Organized Manufacturing Sector Research Articles

Comparison of Cobb-Douglas Production Function with Deep Learning Models to Estimate the Productivity of Organised Manufacturing Sector - Indian Perspective

The production function defines the output of a firm, industry, or economy based on various combinations of inputs. This study focuses on estimating the production function of India's manufacturing sector and determining the relationship between its inputs and outputs. To predict the output, measured as Gross Value Added (GVA), the study applies the Cobb Douglas Production Function (CDPF) along with several deep learning techniques, including Feedforward, Recurrent, Long Short-Term Memory (LSTM), and Bidirectional LSTM networks. Model performance is evaluated using metrics like Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the Cobb Douglas Production Function outperforms the deep learning models in predicting GVA for the organized manufacturing sector in India.

Estimating Determinants of Contractualization of Workers in Organized Sector in India

The structural adjustment programme adopted in 1990 led to the removal of all existing pre-reform regulations and restrictions imposed on the Indian industries. This radically transformed India’s trade relations with international markets. The liberalization of Indian economy on one hand accelerated foreign capital as well as technology influx while on the other hand increased competition for the domestic economy. Thus the increased openness and flexibility arising from the economic transformation prompted the domestic producers to resort to alternative informal routes of employment thereby leading to increased contractualization of employment substituting of regular workers by contract workers in the manufacturing industries. Thus this paper is an empirical study which focuses on exploring and identifying the industry specific factors which are affecting the degree of casualization in Organized manufacturing sector of India. Panel regression using STATA has been used and the econometric analysis reveals that average firm size, value of output, capital intensity, profit per worker and welfare benefits for workers have considerable favourable effect on the employment of contractual workers while trade union activity and the ratio of male to female workers employed have significant negative impact on contract worker employment in the manufacturing industries of the organized sector in India. Keywords: contractualization, capital intensity, trade union activity, feminization, panel regression JEL Classification: C33, J21, L60

Globalisation and Performance of Manufacturing Sector in Punjab and Tamil Nadu: Implications for Employment and Wages

The present study is a comparative analysis of the performance of the organised manufacturing sector of Punjab and Tamil Nadu. The aggregate and the disaggregate picture reveals that globalisation-led growth of the manufacturing sector in Tamil Nadu has transformed its economy to a highly industrialised economy in Indian Union, and Punjab remained far behind in this context. However, the implications of the performance of the manufacturing sector on employment and wage share have shown common characteristics in both states. The increased capital intensity in the globalisation era has widened the gap between labour productivity and wage share in both states. Since 1991, the emoluments (capitalist share) have increased at a higher rate than the real wages. The decomposition analysis of wage share shows that the falling labour–capital ratio and increased contractualisation of the labour market are some of the important reasons behind the low growth of employment and fall in the wage share. Moreover, the disaggregated analysis reveals a secular increase in the use of capital per unit of labour in labour-intensive industries that has further reduced the employment elasticity of gross value added (GVA) in both states. It also highlights that regional heterogeneity plays an important role in the performance of a particular industry with respect to employment and wage share. JEL Codes: F60, J24, L60

Empirical evidence of Goodwin’s growth cycle in the Indian organised manufacturing sector

PurposeThe aim of the paper is to investigate Goodwin’s growth cycle in the Indian organised manufacturing industries.Design/methodology/approachThe methodology is based on bi-variate differential equation, econometrics model like log-linear regression and Autoregressive Distributed Lag model. An empirical investigation is conducted on data from the Annual Survey of Industries from 1980 to 2018 time period.FindingsThe results indicate that though the original Goodwin model estimates deviated from data estimates, its modified (neo-Goodwin) model are found to be equivalent to the data estimates. Moreover, in contrast to the original model, the capital accumulation rate (investment to profit ratio) is not assumed to be unitary in the modified Goodwin model. Furthermore, the labour market-led and cost effect conditions of the Goodwin cycle are empirically verified by investigating the interdependency between employment rate and wage share. Lastly, the short- and long-run Goodwin cycles are observed to be moving in anti-clockwise direction in the employment rate and wage share bi-dimensional plane, thus confirming the existence of profit-led distribution where wage share continuously reducing with high employment.Research limitations/implicationsThis study opens the discussion on application of capitalistic model in the emerging economy and also suggests to incorporate some theoretical models like Kaldorian, Keynesian, Kaleckian or Schumpetrian into the Goodwin cycle.Originality/valueThis is the first paper which empirically examines the capitalistic nature of Indian organised manufacturing industries through the lens of Goodwin growth cycle and then extend it to the Neo-Goodwin model by relaxing one of the unrealistic assumption regarding unitary investment to profit ratio.

Analysis of the carbon emissions trend in the Indian manufacturing sector: a decomposition and decoupling approach.

