Abstract
This paper examines the long-run effects of natural disasters on population density growth across US counties during the period of 1960–2000. Detailed data for measuring the number and intensity of three types of major natural disasters (earthquake, tornado, and hurricane) are collected and incorporated into the empirical models. We do not find any significant adverse long-run growth effects of natural disasters. Weak evidence of minor tornadoes being positively correlated to growth is provided. Results also indicate that disasters have negligible indirect effects on county population density growth through impacting the county characteristics.
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Notes
As our conceptual model in the appendix will show, it is based on the assumption that consumers are homogeneous and migration across regions is free and costless.
Later in this paper, we will report result from using a different spatial weight matrix.
Changes in county boundaries are documented by Bureau of Census. For more information, see http://www.census.gov/geo/www/tiger/ctychng.html. Most of such changes are that a county is split into two or more counties. We recombine such “split” counties so that counties in the data sample have constant boundaries in our sample.
The website is http://www.ngdc.noaa.gov/nndc/struts/form?t=101650&s=1&d=1. We also checked data available on the website http://earthquake.usgs.gov/research/data/centennial.php. Earthquakes from either source are included in our dataset.
It is available online at http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwEvent~Storms.
NOAA Coastal Services Center identifies coastal counties which are affected by hurricanes from 1900 through 2000; details are available online at http://maps.csc.noaa.gov/hurricanes/pop.jsp (accessed February 24, 2007) and http://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html. In a reply to the author’s query, Edward Rappaport at NOAA noted in an email that “we're unaware of any study assessing the meteorological impact (e.g., category) for inland counties.” Missing information for affected inland counties in the dataset may bias our results as often the next county inland is hit just as badly as the coast.
We also tried the measures which weight the number of disasters in the years equally. Qualitatively, the same results are obtained.
We use log of (1 + the disaster measures) in the model and add “1” because the disaster measures equal zero for many counties.
The focus of this paper is on the effects of natural disasters. It is noted that the regression results confirm the well-known facts that counties with nicer weather and western counties attract more people in the past decades. Estimated coefficients for those variables are also statistically significant in income and employment regressions. Results are available from the author upon request.
Data on income inequality in 1959 for several counties are missing, so we have a slightly smaller data sample in this round of regression exercises. We note that missing values for socioeconomic characteristics in year 1960 in several counties can bias the estimates if they are not random. However, we do not have information to evaluate it.
Rappaport and Sachs (2003) use coastal proximity measures (i.e., harbor proximity and the ratio of a county’s shoreline to its total area) to distinguish the two effects. They find that the coastal concentration of economic activity “derives primarily from a productivity effect but also, increasingly, from a quality of life effect.” Shapiro (2006) uses data on wages, rents, and house values to calibrate a model and find that “roughly 60% of employment growth effect of college graduates is due to enhanced productivity growth, the rest being caused by growth in the quality of life.”.
This result of presidential declaration having adverse impacts is contrary to our expectation. Possible reasons include that only those most costly events are declared. Another possibility is that the estimate could be biased. We should also note that the inclusion of presidential declarations in the regression with natural disaster can be potentially problematic because, among other reasons, it is not a perfect measure of relief efforts and there are various determinants of disaster payments (Garrett and Sobel 2003). The detailed results are available upon request from the author.
As the number of counties hit by earthquake or hurricane is very small, we exclude only those affected counties with the highest or lowest measures for the two disasters.
I thank a reviewer for pointing it out.
In the empirical analysis, we focus on population density growth, but not population growth. The main reason is that land areas for some counties in our sample have changed slightly even when we make the county boundaries consistent over the years. Note that change in population density is equal to change in population when the land area is constant between 2 years. In that case, our model is the same as the one developed by Glaeser et al. (1995) and many others in the regional literature.
