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This paper aims to revisit the relationship between intangible capital and labour productivity growth using the largest, up-to-date macro database (2000–2015) available to corroborate the econometric findings of earlier work and to generate novel econometric evidence by accounting for times of crisis (2008–2013) and economic recovery (2014–2015).

To achieve these aims, this paper employs a cross-country growth accounting econometric estimation approach using the largest, up-to-date database available encompassing 16 EU countries over the period 2000–2015. The paper accounts for times of crisis (2008–2013) and of economic recovery (2014–2015). It separately estimates the contribution of three distinct dimensions of intangible capital: (1) computerized information, (2) innovative property and (3) economic competencies.

First, when accounting for intangibles, the paper finds that these intangibles have become the dominant source of labour productivity growth in the EU, explaining up to 66 percent of growth. Second, when accounting for times of crisis (2008–2013), in contrast to tangible capital, the paper detects a solid positive relationship between intangibles and labour productivity growth. Third, when accounting for the economic recovery (2014–2015), the paper finds a highly significant and remarkably strong relationship between intangible capital and labour productivity growth.

This paper corroborates the importance of intangibles for labour productivity growth and thereby underlines the necessity to incorporate intangibles into today's national accounting frameworks in order to correctly depict the levels of capital investment being made in European economies. These levels are significantly higher than those currently reflected in the official statistics.

The purpose of this paper is to investigate the factors influencing the total factor productivity (TFP) gap between the USA and eight Latin American countries for the period of 1970-2000.

The paper provides an explicit application of TFP estimation by employing a growth accounting approach (Solow Residual) in the presence of non-constant returns to scale and a non-parametric approach (DEA – Malmquist Index) while relaxing the scale-related constraint. A macro-based economic model of innovator and follower countries is employed to explore the linkage between technology gaps and innovations, labor productivity, trade openness, foreign direct investment, and adult workforce illiteracy rates. A pooled model and a fixed effects model are used to determine the factors of the technology gap between the innovator and the follower countries.

The results show that the labor productivity gap, adult work force illiteracy rates, patent filing gap, and trade openness are significant determinants of the technology gap between innovator and follower country.

Latin American countries would benefit from the technology diffusion from an innovator country; but a minimum threshold of human capital, such as adult workforce illiteracy rates and patent filing has to be met. The authors find government policies on trade openness also have large effects on technology limitations in foreign countries.

This paper is of value to researchers, policy makers, and economic development specialists trying to improve the rate of technology adoption and innovation.

The purpose of this paper is to incorporate the spillover effects of trade on East Asian productivity, namely China, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore and Thailand.

This study attempts to fill in the gaps of previous studies by developing applications of extensive growth theory that shows the trade spillover effects on productivity growth of ASEAN 5 plus3. It further provides a meaningful statistical analysis in which, the first step of the estimation to get the coefficients of the explanatory variables that has been used by econometric approach. It can be restated here that in addition, a second step that plugs the parameters of the variables into the model in order to compute the contribution rates of productivity indicators including the calculation of the residual of the model (total factor productivity – TFP) and GDP contributions being used by growth accounting approach. The TFP is considered be trade spillover effects indicator that is showed the technology transfer to domestic firms and human capital skills upgrading.

The paper finds that there was a little contribution of exports and imports to TFP growth in these countries during all the periods of study. It confirms that high physical capital input growth resulted in high gross domestic product (GDP) contribution and low TFP contribution with insignificant technological progress experiences by most of these countries, with the exception of Japan and to some extent, South Korea.

In this respect, the trade spillover effects had transferred technology and developed human capital skills to a greater extent in the cases of Japan and Republic of Korea and their economies considered to be productivity driven economies.

This paper aims to provide an up-to-date analysis of the productivity in the agricultural sector within the states and union territories of India. Despite agriculture’s diminishing role as a share of overall gross domestic product (GDP) in India, it plays a crucial role by providing a large proportion of jobs to the workforce. Recognising agriculture’s central role in the economy as well as the significant diversity between the states in terms of resources, this paper estimates the total factor productivity (TFP) for Indian crops at the state level from 2000 to 2010 using both the growth accounting and the Malmquist Index Data Envelopment Analysis methodologies. The results highlight the possibility of increasing production with existing technologies by focusing on efficient resource deployment and enhanced management techniques.

The paper utilizes both growth accounting and the Malmquist Index Data Envelopment Analysis methodologies to estimate the growth of TFP at the regional level at the sub-national level (for states and union territories).

The results highlight the wide variations in the performance of states with respect to growth in TFP for the period 2000-2010. At the regional level, the Western region experienced the largest TFP growth, while the Eastern region experienced the lowest. At the state level, Gujarat registered the highest TFP growth, while Bihar emerged as a laggard with the lowest growth in TFP.

The results highlight the possibility of increasing production with existing technologies by focusing on efficient resource deployment and enhanced management techniques.

Although most of the existing literature focuses on national level analysis for India, this paper provides an up-to-date analysis of the productivity in the agricultural sector within the states and union territories of India. Correspondingly, the results are more applicable for these sub-national economies and offer more relevant policy implications.

