Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

中文翻译：

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更好地衡量印度的生产力：化学和化学产品行业的案例

目的

对印度工业生产率情景早期研究的简要回顾表明，大多数研究分析都局限于衡量生产率的参数方法或增长核算方法。同时，少数基于非参数[即马姆奎斯特生产率指数(MPI)]的研究忽视了规模报酬条件以及距离函数估计中涉及的偏差。在这种背景下，本研究旨在提供一种稳健的生产率衡量标准，其中考虑了规模回报假设并纠正生产率估计中涉及的偏差。

设计/方法论/途径

本研究对印度化学和化学产品行业存在的规模回报进行了实证检验。测试结果表明 Ray 和 Desli (1997) 的 MPI 方法是适合当前情况的方法。最初，使用传统的 Ray 和 Desli (1997) 估计和分解 2001 年至 2017 年期间的 MPI。随后，为了纠正用于 MPI 估计的效率分数估计中的偏差，Simar 和 Wilson (2007) 的自举算法已扩展到 MPI 估计的背景中。

发现

传统 Malmquist 生产率估算的结果证明，在所考虑的 16 年中，有 7 年全要素生产率 (TFP) 有所提高。相反，全要素生产率仅在偏差修正后的四年内出现增长。在 MPI 的尺度变化因子分量的情况下，发现两种测量之间存在更大的差异。

实际影响

技术变革（TC）因素对TFP产生正向影响，而规模变化因素（SCF）则恶化该行业的TFP状况。这些公司应该确定并在最佳运营规模下运营，同时获得技术变革的好处。从方法论的角度来看，研究人员应考虑 TFP 估算中可能出现的偏差，并尽可能使用更大的样本。

原创性/价值

本文为现有的工业生产力文献带来了新的视角。与早期的研究相比，本研究通过实证检验了所考虑行业的规模回报，并使用最合适的方法来衡量生产率。分析了抽样偏差对全要素生产率及其组成部分的影响。