .O55 2.907 -008

--

Sig. .O01 .O55 .O1 3 -156 645 .O91 .O37 .703 .O01 .O00 .O27 .774 .126 .815 .O1 1 .O1 5 .O1 6 -304 .O88 -927

Classification J

Predicted I I 1 percent

95% Confidence lnterval for

Observed high

Ex Lower Bound

941 1.352 .654 362 .959

1.003 .854

4.297 10.338

B ) Upper Bound

1 .O01 12.664 1 .O70 1 .O65 1.765 t ,085 1.263

416.920 801.304

h i ~ h 26

med 3

med low Overall Percentage

low 4

7 5

32.5%

Correct , 78.8%

23 10

31.6%

10 29

35.9%

57.5% 65.9% 66.7%

Binary Logistic Regression (Without period dummy)

Case Placessing Summary

Valid Missing Total 117

high=upto0.8 and high

Model Fittlng Information

N I

56

Final 1 110.789

Model lntercept Only

Pseudo RSquare

Nagelkerke McFadden

~ikelihood 161.983

Likelihood Ratio Tests

Effect lntercept PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAD DEMOCRAC IDASlA IDLAT

-2 Log Likelihood of

Red uced Model

135.723 1 12.025 129.207 1 13.345 1 1 1.673 1 16.576 120.456 1 lO.79O 123.898 138.41 7

-- - -

Chi-Square 24.934

1.236 18.418 2.557 .884

5.787 9.667 .O01

13.109 27.629

-

Sig. .O00 .266 .O00 . i l 0 347 .O16 .O02 .973 .O00 .O00

The chi-square statistic is the difference in -2 log-likelihoods between the final mode1 and a reduced model. The reduced model is fomed by omitting an effect from the final moûel. The nuIl hypothesis is that al1 parameters of that effect are 0.

high=uptd).8 and W a 0 . 8 hish lntercept

PRFORAR POPGR GDP-AGR TDSlGNP AGGR ROAO DEMOCRAC IDASlA IDLAT

Std. Enor - 1.995 .O12 .553 .O92 .O22 . i l 6 .O19 .O78 .957 .97 1 -

Wald 17.385 1.198

13.939 2.488 .874

5.398 8.345 .O01

11.315 18.586

Classification k -

Predicted

I I percent

Sig. .O00 .274 .O00 -1 15 .350 .O20 .O04 .973 .O01 .O00

Obsewed high

95% Confidence Interval for Ex

L m r Bound

363 2.664 -721 .977

1 .O43 1.018 .MO

3.831 9.810

high 1 low 41 1 15

Upper Bound

Correct 73.2%

low Overall Percentage

*

9 42.7%

52 57.3%

85.2% 79.5%

OLS Regression without interactive dummies ( w i t hout pe r i o d dummy )

Dependent Va r iab le : def

Analysis of Variance

Sum o f Mean Source DF Squares Square F Value P r > F Mode1 9 31 ,58809 3.50979 5.80 < . O001 E r r o r 1 07 64.76006 0.60523 Corrected T o t a l 116 96.3481 5

Root MSE 0.77797 R-Square O . 3279 Dependent Mean 1.11187 A d j R-Sq 0.2713 Coef f Var 69.96904

Parameter Est imates

Var iab le DF

In te rcep t 1 PRFORAR 1 POPGR 1 GDP-AGR 1 TDS/ GNP 1 AGGR 1 ROAD 1 DEMOCRAC 1 IDAS I A 1 IDLAT 1

Parameter Standard Consistent Estimate E r r o r t Value Pr > l t 1 t value

Test o f F i r s t and Second Moment S p e c i f i c a t i o n

DF Chi-Square Pr > C h i Sq

Amendix - 16

OLS Regression with interactive dummies ( w i t hout p e r i o d dummy )

Ana lys is o f Variance

Sum of Mean Source OF Squares Square F Value Pr > F

Mode1 29 87.00126 3.00004 27.92 <. O001 Er ro r 87 9.34689 0.10744 Corrected T o t a l 116 96.34815

Root MSE 0.32777 R-Square 0.9030 Dependent Mean 1.11187 Adj R-Sq O. 8707 Coeff Var 29.47937

Parameter Estimates

Var iab le

INTERCEPT PRFORAR POPGR GDP-AGR TDSI GNP AGGR ROAD DEMOCRAC IDASIA IDLAT HIGH PRFORARHI POPGRHI GDP-AGRHI TDS-GNPHI AGGRHI ROADHI DEMOCRACHI IDASIAHI IDLATHI MED

