Is there an environmental Kuznets curve for deforestation?

Journal of Development Economics Vol. 58 Ž1999. 231–244

Is there an environmental Kuznets curve for deforestation? Gary Koop

a,)

, Lise Tole

b

a

Department of Economics, UniÕersity of Edinburgh, William Robertson Building, 50 George Square, Edinburgh, EH8 9JY, UK b Institute of Ecology and Resource Management, UniÕersity of Edinburgh, Edinburgh, EH8 9JY, UK Received 30 January 1996; accepted 30 January 1998

Abstract This paper examines the relationship between deforestation and gross domestic product ŽGDP. per capita; in particular, whether an inverted-U relationship Žindicative of worsening then improving deforestation. exists between them. We note that previous work has used models with restrictive assumptions, and recommend a more flexible random coefficients specification which allows for a greater degree of cross-country heterogeneity. Empirical results using data for 76 developing countries between 1961–1992 suggest that the inverted-U shaped relationship observed in other studies does not appear to be an empirical regularity with our less restrictive specification. Statistical tests indicate that this specification is supported by the data. We argue that such results are not surprising in view of the wide diversity of physical and social characteristics that exist across countries. q 1999 Elsevier Science B.V. All rights reserved. Keywords: Environmental quality; Economic growth; Tropical deforestation; Random coefficients model

1. Introduction The environmental effects of economic growth have received increasing attention from economists in recent years. Cropper and Griffiths Ž1994., Grossman and Krueger Ž1995., Holtz-Eakin and Selden Ž1995., Selden and Song Ž1994. and )

Corresponding [email protected]

author. Tel.: q44-131-650-8359; fax: q44-131-650-4514; e-mail:

0304-3878r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 3 0 4 - 3 8 7 8 Ž 9 8 . 0 0 1 1 0 - 2

232

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

Shafik Ž1994. are among several recent papers that investigate the relationship between environmental destruction Že.g., deforestation, CO 2 , NO x or SO 2 emissions. and per capita gross domestic product ŽGDP.. One hypothesis of interest is whether such environmental disamenities increase monotonically with GDP or whether there is some level at which they may be said to decline. The latter relationship, embodied by an inverted-U curve, has come to be known as the ‘environmental Kuznets curve’. Empirical results in the literature are mixed depending on which environmental measure and which set of countries are used; however, several studies have found strong evidence for the presence of such a relationship. Most of these studies use panel data, i.e., data on individual countries observed over time. The econometric techniques used are variants of regression methods. The simplest regression formulation assumes constant coefficients across countries, which amounts to saying that every country has the same environmental Kuznets curve. However, with panel data, it is possible to free up this restriction, and most of the above papers allow the regression intercept to vary across countries. Underlying this approach is the assumption that, while the environmentrGDP relationship varies across countries, it varies in a very restricted way only Ži.e., if it exists, every country has an environmental Kuznets curve with the same shape, albeit the level of this curve may vary across countries. To put it another way: every country has the same turning point where environmental degradation starts declining, but the amount of environmental degradation at this point can differ.. Given the variety of socialreconomicrpolitical and biophysical factors that may affect forest cover from country to country, this assumption of a high degree of common structure is probably unwarranted. In contrast to these works, this paper uses a random coefficients model which allows for more cross-country heterogeneity in the shape of the environmentalr growth relationship. We take as our measure of environmental quality, changes in forest cover Ždeforestation.. Cropper and Griffiths Ž1994. and Shafik Ž1994. are two prominent papers that have investigated this relationship from the perspective. Shafik Ž1994. finds that forest cover loss exhibits a weak inverted-U relationship with income, while Cropper and Griffiths Ž1994. find such a pattern for Africa and Latin America but not for Asia. We begin with a simple regression model, then consider fixed and random effects models, and conclude with a discussion of estimates from a random coefficients model. Empirical results indicate that a significant environmental Kuznets curve exists in the simple regression, but it is gradually lost when the specification is freed up. Tests also strongly indicate that the less restrictive specifications are favored by the data. A story consistent with our empirical results is that the deforestationrGDP relationship varies quite considerably across countries, and that the environmental Kuznets curve found in the simple regressions could reflect restrictive assumptions. Overall, we conclude that, at least for forest cover, there is little evidence for the existence of an environmental Kuznets curve.

