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Production-related industrial zones, super structures and infrastructures are constructed by the construction industry. Nearly all industries and their environmental emissions are influenced by the construction industry including its sub-industries, companies and their supply chains. Furthermore, cities play an important role in economic growth. Cities are hubs for productivity, production, supply and demand, and innovation with the help of their human capital and built environment (e.g. offices, factories, industrial zones, infrastructures, etc.).

Industrial growth fosters urbanisation which is vital for the supply side in the economy to reach to the human resources. Urbanisation which supports industrial growth obstacles industries’ efficiency due to urbanisation problems (e.g. traffic, air and water pollution, health problems).

Construction industry and its sub-industries affect total factor productivity growth in nearly all industries. Construction industry can be a facilitator industry for economic growth and industrial growth considering total factor productivity growth and environment aspects. All industries’ green and sustainable total factor productivity growth can be supported by rethinking construction industry, its sub-industries and their outputs (e.g. construction materials, built environment, cities) as well as construction project management processes.

This chapter aims to introduce carbon capturing smart construction industry model to foster green and sustainable total factor productivity growth of industries. This chapter emphasises current and potential roles of construction industry, its sub-industries and their outputs in fostering other industries’ growth through green and sustainable total factor productivity growth. It focusses on carbon capturing technologies and design at different levels. Furthermore, this chapter emphasises cities’ role in green and sustainable total factor productivity growth. This chapter provides recommendations for construction industry policies and carbon capturing cities/built environment model to solve urbanisation problems and to foster industrial growth and green and sustainable total factor productivity growth. This chapter is expected to be useful to all stakeholders of the construction industry, policy makers, and researchers in the relevant field.

The hypothesis that the growth in total GNP can also be explained by other factors than the growth in total inputs (capital and labour) and their respective productivities is analysed by the use of 1960‐1985 OECD country data. The OLS estimations of the models of embodied and disembodied technical change in both capital services as measured by the R&D expenditures and labour productivity as measured by investment expenditures in education and health showed very significant results. However, despite the inclusion of these expenditures in the aggregate production function, GNP growth has not been fully exhausted in all OECD countries. Indeed, the unexplained residual which was computed for these countries turned out to be of non‐negligible magnitude and growth. The assumed non‐factor sources of growth containing the unexplained residual which may not be associated by the movement along a production function would include non‐quantifiable political, social and institutional forces which, in some cases, might interact to speed or adversely delay growth unless they remain stable or improved.

This study aims to estimate total factor productivity (TFP) growth for the post-2008 period for selected industries in the manufacturing sector at NIC 3-digit. Total factor productivity growth (TFPG) estimates are based on the theoretical framework provided by studies such Hall (1988), Abraham et al. (2009) and Crepon et al. (2005) that incorporate market imperfection in labour and product market, thereby modifying the traditional TFP estimation as Solow Residual.

Based on the theoretical model that incorporates market imperfections in labour as well as product market in modifying the TFP estimates using the Levinsohn–Petrin framework of empirical estimation, the authors have calculated industry wise TFPG for 62 industries at NIC 3-digit level.

The study finds three distinct trends: first, there are considerable industrial disparities in productivity growth in terms of TFP. The estimates have been found to be higher than the conventional Solow Residual for most industries, indicating the role played by market imperfections in affecting the conventional measure of productivity growth. Second, estimates of bargaining power are found to be lower than those compared to the earlier estimates in Maiti (2013) for the Indian organised manufacturing case for 1998-2005. This observation is commensurate with the observation in recent years of a falling share in labour wage in total output in organised manufacturing sector. Finally, the study also found a statistically significant contribution of greater mechanisation on TFPG while an adverse effect of the rising dependence of organised manufacturing on contractual labour.

The role of market imperfections in measuring TFPG has been undertaken, and it has been found to be an important factor, as the estimated measures vary from the conventional measures of TFPG. Moreover, the study has considered a very recent period from 2008-2015 in estimating TFPG, as well as analysing the factors behind the trends in TFPG at industrial level.

The purpose of this paper is to examine the performance of small manufacturing enterprises (SMEs) in India during the pre‐reforms (prior to 1991) and reforms period (1991 onwards) with focus on 15 major states from different levels of development.

In order to capture variation across different categories of states, 15 major states in India have been classified into high‐, middle‐ and low‐income states. Further, to capture productivity growth in the sector during the pre‐reforms and reforms period, both partial factor productivity and total factor productivity method (growth accounting approach) have been adopted. The analysis is based on different rounds of nationwide survey conducted by the National Sample Survey Organization (NSSO) of the Government of India during 1978‐2001.

The findings of the study reveal erosion in growth of output in the SMEs during the reforms period as compared to the pre‐reforms period with variation across different categories of states. The decline in growth of output during the reforms period can be primarily on account of fall in growth of employment and investment. The total factor productivity growth has also declined during the reforms period suggesting the need to enhance the level of technical efficiency and skills of the labour force in the sector. This is noticed in spite of major role played by the SMEs in providing employment (80 per cent of the total manufacturing sector employment) opportunities and in generating output (contributes 60 per cent of net domestic product) in the country.

