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This paper investigates discrimination against women within the Brazilian labour market using firm-level data from the World Bank Investment Climate Survey. The purpose of this paper is to determine whether the female employees in the Brazilian labour market are paid less than their productivity warrants due to the existence of discrimination.

Based on employer discrimination model proposed by Becker (1971) that considered the proportion of female employees as a proxy for the extent of discrimination, the authors estimate the profit function using OLS analysis, and regress it on the proportion of female employees and other firm characteristics. To address the endogeneity problem caused by unobservable productivity shocks, the authors employed the methods proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003), respectively.

The results indicate that the proportion of female employees has positive effect on firms’ profit in 2002, but has no effect in 2007. This finding gives evidence of the existence of discrimination against female employees within the Brazilian labour market in the early 2000s, while the gender discrimination was reduced overtime.

This paper’s main contribution is to provide an approach that differs from that of previous research to determine whether discrimination exists within the Brazilian labour market. This paper also provides policy insights for Brazilian labour market.

The purpose of this paper is to investigate the linkage between exchange rate exposure and firms’ productivity in India. This study also tries to compare the effects of exchange rate exposure on the productivity of the pre- and post-financial crisis periods and also between export- and import-oriented firms.

By using the annual data of 232 manufacturing and service sector firms for the period of 2000-2013, this paper examines the exchange rate exposure and firms’ performance in India. In the first stage, the two-factor regression model, Adler and Dumas, is used, and in the second stage, the Levinsohn and Petrin (2003) approach is used for estimating the total factor productivity of significant firms. Finally, for examining the relationship between exchange rate exposure and productivity, the instrumental variable panel data regression model is used.

This study observes that a negative relation exists between the appreciation of exchange rate exposure and firms’ productivity. The study also reveals that the export-oriented firms make loss during exchange rate appreciation which decreases the productivity. The financial crisis has the negative impact on productivity, as well.

Although there are an ample number of studies which examined the effects of the exchange rate on firm’s export, growth, investment, however impact of exchange rate exposure on productivity at firm level is scanty. This study tries not only to compare the effects of exchange rate exposure on the productivity of the pre- and post-financial crisis periods but also between the export- and import-oriented firms which is another innovation of this study.

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.

Because of growing energy consumption and increasing absolute CO₂ emissions, the recent calibrations about the environmental sustainability across the globe have mandated to achieve the minimal energy consumption through employing energy-efficient technology. This study aims to estimate linkage between simple measure of energy efficiency indicator that is reciprocal of energy intensity and total factor productivity (TFP) in case of Indian paper industry for 21 major states. In addition, the study incorporates the other control variables like labour productivity, capital utilization and structure of paper industry to scrutinize their likely impact on energy efficiency performance of the industry.

To derive the plausible estimates of TFP, the study applies the much celebrated Levinsohn and Petrin (2003) methodology. Using the regional level data for the period 2001-2013, the study employs instrumental variable-generalized method of moments (GMM-IV) technique to examine the nature of relationship among the variables involved in the analysis.

An elementary examination of energy intensity shows that not all states are equally energy intensive. States like Goa, Rajasthan, Jharkhand and Tamil Nadu are less energy intensive, whereas Uttar Pradesh, Kerala, Chhattisgarh, Assam and Punjab are most energy-intensive states on the basis of their state averages over the whole study period. The results estimated through GMM-IV show that increasing level of TFP is associated with lower level of energy per unit of output. Along this better skills and capacity utilization are also found to have positive impact on energy efficiency performance of industry. However, the potential heterogeneity within the structure of industry itself is found responsible for its higher energy intensity.

States should ensure and undertake substantial investment projects in the research and development of energy-efficient technology and that targeted allocations could be reinforced for more fruitful results. Factors aiming at improving the labour productivity should be given extra emphasis together with capital deepening and widening, needed for energy conservation and environmental sustainability. Given the dependence of structure of paper industry on the multitude of factors like regional inequality, economic growth, industrial structure and the resource endowment together with the issues of fragmented sizes, poor infrastructure and availability and affordability of raw materials etc., states should actively promote the coordination and cooperation among themselves to reap the benefits of technological advancements through technological spill overs. In addition, owing to their respective state autonomies, state governments should set their own energy saving targets by taking into account the respective potentials and opportunities for the different industries. Despite the requirement of energy-efficient innovations, however, the cons of technological advancements and the legal frameworks on the employment structure and distributional status should be taken care of before their adoption and execution.

To the best of our knowledge, this is the first study that empirically examines the linkage between energy efficiency and TFP in case of Indian paper industry. The application of improved methods like Levinsohn and Petrin (2003) to derive the TFP measure and the use of GMM-IV to account for potential econometric problems like that of endogeneity will again add to the novelty of study.

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 study aims to estimate total factor productivity (TFP) growth for the post-2008 period for selected industries in the manufacturing sector at NIC 3-digit. Total factor productivity growth (TFPG) estimates are based on the theoretical framework provided by studies such Hall (1988), Abraham et al. (2009) and Crepon et al. (2005) that incorporate market imperfection in labour and product market, thereby modifying the traditional TFP estimation as Solow Residual.

