Causal Inference in Panel Data With Application to Estimating Race-of-Interviewer Effects in the General Social Survey

Abstract

In this article, we review popular parametric models for analyzing panel data and introduce the latest advances in matching methods for panel data analysis. To the extent that the parametric models and the matching methods offer distinct advantages for drawing causal inference, we suggest using both to cross-validate the evidence. We demonstrate how to use these methods by examining race-of-interviewer effects (ROIE) in the 2006 to 2010 panel data of the General Social Survey. We find that ROIE mostly concentrate on race-related outcomes and may vary by respondent’s race for some outcomes. But we find no statistically significant evidence that ROIE vary by the interview mode (i.e., in person vs. by phone). Our study has both methodological and substantive implications for future research.