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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.

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.

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.

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.

The goal of this study is to investigate the nexus among TFP (total factor productivity), IT (information technology) capital accumulation, skills and key plant variables of 34 Indian industries for the period of 2009–2015.

Annual Survey of Industries (ASI) data series are extracted and formulated using Microsoft SQL server. The authors employ Wooldridge (2009) technique to estimate productivity. To investigate the linkages among productivity, IT, skills and key plant variables, the authors estimate specifications using system generalized method of moments (sys-GMM). Advanced estimation techniques such as Heckman two-step process, probit equations, inverse Mills ratio and panel cointegration are applied to overcome problems of nonstationarity, omitted variables, endogeneity and reverse causality.

The results indicate that the level of IT capital influences the TFP of Indian industries, so does the level of skilled workers. The outcome suggests that intermediate capital goods, location and ownership type enable the strength of IT capital and that in turn boosts productivity. The authors fail to find any impact of regional factors and contractual labor on IT capital and productivity. While medium-level gender diversity is statistically significant to influence productivity, however, no complementarities exist between gender diversity and IT capital accumulation. The results also indicate that IT demand of Indian industries is sensitive to availability of skilled workforce, fuel and electricity and access to short-term funding.

To the authors' knowledge, this is the first study to investigate the nexus among TFP, IT capital accumulation, skills and organizational factors using ASI unit level data. Besides this, the paper offers two more novelties. First, it uses Wooldridge (2009) technique to estimate productivity, which is used by a handful of studies in the context of India. Second, the study identifies factors that impact productivity growth, IT demand and its adoption in Indian industries and thus contributes to growth and development literature.