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

233

2. Methodological issues As noted previously, this paper aims to add to the small but rapidly growing body of empirical literature on growth and the environment by an investigation of the nature of the interaction between changing forest cover and income. To this end, the paper estimates and tests more flexible specifications than are currently used in such studies, and argues for their appropriateness for analyzing relationships of this type. Several previous empirical studies on deforestation Žsee Allen and Barnes, 1985; Kahn and McDonald, 1994; Palo et al., 1994; Rudel, 1994; Tole, 1998. use cross-country data to measure, among other factors, the relationship between income and deforestation. These studies use models of the form: k

yi s a q Ý b j x i j ,

Ž 1.

js1

where yi is deforestation in country i Ž i s 1, . . . , N . and x i j is explanatory variable j in country i. This model implicitly assumes that a common structure exists across all countries; that is, that the effect on deforestation of changes in any given explanatory variable will be the same for every country ŽIn other words, every country will have the same a and b j . More intuitively, an extra 1% increase in variable x in Indonesia will have exactly the same effect on forest cover as an extra 1% increase in variable x in Nigeria... 1 Although cross-sectional models of this form have certainly been useful for establishing simple stylized patterns across countries, they are nevertheless based upon the dubious assumption that the interaction between income and the environment is the same for all countries. As stressed previously, countries are too diverse in the factors that may affect this relationship to make this assumption. Some papers ŽCropper and Griffiths, 1994; Selden and Song, 1994; Grossman and Krueger, 1995; Holtz-Eakin and Selden, 1995. attempt to avoid the weakness of cross-sectional studies by using panel data Žie. data for t s 1, . . . ,T time periods for i s 1, . . . , N countries. instead, and by employing fixed or random effects methods to estimate models of the form: k

yi t s a i q Ý b j x i t j .

Ž 2.

js1

Note that this model has an i subscript on the intercept Žan individual effect. that allows for the intercept of the Kuznets curve to vary in every country. Despite the inclusion of fixed country effects, the b j s, which measure the effect of the 1 If explanatory variables include, say, income and income squared, then this intuition becomes slightly more complicated. In this case, the marginal effect of income would be a linear function of income.

234

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

explanatory variables, are the same for every country for every time period. In other words, this model retains the restrictive assumption of commonality of cross-country studies. The estimated relationship has the same functional form in every country in every time period and Žwhere applicable. the same turning point Žalthough the presence of an individual effect will allow the relationship to be shifted up or down depending on the country in question.. In short, while it incorporates individual differences into the estimation, the model nevertheless imposes the strict assumption that the shape of the measured relationship will be exactly the same for every country. Once again, given the great variation that can exist in individual growthrenvironment interactions, this assumption is something that we may wish to test rather than just impose. This is not to say that there is no common structure to these interactions whatsoever. Indeed, the assumption of a common structure within a country over time is probably more reasonable than that of a common structure across countries. Indonesia in the 1970s undoubtedly has more similarities to Indonesia in 1980 than it does, say, to Costa Rica at any given period in time. The physical Žgeoecological, resource endowment. and social Žpoliticalreconomicrcultural. features are what determine each country’s distinctive growthrenvironmental outcomes, and these features remain more or less constant over time. Assuming a common structure within countries over time we can write: k

yi t s a i q Ý b i j x i t j .

Ž 3.

js1

This model allows for the b i j s to differ across but not within countries over time. That is, it allows for specific features in each individual country to enter into the estimation of the environmentrdevelopment relationship. In view of the likely differences in country structures, this model is a more sensible starting point for analysis than are the highly restrictive models above. However, it too has its weakness. By allowing every coefficient in each country to be different, it precludes any cross-country similarities from entering the analysis. This, too, is unreasonable given that we would expect some similarities in developmentrenvironment outcomes to exist across countries. Thus, while models such as Ž1. and Ž2. may be too restrictive to measure appropriately, the complexity of the relationship between the environment and development, Eq. Ž3. is probably a little too flexible. If nothing else, it would involve estimating N Ž k q 1. coefficients; that is, running a separate regression for each country using time series data for each country. A reasonable compromise model would possess some of the flexibility of Eq. Ž3. yet also impose some of the common cross-country structure in Eq. Ž2.. One that incorporates both features is the random coefficients model given by Eq. Ž3., in which b i s Ž a i , b i1 , . . . , b i k .X is assumed to be drawn from some common distribution:

