Recent Advances in Causal Analysis of the Stochastic Frontier Model

TL;DR

This paper reviews recent progress in applying causal inference methods to the stochastic frontier model, highlighting modeling approaches, empirical issues, and core findings relevant for productivity analysis.

Contribution

It synthesizes emerging literature on integrating causal inference with stochastic frontier models, addressing challenges and outlining future research directions.

Findings

Causal analysis can be applied to stochastic frontier models with appropriate modifications.

Existing work has demonstrated preliminary success in this integration.

The review identifies key empirical issues and potential solutions.

Abstract

Causal inference methods (instrumental variables, difference-in-differences, regression discontinuity, etc.) are primary tools used across many social science milieus. One area where their application has lagged however, is in the study of productivity and efficiency. A main reason for this is that the nature of the stochastic frontier model does not immediately lend itself to a causal framework when interest hinges on an error component of the model. This paper reviews the nascent literature on attempts to merge the stochastic frontier literature with causal inference methods. We discuss modeling approaches and empirical issues that are likely to be relevant for applied researchers in this area. This review shows how this model can be easily put within the confines of causal analysis, reviews existing work that has already made inroads in this area, addresses challenges that have yet to be met and discusses core findings.