social determinants of deforestation in developing countries: a cross-national study

Social Determinants of Deforestation in Developing Countries: A Cross-National StudyAuthor(s): Karen Ehrhardt-MartinezSource: Social Forces, Vol. 77, No. 2 (Dec., 1998), pp. 567-586Published by: Oxford University PressStable URL: http://www.jstor.org/stable/3005539 .

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Social Determinants of Deforestation in Developing Countries: A Cross-National Study*

KAREN EHRHARDT-MARTINEZ, The Ohio State University

Abstract

This study examines the social forces that drive deforestation. Neo-Malthusian, modernization, and dependency theories are applied in a cross-national comparison of 51 developing countries. Multiple regression techniques are applied to estimate the rate of deforestation using the level of urbanization, economic growth rate, population growth rate, level of sectoral inequality, rate of change in primary product exports, and rate of change in tertiary education. Results support modernization theory, indicating that the level of urbanization has a curvilinear effect on the rate of deforestation, that economic growth contributes to deforestation, and that sectoral inequality reduces the rate of deforestation. In support of neo-Malthusian theory, population growth results in higher rates of deforestation. Tertiary education has a mild negative effect on the rate of deforestation, whereas the effect of trade dependency is insignificant.

Although deforestation is a phenomenon as old as agriculture, current concern over forest loss is justified by the sheer scale of modem-day destruction. According to estimates by the Food and Agricultural Organization (FAO) (1995), between 100,000 and 200,000 square kilometers of forest area are lost each year. A 1992 National Research Council (NRC) report estimates that deforestation in tropical moist forests alone is responsible for the extinction of 17,500 species per year, resulting in habitat destruction, ecosystem simplification, and climate change. Tropical deforestation also accounts for an estimated 22.9% of global carbon

* I would like to thank Edward Crenshaw, Craig Jenkins, and Robert Kaufman for suggestions and comments. Address all comments to the author, Department of Sociology, The Ohio State University, 300 Bricker Hall, 190 North Oval Mall, Columbus, Ohio 43210-1353. E-mail: ehrhardt-martine.1 @osu. edu.

(?3 The University of North Carolina Press Social Forces, December 1998, 77(2):567-586



568 / Social Forces 77:2, December 1998

dioxide emissions (NRC 1992), the primary contributor to global warming, and is the source of a variety of local ecological and economic problems such as soil erosion, desertification, and flooding.

As a measure of human impact on the natural environment, deforestation is a question of both sociological and theoretical import. While human ecologists have often pondered the effects of the environment on social organization, only recently have sociologists considered the impact of societies on the natural environment. Deforestation, as a specific subject of study, is particularly salient to sociological research, given that the felling of trees largely results from human activities. Despite its importance, most empirical studies to date (principally by geographers, demographers, and economists) have been essentially atheoretical. The lack of theoretical grounding retards the accumulation of knowledge by reducing the generalizability and explanatory power of research findings. Nevertheless, selected theories of social change have been suggested. Environmental degradation and deforestation in particular have been hypothesized to result primarily from three sources of change: population growth, modernization, and dependent development. Although all three have been hypothesized to increase deforestation, this article uncovers hidden complexities in their relationships that yield unanticipated outcomes. As a measure of modernization, for example, urbanization is shown to have a curvilinear effect on the rate of deforestation, resulting in lower rates of deforestation at the highest levels of urbanization. Two previously unexplored measures, sectoral inequality and change in tertiary education, are also shown to reduce the rate of deforestation.

A Snapshot of Forests and Forest Loss

United Nations data on 179 countries worldwide (FAO 1995) indicate a cumulative land area of approximately 12.9 billion hectares, of which approximately 3.4 billion hectares (26.6%) were considered to be forested in 1990. The 36 developed countries held 5.3 billion hectares of the total land area, and developing countries held nearly 7.6 billion hectares - 41% and 59% of the total land area, respectively. It is interesting to note that forests cover approximately the same percentage of land area in both developed (26.8%) and developing (26.5%) countries. Nevertheless, a more divergent pattern of forest cover is evident when considering the differences between tropical and nontropical countries. Forest cover is much greater in tropical countries (37.4%) than in nontropical countries (7.7%). This is noteworthy since approximately 80% of all developing countries are tropical. Table 1 shows the distribution of forest cover for tropical and nontropical areas of Africa, Asia, and Latin America.