b i s b q Õi ,

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

235

with EŽ Õi . s 0, EŽ Õi ÕXi . s V and EŽ Õj ÕiX . s 0 for i / j. The shape of the GDPrdeforestation relationship in country i will depend on b i . The parameter vector b can be thought of as defining the average GDPrdeforestation relationship, but individual countries can differ from this average. If V is small, then differences from the average will be small, and thus, it may make sense to use b to investigate the existence of the environmental Kuznets curve on average. However, if V is large, then cross-country heterogeneity is large, and it becomes meaningless to assume that b is an appropriate tool for analyzing this relationship. The econometric techniques necessary to estimate and test these various specifications can be found in many econometrics textbooks Že.g., Greene, 1997.. In Section 4, we begin with a simple pooled regression model wmodel Ž1. where all time series and cross-country observations are assumed to obey the same regression relationshipx, then estimate random and fixed effects versions of model Ž2., before turning to the estimation of the random coefficients model. For each specification, we begin by regressing deforestation on GDP per capita and GDP per capita squared using data for all countries. This enables us to investigate the environmental Kuznets curve in its purest form; we refer to it as our ‘Basic Model’. Next, we consider adding additional explanatory variables. Our ‘Extended Model’ allows us to determine what happens to the environmental Kuznets curve after explanatory variables which might capture cross-country differences are added. Alternatively, an environmental Kuznets curve may exist in some regions but not in others—a possibility we investigate by estimating and testing for all specifications using data for all countries, and then separately for each of Africa, Latin America and Asia, respectively. Details on the countries and data used in the empirical analysis are given in Section 3 and appendices.

3. Data Deforestation is defined in the study as the percentage annual decrease in forest area. Data on forest cover loss for 76 tropical developing countries are derived from the FAO Production Yearbook data for various years between the period, 1961–1992. Although the FAO Production data are not the only existing source of global land forest data, they are nevertheless the most comprehensive, covering more countries and spanning a longer period than any other source ŽAllen and Barnes, 1985.. Its broad definition of forest cover allows for great versatility of measurement with respect to changes in a wide variety of forest vegetative types Že.g., open and closed forests, woodland, plantations, forest fallow and wooded savannah.. All woody vegetations are included under this definition. More specifically, forests and woodland refer to land under natural or planted stands of trees, regardless of productivity, in addition to land that has been cleared of forests but

236

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

will be reforested in the near future. This broad definition largely avoids problems associated with inconsistencies in definition arising from the use of more restrictive forest definitions. 2 Although all types of forest cover are represented by the data, we include in this study only developing countries whose predominant ecosystem is tropical. 3 The GDP per capita is taken from the Penn World Table, which is adjusted according to a common set of international prices. Two demographic measures— population density and change in population—important for their impacts on the consumption and production processes that fuel economic growth and environmental degradation— are also included in the study. Population density is measured as the number of people per hectare of land area; and population growth, as the percentage annual increase in total population. Both indicators are derived from FAO Production data and the Penn World Table. A list of countries, years, and data definitions are given in Appendix A.

4. Empirical results Tables 1–5 report the empirical results of this study. Table 1 presents summary statistics for the data and Tables 2–5 contain estimates and tests for the pooled regression, fixed effects, random effects and random coefficients models, respectively. Most econometric details are relegated to footnotes and the reader who is not interested in such detail can skip these footnotes. 2 A drawback of the data is that they rely heavily on individual country government surveys to supply information. Survey data have a strong subjective bias, and government officials may under-report the extent of forest depletion for political purposes. Where official or semi-official figures are unavailable, estimates are made, compounding problems of accuracy. Moreover, the data also do not distinguish between forest types. Thus, even land which has lost all its primary forest and which has been replanted with monocultural tree plantations is included in the definition of forest cover, as are heavily degraded forests, which for all intent and purposes have ceased to resemble viable ecosystems. Finally, studies measuring the relationship between growth and forest depletion may underestimate the contribution of development to the deforestation process, since the amount of forest cover at any given period of time is always a by-product of previous land-clearance activities, the impact of which on forest cover may or may not be recent enough to be included in the data ŽSee Pearce and Brown, 1994 for a discussion of the methodological issues surrounding the measurement of tropical forests.. 3 In this paper, we adopt the FAO Ž1993. definition of tropical. Tropical forests encompass a wide variety of ecosystems Že.g., evergreen, semi-evergreen, semi-deciduous, deciduous, premontane, woodlands, alpine, savannah and shrub. in countries receiving between 200 to 2000 mm of rainfall annually. Tropical forest ecosystems dominate the developing world, and it is their progressive destruction, more than that of any other ecosystem, which is causing the greatest international alarm. This is in response to the rapid rate at which they are being depleted, their unique biodiversity, and the important ecological functions and material contributions they make to human welfare ŽMyers, 1993; Pearce and Brown, 1994..

