Matching Without Groups
Optimal matching without groups, or optimal nonbipartite matching, offers many additional options for matched designs in both observational studies and experiments. One starts with a square, symmetric distance matrix with one row and one column for each subject recording the distance between any two subjects. Then the subjects are divided into pairs to minimize the total distance within pairs. The method may be used to match with doses of treatment, or with multiple control groups, or as an aid to risk-set matching. An extended discussion of Card and Krueger’s study of the minimum wage is used to illustrate.
KeywordsPropensity Score Minimum Wage Distance Matrix Statist Assoc Wage Increase
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- Cahuc, P., Zylberberg, A.: Labor Economics. Cambridge, MA: MIT Press (2004)Google Scholar
- Card, D., Krueger, A.B.: Minimum wages and employment: A case study of the fast-food industry in New Jersey and Pennsylvania. Am Econ Rev 84, 772–793 (1994)Google Scholar
- Card, D., Krueger, A.B.: Myth and Measurement: The New Economics of the Minimum Wage. Princeton, NJ: Princeton University Press (1995) Data: http://www.irs.princeton.edu/
- Cook, W., Rohe, A. : Computing minimum-weight perfect matchings. INFORMS J Comput 11, 138–148 (1999) Software: http://www2.isye.gatech.edu/~wcook/
- Joffe, M.M., Rosenbaum, P.R.: Propensity scores. Am J Epidemiol 150, 327–333 (1999)Google Scholar
- Lu, B., Greevy, R., Xu, X., Beck, C.: Optimal nonbipartite matching and its statistical applications. Manuscript. (2009)Google Scholar
- R Development Core Team.: R: A Language and Environment for Statistical Computing. Vienna: R Foundation, http://www.R-project.org (2007)
- Stigler, G.J. : The economics of minimum wage legislation. Am Econ Rev 36, 358–365 (1946)Google Scholar