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Public sector corruption and the probability of technological disasters

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Abstract

A growing number of studies have explored the influence of institution on the outcomes of disasters and accidents from the viewpoint of political economy. This paper focuses on the probability of the occurrence of disasters rather than disaster outcomes. Using panel data from 98 countries, this paper examines how public sector corruption is associated with the probability of technological disasters. It was found that public sector corruption raises the probability of technological disasters. This result is robust when endogeneity bias is controlled.

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Notes

  1. Corruption in general is defined as the use of public office for private gains (Bardhan 1997). The main forms of corruption include bribes received by public officials, the embezzlement by public officials of resources that they are entrusted to administer, fraud in the form of manipulating information to further the personal interests of public officials, extortion, and favoritism (Andivg and Fjeldstad 2001).

  2. Kahn (2005) provides evidence that area dummies, absolute value of latitude, and land area are important determinants in the occurrence of natural disasters, whereas GDP per capita is not considered to be a determinant.

  3. Intuitively, there is a wide range of causal factors through which corruption may increase the risk of failure. It is plausible that corruption decreases the incentive to adopt safety measures when the cost of obtaining a particular authorization with a bribe is lower than the cost of providing the safety measures.

  4. The licensing hypothesis requires that safety regulations be in place. However, corruption can reduce the level of regulation. This corruption effect appears dependent on the degree of democracy. As explained in the Sect. 3, country dummies are included as independent variables to control the degree of democracy.

  5. In the regression estimations in this paper, per capita income is included as an independent variable and thus the income effect is controlled for. Hence, the indirect effect of corruption on disasters through income level is not captured in the coefficient of corruption.

  6. Kellenberg and Mobarak (2008) suggested that the relationship between GDP levels and the damage caused by natural disasters takes an inverted U shape, rather than being monotonically negative.

  7. Media is also considered to be a critical determinant of the damage caused by natural disasters (Eisensee and Strömberg 2007).

  8. Disasters have both direct and indirect detrimental effects on economic conditions. One indirect effect is the distortion of allocation through political economy channels. Garret and Sobel (2003) examined the flow of Federal Emergency Management Administration money and found that nearly half of all disaster relief is motivated politically rather than by need. Sobel and Leeson (2006) explored the outcome of Hurricane Katrina and argued that it is difficult for a centralized agency to make the best use of dispersed information to coordinate the demand for available supplies. The damage caused by Hurricane Katrina was magnified because of a massive governmental failure (Shughart 2006). Congleton (2006) pointed out that the cause of the catastrophe that followed Katrina can be attributed to an interaction between the geographical features of New Orleans and the failure of the New Orleans levee system.

  9. The International Monetary Fund (IMF) has stated that corruption causes public finance to be ineffective in the enhancement of economic development (Hillman 2004).

  10. Golden and Picci (2005) argued that the activities surrounding public works construction are the classic locus of illegal monetary activities between public officials and business. They also developed an objective measure of corruption. The measure calculates the difference between the physical quantities of public infrastructure and the cumulative price that the government pays for public capital stocks. Where the difference is greater between the money spent and the existing physical infrastructure—indicating that more money has been siphoned off in corrupt transactions—higher levels of corruption exist.

  11. Jain (2001) provided a literature review of classic research and introduced the current debate among researchers.

  12. According to the Centre for Research on the Epidemiology of Disasters, technological disasters can be categorized into three categories: industrial, miscellaneous, and transport accidents. http://www.emdat.be/explanatory-notes (accessed on June 15, 2011).

  13. The number of technological disasters was sourced from the International Disaster Database. http://www.emdat.be (accessed on June 1, 2011).

  14. In addition to data regarding the number of technological disasters, EM-DAT also provides various indexes for damage caused by disasters such as estimated damage costs (US), number of homeless, number

    of injured, and number of deaths. This paper, however, focuses on the determinants of accidents rather than the determinants of damage. Hence, indexes for damage caused by disasters are not used in this paper.

  15. Available from http://info.worldbank.org/governance/wgi/index.asp (accessed on June 1, 2011).

  16. Transparency International also provides the proxy for corruption. This data covers 1995 to 2010, which is a shorter period than the ICRG corruption index. The number of countries included in the data from Transparency International is smaller than in the World Bank corruption index. That is, the data from Transparency International are not as helpful. Therefore, this paper does not use those data in estimations.

  17. It is important to consider the difference between the accident categories to examine the influence of corruption. However, it is beyond scope of this paper to do so in detail. Future research could deal with this issue.

  18. As indicated by Fig. 1, there are some outliers. In this case, per capital technological disasters are a likely alternative measure and so could be used as a dependent variable. However, in all estimations, population has been already included as an independent variable. This means that the scale of each county has been controlled for. That is, the outlier bias is, to a certain extent, alleviated.

  19. When \(y\) is a dependent variable, “for strictly positive variables, we often use the natural log transformation, log\((y)\), and use a linear model. This approach is not possible in interesting count data applications, where \(y\) takes on the value zero for nontrivial fraction of the population.” (Wooldridge 2002, 645).

  20. An instrumental variables negative binominal model is more appropriate. However, a method such as this has not been developed. The IV Poisson model is considered to be the second-best model and so is used in this paper. For the estimation, I used the IV Poisson model procedure outlined in Stata. I thank a referee for his/her suggestion to use the IV Poisson model.

  21. Freille et al. (2007) suggested that political and economic influences on the media were strongly related to corruption.

  22. Previous works generally used the percentage of Protestants to examine corruption. In this paper, however, these data are not used because they did not create a good fit with the estimated model when used as an independent variable.

  23. It is available at http://www.economics.harvard.edu/faculty/shleifer/dataset (accessed on May 1, 2011).

  24. In contrast to Catholics, “Protestantism leads to a civil society that more effectively monitors the state” (Gokcekus 2008, 59).

  25. One would think that institutional factors may matter and should be included as independent variables in Eq. (1) rather than used as instruments in Eq. (2). However, in Eq. (1) the time-invariant features are captured by country dummies, and therefore instrumental variables such as the legal origin dummies and the proxy for religion are removed.

  26. Period dummies are not included because the estimation does not reach convergence if period dummies are included.

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Acknowledgments

I gratefully acknowledge financial support in the form of research grants from the Japan Center for Economic Research as well as the Japanese Society for the Promotion of Science (Foundation (C) 22530294).

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  1. Seinan Gakuin University, Nishijin, Japan

    Eiji Yamamura

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  1. Eiji Yamamura
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Correspondence to Eiji Yamamura.

Appendix

Appendix

See Tables 6 and 7.

Table 6 List of countries used in the analysis
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Table 7 Types of technological disasters (number of accidents)
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Yamamura, E. Public sector corruption and the probability of technological disasters. Econ Gov 14, 233–255 (2013). https://doi.org/10.1007/s10101-013-0125-2

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