The Impact of Natural Disasters on Income Inequality: Analysis using Panel Data during the Period 1970 to 2004

Abstract

Although natural disasters have been found to influence economic growth, their impact on income inequality has not yet been explored. This paper uses cross-country panel data during the period 1965 to 2004 to examine how the occurrence of natural disasters has affected income inequality. The major findings of this study are that although natural disasters have increased income inequality in the short (5 years) term, this effect disappears in the long term (10 years). These findings are observed even after the fixed effects of year and country are controlled for.

Key Words:

• Natural disasters
• income inequality

JEL Classifications:

• D63
• Q54

Notes

1 Natural disasters are observed to have had a significant impact on poverty level and human development (Rodriguez-Oreggia et al., 2013).

2 Inequality possibly increases the number of traffic fatalities (Anbarci, Estcaleras, & Register, 2009).

3 Natural disasters are found to enhance social trust (Toya & Skidmore, 2013b), and are regarded as a positive externality. In contrast, natural disasters have been found to increase corruption in the public sector, and are regarded as a negative externality (Yamamura, 2014).

4 In the case of the Hanshin Awaji earthquake of 1995, there was a considerable difference in the damage incurred by antiquated wooden buildings and the damage to modern earthquake-proof buildings (Ministry of Land, Infrastructure, Transport and Tourism, 1996, 12).

5 The United Nations University World Institute for Development Economics Research constructed the World Income Inequality Database (WIID); it contains information on national income distributions from several thousand underlying statistical surveys, and WIID data are widely used by researchers. However, the data suffer from international and inter-temporal inconsistencies owing to how the raw data were collected. Therefore, in the WIID, only a portion of the data is considered of high quality. To improve the WIID data, SIDD data is constructed based on WIID Gini coefficients by considering adjustment factors for different scopes of coverage, income definitions, and reference units. Data were obtained from http://salvatorebabones.com/data-downloadshttp://salvatorebabones.com/data-downloads (accessed on 1 June 2011). This paper has used SIDD-3 (which is an interpolated and extrapolated version of SIDD-2) incorporating in-sample and out-of-sample estimates for 1955 to 2005.

6 The year 1969 reported in Figures 1 and 2 is 5-year average values of Gini coefficients and number of disasters between 1965 and 1969. The values in 1974, 1979, 1984, 1989, 1994, 1999, and 2004 are calculated in the same way.

7 Data were obtained from http://www.emdat.behttp://www.emdat.be (accessed on 1 June 2011). Disasters included in the EM-DAT database must fulfill at least one of the following criteria: (1) 10 or more reported fatalities; (2) more than 100 people affected; (3) declaration of a state of emergency; and (4) a call for international assistance.

8 Types can be divided into drought, earthquake, extreme temperature, flood, mass movement dry, mass movement wet, storm volcano and wildfire.

9 The results do not vary for other disaster types.

10 The ethnic (religious) polarization index can be defined as: Where is the proportion of the population who profess to belong to a given ethnic group i. This index measures the normalized distance of a particular distribution of ethnic groups within a bimodal distribution. Here, ethnic group is represented as i for country j. The index can be calculated for each country.

11 Legal origin dummies and a measure of democracy are available at http://www.economics.harvard.edu/faculty/shleifer/datasethttp://www.economics.harvard.edu/faculty/shleifer/dataset (accessed on 1 June 2011).

12 For a robustness check of the results, I also conducted alternative estimations where the number of deaths in disasters was used as the key independent variable. However, there is no statistical significance between Gini coefficients and the number of deaths in disasters. This might be in part because of an attenuation bias caused by measurement error. Thus, care should be taken when interpreting the results. The estimation results are not reported but are available upon request.