Arbeitspapier
Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions
Estimation of the causal effect of a binary treatment on outcomes often requires conditioning on covariates to address selection on observed variables. This is not straightforward when one or more of the covariates are measured with error. Here, we present a new semi-parametric estimator that addresses this issue. In particular, we focus on inverse propensity score weighting estimators when the propensity score is of an unknown functional form and some covariates are subject to classical measurement error. Our proposed solution involves deconvolution kernel estimators of the propensity score and the regression function weighted by a deconvolution kernel density estimator. Simulations and replication of a study examining the impact of two financial literacy interventions on the business practices of entrepreneurs show our estimator to be valuable to empirical researchers.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 13893
- Klassifikation
-
Wirtschaft
Methodological Issues: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Banks; Depository Institutions; Micro Finance Institutions; Mortgages
- Thema
-
program evaluation
measurement error
propensity score
unconfoundedness
financial literacy
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Dong, Hao
Millimet, Daniel L.
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute of Labor Economics (IZA)
- (wo)
-
Bonn
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Dong, Hao
- Millimet, Daniel L.
- Institute of Labor Economics (IZA)
Entstanden
- 2020
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