India,Palm Oil,and Ecologically Unequal Exchange: A Cross-national Analysis of Forest Loss

Abstract

Drawing on ecologically unequal exchange theory and previous research, we assess whether palm exports from low- and middle-income nations to India increase forest loss in exporting nations. Using ordinary least squares (OLS) regression for a sample of 91 low- and middle-income nations, we find support for our main hypothesis that palm exports sent from low- and middle-income nations to India are related to increased forest loss in the exporting nations. Our findings refine and expand upon ecologically unequal exchange theory by demonstrating that India, a middle-income nation, nevertheless is capable of positioning itself favorably in trading opportunities with other low- and middle-income nations. As India meets its needs for palm oil from abroad which is central to its economic growth and industrialization, their low- and middle-income trading partners bear more of the burden of environmental harms from the extraction and export of palm oil.

Keywords

India forest loss palm oil ecologically unequal exchange

Introduction

Following the collapse of the Soviet Union, a major donor to India during the 1970s and 1980s, and a concomitant balance of payment crisis, India instituted neo-liberal economic reforms that ushered in rapid industrialization, urbanization, and economic growth (Chakrabarti, Dhar, and Dasgupta 2015). India’s economic growth has resulted from a strategy that emphasizes creating a manufacturing sector producing for export. During this time, India’s economic growth also resulted in changing consumption patterns with its growing population consuming more food than ever before and deriving larger portions of their diets from meat, diary, and processed foods (Hubacek, Guan, and Barua 2007). Initially, India utilized its own natural resources to support its economic growth and increased consumption. However, this decision resulted in increased forest loss, soil erosion, air pollution, and water scarcity in India (Government of India’s Ministry of Statistics 2018).

Furthermore, these factors coupled with issues related to distribution, food storage, and domestic production capabilities have placed a strain on India’s ability to meet its own demand agricultural products, particularly palm oil (Qazi 2018). Thus, it has turned to other nations to help meet the demand. India is the world’s largest palm oil importer, bringing 10 million metric tons of palm oil or 15 percent of the global supply into the country where it is used for cooking, consumer products, and manufacturing (Mishra and Tapsall 2017).

India has sought to increase palm oil imports from low- and middle-income nations in a variety of ways. First, it provides bilateral financing to other low- and middle-income governments to buy Indian machinery that will be used to clear forests to grow palm, to build refining mills, and to construct roads connecting the forests and mills to ports for export (Fuchs and Vadlamannati 2013). Second, India offers engineering services for construction projects and institutional capacity building to set up agricultural markets and distribution networks (McCormick 2008). Third, it has signed various free trade agreements which allowed Indian companies to invest in the palm sectors of other countries to eliminate barriers to free trade (Ratna and Kallummal 2013). Fourth, India supports outward foreign investment by offering lines of credit and risk insurance from the Export-Import Bank of India (Mishra and Tapsall 2017). India’s demand for palm oil and its efforts to meet the demand have led academics, policy makers, and government officials to question if its trade in palm may be adversely affecting the natural environment elsewhere (Bawa et al. 2010). This is because palm oil expansion often involves the bulldozing or setting on fire forests to make way for palm plantations Nellemann 2007). Roads are built through forests to facilitate the palm oil’s export which also encourages migration into previously inaccessible areas where people clear forests for companies in the palm sector or raise subsistence crops (Yui and Yeh 2013).

It is not surprising that 40 percent or 2.9 million hectares of forest (an area larger than the U.S. state of Maryland) were lost in Indonesia, the world’s largest palm oil producer and exporter to India, as a result of palm production between 2000 and 2010, with similar patterns being found in other palm oil producing nations like Malaysia, Thailand, Nigeria, and Ecuador among others (Baron, Rival, and Marichal 2017). Furthermore, clearing forests for palm plantations eliminates a country’s “carbon sink” capacity or the ability of forests to remove carbon dioxide from the atmosphere (Reijnders and Huijbregts 2008) and contributes to biodiversity loss affecting up to 54 percent of threatened mammals and 64 percent of threatened birds globally (International Union of Concerned Scientists 2018).

However, despite such statistics and the adverse consequences of palm oil production, whether India’s demand is related to increased forest loss in other low- and middle-income nations that export palm oil to it remains an elusive empirical question and is subjected to speculation. This lack of research is surprising because Stephen G. Bunker (1985), drawing on previous political-economic theory like Arghiri Emmanuel (1972), theorized how exports from low- and middle-income nations to high-income nations tend to adversely affect the natural environments of low- and middle-income nations through the process of unequal exchange. Furthermore, social scientists using cross-national data have tested and found support for Bunker’s (1985) theory of ecologically unequal exchange. Similarly, Mariko Frame (2019) and David Ciplet and J. Timmons Roberts (2019) have refined the theory to consider how unequal exchange may also be driven by middle-income nations (not just high-income nations) as their pursuit of industrialization puts pressures on other low- and middle-income nations.

We draw on the theory of ecologically unequal exchange, existing cross-national research that links it to forest loss, and recent refinements to the theory as the starting points for our study. However, we move the research frontier forward by addressing a crucial gap: No research has examined if palm exports from low- and middle-income nations to India, as the world’s largest importer of palm oil, increase forest loss in exporting low- and middle-income nations.

We now turn to a review of the theory of ecologically unequal exchange and the related empirical research concerning forest loss. We then argue why it is important to extend and apply the theory to India, with its potential implications for forests in low- and middle-income nations that export to India. We then describe our research methodology including our sample, variables, modeling strategy, and findings. Our analytic strategy includes specific tests of other theoretical perspectives including treadmill of production, world polity, and neo-Malthusian theory, which inform our model specifications. We conclude with the implications of our findings, limitations of the study, and corresponding directions for future research.