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Acknowledgements
This paper is based on a chapter of my PhD dissertation at Oregon State University. I thank Edward Rappaport for explaining the hurricane data and Jordan Rappaport for sharing his county-level socioeconomic data. Thanks also to Steven Brakman, Munisamy Gopinath, Ingmar R. Prucha, Jordan Rappaport, JunJie Wu, seminar participants at various institutions, three anonymous referees, and the editor for helpful comments and suggestions. Gina Wang provided excellent assistance in assembling the datasets. The research is partially supported by China Institute for WTO Studies, UIBE (Grant #: 13ZXWTO04). The usual disclaimers apply.
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Appendix
Appendix
1.1 Conceptual framework
Consider an economy composed of a set of counties \( i \in \left\{ {1,2, \ldots , I} \right\} \). Suppose that population in county \( i \) at time \( t \) is represented by \( L_{it} \). Following Ciccone and Hall (1996), we assume that the aggregate production function for county \( i \) is given by
where \( A_{it} \) denotes the level of productivity. \( S_{it} \) denotes total land area. Wage rate for an individual in this county, \( R_{it} \), is the marginal product of labor, thus
A representative individual in county \( i \) at time \( t \) derives utility, \( V_{it} \), from local quality of life and wage rate she received:
where \( Q_{it} ( {\frac{{L_{it} }}{{S_{it} }}} )^{ - \alpha } \) is an index of local quality of life in which \( Q_{it} \) denotes exogenous environmental amenities such as weather; \( ( {\frac{{L_{it} }}{{S_{it} }}})^{ - \alpha } \) represents congestion effects in this county with \( \alpha > 0 \). This is linked to previous studies, suggesting that amenities play an important role with respect to migration destinations (Knapp and Graves 1989).
Let \( l_{it} \equiv \frac{{L_{it} }}{{S_{it} }} \) denote population density.Footnote 16 Combining (A2) and (A3) gives that
Assume that migration across counties is free and costless. In equilibrium, utility levels across counties are equal. Let \( V_{t} \) denote equilibrium level of utility at time period \( t \), then \( V_{t} \equiv V_{it} \) for any county \( i \). Thus, Eq. (A4) implies that,
Growth rate in population density between 2 years can be expressed as
Natural disasters may contribute to the growth of local productivity, \( A_{it} \), by encouraging the adoption of new production technologies by firms through replacing the damaged machines and equipment in the afflicted regions (Tol and Leek 1999). Affected locations also benefit from higher growth of total assets in firms (Leiter et al. 2009) and a massive inflow of capital for rebuilding, and from technological innovations in the construction sector (Albala-Bertrand 1993). On the other hand, disasters could have adverse productivity effects through destroying physical capital stock, lowering marginal product of labor. Thus, the net effects of natural disasters on local productivity growth are not clear in theory.
In addition to productivity effects, the occurrences of natural disaster events also change the level of quality of life, \( Q_{it} \), by directly destroying amenity structures, damaging transportation system, and causing inconvenience to daily life for people in the affected regions. However, some case studies find that natural disasters improve local quality of life through promoting and reinforcing a sense of regional identity among population in the affected region (Geipel 1982). Thus, the net effects on quality of life are also ambiguous.
To describe the dynamics of productivity and quality of life, \( A_{it} \) and \( Q_{it} \) are assumed to change over time and are functions of natural disaster events and other characteristics of the associated county (Glaeser et al. 1995). Formally,
and
where \( D_{it} \) is the vector of measures of natural disasters and \( C_{it} \) is the vector of some other natural, socioeconomic, and demographic characteristics of the associated county. The error terms in the two processes are represented by \( v_{i,t + 1} \) and \( \mu_{i,t + 1} \), respectively. Neither of the two error terms is correlated with natural disasters or county characteristics. It is well documented in the regional literature that locational fundamentals such as weather and coastal proximity have impacts on regional growth. The growth literature also finds substantial evidence on the importance of human capital, political and social characteristics, industrial composition, and many other factors for population growth. Those related variables are grouped in \( C_{it} \). Combining (A5), (A6), and (A7) yields the expression for population density change between two periods:
where \( u_{i,t + 1} = \frac{1}{1 + \alpha - \gamma }[ { - \log ( {\frac{{V_{i,t + 1} }}{{V_{it} }}} ) + v_{i,t + 1} + \mu_{i,t + 1} } ] \).