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A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

This paper aims to evaluate the performance of basic metals industry in India and analyze its determinants, using data envelopment analysis (DEA) method. It also intends to compare the results through conventional two-stage and bootstrap-based inferences.

Considering technical efficiency as a measure of performance, this paper specifically investigates whether the participation of a firm in the global market affects its performance. The conventional two-stage procedure is used to test the export intensity and firm performance nexus. The bootstrap-based algorithms (by Simar and Wilson, 2007) are used to correct the bias and serial correlation issues involved in the conventional approach.

The result shows a negative relation between export intensity and firm performance while following the conventional procedure. Even after accounting for serial correlation, the relation remains more or less similar to that of conventional analysis. However, a strong negative relation between export intensity and firm performance is not observed in a more reliable inference obtained after correcting for possible bias as well as serial correlation.

This paper is based on cross-sectional analysis, and a more reliable result can be obtained by considering a larger sample and longer period.

This paper shows how the conventional two-stage procedure may result in misleading inferences due to bias in the estimation of efficiency scores and the serial correlation during the second stage inferential analysis. This paper also empirically exemplifies how the double bootstrap DEA procedure can overcome these limitations of the conventional two-stage approach.

This paper examines the technical efficiency (TE) of Indian commercial banks during 1998–2015.

This study uses mathematical programming-based data envelopment analysis (DEA) methodology to measure technical efficiency of Indian banks. Further, Simar and Wilson (2007) double bootstrap procedure is applied to examine the determinants of efficiency of the Indian banks, by examining the effects of various bank specific and other contextual variables.

The results indicate substantial upward bias in the conventional efficiency estimates of the Indian commercial banks. Needless to note, such upward bias is consistent with the theoretical postulates. The bootstrapped regression results show that increasing capital adequacy ratio is positively associated with bank efficiency. The popular belief that non-performing assets have a dampening effect on performance of banks is validated. Among others, ownership category is observed to be an important determining factor of bank efficiency. Specifically, state-owned banks (SOBs) are relatively lagging behind the foreign banks. Moreover, larger banks are observed to have a significantly higher level of efficiency, therefore, recent official policy initiatives toward consolidation of SOBs are validated.

As this study uses Simar and Wilson (2007) bootstrap approach, it enables the authors to have an estimate of the extent of bias in the traditional DEA TE scores. It also helps us drawing consistent inferences by rectifying the problem of serial correlation in the conventional second stage regression in this regard.