Although these statistics paint an interesting picture of the current state of the world's forest cover, the focus of concern is forest change and, more specifically,



Deforestation in Developing Countries / 569 TABLE 1: Forests and Forest Loss by Level of Development and Region

Annual Land Natural Percent Deforestation

N Areaa Foresta Forested Rate (%) (1990) (1990) (1980-90)

Al countries 179 12,935 3,442 26.6 Developed countries 36 5,342 1,432 26.8 Less developed countries 143 7,593 2,010 26.5 .8

Tropical 115 4,788 1,792 37.4 .8 Nontropical 28 2,805 217 7.7 .5

Africa 53 2,964 545 18.4 Tropical 45 2,237 530 23.7 .7 Nontropical 8 727 15 2.1 .9

Asia 46 2,613 497 19.0 Tropical 29 901 338 37.5 1.2 Nontropical 17 1,712 159 9.3 .4

LatinAmerica/Caribbean 44 2,016 967 48.0 Tropical 41 1,650 924 56.0 .8 Nontropical 3 366 43 11.7 .6

Source: FAO 1995 a Millions of hectares b Maynot add due to rounding

forest loss. Although it is clear that a full account of forest change originates in the distant past and should be considered in terms of its long history, both data and space are limited, and therefore the following description is but a snapshot of the deforestation that occurred from 1980 to 1990. Statistics developed by the FAO (1995) indicate that forests and wooded lands continue to be cleared in both developed and developing countries at an average annual rate of .2%. Whereas deforestation in developed countries has reached very low levels (including net reforestation in some European countries), it accounts for the loss of 16.3 million hectares of forest per year in developing countries - an area a proximately twice the size of Portugal. An estimated 15.4 million hectares of forest loss occurs each year in tropical regions, with the worst losses occurring in Latin America and the Caribbean (7.4 million ha), followed by Africa (4.1 million ha) and Asia (3.9 million ha). Subregionally, the highest rates of deforestation are occurring in the East Sahelian and West Africa, tropical South Africa, continental and insular Asia, Central America, and Mexico.

The causes of deforestation are complex, varying by both country and region. The proximate causes of the vast majority of forest loss can be attributed to the actions of people involved in clearing land for agriculture or cattle, harvesting timber or mineral resources, and building roads or expanding cities. Nevertheless,




these proximate causes are merely symptoms of more complex social processes. For example, a variety of factors could spur the dearing of additional tracts of agricultural land, induding an expanding population (more mouths to feed) and a government's efforts to reduce the national trade imbalances through the expansion of agricultural exports. Similarly, logging and mining reflect national and international economic concerns as well as a country's level of technological advancement. This artide attempts to understand deforestation by uncovering the underlying aggregate-level social processes. The following section highlights the pertinent theory and literature as a framework for understanding.

Theoretical Frameworks and Literature Review

The longest-standing arguments regarding the negative effects of society on the environment focus on population-based change. These arguments are rooted in the Malthusian perspective that expanding populations eliminate gains in agricultural productivity (or efficiency) and, as a result, require the dearing of vast additional tracts of land. More current arguments (Commoner 1994; Ehrlich & Ehrlich 1991), however, expand the population equation to address the effects of technological changes, such as developments in agriculture and transportation technology that increase yields and decrease losses of perishable foods. Nevertheless, many neo-Malthusians still point to population growth as the primary cause of environmental degradation, arguing that population growth will outstrip any technological gains that might preserve forests. Although most studies have found population growth to be an important factor contributing to deforestation (Inman 1992; Rudel 1989; Southgate 1994), others have failed to even incorporate it into their studies (Shafik 1994). As suggested by neo-Malthusian theory and past research, this artide posits that higher rates of population growth in developing countries result in higher rates of deforestation, all else remaining constant.

As opposed to population-based arguments, the modernization perspective argues that environmental degradation is a function of the level and rate of development within a given country. Based on a continuum of development from traditional to modern (or, as some argue, postmodern), this perspective considers various socioeconomic characteristics such as economic growth, technology, urbanization, transportation, education, and industrialization as indicators of development. In this vein, prior socioenvironmental research has focused on the relationship between economic growth and deforestation. Inman (1992) and Rudel (1989), for example, argue that economic development will increase deforestation by expanding the availability of capital for productive ventures such as mining, logging, and plantation agriculture. These researchers posit that countries with greater economic assets will experience growing capital investments resulting in the expanded exploitation of the natural resource base. Other researchers (Allan &



Deforestation in Developing Countries / 571

Barnes 1985), however, have found no significant relationship between economic growth and deforestation. As suggested by modernization theory and more recent research, this article posits that (holding all else constant) the greater the rate of economic growth in developing countries, the greater the rate of deforestation.

Although less prominent in the literature, the degree to which a country's population is concentrated in urban centers is also a measure of modernization, with clear implications with regard to population pressure on forest resources. According to Kasarda and Crenshaw (1991), the rapid declines in mortality rates that led to rapid population growth in many developing nations promoted rural- to-urban migration as a result of rural-push and urban-pull factors. The positive association between urbanization, agglomeration economies, and technological innovation is linked to increased efficiencies and the development of alternative resources, thereby reducing the demand on forest resources. Therefore, urban concentration may be a better indicator of modernization than the purely economic measure of gross domestic production.

