Viewpoint: Quantifying residential self-selection effects: A review of methods and findings from applications of propensity score and sample selection approaches

Patricia L. Mokhtarian

Georgia Institute of Technology

David van Herick

University of California, Davis

DOI: https://doi.org/10.5198/jtlu.2016.788

Keywords: transport, land use, residential self-selection


Abstract

The phenomenon whereby individuals self-select into their residential environment based on previously determined preferences for how to travel is known as residential self-selection (RSS). Numerous studies have investigated the influence of RSS on the estimated effect of the built environment on travel behavior. However, surprisingly few have actually quantified its effect in terms of partitioning the total influence of the built environment (BE) on travel behavior into a component attributable to RSS and one attributable to the built environment itself. This paper reviews 10 analyses (found in seven studies) that have quantified the proportion of the total influence of the built environment that is due to the BE itself (which we call the BEP), using either propensity-score or sample-selection approaches to control for RSS. After first outlining the basics of each approach, we then explain the various methods used to compute the BEP, followed by a discussion of the empirical results. The estimated BEPs vary widely, ranging from 34 percent to 98 percent. A number of reasons for these disparities are suggested, but there is considerable divergence in estimates even when many of these factors are held constant. Additional research is called for to better understand the circumstances under which the BEP is higher or lower.

Author Biographies

Patricia L. Mokhtarian, Georgia Institute of Technology

Professor, School of Civil and Environmental Engineering

David van Herick, University of California, Davis

PhD candidate, Department of Civil and Environmental Engineering

References

Bhat, CR & Eluru N 2009, 'A copula-based approach to accommodate residential self-selection effects in travel behavior modeling', Transportation Research Part B: Methodological, vol. 43, no. 7, pp. 749-765.

Cao, X 2009, 'Disentangling the influence of neighborhood type and self-selection on driving behavior: an application of sample selection model', Transportation, vol. 36, pp. 207-222.

Cao, X 2010, 'Exploring causal effects of neighborhood type on walking behavior using stratification on the propensity score. Environment and Planning A, vol. 42, no. 2, pp. 487–504.

Cao, X 2015, 'The effects of neighbourhood type and self-selection on driving: a case study of Northern California, Chapter 10 in: International Handbook on Transport and Development, 2015, (Eds): Robin Hickman, Moshe Givoni, David Bonilla, David Banister, Cheltenham, Edward Elgar.

Cao, X & Fan, Y 2012, 'Exploring the influences of density on travel behavior using propensity score matching', Environment and Planning B: Planning and Design, vol. 39, no. 3, pp. 459–470.

Cao, X, Mokhtarian PL, & Handy SL 2011, 'Examining the impacts of residential self-selection on travel behavior: methodologies and empirical findings', Chapter 1 in Elisabetta Venezia, ed., Urban Sustainable Mobility. Milan: FrancoAngeli, pp. 15-100. Available as Research Report UCD-ITS-RR-08-25, http://www.its.ucdavis.edu/research/publications/publication-detail/?pub_id=1194.

Cao, X, Xu, Z, & Fan, Y 2010, 'Exploring the connections among residential location, self-selection, and driving: propensity score matching with multiple treatments', Transportation Research Part A: Policy and Practice, vol. 44, no. 10, pp. 797–805.

Cook, TD, Shadish, WR, & Wong, VC 2008, ‘Three conditions under which experiments and observational studies produce comparable causal estimates: new findings from within-study comparisons’, Journal of Policy Analysis and Management, vol. 27, no. 4, pp. 724-750.

D'Agostino, RB Jr. 1998, 'Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group', Statistics in Medicine, vol. 17, no. 19, pp. 2265-2281.

Heckman, JJ 1979, 'Sample selection as a specification error', Econometrica, vol. 47, no. 1, pp. 153-161.

Heckman, JJ 1990, 'Varieties of selection bias', The American Economic Review, vol. 80, no. 2, pp. 313-318.

Heckman, JJ, Tobias, JL & Vytlacil, EJ 2001, 'Four parameters of interest in the evaluation of social programs', Southern Economic Journal, vol. 68, no. 2, pp. 210-223.

Heckman, JJ & Vytlacil, EJ 2005, 'Structural equations, treatment effects, and economic policy evaluation', Econometrica, vol. 73, no. 3, pp.669-738.

Joh, K, Mai Thi, N & Boarnet, MG 2012, 'Can built and social environmental factors encourage walking among individuals with negative walking attitudes?', Journal of Planning Education and Research, vol. 32, no. 2, pp. 219-236.

Larco, N, Steiner, B, Stockard, J & West, A 2012, 'Pedestrian-friendly environments and active travel for residents of multifamily housing: the role of preferences and perceptions', Environment and Behavior, vol. 44, no. 3, pp. 303–333.

Oakes, MJ & Johnson, PJ 2006, 'Propensity score matching for social epidemiology', In: Oakes, M.J., Kaufman, J.S. (Eds.), Methods in Epidemiology. John Wiley & Sons, Inc., New York, pp. 370–392.

Rosenbaum, PR & Rubin, DB 1983, 'The central role of the propensity score in observation studies for causal effects', Biometrika, vol. 70, no. 1, pp. 41-55.

Rosenbaum, PR & Rubin, DB 1984, 'Reducing bias in observational studies using subclassification on the propensity score', Journal of the American Statistical Association, vol. 79, no. 387, pp. 516-524.

Shadish, WR, Clark, MH, & Steiner, PM 2008, ‘Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments’, Journal of the American Statistical Association, vol. 103, no. 484, pp. 1334-1356.

Winship, C & Morgan, SL 1999, 'The estimation of causal effects from observational data', Annual Review of Sociology, vol. 25, pp. 659-707.

Zhou, B & Kockelman, K 2008, 'Self-selection in home choice: use of treatment effects in evaluating the relationship between the built environment and travel behavior', Transportation Research Record, no. 2077, pp. 54-61.