Based on the theoretical model that incorporates market imperfections in labour as well as product market in modifying the TFP estimates using the Levinsohn–Petrin framework of empirical estimation, the authors have calculated industry wise TFPG for 62 industries at NIC 3-digit level.

The study finds three distinct trends: first, there are considerable industrial disparities in productivity growth in terms of TFP. The estimates have been found to be higher than the conventional Solow Residual for most industries, indicating the role played by market imperfections in affecting the conventional measure of productivity growth. Second, estimates of bargaining power are found to be lower than those compared to the earlier estimates in Maiti (2013) for the Indian organised manufacturing case for 1998-2005. This observation is commensurate with the observation in recent years of a falling share in labour wage in total output in organised manufacturing sector. Finally, the study also found a statistically significant contribution of greater mechanisation on TFPG while an adverse effect of the rising dependence of organised manufacturing on contractual labour.

The role of market imperfections in measuring TFPG has been undertaken, and it has been found to be an important factor, as the estimated measures vary from the conventional measures of TFPG. Moreover, the study has considered a very recent period from 2008-2015 in estimating TFPG, as well as analysing the factors behind the trends in TFPG at industrial level.

The purpose of this paper is to compare and analyze the modern productivity estimation techniques, namely, Levinsohn and Petrin (LP, 2003), Ackerberg Caves and Frazer (ACF, 2006), Wooldridge (2009) and Mollisi and Rovigatti (MR, 2017) on unit-level data of 32 Indian industries for the period 2009-2015.

The paper first analyzes different issues encountered in total factor productivity (TFP) measurement. It then categorizes the productivity estimation techniques into three logical generations, namely, traditional, new and advanced. Next, it selects four contemporary estimation techniques, computes the industrial TFP for Indian states by using them and investigates their empirical outcomes. The paper also performs the robustness check to ascertain, which estimation technique is more robust.

The result indicates that the TFP growth of Indian industries have differed greatly over this seven-years of period, but the estimates are sensitive to the techniques used. Further results suggest that ACF and Wooldridge yield the consistent outcomes as compared to LP and MR. The robustness test confirms Wooldridge to be the most robust contemporary technique for productivity estimation followed by ACF and LP.

To the authors’ knowledge, this is the first study that compares the contemporary productivity estimation techniques. In this backdrop, this paper offers two novelties. First, it uses advanced production estimation techniques to compute TFP of 32 diverse industries of an emerging economy: India. Second, it addresses the fitment of estimation techniques by drawing a comparison and by conducting a robustness test, hence, contributing to the limited literature on comparing contemporary productivity estimation techniques.

This paper aims to examine the interrelation between two innovating strategies (product and process) on total factor productivity (TFP) growth and the dynamic linkages between these strategies, for Colombia. The authors first explore whether ex ante more productive firms are those that introduce innovations (the self-selection hypothesis) and if the introduction of innovations boosts TFP growth (the returns-to-innovation hypothesis). Second, the authors study the firm’s joint dynamic decision to implement process and/or product innovations. The authors use Colombian manufacturing data from the Annual Manufacturing and the Technological Development and Innovation Surveys.

This study uses a four-stage procedure. First, the authors estimate TFP using a modified version of Olley and Pakes (1996) and Levinsohn and Petrin (2003), proposed by De Loecker (2010), that implements an endogenous Markov process where past firm innovations are endogenized. This TFP would be estimated by GMM, Wooldridge (2009). Second, the authors use multivariate discrete choice models to test the self-selection hypothesis. Third, the authors explore, using multi-value treatment evaluation techniques, the life span of the impact of innovations on productivity growth (returns to innovation hypothesis). Fourth, the authors analyse the joint likelihood of implementing process and product innovations using dynamic panel data bivariate probit models.

The investigation reveals that the self-selection effect is notably more pronounced in the adoption of process innovations only, as opposed to the adoption of product innovations only or the simultaneous adoption of both process and product innovations. Moreover, our results uncover distinct temporal patterns concerning innovation returns. Specifically, process innovations yield immediate benefits, whereas implementing both product innovations only and jointly process and product innovations exhibit significant, albeit delayed, advantages. Finally, the analysis confirms the existence of dynamic interconnections between the adoption of process and product innovations.

The contribution of this work to the literature is manifold. First, the authors thoroughly investigate the relationship between the implementation of process and product innovations and productivity for Colombian manufacturing explicitly recognising that firms’ decisions of adopting product and process innovations are very likely interrelated. Therefore, the authors start exploring the self-selection and the returns to innovation hypotheses accounting for the fact that firms might implement process innovations only, product innovations only and both process and product innovations. In the analysis of the returns of innovation, the fact that firms may choose among a menu of three innovation strategies implies the use of evaluation methods for multi-value treatments. Second, the authors study the dynamic inter-linkages between the decisions to implement process and/or product innovations, that remains under studied, at least for emerging economies. Third, the estimation of TFP is performed using an endogenous Markov process, where past firms’ innovations are endogenized.