Compounding these discussions are socioenvironmental studies that suggest that the relationship between modernization and environmental change may not be linear. In a theoretical discussion on the determinants of carbon dioxide emissions, for example, Crenshaw and Jenkins (1996) hypothesize that per capita emissions increase from low to intermediate levels of development but decline at relatively high levels of development. This curvilinear relationship is the result of the increased energy efficiency related to economic complexity and competition. The relationship between development and deforestation may be equally complex, because low to intermediate levels of development seem to be characterized by high rates of deforestation as countries rely on forests for a wider variety of products (including charcoal), while countries at higher levels of development use wood alternatives, more efficient production technologies, and stricter forest management practices resulting in lower rates of deforestation. Moreover, the shift from a heavily industrial economy to one with a greater reliance on services, a progression characteristic of more "modern" nations, also relieves some of the demand for forest resources. Sussman, Green, and Sussman (1994) document the early stages of this social phenomenon in their case study of deforestation in Madagascar. Given these findings, this article posits that the rate of deforestation will increase from low to intermediate levels of urbanization but decline at relatively high levels of urbanization (all else remaining constant).

The high levels of sectoral inequality are associated with the modernization process and are hypothesized to result from a mismatch between demographic and economic growth (Kasarda & Crenshaw 1991). These suggest a negative impact on deforestation. More specifically, higher wages associated with higher levels of productivity per worker in urban areas create sectoral imbalances, often resulting in uncontrolled rural-urban migration as higher incomes and other opportunities attract rural residents. This migration pattern reduces the pressure on forest



572 I Social Forces 77:2, December 1998

resources as people move away from land-based systems of production. Moreover, because sectoral inequality can be translated as the large gap created by high levels of urban productivity and low levels of rural productivity, high scores also imply a system of labor-intensive and possibly extensive agriculture associated with minimal capital investments and low levels of mechanization. Since the expansion of agricultural production is highly dependent on labor inputs at this level of development, countries with the greatest sectoral inequality and therefore the least growth in rural populations (which is negative in many countries) are less likely to experience deforestation resulting from agricultural expansion. Low sectoral inequality, on the other hand, implies a more highly productive agricultural sector (relative to industry and services), which frequently results from agricultural mechanization and the harvesting of forest resources. Therefore, this article posits that, all else being constant, the greater the level of sectoral inequality in developing countries, the lower the rate of deforestation.

In contrast to modernization theory, dependency theory focuses on the relationships between countries and their position and role in the global economy. Countries that modernization theorists label as "developed" are placed at the core of a world system in which they dominate cutting-edge technologies, international capital, and noncore countries. Less developed countries, characterized by their reliance on labor-intensive systems of production and dependence on foreign sources of capital and technology, are placed in the periphery and semiperiphery. Within this system, deforestation may result from three types of dependency. export! trade dependency, debt dependency, and foreign capital penetration. Export dependency consists of trade relationships between core and periphery countries in which dependent countries must import high-priced, technologically advanced manufactured goods (needed for the expansion of local production capacity) from the core countries and pay for these imports through the exploitation of their own natural resources. Exports from dependent countries are primarily concentrated in mining, logging, and the expansion of agricultural production; this reliance on primary products commonly results-in deforestation.

Similarly, debt dependency has often resulted from international loans in which peripheral countries borrow capital from core lenders. Again, exports assume a key role as a generator of the foreign currency required to repay international lenders and are expanded at the cost of the environment. Finally, foreign investment (particularly in the extractives sector) has also been connected to environmental degradation and deforestation. Peripheral countries, often eager to attract foreign investment in efforts to expand local production, employment, and technology, may compete with one another by providing a variety of economic incentives, induding wage and tax reductions and/or regulatory concessions that often include exemptions to environmental laws (Leonard 1988). Nevertheless, Rudel's (1989) study did not find support for the suggested positive relationship between dependency and deforestation. Despite these findings, this article posits that the




greater the rate of dependency on core countries, the greater the rate of deforestation in developing countries (holding all else constant).

Like modernization theory, dependency theory addresses the consequences of inequality in social and economic systems that are believed to be rooted in the disarticulation caused by the historically determined stratification of nations. Colonialism, capitalist imperialism, and the precedence of trade relationships established by early industrializers are blamed for the establishment of land inequality and a static system of international domination. The linkage between rural poverty and deforestation has also been well established (Harrison 1992) such that poor rural households are more likely to use wood as a source of fuel and participate in colonization/resettlement schemes. As opposed to modernization theory, therefore, dependency theory indicates that high levels of sectoral inequality will result in increased rates of deforestation as the rural poor become increasingly impoverished, rely more heavily on forest resources, and search for nonagricultural activities to supplement or replace their agricultural income.

