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Total factor productivity growth (TFPG) is not only the technological progress. It is a boarder concept. It may be the increase in the productivity of inputs, economies of scale, capacity utilization, technological progress, etc. In this study, we have tried to estimate TFPG and its components for the manufacturing industries of West Bengal and overall India, for the period 1980–1981 to 2016–2017, using stochastic frontier approach. The main data source of this study is the Annual Survey of Industries (ASI), which is published by the Central Statistical Organization, Government of India. The entire period has been sub-divided into two phases; pre-World Trade Organization (WTO) regime (1980–1981 to 1995–1996) and post-WTO regime (1995–1996 to 2016–2017). This study also tries to make a comparative analysis between the TFPG of manufacturing sector of West Bengal and all India level for the time period mentioned above. For West Bengal, TFPG has decreased for the post-WTO regime and it has increased in post-WTO regime for overall.

This study attempts to analyze energy intensity, capacity utilization (CU), output and productivity growth of aggregate manufacturing sector in India during the period 1980–1981 to 2016–2017. A decadal analysis as well as a comparison between pre- and post-liberalization period of productivity growth is also made. Total factor productivity growth (TFPG) is also adjusted with CU to obtain adjusted TFPG. The trend in energy intensity is also analyzed to answer the question of sustainability. Results shows that TFPG declined in the post-reform period, highlighting the fact that liberalization process has its adverse impact on productivity growth. From the study it is observed that a declining trend in adjusted TFPG in the post-reform period, but the rate of decline is higher. Energy intensity and CU of the Indian manufacturing industries is found to be increasing over the study period. Increasing energy intensity, quite significantly, would increase the level of pollution generated by the manufacturing industries. So, interestingly enough, this may lead to conclude that the growth of the manufacturing industries is not in line with the basic essence of sustainable development.

Pharmaceutical industry is one of the sunrise industries in the Indian manufacturing sector. It has flourished in the recent past. This chapter makes a comparative analysis of the productivity growth of Indian pharmaceutical industry using production function approach and adopting two distinct measures of labour input and also explains whether the growth and productivity is eco-friendly or not. Annual Survey of Industries (ASI) data is considered as data base and the time period 1980–1981 to 2016–2017 is considered which is sub-divided into four periods (1980–1981 to 1989–1990; 1990–1991 to 1999–2000; 2000–2001 to 2009–2010; and rest of the period). The pattern of result for both the measures are more or less in the same direction. A remarkable growth in total factor productivity (TFP) is observed after the initiation of new economic policy for both the method used. So far as the environmental issues are concerned, this industry seems to have been polluting the environment, as per unit use of energy is increasing over time.

This chapter analyses the efficiency of African airlines using a two-stage network DEA (Data Envelopment Analysis) model. Network DEA models usually take into account the production process with intermediate inputs derived from the first stage and a second stage that departs from it. This fundamental feature enables one to view the airline production process as a carry-over activity. The analysis covers the 2010–2013 period. The relative efficiency ranks are presented and policy implications are derived.

The Association of Southeast Asian Nations (ASEAN) members want to efficiently promote the flow of commodities and personnel within its service areas under given limited resources. Based on panel data from 2007 to 2014, this study applies the output-oriented data envelopment analysis method and focuses on the disaggregated output efficiencies of 42 ASEAN airports. Results show that the international airports of ASEAN members have significantly better output efficiency for passenger and movement output than regional airports. This work provides a relatively fair perspective in evaluating ASEAN’s airport operating efficiency. It helps policymakers measure the frontier forward or backward shift of an airport over the research period, in order to reveal the characteristics of airport efficiency and to present a new interpretation along with managerial implications.

The chapter examines the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries as well as total manufacturing industry of Gujarat during the period from 1981–82 to 2010–11, using a stochastic frontier approach. The empirical finding clearly states that although factor accumulations as well as resource allocations in most of the 2-digit manufacturing industries of the state have been improved during the postreform period, technological progress (TP) and technical efficiency change (TEC) of the same have deteriorated in most industries of the state during that period. As a result TFPG in the major manufacturing industries as well as total manufacturing industry of the state have declined because the combined effect of their improvement in scale effect (SC) and allocation efficiency effect (AEC) could not offset the declining effect of both the TP and TEC of the same during that period. In this context, the government should take some policy initiatives to improve productive efficiency of the organized manufacturing industries in Gujarat. Once efficiency increases, it enhances competitiveness, thereby increasing productivity growth and its different sources of organized manufacturing industries of the state.

