Modeling Impact of Hurricane Damages on Income Distribution in the Coastal U.S.

Abstract

This article examines the impact of catastrophic hurricane events on income distribution in hurricane states in the United States. Media claims have been made and the perception created that the most damaging impact of hurricanes is on the lowest income population in the affected states. If these claims are true, they may have serious implications for the insurance industry and government policy makers. We develop a panel data, fixed effects econometric model that includes hurricane-impacted states as cross-sections using annual data for a period of almost 100 years. The Gini coefficient is used as a measure of income inequality, and is a function of normalized hurricane economic damages, gross domestic product (GDP), a set of socioeconomic variables that serves as a control, time trend, and cross-sectional dummy variables. Findings indicate that for every 100 billion US dollars in hurricane economic damages there is an increase in income inequality by 5.4 % as measured by Gini coefficient. Political, sociodemographic, and economic variables are also significant. These include such variables as the political party controlling the U.S. Senate, the proportion of nonwhite population by state, and GDP. Time trend is a positive and significant variable, suggesting an increase in income inequality over time. There are significant differences among the states included in the study. Our results demonstrate that different segments of the population are differently impacted by hurricanes and suggest how that differential impact could be considered in future government policies and business decisions, particularly those made by the insurance industry.

Modeling Impact of Natural Hazard-Induced Disasters on Income Distribution in the United States

“…To be consistent with the previous research (Miljkovic and Miljkovic 2014), the data on property losses (PL) from past hurricanes were obtained from Pielke et al (2008) for the period prior to 2006. These hurricane-caused property losses were further normalized at current value to account for wealth adjusted by inflation and percent change in population growth using the methodology proposed by Brunkard et al (2008).…”

“…The proportion of explained variability in the model is measured with the coefficient of determination, R 2 . As noted by Miljkovic and Miljkovic (2014), it would be ideal to be able to convert the data into natural logarithms to make the coefficient estimates reported in the form of elasticity; however, the numerous zeroes presented in economic losses prevent this transformation.…”

“…According to Miljkovic and Miljkovic (2014), people over age 65 are most vulnerable to natural hazard-induced disasters. Therefore, this group is believed to reflect important changes in the sociodemographic profile of the population in response to disasters.…”

“…Miljkovic and Miljkovic (2014) showed that the damages from hurricane events have tended to further exacerbate income inequalities in the coastal states of the United States throughout the last century. If hurricanes continue to increase income inequalities in affected areas, relatively poorer people will continue to become poorer as time goes by, and the consequences will become critical for both the insurance industry and policymakers.…”

“…This study is motivated by the recent study by Miljkovic and Miljkovic (2014) on modeling the impact of hurricane damages on income distribution in the coastal United States. These authors found that an increase in normalized economic damages of USD 100 billion would lead to an increase in income inequality by 5.4% in the hurricane states of the United States, based on the data for the period 1910-2005 and measured by the Gini coefficient.…”