Skip to main content

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Notes

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

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

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

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

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

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

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

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

  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. 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. 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).

References

  • Ades, A., & Di Tella, R. (1999). Rents, competition, and corruption. American Economic Review, 89, 982–993.

    Article  Google Scholar 

  • Albala-Bertrand, J. (1993). Political economy of large natural disasters. Oxford: Claredon Press.

    Google Scholar 

  • Anbarci, N., Escaleras, M., & Register, C. (2005). Earthquake fatalities: The interaction of nature and political economy. Journal of Public Economics, 89, 1907–1933.

    Article  Google Scholar 

  • Anbarci, N., Escaleras, M., & Register, C. (2006). Traffic fatalities and public sector corruption. Kyklos, 59(3), 327–344.

    Article  Google Scholar 

  • Apergis, N., Dincer, O., & Payne, J. (2010). The relationship between corruption and income inequality in U.S. states: Evidence from a panel co-integration and error correction model. Public Choice, 145(1), 125–135.

    Article  Google Scholar 

  • Baland, J. M., & Francois, P. (2000). Rent-seeking and resource booms. Journal of Development Economics, 61(2), 527–542.

    Article  Google Scholar 

  • Boettke, P., Chamlee, E., Gordon, P., Ikeda, S., Leeson, P. T., & Sobel, R. (2007). The political, economic, and social aspects of Katrina. Southern Economic Journal, 74(2), 363–376.

    Google Scholar 

  • Brollo, F., Nannicini, T., Perotti, R., & Tabellini, G. (2013). The political resource curse. American Economic Review, 103(5), 1759–1796.

    Article  Google Scholar 

  • Chamlee-wright, E. (2010). The cultural and political economy of recovery: Social learning in a post-disaster environment. New York: Routledge.

    Google Scholar 

  • Cuaresma, J. C., Hlouskova, J., & Obersteiner, M. (2008). Natural disasters as creative destruction? Evidence from developing countries. Economic Inquiry, 46(2), 214–226.

    Article  Google Scholar 

  • Daily Yomiuri. (2013). Government launches into reconstruction fund misuse, Daily Yomiuri, May 12, 2013.

  • Djankov, S., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2003). Courts. Quarterly Journal of Economics, 118, 453–517.

    Article  Google Scholar 

  • Djankov, S., Montalvo, J., & Reynal-Querol, M. (2008). The curse of aid. Journal of Economic Growth, 13, 169–194.

    Article  Google Scholar 

  • Dreher, A., & Schneider, F. (2010). Corruption and the shadow economy: An empirical analysis. Public Choice, 144(1), 215–238.

    Article  Google Scholar 

  • Easterly, W., & Levine, R. (1997). Africa’s growth tragedy: Policies and ethnic divisions. Quarterly Journal of Economics, 112(4), 1203–1250.

    Article  Google Scholar 

  • Eisensee, T., & Strömberg, D. (2007). News droughts, news floods, and U.S. disaster relief. Quarterly Journal of Economics, 122(2), 693–728.

    Article  Google Scholar 

  • Escaleras, M., Anbarci, N., & Register, C. (2007). Public sector corruption and major earthquakes: A potentially deadly interaction. Public Choice, 132(1), 209–230.

    Article  Google Scholar 

  • Escaleras, M., Lin, S., & Register, C. (2010). Freedom of information acts and public sector corruption. Public Choice, 145(3), 435–460.

    Article  Google Scholar 

  • Escaleras, M., & Register, C. A. (2008). Mitigating natural disasters through collective action: The effectiveness of Tsunami early warnings. Southern Economic Journal, 74(4), 1017–1034.

    Google Scholar 

  • Garret, T., & Sobel, R. (2003). The political economy of FEMA disaster payment. Economic Inquiry, 41, 496–509.

    Article  Google Scholar 

  • Glaeser, E. L., & Saks, R. E. (2006). Corruption in America. Journal of Public Economics, 90(6–7), 1407–1430.

    Google Scholar 

  • Gokcekus, O. (2008). Is it protestant tradition or current protestant population that affects corruption? Economics Letters, 99, 59–62.

    Article  Google Scholar 

  • Jaffe, D., & Russell, T. (2008). Financing catastrophe insurance: A new proposal. In J. M. Quigley & L. A. Rosenthal (Eds.), Risking house and home: Disasters, cities, public policy. San Francisco: Berkley Public Policy Press.

    Google Scholar 

  • Jain, A. (2001). Corruption: A review. Journal of Economic Surveys, 15, 71–121.

    Article  Google Scholar 

  • Japan Times. (2012). Misuse of reconstruction funds (Editorials), Japan Times, October 20, 2012.

  • Johnson, N., La Fountain, C., & Yamarik, S. (2011). Corruption is bad for growth (even in the United States). Public Choice, 147, 377–393.

    Article  Google Scholar 

  • Kahn, M. (2005). The death toll from natural disasters: The role of income, geography and institutions. Review of Economics and Statistics, 87(2), 271–284.

    Article  Google Scholar 

  • Kellenberg, D., & Mobarak, A. M. (2008). Does rising income increase or decrease damage risk from natural disasters? Journal of Urban Economics, 63, 788–802.

    Article  Google Scholar 

  • Kurosaki, T. (2013). Vulnerability of household consumption to floods and droughts in developing countries: evidence from Pakistan. Center for Economic Institutions Working Paper Series (Hitotsubashi University), no. 2012-10.

