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.
|Date of creation:||Jul 2009|
|Date of revision:||Mar 2010|
|Contact details of provider:|| Postal: 1455, de Maisonneuve Blvd, Montréal, Québec, H3G 1M8|
Phone: (514) 848-3900
Fax: (514) 848-4536
Web page: http://economics.concordia.ca
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- Artem Prokhorov, 2008.
"On relative efficiency of Quasi-MLE and GMM estimators of covariance structure models,"
08004, Concordia University, Department of Economics.
- Prokhorov, Artem, 2009. "On relative efficiency of quasi-MLE and GMM estimators of covariance structure models," Economics Letters, Elsevier, vol. 102(1), pages 4-6, January.
- Albert Satorra, 1993.
"On the asymptotic optimality of alternative minimum-distance estimators in linear latent-variable models,"
Economics Working Papers
35, Department of Economics and Business, Universitat Pompeu Fabra.
- 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.
- Whitney K. Newey & Richard J. Smith, 2004.
"Higher Order Properties of Gmm and Generalized Empirical Likelihood Estimators,"
Econometric Society, vol. 72(1), pages 219-255, 01.
- Whitney Newey & Richard Smith, 2003. "Higher order properties of GMM and generalised empirical likelihood estimators," CeMMAP working papers CWP04/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
- Todd E. Clark, 1995.
"Small sample properties of estimators of non-linear models of covariance structure,"
Research Working Paper
95-01, Federal Reserve Bank of Kansas City.
- Clark, Todd E, 1996. "Small-Sample Properties of Estimators of Nonlinear Models of Covariance Structure," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 367-373, July.
- 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.
- Stanislav Anatolyev, 2005. "GMM, GEL, Serial Correlation, and Asymptotic Bias," Econometrica, Econometric Society, vol. 73(3), pages 983-1002, 05.
- Joseph G. Altonji & Lewis M. Segal, 1994.
"Small sample bias in GMM estimation of covariance structures,"
Working Paper Series, Macroeconomic Issues
94-8, Federal Reserve Bank of Chicago.
- Altonji, Joseph G & Segal, Lewis M, 1996. "Small-Sample Bias in GMM Estimation of Covariance Structures," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(3), pages 353-366, July.
- Joseph G. Altonji & Lewis M. Segal, 1994. "Small Sample Bias in GMM Estimation of Covariance Structures," NBER Technical Working Papers 0156, National Bureau of Economic Research, Inc.
- Joel L. Horowitz, 1996. "Bootstrap Methods For Covariance Structures," Econometrics 9610003, EconWPA.
- Haruhiko Ogasawara, 2004. "Asymptotic biases in exploratory factor analysis and structural equation modeling," Psychometrika, Springer;The Psychometric Society, vol. 69(2), pages 235-256, June.
When requesting a correction, please mention this item's handle: RePEc:crd:wpaper:09009. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Economics Department)
If references are entirely missing, you can add them using this form.