Does selection of productivity estimation techniques matter?

Purpose: The purpose of this paper is to compare and analyze the modern productivity estimation techniques, namely, Levinsohn and Petrin (LP, 2003), Ackerberg Caves and Frazer (ACF, 2006), Wooldridge (2009) and Mollisi and Rovigatti (MR, 2017) on unit-level data of 32 Indian industries for the period 2009-2015. Design/methodology/approach: The paper first analyzes different issues encountered in total factor productivity (TFP) measurement. It then categorizes the productivity estimation techniques into three logical generations, namely, traditional, new and advanced. Next, it selects four contemporary estimation techniques, computes the industrial TFP for Indian states by using them and investigates their empirical outcomes. The paper also performs the robustness check to ascertain, which estimation technique is more robust. Findings: The result indicates that the TFP growth of Indian industries have differed greatly over this seven-years of period, but the estimates are sensitive to the techniques used. Further results suggest that ACF and Wooldridge yield the consistent outcomes as compared to LP and MR. The robustness test confirms Wooldridge to be the most robust contemporary technique for productivity estimation followed by ACF and LP. Originality/value: To the authors’ knowledge, this is the first study that compares the contemporary productivity estimation techniques. In this backdrop, this paper offers two novelties. First, it uses advanced production estimation techniques to compute TFP of 32 diverse industries of an emerging economy: India. Second, it addresses the fitment of estimation techniques by drawing a comparison and by conducting a robustness test, hence, contributing to the limited literature on comparing contemporary productivity estimation techniques.