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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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  • Timothy J. Bartik

    ()
    (W.E. Upjohn Institute for Employment Research)

  • J.S. Butler

    (Vanderbilt University)

  • Jin Tan Liu

    (The Institute of Economics Academia Sinica)

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 estimation have been well-explored (see Manski (1975,1985)), our paper presents one of the first empirical applications of maximum score techniques. Maximum score estimation of our residential mobility model yields similar parameter estimates to probit estimation, but much smaller standard errors. This advantage of greater precision may prove attractive to other researchers. We use our estimates of the residential mobility model to calculate the value to households of remaining at their current dwelling rather than being forced to move out. We use the household's mobility response to rent changes to infer a monetary value of remaining in the current dwelling. Our calculations indicate that the typical low-income renter household is willing to pay at least 8 percent of its annual income to avoid being forced out of its current dwelling. These "psychological moving costs" increase greatly for older or longer tenure households. Large "psychological moving costs" have important implications for public policy towards low-income neighborhoods. Neighborhood improvement policies or private market forces may displace low-income renters. If the losses suffered by low-income renters due to being forced out of their current dwelling unit are significant, as indicated in this paper, then it is important to include these losses in any evaluation of the net benefits of a neighborhood improvement program. In addition, policy makers might want to consider policies to prevent or compensate for privately-induced displacement. Estimates of the monetary value of low-income renters' psychological moving costs are important to determining the effects of these policies, and deciding appropriate compensation. Finally, this paper uses the residential mobility estimates to infer the willingness t pay (WTP) of low-income renters for neighborhood amenities such as the physical condition of the neighborhood, neighborhood school quality, and the safety of the neighborhood from crime. The relative responsiveness of household mobility to changes in these neighborhood amenities, versus changes in rents, implicitly reveals households' monetary valuations of these amenities. The more common approach to measuring household WTP for neighborhood amenities is the hedonic price approach, which relies n the equilibrium relationship between housing prices and amenities. The calculations in this paper suggest that mobility-based WTP estimates for amenities may often be greater than hedonic based estimates of WTP. We consider which approach is more accurate. Section 1 of the paper presents our econometrics, specification, and data. Section 2 presents the results. Section 3 is the conclusion.

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Bibliographic Info

Paper provided by W.E. Upjohn Institute for Employment Research in its series Upjohn Working Papers and Journal Articles with number 90-01.

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Date of creation: Aug 1990
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Handle: RePEc:upj:weupjo:90-01

Note: A revised version of this paper appears in Journal of Urban Economics, Vol. 32, No. 2 (September 1992), pp. 233-256.
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Keywords: residential; mobility; determinants; Bartik; low-income; renters;

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References

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  1. 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.
  2. Bolton, R., 1989. "An Economic Interpretation Of A Sense Of Place," Department of Economics Working Papers 130, Department of Economics, Williams College.
  3. Dunn, L. F., 1979. "Measuring the value of community," Journal of Urban Economics, Elsevier, vol. 6(3), pages 371-382, July.
  4. 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.
  5. Manski, Charles F. & Thompson, T. Scott, 1986. "Operational characteristics of maximum score estimation," Journal of Econometrics, Elsevier, vol. 32(1), pages 85-108, June.
  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. Dynarski, Mark, 1985. "Housing demand and disequilibrium," Journal of Urban Economics, Elsevier, vol. 17(1), pages 42-57, January.
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Cited by:
  1. Kan, Kamhon, 2007. "Residential mobility and social capital," Journal of Urban Economics, Elsevier, vol. 61(3), pages 436-457, May.
  2. Gregory Kordas, 2006. "Smoothed binary regression quantiles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 387-407.
  3. 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.
  4. Aaronson, Daniel, 1999. "The Effect of School Finance Reform on Population Heterogeneity," National Tax Journal, National Tax Association, vol. 52(n. 1), pages 5-29, March.
  5. Jos Van Ommeren & Marnix Koopman, 2010. "Public Housing and the Value of Apartment Quality to Households," Tinbergen Institute Discussion Papers 10-085/3, Tinbergen Institute.
  6. Teemu Lyytikäinen, 2008. "Studies on the Effects of Property Taxation, Rent Control and Housing Allowances," Research Reports 140, Government Institute for Economic Research Finland (VATT).
  7. van Ommeren, Jos & Fosgerau, Mogens, 2008. "Workers' marginal costs of commuting," MPRA Paper 12010, University Library of Munich, Germany.
  8. Florios, Kostas & Skouras, Spyros, 2008. "Exact computation of max weighted score estimators," Journal of Econometrics, Elsevier, vol. 146(1), pages 86-91, September.
  9. Cragg, M. & Kahn, M., 1995. "New Estimates on Climate Demand: Evidence from Location Choice," Discussion Papers 1995_34, Columbia University, Department of Economics.
  10. 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.
  11. 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.
  12. Wenli Li & Haiyong Liu & Rui Yao, 2009. "Housing over time and over the life cycle: a structural estimation," Working Papers 09-7, Federal Reserve Bank of Philadelphia.
  13. 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, 02.
  14. Jos Van Ommeren & Marnix Koopman, 2010. "Public Housing and the Value of Apartment Quality to Households," Tinbergen Institute Discussion Papers 10-085/3, Tinbergen Institute.
  15. 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.
  16. Ommeren, Jos van & Berg, Gerard J. van den & Gorter, Cees, 1998. "Estimating the marginal willingness to pay for commuting," Serie Research Memoranda 0046, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
  17. 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.

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