Ecologically Unequal Exchange Theory and Research

The theory of ecologically unequal exchange has its origins in political-economic writings of the 1960s and 1970s. Of particular note, Emmanuel (1972) argues that international trade between high- and low- and middle-income nations is inherently unequal. This is the result of the mobility of investment capital from high-income nations relative to the immobility of labor in low- and middle-income nations. A situation arises where “surplus value” is transferred from low- and middle-income nations and relocated to high-income nations. Put differently, high-income nations utilize the cheap labor of low- and middle-income nations to produce agricultural goods and extract natural resources. These products are then exported back to high-income nations where they are transformed from the raw materials into finished commodities, thereby “adding value” in the high-income nations (Emmanuel 1972). In the end, unequal exchange allows high-income nations to maintain and expand their manufacturing and industries. However, low- and middle-income nations remain locked into low-wage, labor-intensive, and extractive economic activities and fail to establish their own industrial or manufacturing sectors. Instead, most finished products are sold back to them from high-income nations after value has been added (Emmanuel 1972).

With this idea as his starting point, especially the extractive nature of production in low- and middle-income nations, Bunker (1985) extends the idea of unequal exchange to the natural environment. How does the process of ecologically unequal exchange play out and what are its impacts on the natural environment? As noted above, high-income nations are advantageously situated within the global economy and are able to secure favorable terms of trade (Hornborg 2003). This advantageous position is the result of the prices of exports from low- and middle-income nations, largely natural resources and agricultural products, consistently falling in value relative to the prices of manufactured goods and services exported by high-income nations (A. G. Frank 1967). The falling prices are the result of several factors. They include competitive market pressures as a number of low- and middle-income nations pursue their “comparative advantage” in similar export markets, along with other factors noted by Emmanuel (1972)—such as the availability of a large, low-wage workforce, and nations failing to develop their own manufacturing sectors and being reliant on importing such goods from high-income nations.

Philip McMichael (2004) describes how various organizations further facilitate this process. The World Bank and International Monetary Fund, for instance, do so through their lending. The World Bank’s investment loans finance infrastructure that helps bring agricultural and natural resource exports to market (Rich 1994). The International Monetary Fund’s structural adjustment loans require indebted nations to adopt macro-economic policy reforms centered upon increasing exports to receive the loans (Sommer, Shandra, and Restivo 2017). The reforms require indebted nations to liberalize trade by offering various economic incentives and regulatory concessions to multinational companies from high-income nations (Frey 2012). Finally, high-income nations finance projects in extractive sectors like agriculture with their export credit agencies (Swamy 2017).

In the end, it takes more agricultural and natural resource exports to buy manufacturing and service imports from high-income nations (Muradian and Martinez-Alier 2001). A low- or middle-income nation can be very successful at increasing its exports, but, in return for the sale of their exports, they get fewer, not more, imports (Giljum and Eisenmenger 2004). Andrew K. Jorgenson (2009) finds that this process translates into increased forest loss in low- and middle-income nations as they expand exports just to keep levels of manufactured and services imports from high-income nations from declining.

The empirical research of ecologically unequal exchange theory has become quite popular with social scientists. Jennifer E. Givens, Xiaorui Huang, and Andrew K. Jorgenson (2019) published a review that discusses how ecologically unequal exchange has been applied to various outcomes. The first empirical study on forest loss carried out by Andrew K. Jorgenson (2006) finds that total export flows weighted by level of gross domestic product correspond with increased forest loss. Following this research, Andrew K. Jorgenson, Christopher Dick, and Kelly Austin (2010) use a more refined measure (a weighted index of agricultural, forestry, and mining exports) and find that such export flows from low- and middle-income nations to high-income nations are related to increased forest loss. John M. Shandra, Christopher Leckband, and Bruce London (2009) refine the measurement further and demonstrate that forestry exports of this sort are related to increased forest loss in the exporting low- and middle-income nations. Similarly, Jamie M. Sommer, John M. Shandra, and Carolyn Coburn (2019) examine how mining exports sent from low- and middle-income nations to high-income nations negatively affect forests in exporting nations. The authors find evidence of a moderated relationship, where mining exports were related to increased forest loss in repressive nations but not more democratic ones. Several other studies link increased forest loss in low- and middle-income nations to the ecologically unequal exchange of cattle (Austin 2010a),¹ soy (Austin 2010b), coffee (Austin 2012, 2017), chocolate (Noble 2017), and palm (Henderson and Shorette 2017) with high-income nations.

The preceding discussion of theory and empirical research on ecologically unequal exchange describes how high-income nations “externalize” their forest loss to low- and middle-income nations. However, the focus had remained on trade relations between high-income nations and their low- and middle-income trading partners. As noted above, Frame (2019) extends ecologically unequal exchange theory by demonstrating the necessity of considering how middle-income nations may be engaging in ecologically unequal exchange with other middle-income or low-income nations. Frame provides a case study of Cambodia to demonstrate the impact on forests.

Frame (2019) begins by describing how China, Malaysia, Thailand, and Vietnam increasingly consume minerals, fossil fuels, wood, and other construction materials as they industrialized from 1970 to 2005. The author then goes on to show the degree to which each nation meets its consumption of these goods by importing them from elsewhere. China meets its demand for fossil fuels and minerals and has become a net importer, while Malaysia is still a net exporter of minerals, fossil fuels, wood, and construction material; this trend is slowing. Vietnam meets its demand for wood and construction material through imports, while Thailand meets its demand for construction materials and minerals from abroad. Most importantly, Frame (2019) describes the process by which these middle-income nations are meeting their demands. She describes how governments and companies purchase land concessions in Cambodia to expand activities related to the imports that each country needs. Unexpectedly, Frame (2019) notes that forest loss increased in Cambodia with more investments to support exports to China, Malaysia, Thailand, and Vietnam.

From this discussion, we argue that the theory of ecologically unequal exchange should be applied to India, with an emphasis on the palm sector, and its trade relations with other middle- and low-income nations. Having reviewed the limited research that exists on the topic, we now turn to our derivation of an appropriate hypothesis related to exports of palm oil from low- and middle-income nations to India.

