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This study aims to explore the long-run equilibrium relationship between India’s real exchange rate and sectoral productivity trends using internationally comparable KLEMS databases on productivity for India, China, Euro area, the USA, the UK and Japan.

This study uses pooled mean group estimations for panel data suggested by Pesaran et al. (1999). This method is chosen because of the presence of variables with different orders of integration.

The results find support for an “extended” Balassa–Samuelson (BS) hypothesis which allows labour market frictions that does not allow for wage equalisation between traded and non-traded sectors within a country. This mechanism continues to find some support when we separate out distribution sector that comprises wholesale and retail trade in the domestic services sector. The empirical evidence suggests that India’s real exchange rate is anchored to domestic fundamentals and is closely aligned to its fair value over a medium to long-time horizon.

To the best of the authors’ knowledge, unlike the available literature, which uses aggregate per-capita income as proxy for a country’s productivity growth, this paper perhaps makes the first attempt to validate the BS hypothesis by accounting for productivity differential at the sectoral levels using KLEMS data across countries. Moreover, this study takes the country’s productivity improvement rather than using a basket of countries, a prevalent practice in the literature. While this paper uses India’s data, which witnessed a prolonged appreciation in its real effective exchange rate and rapid technological progress, the authors believe its findings and policy implications could be applicable to the similar emerging market economies.

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.

This study aims to analyze the structure and trend in employment in the Indian economy between 1980-8081 and 2015-2016.

Use of India KLEMS data set. Estimate growth rate of employment and discuss employment prospects using “Point” employment elasticity.

Whilst India’s GDP growth rate has been quite impressive since the reforms of 1991, the rate of employment growth, especially in the recent period of 2003-2015, has been quite slow (1 per cent) with low employment elasticity (0.1). The pattern of employment growth has also been imbalanced with slow rate of employment growth in manufacturing and rapid growth rate in the construction sector. India now also has low labour force participation rate and a large share of informal employment in the economy.

The limitation is the lack of reliable data on employment for the recent period.

With overall low employment elasticity, India would have to explore sectors where more employment opportunities could be created.

India has to create not only more jobs but also “good” jobs.

The India KLEMS data provide a time series for employment, which has been used in this paper to find “Point” elasticity instead of arc elasticity of employment and is an improvement over existing employment elasticity estimates.

The purpose of this paper is to investigate which sectors are more vulnerable to human capital depreciation, with an emphasis on potential differences in skills and in ICT intensities.

The authors estimate an extended Mincerian earnings equation based on Neuman and Weiss’s (1995) model using the EU-KLEMS international database for 15 sectors for the period from 1980 to 2005. The authors also test structural ruptures in earnings and human capital depreciation in the labor market per decade controlling by technological intensity.

Human capital depreciation ranges from 1 to 6 percent. It is mainly significant in skill-intensive sectors regardless of the sector’s technological intensity. The analysis of structural breaks shows that human capital value indeed changed from decade to decade. It even appreciated in low skill-intensive sectors in the 1980s and in the high skill-intensive during the 1990s. Appreciation though, was mainly skill-biased.

Information about on-the-job-training and non-cognitive skills that can also affect human capital depreciation are not included due to lack of data.

To prevent human capital from depreciating in particular sectors and periods educational systems should provide the tools for ongoing lifelong learning at all skills levels. Education is subject to dynamic effects that should be addressed to increase the potential benefits of technological change.

First, instead of using cross-section analysis which is considered to be a pitfall in studying the depreciation of knowledge, the authors observe its dynamic on a longitudinal basis. Second, the international macro-sectoral approach goes beyond limited micro-sectoral analysis in certain countries.

In this chapter, using a combination of long-run and sign restrictions to identify aggregate monetary and productivity factors, I find that the monetary factor is responsible for long swings in nominal variables but has little effect on fluctuations in output, real wage, or labor input growth. The productivity factor in addition to increasing output growth and real wage growth in the short and long run, also results in increases in labor input and decreases in prices, but the quantitative effect of the productivity factor on labor input is relatively small. These results are robust to the number of factors included in the model and to alternative priors about the short-run effects of the monetary factor, and to the inclusion of oil prices. Oil prices, in fact, appear to be largely driven by the other aggregate factors.

This study asks whether working in a R&D intensive industry affects a worker's wage profile. If R&D investment translates into transferable human capital or knowledge, workers' mobility constitutes a negative externality from the point of view of the firm/industry that bears the cost of R&D activities. A steepening of the wage profile would address such externality.

Using PSID data combined with US BEA data on US manufacturing industries' R&D intensities between 1981 and 1992, regression analysis is used to explore the hypothesis that, similarly to general training, industry R&D steepens a worker's wage‐experience profile.

In general the evidence is mixed. The results obtained from biennial wage growth regressions support to some extent the hypothesis that exposure to R&D activities allows a specific group of workers to accumulate general human capital for which they pay a positive price in early stages of their career.

An important caveat applies to the results. Unlike previous research by Møen who uses firm level R&D, the results found in this study are generated by using industry level R&D, which, being possibly affected by severe measurement errors, may bias the estimated coefficients towards zero.

This study complements Møen's evidence based on Norwegian wages with the effects of industry‐specific R&D intensities on the earnings profile in US manufacturing industries. By investigating whether industry R&D affects the return to experience and/or to tenure this study addresses an overlooked issue of which type of skills R&D allows workers to accumulate.