×

An odd couple: monotone instrumental variables and binary treatments. (English) Zbl 1491.62255

Summary: This article investigates Monotone Instrumental Variables (MIV) and their ability to aid in identifying treatment effects when the treatment is binary in a nonparametric bounding framework. I show that an MIV can only aid in identification beyond that of a Monotone Treatment Selection assumption if for some region of the instrument the observed conditional-on-received-treatment outcomes exhibit monotonicity in the instrument in the opposite direction as that assumed by the MIV in a Simpson’s Paradox-like fashion. Furthermore, an MIV can only aid in identification beyond that of a Monotone Treatment Response assumption if for some region of the instrument either the above Simpson’s Paradox-like relationship exists or the instrument’s indirect effect on the outcome (as through its influence on treatment selection) is the opposite of its direct effect as assumed by the MIV. The implications of the main findings for empirical work are discussed and the results are highlighted with an application investigating the effect of criminal convictions on job match quality using data from the 1997 National Longitudinal Survey of the Youth. Though the main results are shown to hold only for the binary treatment case in general, they are shown to have important implications for the multi-valued treatment case as well.

MSC:

62P20 Applications of statistics to economics

References:

[1] Black, D., Discrimination in an equilibrium search model, Journal of Labor Economics, 13, 309-334 (1995)
[2] de Haan, M., The effect of parents’ schooling on child’s schooling: A nonparametric bounds analysis, Journal of Labor Economics, 29, 4, 859-892 (2011)
[3] Gerfin, M.; Schellhorn, M., Nonparametric bounds on the effect of deductibles in health care insurance on doctor visits: Swiss evidence, Health Economics, 15, 1011-1020 (2006)
[4] Gonzalez, L., Nonparametric bounds on the returns to language skills, Journal of Applied Econometrics, 20, 771-795 (2005)
[5] Holzer, H. J. (2007)
[6] Imbens, G.; Angrist, J., Identification and estimation of local average treatment effects, Econometrica, 62, 467-475 (1994) · Zbl 0800.90648
[7] Kang, C., Family size and educational investments in children: Evidence from private tutoring expenditures in South Korea, Oxford Bulletin of Economics and Statistics, 73, 59-78 (2011)
[8] Kreider, B.; Pepper, J., Disability and employment: Reevaluating the evidence in light of reporting errors, Journal of the American Statistical Association, 102, 432-441 (2007) · Zbl 1134.62397
[9] Manski, C., Nonparametric bounds on treatment effects, American Economic Review, Papers and Procedings, 80, 319-323 (1990)
[10] Manski, C., Monotone treatment response, Econometrica, 65, 1311-1334 (1997) · Zbl 0906.62126
[11] Manski, C., Partial Identification of Probability Distributions (2003), New York: Springer-Verlag, New York · Zbl 1047.62001
[12] Manski, C.; Pepper, J., Monotone instrumental variables: With an application to the returns to schooling, Econometrica, 68, 997-1010 (2000) · Zbl 1016.62133
[13] Nevo, A.; Rosen, A. M., Identification with imperfect instruments, Review of Economics and Statistics, 94, 659-671 (2012)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.