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The study gives new evidence on the effects of public debt on economic growth in India with key macroeconomic indicators from 1980 to 2019. In the past decade, and after the COVID-19 pandemic, there is a substantial rise in public debt, which reached 90% of the GDP in April 2021. Therefore, it is imperative to study the impact of different public debt sources on the Indian economy to help policymakers frame informed debt management policies. The long-run equilibrium relationship and cointegrating coefficients are calculated using Johansen cointegration and fully modified ordinary least square techniques. Toda and Yamamoto's (1995) Granger causality test is used as a short-run diagnostic test for the long-run equilibrium relationship. The study's major findings suggest that domestic debt, total factor productivity (TFP) and exports are the major determinants of economic development in the long run. In contrast, economic prosperity determines the growth of external debt, debt service payments and TFP in the short run. It is recommended that the government should control and channel public debt productively for favourable growth effects. [ABSTRACT FROM AUTHOR]

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The study re-examines convergence among Indian states by estimating their total factor productivity (TFP) for the period 1990/91–2018/19. The novelty of the study is the decomposition of TFP change into efficiency change and technological change using the Malmquist index in a spatial dependence framework to test convergence. Findings reveal that, on average, productivity increased at an aggregate level for the entire period on account of the impressive growth of technological innovation. However, technological growth convergence could not lead to productivity growth convergence in the second sub-period because of no convergence in efficiency growth. Alongside, while we witness significant spatial spillovers in both technology and efficiency, convergence was observed only with respect to technology growth in the second sub-period. The congruence of technological convergence and significant spatial spillovers indicates that while states may be converging in technology, they do not do so independently but display movements similar to their regional neighbours. [ABSTRACT FROM AUTHOR]

India is often referred to as the next development superpower, and generally, becoming a large-scale industrialization center is seen as an achievable goal for the country. This article investigates the output elasticity, substitution elasticity, and technological advancement between the various factors (i.e., labor, capital, and energy use) in the industrial sector of India. To investigate the factor's productivity, a trans-log production function was applied; however, ridge regression was used to analyze the various parameters to check the multicollinearity issue. The results show that (1) the analyzed inputs are optimistic and return-to-scale averages of 1.18, 1.41, and 1.24 between labor, capital, and energy, respectively, are increasing; (2) the pairs substitution between labor--industrial energy utilization and capital--industrial energy consumption is found to be 0.96 and 0.98, respectively, on average, indicating that capital, labor, and energy are good substitutes that need more attention in the production process; and (3) the technological progress between factors ranges from -0.4 to 0.02, in which labor--energy and capital--energy utilizations provide quicker outcomes than a capital--labor utilization. Finally, the industrial sector can attain maximum productivity if capital and skilled labor are improved under the sustainable development goals, as energy and capital are optimized for maximum efficiency. Finally, energy substitution and low-carbon technological efforts can be better suited for attaining dual-carbon goals in the industrial sector. [ABSTRACT FROM AUTHOR]

While promoting manufacturing growth is imperative to boost economic growth, potential adverse consequences on natural resources and ecology have created sustainability-related concerns and challenges. In India, with the reform processes deepening further and the business environment becoming more market-oriented over the years, the adoption of appropriate measures for sustainable manufacturing growth under such conditions would require a deeper investigation of the underlying dynamics, particularly to address the impacts of external shocks. This is especially true when external shocks alter market dynamics and, hence, input is used significantly in the manufacturing sector. Given this backdrop, the present paper examines how the major external shocks to the Indian economy during in the post-reform era have altered the resource use patterns in selected manufacturing industries. The paper uses secondary data collected from the KLEMS database of the Reserve Bank of India for the period from 1980–81 to 2019–20 and applies descriptive statistics and econometric techniques to address the research objective. It is found that energy intensity, vertical integration, capital intensity, and rate of growth of total factor productivity decreased over the decades, whereas labour productivity experienced an increasing trend in most industries. However, the trends are mixed in the case of material intensity. Further, the regression results indicate significant effects of external shocks on resource use, factor productivity, and structural changes. There are also industry-specific differences in the nature and extent of such changes. Hence, a deeper investigation of the underlying factors and impacts at a disaggregate industry level is necessary to draw more robust insights. [ABSTRACT FROM AUTHOR]

