Abstract
There have been several cross-national studies published in the world polity theoretical tradition that find a strong correlation between nations with high levels of environmental nongovernmental organizations (NGOs) and low levels of various forms of environmental degradation. However, these studies neglect the role that women’s NGOs potentially play in this process. We seek to address this gap by conducting a cross-national study of the association between women’s NGOs and deforestation. We examine this relationship because deforestation often translates into increased household labor, loss of income, and impaired health for women and, as a result, women’s non-governmental organizations have become increasingly involved in dealing with these problems often by protecting forests. We use data from a sample of 61 nations for the period of 1990–2005. We find substantial support for world polity theory that both high levels of women’s and environmental NGOs per capita are associated with lower rates of deforestation. We also find that high levels of debt service and structural adjustment are correlated with higher rates of forest loss. We conclude with a discussion of findings, policy implications, and possible future research directions.
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Notes
The following nations are included in the analysis: Albania, Algeria, Angola, Argentina, Bangladesh, Bolivia, Brazil, Bulgaria, Burkina Faso, Central African Republic, Chad, Chile, China, Columbia, Congo, Costa Rica, Ecuador, El Salvador, Ethiopia, Gambia, Ghana, Guatemala, Guinea, Guinea-Bissau, Honduras, Hungary, India, Jamaica, Kenya, Lesotho, Madagascar, Malawi, Malaysia, Mexico, Mongolia, Mozambique, Nepal, Nicaragua, Nigeria, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Romania, Rwanda, Senegal, Sierra Leone, South Africa, Sri Lanka, Swaziland, Tanzania, Thailand, Togo, Trinidad, Uganda, Uruguay, Zambia, and Zimbabwe.
Ehrhardt-Martinez et al. (2002) find that an environmental Kuznets curve exists between gross domestic product per capita and deforestation. We test this hypothesis using a quadratic polynomial equation in which the gross domestic product per capita and its square are entered into the same model. If this relationship exists, the sign of the coefficient for the level of development should be positive and the sign of the coefficient for the squared term should be negative with this term being statistically significant. To reduce problems of multicollinearity, we begin by centering the linear term around its mean. We then square the centered term. Finally, we include the centered linear term and squared term in our models (York et al. 2003). The coefficients for the squared term are negative but fail to achieve statistical significance. We also tested for the possibility of an environmental Kuznets curve between a nation’s level of urbanization and deforestation (Ehrhardt-Martinez 1998). We use the same procedure described above to test this hypothesis but find no support for it.
Bollen and Paxton (2000) argue that non-random measurement error arising from the subjective perceptions of judges affect all cross-national measures of democracy to some degree. This bias may distort comparisons across nations, undermining the empirical results that ignore it. Therefore, we also estimate our models using the level of democracy or autocracy in a nation using data from the Polity IV Project (2005). This measure ranges from—10 (autocracy) to 10 (democracy). The results using this measure are very similar to the results reported for the measurements of democracy in Table 1. We do not present these results for sake of space.
We also included a series of dummy variables to account for findings that may arise out of geographical circumstances, which cannot be accounted for by the independent variables in the model (Shandra 2007c). These dummy variables identify a nation as being located in Latin America and the Caribbean, Asia, or Africa. The reference category includes nations in Europe. The coefficients for the geographical control variables failed to predict any significant variation in deforestation. The other findings remained similar to the results reported in Table 2. We do not present the results for sake of space, but they are available from the authors upon request.
A problem that commonly arises in cross-national research is that of missing data. Statistical procedures such as multivariate regression analysis generally assume that each country has complete data. However, for numerous reasons, countries may be missing values on one or more of the variables under investigation. When this is the case, questions may emerge about the extent to which inferences about the parameters and tests of statistics are influenced by the presence of incomplete data. When using listwise deletion, for example, the effective sample size only includes those nations with complete records, and, consequently, this number can be substantially smaller than the original sample size if missing observations are scattered across many nations. Further, nations that are excluded will often be the poorer countries that have fewer resources to allocate toward record keeping. Thus, the final sample may not be representative of the poorest nations. We attempt to determine if our estimates are biased by listwise deletion by using Arbuckle’s (1996) full information maximum likelihood estimation routine to handle incomplete data. This approach has been used by Jorgenson (2003), Paxton (2002), and Kentor (2001). The size and significance of the full information maximum likelihood estimates are remarkably similar to the listwise deletion estimates, providing little evidence that the listwise deletion results are biased.
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Shandra, J.M., Shandra, C.L. & London, B. Women, non-governmental organizations, and deforestation: a cross-national study. Popul Environ 30, 48–72 (2008). https://doi.org/10.1007/s11111-008-0073-x
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DOI: https://doi.org/10.1007/s11111-008-0073-x