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This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

The purpose of this paper is to apply a stochastic Frontier production model to Canadian manufacturing industries, to investigate the sources of total factor productivity (TFP) growth. As productivity (growth) appears to be the single most important determinant of a nation's living standard or its level of real income over long periods of time, it is important to better understand the sources of productivity growth. In Canada, TFP growth is the major contributing factor (relative to changes in capital intensity) to labour productivity growth, particularly in manufacturing sector. However, the TFP gap is also the main source of labour productivity gap between Canada and other industrialized (Organization for Economic Co‐operation and Development) countries in recent years.

In this paper, a stochastic Frontier production model is applied to Canadian manufacturing industries to investigate the sources of TFP growth. Using a comprehensive panel data set of 18 industries over the period 1990‐2005 and the approach proposed by Kumbhakar et al. and Kumbhakar and Lovell, TFP growth is decomposed into technological progress (TP), changes in technical efficiency, changes in allocative efficiency and scale effects.

The decomposition reveals that during the period under study, TP has been the main driving force of productivity growth, while negative efficiency changes observed in certain industries have contributed to reduce average productivity growth. In addition, the empirical results show that research and development expenditure, information and communications technology investment, as well as trade openness exert a positive impact on productivity growth through the channel of efficiency gains.

The author argues that the decomposition carried out in this study may be very helpful to elicit the correct diagnosis of Canada's productivity problem and develop effective policies to reverse the situation, thereby reducing Canada's lagging productivity gap.

This paper aims to measure and compare agricultural total factor productivity (TFP) growth in China and India and relates TFP growth in each country to policy milestones and investment in agricultural research.

TFP is measured using a non‐parametric Malmquist index which allows the decomposition of TFP growth into its components: efficiency and technical change.

Comparing TFP growth in China and India it is found that efficiency improvement played a dominant role in promoting TFP growth in China, while technical change has also contributed positively. In India, the major source of productivity improvement came from technical change, as efficiency barely changed over the last three decades, which explains lower TFP growth than in China. Agricultural research has significantly contributed to improve agricultural productivity in both China and India. Even today, returns to agricultural R&D investments are very high, with benefit/cost ratios ranging from 20.7 to 9.6 in China and from 29.6 to 14.8 in India.

The applied methodology and the comparison between TFP growth patterns contribute to a better understanding of the consequences that the different approaches to agricultural reform followed by China and India had on the performance of agriculture in both countries.

This paper aims to conduct inter‐country analysis of agricultural productivity growth in transition countries in Asia and Europe. This paper pays particular attention to the magnitude and direction of productivity growth over different stages of their market reforms.

The paper adopts a nonparametric Malmquist index approach using an output distance function to measure productivity growth and decompose it into its associated components. The empirical analysis is performed using the most recent FAO data set of 35 transition countries in Asia and Europe over the period of 1979‐2004.

The paper shows that decomposition analysis of productivity growth differs considerably at different stages of the transition period. This study presents supporting evidence that serious improvements in performance and efficiency, as well as continued technology transfer and adoption are required for transition economies to meet the demand for food and anticipated increases in world population.

A comprehensive picture about the agricultural performance of the transition countries has somehow been missing in the literature. This study fills this gap by analyzing the productivity in these transition countries.

This paper deals with some structural indicators and their evolution, in China and its regions, over the period 1981-2010.

The paper uses a quantitative approach. Linear programming and structural growth decompositions were used. The authors first produce estimates of the optimal productivities of incremental capital and the optimal incremental income elasticity of capital by means of a linear programming exercise. They then produce an accounting growth decomposition to assess the changes in the contribution of capital productivity, capital intensity and labour participation to the growth rate of output per capita. Finally, they combine an accounting growth decomposition with a standard production function, growth accounting, decomposition to assess the contribution of both capital productivity and capital intensity to total factor productivity (TFP). They also show in the Appendix the difference in the TFP growth contribution when marginal elasticities are assumed variable over time and when scale returns are assumed to be increasing rather than constant.

The main conclusion of the paper is that capital intensity, rather than capital productivity or labour participation, has been the main growth contributor. Capital productivity has fallen, while capital intensity has increased significantly, but that does not mean that quantity in itself, rather than quality, is behind such growth, as total factor productivity, which is significantly more than engineering technical change, has been relatively important over the period.