There is a growing emphasis on fostering green growth and lowering carbon emissions in order to achieve sustainable economic development. This study uses the Tapio decoupling model and analyzes the factors influencing changes in carbon emissions from manufacturing in India utilizing the log mean Divisia index (LMDI) techniques. Furthermore, the nexus between carbon emission intensity, information and communication technology (ICT), total factor productivity (TFP), skill, and energy intensity has been analyzed using the system-GMM approach. It is based on the plant-level Annual Survey of Industries (ASI) datasets for the organized manufacturing sector of India from 2001 to 2002 to 2019 2020 for the major 21 Indian states/UT. The findings reflect the presence of weak decoupling in the manufacturing sector both at the aggregate level and in states. This indicates that both output and emissions are increasing; however, output growth surpasses emission growth, which signifies an effort to transition towards more environmentally friendly production methods and enhanced energy efficiency. The output and population effect are found to be leading factors in carbon emissions, while energy intensity is found to be reducing the effect. Further, the system-GMM estimates show that ICT and energy intensity positively affect total factor productivity, while with an increase in carbon emission intensity, productivity declines. The study confirms the existence of an inverted N-shaped Kuznets curve in the sector. This present study will contribute to formulating energy and environmental strategies to reduce emissions and promote adopting cleaner energy sources. These efforts will facilitate the attainment of carbon neutrality and enhance energy efficiency within the sector.

Does carbon intensity affect technical efficiency? An empirical assessment of manufacturing industries in Maharashtra, Odisha, and India

Technical progress has a tremendous potential to reduce carbon dioxide emissions by reducing energy consumption, a major concern across production units. However, the existing empirical literature concerning technical efficiency and carbon intensity is scanty. Thus, this paper examines the relationship between technical efficiency and carbon intensity for the organized manufacturing sector of two states, Maharashtra and Odisha, and the all-India level from 2001 to 2018. The paper uses data envelopment analysis to estimate technical efficiency scores. It applies the 2006 Intergovernmental Panel on Climate Change Tier 1 methodology for estimating carbon intensity for each 3-digit manufacturing industry in all three sample cases. The study has used static panel regression and fractional logit regression techniques to examine the deterministic relationship between technical efficiency and carbon intensity. The result shows that technical efficiency is highly sensitive to carbon intensity in the Indian manufacturing industries. The findings also addressed that the size of the industries also reduces the technical performance of manufacturing units. This paper also confirmed that increased profit could boost the Indian manufacturing industries’ technical efficiency. Thus, this study addresses that carbon intensity as a proxy for the manufacturing sector’s potential to affect climate change plays a crucial role in explaining the technical efficiency variations across industries. Thus, it calls for better policies aimed at reducing the emissions of industries specifically to achieve sustainable growth for the Indian manufacturing sector.

Degree of Monopoly and Distribution of Income in India’s Organised Manufacturing Sector

Degree of Monopoly and Distribution of Income in India’s Organised Manufacturing Sector

Nature of agglomeration of the manufacturing sector –a study of Indian Districts


 The paper estimates the degree of agglomeration of the Indian organised manufacturing sector and examines its evolution pattern across districts over the period 2000-01 to 2009-10. The estimation of the degree of industrial agglomeration is based on plant-level data from the Annual Survey of Industries. The paper uses the spatially-weighted Ellison Glaser Index to control for the inter-district spillover effect. The overall degree of agglomeration has been moderate and, over time, it registered a declining trend. While analysing the nature of industrial agglomeration, it has been observed that most of the low-tech and medium-low-tech industries are found to be highly agglomerated. 42% of the highly agglomerated industries are also highly polluting in nature. During the period 2000-01 to 2009-10, the second-tier cities observed a rise in the number of plants belonging to the polluting industries. High-tech industries are found to be concentrated in the already industrialised states. In contrast to this, the medium-high-tech industries have been spreading across districts. The distribution of low-tech industries is found to be even across the districts.

Economic Liberalisation, Structural Change and Productivity Growth in Indian Organised Manufacturing Sector, 1991–2016

Economic Liberalisation, Structural Change and Productivity Growth in Indian Organised Manufacturing Sector, 1991–2016

Informalization of the formal sector: Evidence from India's manufacturing industries

Abstract The employment structure of India's formal manufacturing sector has undergone substantial changes since the early 2000s with a steep rise in the use of contract workers in place of directly hired workers. Much of the existing literature has attributed the widespread use of contract labour to India's rigid employment protection legislation. The resulting informalisation of the workforce in the organised sector raises concerns about the sustainability of employment growth. Using a 14-year plant-level panel data from the Annual Survey of Industries, we find that in addition to labour market rigidities and the existence of a wage differential between contract and directly hired workers, plants in the organised manufacturing sector have another important incentive to hire contract workers. Plants appear to be using contract workers to their strategic advantage against the directly hired workers to keep their bargaining power and wage demand in check. Importantly, the strength of this bargaining channel varies across plants depending on their capital intensity of production, size and existing contract worker intensity.