India, Palm, and Forests

How might India be facilitating ecologically unequal exchange, and what are the impacts of palm oil exports from low- and middle-income nations to India on the forests in those exporting nations? To answer these questions, we now turn to a discussion of strategies used by India to successfully match imports to meet demand and offer several reasons why palm exports to India may result in increased forest loss.

The earliest strategy employed by India to increase their palm imports involved providing loans and grants to support the agricultural sectors of other low- and middle-income nations (Fuchs and Vadlamannati 2013). This most often involved funding for recipient nations to purchase seeds, fertilizers, pesticides, and irrigation systems from India (Banerjee 1982). In the palm sector, India helps recipient nations buy machinery to clear forests to grow palm, build refining mills, and construct roads to connect the forests and mills to export markets (Export-Import Bank of India 2011).

India creates a demand for its investments by offering technical services and institution building to other low- and middle-income nations. The technical training includes engineering services and sharing best practices on using seeds, pesticides, irrigation, and fertilizers to increase agricultural productivity (Export-Import Bank of India 2017). It also involves establishing research centers that facilitate knowledge transfers with Indian scientists and researchers traveling abroad to share their experiences. The priority areas of cooperation involve horticulture, crop science, fisheries, animal science, natural resource management, and agricultural extension services (Export-Import Bank of India 2017). There is an emphasis on promoting policies to government officials from other nations that emphasize implementing neo-liberal economic reforms undertaken by India that helped it “industrialize” (McCormick 2008).

In the palm sector, India’s institution building involves sharing research on the adoption of new varieties of the fruit and improving competitiveness of small-scale land holders producing palm for export (Mishra and Tapsall 2017). A major initiative involves helping recipient nations create or improve agricultural markets and distribution networks in an effort to increase production and exports of palm—a plan that may well increase forest loss by increasing exports. India also offers engineering services to help with the successful completion of projects like mills and roads, which has the potential to increase palm oil production and exports, and, in turn, forest loss (Paul 2014).

India has also taken steps domestically, which are in line with the neo-liberal economic policies it promotes abroad, to ensure access to natural resources. These steps include becoming a member of the World Trade Organization during the 1990s and, consequently, coming into compliance with its rules in subsequent years (Persaud and Landes 2007). Of particular note to this study, India eliminated quantitative trade restrictions on palm and replaced them with low tariffs (Persaud and Landes 2007). However, India also completed various multilateral and bilateral trade agreements with other low- and middle-income nations, which eliminated many tariffs completely (Ratna and Kallummal 2013).

These free trade agreements have lowered the price of palm oil in India and, as a result, consumers are using more of it (Schleifer 2016). This demand may be increasing forest loss in exporting nations as companies look to increase exports to India to meet this demand increase. Furthermore, palm oil prices are also lower in India because 90 percent of it is sold without a brand label into a highly segmented retail market dominated by small markets (Schleifer 2016). Toward this end, Indian consumers have been less likely to purchase sustainably certified palm oil because of the large cost differential between it and conventional palm oil (Schleifer 2016). Thus, palm producers, refiners, and importers in the Indian market have been less likely to participate in environmental certification schemes, thereby exacerbating the harmful practices associated with producing palm conventionally in other nations (Schleifer 2016).

The free trade agreements have led to another consequence. They make it more profitable for Indian companies investing abroad to send exports back to India to meet the increased consumer demand due to the low cost of palm oil. These investments tend to entail Indian firms involving a trading company to purchase palm fruit or oil from producers in other low- and middle-income nations or entering into a purchasing agreement with suppliers (Malone 2011). Indian companies may also form public-private partnerships with a host government leasing land to grow and refine palm for export back to India or purchase forest concessions when not prevented by a host country’s laws to expand palm production (Malone 2011).

In many instances, Indian firms receive below market rate lines of credit and discounted risk insurance from the Export-Import Bank of India (Mishra and Tapsall 2017). These products allow Indian companies seeking to expand their palm oil operations abroad and to increase exports back to India (Export-Import Bank of India 2011). The Export-Import Bank of India also loans money to foreign firms or governments to purchase engineering services to help in the construction of mills or equipment needed to clear forests to plant, harvest, and refine palm.

A notable example of how Indian companies with support of the Export-Import Bank of India may lead to forest loss involves Indonesia. According to Greenpeace (2007), Indonesia’s Duta Palm has cleared peat forests with fires while their legal concessions remained largely forested and unplanted with palm. However, palm was grown on the illegally cleared land, which was then exported under contract to India by several companies including Adani Wilmar, Ruchi Soya, Cargill, Emami, Gokul Refoils, and VVF in India (Greenpeace 2007). Furthermore, Wilmar, the world’s largest processor and merchandiser of palm, sourced substantial amounts of palm from Duta Palm and then sold it under contract to its subsidiary in India (Greenpeace 2007).

This discussion describes the strategies that India employs to facilitate its demand for palm through imports. When these policies and practices are viewed through the theory of ecologically unequal exchange, this leads us to our main hypothesis: Higher levels of palm oil exports from low- and middle-income nations to India should be related to increased forest loss in the exporting low- and middle- income nations. Before turning to a discussion of the results of our analysis, we review the measurement of our variables and discuss the statistical model used to test the hypothesis.

Method

Dependent Variable

Forest loss

Until recently, cross-national research on forest loss was largely based on data made available in the United Nation’s Food and Agriculture Organization’s Global Forest Resources Assessment (e.g., Shandra, Rademacher, and Coburn 2016). However, the reliability of these data has been called into question because they are gathered utilizing data collection methods that vary from nation to nation (Grainger 2008). In some nations, forestry statistics are highly reliable because they are based on remote sensing surveys (Food and Agriculture Organization 2015). In other nations, estimates may be of low reliability because they are based on expert opinions or extrapolated from an outdated forest inventory (Grainger 2008). Thus, in an attempt to improve on these shortcomings, we use newly available data on forest loss derived from high-resolution satellite imagery (30 × 30 m²) to eliminate this potential source of error. The data may be obtained online from the World Resources Institute’s (2016) Global Forest Watch Web page. See Mathew C. Hansen, Stephen V. Stehman, and Peter V. Potapov (2010) for an in-depth discussion of the methodology used to arrive at the estimates.