On account of non‐availability of annual data, the study relied on data collected by the NSSO of the Government of India periodically. In addition, the study did not examine the factors that explain decline in productivity growth in the sector.

There is a large body of literature on regional growth and productivity in the Indian manufacturing sector but most of the studies have considered only the organized manufacturing sector. This study contributes to the literature by analyzing the inter‐state variation in growth and productivity performance of SMEs in the pre‐reforms and reforms periods.

The purpose of this paper is to measure the Malmquist Productivity Index and its different components such as technological change, technical efficiency change and the change in scale efficiency in the Indian food industry during the period spanning 1988‐1989 to 2004‐2005. Further, it examines the variation in productivity and its components with respect to the factors internal to the firms.

The technique of data envelopment analysis has been used to measure productivity index and its different components under the assumption of variable returns to scale. Further, log‐linear regression model has been used to explain the variation in productivity and its components with respect to certain factors internal to the firms.

In spite of a strong agricultural base and being the third largest producer of food products in the world, India's food processing industry is far from tapping its full potential as a result of a low rate of technological progress/regress on the one hand and increasing inefficiencies of the firms on the other hand. It is necessary to encourage imports along with R&D to ensure faster technological progress in the Indian food industry. However, the technological possibilities depend on the mode of organization and various economic and institutional factors. Therefore, bold institutional changes are to be made side‐by‐side in order that inefficiency is substantially reduced.

The present study evaluates the contribution of technological change, technical efficiency change and scale efficiency change to total factor productivity growth in the Indian food processing industry by using the firm‐level data, collected from the Centre for Monitoring Indian Economy (CMIE). It further examines the impact of some common factors internal to the firms on their performance.

The purpose of this paper is to explore the effects of cross-border acquisitions on the efficiency and productivity of acquired companies in the cement industry in the context of a transitional economy.

The Data Envelopment Analysis (DEA) and Malmquist Productivity Index were used to assess the efficiency and productivity of the acquired companies over the period 2000–2018. DEA and Malmquist index are combined with bootstrapping to perform succinct statistical inferences for determining the accuracy of results. The study assesses partial efficiency and productivity of three inputs: material, capital and labour, as well as the total factor efficiency and productivity of the acquired companies in the short and long term after the acquisitions.

The research results suggest that efficiency of material, efficiency of labour and the total factor efficiency of the acquired companies are higher after the acquisitions than before, while efficiency of capital is lower. In addition, the results show that the acquisitions had a positive impact on total factor productivity of the acquired companies.

The results of this study have practical implications for managers, especially for policy-makers and industry analysts in deciding whether to encourage or discourage cross-border acquisitions in transitional economies.

The study contributes to a better understanding of the impact of cross-border acquisitions on efficiency and productivity of acquired companies in the manufacturing industry. Research in transitional economies related to subject matter is limited, and this study is the first empirical investigation of the effect of cross-border acquisitions on the efficiency and productivity in the cement industry in Serbia by applying the Data Envelopment Analysis.

This chapter measures total factor productivity growth (TFPG) using Malmquist productivity index (MPI) and the growth of MPI of Indian Textile Industry employing nonparametric data envelopment analysis (DEA), during 1995–2016, exploring company (firm) level Center for Monitoring of Indian Economy (CMIE) Prowess data; examines whether TFPG has improved after the withdrawal of multifiber trade agreement (MFA) since 2005; decomposes TFPG into technical change (TC), technical efficiency change (TEC), and scale efficiency change (SEC); and explains the factors behind the movement of TFPG, considering the effect of R&D (RD), exports (EX), marketing expenditures (MKTs) advertisement expenditures (ADVs), imports (IMP), using second-stage panel regression. Empirical evidence supports fluctuating pattern of TFPG during 1995–2016, with a marginal declining tendency. TFPG has increased in 1999–2000, 2000–01, 2009–10, and 2012–13. After dismantling MFA, MPI level has significantly declined, with an increase in its growth rate, but the increase is not statistically significant. The effect of EX, RD, ADV are nonlinear, U-shaped, and IMP and MKT are inverted U-shaped, implying that the sign effect of any variable depends on its size. There are joint interaction effects of (a) RD and EX; RD and MKT which are positive, (b) ADV and MKT as represented by the ratio (ADV/MKT), having nonlinear inverted U-shaped relation. The joint interaction effect supports that the impact of one variable depends on the magnitude of other. The marginal effect of EX, IMP, and ADV are positive; increase in these variables promotes TFPG. The greater role of ADV over MKT is evident. The marginal effect of RD is negative; the average level of RD is too low to generate positive effects, and, thus, there is an urgency of increasing RD. The promising part of the decomposition analysis is that highest contribution to growth rate of TFPG is the growth rate of TEC followed by growth rate of TC, and thus by increasing TEC and TC, higher growth rate of TFPG is achieved and may be beneficial in the long run and may lead to absorption of economic shocks for an economy facing recession in its output growth. Some policy suggestions are made for boosting up TFPG.