Foreign firms and domestic multinationals have certain internal advantages which may spillover to domestic firms. However, due to heterogeneity across multinationals, it is not necessary that the effect of the spillovers generated by the foreign firm and that generated by the domestic multinational be similar. The purpose of this paper is to empirically find out if the spillovers generated are similar or different in nature.

The study's results are based on a panel regression analysis of 578 firms in the Indian pharmaceutical industry from 1995‐2006. Fixed effects as well as the Levinsohn Petrin methodology are used to analyze the research question.

The paper finds that there are differences in the characteristics of foreign firms and Indian multinationals. It also finds differences in the research and development (R&D) spillover effects from foreign firms and those from Indian multinationals. The knowledge or R&D spillover effect of foreign firms on domestic firms is found to be negative, which is interpreted as movement of labor to foreign firms. Indian multinationals seem to have no spillover effect on domestic firms in the Indian pharmaceutical industry. The study also finds that the presence of foreign firms in the Indian pharmaceutical industry has not had a productivity hampering effect on domestic firms. Finally, the study finds some evidence to believe that spillovers in the Indian pharmaceutical industry may vary with size of the domestic firm.

There are very few papers in literature that empirically try to find similarity or differences between spillover effects due to foreign firms and those due to domestic multinationals. The study also tries to discern if these spillovers vary with respect to the size of the domestic firm.

There is plenty of individual-level evidence, based on the estimation of Mincerian equations, showing that better-educated individuals earn more. This is usually interpreted as a proof that education raises labour productivity. Some macroeconomists, analysing cross-country time series, also support the idea that the continuous expansion of education has contributed positively to growth. Surprisingly, most economists with an interest in human capital have neglected the level of the firm to study the education-productivity-wage nexus. And the few published works considering firm-level evidence are lacking a proper strategy to cope with the endogeneity problem inherent to the estimation production and wage functions. The purpose of this paper is to aim at providing estimates of the causal effect of education on productivity and wage labour costs.

This paper taps into a rich, firm-level, Belgian panel database that contains information on productivity, labour cost and the workforce’s educational attainment to deliver estimates of the causal effect of education on productivity and wage/labour costs. Therefore, it exclusively resorts to within firm changes to deal with time-invariant heterogeneity bias. What is more, it addresses the risk of simultaneity bias (endogeneity of firms’ education-mix choices in the short run) using the structural approach suggested by Ackerberg et al. (2006), alongside more traditional system-GMM methods (Blundell and Bond, 1998) where lagged values of labour inputs are used as instruments.

Results suggest that human capital, in particular larger shares of university-educated workers inside firms, translate into significantly higher firm-level labour productivity, and that labour costs are relatively well aligned on education-driven labour productivity differences. In other words, the authors find evidence that the Mincerian relationship between education and individual wages is driven by a strong positive link between education and firm-level productivity.

Surprisingly, most economists with an interest in human capital have neglected the level of the firm to study the education-productivity-pay nexus. Other characteristics of the workforce, like gender or age have been much more investigated at the level of the firm by industrial or labour economists (Hellerstein et al., 1999; Aubert and Crépon, 2003; Hellerstein and Neumark, 2007; Vandenberghe, 2011a, b, 2012; Rigo et al., 2012; Dostie, 2011; van Ours and Stoeldraijer, 2011). At present, the small literature based on firm-level evidence provides some suggestive evidence of the link between education, productivity and pay at the level of firms. Examples are Hægeland and Klette (1999); Haltiwanger et al. (1999). Other notable papers examining a similar question are Galindo-Rueda and Haskel (2005), Prskawetz et al. (2007) and Turcotte and Whewell Rennison (2004). But, despite offering plausible and intuitive results, many of the above studies essentially rely on cross-sectional evidence and most of them do not tackle the two crucial aspects of the endogeneity problem affecting the estimation of production and wage functions (Griliches and Mairesse, 1995): first, heterogeneity bias (unobserved time-invariant determinants of firms’ productivity that may be correlated to the workforce structure) and second, simultaneity bias (endogeneity in input choice, in the short-run, that includes the workforce mix of the firm). While the authors know that labour productivity is highly heterogeneous across firms (Syverson, 2011), only Haltiwanger et al. (1999) control for firm level-unobservables using firm-fixed effects. The problem of simultaneity has also generally been overlooked. Certain short-term productivity shocks affecting the choice of labour inputs, can be anticipated by the firms and influence their employment decision and thus the workforce mix. Yet these shocks and the resulting decisions by firms’ manager are unobservable by the econometrician. Hægeland and Klette (1999) try to solve this problem by proxying productivity shocks with intermediate goods, but their methodology inspired by Levinsohn and Petrin (2003) suffers from serious identification issues due to collinearity between labour and intermediate goods (Ackerberg et al., 2006).