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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Public sector corruption is also observed to increase the frequency of technological disasters (Yamamura 2013).
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).
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).
The empirical results of this paper do not change when other classifications are employed.
Definitions of classifications can be found on the EM-DAT website http://www.emdat.be/glossary/9 (accessed on December 7, 2013).
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.
Natural disaster data were sourced from the International Disaster Database. http://www.emdat.be (accessed on August 25, 2013).
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.
Countries included in the sample can be seen at the author’s website: (https://www.seinan-gu.ac.jp/~yamaei/).
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)
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).
Tables show the results of control variables are available at the author’s website (https://www.seinan-gu.ac.jp/~yamaei/).
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.
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/).
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).
Ades, A., & Di Tella, R. (1999). Rents, competition, and corruption. American Economic Review, 89, 982–993.
Albala-Bertrand, J. (1993). Political economy of large natural disasters. Oxford: Claredon Press.
Anbarci, N., Escaleras, M., & Register, C. (2005). Earthquake fatalities: The interaction of nature and political economy. Journal of Public Economics, 89, 1907–1933.
Anbarci, N., Escaleras, M., & Register, C. (2006). Traffic fatalities and public sector corruption. Kyklos, 59(3), 327–344.
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.
Baland, J. M., & Francois, P. (2000). Rent-seeking and resource booms. Journal of Development Economics, 61(2), 527–542.
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.
Brollo, F., Nannicini, T., Perotti, R., & Tabellini, G. (2013). The political resource curse. American Economic Review, 103(5), 1759–1796.
Chamlee-wright, E. (2010). The cultural and political economy of recovery: Social learning in a post-disaster environment. New York: Routledge.
Cuaresma, J. C., Hlouskova, J., & Obersteiner, M. (2008). Natural disasters as creative destruction? Evidence from developing countries. Economic Inquiry, 46(2), 214–226.
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.
Djankov, S., Montalvo, J., & Reynal-Querol, M. (2008). The curse of aid. Journal of Economic Growth, 13, 169–194.
Dreher, A., & Schneider, F. (2010). Corruption and the shadow economy: An empirical analysis. Public Choice, 144(1), 215–238.
Easterly, W., & Levine, R. (1997). Africa’s growth tragedy: Policies and ethnic divisions. Quarterly Journal of Economics, 112(4), 1203–1250.
Eisensee, T., & Strömberg, D. (2007). News droughts, news floods, and U.S. disaster relief. Quarterly Journal of Economics, 122(2), 693–728.
Escaleras, M., Anbarci, N., & Register, C. (2007). Public sector corruption and major earthquakes: A potentially deadly interaction. Public Choice, 132(1), 209–230.
Escaleras, M., Lin, S., & Register, C. (2010). Freedom of information acts and public sector corruption. Public Choice, 145(3), 435–460.
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.
Garret, T., & Sobel, R. (2003). The political economy of FEMA disaster payment. Economic Inquiry, 41, 496–509.
Glaeser, E. L., & Saks, R. E. (2006). Corruption in America. Journal of Public Economics, 90(6–7), 1407–1430.
Gokcekus, O. (2008). Is it protestant tradition or current protestant population that affects corruption? Economics Letters, 99, 59–62.
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.
Jain, A. (2001). Corruption: A review. Journal of Economic Surveys, 15, 71–121.
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.
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.
Kellenberg, D., & Mobarak, A. M. (2008). Does rising income increase or decrease damage risk from natural disasters? Journal of Urban Economics, 63, 788–802.
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.
Leeson, P. T., & Sobel, R. (2008). Weathering corruption. Journal of Law and Economics, 51, 667–681.
Leff, N. H. (1964). Economic development through bureaucratic corruption. American Behavioral Scientist, 82(2), 337–341.
Luechinger, S., & Saschkly, P. A. (2009). Valuing flood disasters using the life satisfaction approach. Journal of Public Economics, 93, 620–633.
Lui, F. T. (1985). An equilibrium queuing model of bribery. Journal of Political Economy, 93(4), 760–781.
Mauro, P. (1995). Corruption and growth. Quarterly Journal of Economics, 110, 681–712.
Niskanen, W. A. (1971). Bureaucracy and representative government. Chicago: Aldine-Atherton.
Paldam, M. (2001). Corruption and religion adding to the economic model. Kyklos, 54(2–3), 383–413.
Pedro, V. (2010). Does oil corrupt? Evidence from a natural experiment in West Africa. Journal of Development Economics, 92(1), 28–38.
Pellegrini, L., & Gerlagh, R. (2008). Causes of corruption: A survey of cross-country analyses and extended results. Economic Governance, 9, 245–263.
Robinson, J. A., Torvik, R., & Verdier, T. (2006). Political foundation of the resource curse. Journal of Development Economics, 79(1), 447–468.
Serra, D. (2006). Empirical determinants of corruption: A sensitivity analysis. Public Choice, 126, 225–256.
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.
Shleifer, A., & Vishny, R. (1993). Corruption. Quarterly Journal of Economics, 108, 599–617.
Shughart, W. F, I. I. (2006). Katrinanomics: The politics and economics of disaster relief. Public Choice, 127, 31–53.
Shughart, W. F, I. I. (2011). Disaster relief as a bad public good. Independent Review, 15(4), 519–539.
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.
Skidmore, M., & Toya, H. (2002). Do natural disasters promote long-run growth? Economic Inquiry, 40(4), 664–687.
Sobel, R., & Leeson, P. (2006). Government’s response to Hurricane Katrina: A public choice analysis. Public Choice, 127, 55–73.
Strobl, E. (2011). The economic growth impact of hurricanes: Evidence from U.S. coastal countries. Review of Economics and Statistics, 93(2), 575–589.
Svensson, J. (2000). Foreign aid and rent seeking. Journal of International Economics, 51, 437–461.
Swaleheen, M. (2011). Economic growth with endogenous corruption: An empirical study. Public Choice, 146(1), 23–41.
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.
Toya, H., & Skidmore, M. (2007). Economic development and the impacts of natural disasters. Economics Letters, 94(1), 20–25.
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.
Vigdor, J. L. (2009). Book review of “Rising house and home: disasters, cities, public policy”. Journal of Economic Literature, 47(4), 1156–1157.
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.
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.
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