Both the use of linear programming to assess the evolution of incremental capital productivity and the decomposition of TFP.

The purpose of this paper is to analyze agricultural total factor productivity (TFP) and input redundancies in different regions of China, and to bring out the policy implications for improving efficiency in agricultural production as well as environment protection.

Based on the provincial panel data during 1995-2014, the agricultural productivity of China and its regional disparity are analyzed. First, the agricultural TFP and its decomposition are dynamically evaluated by means of data envelopment analysis-Malmquist productivity index. Second, the agricultural radial production efficiency in year 2014 and the input redundancy changes from 1995 to 2014 are measured based on the BCC-slacks-based measure model.

The results showed that the overall agricultural TFP of China grew 4.3 percent annually during 1995-2014, mainly as a result of technical progress. However, the declines of technical efficiency and scale efficiency slowed down the agricultural TFP growth. The TFP growth in the Western region and Central region far exceeded the Eastern region in last few years. In 2014, most effective decision-making units were in the Western region. The input redundancies in the agricultural production increased substantially after 2006, especially for the pesticide use amount, reservoir capacity and agricultural machinery power.

Combining the dynamic and static analyses, the paper fulfilled the study of China’s agricultural productivity and the input redundancies in recent years, and also presented the regional disparities.

The purpose of this paper is to empirically study the impact of product patent regime on the productivity of different categories such as ownership, R&D, size and product-wise of Indian pharmaceutical firms using non-parametric data envelopment analysis.

The present study has applied Ray and Desli’s Malmquist productivity index and its decomposition to measure total factor productivity (TFP) change, pure technical efficiency change, scale efficiency change and technical change under variable returns to scale (VRS) technology assumption for 141 Indian pharmaceutical firms during 2000-2001 to 2014-2015.

The study found the negligible impact of product patent regime on productivity. The technological change has played a positive role in the growth of productivity, whereas technical efficiency change depicts the judicious utilization of resources for improving performance. From the results, it is found that R&D intensive firms depict better stability in the TFP than the non-R&D firms. However, Granger causality between R&D and productivity found no relationship. Productivity is more directly affected by investment in fixed assets rather than in R&D, which focusses on incremental value additions in a largely branded/plain generic product market. In case of ownership, private foreign firms found to have registered progress in TFP while others have recorded marginal regress, which probably could be attributed to the superior marketing and management skills of the foreign firms, besides possessing proprietary technology. Both small and large firms have shown positive growth in the new regime as compared to the pre-patent regime. These small firms are able to compete with large firms because of their up-gradation of the technological base by improving access to better foreign technology. TFP growth for all the firms can be attributed to improvement in technology, and innovation in terms of high capital-output ratio. Further, the paper tried to identify the determinants of productivity from panel random effect regression, and it is found that export intensity, age and the new patent regime have negative and significant relationship with productivity, whereas other variables such as R&D, ownership, size and capital imports are insignificant. In the end, the results of sensitivity analysis have confirmed the validity of the selected variables.

The results suggest that Indian pharmaceutical firms need substantive improvement in TFP by improving managerial and scale efficiency. Indian pharmaceutical industry (IPI) needs to improve productivity across the network and drive cost excellence initiatives across the spend base through operational excellence and digital initiatives. The results of this paper can be applied in framing policies for future growth and improvement in the productivity of IPI.

The paper aims to make several new contributions to the existing literature. Most of the research papers only analysed TFP of the industry as a whole and detailed firm-wise analysis is needed to capture the true impact at a unit level. This study has analysed the impact of different categories such as ownership, R&D, size and product-wise, and determinants of productivity. The study has used a broader time period and larger panel data to predict the better picture.

The Indian garment industry has witnessed a significant change since the inception of the New Textile Policy 2000 that suggests removing the industry from the list of small‐scale industries with a view to improving its competitiveness in the global market. As productivity is the driving factor in enhancing the competitiveness of any decision‐making entity (firm), a study of total factor productivity (TFP) and its sources can provide vital inputs to a firm for improving its competitiveness. Keeping this as a backdrop, the paper attempts to measure the TFP in the Indian garment‐manufacturing firms; identify sources of the TFP; and suggest measures for the firms to enhance their productivity.

