Do debit cards increase household spending? Evidence from a semiparametric causal analysis of a survey
Abstract
© Institute of Mathematical Statistics, 2014.Motivated by recent findings in the field
of consumer science, this paper evaluates the causal effect of debit cards on household
consumption using population-based data from the Italy Survey on Household Income
and Wealth (SHIW). Within the Rubin Causal Model, we focus on the estimand of population
average treatment effect for the treated (PATT). We consider three existing estimators,
based on regression, mixed matching and regression, propensity score weighting, and
propose a new doubly-robust estimator. Semiparametric specification based on power
series for the potential outcomes and the propensity score is adopted. Cross-validation
is used to select the order of the power series. We conduct a simulation study to
compare the performance of the estimators. The key assumptions, overlap and unconfoundedness,
are systematically assessed and validated in the application. Our empirical results
suggest statistically significant positive effects of debit cards on the monthly household
spending in Italy.
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https://hdl.handle.net/10161/10303Published Version (Please cite this version)
10.1214/14-AOAS784Publication Info
Mercatanti, A; & Li, F (2014). Do debit cards increase household spending? Evidence from a semiparametric causal
analysis of a survey. Annals of Applied Statistics, 8(4). pp. 2485-2508. 10.1214/14-AOAS784. Retrieved from https://hdl.handle.net/10161/10303.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Fan Li
Professor of Statistical Science
My main research interest is causal inference and its applications to health, policy
and social science. I also work on the interface between causal inference and machine
learning. I have developed methods for propensity score, clinical trials, randomized
experiments (e.g. A/B testing), difference-in-differences, regression discontinuity
designs, representation learning. I also work on Bayesian analysis and statistical
methods for missing data. I am serving as the editor for social science, bios

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