Working Paper: NBER ID: w23144
Authors: Joshua D. Angrist; Jrnsteffen Pischke
Abstract: The past half-century has seen economic research become increasingly empirical, while the nature of empirical economic research has also changed. In the 1960s and 1970s, an empirical economist’s typical mission was to “explain” economic variables like wages or GDP growth. Applied econometrics has since evolved to prioritize the estimation of specific causal effects and empirical policy analysis over general models of outcome determination. Yet econometric instruction remains mostly abstract, focusing on the search for “true models” and technical concerns associated with classical regression assumptions. Questions of research design and causality still take a back seat in the classroom, in spite of having risen to the top of the modern empirical agenda. This essay traces the divergent development of econometric teaching and empirical practice, arguing for a pedagogical paradigm shift.
Keywords: No keywords provided
JEL Codes: A22
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| shift towards design-based empirical work in econometrics (C51) | change in how regression is taught (C29) |
| change in how regression is taught (C29) | focus on controlling for confounding factors (C90) |
| focus on controlling for confounding factors (C90) | more reliable estimates of causal effects (C51) |
| traditional approach to regression (C29) | inadequate for modern empirical research (C90) |
| modern econometric paradigm (C51) | treats regression as a tool for causal analysis (C32) |
| change in how regression is taught (C29) | clear distinction between causal and control variables (C32) |
| design-based approach to teaching econometrics (C50) | understanding causal inference (C32) |