In this study, the authors have tried to estimate and examine the relationship between energy intensity (EI), capacity utilisation (CU) and total factor productivity (TFP) of Indian Iron and steel industry for the time period 1980–1981 to 2016–2017. The estimation of TFP and its growth is based on three inputs capital (K), labour (L) and energy (E) using transcendental logarithmic cost function. Besides, capital and labour factor inputs, the estimation of CU also take into account the input, energy. It also considers the variable, price of energy and uses a variable cost (VC) function. This study also takes into account the concept of adjusting total factor productivity growth (TFPG) by CU. From our result, this study observes that CU increased over time. Again, Iron and Steel industry has become energy intensive (increasing EI) over the time period taken up for the study. On the other hand, TFPG has declined in the liberalised era. The preliminary idea that we get from our result is that TFPG is negatively related to the extent of pollution generated by energy intensive nature of the Indian Iron and Steel industry, which is in line with reality. To come out of this situation, we may prescribe the use of green technology and green energy sources for this industry.

In the present knowledge economy, intellectual capital (IC) is regarded as one of the significant determinants of efficiency, profitability, and ultimately value of a firm. This chapter empirically investigates the ramifications of the IC on the level of efficiency of the firm. In addition, exploration of the changing dynamics in the relationship between IC and firm level efficiency in the face of global economic crisis is of special interest of this chapter. In attaining the objectives of the study, a comprehensive database of 299 manufacturing firms (chosen randomly from a stratification of six BSE manufacturing industry subsectors) were utilized during the period from 1999–2000 to 2013–2014. Firm level efficiency scores and implications of IC (as measured by employing Pulic's Value Added Intellectual Capital Model) on the level of efficiency of the firms were examined simultaneously using Stochastic Frontier Analysis. Empirical results revealed that IC significantly determines the efficiency of the manufacturing firms during the period of study. However, the impact of financial crisis was not robust in changing the synergy between efficiency and IC. Size, age, and leverage were also found to be significant determinants of efficiency during the period of study.

World economies including India have been moving toward recession. To combat this recession more employment generation through investment is required in a highly populated economy like India. Since unorganized manufacturing enterprises (UMEs) provide employment to a huge mass in India, therefore its growth and productivity is a matter of concern in the Indian economy. The present study analyzes the growth and productivity of UMEs on the basis of the latest two rounds of NSSO unit level data incorporating all states and union territories (UTs) of India. It reveals that the growth of UMEs, employment, gross value added (GVA) and fixed assets widely varied across states/UTs, and these growth rates were substantially high in a number of states during 2010–11 and 2015–16. In most of the states/UTs the labor productivity of UMEs has increased significantly but not the capital productivity. Our analysis supports the theoretical relationship among growth of employment, GVA, and labor productivity. Therefore, the government has to make deliberate attempts to increase the growth of UMEs on one side and raise productivities of UMEs through skill developments on the other side to overcome the problem of unemployment in particular and expedite the growth of the Indian economy in general to combat the global economic recession.

The unorganised manufacturing sector contributes one third share of overall manufacturing employment and one fifth share of gross value added of the manufacturing sector. Despite its important role in large-scale employment generation, this sector is neglected by the researchers as well as by the policy makers as compared to the focus given on the organised manufacturing sector. The issues of energy intensity, environment emissions and growth of unorganised manufacturing enterprises (UMEs) remain unexplored. The present chapter attempts to estimate the CO₂ emission and emission intensity (EI) across UMEs on the basis of NSSO Unit Level data of 62nd, 67th and 73rd rounds. It also analyses the growth of UMEs in relation to CO₂ emission and EI. The nature of the sector is very much dispersed. Our study reveals that a portion of unorganised enterprises did not use any energy in their production activities and used manually operated instruments like – handlooms, weaving machines, hand-operated oil and rice mills, etc. The main energy inputs of UMEs are electricity and fuel & lubricants. The CO₂ emission is relatively less in UMEs compared to organised manufacturing enterprises. Across the unorganised manufacturing industries, the higher CO₂ emission are observed in manufacturing of food product industry and other non-metallic mineral industry. The study found that CO₂ EI of UMEs depends on firm-level characteristics like perennial nature, establishment type, urban location and expanding growth status. However, capital intensive UMEs are more polluting.