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An Empirical Study of the Impact of Corruption on Environmental Performance: Evidence from Panel Data

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Abstract

This paper extends the empirical debate on the effects of corruption on environmental degradation by considering a recently available measure of environmental quality, the Environmental Performance Index. This indicator is more comprehensive than the measures of air pollutant emissions commonly used in the literature and, in particular, can also capture the impact of pollution on human health. This allows for a better understanding of the actual effects of a wide range of human activities on the ecosystem. From a panel data analysis, two regularities emerge. First, corruption deteriorates the overall environmental quality. This effect is robust and persistent. Second, our findings highlight the improvement of environmental quality as income rises, even at an initial level of development. This is not in contradiction with the EKC hypothesis because an increase in income levels provides positive externalities on the whole environmental quality by compensating the mere negative effects induced by industrialization on the emission levels. As a consequence, in emerging economies, policies fighting corruption and enhancing development are very likely to improve the environmental performances.

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Notes

  1. More recently, Lapatinas et al. (2014) have explored an additional channel through which the effect of corruption on environmental quality may take place. Environmental protection may involve high technology investment, and consequently environmental protection can be considered a rent-seeking activity. Thus, in the presence of corruption, better technology and larger budgets allocated for the environment are not necessarily associated with better environmental outcomes. Hence, the effectiveness of green policies also depends on the extent of corruption and on rent-seeking associated with green technologies.

  2. See the pioneering works by Shafik (1994), Holtz-Eakin and Selden (1995), and Grossman and Krueger (1995).

  3. WEF/YCELP/CIESIN (2001).

  4. This index has been developed by Yale University and Columbia University in collaboration with the World Economic Forum and the Joint Research Centre of the European Commission. For more details on the database, see http://sedac.ciesin.columbia.edu/data/collection/epi. For methodology and further details, see Hsu et al. (2014).

  5. In the literature, the scale effect has been identified according to two different approaches depending on whether the dependent variable has to do with emissions or concentrations. We follow the approach of Stern (2002, (2004), Hamilton and Turton (2002), Cole (2007), and Tsurumi and Managi (2010) in which the dependent variable denoted the level of emissions of some pollutants and the scale effect was captured by the GDP per capita.

  6. As expected, there is strong collinearity between the logarithm of GDP per capita and its squared term. As suggested by Aiken and West (1991) and Bryk and Raudenbush (2002) in order to mitigate multicollinearity we centered the linear term around its sample mean before creating the power. Notice that the interpretation of the coefficients remains the same.

  7. The prior of the effect of Agriculture on EPI is undefined. On the one hand, agriculture is supposed to be a green sector anticipating the stage of industrialization. On the other hand, the primary sector, including both crop and meat production, is one of the leading producers of GHGs. It is estimated that agriculture is responsible for 15 to 35 % of global anthropogenic GHG emissions, depending on whether or not deforestation is accounted for (Pandey 2009; Goel et al. 2013). Furthermore, countries with an important agricultural sector are exposed to powerful lobbies that prevent the introduction and applications of more stringent environmental laws and regulations (Fredriksson and Svensson 2003). This variable can also potentially capture the soil and water contamination through pesticides and fertilizers.

  8. High-income non-OECD countries are primarily oil exporting countries (Galeotti et al. 2006).

  9. For a detailed analysis of the methodological issues regarding this indicator, see Kaufmann et al. (2010). The database is available at http://info.worldbank.org/governance/wgi/index.aspx#home.

  10. Child Mortality is the only indicator that estimates the scores according to how well countries perform with regard to the best performing countries. This indicator accounts for 13.3 % of the whole EPI score.

  11. Among other available indices of corruption, it is worth mentioning the ICRG corruption index that measures the extent to which “high government officials are likely to demand special payments” as well as the extent to which “bribes connected with import and export licenses, exchange controls, tax assessment, policy protection, or loans” are generally expected throughout lower levels of government. This index has been used by Knack and Keefer (1995), Fredriksson and Svensson (2003), Damania et al. (2003), Cole (2007), Leitão (2010), Goel et al. (2013). However, within a given country, this measure is quite constant. Another cross-country proxy for the level of corruption is the measure of the control of corruption index (CPI) from Transparency International. This measure has been used in many investigations, including Lapatinas et al. (2014), and Niu and Li (2014). However, this index is not quite amenable to time series interpretation.

  12. Notice that all country scores are accompanied by standard errors. The standard errors reflect the number of sources available for a country and the extent to which these sources agree with each other (with more sources and more agreement leading to smaller standard errors).

