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Large Locational Differences in Unemployment Despite High Labor Mobility: Impact of Moving Cost on Aggregate Unemployment and Welfare

In the U.S., the cross-state differences in unemployment rates are large - for instance, large compared to variations in the national unemployment rate over time. At the same time, there is considerable labor mobility within the U.S.; in fact, enough that, if migration arbitrages differences in unemployment, one might expect very low cross-state differences in unemployment. This paper develops a multi-sector equilibrium model that can account for high cross-state mobility and large variability in unemployment rates across states. The model allows for explicit treatment of net and gross mobility across local labor markets and within-market job search frictions. The prediction of the model is consistent with procyclicality of gross mobility in the U.S.. The model generates a striking result: that unemployment is a U-shaped function of moving cost. However, evaluated at moving costs which are empirically relevant, a marginal decrease in the moving cost reduces aggregate unemployment. Using the model, several policy experiments are conducted. These show that the government can reduce aggregate unemployment substantially by subsidizing workers' moving expenses. Such policy is welfare-improving despite being financed by taxes imposed on workers. The model also provides insights into the impacts of homeownership, city size, and an aging population on aggregate unemployment.

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Paper provided by Concordia University, Department of Economics in its series Working Papers with number 09009.

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Length: 5 pages
Date of creation: Jul 2009
Date of revision: Mar 2010
Handle: RePEc:crd:wpaper:09009
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  1. Haruhiko Ogasawara, 2004. "Asymptotic biases in exploratory factor analysis and structural equation modeling," Psychometrika, Springer, vol. 69(2), pages 235-256, June.
  2. Rilstone, Paul & Srivastava, V. K. & Ullah, Aman, 1996. "The second-order bias and mean squared error of nonlinear estimators," Journal of Econometrics, Elsevier, vol. 75(2), pages 369-395, December.
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  7. Ogasawara, Haruhiko, 2005. "Asymptotic robustness of the asymptotic biases in structural equation modeling," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 771-783, June.
  8. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
  9. Satorra, Albert & Neudecker, Heinz, 1994. "On the Asymptotic Optimality of Alternative Minimum-Distance Estimators in Linear Latent-Variable Models," Econometric Theory, Cambridge University Press, vol. 10(05), pages 867-883, December.
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  11. Stanislav Anatolyev, 2005. "GMM, GEL, Serial Correlation, and Asymptotic Bias," Econometrica, Econometric Society, vol. 73(3), pages 983-1002, 05.
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