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Journal of Quantitative Economics

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23-09-2016 | Original Article

Do Technological Conditions of Production Explain Industrial Growth? The Indian Manufacturing, 1998–1999 to 2007–2008

Authors: Alokesh Barua, Bishwanath Goldar, Himani Sharma, Priyanta Ghosh

Published in: Journal of Quantitative Economics | Issue 3/2017

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Abstract

The paper tries to assess whether the technological conditions of production can explain the sluggishness in growth in Indian manufacturing industries reflected in a stagnant share in aggregate GDP. For this purpose, the returns to scale and elasticity of factor substitution are estimated for various two-digit manufacturing industries of India for the years 1998–1999 to 2007–2008 using the translog production function specification. Most of the previous research of this kind was undertaken by using either aggregate level time-series or state-wise aggregate cross-section data. The recent availability of factory (plant) level panel data has motivated us to re-estimate the parameters of the production function for the Indian manufacturing using factory-level data. Our results clearly indicate presence of significant scale economies. We observe that the capital-labour elasticity of substitution varies across industries, being a little above one or less than one in nearly half of the cases. A multiple regression analysis has been undertaken with the help of industry-level panel data for the years 1998–1999 to 2007–2008 to find out if the manufacturing growth rate is conditioned by the parameters of the production function. Our results indicate that production function parameters do exert an important influence on the rate of growth.

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Over the last two decades (1990–2013), the share of manufacturing in GDP has remained stagnant with a possible marginal decline. This prolonged and severe stagnation caused serious policy concern to arrest and reverse the slowdown. See Babu and Natarajan (2013).

The 5-yearly average GDP growth rate in India increased from 5.24 % in 1991–1995 to 8.7 % in 2006–2010 but since 2011 the growth rate had started declining. See Barua and Sawhney (2015).

See Babu and Natarajan (2013).

For an exhaustive survey of the estimates of production function for Indian manufacturing industries see Barua (1985). A subsequent survey of production function estimates for Indian manufacturing is available in Goldar (1997).

Internal economies are the gains (lowering of costs) taking place within a firm as its scale of production goes up. External economies are the gains (improved productivity and cost reduction) that occur in a firm as the size of the industry goes up. The industry size may go up as many firms become larger in size or new firms are established or both. The sources of external economies include knowledge spillover, labour-market pooling and specialized capital input (Krugman 2009). Evidently, if one does not use firm/plant level data and instead undertakes an analysis based on industry level data, one would not be able to assess the internal economies, but may be able to get an assessment of external economies.

Though we had access to data for later years, the change in industrial classification introduced in ASI data since 2008–2009 makes it difficult to combine the estimates of RTS and ES for years up to 2007–2008 and those for later years. Another concern is that the global economic crisis began from 2008/2009 and we considered it appropriate to confine our analysis to the period prior to the crisis.

Since data for each year is considered separately for the analysis, deflation of value added and capital stock in order to bring them at constant prices has not been done. However, for an alternate estimate based on panel data (discussed later), deflation of value added and capital stock has been done.

For derivation of the elasticity of substitution from a general production function, see Henderson and Quandt (1980, p. 73) and Debertin (2012, p.204), and for derivation in the case of a linear homogeneous production function which is a special case, see McFadden (1978, p. 78).

To apply the Olley–Pakes or Livinsohn–Petrin methodology, the sample will have to be drastically reduced to those plants that are repeated in consecutive years for a sufficient number of years. Since the models are estimated separately for each two-digit industry, this will be a serious disadvantage as the number of observations will go down considerably and the sample will get biased towards relatively larger factories (the reason is that the factories with 100 or more workers which are included in the census sector of ASI tend to get covered each year, whereas smaller factories will get covered only if they are chosen in the sample with the consequence that they get less frequently covered).

Information on the application of the delta method for computation of standard errors is available on the internet. See, for instance, Explanation of the Delta Method, STATA Data Analysis and Software, http://www.stata.com/support/faqs/statistics/delta-method/ (accessed on 6 September 2016).

The equation estimated is: ln(Y/L)=c+\(\upvarphi \) ln(w) +\(\uppsi \) ln(L)+\(\upvarepsilon \), where Y denotes value added (output), w wage rate and L labour input. \(\upvarepsilon \) is the error term. After obtaining estimates of \(\upvarphi \) and \(\uppsi \), estimates of returns to scale and elasticity of substitution can be derived by solving two equations.

These estimates are not presented in the paper to save space but can be obtained from the authors on request.

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Title: Do Technological Conditions of Production Explain Industrial Growth? The Indian Manufacturing, 1998–1999 to 2007–2008
Authors: Alokesh Barua
Bishwanath Goldar
Himani Sharma
Priyanta Ghosh
Publication date: 23-09-2016
Publisher: Springer India
Published in: Journal of Quantitative Economics / Issue 3/2017
Print ISSN: 0971-1554
Electronic ISSN: 2364-1045
DOI: https://doi.org/10.1007/s40953-016-0060-5

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