EFFICIENCY AND PRODUCTIVITY ANALYSES OF THE INDIAN GARMENT INDUSTRY

Abstract

Until 2000, the Indian garment industry was reserved for the small-scale sector to protect the employment in rural and urban areas. This restriction adversely affected investment in plant and machinery, technology up-gradation, skill up-gradation of operators, and economies of scale, and consequently quality, efficiency and productivity in the industry. In addition, there were restrictions on import and export of garments in the global market under Multi Fibre Agreement (MFA) during the period 1974-2004. To remove bottlenecks of the industry and make it globally competitive, the Government of India de-reserved the industry from the SSI list in 2001. With the de-reservation and removal of the MFA restrictions on garment trade in January 2005, the medium and large-scale firms have also entered in the production and trade of garment products. These policy changes would increase competitiveness of the industry in domestic and international markets, putting a lot of pressure on the SSI units to produce garments with a competitive price. To retain competitiveness, the need is to make continuous improvement in efficiency and productivity of the garment firms. This improvement is also required to measure and compare with the benchmarks to know the excess use of inputs and deficiency in outputs. In the garment industry, Partial factor productivity (PFP) approach is generally used to measure the productivity of worker and machine. The most important limitation of this approach is an inappropriateness of making decisions based on one single ratio when there are many inputs and outputs. In fact, in garment production, essential inputs required are machine operators, stitching machines, raw materials and energy. Most sophisticated analytical tools are required to evaluate the efficiency and productivity of the garment industry. Linear programming based Data Envelopment Analysis (DEA) technique happens to be appropriate for such evaluation. DEA can handle multiple inputs and outputs and does not require any assumption of a functional form relating inputs to outputs. Thus, it is well suited for comparative performance analysis of the industry. By a critical examination of the available literature, it is found that DEA-based studies on Indian garment industry are extremely limited. Keeping this as a backdrop, this study attempts to estimate the technical efficiency and TFP growth in the Indian garment industry, identify their determinants, and suggest measures to enhance productivity and efficiency in the industry. The first objective of this work is to study the profile, growth and efficiency trends of the Indian garment industry. To achieve this, we use ASI data (factory sector) of the industry for the period 1981-82 to 2005-06. Value of output is used as an output variable and the number of iii employees, fixed capital, raw material and power &fuel as inputs. The compound annual growth rates in inputs and outputs, and some technical ratios are calculated to study the growth trends. CGR and BCCmodels are used to estimate the overall technical efficiency (OTE), pure technical efficiency (PTE) and scale efficiency (SE) of the industry. Further, Tobit regression model is applied to understand the determinants ofinefficiency.. The results ofDEA models reveal that the industry achieves 91.7 percent OTE, 92.7 percent PTE and 98.9 percent SE. The PTE causes relatively more variations inthe OTE than the SE. Based on the estimated OTE scores; it is found that the performance of the industry during the pre-reform period is relatively better than the post-reform period. Slack analysis shows high magnitude of slacks in number of employees and fixed capital. Regression analysis reveals that the labour productivity has positive effect on OTE, PTE and SE, whereas the capital intensity is inversely associated with the OTE and PTE. Besides, the outstanding loan has negative impacton SE and OTEof the industry. Secondly, we estimate the technical efficiency and its determinants of the individual garment firms for the year 2004-05. The ASI unit level data of 275 firms are used to measure the OTE, PTE, SE, slacks and returns to scale. Value of output is used as an output variable and the number of workers, numberof supervisory & managerial staff, raw material consumption, power &fuel consumption and plant &machinery as input variables. CCR andBCC models with output orientation are used to estimate the OTE, PTE and SE of the individual firms. Further, Jackknifing technique is used to detect outliers and study robustness of the efficiency scores. Slack analysis is done to identify the slacks in inputs and output. Tobit regression is conducted to know the effects of several background variables on the efficiency of the firms. Finally, efficiency scores of firms are also compared by location and scale-size. The results show that the average OTE, PTE and SE of all the 275 firms are 0.705, 0.773 and 0.912 respectively. Slack analysis shows the high amount of slacks in plant & machinery, number of workers and supervisory & managerial staff. Returns to scale analysis reveals that most of the firms have operated at DRS during the reference year. Karl Pearson and Spearman correlation coefficients suggest that the efficiency scores and rankings of the firms are stable. The state-wise analysis reveals that the firms in Delhi state are more OTE efficient as compared to the firms in other states of India. The scale-wise and rural-urban analyses show that the micro-scale firms and urban firms are more efficient than their counterparts. Lastly, Tobit regression results indicate that labour productivity, wages per employee and labour-staff ratios have positive impact on efficiency scores, whereas, capital intensity and outstanding loan show the negative impact. IV Last objective of the study is to estimate the total factor productivity (TFP) growth and its components in the garment firms. The panel data of 50 firms are obtained from the Capitaline database for 1998-99 to 2007-08 to estimate the total factor productivity change (TFPCH), technological change (TECHCH) and efficiency change (EFCH). The inputs are wages & salaries, raw material consumption, power &fuel consumption, plant &machinery and output as value of output. Malmquist Productivity Index (MPI) is used to evaluate the TFPCH and its components. An attempt has been also made to study the variation in TFP in the firms across the regions, scale-size, production method and market orientation. Regression analysis is also carried out to identify the determinants of TFP growth in the firms. The results of MPI model reveal that on an average, the TFP in the firms has increased by a rate of 0.8 percent per annum, mainly due to technical progress. Looking at MFA-phase out and post-MFA periods, wc observe that the TFPCH has increased in the later period, which is due to gain in EFCH. Region-wise TFP analysis reveals that TFP growth is positive in northern and southern regions, while western region shows no progress in TFPCFI. Scale-wise analysis shows that the small-scale firms are more productive in comparison to medium and large-scale firms. TFP analysis of woven and knitted firms shows that the woven firms achieve higher TFP growth than the knitted firms. Moreover, TFP comparison of firms according to their market orientation shows the similar progress of TFP in domestic and export firms. The determinants of TFP growth demonstrate that breakdown in plant & machinery and capital utilisation have negative impact on the TFPCH, while gross output per unit of electricity and firm age show the positive impact. All the three analyses (time series, cross sectional and panel data) reveal that the PTE affects the OTE more than the SE. It is recommended that the industry should first improve the PTE and then focus on improving the SE to raise the overall performance of the firms. For improvement in PTE, skilled labour and staffare required. Atthe firm level, in-house training for machine operators should be provided to improve the skill. At Government level, the Apparel Export Promotion Council can plan to set up a number of Apparel Training Centres widely spread across the garment producing states of India. In addition, the industry needs to provide the attractive wages & salaries to the employees to attract the skilled manpower, which will result improvement in the PTE. Regression analysis shows negative impact of outstanding loan on the performance of the industry. In this direction, Technology Upgradation Fund Scheme can help the garment firms to have easy access of the bank loan for their modernisation and upgradation. To avoid losses due to breakdown in plant & machinery, the firms should have skilled technicians and scheduled machine maintenance. In order to improve the cost competitiveness, uninterrupted power supply may be provided to the garment manufacturers. Keeping in view the global competition, the firms should make yearly self-assessment to evaluate their own performance with the competitors.