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Mining Exports Flows, Repression, and Forest Loss: A Cross-National Test of Ecologically Unequal Exchange

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Ecologically Unequal Exchange

Abstract

We draw on the theory of ecologically unequal exchange to inform our study of how mining exports sent from poor to rich nations affect forests in poor nations. Using ordinary least squares regression for a sample of 61 low- and middle-income nations, we find little support for this hypothesis. However, we refine it by considering how repressive nations facilitate ecologically unequal exchange in the mining sector. We argue that repressive nations create a “good business climate” for mining companies via economic incentives (e.g., tax holidays) and regulatory concessions (e.g., environmental law exemptions, labor flexibility, and imposed political stability). The key finding is that mining export flows increase forest loss more in repressive rather than democratic nations.

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Notes

  1. 1.

    We limit our discussion to the cross-national literature on forest loss because it is the most relevant and considerations of space. However, we do acknowledge that there is a larger cross-national literature on EUE that includes the analysis of ecological footprints (e.g., Jorgenson 2005), carbon dioxide emissions (e.g., Jorgenson 2012), industrial water pollution (e.g., Shandra, Shor, and London 2009c), and biodiversity loss (e.g., Shandra et al. 2009a). Austin (2012) demonstrates that EUE adversely impacts not only forests but also educational attainment and malnutrition.

  2. 2.

    The following 61 nations are included in the analysis. They are Algeria, Angola, Argentina, Bangladesh, Benin, Bolivia, Brazil, Bulgaria, Burkina Faso, Central African Rep, Chad, Chile, China, Colombia, Congo, Cote d’Ivoire, El Salvador, Gabon, Gambia, Ghana, Guatemala, Guinea, Guinea-Bissau, Honduras, Hungary, India, Indonesia, Jamaica, Kenya, Madagascar, Malawi, Malaysia, Mali, Mauritius, Mexico, Mauritania, Mongolia, Mozambique, Nepal, Nicaragua, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Rwanda, Senegal, Sierra Leone, Sri Lanka, Tanzania, Thailand, Togo, Trinidad, Turkey, Uganda, Uruguay, Zambia, and Zimbabwe.

  3. 3.

    We deal with issues of linearity by taking the natural log of skewed variables. We acknowledge that this complicates the interpretation of the effects of variables. One way to deal with this issue is to use an elasticity model where all variables are logged with the exception of dummy variables (York et al. 2003). We reran the model using an elasticity model and results are similar. However, we do see a marked drop in the R-square values, indicating potential problems with model fit. This is not surprising as most variables approximate linearity prior to transformation and taking the natural log yields a relationship that does not approximate linearity. To further increase the reliability of the results, however, we also run the models with no variables transformed and keeping the original metrics for everything. This (again not ideal since some variables are not linear) yields substantively similar findings to the model presented but with substantially lower R-square values.

  4. 4.

    We also ran the analysis using a robust regression model that uses iteratively reweighted least squares with Huber and biweight functions tuned for 95 percent Gaussian efficiency to ensure there are no problems with outliers (Dietz, Frey, and Kalof 1987). Put simply, this model assigns a weight to each observation used in the analysis with higher weights given to observations with smaller residuals and lower weights given to observations with higher residuals (Dietz et al. 1987). The results of the robust regression are similar to the results obtained by ordinary least squares. This enhances the reliability of the findings. We report the ordinary least squares results because they are more easily interpreted.

  5. 5.

    We use a weighted mining export flow variable. It is possible that this measure may be capturing the same underlying structural relationships as total export flows. However, we do not think this is the case. First, we find a fairly low bivariate correlation between mining export flows and export flow measures in other sectors. For instance, the bivariate correlation between mining and agricultural export flows equals 0.104 in our sample. The bivariate correlation with forestry export flows is equal to 0.130 in the sample. The bivariate correlation with total exports flows from poor to rich nations is equal to 0.150 for the sample. At the same time, there is a theoretical rationale for considering only mining exports in a study of forest loss. We review the reasons why EUE theory expects mining export flows to increase forest loss. However, there is research suggesting that oil, gas, and mineral exports may have a negligible impact on forest loss (Rudel 2013). Rudel (2013) argues that the discovery of mineral, oil, or natural gas deposits in sub-Saharan Africa triggers economic booms in extractive sectors, which slows growth in the agricultural sector. This is because extractive sector booms create a demand for labor, which makes it more expensive in a country. With workers seeking higher wages in mines or oil fields, labor shortages emerge especially in agriculture where wages tend to be lower. As a result, fields lay fallow (Rudel 2013). Further, oil, gas, and mineral exports tend to drive up the price of a country’s currency, which makes its agricultural exports more expensive relative to a country with a devalued currency exporting similar crops (Rudel 2013). In both instances, there is less pressure to clear forests to expand agriculture (Rudel 2013).

  6. 6.

    We consider how other aspects of a country’s exports may affect forest loss. These include total mining exports and total exports. The coefficients for these variables fail to reach a level of statistical significance.

  7. 7.

    We examine how other measures of a country’s macroeconomic conditions affect forest loss. These include international trade, balance of payments, currency reserves, and exchange rates. These data can be obtained from the World Bank (2015). The coefficients for each measure failed to reach a level of statistical significance, but the other results are similar to the findings in Table 7.2.

  8. 8.

    We include economic activity value added from agriculture, forestry, and mining as a percent of gross domestic product to account for a country’s domestic economy structure. We would expect this measure to be associated with higher rates of deforestation because these types of economic activities are presumed to put greater pressure on forests than other types of economic activities (e.g., services and manufacturing). This variable does not explain any significant variation in forest loss.

  9. 9.

    We classify forestry statistics as being highly reliable if they are based on remote sensing survey or current national field sampling estimates (Shandra, Shandra, and London 2008). We classify forestry statistics as having low reliability if they are based on expert estimates, which often involves extrapolation from an outdated national inventory. As such, we include a dummy variable to measure the reliability of the deforestation data, identifying those nations in which forest cover measures are based upon remote sensing surveys or current national field sampling estimates and are, therefore, of higher quality (1 = high data quality). The reference category includes nations whose forestry estimates are based upon expert estimates or an outdated inventory (0 = low data quality). The coefficients for the data quality measure fail to reach a level of statistical significance.

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Authors and Affiliations

  1. Department of Sociology, State University of New York at Stony Brook, Stony Brook, NY, USA

    Jamie M. Sommer, John M. Shandra & Carolyn Coburn

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  1. Jamie M. Sommer
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  2. John M. Shandra
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Correspondence to Jamie M. Sommer .

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  1. University of Tennessee, Knoxville, TN, USA

    R. Scott Frey , Paul K. Gellert  & Harry F. Dahms ,  & 

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Sommer, J.M., Shandra, J.M., Coburn, C. (2019). Mining Exports Flows, Repression, and Forest Loss: A Cross-National Test of Ecologically Unequal Exchange. In: Frey, R.S., Gellert, P.K., Dahms, H.F. (eds) Ecologically Unequal Exchange. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-89740-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-89740-0_7

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