Propensity Score Weighting with Mismeasured Covariates: An Application to Two Financial Literacy Interventions

Abstract

Estimation of the causal effect of a binary treatment on outcomes often requires conditioning on covariates to address selection concerning 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.

Empowering Financial Education by Banks—Social Media as a Modern Channel

“…Although the effects of financial education are not simple to measure because of the range of objectives, audiences, activities, and timing (Hathaway and Khatiwada 2008;Rieger 2020;Dong and Millimet 2020), reviews of the existing literature suggest that financial education generates mostly promising results (Braunstein and Welch 2002;Fox et al 2005;Hathaway and Khatiwada 2008;Lyons et al 2006;Schuchardt et al 2009). The benefits of financial literacy have been empirically demonstrated for a variety of financial decisions and behaviors.…”