Finally, the effect of these structural features of societies may be offset or augmented by increased environmental awareness and mobilization. Of particular note is the link between environmentalism and higher levels of education in a variety of Western countries (Garner 1996). Expansion of higher education may increase the diffusion of Western environmentalism and support for global efforts to reduce deforestation. Universities worldwide also often serve as catalysts of social movement providing a means to foment environmental activism. Finally, the growth in the number of university-educated citizens increases local capacity to adapt technologies that have been diffused from technologically advanced countries as well as to develop new technologies that are appropriate to local environmental conditions. As a result, this article posits that the greater the rate of growth in tertiary education in developing countries, the lower their rate of deforestation (all else remaining constant).

Critique

Existing empirical research on the causes of deforestation can be characterized as largely atheoretical and methodologically lax. First, as mentioned earlier, previous research on the social causes of deforestation has had limited value because of its omission of theoretical frameworks. Although some prior studies attempt to test hypotheses, most barely mention the theories on which they are based, and the vast majority fail to clearly specify the theoretical linkages and development of indicators that facilitate cumulative knowledge. Rudel (1989), for example, mentions the "Malthusian necessity of a growing population" and the potential argument that "deforestation has its origins in the classical forms of dependency" (328), but he leaves the theoretical basis for his approach unstated.




Similarly, Inman (1992) discusses the variety of perspectives offered by neo- Malthusian, modernization, and dependency theories but does not adequately explore the theoretical underpinnings, thereby failing to establish the linkage between theoretical indicators and theory. The complete absence of theoretical references in studies by Shafik (1994) and others (Capistrano 1994; Kahn & McDonald 1994) leads to the conclusion that these studies have not been guided by deductive theory.

The absence of theory in these studies is problematic because the choice of variables and models remains unguided. Statistical models are specified by an undetermined set of criteria such that variables may be chosen according solely to data accessibility or other ad hoc criteria. Subsequent models and analysis, therefore, have often been found to have no relationship to earlier, equally atheoretical studies. This lack of a systematic approach reduces the generalizabiity of research findings and retards the accumulation of knowledge.

Finally, previous research can be characterized as methodologically lax in its modeling and measurement of deforestation. Most studies (Allen & Barnes 1985; Inman 1992; Rudel 1989) use a change score model in which the forest area during a particular year is regressed on the forest area during an earlier year, along with other independent variables. This type of model measures the raw change in forest area during a brief (approximately five-year) period, which may be adequate for policy making but not for testing theory. Theory testing requires a more rigorous measurement of deforestation that is comparable across countries regardless of land and forest area. The rate of deforestation provides such a measurement. To date, only one study has employed a rate model (Shafik 1994), but it fails to adequately test the theoretical approaches discussed herein.

Having highlighted the theoretical and empirical considerations, the purpose of this article is to propose a model of deforestation that tests the various assertions of neo-Malthusian, modernization, and dependency theory using a cross-national framework and current, reliable data.

Data and Variables

The original sample consists of 88 developing countries dispersed throughout Asia, Africa, and Latin America. The analysis focuses on developing countries rather than on all nations of the world because of the salience of deforestation in the developing countries and the absence of net deforestation in the "developed" countries (largely the result of successful reforestation efforts and regrowth). This more narrow focus is further supported by the work of Burns et al. (1994), which suggests that the causes of deforestation may differ from those of reforestation. Nevertheless, the exclusion of developed countries is likely to result in more




conservative statistical estimates for some variables, such as urbanization and dependency level.

The sample of 88 developing countries was selected based on two criteria: the availability of appropriate country-level data on forest area and deforestation as reported by the FAO (1995) and the existence of a minimum of 5% forest cover as a percentage of total land area. Countries with less than 5% forest area were excluded because of a hypothesized ceiling effect on the rate of deforestation related to accessibility and relative resource abundance. With continued deforestation, countries reach a threshold as they deplete their most accessible and valuable forest stocks until only the most inaccessible forests remain. The cost of deforesting the remaining stands outweighs the benefits, particularly when the same resources are available from external sources. Final regression results are based on measures from 51 developing countries (which exclude 3 outliers) as a result of missing data on sectoral inequality, real gross domestic product (GDP), and tertiary education.

Ordinary least-squares regression was used to regress the rate of deforestation on the predictor variables. Figure 1 represents the causal model, in which the theoretical variables have been replaced with their operational counterparts to emphasize the longitudinal design. Independent variables are lagged to varying degrees (see operational definitions below) to ensure the temporal ordering of effects.