We calculate forest loss in the following way. First, we set a minimum tree cover canopy density level upon which to base the estimates. We follow Thomas K. Rudel (2017) and set the minimum tree cover canopy density equal to 75 percent or greater to represent wet forests. The tree cover density for a nation represents the estimated percentage of a pixel taken from satellite imagery that is covered by tree canopy (World Resources Institute 2016). Second, we obtain the amount of a nation’s land area in hectares with the corresponding minimum tree cover canopy density. These data are measured in the year 2000. Third, using the satellite imagery data, we calculate the number of hectares cleared from 2001 to 2014 in the area for each nation. Fourth, we divide the total amount of hectares cleared by the total forest size which yields the rate of forest loss over this time period (Rudel 2017). We take the square root of the forest loss rate because it is skewed (Table 1).

Table 1.

Descriptive Statistics and Bivariate Correlation Matrix for Forest Loss Analysis (2001–2014).

Variables	M	SD	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
(1) Forest loss	0.307	0.226	1.000
(2) Palm sector exports to India	2.091	9.791	.052	1.000
(3) Total palm exports	0.002	0.007	−.029	.162	1.000
(4) Gross domestic product per capita	6.650	1.034	−.197	.251	.066	1.000
(5) Agricultural land area	43.754	20.689	.194	−.026	−.270	−.141	1.000
(6) Total palm sector production	266.033	1,514.559	.057	.391	.470	.149	−.148	1.000
(7) Democracy	4.055	1.605	−.030	−.117	−.104	.442	.116	.023	1.000
(8) Protected forest area	2.341	0.932	.145	.003	.042	.046	.006	.039	−.026	1.000
(9) Economic growth rate	3.870	4.610	−.130	−.033	−.079	−.060	.039	.108	.207	.002	1.000
(10) Non-governmental organizations	4.049	8.652	−.033	−.077	−.044	.233	−.295	−.058	−.315	−.066	.113	1.000
(11) Total population growth rate	0.201	0.136	.372	−.049	.120	−.285	−.190	.043	.071	.174	−.122	.086	1.000

Independent Variables

We focus on ecologically unequal exchange as the main theoretical perspective informing this study because it has yet to be applied to India. However, we draw upon other theoretical perspectives, which serve as potential alternate explanations, to inform our model specifications. We provide a brief overview of the perspectives that we draw on to inform our models along with their relationship to particular variables. From our extensive discussion of ecologically unequal exchange theory, above, we now turn to a discussion of the two variables we use to measure concepts central to this perspective. Also note that all variables can be obtained from the World Bank (2016) unless otherwise indicated.

Ecologically unequal exchange theory and variables

Palm sector export flows to India

We include flows of palm exports from low- and middle- income nations to India to test if the ecologically unequal exchange of palm is adversely affecting forests in the exporting nations. The data are measured for 2000 and may be obtained from the United Nations Commodity Trade Statistics Database (2017). This database reports export statistics in constant U.S. dollars for each nation by commodity and trading partner (United Nations Commodity Trade Statistics Database 2017). We specify India as the trading partner for each low- and middle-income nation. We then use the third revision of the Standard International Trade Classification of palm exports to India. These codes include 4222 for palm oil and its fractions and 4224 for palm kernel oil and its fractions (United Nations Commodity Trade Statistics Database 2017). We also obtain data on total palm and palm kernel oil and their fractions exports for each nation, which we use as the denominator for this variable. According to ecologically unequal exchange theory, we hypothesize that higher levels of palm exports sent from low- and middle-income nations to India are associated with higher rates of forest loss in exporting nations.

Total palm exports

We also control for a country’s total palm exports to determine if it is the directionality of palm exports, overall levels of palm exports, or both, which contribute to forest loss (Shandra, Leckband, and London 2009). Therefore, we include total palm exports as a percentage of total agricultural exports. The data may be obtained from the United Nations Commodity Trade Statistics Database (2017). We expect that higher levels of palm exports correspond with more forest loss.

Treadmill of production theory and variables

According to Allan Schnaiberg and Kenneth A. Gould (1994), the treadmill of production is the central feature of modern economics, which also leads to environmental degradation. To keep profits up, companies must constantly expand production. However, companies also seek to lower labor costs so they invest in economically efficient technologies that have higher yields per unit of labor (Schnaiberg 1980). Toward this end, “costly” workers are displaced and, as a result, unemployment increases. In an effort to address this issue, companies again expand production. The workers, looking for jobs, are sucked into supporting the increased production as it creates employment opportunities for them (Schnaiberg and Gould 1994). Furthermore, governments facilitate the economic expansion by bailing out bankrupt companies, negotiating free trade agreements, and protecting private property, in an effort to capture the tax revenues from a growing economy (Jorgenson 2016). Through the continual expansion of production, the treadmill increases environmental degradation like forest loss by placing more and more demands on natural resources (Schnaiberg 1980).

Economic growth

From this discussion, we include the average annual economic growth rate from 1990 to 2000. From the treadmill of production theory, we expect that high rates of economic growth should be related to increased forest loss because low- and middle-income nations experiencing economic gain invest money in environmentally damaging activities to absorb unemployed workers, which leads to forest loss.

Total palm oil production

We also include the total of palm oil produced by low- and middle-income nations. These data may be obtained from the United States Department of Agriculture’s Foreign Agricultural Services (2018). We expect that low- and middle-income nations with higher levels of palm oil production should correspond with increased forest loss. According to treadmill of production theory, a low- or middle-income nation with higher levels of palm oil production should have more forest loss due to more and more forests being cleared with machinery or fire to keep palm production high and the economy growing.

