Impact of natural disaster on public sector corruption

Abstract

This paper uses inter-country panel data from 1990 through 2010 to examine how the occurrence of natural disasters affects corruption within the public sector. For a closer analysis, disaster is classified into various categories, including general floods, other floods, tropical storms, other storms, earthquakes, volcanic eruptions, and landslides. Furthermore, this paper explores whether natural disasters have different impacts on corruption levels in developed and developing countries. The study reveals a number of novel findings. (1) Natural disasters that cause substantial damage increase public sector corruption in both developing and developed countries. (2) Natural disasters have a greater impact on public sector corruption in developed countries than in developing countries. (3) In developed countries, natural disaster frequency has a significant impact on the level of corruption. Hence, foreseeable disasters increase corruption in general. In developed countries, an incentive may exist to live within disaster-prone areas because of the potential for disaster compensation payments.

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Notes

  1. 1.

    Specifically, in recent years researchers have investigated the impact of natural disasters on economic growth (Skidmore and Toya 2002; Strobl 2011), death toll (Anbarci et al. 2005; Kahn 2005; Toya and Skidmore 2007), and trust (Toya and Skidmore 2013).

  2. 2.

    Public sector corruption is also observed to increase the frequency of technological disasters (Yamamura 2013).

  3. 3.

    Some studies explore the relation between disasters and moral hazard issues (Simmons et al. 2002; Shiue 2004).

  4. 4.

    There are few empirical analyses of corruption before the 1990s (partly because of data limitations), although a number of classical anecdotal and theoretical research works exist (Leff 1964; Lui 1985; Shleifer and Vishny 1993; Jain 2001). The seminal work of Mauro (1995) was the first to explore the effects of corruption empirically, and there were a significant number of subsequent studies (e.g., Anbarci et al. 2006; Glaeser and Saks 2006; Apergis et al. 2010; Dreher and Schneider 2010; Escaleras et al. 2010; Johnson et al. 2011; Swaleheen 2011).

  5. 5.

    Numerous studies have attempted to ascertain the determinants of corruption (Treisman 2000; Paldam 2001; Serra 2006; Pellegrini and Gerlagh 2008).

  6. 6.

    ICRG data scores corruption with a range of 0–6, with 6 indicating no corruption. In this paper, to simplify matters, the score is inverted. Thus, countries with an ICRG score of 6 are given a value of 0 in this paper (i.e., no corruption) and countries with an ICRG score of 0 are given a value of 6 (i.e., very corrupt).

  7. 7.

    The empirical results of this paper do not change when other classifications are employed.

  8. 8.

    Definitions of classifications can be found on the EM-DAT website http://www.emdat.be/glossary/9 (accessed on December 7, 2013).

  9. 9.

    EM-DAT offers alternative ways of measuring the cost of a disaster, including fatalities or injuries. Their relative values for each disaster are almost identical to those illustrated in Fig. 4. Hence, the argument of this paper does not change if other values are used to measure the cost of a disaster.

  10. 10.

    Natural disaster data were sourced from the International Disaster Database. http://www.emdat.be (accessed on August 25, 2013).

  11. 11.

    When we compare the landmass of Canada and the United States., attention should be paid to population size. (1) The population of Canada is approximately 34 million, while the US population (about 314 million) is almost ten times that. (2) Vast areas of Canada are unpopulated or have very low population densities, thus many natural disasters would go unreported as they did not affect the human population.

  12. 12.

    Countries included in the sample can be seen at the author’s website: (https://www.seinan-gu.ac.jp/~yamaei/).

  13. 13.

    In the regression estimation, various control variables are included. Latitude, the highest point of elevation, lowest point of elevation, percentage of land area where elevation is below 5 meters, agricultural sector ratio and industrial sector ratio (percentages of GDP) are available from World Bank (2010).

    The percent of the population belonging to the Catholic Church is used by Easterly and Levine (1997). The data are available from http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:20700002~pagePK:64214825~piPK:64214943~theSitePK:469382,00.html (accessed June 2, 2011). Legal origin dummies and measure of democracy are available at http://www.economics.harvard.edu/faculty/shleifer/dataset (accessed on June 1, 2011)

  14. 14.

    The data were available at the website of Penn World Table. https://pwt.sas.upenn.edu/php_site/pwt71/pwt71_form.php (accessed on August 25, 2013).

  15. 15.

    Tables show the results of control variables are available at the author’s website (https://www.seinan-gu.ac.jp/~yamaei/).

  16. 16.

    The association between natural disasters in year t−3 and corruption in year t disappears; natural disasters in year t−3 therefore are not included.

  17. 17.

    Figures illustrating average damage levels for the non-OECD and OECD care available on the author’s website (https://www.seinan-gu.ac.jp/~yamaei/).

  18. 18.

    In the U.S., the National Flood Insurance Program causes a moral hazard problem. “The program dramatically distorts the signaling mechanism that would otherwise guide property owners away from the areas prone to flooding from any source” (Chamlee-Wright 2010, p. 140).

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Acknowledgments

I would like to thank the insightful comments of two anonymous referees and professor William F. Shughart II (editor in chief), which have improved this article considerably. I am responsible for all remaining errors. I gratefully acknowledge financial support from the Japanese Society for the Promotion of Science (Grant-in-Aid for Scientific Research (C): 25380347 (Principal Investigator: Eiji Yamamura)).

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Yamamura, E. Impact of natural disaster on public sector corruption. Public Choice 161, 385–405 (2014). https://doi.org/10.1007/s11127-014-0154-6

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Keywords

  • Corruption
  • Institution
  • Disasters
  • Risk

JEL classification

  • D73
  • D81
  • Q54