Parameter DF Estimate

PRFORARME 1 O . O0251

Standard E r r o r t V a l u e

Consistent Pr > I t l t - v a l u e

POPGRME GDP-AGRME TDS-GNPME AGGRME ROADME DEMOCRACME IDASIAME IDLATME

Test of First and Second Moment Specif ication

DF Chi-Square Pr>ChiSq

OLS Regression witb interactive dummies after random changes in the rates of deforestation

Dependent V a r i a b l e : d e f l

A n a l y s i s o f V a r i a n c e

S o u r c e Sum o f Mean

DF S q u a r e s S q u a r e F V a l u e P r > F

Mode1 29 93.18168 3.21316 28.57 <.O001 E r r o r 8 7 9.78307 0.11245 C o r r e c t e d T o t a l 116 102 .96475

R o o t MSE 0.33533 R - S q u a r e O. 9050 Dependent Mean 1.14309 A d j R - S q O. 8733 C o e f f V a r 29.33581

V a r i a b l e

INTERCEPT PRFORAR POPGR GDPAGR TDS /GNP AGGR ROAD DEMOCRAC IDASIA IDLAT HIGH PRFORARHI POPGRHI GDPAGRHI TDS-GNPHI AGGRHI ROADHI DEMOCRACHI IDASIAHI

P a r a m e t e r E s t i m a t e s

P a r a m e t e r S t a n d a r d DF E s t i m a t e E r r o r t V a l u e

C o n s i s t e n t P r > / t l t - v a l u e

IDLATHI MED PRFORARME POPGRME GDPAGRME TDS-GNPME AGGRME ROADME DEMOCRACME IDASIAME IDLATME

Test o f F i r s t and Second Moment Spec i f ica t ion

DF Chi-Square P r > C h i . Sq

Multinomial logistic regression after the cut-off points are reduced by 7 percent

Case Processing Summary

I poinl are reduced med by 7 percent low I ::I DEF after the cutoff high

Model Çitting Information

N 34

Pseudo R-Squan,

Nagel kerke McFadden

Model lntercept Only Final

Likelihood Ratio Tests

Effect l ntercept PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAD DEMOCRAC l DASlA IDLAT

-2 Log Likelihood

255.983 187.814

-2 Log Likeli hood of

Reduced Model 201.546

Chi-Square

68.169

The chi-square statistic is the difference in -2 log-likelihoods between the final model and a reduced model. The redu&d model is formed by omitting an effect from the final model. The nuIl hypothesis is that al1 parameters of that effect are 0.

d f

18

Sig.

.O00

DEF afbr the aitoff points are reduced 1

PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAD DEMOCRAC IDASIA

by 7 percent h m lnterœpt

B -6.667

Classification

IOLAT ned lntercept

PRFORAR POPGR GDP-AGR TDSGNP AGGR ROAD DEMOCRAC IDASlA l DLAT

Wald 10.970 3.385 6.820 2.002 -221

2.730 4.61 7 -243

9.556 15.712 4.572 -127

2.040 .O77

6.664 6.066 5.656 .923

2.829 .O24 -

4.212 -3.962

-4.706-03 .742

2.727E92 6.279E92

.317 5.098E-02 8.856E-02

-2.005 .148

Sig. .O01 .O66 .O09 -157 .638 -098 .O32 .622 .O02 .O00 .O32 -721 .153 .781 .O1 0 .O1 4 .O1 7 .337 .O93 .876

Predicted I I 1 percent

Observed high

95% Confidence lntekal for

med low Overall Percentage

Er lower Bound

944 1 A46 664 363 .955

1 .O04 A67

3.561 8.410

.970

.759 ,048

1.015 1 .O67 1 .O09 .912

1 -302E-02 .180

high 26

8) Upper Bound

1 .O02 13.269

1 .O68 1.064 1.725 1 .O86 1.269

290.040 541 -800

1.021 5.813 1.245 1.117 1 -766 1 .O97 1.309 1.393 7.454

8 5

33.3%

med 4 23 9

30.8%

low 4

Correct 76.5%

9 29

35.9%

57.5% 67.4% 66.7%

Amendix - 19

Multinomial Iogistic regression after the cut-off points are increased by 7 percent

Case Processing Summary

DEF after the cutoff high point are increased med by 7 percent low

Valid Missing Total

Model Fitting Information

1 Mûdel O lntercept nly

1 Final

-2 Log Likelihood

248.697 1 73-21 0

Pseudo RSquare

Nagel kerke McFadden -304

Likelihood Ratio Tests

Effect lntercept PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAD DEMOCRAC IDASlA IDLAT

-2 Log Likelihood of

Reduced Model

200.822 Chi-Square

27.61 1 Sig.