World polity theory and variable

The world polity perspective holds that international non-governmental organizations play an important role in creating and reinforcing world cultural norms on issues like human rights, gender equality, and environmentalism among others (Boli and Thomas 1999). In terms of environmentalism, international non-governmental organizations involve themselves in global political processes by helping shape the language of international treaties dealing with the environment, thereby influencing the normative content of global institutions and governments (Smith 1995). In the absence of resources and formal mechanisms of enforcement, international non-governmental organizations monitor adoption and compliance with environmental agreements by governments (Shandra 2007). Thus, they are able to point out embarrassing failures and hypocrisies of nations, putting pressure on governments to ratify treaties, adapt their behaviors to international norms, and meet their treaty obligations (Hafner-Burton and Tsutsui 2005).

It is also important to note that international non-governmental organizations help mobilize support for solutions to environmental problems when national avenues are either inadequate or blocked (Keck and Sikkink 1998). It has become common for them to provide support for conservation efforts at local levels (Schofer and Hironaka 2005). In this regard, international non-governmental organizations fund sub-national environmental protection efforts and social movement activity around the environment (Smith and Wiest 2005). In such instances, governments are “squeezed” from above and from below to conform to world cultural norms and attend to environmental problems like forest loss (Schofer and Hironaka 2005).

Non-governmental organizations

Accordingly, we include the number of international non-governmental organizations working on environmental and animal rights issues in a nation for 2000. The data are collected by Jackie Smith and Dawn Wiest (2005) from the Yearbook of International Associations. We divide it by a country’s population size to standardize the variable. According to world polity theory, higher levels of non-governmental organizations are associated with lower rates of forest loss (Schofer and Hironaka 2005). This may be the case because non-governmental organizations finance local conservation projects, support social movement activity, and shape the language of environmental laws (Shandra 2007).

Neo-Malthusian theory and variable

According to neo-Malthusian theory, demographic factors, especially population growth, are a prominent cause of social problems (Ehrlich and Ehrlich 2009). The arguments, rooted in Malthus’s (1986) well-known assertion that geometric growth in population would outstrip arithmetic growth in the means of subsistence, led to the pessimistic conclusion that “carrying capacity” problems would be inevitable if population growth outpaces finite environmental resources such as land, food, and water. This focus on environmental resource shortages has been extended in recent years to argue that population growth is a major cause of forest loss (Ehrlich and Ehrlich 2009). The general idea holds that increases in population growth drive extraction, consumption, and production activities (Pimentel and Pimentel 1999). As a result, growing populations in low- and middle-income nations clear forests to expand agriculture and other extractive activities (Rudel and Roper 1997).

Total population growth

From this discussion, we include the average annual percentage change in total population growth from 1990 to 2000. According to neo-Malthusian theory, we hypothesize that higher rates of population growth correspond with increased forest loss.

Control variables

We include four additional control variables in the analysis that do not correspond completely with a given theory but that have been shown to be important predictors of forest loss in previous cross-national research.² They include the following.

Gross domestic product

We include gross domestic product per capita for 2000 in our models. This variable is logged to correct for its skewed distribution. We expect that higher levels of gross domestic product per capita should correspond with increased forest loss. This is because as low- and middle-income nations become wealthier, they move away from limited production subsistence agriculture to large-scale commercial agriculture and other extractive activities like mining and forestry to stimulate economic growth (Ehrhardt-Martinez 1998).

Agricultural sector size

We also include a variable that measures the size of a country’s agricultural sector to determine if it is country’s overall agricultural sector or its domestic palm oil production that is related to forest loss (Sommer et al. 2017). This variable includes the total amount of agricultural land as a percentage of total land area. It is logged to correct for skewness. We hypothesize that a larger domestic agricultural sector in a low- and middle-income nation should correspond with more forest loss because forests are often cleared to make way for other agricultural activities including cattle ranching or growing subsistence crops (Ehrhardt-Martinez, Crenshaw, and Jenkins 2002).

Democracy

We use the average of Freedom House’s (2005) political rights and civil liberties scales to measure democracy. Political rights reflect whether a nation is governed by democratically elected representatives and has fair, open, and inclusive elections. Civil liberties reflect whether a nation has freedom of press, freedom of assembly, general personal freedom, freedom of private organizations, and freedom of private property. The variables are measured on a seven-point scale: free (1–2), partially free (3–5), and not free (6–7). We multiply the average of the two variables by negative one so that high values correspond with a high level of democracy. We argue that higher levels of democracy are associated with lower rates of forest loss because democratic nations are more likely to put into place environmental regulations due to political activism demanded by their citizens. Furthermore, elected officials must meet these demands because of accountability at the ballot box (Li and Reuveny 2006).

Protected forest area

We use the percentage of protected forest area to measure a government’s commitment to conservation (D. J. Frank 1999). These data may be obtained from the Food and Agriculture Organization (2015) and are logged to correct for skewness. We expect that higher levels of protected forest area should correspond with less forest loss because such areas are not open to extractive activities (Lewis 2003). These protected areas often result from the demands of citizens in a democratic nation (Marquart-Pyatt 2008).

Statistical Model

Our sample is in the appendix. We analyze the cross-national data using ordinary least squares regression in Stata 14. It is denoted by the following formula:

y_{i} = a + b_{1} X_{1} + b_{2} X_{2} + \dots + b_{k} X_{k} + e_{i},

where y_i is the dependent variable for each country, a is the constant, b₁ to b_k are the unstandardized coefficients for each independent variable, X_k is the independent variable for each country, and e_i is the error term for each county.

The validity of inferences depends on ensuring that we are not violating any regression assumptions. First, we calculate the mean and highest variance inflation factor scores for each model. We report the values in Table 2. There does not appear to be any potential problems with multicollinearity because mean and highest variance inflation factor scores do not exceed a value of 2.5 (Tabachnick and Fidell 2013).

Table 2.

Ordinary Least Squares Regression Estimates for Forest Loss Including Palm Exports to India (2001–2014).

