Technical efficiency and its determinants in the Indian garment industry

Abstract

One of the recommendations of the Textile Expert Committee, India, was to set a Technology Demonstration Centre to address the issues related to improvement in productivity and efficiency of the Indian garment industry. The committee identified the key factors responsible for the low level of production efficiency, which were low labour and capital productivities, high capital cost, outdated technology and low-skilled workers. In the light of subsequent recommendations made by the committee, the researchers examined the technical efficiency of the Indian garment industry. For this, a two-stage analytical process using cross-sectional data from 275 Indian garment firms spread across various states in India for the year 2004-2005 was adopted. In the first stage, data envelopment analysis was used to measure technical efficiency scores. Subsequently, in the second stage, Tobit regression was used to study the factors affecting the technical efficiency of the firms. The results of the study show that on an average, a garment firm could increase its output by 32.1% with the existing level of inputs by way of improving its input-use efficiency and adjusting plant size to the optimum level. Further, the overall technical efficiency scores were found to be more sensitive to the variation in the pure technical efficiency (PTE) scores than that in the scale efficiency scores. Lower PTE scores in all the states indicated the poor performance of the industry in converting inputs into output. The results of the study also showed that the micro-sized firms were more efficient in utilizing resources than the small and medium ones. The Tobit regression results confirmed that investment in plant and machinery and outstanding loans did have a negative impact on a firm's efficiency. By way of contrast, efficiency was found to be positively associated with labour productivity, wages per employee and labour-staff ratio.

Technical efficiency and its determinants in the Indian textile garments industry

“…The extent and intensity of the factors use, namely, fuel, capital and labor, and decide the success of the industry. Nevertheless, the policy restrictions have affected the firms' investment and machinery that prevented firms from achieving economies of scale (Joshi and Singh, 2012).…”

“…Joshi and Singh's study (2012) provided a complete overview of the manufacturing sector and trends since independence. In the Indian context, only a few studies have been carried out on the textile industry's technical efficiency-related issues (Bhandari and Ray, 2012;Bhaskaran, 2013;De Jorge-Moreno and Carrasco, 2015;Joshi and Singh, 2012). Recent developments in the Industry 4.0 revolution have created constructive disruption (Schumpeter, 1954) and shaken the foundation of the manufacturing industry.…”

Three decades of export competitiveness literature: systematic review, synthesis and future research agenda

“…Previous research on the methodological domain of EC has evaluated trends by mostly considering empirical studies only. A number of attempts have been made by previous researchers to understand the methodologies adopted; for instance, two stage least square method (Frankel and Romer, 1999; Morgan, 2013), constant market share model (Joshi and Singh, 2012), simple general equilibrium model of EC (Helpman, 1984), RCA (Qureshi and Wan, 2008), MNL model (Rettab and Rao, 2009), gravity model (Bergstrand, 1985; Fink et al , 2005), tobit model (Kumar and Siddharthan, 1994), auto regressive conditional heteroscedasticity model (McKenzie and Brooks, 1997), GARCH model (Kroner and Lastrapes, 1993), probit, logit and tobit and MNL logistic (Hung et al , 2004). We make a strong point here that authors should compile more studies to put forward a more comprehensive integration of all the relevant facets of the methodological domain.…”

Dynamics of MSME Industry in Uttar Pradesh and Maharashtra: An Analysis of Sugar Firms

“…NIC 1998 and 2004 for sugar mills is 15,421 and 10,721 for NIC 2008. After reviewing the literature, it was found that Abdulla (2017), Bhandari and Vipin (2016), Mukherjee (2008), Mukherjee and Ray (2004) and Joshi and Singh (2012) also used ex-factory value of output as an output variable and fixed capital, fuels consumed, wages salary and raw material consumed as input variables to assess the efficiency of manufacturing sector in India. The variables used in the study deflated by using appropriate deflator.…”