BAYESIAN MODEL AVERAGING FOR PROPENSITY SCORE MATCHING IN TAX REBATE

Riccardo (Jack) Lucchetti, Luca Pedini and Claudia Pigini

No 457, Working Papers from Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali

Abstract: Propensity Score Matching is a popular approach to evaluate treatment effects in observational studies. However, when building the underlying propensity score model practitioners often overlook the issue of model uncertainty and its consequences. We tackle this problem by Bayesian Model Averaging (BMA) with an application to the 2014 Italian tax credit reform (the so-called "Renzi bonus"). Model uncertainty has a great impact on the estimated treatment effects. BMA-based estimates point towards a significant effect of the rebate on food consumption only for liquidity constrained house- holds; conversely, model selection procedures sometimes produce results incompatible with the consumption smoothing hypothesis.

Keywords: 2014 Italian Tax Credit Reform; Bayesian Model Averaging; Model Uncertainty; Propensity Score Matching; Reversible Jump Markov Chain Monte Carlo; Tax Rebate Policies (search for similar items in EconPapers)
JEL-codes: C11 C52 D12 (search for similar items in EconPapers)
Pages: 28
Date: 2021-06
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-pub
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:anc:wpaper:457

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