Independent variables	Model (2.1)	Model (2.2)	Model (2.3)	Model (2.4)	Model (2.5)	Model (2.6)
Palm sector exports to India, 2000	.001*	.001*	.003**	.001*	.002*	.001**
	.052	.058	.112	.061	.070	.064
	(.001)	(.001)	(.001)	(.001)	(.001)	(.001)
Total palm exports, 2000		−1.168	−1.312	−1.604	−1.956	−2.568
		−.038	−.043	−.052	−.064	−.084
		(1.556)	(2.232)	(1.636)	(1.882)	(1.628)
Gross domestic product per capita, 2000			−.050*	−.046*	−.064*	−.035
			−.230	−.213	−.296	−.160
			(.021)	(.023)	(.025)	(.004)
Economic growth rate, 1990–2000			−.007	−.008	−.007	−.005
			−.143	−.166	−.162	−.102
			(.004)	(.005)	(.005)	(.004)
Total palm sector production, 2000				.002**	.002*	.003**
				.133	.154	.130
				(.001)	(.001)	(.001)
Agricultural land area, 2000				.002	.002*	.002*
				.177	.208	.268
				(.001)	(.001)	(.001)
Democracy, 2000					−.017	−.018
					−.118	−.127
					(.022)	(.020)
Protected forest area, 2000					.038	.021
					.158	.086
					(.023)	(.022)
Non-governmental organizations, 2000					.003	.001
					.101	.038
					(.003)	(.003)
Total population growth rate, 1990–2000						.600***
						.363
						(.162)
Constant	.305***	.306***	.666***	.558**	.629*	.328
	(.025)	(.026)	(.164)	(.202)	(.282)	(.280)
R ²	.003	.004	.071	.110	.157	.260
No. of countries	91	91	91	91	91	91
Highest variance inflation factor score	1.00	1.03	1.09	1.53	1.57	1.57
Mean variance inflation factor score	1.00	1.03	1.05	1.23	1.30	1.34

Note. The first number is the unstandardized coefficient, the second is the standardized coefficient, and the third number in parentheses is the robust standard error.

p < .05. **p < .01. ***p < .001 (one-tailed test).

Second, to help satisfy the linearity assumption, we use Stata 14’s ladder and gladder commands to determine if a variable is normally distributed or needs to be transformed. The ladder command reports a chi-square test for eight different transformations. The null hypothesis for the chi-square test is that a specific transformation approximates normality (Tukey 1977). If the chi-square statistic is statistically significant, then we reject the null hypothesis and conclude that the specified transformation does not approximate normality (Tukey 1977). We confirm the statistical tests by visually inspecting graphical distributions for each variable using the gladder command. We transform variables based on the results of this procedure and note any transformations below (Tabachnick and Fidell 2013). We then visually inspect the bivariate association between the dependent variable and each predictor to ensure linearity.

Third, we calculate the standardized residuals to determine if outliers are a problem. We identify Namibia as a multivariate outlier because its standardized residuals exceed an absolute value of 2.5 (Tabachnick and Fidell 2013). We remove it from the analysis and report results based on its exclusion. We also examined Cook’s distance statistics to detect influential cases. The results indicate no potential problems with influential cases.

Fourth, we calculate Breusch-Pagan’s heteroscedasticity tests for each model. The null hypothesis for this chi-square test is that the error variances are homoscedastic or equally distributed (Tabachnick and Fidell 2013). The coefficients for the chi-square statistics reach a level of significance in most models, indicating potential problems with heteroscedasticity (Tabachnick and Fidell 2013). We report robust standard errors to help address this issue.

Findings

In Table 2, we present the ordinary least squares regression estimates of forest loss. The first number presented is the unstandardized coefficient, the second number is the standardized coefficient, and the third number in parentheses is the robust standard error. We report one-tailed significance tests because of the directional nature of the hypotheses.

In Model (2.1), we include our main indicator related to the theory of ecologically unequal exchange, palm exports to India from other low- and middle-income nations. In Model (2.2), we add total palm exports—an important control when testing ecologically unequal exchange theory that allows us to determine if it is the overall exports, the flow of exports to specific locations lost, or both that are related to forest loss. In Model (2.3), we add economic factors. These variables include gross domestic product per capita and the economic growth rate. In Model (2.4), we add variables related to palm oil production and a country’s agricultural sector size. In Model (2.5), we add political factors including democracy, non-governmental organizations, and protected forest area. In Model (2.6), we include our demographic factor, the total population growth rate, which is related to neo-Malthusian theory. We use this block modeling strategy to demonstrate the reliability of the ecologically unequal exchange theory finding—palm oil exports from low- and middle-income nations to India—in light of alternative theoretical explanations (London and Ross 1995).

Let us begin by examining the palm export variables. We find support for ecologically unequal exchange theory that palm oil exports from low- and middle-income nations to India are related to increased forest loss in the exporting nations. The coefficients for this measure are positive and significant in all six models despite what other variables are included. However, we find that total palm exports are not related to forest loss. The coefficients for this variable do not reach a level of significance in Table 2. Taken together, we find that it is the directional aspect of trade as suggested by the theory and not the overall level of trade that leads to increased forest loss in low- and middle-income nations. We contextualize the impact of this variable on forests in the “Discussion and Conclusion” section of the paper.

We do find some support for other theoretical perspectives as well. First, we find that certain aspects of treadmill of production theory are related to forest loss. In particular, higher levels of total palm production correspond with increased forest loss. The coefficients for this variable are positive and significant in the models in which the variable is included. It is important to note that this variable is significant even when controlling for a country’s agricultural sector size, which is also related to increased forest loss. Second, we find support for neo-Malthusian theory. The coefficients for population growth are positive and significant in Table 2. Third, we find that higher levels of gross domestic product per capita are related to less forest loss. The coefficients for this variable are negative and significant in all but one model in which it is included.

