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• 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]