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The Annals of Regional Science

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03-06-2024 | Special Issue Paper

Climate-related disaster exposure and regional migration

Authors: Yong Chen, Myungjin Kim, Sultana Fouzia

Published in: The Annals of Regional Science | Issue 2/2024

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Abstract

In contrast to the existing literature focusing on post-disaster regional impacts, we illustrate how the perception of disaster exposure affects regional population flows through household location decisions using a quantitative spatial economics model. More importantly, the quantitative spatial economics model helps identify critical drivers for regional migration that motivate the subsequent empirical analyses. A generalized additive model is applied to US county-level data to capture the nonlinear impact of disaster exposure on migration. The regional migration is not responsive to small and moderate disaster exposures. However, counties subject to severe disaster exposure experience significantly slower net inmigration.

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The short and medium run in this paper refers to 0-5 and 6-10 years, respectively. The long run refers to over ten years.

Under the model setting, the functional form of $f(\cdot )$ can be uniquely and explicitly determined. However, the specific functional form in Eq. (7) is derived under strong assumptions like consumer preference, firm technology, and specific market structure. This means that the specific functional form in Eq. (31) may not hold true in general. To abstract from the specific functional forms, we keep the functional form in its generic form and leave the analytical solution in the appendix.

When $\sigma >1$, which is generally true in reality and existing literature (Redding and Rossi-Hansberg 2017), we have:

$$\frac{{\partial Li}}{{\partial Di}} \le 0,\frac{{\partial Li}}{{\partial Ai}} > 0,\frac{{\partial Li}}{{\partial Pi}} < 0$$

See Appendix 1 for detailed derivations.

We use thin plate regression splines (TPRS) (Wood 2003) because TPRS uses a general-purpose spline basis and circumvents the complexity in the placement of knots in traditional spline methods.

These are evaluated as $2{\hat{\beta }}\log (D_i)$, where $D_i$ takes the value of min, mean and max of the disaster exposure as reported in Table 1.

The average slope in the lower 90 percentile of the function is calculated as

$$\begin{aligned} \sum _{i=1}^{90} \frac{g_1(D(i+1))-g_1(D(i))}{D(i+1)-D(i)}=-0.0014, \end{aligned}$$

where D(i) denotes the i-th percentile of the ordered exposure (D) value.

The response function for local price is the only result that is sensitive to the exclusion of these control variables X. It also becomes statistically insignificant.

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Title: Climate-related disaster exposure and regional migration
Authors: Yong Chen
Myungjin Kim
Sultana Fouzia
Publication date: 03-06-2024
Publisher: Springer Berlin Heidelberg
Published in: The Annals of Regional Science / Issue 2/2024
Print ISSN: 0570-1864
Electronic ISSN: 1432-0592
DOI: https://doi.org/10.1007/s00168-024-01276-z

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