Academic journal article Indian Journal of Industrial Relations

Productivity Growth of Indian Manufacturing: Panel Estimation of Stochastic Production Frontier

Academic journal article Indian Journal of Industrial Relations

Productivity Growth of Indian Manufacturing: Panel Estimation of Stochastic Production Frontier

Article excerpt

Along with technological progress, changes in technical efficiency, scale effect and changes in allocative efficiency can also contribute to productivity growth. The present study used the stochastic frontier production approach to decompose sources of TFPG of organized manufacturing into technological progress, changes in technical efficiency, scale effect and changes in allocative efficiency during 1981/ 82-2010/11. According to the results, technical inefficiency, though exists, is time invariant and technological progress (TP) became the main contributor to TFPG of the sector during 1981/82-2010/11. Furthermore, TFPG of organized manufacturing in most states in India declined during the post-reform period due to the decline in technological progress.

Introduction

Most of the studies relating to productivity growth in Indian manufacturing considered technological progress to be the unique source of total factor productivity growth (TFPG) and it can be shown by the shift in production possibility frontier over time. However, some recent studies (Aigner, Lovell & Schmidt, 1977; Meeusen & Van den Broeck, 1977) have used a stochastic frontier production model that allows decomposing TFPG into two components: technological progress (TP) and change in technical efficiency (TE). Later, studies by, among others, Nishimizu and Page (1982), Kumbhakar (1990), Fecher and Perelman (1992), Domazlicky and Weber (1998) have been focusing on decomposition of TFPG using Stochastic Frontier Approach. Some studies have extended their analyses to deal with the issues of scale effect and allocative efficiency effect. By applying a flexible stochastic translog production function, Kumbhakar and Lovell (2000), Kim and Han (2001) and Sharma et al (2007) decomposed TFPG into four components: changes in technological progress, changes in technical efficiency, economic scale effect and changes in allocative efficiency.

In the present study we have used the stochastic production frontier approach to decompose TFPG of the total organized manufacturing industries in India and fifteen major industrialized states assuming that manufacturing industries in the states are not able to fully utilize the existing resources and technology because of various non-price and organizational factors that might have led to technical inefficiencies in production. Using panel data of the organized manufacturing industries of the states as well as all-India over a period from 1981-82 to 2010-11, pre-reform period (1981-82 to 1990-91), post-reform-period (1991-92 to 2010-11) and also during the two decades in the post-reform period (1991-92 to 2000-01 and 200102 to 2010-11], we have decomposed TFPG of the organized manufacturing sector into technological progress, changes in technical efficiency, scale effect and allocative efficiency effect. This decomposition of TFPG of Indian manufacturing has also been made for the pre-and post reform periods, and also for different decades in order to examine the trend and variations in the TFPG and its different components, during these sub-periods.

Decomposition of TFPG

Stochastic frontier model was first developed by Aigner, Lovell and Schmidt (1977) and Meeusem Van den Broeck (1977) and it was later extended by Pit and Lee (1981), Schmidt and Sickles (1984), Kumbhakar (1990) and Battese and Coelli (1992) to allow for panel data regression estimation in which technical efficiency and technological progress vary over time and across different production units. Here we discuss the methodology used in the efficiency literature for estimating stochastic production frontier and the decomposition of TFPG. We start with a standard stochastic frontier model that can be estimated using panel data. The model is written as:

[y.sub.it] = f([x.sub.it], [beta], t) exp ([v.sub.it] - [u.sub.it])--(1)

where [y.sub.it] represents the output of the i-th production unit (i=1 . …

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