.000

The chi-square statistic is the differenœ in -2 log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The nuIl hypothesis is that al1 parameters of that effect are 0.

Std. Enor 2.358

.O1 6 -637 .138 .O28 -173 .O21 -104

1.308 1.288

DEF after the wtoff point are incieased by 7 percent high lnterœpt

PRFORAR POPGR GDP-AGR 1 DSIGNP AGGR ROAD DEMOCRAC IDASIA IDLAT

med Intercept PRFORAR POPGR GDP-AGR TDSIGNP AGGR ROAO DEMOCRAC IDASIA I O U T

Wald l6.ûû2 2.896 9.335 1 -744 .444

2.338 4.239 .O82

15.81 1 21 S3l

B -9.431

-2.79502 1.945 -. 182

1.855E-02 -265

6.395E-02 2.979E-02

5.200 5.979 -7.343

7.782503 1 -577 -.151

4.091 E-02 -334

3.326E-02 6.855E-02

227 1 .192

Classification

Predicted

I I 1 percent

Sig. .O00 .O89 .O02 .187 .505 .126 .O39 .774 .O00 .O00 .O00 .565 .O04 .121 .O79 .O08 .O03 .421 .838 223

Observed high

95% Confidence lntervaf for Ex

Lower Bound

-942 2.009

.637 -965 -928

1 .O02 A41

13.973 31.608

981 1.648

.711

.995 1 .O90 f .O21 -906 -144 -483

high 22

B ) Upper Bound

1 .O04 24.370 1 .O92 1 .O76 1.829 1 .O90 1.263

2353.976 4935.332

1 .O35 14.228 1 .O41 1 .O90 1.789 1.111 1.265

10.963 22.456

med 3

med low Overall Percentage ;

low 5

Correct 73.3%

8 6

30.8%

13 5

17.9%

12 43

51.3%

39.4% 79.6% 66.7%

Aopendix - 20 Calculation of probability of deforestation for different countries

~hutan Botswana Botswana Brazil Brazil Burundi Burundi Cameroon Cameroon CenAfrRep CenAfrRep Colom bia Congo Congo C8te d'Ivoire Gabon Gabon Guinea-Biss;: Guyana Guyana Haiti lndia lndia indonesia Kenya Kenya Liberia Mauritania

Co~ntïy Bangladesh

1 Mauritania 1 3 0.5 2.7 -0.5 6.9 1.7 11.0 O O O O -1.78 -0.44 0.17 0.64 0.09 0.35 0.551 IDPERIOD variable is displayed to indicate the period to which the observations belong

DEF PRFORAR POOGR GDP-AGR TOSlGNP AGGR ROAD DEMOCRAC IDASU IDLAT IDPERIOD* 4 45, ,La e L2 P,(high) P,(med) P3(bw) 3 5.0 2.2 5.4 -4.5 1.1 7.9 6 1 O 1-0 .11 -3.24 0.90 0.04 0.46 0.02 0.52

Zountry Mozambique Niger Niger Papua New ( Papua New ( Peru Peru Rwanda Rwanda Senegal SierraLeone Somalia Vietnam Zimbabwe

Angola Angola Benin Benin Bolivia Burkina Fasc Burkina Fasc Chad Chad Colombia C6te d'Ivoire Gam bia Gambia Ghana Ghana Guinea Guinea-Biss lndonesia Madagascar Madagascar

162

iEF PRFORAR PûûGR GDP-AGR TDWGNP AGGR ROAD DEPOCRAC I D M U IDUT IDPERlQD L i L2 eL' e L2 P,(high) P2(ITlad) P3(10w) 21.7 4.6 16.9 2.4 18.6 1 -3.03 0.81 0.05 2.25 0.68 0.30

Country Malawi Mali Mali Mexico Mexico Mozambique Ne pal Nepal Nigeria Nigeria Senegal Tanzania, UI Trinidad and Uganda Uganda Venezuela Venezuela Zambia Zambia Zimbabwe

Cam bodia Costa Rica Costa Rica Dorninican R Dominican R Ecuador Ecuador Guatemala Guatemala Honduras Honduras Malawi Malaysia Malaysia Pakistan Pakistan Panama Panama Paraguay Paraguay Philippines Philippines SierraLeone Sri Lanka Thailand Thailand Togo Two Trinidad and El Salvador El Salvador Nicaragua