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

237

Table 1 Summary statistics for data

Percentage of decrease in forest cover GDP per capita Ž$000. Percentage of increase in GDP People per hectare of land Percentage of increase in population

Mean

Standard deviation

0.007 1.726 0.013 0.637 0.025

0.019 1.701 0.072 1.024 0.016

An examination of Table 2 indicates strong support for the environmental Kuznets curve when both the Basic and Extended models are used. The row labelled ‘Kuznets curve’ indicates whether the signs on the estimated coefficients imply an inverted-U shape. Except for Latin America, the GDPrdeforestation relationship exhibits this shape. The row in the table labelled ‘F-statistics for Kuznets’ represents the F-statistics for testing whether the coefficients on GDP and GDP 2 are jointly equal to zero. With the exception of Asia, these Kuznets coefficients are statistically significant. However, in Table 3, we include a row labelled ‘F-statistics for Pooled Model’ which uses the fixed effects model to test whether the assumptions underlying the Table 2 Estimates for pooled regression models Žstandard errors in parentheses.

Basic model GDP GDP 2 Turning point Kuznets curve F-statistics for Kuznets Extended model GDP GDP 2 Change in GDP Population density Change in population Turning point Kuznets curve F-statistics for Kuznets

All countries

Africa

Latin America

Asia

0.0014 Ž0.0006. y0.0002 Ž0.0001. 3.6408 Ž0.6226. Yes 4.1017)

0.0020 Ž0.0009. y0.0007 Ž0.0002. 1.5319 Ž0.4064. Yes 10.7685))

y0.0037 Ž0.0008. 0.0002 Ž0.0001. 8.8918 Ž1.4723. No 25.2559))

0.0112 Ž0.0079. y0.0016 Ž0.0016. 3.5978 Ž1.5331. Yes 1.6999

0.0016 Ž0.0006. y0.0002 Ž0.0001. y0.0118 Ž0.0057. y0.0007 Ž0.0004. 0.0286 Ž0.0254.

0.0002 Ž0.0009. y0.0007 Ž0.0002. y0.0022 Ž0.0029. 0.0009 Ž0.0005. 0.0202 Ž0.0113.

y0.0034 Ž0.0022. 0.0002 Ž0.0001. y0.0246 Ž0.0083. y0.0003 Ž0.0006. 0.2169 Ž0.0522.

0.0112 Ž0.0079. y0.0015 Ž0.0016. y0.0331 Ž0.0387. 0.0021 Ž0.0013. y0.2793 Ž0.4988.

3.9232 Ž0.6026. Yes 3.990)

1.6364 Ž0.2763. Yes 10.6441))

8.4952 Ž1.3818. No 18.8380))

3.7106 Ž1.7218. Yes 1.7261

Ži. )) and ) indicate statistical significance at the 1% and 5% levels, respectively. Žii. ‘Kuznets curve’ indicates whether coefficients on GDP and GDP 2 imply an inverted-U shape. Žiii. ‘F-statistics for Kuznets’ tests whether coefficients on GDP and GDP 2 are jointly zero.

238

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

Table 3 Estimates for fixed effects models Žstandard errors in parentheses.

Basic model GDP GDP 2 Turning point Kuznets curve F-statistics for Kuznets F-statistics for pooled model Extended model GDP GDP 2 Change in GDP Population density Change in population Turning point Kuznets curve F-statistics for Kuznets F-statistics for pooled model

All countries

Africa

Latin America

Asia

0.0010 Ž0.0015. y2.3=10y5 Ž0.0001. 20.9780 Ž97.8656. Yes 0.5160

0.0014 Ž0.0009. y0.0002 Ž0.0001. 4.0129 Ž1.5997. Yes 1.2824

0.0051 Ž0.0013. y0.0003 Ž0.0001. 9.5732 Ž1.9312. Yes 8.6596))

y0.0040 Ž0.0124. 0.0002 Ž0.0021. 13.273 Ž149.518. No 0.3042

8.9971))

72.6567))

19.1600))

4.1632))

0.0002 Ž0.0015. 5.60=10y6 Ž0.0001. y0.0097 Ž0.0052. 0.0048 Ž0.0016.

0.0008 Ž0.0009. y0.0001 Ž0.0001. y0.0022 Ž0.0016. 0.0050 Ž0.0010.

