Can extreme rainfall trigger democratic change? The role of flood-induced corruption

Abstract

Using a new dataset of extreme rainfall covering 130 countries from 1979 to 2009, this paper investigates whether and how extreme rainfall-driven flooding affects democratic conditions. Our key finding indicates that extreme rainfall-induced flooding exerts two opposing effects on democracy. On one hand, flooding leads to corruption in the chains of emergency relief distribution and other post-disaster assistance, which in turn impels the citizenry to demand more democracy. On the other hand, flooding induces autocratic tendencies in incumbent regimes because efficient post-disaster management with no dissent, chaos or plunder might require government to undertake repressive actions. The net estimated effect is an improvement in democratic conditions.

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Fig. 1
Fig. 2

Notes

  1. 1.

    On a related, but nevertheless distinct topic, Sobel and Leeson (2006), Schultz and Libman (2015) and Escaleras and Register (2012) show how decentralized political institutions and local knowledge contribute to government effectiveness in responding to natural disasters.

  2. 2.

    A well-known historical example is the severe 1970 flooding in Eastern Pakistan, which acted as a catalyst for Bangladesh’s Liberation War in 1971.

  3. 3.

    Earlier evidence on the public reaction against corruption is provided by Peters and Welch (1980), who show that corruption charges against candidates reduce the votes these candidates receive in US congressional elections by 6–11%. See also Welch and Hibbing (1997).

  4. 4.

    Escaleras et al. (2007) show that in countries with more corruption, earthquakes are more deadly.

  5. 5.

    The off-equilibrium prediction of the model is that rampant corruption in the flood year is followed by less democracy in the subsequent year, but then the regime faces an insurgency. The model also implies that preventing corruption after flooding events can go hand in hand with autocracy off-the-equilbrium.

  6. 6.

    Of more than 2100 court cases opened to investigate the death of 17,280 people, the judiciary was able to punish only one contractor, Veli Göçer, who was sentenced to 7.5 years (for a total of 195 deaths in the sites he built) and became a public name. Hundreds of other contractors escaped punishment.

  7. 7.

    Each year, approximately 320,000 km3 of water evaporates from the oceans and 60,000 km3 evaporates from lakes, lagoons and streams. Of the total of 380,000 km3 of evaporation, approximately 284,000 km3 falls back into the world’s oceans as precipitation and 96,000 onto the land surface, creating the hydrological cycle.

  8. 8.

    Cherrapunji in northeast India experiences the world's heaviest rainfall of up to approximately 10,922 mm (430 in.) per year. In the United States, the heaviest rainfall amounts—up to 1778 mm (70 in.)—are experienced in the southeast, followed by moderate annual accumulations, from 762–1270 mm (30–50 in.), in the eastern United States, and smaller accumulations, 381–1016 mm (15–40 in.), in the central plains.

  9. 9.

    The contemporary hydrology literature demonstrates the relationship between runoff and flood severity. See Sui and Koehler (2001) and Cunderlik and Burn (2002).

  10. 10.

    The correlation between our measure and alternative data sources such as the National Center for Environment Prediction and the UN Food and Agricultural Organization agro-climatic database exceeds 0.8.

  11. 11.

    Adopting the standard deviation of monthly total rainfall in a given year for each 2.5° node to measure extreme rainfall yields qualitatively similar findings.

  12. 12.

    Our results remain qualitatively similar using the 95th, 85th, 80th and 75th percentile thresholds.

  13. 13.

    For example, Makkah Province in Saudi Arabia faces severe seasonal flash floods notwithstanding that it is situated in an arid area characterized by high temperatures and low rainfall.

  14. 14.

    The exclusion of smaller countries, such as country C in Fig. 2, is unlikely to affect our results, as we employ a large panel of 130 countries and capture extreme rainfall variations on a small-scale interval, i.e., 2.5° × 2.5°.

  15. 15.

    See Starr (1991), Starr and Lindborg (2003) and Leeson and Dean (2009).

  16. 16.

    Our sample indicates that 25% of flooding events around the world during the 1979–2009 period affected at least 1% or more of a country’s population, on average.

  17. 17.

    We would like to thank an anonymous reviewer for having revealed to us the lags in the timing of variables and their potential implications.

  18. 18.

    We take the natural log of the per capita agricultural output to maintain the underlying data distribution uniformly symmetric.

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Acknowledgements

We would like to thank Markus Brückner, Cahit Guven, Cem Karayalcin, Philip Keefer, Jakob Madsen, Ilan Noy, Paul Raschky, and Jeffrey Wooldridge for very useful input into this paper. All errors are our own.

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Correspondence to Mehmet Ali Ulubaşoğlu.

Appendices

Appendix 1

See Table 6.

Table 6 Total number of nodes used in deriving extreme rainfall estimates in each country

Appendix 2

See Table 7.

Table 7 Data definitions and sources

Appendix 3: Data on other variables

The Polity IV project revised combined Polity score (i.e., Polity2), ranging from −10 to 10 (i.e., autocracy to democracy), is taken as the measure of democracy (Marshall and Jaggers 2005). It estimates the level of democracy based on the competitiveness of political participation, the openness and competitiveness of executive recruitment and constraints on the executives. In spite of having several methodological shortcomings, the Polity2 score is—arguably—the most accurate measure of democracy, thus is widely used in the literature (see Glaeser et al. 2004).

Our income measure—that is, real GDP per capita—and the population size are sourced from the Penn World Tables (PWT), version 7.0 (Heston et al. 2011), which provides data for the period 1950– 2009. The agriculture value-added data—measured as the share of the population for each country in a given year—are from the UN Statistical Division (UNSTAT, 2012).Footnote 18

The corruption index dataset—ranging from 0 to 6, in which higher values refer to higher political risk of involvement in corruption—are obtained from the PRS’s International country risk guide (see PRS-ICRG 2007). This PRS measure captures the corruption within the political system that is a threat to foreign investment by distorting the economic and financial environment, reducing the efficiency of government and business by enabling people to assume positions of power through patronage rather than ability, and introducing inherent instability into the political process (PRS-ICRG 2007).

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Rahman, M.H., Anbarci, N., Bhattacharya, P.S. et al. Can extreme rainfall trigger democratic change? The role of flood-induced corruption. Public Choice 171, 331–358 (2017). https://doi.org/10.1007/s11127-017-0440-1

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Keywords

  • Extreme rainfall shocks
  • Flood severity
  • Corruption
  • Democracy

JEL Classification

  • O0
  • P0