OPERATIONALIZATION OF VARIABLES

This study uses secondary data collected primarily from FAO (1995) and World Bank (1994) documents. The rate of deforestation is the annual average percent of change in forest area based on FAO measures of forest area for 1980 and 1990 (FAO 1995). It is important to note that this study calculates deforestation as a positive value.

Development level is measured as the percent of the total population living in urban areas in 1980 (World Bank 1994). A quadratic term was also created to test for a curvilinear relationship. Economic growth rate is measured as the average annual percent of change in the real GDP per capita between 1970 and 1980, as reported by Summers and Heston (1991). Real GDP is a better measure of economic growth because it controls for inflation and purchasing power and creates a standard economic unit of measurement across countries (1985 international dollars). Economic growth is measured for the ten-year period preceding deforestation in order to allow for the growth of investment capital and the development of deforesting enterprises.

Population growth rate is calculated as the average annual percent of change in total population between 1965 and 1980 using World Bank (1994) data.1 The measurement of sectoral inequality, as reported in the World Handbook of Political



576 I Social Forces 77:2, December 1998

FIGURE 1: Causal Model of the Social Determinants of Deforestation in Developing Countries

Poptuation Growrth \

Economct -

Growrth Razte\

I,evel of\ \

Urbantz at ) ion ip

RatE of Changec + Rate of In oPf nonfe pimaryex tu9orld Ban

Level of Sectoral Inequality /

R ate of Change i In Tertiary Edua. 9

BaB ( Year T Forest Stock

and Social Indicators (Taylor & Jodice 1983), is a GINI coeffipcient that measures the relative inequality of production per unit of labor across three national economic sectors -agriculture, industry, and services. Scores, with a range from zero- (perfect equality) to one (complete inequality), denote the variation in per worker productivity between sectors in 1970.

Export dependency is measured as the average annual percent of change in the amount of nongoelpricmeary exorts duringlthe period 1970-80 (World Bank 1994). Education measures the rate of change in the average annual percent of the total population rece'iv'ing tertiary education between 1965 and 1980 based on World Bank (1994) statistics. 'The lag in measurement is designed to account for the lag between education and implementation. -

Finally, a geographic variable is added to control for the potentially biasing-effect of the size of existing forest stocks as well as to control for the relative abundance of forest resources within a given country (the percent of forest cover). The control variable, a geometric mean, is calculated using FAO (1995) data on forest area in




1980 which is then weighted by the percentage of forest coverage in the same year. Forest stock is formulated by taking the square root of the product of forest area and the percent of forest coverage in a given country.

By controlling for forest size, the model eliminates statistical findings that are simply an artifact related to a given country's abundance of resources. Countries with large forests, for example, have a greater potential for deforestation in absolute terms than do countries with small areas of forest cover. Similarly, the rate of deforestation is likely to be lower in countries with greater expanses of forested lands, whereas higher rates are easily achieved in countries with minimal forest cover. Just as the abundance of forest resources (forest area) is hypothesized to have an impact on deforestation, so too is its relative abundance (percent of forested area). Decisions to deforest, whether by governmental action or inaction, are partially based on the perception of resource abundance, which is a function of a country's total area. Countries that have a mere 5 to 10% of their land area forested, whether as a result of past deforestation or climatic reasons, are less likely to continue the same rate of exploitation. This weight is particularly important for large countries, because their absolute forest area may always remain large while the percent of forest cover declines substantially in terms of prior forest cover. Absolute forest stock has been used as a control in a number of studies (Inman 1992; Rudel 1989), and relative forest stock has been used in others (Allen & Barnes 1985; Rudel 1989).

RELIABILITY AND MEASUREMENT

As in all previous studies of the social causes of deforestation, in this study the reliability of data on forest area was questionable and required assessment. Analysis of the correlation between FAO (1995) data reliability scores and the dependent variable reveal the correlation to be less than .1, indicating that deforestation scores are not correlated with measurement techniques or reliability scores.