0.0035 Ž0.0014. y0.0002 Ž0.0001. y0.0139 Ž0.0068. 0.0121 Ž0.0029.

y0.0067 Ž0.0149. 0.0006 Ž0.0024. y0.0366 Ž0.0376. 0.0049 Ž0.0049.

y0.0110 Ž0.0248.

y0.0039 Ž0.0065. y0.0287 Ž0.0546. 0.1655 Ž0.8188.

y19.0797 Ž592.63. No 0.0574

4.3731 Ž3.2107. Yes 0.4904

7.5307 Ž1.6103. Yes 3.1570)

6.0405 Ž13.8711. No 0.2753

9.0384))

74.5398))

18.8561))

4.2205))

Note: ‘F-statistics for pooled model’ tests fixed effects vs. pooled model as described in Greene Ž1997., pp. 617–618. Ži. )) and ) indicate statistical significance at the 1% and 5% levels, respectively. Žii. ‘Kuznets curve’ indicates whether coefficients on GDP and GDP 2 imply an inverted-U shape. Žiii. ‘F-statistics for Kuznets’ tests whether coefficients on GDP and GDP 2 are jointly zero.

pooled model are appropriate. 4 This test strongly rejects in all cases, indicating that the pooled model is rejected in favor of the fixed effects model. The row labelled ‘LM statistics for pooled model’ in Table 4 presents results for tests of the pooled model against the random effects model. 5 Once again, the pooled model is 4 Note that the fixed effects model can be written as a simple regression model with no intercept and dummy variables for all countries. The ‘F-statistics for pooled model’ row is the standard F-statistics for testing whether the coefficients on these dummy variables are all equal to one another. 5 The random effects model assumes that the intercepts are drawn from some distribution. If the variance of this distribution goes to zero, then the random effects are all constant, i.e., the model collapses down to the pooled model. The ‘LM statistics for pooled model’ is the standard LM statistic for testing whether this variance equals zero. Greene Ž1997., pp. 628–629, provides the exact form of this test statistic which has a Chi-squared distribution with one degree of freedom. Note that this test has only an asymptotic justification.

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

239

Table 4 Estimates for random effects models Žstandard errors in parentheses.

Basic model GDP GDP 2 Turning point Kuznets curve F-statistics for Kuznets LM statistics for pooled model Hausman statistics Extended model GDP GDP 2 Change in GDP Population density Change in population Turning point Kuznets curve F-statistics for Kuznets LM statistics for pooled model Hausman statistics

All countries

Africa

Latin America

Asia

0.0007 Ž0.0011. y0.0001 Ž0.0001. 4.7960 Ž3.5711. Yes 0.1930

0.0012 Ž0.0009. y0.0002 Ž0.0002. 3.6712 Ž1.4948. Yes 1.0004

0.0024 Ž0.0033. y0.0002 Ž0.0001. 7.3180 Ž1.8311. Yes 1.8300

0.0040 Ž0.0104. y0.0008 Ž0.0019. 2.4693 Ž2.1737. Yes 0.0899

1587.69))

8043.71))

1223.60))

40.6151))

1.9819

1.2380

25.6981))

1.9323

0.0007 Ž0.0011. y0.0001 Ž0.0001. y0.0105 Ž0.0053. 0.0009 Ž0.0010. y0.0052 Ž0.0249.

0.0009 Ž0.0009. y0.0001 Ž0.0002. y0.0023 Ž0.0016. 0.0046 Ž0.0009. y0.0035

0.0019 Ž0.0013. y0.0001 Ž0.0001. y0.0195 Ž0.0070. 0.0022 Ž0.0016. y0.0247 Ž0.0552.

0.0038 Ž0.0115. y0.0008 Ž0.0020. y0.0368 Ž0.0380. 0.0009 Ž0.0027. 0.1869 Ž0.6968.

4.6729 Ž3.4492. Yes 0.2062

3.9584 Ž2.4484. Yes 0.5229

6.6203 Ž1.8475. Yes 1.1107

2.5097 Ž2.6619. Yes 0.0710

1529.44))

7986.32))

883.777))

38.2773))

5.6037)

4.5937)

37.1899))

3.2989

Ži. ‘LM statistics for pooled model’ tests random effects vs. pooled model as described in Greene Ž1997., pp. 628–629. Žii. ‘Hausman statistics’ tests random vs. fixed effects model as described in Greene Ž1997., pp. 632–633. Žiii. )) and ) indicate statistical significance at the 1% and 5% level, respectively. Živ. ‘Kuznets curve’ indicates whether coefficients on GDP and GDP 2 imply an inverted-U shape. Žv. ‘F-statistics for Kuznets’ tests whether coefficients on GDP and GDP 2 are jointly zero.

strongly rejected, this time in favor of the random effects model. Hence, the results in Table 1 are based on restrictive assumptions that are rejected by the data and, thus, should not be used for making inferences about the existence of an environmental Kuznets curve. Formally, these tests indicate that the pooled model will yield biased and inconsistent estimates of the regression coefficients. The results for the fixed effects model are weakly supportive of the existence of an environmental Kuznets curve. However, with the exception of Latin America, the Kuznets coefficients are not statistically significant. In addition, for Asia, the inverted-U relationship does not appear to hold. The random effects model ŽTable 4. yields similar results. An inverted-U relationship is estimated for most country groups, but the relationship is not statistically significant.