  • La Porta, R., Lopez de Silanes, F., Shleifer, A., & Vishni, R. (1999). Quality of government. Journal of Law Economics and Organization, 15(1), 222–279.

    Article  Google Scholar 

  • Leeson, P. T., & Sobel, R. (2008). Weathering corruption. Journal of Law and Economics, 51, 667–681.

    Article  Google Scholar 

  • Leff, N. H. (1964). Economic development through bureaucratic corruption. American Behavioral Scientist, 82(2), 337–341.

    Google Scholar 

  • Luechinger, S., & Saschkly, P. A. (2009). Valuing flood disasters using the life satisfaction approach. Journal of Public Economics, 93, 620–633.

    Article  Google Scholar 

  • Lui, F. T. (1985). An equilibrium queuing model of bribery. Journal of Political Economy, 93(4), 760–781.

    Article  Google Scholar 

  • Mauro, P. (1995). Corruption and growth. Quarterly Journal of Economics, 110, 681–712.

    Article  Google Scholar 

  • Niskanen, W. A. (1971). Bureaucracy and representative government. Chicago: Aldine-Atherton.

    Google Scholar 

  • Paldam, M. (2001). Corruption and religion adding to the economic model. Kyklos, 54(2–3), 383–413.

    Article  Google Scholar 

  • Pedro, V. (2010). Does oil corrupt? Evidence from a natural experiment in West Africa. Journal of Development Economics, 92(1), 28–38.

    Article  Google Scholar 

  • Pellegrini, L., & Gerlagh, R. (2008). Causes of corruption: A survey of cross-country analyses and extended results. Economic Governance, 9, 245–263.

    Article  Google Scholar 

  • Robinson, J. A., Torvik, R., & Verdier, T. (2006). Political foundation of the resource curse. Journal of Development Economics, 79(1), 447–468.

    Article  Google Scholar 

  • Serra, D. (2006). Empirical determinants of corruption: A sensitivity analysis. Public Choice, 126, 225–256.

    Article  Google Scholar 

  • Shiue, C. H. (2004). Local granaries and central government disaster relief: Moral hazard and intergovernmental finance in eighteenth- and nineteenth-century China. Journal of Economic History, 64(1), 100–124.

    Article  Google Scholar 

  • Shleifer, A., & Vishny, R. (1993). Corruption. Quarterly Journal of Economics, 108, 599–617.

    Article  Google Scholar 

  • Shughart, W. F, I. I. (2006). Katrinanomics: The politics and economics of disaster relief. Public Choice, 127, 31–53.

    Article  Google Scholar 

  • Shughart, W. F, I. I. (2011). Disaster relief as a bad public good. Independent Review, 15(4), 519–539.

    Google Scholar 

  • Simmons, K. M., Kruse, J. B., & Smith, D. A. (2002). Valuing mitigation: Real estate market response to hurricane loss reduction measures. Southern Economic Journal, 68(3), 660–671.

    Article  Google Scholar 

  • Skidmore, M., & Toya, H. (2002). Do natural disasters promote long-run growth? Economic Inquiry, 40(4), 664–687.

    Article  Google Scholar 

  • Sobel, R., & Leeson, P. (2006). Government’s response to Hurricane Katrina: A public choice analysis. Public Choice, 127, 55–73.

    Article  Google Scholar 

  • Strobl, E. (2011). The economic growth impact of hurricanes: Evidence from U.S. coastal countries. Review of Economics and Statistics, 93(2), 575–589.

    Article  Google Scholar 

  • Svensson, J. (2000). Foreign aid and rent seeking. Journal of International Economics, 51, 437–461.

    Article  Google Scholar 

  • Swaleheen, M. (2011). Economic growth with endogenous corruption: An empirical study. Public Choice, 146(1), 23–41.

    Article  Google Scholar 

  • Tanzi, B., & Davoodi, H. (1997). Corruption, public investment, and growth. IMF working paper WP/97/139.

  • Torvik, R. (2002). Natural resources, rent seeking and welfare. Journal of Development Economics, 67(2), 455–470.

    Article  Google Scholar 

  • Toya, H., & Skidmore, M. (2007). Economic development and the impacts of natural disasters. Economics Letters, 94(1), 20–25.

    Article  Google Scholar 

  • Toya, H., & Skidmore, M. (2013). Do natural disasters enhance societal trust? CESifo Working papers 3905.

  • Treisman, D. (2000). The causes of corruption: A cross-national study. Journal of Public Economics, 76(3), 399–457.

    Article  Google Scholar 

  • Vigdor, J. L. (2009). Book review of “Rising house and home: disasters, cities, public policy”. Journal of Economic Literature, 47(4), 1156–1157.

    Google Scholar 

  • World Bank. (2010). World development indicators 2010 on CD-ROM. The World Bank.

  • Yamamura, E. (2013). Public sector corruption and the probability of technological disasters. Economics of Governance, 14(3), 233–255.

    Article  Google Scholar 

  • Zanjani, G. (2008). Public versus private underwriting of catastrophic risk: Lessons from the California earthquake authority. In J. M. Quigley & L. A. Rosenthal (Eds.), Risking house and home: Disasters, cities, public policy. San Francisco: Berkley Public Policy Press.

    Google Scholar 

Download references

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)).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eiji Yamamura.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11127-014-0154-6

Keywords

  • Corruption
  • Institution
  • Disasters
  • Risk

JEL classification

  • D73
  • D81
  • Q54