However, we did not find support for other aspects of these theoretical perspectives in this study. First, we do not find support for certain aspects of treadmill of production theory. The coefficients for economic growth are not statistically significant in this study. Second, we do not find support for world polity theory because the coefficients for the non-governmental organization variable are not significant. In addition, other political variables—a nation’s level of democracy and the size of its protected forest area—are not associated with forest loss.

We note that an independent variable may fail to reach statistical significance for a variety of reasons including sharing variance with other variables in our model specifications, particularities about the data collection methodologies, or characteristics specific to this particular study including how India goes about facilitating ecologically unequal exchange. Future research may be able to explore why such factors are theorized to contribute to forest loss but are not significant in these model specifications.

Discussion and Conclusion

We began our study by reviewing the theory of ecologically unequal exchange and the existing cross-national research on the topic and by responding calls to apply it to industrializing middle-income nations. We fill a gap in the cross-national literature by applying ecologically unequal exchange to India. In doing so, we find support for the theory of ecologically unequal exchange with palm oil exports sent from other low- and middle-income nations to India being related to increased forest loss in the exporting nations. The coefficients for this variable are positive and significant across several different model specifications.

We should contextualize our main finding regarding the impact of ecologically unequal exchange of palm oil to India on forests. To do so, let us consider the case of Belize, using its actual data. In 2000, the county had a forest area equal to 1,600,000 hectares or 6,175 square miles, and 151,000 hectares or 580 square miles of forest were cleared from 2001 to 2014 (World Resources Institute 2016). Note that we selected Belize in this example because it has a forest loss rate similar to the forest loss rate that Model (2.7) predicts would have been cleared in a low- or middle-income nation that does not export any palm oil to India, holding all other independent variables at their mean values. From our models, we can predict the marginal effects of exports to India on forests. For a country like Belize, if 10 percent of its total palm exports are sent to India, then 163,500 hectares of forest are predicted to be lost during the period of 2001 to 2014, which amounts to an additional 12,500 hectares or approximately 50 square miles of forest. This amount is approximately equal to 1 percent of Belize’s total forest area. If a country like Belize sent 50 percent of its total palm exports to India (holding all other independent variables at their means), then 213,500 hectares of forest would be predicted to be cleared during this period. This amount translates to approximately 62,500 hectares, 250 square miles, or about 4 percent of Belize’s total forest area being cleared, compared with an equivalent country that does not export palm to India. As this example illustrates, it is not total exports but the particular configurations of trade relations that can impact forest loss in a nation.

While the contentions of the theory of ecologically unequal exchange are supported in our analysis, we also find that other theoretical perspectives also explain a portion of forest loss. Certain aspects of treadmill of production theory explain significant variation in forest loss—total palm sector production. Thus, efforts to lower reliance on palm production may be necessary to conserve forests. We also find that population growth is related to increased forest loss, providing support for neo-Malthusian theory and confirming the continued importance of demographic factors in explaining forest loss that has been noted much earlier by Rudel (1989).

Furthermore, we find that gross domestic product per capita is related to less forest loss. The finding contradicts our hypothesis that as nations become wealthier, they move away from subsistence agriculture to mechanized agriculture and other extractive activities like mining and forestry to stimulate economic growth (Ehrhardt-Martinez 1998). However, Thomas J. Burns, Edward L. Kick, and Byron L. Davis (2003) explain why such a finding may exist and correspond with our results. The authors suggest that a transformation is happening in the forestry sector where it has become increasingly common for a company to be headquartered in a middle-income nation—not only high-income nations—and support logging operations in other low- or middle-income nations. Burns et al. (2003) refer to this outsourcing of forest loss as “recursive exploitation,” where a middle-income nation may still be at a relative disadvantage to high-income nations in terms of trade but may able to secure more favorable terms of trade with other low- and middle-income nations—see also John M. Shandra, Michel Restivo, and Jamie M. Sommer (2019). We suggest that this may be what is going on in the palm oil sector given our main finding related to ecologically unequal exchange and India.

From our main finding, we arrive at an important theoretical contribution. We agree with Frame (2019) and Ciplet and Roberts (2019) that it is essential for social scientists utilizing ecologically unequal exchange theory in their research to consider if rapidly industrializing middle-income nations, such as India, may be affecting the natural environment in other low- and middle-income nations in pursuit of their own industrialization. This can occur when they externalize environmental harms by importing various natural resources from elsewhere. In the end, such scholarship has the potential to refine thinking around ecologically unequal exchange and move beyond considering only how trade with high-income nations affects the natural environment of low- and middle-income nations. However, it is also important to consider how political, economic, demographic, and social processes described by other perspectives including the treadmill of production and neo-Malthusian theories are related to forest loss. A failure to do so would have led to an incomplete understanding of the factors that shape it. Overall, our analysis supports ecologically unequal exchange theory. Our findings support the idea that rapidly industrializing nations like India play an intermediary role in ecologically unequal exchanges, driving forest loss in low- and middle-income nations. Our findings also support neo-Malthusian theory. However, we find limited support for certain aspects of treadmill of production theory, and no support for world polity theory.

There are methodological implications related to the theoretical implications. It is now easier to obtain data from the United Nations Commodity Trade Statistics Database (2017) on export flows by sector (Stretesky and Lynch 2009), commodity (Henderson and Shorette 2017), and trading partners (Huang 2018). The availability of such data means sociologists can test theoretical propositions with more specificity than had been possible in the past. We demonstrate the utility of this approach here by considering a specific commodity (palm) flowing from low- and middle-income nations to a specific trading partner (India). We hope that other social scientists follow our lead and use these detailed data to refine ecologically unequal exchange theory and to offer more specific policies suggestions.