240

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

The random coefficients model implicitly assumes that the individual effects are uncorrelated with the regressors. The Hausman statistics can be used to test this assumption. 6 A rejection indicates that coefficient estimates from the random effects model are biased and inconsistent and, hence, that the fixed effects model is preferable. Table 4 presents results from this test. It is evident that in some cases, the fixed effects model is preferable and that in others, the more parsimonious random effects model is to be preferred. For future reference, note that the Hausman statistics rejects the random effects model in favor of the fixed effects model for Latin America. However, in most cases, the models discussed above are all rejected in favor of the random coefficients model. Table 5 contains rows labelled ‘Test statistics for constant coefficient’ and ‘Test statistics for constant slopes’. 7 As expected, the constant coefficients implicit in the pooled model are rejected massively in all cases. Furthermore, the constant slope assumption implicit in the fixed and random effects models is also rejected, the only exceptions being Africa and Latin America for the Basic Model. Subject to the exceptions, then, all results from Tables 2–4 are based on assumptions that are rejected by the data. Turning to the random coefficients model, we stress that it specifies that each country has its own environmental Kuznets curve. The estimates in Table 5 are averages across all countries Ži.e., what we have called b in Section 2.. Given the strong rejections of models which assume constant structure across countries, country-specific coefficients are likely very different from cross-country averages and it is questionable to use the latter to form an estimated environmental Kuznets curve. 8 Ignoring this problem, we still find that the Kuznets coefficients are neither significant nor do

6 The Hausman test is based on the observation that, if the individual effects are uncorrelated with the regressors, then both fixed and random effects estimators are consistent and the random effects estimator will be more efficient Žsince the fixed effects estimator has so many more parameters to estimate.. However, if random effects are correlated with regressors, then the random effects estimator is inconsistent. The Hausman test statistics uses a specific metric to measure the difference between the fixed and random effects estimates. Intuitively, if fixed and random effects estimates are very different, this suggests that random effects estimator is inconsistent. Formally, the Hausman test rejects the random effects model if the test statistics is greater than a critical value taken from the Chi-squared distribution with k degrees of freedom. The exact form of the Hausman test is given in Greene Ž1997., pp. 632–633. This test only has an asymptotic justification. 7 These test statistics are described in Greene Ž1997., pp. 672–674. They are based on the differences between OLS estimates one country at a time and a weighted average of the OLS estimates across all countries. Intuitively, if parameters are constant across countries, then OLS estimates in particular countries should not differ much from the overall average OLS estimate. The test statistics has an asymptotic distribution which is Chi-squared with Ž k q1.=Ž N y1. and k =Ž N y1. degrees of freedom for the constant coefficients and only constant slopes hypotheses. Note that this is formally a test only of parameter constancy. A rejection indicates that parameters vary across countries. The random coefficients model is one model which exhibits such variation, but others are possible. 8 That is, if b i is very different from b , it is questionable to think that the latter has much to say about former.

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

241

Table 5 Estimates for random coefficients models Žstandard errors in parentheses.

Basic model GDP GDP 2 Turning point Kuznets Curve F-statistics for Kuznets Test statistics for constant coefficient Test statistics for constant slopes Extended model GDP GDP 2 Change in GDP Population density Change in population Turning point Kuznets curve F-statistics for Kuznets Test statistics for constant coefficient Test statistics for constant slopes

All countries

Africa

Latin America

Asia

y0.0018 Ž0.0196. 0.0016 Ž0.0097. 0.5758 Ž3.2740. No 0.0228

y0.0025 Ž0.0201. 0.0043 Ž0.0136. 0.2913 Ž1.5452. No 0.1286

y0.0036 Ž0.0106. 0.0016 Ž0.0041. 1.1505 Ž0.0041. No 0.0742

0.0025 Ž0.0355. y0.0007 Ž0.0151. 1.8535 Ž16.8020. Yes 0.0081

58,758.10))

48,118.53))

4,295.368))

1,669.543))

354.281))

20.4714

28.3021

301.2366))

0.0002 Ž0.0130. y0.0003 Ž0.0134. y0.0016 Ž0.0140. 0.0152 Ž0.0420. 0.0171 Ž0.2965.

0.0012 Ž0.0179. y0.0004 Ž0.0144. y0.0001 Ž0.0013. 0.0238 Ž0.0235. y0.0063 Ž0.0545.

y0.0020 Ž0.0195. 0.0007 Ž0.0097. y0.0012 Ž0.0100. 0.0033 Ž0.0680. 0.0155 Ž0.1235.

