Abstract
Theoretical and empirical studies have shown that democracy and corruption influence environmental policies. In this chapter, we empirically analyse the relative importance of these determinants of environmental policy. When these variables are jointly included as explanatory variables in a multiple regression analysis, we find that corruption stands out as a substantial and significant determinant of environmental policies, while proxies for democracy have an insignificant impact. Nevertheless, democracy could affect environmental policy stringency given that countries with a history of democratic rule tend to be less corrupt (see Chap. 3). A discussion of our results in the context of the Environmental Kuznets Curve literature follows. We argue that improving environmental quality following increasing income is less probable in developing countries with institutional disarray. Finally, and more optimistically, when considering our results in the context of institutions and growth, we conclude that there is scope for reaping a double dividend: reductions in corruption induce higher economic growth rates and stricter environmental policies.
This chapter is a slightly modified version of Pellegrini and Gerlagh 2006.
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- 1.
Similarly, Hardin (1968) argued—in his famous article on The Tragedy of the Commons—that coercion is the only way to limit human population growth and that “freedom to breed will bring ruin to all” (p. 1248). It must be noted though that the type of coercion proposed by Hardin is “mutual” and “mutually agreed upon” and that he argues that that is not a real loss of freedom.
- 2.
van Beers and van den Bergh (1997, 2003) suggest a distinction between input- and output-oriented indicators, where the former “are based on input efforts devoted to environmental protection” and the latter on “concrete results of environmental regulations” (pp. 33–34). The authors prefer output indicators because they reflect countervailing policy interventions (e.g. when subsidies are given to a sector characterized by environmental regulations that increase costs) (van Beers and van den Bergh 2003, p. 17). The indexes we use do not have this particular shortcoming because they are comprehensive indexes that would reflect countervailing policies (e.g. the environmental regulatory regime index includes a variable describing the existence of subsidies that encourage the inefficient use of energy or materials). In any case, the alternative indicators used by the authors were available for much smaller sample sizes than the ones used here.
- 3.
The analysis studied the probability that countries signed the agreement by 1985 or 1987. Since most countries, democracies as well as non-democracies, have signed the convention nowadays, the finding that democracies are more likely to sign the convention seems less valid. However, the evidence can indicate that democracies, if compared to autocracies, are faster to tackle environmental issues (see Neumayer 2002).
- 4.
Fredriksson and Millimetet support their finding with econometric data at state level for the United States, using as a proxy for corruption the number of civil servants tried for crimes related to bribery as a share of the total number of public employees. Such a proxy has the obvious shortcoming that it can reflect judicial efficiency. Furthermore, trials are held only after some time following crimes, so there is a considerable time lag between the moment when the corruption-related crime is committed and its relevance in the index. Judiciary initiatives can be a symptom of anticorruption campaigns rather than corruption itself.
- 5.
The ICRG index, which has been used extensively in the empirical literature (beginning with Knack and Keefer 1995 who used it in an analysis of institutions on economic performance), measures political risk associated with corruption, rather than corruption itself. As such, the proxy for corruption chosen by the authors seems to be very much causally related to the other independent variable they use for political instability (see Lambsdorff 1999).
- 6.
The authors do not produce a robustness analysis of the finding and their measure of democracy is from the Freedom House. This measure of democracy has been criticized because of opacity and ideological bias (see also footnote 106).
- 7.
More technically, if the real relation among three variables is given by \(y = {\beta _1} + {\beta _2}{X_2} + {\beta _3}{X_3} + u\), the two independent variables are correlated (so that \({X_3} = {\gamma _1} + {\gamma _2}{X_2} + \varepsilon \)) and we omit variable X 3; then in a regression we will find \(y = {\beta _1} + {\beta _3}{\gamma _1} + ({\beta _2} + {\beta _3}{\gamma _2}){X_2} + \varepsilon + u \). Therefore, the estimation bias of the coefficient of the included variable X 2 will be equal to \({\beta _3}{\gamma _2}\).
- 8.
Paldam (2002) also reaches conclusions similar to Treisman. In Chap. 3, we have checked their results on the recently available and much larger World Bank database (Kaufmann et al. 2004) of corruption perception and we find that only three decades of uninterrupted democracy correlate to lower contemporary levels of corruption. Sandholtz and Koetzle (2000), Sung (2004) and Chowdhury (2004), on the contrary, find that democracy tends to correlate to lower corruption, but they use a very limited set of explanatory variables in their econometric models. Overall, we find that there is a lack of empirical evidence for an effect of contemporary democracy on contemporary corruption levels. The possibility of causality in the longer run should still be kept in mind as it could provide to democracy a way to affect environmental policies indirectly.
- 9.
The democracy score of Singapore in 2001 (from Polity IV) was 4 and the corruption perception index (from Transparency International) was 0.8 (both indexes are on a 0–0 scale with higher values indicating higher democracy and higher corruption, respectively). In the same year Italy had a democracy score of 10 and a corruption score of 4.8.
- 10.
