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Complementarity And Identification

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  • Twinam, Tate

Abstract

This paper examines the identification power of assumptions that formalize the notion of complementarity in the context of a nonparametric bounds analysis of treatment response. I extend the literature on partial identification via shape restrictions by exploiting cross-dimensional restrictions on treatment response when treatments are multidimensional; the assumption of supermodularity can strengthen bounds on average treatment effects in studies of policy complementarity. This restriction can be combined with a statistical independence assumption to derive improved bounds on treatment effect distributions, aiding in the evaluation of complex randomized controlled trials. Complementarities arising from treatment effect heterogeneity can be incorporated through supermodular instrumental variables to strengthen identification in studies with one or multiple treatments. An application examining the long-run impact of zoning on the evolution of urban spatial structure illustrates the value of the proposed identification methods.

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  • Twinam, Tate, 2017. "Complementarity And Identification," Econometric Theory, Cambridge University Press, vol. 33(5), pages 1154-1185, October.
    • Handle: RePEc:cup:etheor:v:33:y:2017:i:05:p:1154-1185_00
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    Cited by:

    1. Allen, James & Mahumane, Arlete & Riddell, James & Rosenblat, Tanya & Yang, Dean & Yu, Hang, 2022. "Teaching and incentives: Substitutes or complements?," Economics of Education Review, Elsevier, vol. 91(C).
    2. Eggenberger, Christian & Backes-Gellner, Uschi, 2023. "IT skills, occupation specificity and job separations," Economics of Education Review, Elsevier, vol. 92(C).
    3. Doerr Annabelle & Strittmatter Anthony, 2021. "Identifying Causal Channels of Policy Reforms with Multiple Treatments and Different Types of Selection," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 67-88, January.

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