0.0008 Ž0.0389. y0.0005 Ž0.0235. y0.0001 Ž0.0311. y0.0009 Ž0.0229. 0.0076 Ž0.7073.

0.5013 Ž27.068. Yes 0.0002

1.5098 Ž32.9523. Yes 0.0093

1.5369 Ž1.2504. No 0.0128

0.7969 Ž14.6113. Yes 0.0002

3,225,394))

2,578,136))

587,334.82))

2,261.758))

5,340.017))

2,028.408))

2,297.839))

301.2366))

Note: ‘Test statistics for constant coefficient’ and ‘Test statistics for constant slopes’ tests for parameter constancy as described in Greene Ž1997., p. 673. Ži. )) and ) indicate statistical significance at the 1% and 5% levels, respectively. Žii. ‘Kuznets curve’ indicates whether coefficients on GDP and GDP 2 imply an inverted-U shape. Žiii. ‘F-statistics for Kuznets’ tests whether coefficients on GDP and GDP 2 are jointly zero.

they yield an inverted-U relationship. Estimates of turning points have enormous standard errors, and there is no evidence of a statistically significant environmental Kuznets curve. These findings are due to the large amount of cross-country heterogeneity in the data, which is present even after the extra explanatory variables in the extended model are added. Econometrically speaking, the distribution of b i has enormous variance Ži.e., V is large. and this feeds through to the standard errors on all coefficients and the estimate of the turning point. It is worthwhile briefly discussing the two exceptions, Africa and Asia, in the case of the Basic Model. First, it should be noted that the extra regressors included in the Extended Model are Žat least in Tables 3 and 4. statistically significant, thus implying that the Basic Model is irrelevant. Nevertheless, on examination of the

242

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

Hausman tests for the Basic Model, it would appear that the fixed effects specification is favored for Latin America, and the random effects specification, for Africa. Examining the relevant tables, we see that a statistically significant Kuznets curve does indeed appear for Latin America but not for Africa. We also note that the turning point for Latin America occurs at $8660—a value of GDP per capita much higher than current values for any Latin American country in our sample. Cropper and Griffiths Ž1994., using a slightly different set of regressors and a fixed effects specification, find that an inverted-U shaped relationship holds for both Latin America and Africa. It should be noted that the turning points in the inverted-U relationship that Cropper and Griffiths Ž1994. find are quite high Ž$4760 for Africa and $5420 for Latin America.. The authors stress quite rightly that major damage to forests may occur well before these turning points are reached. It is worthwhile to briefly mention findings for the extra explanatory variables included in our extended regression. We hesitate to make strong statements since nothing is significant for the random coefficients model and this is the model preferred by the data. However, it does seem that GDP growth is negatively associated with deforestation in most cases Ži.e., faster growth is associated with less deforestation.. Population density does tend to be positively associated with deforestation, while results for population growth are mixed. Before completing our discussion of empirical results, we offer a brief econometric digression. In the previous discussion, we have used statistical tests to select a model that is ‘best’ in some sense. However, just because one model is ‘best’ does not necessarily mean the others are completely wrong. In particular, the fixed effects model provides consistent estimates of the cross-country average coefficients Ži.e., b . even if the slope coefficients vary across countries. 9 We have placed relatively little weight on our fixed effects results since: Ži. results from the random coefficients model indicate that the cross-country average Kuznets curve which the fixed effects model yields is of little interest; Žii. the statistical tests indicate that fixed effects estimation will be inefficient; and Žiii. in practice, the fixed effects model often is over-parameterized Žanother source of inefficiency.. However, others may wish to take the fixed effects results more seriously since they are consistent. We stress that, even if one looks only at the fixed effects results, there is little statistically significant evidence of an environmental Kuznets curve. That is, the basic message of this paper is not undermined by considering only fixed effects results.

9

Random coefficients can be thought of as a problem of heteroskedasticity. Hence, using the fixed effects model is essentially the same as ignoring the latter problem. It is well-known that ignoring heteroskedasticity will result in consistent but inefficient estimators for the regression coefficients. Estimators for standard errors will be inconsistent.

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

243

5. Concluding remarks This paper examines the relationship between deforestation and GDP per capita and finds no statistically significant empirical regularity between them. Only if extreme assumptions are made about the commonality of structure across countries does any empirical regularity appear—the inverted U-shaped relationship, found in other studies—indicative of worsening then improving environmental quality with growth. However, we argue that such assumptions are unreasonable in view of the statistical evidence and the great diversity in environmental and social characteristics which exists across countries. In contrast to previous studies, we adopt a random coefficients approach which allows for both country differences and similarities to enter into the analysis. Rather than uniformly imposing structure on the data, the method adopted in this paper gives a more realistic assessment of the developmentrdeforestation nexus than currently exists in the literature.

