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The purpose of this paper is to analyse econometrically determinants of total factor productivity (TFP) in Indian manufacturing plants with a focus on the influence of services input on productivity.

Plant-level data drawn from Annual Survey of Industries for the years 1998-1999 to 2012-2013 are used for the estimation of TFP at plant-level by applying the Levinsohn–Petrin methodology. Econometric models are estimated to explain variations in plant-level TFP. The explanatory variables used are services input intensity (split into manufacturing services purchased and other services), the share of information communication technology (ICT) assets in total fixed capital stock, the share of contract workers in total workers and the share of imported materials out of total materials used, with plant size taken as a control variable. Model estimation is done by applying the fixed effects model.

Econometric results indicate that services input and ICT intensity have a significant positive effect on productivity of manufacturing plants in India. Use of imported materials raises productivity, whereas the use of contract workers in place of regular workers tends to lower productivity. The impact of imported materials on TFP of manufacturing plants seems to be relatively bigger for labour-intensive, low technology industries.

Care has been taken for TFP measurement. Analysis of the impact of services input on TFP has been undertaken for Indian manufacturing using plant-level data for the first time.

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.

Since the announcement of the new series of national accounts for India (with base 2011-12) in January 2015, there has been endless controversy over the new gross domestic product (GDP) growth numbers, particularly in respect of growth of Indian manufacturing. The purpose of this paper is to highlight certain policy issues concerning India’s system of national accounts, in the context of the methodological changes made in the new national accounts series, and to check the validity of the view held by some critics that the new series has significantly overstated the growth rate in real gross value added in manufacturing in recent years.

The paper presents a brief, selective review of the literature that has emerged on the new series of national accounts. A close look is taken at the available data on real gross value added growth in Indian manufacturing in conjunction with data on growth in India’s exports and in outstanding non-food commercial bank credit. Analysis of these data is undertaken with the help of a table and some graphs.

The paper finds that there is not enough basis to believe and argue that the GDP estimates in the new series of national accounts significantly overstate the true manufacturing sector growth in India.

Rates of manufacturing output growth in recent years indicated by the new series of national accounts for India are subjected to careful scrutiny by contrasting yearly growth rates in manufacturing output with those in India’s non-oil exports and in outstanding non-food commercial bank credit.

The purpose of this paper is to assess the impact of India’s trade liberalization during the late 1990s and 2000s on productivity of manufacturing firms and verify whether the productivity-enhancing impact of reductions in input tariffs was greater than that of output tariff cuts, as found in some earlier studies.

Firm-level (company-level) data drawn from Prowess database are used for the estimation of total factor productivity (TFP) at the firm level, done by using the Levinsohn–Petrin methodology. Econometric models are estimated to explain firm-level TFP. The explanatory variables used are output and input tariff rates and quantitative restrictions on imports at the industry level and firm characteristics such as firm size, export intensity and import intensity. Firm-level panel data for 2002-2010 or for a longer period 1998-2010 are used for the estimation of econometric models. Model estimation is done by applying the fixed-effects model and IV-2SLS, 3SLS estimators and EC2SLS estimators.

Trade liberalization had a significant positive effect on the productivity of Indian manufacturing firms. The lowering of output tariff had a greater beneficial impact on TFP of Indian manufacturing firms than the lowering of tariff on intermediate inputs.

Good deal of care has been taken in the measurement of output and inputs for the purpose of TFP measurement. Two alternative frameworks, gross output and value added, are used. This helps in making a better estimate of the impact of trade liberalization on TFP.

This paper aims to explore whether firms’ performance determines innovation using a sample of Indian manufacturing firms. The impact of innovation on firms’ performance across specific countries has been discussed in the literature. However, the effect of firms’ performance on innovation output, especially for a developing country like India, remains an open question. Against this backdrop, this paper investigates whether firms’ performance determines innovation in Indian manufacturing firms.

The authors use patent filing information to instrument innovation and total factor productivity to instrument firms’ performance. The patent data are collected from the Patent Search and Analysis Software database and firm-level data from the Centre for Monitoring Indian Economy’s Prowess database. The study uses a sample of 309 Indian manufacturing firms from 2005 to 2021. Given the count nature of the data set used in this study coupled with over-dispersion issues, the authors have used the negative binomial regression to estimate the empirical specification of the models. There could be a possible problem of endogeneity due to the contemporary nature of innovation and firms’ performance. Therefore, to address the possible issues of endogeneity in the model, the authors have used the Generalized Method of Moments (GMM) estimators for more robustness checks of the empirical results.

The empirical results exhibit a positive and significant impact of firms’ performance on the innovation output, validating that firms’ performance determines innovation in Indian manufacturing firms. The posterior estimation results using GMM estimation also corroborate that firms’ productivity is a determining factor for the innovation output of Indian manufacturing firms. Furthermore, empirical results exhibit that the ex ante innovativeness of the firms substantially affects the current innovation. This validates that the firms’ prior experience, learning by doing and past innovative efforts are more likely to precipitate more innovation in the current period.

This paper’s main contribution is empirically estimating whether firms’ performance determines innovation, which is hardly discussed in the existing innovation literature, specifically using Indian manufacturing industries. Further, it adds to the existing literature in two other prominent ways. First, this paper investigates whether firms require ex ante expertise to innovate or if a firm starting from scratch can innovate significantly without any hindrances. Second, it enriches the literature by instrumenting innovation in output terms with the patent application against input measures of innovation, such as research and development expenditures, acquisition of machinery and equipment, while discussing the relationship between firms’ performance and innovation, specifically in the context of a developing economy like India.

This paper tries to explain variation in company-level innovation activities based on certain firm-level characteristics in India between 2010 and 2020.

Probit and Heckman’s two-step estimation based on panel data consisting of annual consolidated financial statements of 8,529 companies.

Firm-level innovation activities were associated with larger company size, lower age, higher access to digital assets and voluntary expenses on environmental sustainability and social responsibility.

This paper is based on company-level financial statements and hence does not include aspects related to human capital, managerial capacities, participation in the global value chain and collaboration with other industries or academia.

This paper uses alternative measures of innovation such as promotional expenses and holding of intangible assets by companies in India alongside R&D expenditure, which is largely used in the past literature. The company characteristics also include emerging areas such as digitalisation, and spending on environmental sustainability and social responsibility, in addition to the conventional factors such as firm size, age and exposure to exports.

The purpose of this paper is to study the spillover effects of foreign direct investment (FDI) on skilled–unskilled wage inequality in the Indian manufacturing industries.

The authors show theoretically with a model of spillover that if foreign firms (receiving FDI) have a negative spillover effect on domestic firms (not receiving FDI), then the level of capital and skilled workers in the domestic firms falls down. Consequently, the authors conduct an empirical analysis by using system GMM estimation technique on the firm-level data of the Indian organised manufacturing sector.

The authors show that wage inequality worsens when there is negative spillover effects like competition spillover or skill spillover effect of FDI in India.

To the best of the authors’ knowledge, this is the first attempt to measure the various spillover effects of FDI on the wage inequality in the Indian manufacturing industries by using firm-level data.