Multiple regression equations were tested for problems of multicolinearity using variance inflation factor statistics with a critical value of 4. Resulting scores ranged from 1.2 to 1.6 (excepting the scores for the quadratic term, which are expected to be colinear). Outlier analysis performed using Rstudent and DfBeta scores suggested that data from three countries were influencing the regression results. The most influential of these cases, in order of magnitude of influence, were Jamaica, Thailand, and the Congo. Jamaica, although limited in forest resources, pursued policies of aggressive deforestation during the period under review, attaining an average annual rate of 5.3% (4.72 standard deviations from the sample mean). Similarly aggressive policies were pursued by Thailand, resulting in high rates of deforestation. These high rates are particularly unusual given Thailand's overall growth in tertiary education and its strong economic growth based in an expanding industrial sector. Finally, results for the Congo are extremely




suspect given that baseline deforestation data were developed from 1967 survey results, the oldest set of survey results for countries included in this study. These three cases were dropped cumulatively from the full regression model, resulting in an increase in the level of variance explained by the independent variables. However, while the results were strengthened, the signs of the results were not changed by the exclusion of these cases. The need to remove these cases from the analysis supports the claims of researchers who argue that cross-national research cannot capture all the nuances and complexities found in the world and lends credence to the use of case studies. Nevertheless, the purpose of this research is to capture the generalizable patterns that provide a widely recognizable picture, albeit without the fine details that are the strength of case studies.

Analysis

Hypothesis testing proceeded in three stages: an assessment of bivariate correlations (Table 2), testing of the relationships between variables through multivariate regression (Table 3), and reconsideration of the regressions based on the results of outlier analysis. The average annual rate of deforestation in the sample ranged from O to 2.8% per year, with the mean at 1.6% per year. The rate of deforestation was most highly correlated with the population growth rate (.27) and level of sectoral inequality (-.20). These results indicate that countries experiencing greater increases in their rate of population growth are likely to deforest at a faster rate and that countries experiencing high levels of sectoral inequality will likely experience lower rates of deforestation. These findings provide preliminary support for neo-Malthusian and modernization arguments.

Two other variables - exports and forest stock - also show mild, negative correlations with the dependent variable. The negative correlation between primary product exports and the rate of deforestation contradicts the expected relationship as suggested by dependency theory, indicating instead that declines in the rate of primary product exports are associated with increases in deforestation. Nevertheless, the negative correlation between forest stock and the rate of deforestation confirms the expectation that the rate of deforestation is partially a function of the forest stock, such that countries with large starting stocks will deforest at a slower rate than countries with less forest area.

Table 3 presents the results of the various models regressing the deforestation rate of 51 developing countries on selected independent variables. Beginning with model 1, independent variables are stepped into the regression equation in order to highlight the effects of each variable. The first model simply controls for forest stock, while models 2 through 7 add measures of urbanization, economic growth, population growth, sectoral inequality, change in primary product exports, and change in tertiary education, respectively. As shown in the final model (Model 7),




all but the export dependency variable show significant relationships with the dependent variable. The results shown in Table 3 are sequenced in order to control for development first, followed by population change and other variables. Nevertheless, the final model remains the same when the data are stepped in using alternative sequences.

The level of urbanization, entered as a second-degree polynomial, is introduced in Model 2. The results indicate a significant negative relationship (p = .001) with deforestation, providing additional support to the hypothesis that the rate of deforestation will increase from low to moderate levels of urbanization but decline at relatively high levels. It is also important to note that the adjusted R2 value jumps between the first and second models from explaining none of the variance to explaining 21% and that the level of significance of the urban variable is maintained throughout the various models. These findings provide strong support for modernization theory.

Although not as stable as the results for urbanization, the rate of economic growth is shown to have a significant, positive relationship with deforestation once the effects of population growth are controlled for. The significance of this relationship is clouded once the model controls for the effects of inequality (high, negative bivariate correlation with economic growth) but reemerges in the full model (p = .05) when controlling for the rate of change in tertiary education.

As predicted by neo-Malthusian theories, the population growth rate is shown to have a significant, positive effect on deforestation. Although significant in Model 4 (p = .05), the level of significance increases (p = .001) once the model is fully specified. The level of sectoral inequality is shown to have a significant, negative relationship with the rate of deforestation (p = .01) in models 5 through 7. The export dependency variable, introduced in model 6, does not attain significance and continues to contradict the theoretical expectation that increased dependency is associated with higher rates of deforestation. Models 6 and 7 were also run using the concentration of primary product exports as a percent of total exports for 1980 and the raw change in this percent between 1970 and 1980 as alternative measures of export dependency in order to model the level of export dependency. As with the initial measure, the alternative measures were not significant predictors of the rate of deforestation.3 Finally, tertiary education is added to the equation to account for the effects of global networks, knowledge, and the mobilization of environmental social movements. As hypothesized, the rate of change in tertiary education is negative and significant (p = .05), indicating that increases in education correspond to decreases in deforestation.