Acknowledgements Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged.

Appendix A. Data The deforestation data are obtained from the FAO’s Agrostat-PC data land use and input diskette and use the study’s forest and woodland variable. This source is also used to obtain total land area for calculating population density. All other variables were obtained from the Penn World Table Žversions 5–6., commonly used data source which correct GDP figures for cross-country price differences to ensure that numbers are comparable across countries. List of countries with years of data availability Angola 61–89 Guatemala 61–92 Bahamas 77–87 Guinea 61–92 Bangladesh 61–92 Guinea-Bissau 61–92 Belize 80–92 Guyana 61–90 Benin 61–91 Haiti 61–89 Bolivia 61–92 Honduras 61–92 Botswana 61–89 India 61–92 Brazil 61–92 Indonesia 61–92 Burkina Faso 61–92 Jamaica 61–91 Burundi 61–92 Kenya 61–92 Cameroon 61–92 Laos 84–91

Papua-New Guinea Paraguay Peru Philippines Puerto Rico Rwanda St. Kitts-Nevis Senegal Sierra Leone Somalia Sri Lanka

61–92 61–92 61–92 61–92 61–89 61–92 83–92 61–91 61–92 61–89 61–92

244

G. Koop, L. Tole r Journal of DeÕelopment Economics 58 (1999) 231–244

Cape Verde C. African Republic Chad Colombia Congo Costa Rica Cote d’Ivoire Djibouti Dominican Republic Ecuador El Salvador Ethiopia Gabon Gambia Ghana Grenada

61–92 61–92 61–92 61–92 61–92 61–92 61–92 70–87 61–92 61–92 61–92 61–86 61–92 61–90 61–92 84–90

Liberia Madagascar Malawi Malaysia Mali Mauritania Mexico Mozambique Myanmar Namibia Nepal Nicaragua Niger Nigeria Pakistan Panama

61–86 61–92 61–92 61–92 61–92 61–92 61–92 61–92 61–89 61–92 61–86 61–90 61–89 61–92 61–92 61–92

Sudan Surinam Tanzania Thailand Togo Trinidad and Tobago Uganda Venezuela Zaire Zambia Zimbabwe

70–91 61–89 61–88 61–92 61–92 61–91 61–92 61–92 61–89 61–91 61–92

References Allen, J.C., Barnes, D.F., 1985. The causes of deforestation in developing countries. Annals of the Association of American Geographers 75, 163–184. Cropper, M., Griffiths, C., 1994. The interaction of population growth and environmental quality. American Economic Review 84, 250–254. FAO, 1993. Forest Resources Assessment 1990: Tropical Countries. FAO, Rome. Greene, W., 1997. Econometric Methods, 3rd Int. edn. Prentice-Hall, Englewood Cliffs, NJ. Grossman, G., Krueger, A., 1995. Economic growth and the environment. Quarterly Journal of Economics 60, 353–377. Holtz-Eakin, D., Selden, T., 1995. Stoking the fires? CO 2 emissions and economic growth. Journal of Public Economics 57, 85–101. Kahn, J., McDonald, J., 1994. International debt and deforestation. In: Brown, K., Pearce, D.W. ŽEds.., The Causes of Tropical Deforestation. UCL Press, pp. 57–67. Myers, N., 1993. Population, environment and development. Environmental Conservation 20, 205–216. Palo, M., 1994. Population and deforestation. In: Pearce, D., Brown, K. ŽEds.., The Causes of Tropical Deforestation. UCL Press, pp. 42–56. Pearce, D., Brown, K., 1994. Saving the world’s tropical forests. In: Pearce, D., Brown, K. ŽEds.., The Causes of Tropical Deforestation. UCL Press, pp. 2–26. Rudel, T., 1994. Population, development and tropical deforestation: a cross-national study. In: Pearce, D., Brown, K. ŽEds.., The Causes of Tropical Deforestation. UCL Press, pp. 96–105. Selden, T., Song, D., 1994. Environmental quality and development: Is there a Kuznets curve for air pollution emissions?. Journal of Environmental Economics and Management 27, 147–162. Shafik, N., 1994. Economic development and environmental quality: an econometric analysis. Oxford Economic Papers 46, 757–773. Tole, L., 1998. Sources of deforestation in tropical developing countries. Environmental Management 22, 19–33.