RELATION BETWEEN REAL LABOR PRODUCTIVITY AND WAGE SHARE IN INDIA’S ORGANIZED MANUFACTURING SECTOR

This paper examines the link between wage share in net value added and real labor productivity in India’s organized manufacturing sector. The empirical results suggest that increase in real labor productivity almost leads to one for one decline in wage share, which implies that gains of real labor productivity gains are not accruing to laborers, instead entire gains of labor productivity increase is being captured by profit earners and it is being manifested in continuously rising profit shares. The data from Annual Survey of Industries (1981-82 to 2013- 14) suggests that overtime there has been a decline in share of wages in net value added, which is matched by a corresponding increase in profit share, this regressive distribution of income has profound implications for the growth of manufacturing sector itself.

What factors drive industrial plants in emerging economies to adopt ISO 14000 environmental management standards: Evidence from the Indian organized manufacturing sector

The factors behind the adoption of ISO 14000 certification by Indian organized manufacturing plants are examined using the Annual Survey of Industries unit-level data for 2016–2018, with supplementary analyses undertaken using such data for 2008–2010 and 2008–2015. While observing that a change in the export status of a plant from non-exporting to exporting will raise its probability of adopting ISO 14000 certification by about 20 percent, the paper also identifies that other plant-level characteristics like size, fuel intensity, financial ability, presence of foreign equity, R&D activity, and adequate managerial staff strength are some of the important drivers of the adoption of ISO 14000 certification. The paper finds that while exporting enhances the probability of adoption of ISO 14000 certification, the probability goes down beyond an export share in the production of 80 percent. The paper adds to the literature by disentangling the pecuniary objectives vis-à-vis the role of environmental and social consciousness of plants’ management in driving the adoption of ISO 14000 certification.

Wage, Profit, Employment and Output in Indian Organized Manufacturing Sector during UPAI & UPA II and NDA I & NDA II (Modi I) Regimes

This paper comparatively examines wages, profits, employment and output in Indian organized manufacturing sector during two political regimes, National Democratic Alliance (NDA) and NDA under the Prime-minister of Mr. Narendra Modi, and United Progressive Alliance (UPA). These four aspects of industrial development in India are analyzed in the context of political economy of neo-liberal reforms and sustainable development goals (SDGs) to be achieved by 2030. By using data of Annual Survey of Industries (ASI), the political economy of the two regimes is comparatively analyzed in the four periods/year, viz., NDA I (1999-2004), UPA I (2004-2009), UPA II (2009-2014) and Modi I (2018-2019). There are four empirical results in this analysis: (i) decrease in share of wages of blue-collar workers to white-collar workers, (ii) higher level and share of blue-collar workers at 79% and constant respective shares of clerks and white-collar workers at 12% and 10% over fourth regimes, (iii) increase in contract blue-collar workers, (iv) increase in feminization of blue-collar workers. These empirical results advocates policy implications for descent work, inclusive industrial and sustainable development in the Covid era.

What Factors Drive Industrial Plants in Emerging Economies to Adopt ISO 14000 Environmental Management Standards: Evidence from the Indian Organized Manufacturing Sector

What Factors Drive Industrial Plants in Emerging Economies to Adopt ISO 14000 Environmental Management Standards: Evidence from the Indian Organized Manufacturing Sector

Technology intensity and Indian manufacturing industries

The significance of technology-intensive industries for the growth and competitiveness of the manufacturing sector has been well documented. Many studies have attempted to define and classify the industries based on their technology intensity. However, very few attempts have been made in this respect from the vantage point of developing counties. In an attempt to fill this gap in the empirical literature, this article examines the technology intensity of organized manufacturing industries in India. A composite index of technology intensity of industries is calculated using the method of principal component analysis. The results of our analysis reveal poor performance of industries in terms of their technology intensity. It was found that only four industries in the Indian organized manufacturing sector spend more than 1 per cent of their sales on R&D. The highest score of ‘composite technology intensity index’ (2.63) is for the industry manufacturing electronic components. This score is, however, found to be far below the level prescribed by OECD for an industry to be categorized as ‘high-tech’. The results of our empirical analysis, therefore, suggest the need for increasing technology intensity for the Indian organized manufacturing industries to achieve competitiveness in the global market.