Using the unstandardized coefficients to calculate the average annual rate of deforestation, the relationship between urbanization level and deforestation is best described as an inverted U-shaped curve. Deforestation rates are shown to accelerate with increasing levels of urbanization until an urban threshold is reached (around 40-50% urban), after which there is a sharp decline. Countries with 10% of their




TABLE 2: Correlation and Basic Statistics - Deforestation Rate Model

1 2 3 4

1. Deforestation 2. Forest stock -.1665 3. Level of urbanization .0288 .2184 4. Urbanization squared -.1034 .1670 .9567* 5. Economic growth rate .1273 .0906 .2740* .3044* 6. Population growth rate .2653 .1159 -.1525 -.2268 7. Level of sectoral

inequality -.2038 .0840 -.3360* -.3899* 8. Rate of change in

primary exports -.1385 .2027 -.1020 -.0346 9. Rate of change in

tertiaryeducation -.0257 -.1780 -.1517 -.1987

Minimum .00 5.12 4.30 18.49 Maximum 2.88 6,500.88 100.00 10,000.00 Mean 1.06 894.55 36.33 1,786.31 Skewness .99 3.59 .92 2.10 Standard deviation .62 1,025.01 21.81 2,096.63 (N = 51)

population living in urban areas, for example, experience an additional .4% increase in their annual rate of deforestation on average, and countries that are 40% and 50% urban experience an additional 1% increase. Beyond 50% urban, however, the effect on the rate of deforestation is attenuated, and extremely high rates of urbanization may be associated with the absence of forest clearing and possibly with reforestation (at least hypothetically).

In terms of average annual economic growth, each percentage gain is associated with a .05% increase in the annual rate of deforestation (on average). Likewise, a 1% increase in the rate of population growth increases deforestation by .38% on average, while a 1% increase in the rate of education results in a .004 decline in the rate of deforestation. Deforestation is also reduced by .02% with each unit increase on the GINI index of sectoral inequality.

A comparison of the adjusted R2 values across the rate models suggests an increased capacity of the full model to explain the variation in the rate of deforestation across countries. The largest jump in explained variance occurs in model 2 with the addition of the urbanization term (from 0 to .21), followed by the addition of the measure of sectoral inequality in model 5 (from .31 to .40) and population growth in model 4 (from .24 to .31).




TABLE 2: Correlation and Basic Statistics - Deforestation Rate Model

5 6 7 8 9

1. Deforestation 2. Forest stock 3. Level of urbanization 4. Urbanization squared 5. Economic growth rate 6. Population growth rate -.3756* 7. Level of sectoral

inequality -.4856* .4522* 8. Rate of change in

primary exports .2515 -.0434 -.0816 9. Rate of change in

tertiary education .0494 .2985* .1680 .0051

Minimum -3.11 1.30 2.70 4.16 -1.40 Maximum 13.37 5.25 73.00 135.25 186.65 Mean 2.51 3.04 32.51 29.75 30.31 Skewness .93 -.08 .59 2.43 2.22 Standard deviation 3.33 .78 15.50 22.22 40.40

* Indicates significant bivariate correlations; p < .05

Discussion and Conclusions

It is clear that the models presented herein provide support for a theoretical framework that draws primarily on neo-Malthusian and modernization arguments. Referring back to the modernization arguments outlined earlier, I found the relationship between the level of urbanization and the rate of deforestation to be curvilinear. This finding suggests that as countries become increasingly urban the rate of deforestation increases until moderate levels of urbanization are achieved, at which point further gains in the predominance of cities implies smaller increases in the rate of tree felling (and at very high levels of urbanization may lead to reforestatation, at least hypothetically). The shifts from a largely rural to a moderately urban population base signals an increased demand for forest resources for use as charcoal as well as various industrial inputs. Conversely, the shift from moderate to predominantly urban status implies the establishment of increased efficiencies related to agglomeration economies, improved technologies, and the use of alternative resources.

Also as suggested by modernization theory, growth in the gross domestic product is found to increase the rate of deforestation when controlling for the level of urbanization. Although countries may share a given level of urbanization, those experiencing greater rates of economic growth will also experience greater rates of




TABLE 3: Unstandardized Coefficients - Regression of Deforestation Rate on Selected Independent Variables

Model 1 Model 2 Model 3

Intercept 1.1539*** -.3495 -.2600 (10.06) (1.41) (1.04)

Geographic Forest stock -.0001 -.0001 -.0001*

(-1.18) (-1.96) (-2.09) Modernization

Level of urbanization -.0476*** -.0491*** (3.83) (4.02)

Urbanization squared -.0004*** -.0005*** (-3.84) (-4.14)

Economic growth rate -.0407 (1.70)

Neo-Malthusian Population growth rate

Inequality

Level of sectoral inequality

Dependency Rate of change in primary exports

Westernization Rate of change in tertiary education

R 2 -.02 -.26 -.30 Adjusted R2 -.00 -.21 -.24 (N = 51)

Note: T values are in parentheses.

deforestation as a result of either increased consumption of primary commodities or increased capital investment in rural economies. In effect, both findings support modernization-based arguments that agglomeration and technological innovations of modernization yield more efficient production and larger increases in prosperity result in increased levels of consumption and/or expanded capital for investments in the exploitation of forest resources, suggesting that deforestation may be a symptom of early economic expansion.