The word democracy derives from the two ancient Greek words: Demos (D ή m o ς), which means “People”, and Kratos (Κr ά τ o ς), which means “Rule”.
- 11.
Also, it is important to recognize that substantive democracy is a primary goal per se, independently from its effects on other welfare variables. When we consider that democracy is a fundamental component of freedom, the point was convincingly made by Sen (1999).
- 12.
Specifically, we used data of the corruption perception index 1997 for Uruguay, the index 1998 for Ghana, Iceland, Jamaica, Morocco, Malawi, Paraguay, Senegal, Tunisia, Tanzania, Zambia, and Zimbabwe and the 1980–1985 for the rest of the sample.
- 13.
We opt for measures of independent variables that refer to previous years, compared to the year of measurement for the dependent variable, because we assume that environmental policy and its implementation requires some time to adapt to the current socio-economic and political situation. In other words, if present conditions are favourable to more stringent environmental policies, we expect that it takes some years for these policies to come about and be implemented.
- 14.
Other measures of democracy are available and have been widely used in the literature (e.g. the ones from the Freedom House). Here, for our robustness checks, we prefer to use Vanhanen’s index of democracy because of its methodological and conceptual differences with the Polity IV index. The main methodological difference is that the index of democracy , once its definition is taken for granted, is independent from experts opinions and as such it is a good test of possible biases in experts’ judgments. Conceptually, it is based on a minimalist definition of democracy (referring to Dahl 1971), while the Polity IV index is one of the most comprehensive indexes. Last, the Polity IV index has been criticized for underestimating the relevance of participation (e.g. Munck and Verkuilen 2002, p. 11), whereas this is a fundamental element in Vanhanen’s index of democracy participation. Furthermore, Freedom House’s indexes have been challenged for their opacity and ideological biases (Deacon 2003; Rosenblum and Salehyan 2004; and Landman 2004), thus, for our robustness checks, we prefer an alternative index that is not based on expert opinions.
- 15.
The Environmental Sustainability Index is a joint project of the World Economic Forum, The Yale Center for Environmental Law and Policy, and the Columbia University Center for International Earth Science Information Network. See www.ciesin.columbia.edu/indicators/ESI/.
- 16.
For a critical assessment of the ESI see Morse and Fraser (2005).
- 17.
The mean of the democracy variable (when scaled 0–10) for the years 1980–1985 is 4.2, 5.9, and 6.4 for the complete sample, for the countries for which the Environmental Policy Stringency Index is available and for the countries for which the Environmental Regulatory Regime Index is available, respectively. The standard deviations of the same democracy variable (in the same order) are: 3.8, 3.9, and 3.6. Therefore, the sample for which the Environmental Regulatory Regime Index has been compiled appears to have a higher mean value of the democracy variable (and slightly lower variance).
- 18.
We use the natural logarithm of income, in accordance with most of the empirical literature, in order to smooth the distribution of the income variable. The other variables are indexes; therefore this transformation is not in order.
- 19.
The standardization procedure does not affect the significance of the coefficients.
- 20.
As a method to check for the possibility of clustering independent variables (maintaining their explanatory power) and discovering possible composed variables, we have tried to use the principal component method adding other institutional quality indexes. The results did not allow for any meaningful grouping of these variables and confirmed our impression on the independent role played by the corruption variable.
- 21.
In order to check for multicollinearity problems, we used a Variance Inflation Factor (VIF). We did not find the VIF to be higher than the usual threshold of 10 for the variables of democracy and corruption in any of our regressions, except for the regressions where interaction factors were introduced.
- 22.
Urbanization rates are typically included in EKC estimations. In our analysis, the coefficient on the urbanization variable can be interpreted as reflecting differences in preferences and political influence of urban and non-urban citizens.
- 23.
- 24.
The Latin America dummy is significant when it is the only regional dummy included, but not together with the other dummies. The omission of the Latin America dummy would not alter our results.
- 25.
We also included an interaction factor of corruption and income, but found it not to be significant in any of the regressions.
- 26.
It must be noted that the turning points of the EKC used for the analysis of Cole and Neumayer (2005) are deemed to be overly optimistic in comparison to other studies (e.g. Harbaugh et al. 2002; Stern 2004), which made use of more complete dataset and improved estimation techniques if compared to the benchmark works of Grossman and Krueger (1995) and to the more recent work of Cole (2003).
- 27.
In our sample the average value of the corruption index was 7.14 for countries with income below US $2000 in 1980, and 3.79 for the countries with income above US $20,000.
- 28.
Data from the Environmental Treaties and Resource Indicators (available at http://sedac.ciesin.columbia.edu/entri) confirm an increasing trend in the number of environmental treaties over time: 70 in the decade 1960–1970, 94 in the decade 1970–1980, 89 in the decade 1980–1990, and 110 for 1990–2000.
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Pellegrini, L. (2011). Corruption, Democracy, and Environmental Policy: An Empirical Contribution to the Debate. In: Corruption, Development and the Environment. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0599-9_5
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