China and India have made significant strides in transforming their agricultural sectors to cut hunger and poverty for the masses through improved agricultural productivity. Given limited land and shift of labor to non-agricultural sector, increasing productivity will continue to be central in agricultural growth in the twenty-first century. The purpose of this paper is to provide comparative analysis of the agricultural total factor productivity (TFP) growth in the two countries. It complements existing literature by examining the evolution and drivers of TFP at disaggregated sub-national level. Richer data allows a deeper understanding of the nature and drivers of TFP growth in the two countries.

This paper applies different analytical framework to address different research questions using data since 1980. China study estimates a parametric output-based distance function using a translog stochastic frontier function. Productivity growth index and its multiple components are calculated using parameters derived from the parametric approach to identify the characteristics of technology such as structural bias. India study first applies data envelopment analysis to estimate the aggregate productivity growth index, technical change (TC), and efficiency change. Next productivity indexes by for traditional crops are estimated using growth accounting framework at state level. Finally, a panel regression links TFP on its determinants.

Several common themes emerge from this comparative study. Faced with similar challenges of limited resources and growing demand, improving productivity is the only way to meet long-term food security. Agriculture sector has performed impressively with annual TFP growth beyond 2 percent in China and between 1 and 2 percent in India since the 1980s. The TFP growth is mainly propelled by technological advance but efficiency had been stagnant or even deteriorated. This study provides a granular picture of within country heterogeneity: fast growth in the North and Northeast part of China, South and East of India.

The study suggests some possible policy interventions to improve agricultural productivity, including investment in agricultural R & D to create advanced production technology, effective extension programs and supportive policies to increase efficiency, and diversification from staple crops for sector-wide growth. The India study suggests certain policies may not be contributing much to productivity growth in the long run due to a negative impact on environment. Further studies are needed to expand the productivity analysis to take into consideration of the negative externalities to the society. Data enhancement to account for quality-adjusted inputs could improve the estimation of productivity growth.

Each country study reveals certain prospects of the agricultural sector and production technology. China analysis statistically confirms the existence of technical inefficiency and technology progress, suggests the translog form is appropriate to capture the production technology and satisfies conditions stipulated in theoretical models. The results indicate TC does not influence the contribution of output or input to the production process. India study pinpoints the lagging productivity growth of traditional crops, which still derives growth from input expansion. Although different states benefited from different crops, sector-wide productivity gain is primarily the result of diversification to high-value crops and livestock products.

Although an East Asian miracle, Singapore has been singled out for experiencing insignificant total factor productivity (TFP) growth, thereby reflecting limited potential for long‐term growth. Examines the validity of this statement for the services sector, which is an important engine of growth for Singapore. This is done using panel data with a stochastic frontier model, which, unlike the conventional growth accounting model used by previous studies, not only decomposes output growth into input growth and TFP growth but further decomposes TFP growth into technological progress and changes in technical efficiency. In addition, the stochastic frontier model incorporates the more realistic non‐neutral shifting production frontier, as opposed to the commonly assumed Hicks‐neutral production technology underlying a production function.

This study aims to explain the role of globalisation on the regional income growth disparities in the states of India and provinces in the People’s Republic of China (PRC).

The authors use two approaches to analyse regional growth disparities: growth accounting and the panel spatial Durbin model.

The growth accounting shows that contributions of growth of capital intensity (GKI) and total factor productivity growth (TFPG) distinguish the high-income (HI) regions from medium-income (MI) and lower-income (LI) regions in India. In the PRC, the contributions of GKI and TFPG in MI regions are slightly higher than HI regions, but significantly higher than the LI regions. The empirical results find that foreign direct investment (FDI), domestic investment, human capital, and interaction of FDI and human capital explain income growth states/provinces in India and the PRC. A region’s income growth and FDI inflows spread the benefit to neighbourhoods in both countries.

The paper contributes by performing a comparative analysis of Indian states and the PRC’s provinces by capturing the neighbourhood effects of economic growth, FDI, investment and human capital and also the interaction effects of FDI with human capital and domestic investment. A comparison of the decomposition of income growth to the growth of factor inputs and efficiency in Indian states and the PRC’s provinces also adds to the existing literature.

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 dynamics of economic growth in India continues to engage economists and still remains much debated. The trends and patterns of growth observed in India have seen acceleration in growth in Indian economy in the period following macroeconomic reforms and policy changes in investment and trade regimes. However, when and how did India transform itself from Hindu rate of growth to the present growth regime continues to be debated.

Using INDIA KLEMS data set, this study provides a distinctive perspective on India’s economic growth. A unique data set comprising 27 sectors of Indian economy at a disaggregate industry level for a period of 30 years, beginning 1980s, attempts to understand the dynamics of India’s growth from the contribution of industries that comprise the Indian economy.

This productivity data set offers a new way of analyzing the dynamics of growth including the sources of growth. The growth empirics allow evaluation of the relative significance of total factor productivity growth vis-a-vis input accumulation in accounting for output growth. In addition, the authors were able to document the industry contributions to aggregate growth. In this way, they were able to analyze the importance of the constituent industries within the different sectors of the economy − agriculture, manufacturing, construction and market, as well as non-market services in accounting for the observed growth in India. In conclusion, the industry perspective offers a new and analytical way of discerning new aspects of India’s march to higher growth regimes in post-1990s era.

A unique data set comprising 27 sectors of Indian economy at a disaggregate industry level for a period of 30 years, beginning 1980s, attempts to understand the dynamics of India’s growth from the contribution of industries that comprise the Indian economy.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.