TABLE 3: Unstandardized Coefficients - Regression of Deforestation Rate on Selected Independent Variables

Model 4 Model 5 Model 6 Model 7

Intercept -.4250 -.0823 -.0245 -.1499 (-1.11) (-.22) (-.06) (-.38)

Geographic Forest stock -.0001* -.0001* -.0001* -.0001*

(-2.50) (-2.25) (-2.03) (-2.24) Modernization

Level of urbanization -.0440*** -.0451*** -.0435*** -.0452*** (3.69) (4.09) (3.74) (4.01)

Urbanization squared -.0004*** -.0005*** -.0004*** -.0005*** (-3.70) (-4.32) (-4.05) (-4.40)

Economic growth rate -.0594* -.0353 -.0381 -.0499* (2.44) (1.47) (1.52) (2.00)

Neo-Malthusian Population growth rate -.2404* -.3214** -.3245** -.3819***

(2.28) (3.17) (3.17) (3.69) Inequality

Level of sectoral -.0160** -.0161** -.0152** inequality (-2.91) (-2.89) (-2.82)

Dependency Rate of change in -.0015 -.0016 primaryexports (-.46) (-.50)

Westernization Rate of change in -.0035* tertiary education (-1.97)

R2 -.37 -.47 -.48 -.52 Adjusted R2 -.31 -.40 -.39 -.43

*p <.05 **p <.01 ***p <.001

High rates of population growth were also found to increase the rate of deforestation in developing countries, even when controlling for the level of urbanization and the rate of economic growth. This finding supports neo- Malthusian arguments that despite economic efficiencies, population growth will result in greater rates of natural resource depletion (possibly through increased levels of per capita consumption). Although production and energy use may be more efficient, resulting in declines in the marginal environmental cost of each additional person, the net change in the rate of deforestation will still be positive. As populations grow in size, the demand for food, building materials, paper, and




other primary products is likely to increase, thus placing additional burdens on forest and land resources.

Findings for sectoral inequality support the modernization rather than the dependency framework. Large discrepancies between the levels of rural and urban productivity and higher urban wages pull rural residents into urban centers, while a variety of disincentives in rural areas make rural-urban migration even more attractive. As populations migrate to urban centers, the pressure on rural lands diminishes, with declining numbers of farms and reduced demand for fuelwood.

This analysis did not find evidence to support the notion that increasing rates of export dependency result in higher rates of deforestation. Although dependency theory suggests that deforestation should be higher in peripheral and semiperipheral countries that have the most unfavorable trade relations with the core, there was no statistical support for this proposition. Support was found, however, for the negative impact of tertiary education on the rate of deforestation. Although the mechanism of change is not clearly defined, potential explanations principally rely on diffusion arguments related to the modernization framework or social movement theory, which suggest that higher rates of change in tertiary education result in lower rates of deforestation net of other effects because of technological diffusion and growing linkages with environmentally minded communities. Growth in the number of university-educated citizens increases local capacity to adapt imported technologies as well as to develop new technologies that are appropriate to local conditions.

This research suggests that if applied independently of each other, neo- Malthusian and modernization frameworks paint only a one-dimensional picture of the interface between society and the environment in less-developed countries. Despite belief to the contrary, these two frameworks are shown to be compatible and must be used together to develop a clearer perspective that integrates both population and development dynamics. This article indicates, for example, that although population growth is strongly related to forest loss, the rate of loss is attenuated by the spacial component of population (rural/urban) that is a characteristic of modernization. Future research in this area should also explore the effects of technology (another component of modernization) on deforestation as well as consider the environmental impact of specific demographic trends. Finally, the effects of tertiary education suggest that social movement theory may also provide additional perspective for those attempting to illustrate the complexities of deforestation.



Deforestation in Developing Countries / 585 Notes

1. Population growth is lagged 15 years to model the time period required for it to have an effect on the natural environment. This time period reflects the aging of the population into adulthood and the consequent establishment of new households, clearing of additional tracts of forest for agriculture, and increased consumption.

2. Other measures were also tested, including a measure of foreign investment and various measures of external debt. Given the more proximate nature of primary product exports, it is not surprising that it was the strongest measure of dependency and therefore used in the model presented in this article.

3. The export dependencyvariable was also measured for the period 1980-90 in order to test for the possibility of a simultaneous effect of export on deforestation. The regression, however, yielded the same results (negative and nonsignificant).

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