In terms of specific policy suggestions from the findings, we offer a few ideas. There needs to be efforts to change India’s consumption patterns with regard to palm oil, as well as global consumption overall. India can develop policies that lead to less consumption of palm from other low- or middle-income nations or consume palm oil that is produced in a more environmentally responsible way. As noted earlier, it is unlikely that consumers in India would voluntarily purchase sustainably certified palm oil because of the cost differential between it and conventional palm oil (Schleifer 2016). Furthermore, Indian palm producers, refiners, and importers are less likely to participate in environmental certification schemes because participation is driven by consumer demand. There is little consumer pressure on companies to participate through threats to profits and reputation—both of which are absent in the Indian market.

As a result, it may be best if India increases its domestic supply of palm either by cultivating more acreage or improving yields (Simlai 2018). The increases can be accomplished with various financial incentives for its farmers. Nevertheless, India’s palm sectors, largely controlled by domestic firms like Ruchi 3F Industries and Godrej Agrovet, who are working with small farmers to produce palm, have historically struggled at this task (Simlai 2018). Thus, India is providing foreign investors with incentives like tax breaks and regulatory concessions like removing land ceilings on palm cultivation in an effort to meet this goal (Government of India, Ministry of Agriculture and Farmer Welfare 2017).

While in the short-term increasing palm production domestically has the potential to improve India’s food security, stimulate its economic growth, and lessen its dependence on other low- and middle-income nations, long-term forecasts suggest that India will begin to experience water shortages by 2030 due to climate change (Busby 2017). Furthermore, we find that domestic palm production is also related to increased forest loss for a nation. If this is the case, then expanding the palm sector may be unfeasible because it takes 80 gallons of water per day to support one tree (Simlai 2018). India may need to consider alternatives to palm like growing coconut trees that only require 13 gallons of water per day to support a tree coupled with a national reforestation program to ensure that any forests removed are replaced (Simlai 2018).

In addition, an effort can be made to change Indian’s consumer preference for alternative cooking oils. For instance, a public health campaign could encourage Indian citizens to cut back on eating processed foods and use healthier cooking oils. Such a campaign would be designed to combat rising incidences of cardiovascular disease in India, but it could have the desirable unintended consequence of reducing forest loss abroad by altering India’s consumption patterns at home.

These non-confrontational policies may prove difficult to implement if rapid economic growth fueled by access to cheap natural resources remains central to India’s industrialization. Put differently, these recommendations can be considered “reformist” because they do not seek to bring about fundamental changes that may be necessary to reduce forest loss (Newell 2001). A more “confrontational” strategy may be needed.

A typical confrontational strategy would entail non-governmental organizations, social movements, and concerned citizens monitoring malfeasance including forest loss, human rights abuses, and land grabs by Indian companies across the palm oil supply chain. This information could then be publicized and used to mobilize consumer boycotts in India. However, such tactics are unlikely to be effective in India. Philip Schleifer (2016) offers some reasons why this may be the case. To begin, India has shown little priority to integrate environmental protection into its governmental policy. Furthermore, as we note earlier, most consumers buy unbranded palm oil from small retail outlets. Thus, any corporate monitoring or consumer boycotts should focus on projects co-financed by the Indian government, the Export-Import Bank of India, the World Bank, and companies and commercial banks from high-income nations working with India’s government or Export-Import Bank. These actors are more likely to be susceptible to such pressure to change production processes than firms in India (Schleifer 2016).

There are some caveats that should be noted and corresponding possible directions for future research. First, we measure palm sector exports sent to India from other low- and middle-income nations at the turn of the century. This cross-sectional analysis is the result of the forest loss data only being available for a single time point (World Resources Institute 2016). However, palm sector exports to India have increased since 2000 (United Nations Commodity Trade Statistics Database 2017), which means that our analysis most likely underestimates the impacts of these flows on forests. Future research that integrates panel data with controls for time-invariant characteristics needs to be carried out when more forest loss data become available to capture the changing magnitude of the exports to India.

Furthermore, India is a leading importer of palm. But it is also increasing imports of other natural resources especially in the forestry sector, which may affect forests in exporting low- and middle-income nations (Sood 2014). Finally, while we have focused on India as the world’s largest importer of palm oil, China is also importing large amounts of palm oil among other natural resources from low- and middle-income nations (Yu, Feng, and Hubacek 2014). It is essential to understand to what extent and in what ways China and other rapidly industrializing middle-income nations, such as Brazil and South Africa, are engaging in ecologically unequal exchange at the expense of the natural environment elsewhere as they pursue their own industrialization (Ciplet and Roberts 2019).

Footnotes

Appendix

We include the following 91 low- and middle-income nations, according to the World Bank’s (2015) classification, in our analysis after list-wise deletion of missing data. The nations with complete data include the following: Albania, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Central African Republic, Chad, China, Colombia, Republic of the Congo, Costa Rica, Cote d’Ivoire, Cuba, Dominican Republic, Ecuador, El Salvador, Ethiopia, Gabon, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Indonesia, Jamaica, Kazakhstan, Kenya, Kyrgyz Republic, People’s Democratic Republic of Laos, Lesotho, Liberia, Madagascar, Malawi, Malaysia, Mali, Mauritania, Mexico, Moldova, Mongolia, Mozambique, Namibia, Nepal, Nicaragua, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Russian Federation, Rwanda, Senegal, Sierra Leone, Solomon Islands, South Africa, Sri Lanka, Suriname, Swaziland, Tajikistan, Tanzania, Thailand, Togo, Turkmenistan, Uganda, Ukraine, Uzbekistan, Vanuatu, Venezuela, Vietnam, Zambia, and Zimbabwe.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jamie M. Sommer

Notes

Author Biographies

Jamie M. Sommer is an assistant professor in the Department of Sociology at the University of South Florida. Her research uses mixed-methods to examine how institutional factors impact global inequality in environment and development outcomes.

Michael Restivo is an assistant professor of Sociology at the State University of New York at Geneseo. His work focuses broadly on the political economy of global environmental problems. He uses quantitative and longitudinal methods to study concerns such as deforestation from a macro comparative perspective.

John M. Shandra is a professor of Sociology at the State University of New York at Stony Brook. His research focuses on how organizations impact environment and development outcomes.

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