The study is based on the firm‐level panel data collected from the Centre for Monitoring Indian Economy for the years 2002‐2007. One output variable, namely, gross sale and four input variables, namely, net fixed assets, wages & salaries, raw material, and energy & fuel, have been selected. The DEA‐based Malmquist Productivity Index (MPI) approach has been applied to measure the TFP.

The Indian garment industry has achieved a moderate average TFP growth rate of 1.7 per cent per annum during the study period. The small‐scale firms are found to be more productive than the medium‐ and large‐scale firms. The decomposition of TFP growth into technical efficiency change (catch‐up effect) and technological change (frontier shift) reveals that the productivity growth is contributed largely by technical efficiency change rather than by technological change.

Earlier studies on the Indian garment industry have applied the partial factor productivity approach, which has several limitations. This paper measures the TFP and identifies its sources through applying a non‐parametric DEA‐based MPI approach. Through this approach, the productivity growth is decomposed into technical efficiency change and technological change. Further, an attempt has also been made to study the variation in the productivity growth rates across location, scale‐size and type of garments.

The objectives of this paper are to estimate the annual Malmquist TFP(total factor productivity) index of Korea and China’s road freight transport with DEA(data envelope analysis) and to decompose the index into technical efficiency change and technology change. In the process of the estimation, we used labor, capital, and fuel as input factors and ton-km of road freight transport as output factor. The panel data of Korea and China’s road freight transport industry from 1985 to 2004 are used. The results of the analysis show several points. First, there was no significant improvement in China’s TFP growth before 1997, but there was continuous growth in TFP since 1997 because of constantly increasing domestic freight transport demand. Second, there was downward trend in Korea’s TFP, especially there was a large reduction of productivity in 1998 because of the huge reduction of road freight transport demand during the period of the economic crisis. Third, the technology improvements play a significant role in the TFP growth and the technical efficiency had negative effects on the TFP growth of Korea. However, the technology improvements as well as the technical efficiency had positive effects on the TFP growth of China’s road freight transport industry.

Foreign firms have certain advantages which may spillover to domestic firms in the form of improvements in total factor productivity (TFP) growth. The purpose of this paper is to empirically observe the presence of TFP spillovers of foreign direct investment (FDI) to domestic firms through analyzing source of TFP growth in Indian drugs and pharmaceutical industry.

This paper examines the sources of TFP spillovers of FDI in Indian drugs and pharmaceutical industry over the period 1999 to 2014. The data of 304 firms has been used for estimation of the growth rates of TFP and its sources under stochastic frontier analyses based Malmquist productivity index framework. For frontier estimation, the Wang and Ho (2010) model has been executed using translog form of production function.

The results show that there exists significant TFP spillover effect from the presence of foreign equity in drugs and pharmaceutical industry of India. The results also show that the major source of TFP fluctuations in the said industry is managerial efficiency that has been significantly affected by FDI spillover variables. In sum, the phenomenon of significant Intra-industry (horizontal) efficiency led productivity spillovers of FDI found valid in case of Indian drugs and pharmaceutical industry.

The number of foreign firms is very less to imitate the significant impact of foreign investment on TFP growth of Indian pharmaceutical industry at aggregated level; and the Wang and Ho (2010) model is failing to capture direct impact of FDI on technological change under Malmquist framework.

Since, there exists dominance of domestic firms in Indian drugs and pharmaceutical industry, the planners should follow the policy which not only attract FDI but also benefit domestic firms; for example, developing modern infrastructure and institution which will further help domestic firms to absorb spillovers provided by the Multinational Corporations and also accelerate the growth and development of the economy.

In no case, the foreign firms should dominate the market share otherwise the efficiency spillover effect will be negative and the domestic firms will be destroyed under the self-centric approach of foreign firms protected by the recent patent laws.

The study is a unique attempt to discuss the production structure and sources of TFP spillovers of FDI in Indian drugs and pharmaceutical industry with such a wide coverage of 304 firms over a period of 16 years under Wang and Ho (2010) model’s framework. The existing studies on TFP spillovers are using either a small sample size of firms or based upon traditional techniques of measuring spillover effects.