  13. As expected, there is strong collinearity between the linear and quadratic terms of the variables Log (GDP) and Industry. In order to mitigate multicollinearity we centered the linear term around its sample mean before creating the power. The same procedure have been carried out when the interaction term is introduced: Log (GDP) and Corruption have been demeaned and then multiplied between each other. Notice that the interpretation of the coefficients remains the same.

  14. Linzer and Staton (2012) provide the measurement of the independence of judiciary. However, it shows 36 missing values compared to the original dataset used in the non-instrumented regressions. The correlation between this measurement and Corruption is equal to −0.7686, while small correlation has been found between this instrument and EPI.

  15. Consider that with the linear-log specification along with a quadratic term in logarithms, changes in the dependent variable would be as follows: \(\Delta {\textit{EPI}}=\beta _2 \frac{\Delta {\textit{GDP}}}{{\textit{GDP}}}+2\beta _3 \frac{\Delta {\textit{GDP}}}{{\textit{GDP}}}\times Log({\textit{GDP}})\).

  16. Population density has been used as an alternative measure to the level of urbanization. However, the variable’s parameter is still insignificant.

  17. As mentioned in Sect. 2, according to the results of the Hausman test [i.e., \(\hbox {X}^{2}(8)=149.08,\, Prob> \hbox {X}^{2}=0.0000\)], we can reject the null hypothesis that errors are not correlated with the regressors. Therefore, it is safer to adopt the fixed effects model instead of the random effects model.

  18. We owe this robustness check to an anonymous referee.

  19. The number of countries exceeding this threshold is 26. The highest value of Weight is 0.451.

  20. The F-test on the significance of the dummy and its two interactions rejects the null that the three coefficients are jointly equal to zero [F(3,152)=5.70, \(Prob>0.001\)].

  21. The classical Hausman test does not reject the null of non-systematic difference in coefficients [i.e., \(\hbox {X}^{2}(17)= 7.59,\, Prob> \hbox {X}^{2}= 0.9746\)]. The Suest version of the Hausman stability test (allowing for robust standard errors) again does not reject the same null hypothesis [i.e., \(\hbox {X}^{2}(18)= 9.81,\, Prob> \hbox { X}^{2}= 0.9379\)].

  22. The parameters have been estimated with a GMM fixed effects, where the fixed effects have been eliminated by applying the first difference transformation. This common procedure is suitable for fully balanced dataset. A first-order panel VAR model was selected according to the MBIC, MAIC and MQIC criteria (Andrews and Lu 2001). For theoretical details on VAR with panel data, see Holtz-Eakin et al. (1988). For the estimation procedure and the routine in Stata, see Love and Zicchino (2006), and Abrigo and Love (2015).

  23. The stability conditions are satisfied since all the eigenvalues lie inside the unit circle.

  24. The Panel VAR Granger-Causality Wald test reports the following results: Corruption Granger-causes EPI [\(\hbox {X}^{2}(1) = 13.861,\, Prob > \hbox { X}^{2 }= 0.000\)], EPI Granger-causes Corruption [\(\hbox {X}^{2}(1) = 1.349, \,Prob > \hbox { X}^{2 }= 0.246\)]; the null hypothesis is no Granger-causality.

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Acknowledgments

The authors thank participants at the third IAERE Annual Conference in Padua (Italy) for comments and suggestions. We also thank two anonymous referees. The usual disclaimer applies.

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    1. Department of Economics, University of Messina, Piazza Pugliatti 1, 98122, Messina, Italy

      Maurizio Lisciandra & Carlo Migliardo

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    1. Maurizio Lisciandra
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    2. Carlo Migliardo
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    Correspondence to Maurizio Lisciandra.

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    Appendix

    Appendix

    Table 6 Robustness checks on the EPI index
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    Fig. 2
    figure 2

    Panel VAR—Graphical Impulse Responses EPI-Corruption Notes: First-order panel VAR model. Errors are 5 % on each side generated by Monte-Carlo with 1000 reps. Standard errors are cluster-adjusted.

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    Table 7 Comparison between \(\hbox {Log} (\hbox {CO}_{2}\)) Emissions and EPI
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    Lisciandra, M., Migliardo, C. An Empirical Study of the Impact of Corruption on Environmental Performance: Evidence from Panel Data. Environ Resource Econ 68, 297–318 (2017). https://doi.org/10.1007/s10640-016-0019-1

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