In this article, we analyse India's transport sector policy. The study examines the two important modes of transport, viz., road and rail, and analyses the efficiency of transport infrastructure from 2011 to 2019. Through Data Envelopment Analysis (DEA), we evaluate modes in terms of the capacity of registered road goods vehicles and railway goods wagons, truck km/train km and energy consumption and corresponding freight traffic. The analysis gives valuable insight into the fact that rail infrastructure is more efficient than road infrastructure. Still, current transport sector policies promote energy-intensive carbon-fuel road vehicles rather than energy-efficient alternative transport modes. The study suggests the changes that may be incorporated in inputs to improve the efficiency of the road infrastructure. The Data Envelopment Analysis (DEA)-Malmquist Index analysis reports the need for total factor productivity improvement in rail transport and the introduction of technological innovations in road and rail transport sector management. The study employs a well-to-wheel (WTW) based index to ascertain the energy consumed and pollutants emitted by freight-carrying road and rail modes between two leading business hubs of India, i.e., Ludhiana and Mumbai. The paper identifies policy gaps and suggests a holistic approach for need-based roadway investment and a time-bound development of railway infrastructure in India. Our study generates valuable implications for India's policy planners and government authorities and provides new insights to other countries. • The study examines road and rail transport, and analyses the efficiency and energy consumption of these modes. • The study suggests the changes to improve the efficiency of the road infrastructure. • The Data Envelopment Analysis (DEA)-Malmquist Index analysis and well-to-wheel (WTW) based index have been employed for the analysis. • The paper suggests a need-based roadway investment approach and a time-bound development of railway infrastructure. [ABSTRACT FROM AUTHOR]

We compare the economic and environmental performances of India and China over the past decade against the Euro Area, Japan, and the USA. India has emerged as the world’s fastest growing large economy, but closer scrutiny suggests this impressive economic performance derives largely from structural factors such as labor force growth and the Balassa-Samuelson effect. Indeed, notwithstanding its superior level of economic development relative to India, China still posts stronger economic gains from investment and total factor productivity growth. While the two economies have grown markedly faster than the three developed economies against which we compare them, both Indian and Chinese growth has come with huge increases in greenhouse gas emissions. Our findings underscore the importance of Chinese and Indian participation in efforts to avoid the more dire impacts of climate change. [ABSTRACT FROM AUTHOR]

• During 1993–2018, market services grew at a faster rate than non-market services. • Applying Domar weights we observed that transport and storage was the highest contributor among market services industries, while public administration has made relatively higher contribution to aggregate non-market services TFP growth. • TFP growth in manufacturing has a significant positive effect on TFP growth of both market services and non-market services. • Market services TFP growth is positively impacted by a spillover effect of TFP growth in India's trade partners. The services have been the driver of India's overall growth since the onset of economic reforms in the 1990s. The value-added share of services increased from 41 percent in 1990 to 53 percent in 2018. On average, the sector grew at 7.5 percent annually during 1993–2018, contributing to about half of the aggregate economic growth. Using the India KLEMS data, we examine the trends in total factor productivity (TFP) in individual industries within services and their contribution to aggregate TFP growth in the sector during 1993–2018. We find that transport and storage was the highest contributor among various market services industries followed by financial services, while public administration made a relatively high contribution to aggregate non-market services TFP growth. Moreover, we investigate the factors driving TFP growth in the services sector, particularly the role of manufacturing TFP – looking into spillover effect from manufacturing to market services and non-market services. Our results suggest that TFP growth in manufacturing had a significant positive impact on TFP growth in the services sector. Also, we find evidence of significant productivity spillover effect to India's services sector from other countries, particularly the developed countries. [ABSTRACT FROM AUTHOR]

This paper investigates income convergence of Indian states in the post-reform period when markets played a greater role in resource allocation. We analyze stochastic convergence of relative per capita incomes of 19 states for the period 1994–2018 by employing a recently developed panel data approach controlling for structural breaks as smooth shifts. Smooth shifts are modelled using a more flexible Fourier approach that does not require identifying the number, date, and form of breaks. The empirical results, contrary to recent empirical findings, do not support evidence in favour of convergence in per capita income among Indian states. Poor infrastructure, lack of adequate financial development, and weak governance structure coupled with low total factor productivity growth seem to be responsible for the divergence of income. The findings suggest that development intervention in the post-reform period has neither been of the required order nor in the desired direction to help the lagging states to catch up with the leading ones. [ABSTRACT FROM AUTHOR]