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Maximum score estimates of the determinants of residential mobility: Implications for the value of residential attachment and neighborhood amenities

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  • Bartik, Timothy J.
  • Butler, J. S.
  • Liu, Jin-Tan

Abstract

This paper examines the determinants of the decision of low-income renters to move out of their current dwelling. Maximum score estimation is shown t be superior to ordinary discrete choice estimation techniques (probit, logit) for this problem, ad for similar discrete choices that require revering a previously optimal decision. The estimation reveals psychological costs of moving for typical low income renters of at least 8 percent of their income; these costs are even higher for older, longer tenure, or minority households. Policies that displace low income renters will have large social costs. In addition, the estimation results are used to calculate implicit household willingness to pay (WTP) for neighborhood amenities. This WTP based on mobility behavior is much greater than WTP estimates derived using hedonic methods, and is argued to be more accurate. This paper uses a semiparametric empirical technique to estimate the determinants of the decision of low-income renters to move out of their dwelling. These estimates show that low-income residents highly value remaining in their dwelling. In addition, these estimates are used to illustrate an alternative method to measure willingness to pay for neighborhood amenities. Moving decisions are usually examined with standard discrete choice models such as probit or logit (e.g., Venti and Wise (1984), or Weinberg, Friedman, and Mayo (1981). But the moving decision presents econometric difficulties for standard discrete choice models. As will be explained in section 1, because the household decision about moving is conditional on having previously preferred the original location, the disturbance term in mobility models is unlikely to follow the simple distributional forms required for probit or logit estimation. Maximum score estimation is an alternative estimation technique for discrete choice models that is robust to unusual distributions of the disturbance term. Although the theoretical properties of maximum score e
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Suggested Citation

  • Bartik, Timothy J. & Butler, J. S. & Liu, Jin-Tan, 1992. "Maximum score estimates of the determinants of residential mobility: Implications for the value of residential attachment and neighborhood amenities," Journal of Urban Economics, Elsevier, vol. 32(2), pages 233-256, September.
  • Handle: RePEc:eee:juecon:v:32:y:1992:i:2:p:233-256
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    References listed on IDEAS

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    1. Timothy J. Bartik & V. Kerry Smith, 1996. "Urban Amenities and Public Policy," Book chapters authored by Upjohn Institute researchers,in: V. Kerry Smith (ed.), Estimating Economic Values for Nature: Methods for Non-Market Valuation, pages 271-318 W.E. Upjohn Institute for Employment Research.
    2. Dynarski, Mark, 1985. "Housing demand and disequilibrium," Journal of Urban Economics, Elsevier, vol. 17(1), pages 42-57, January.
    3. Manski, Charles F. & Thompson, T. Scott, 1986. "Operational characteristics of maximum score estimation," Journal of Econometrics, Elsevier, vol. 32(1), pages 85-108, June.
    4. Bolton, R., 1989. "An Economic Interpretation Of A Sense Of Place," Department of Economics Working Papers 130, Department of Economics, Williams College.
    5. Dunn, L. F., 1979. "Measuring the value of community," Journal of Urban Economics, Elsevier, vol. 6(3), pages 371-382, July.
    6. Venti, Steven F. & Wise, David A., 1984. "Moving and housing expenditure: Transaction costs and disequilibrium," Journal of Public Economics, Elsevier, vol. 23(1-2), pages 207-243.
    7. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
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    Cited by:

    1. Aaronson, Daniel, 1999. "The Effect of School Finance Reform on Population Heterogeneity," National Tax Journal, National Tax Association;National Tax Journal, vol. 52(1), pages 5-29, March.
    2. Wenli Li & Haiyong Liu & Fang Yang & Rui Yao, 2016. "Housing Over Time And Over The Life Cycle: A Structural Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57, pages 1237-1260, November.
    3. Quigley, John M., 2002. "Transactions Costs and Housing Markets," Berkeley Program on Housing and Urban Policy, Working Paper Series qt6pz8p6zt, Berkeley Program on Housing and Urban Policy.
    4. Clark David E. & Nieves Leslie A., 1994. "An Interregional Hedonic Analysis of Noxious Facility Impacts on Local Wages and Property Values," Journal of Environmental Economics and Management, Elsevier, vol. 27(3), pages 235-253, November.
    5. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    6. Van Ommeren, Jos & Fosgerau, Mogens, 2009. "Workers' marginal costs of commuting," Journal of Urban Economics, Elsevier, vol. 65(1), pages 38-47, January.
    7. Van Ommeren, Jos & Koopman, Marnix, 2011. "Public housing and the value of apartment quality to households," Regional Science and Urban Economics, Elsevier, vol. 41(3), pages 207-213, May.
    8. Jos Van Ommeren & Mihails Hazans, 2008. "Workers' Valuation of the Remaining Employment Contract Duration," Economica, London School of Economics and Political Science, vol. 75(297), pages 116-139, February.
    9. Lyytikäinen, Teemu, 2008. "Studies on the Effects of Property Taxation, Rent Control and Housing Allowances," Research Reports 140, VATT Institute for Economic Research.
    10. van Ommeren, Jos & Rietveld, Piet & Nijkamp, Peter, 1999. "Job Moving, Residential Moving, and Commuting: A Search Perspective," Journal of Urban Economics, Elsevier, vol. 46(2), pages 230-253, September.
    11. Ross, Stephen L., 1998. "Racial Differences in Residential and Job Mobility: Evidence Concerning the Spatial Mismatch Hypothesis," Journal of Urban Economics, Elsevier, vol. 43(1), pages 112-135, January.
    12. Gregory Kordas, 2006. "Smoothed binary regression quantiles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 387-407.
    13. Cragg, Michael & Kahn, Matthew, 1997. "New Estimates of Climate Demand: Evidence from Location Choice," Journal of Urban Economics, Elsevier, vol. 42(2), pages 261-284, September.
    14. Van Ommeren, Jos N. & Van der Vlist, Arno J., 2016. "Households' willingness to pay for public housing," Journal of Urban Economics, Elsevier, vol. 92(C), pages 91-105.
    15. Jos van Ommeren & Gerard J. van den Berg & Cees Gorter, 2000. "Estimating the Marginal Willingness to Pay for Commuting," Journal of Regional Science, Wiley Blackwell, vol. 40(3), pages 541-563.
    16. Van Ommeren, Jos & Graaf-de Zijl, Marloes, 2013. "Estimating household demand for housing attributes in rent-controlled markets," Journal of Housing Economics, Elsevier, vol. 22(1), pages 11-19.
    17. Florios, Kostas & Skouras, Spyros, 2008. "Exact computation of max weighted score estimators," Journal of Econometrics, Elsevier, vol. 146(1), pages 86-91, September.
    18. Kan, Kamhon, 2007. "Residential mobility and social capital," Journal of Urban Economics, Elsevier, vol. 61(3), pages 436-457, May.

    More about this item

    JEL classification:

    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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