Higher Education, Vocational Training and Performance of Firms

This article contributes to the debate on the efficacy of traditional forms of education versus vocational training. The effects of technical education (leading to an engineering degree or diploma) and vocational training in engineering on the performance of Indian firms are analysed using regression models based on the Cobb–Douglas production function, enhanced to incorporate education and training. Instrumental variable approach is used to establish the direction of causality. It is found that that when a larger share of workers in a particular sector has a college or university-level technical education or vocational education in technical fields, there is a positive impact on firm performance in those sectors. Further, higher education in a general field seems to consistently benefit the organised manufacturing sector, while some levels of school education appear to benefit the unorganised sectors. JEL codes: I-23; L-60; M-53

Labour Market Regulation and Manufacturing Employment: A Study of Organized Manufacturing Sector Across Indian States

Labour regulation varies across different states in India. Using the state-level labour amendments of the Industrial Dispute Act (IDA), Besley and Burgess (Q J Econ 119:91–134, 2004) classified states as pro-worker and pro-employer states. Bhattacharjea (Indian J Labor Econ 49(2):211–232, 2006; Working paper, Center on Democracy, Development, and The Rule of Law Freeman Spogli Institute for International Studies, Stanford University, 2009) criticized Besley and Burgess’s (2004) paper both in terms of its methodology and in terms of its overall coding of the labour regulation index. The current paper uses the Besley and Burgess’s (2004) methodology, classifies states as flexible and inflexible based on the state-level amendments of the IDA and looks into the issue of manufacturing employment for the three-digit manufacturing industries across Indian states. Data on industrial outputs and employment were extracted from ASI three-digit manufacturing industries for the period from 1980–1981 to 2006–2007. The employment function was estimated for both total workers (measured as ‘total persons engaged’) and production workers (measured as ‘workers’) as dependent variables regressed on the labour market regulation index. The variables are found to be stationary under panel unit root tests. Controlling for industry-fixed and year-fixed effects, we found stringent labour regulation to adversely affect manufacturing employment. We did not find enough evidence to support the hypothesis that labour-intensive industries are worst affected from rigid labour laws. The results are found to be robust under dynamic panel estimation.

Energy Intensity, Productivity and Pollution Loads: Empirical Evidence from Manufacturing Sector of India

We explain the relationship between energy intensity and productivity for the organized manufacturing sector of India. Using data from the secondary sources, we explain the relationships at aggregate, state and industry levels. The novelty of this paper lies in bringing in pollution loads in explaining inter-industry variations in energy intensity. Results of this study indicate that the organized manufacturing sector of India has gained energy efficiency and productivity. We found heterogeneity among Indian states in productivity growth and energy intensity. The results indicate that small states performed well whereas large states fall in the productivity paradox. The productivity dilemma hypothesis is validated at industry level analysis however, results are inconsistent to validate the decoupling growth hypothesis. Pollution loads as classified by Government of India, plays a vital role in explaining energy intensity variations across industries, which calls for better policies aiming at pollutive industries specifically to achieve sustainable growth for the manufacturing sector of the Indian economy.

Wage-differentials and Their Determinants Across Industries in the Organized Manufacturing Sector of India

This article calculates the magnitude of wage differentials across industries in the organized manufacturing sector of India and identifies the major determinants of wage differentiation among the industries. Using data from Annual Survey of Industries in India for the period from 2000–2001 to 2015–2016, this study shows that mean wage is less in labour-intensive industries compared to the capital-intensive industries. The results of panel regression of annual average wage on various industry-specific factors show that productivity of labour is the most important factor in wage determination, and productivity largely depends on capital–labour ratio. The other significant factors in this regard are farm size, amount of profit and proportion of casual and female workers in total employment. Important policy implication of this study is that regulatory wage fixation and wage bargaining outcomes are not as significant as productivity differentials in explaining wage gaps across industries.

Impact of Determinants of Industrial Development on Employment and Wages in Indian Manufacturing

ABSTRACT The present study was undertaken to explore the evolution of the impact of firm-level performance on employment level and wages in the Indian organized manufacturing sector over the period 1989-90 to 2013-14. One of the major components of the economic reform package was the deregulation and de-licensing in the Indian organized manufacturing sector. The impact of firm-level performance on employment and wages were estimated for Indian organized manufacturing sector in major sub-sectors in India during the period from 1989-90 to 2013-14 of the various variables namely profitability ratio, total factor productivity change, technical change, technical efficiency, openness (export-import), investment intensity, raw material intensity and FECI in total factor productivity index, technical efficiency, and technical change. The study exhibited that all explanatory variables except profitability ratio and technical change cost had a positive impact on the employment level. Out of eight variables, four variables such as net of foreign equity capital, investment intensity, TFPCH, and technical efficiency change showed a positive impact on wages and salary ratio and rest of the four variables such as openness intensity, technology acquisition index, profitability ratio, and technical change had negative impact on wages and salary ratio. In this context, the profit ratio should be distributed as per the marginal rule of economics such as the marginal productivity of labour and capital.