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Partial Identification and inference in nonparametric one-to-one matching models

Cristina Gualdani and Shruti Sinha

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Abstract: When the analyst has data on one large market, we study partial identification of the preference parameters in models of one-to-one matching with transfers without imposing parametric distributional restrictions on the agents' unobserved characteristics. We provide a tractable characterisation of the sharp identified set and discuss inference, under various classes of nonparametric distributional assumptions on the agents' unobserved characteristics. We use our methodology to test if the variations in marriage matching patterns observed over time in the U.S. are caused by changes in the agents' preferences for education assortativeness or by a shift in the proportion of educated women.

Date: 2019-02, Revised 2019-10
New Economics Papers: this item is included in nep-des and nep-ecm
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