Productivity Growth in the Manufacturing Sector

In view of discussion of two crises, Asian Financial crisis, 1997 and global meltdown, 2008 spreading over more than two decades, the objective of this article is to present econometrically whether productivity growth across countries can be a remedial measure toward tackling global recession pervaded during recent two or three decades worldwide and also to shed light on the aspect of whether productivity can truly act as a driver of growth of selected six economies like Korea Republic, Japan, India, China, USA, UK, and world economy as a whole. The panel data for the six selected countries for the period 1990–2018 were constructed keeping eyes on the 1997 Asian financial crisis and then the 2008–09 global economic crisis and a random effects model was applied after Hausman test. The empirical findings disclosed that the impacts on the growth of economies (represented by growth of GDP) from the growth rates of the manufacturing sector, labor productivity of manufacturing sector, and labor quantity are positive and statistically significant; while the effects of growth of the capital deepening and labor composition on economic growth of those sampled countries are statistically significant but negative. Some key factors that are likely to affect future productivity performance are centered on some issues like facilitating global learning spillovers; allowing productive firms to thrive; and making the most of human capital that should be taken care of.

Labor productivity always plays an important part in the growth of manufacturing sector of any nation, and certainly, in the growth of GDP as well. Now, the relationship between these three variables has been of interest to the researchers because few nations have experienced labor productivity–led economic growth, whereas for others it has been the other way round. In this chapter we have studied the relationship between labor productivity, manufacturing output, and growth of GDP, for 25 major economies across the globe, covering the period 2000–2015, with the help of simultaneous equation system for individual nations as well as panel data analysis, covering all the nations together. Study of this relationship has, hardly, been done before which is our prime motivation behind the study. Our findings suggest that in most of the nations, these variables have significant impact on one another but there are exceptions as well. Apart from that, there are variables like energy consumption, health status, life expectancy, foreign direct investment, etc., which are significant in influencing these variables. So, policy measure suggests that importance should be given not only on labor productivity or output of manufacturing sector but also on factors that can influence these variables.

During the post-globalization period, tariff imposition on manufacturing trade has a possible effect on the economy of developed and developing nations. Along with the volume and balance of trade, the study accounts for both export and import separately in order to observe their dynamisms under the tariff regime and makes comparisons between developing and developed countries. Using the World Development Indicators and World Integrated Trade Solution databases of World Bank (2020) on China (developing nation) and the United States (developed nation) over the period of 1970–2019, the co-integration tests and thereafter vector error correction models indicate that the relationship between tariff and manufacturing trade is positive and statistically significant.

The aim of this chapter is to determine the relationship between labor productivity and economic development. In this context, the annual data of Turkey on a range of 1970–2017 are included in the study period. On the other hand, these data are tested with the help of Toda Yamamoto causality analysis. Thus, it will be possible to determine whether there is a strong relationship between the two variables. According to the obtained results of the analysis, it is defined that there is a causal relationship from labor productivity to economic growth in Turkey. Based on these results, it can be said that labor productivity should rise in order to increase economic development in Turkey. For this purpose, educational programs in Turkey can be revised with the help of a detailed study. In this process, cooperation with companies to understand the needs in the market plays a key role. Additionally, regulations should also be prepared related to the salaries of the employees. If it can be possible to prevent employees from receiving wages below a certain amount by placing a minimum legal limit on salaries, it will be possible to increase the motivation of the employees. This situation has a positive and significant contribution to the labor productivity.

The contribution of the different sectors in gross domestic product (GDP) growth significantly implies the relative strength of the sectors over the country. The countries are classified by world agencies in terms of regional variation as well as income classification on the basis of the topographical location and international economic strength. This chapter considers the contribution of allied sectors over GDP considering manufacturing as a separate entity under the regional variation and different income classifications. It uses World Bank recent data set of 2010 and 2018 for cross-sectional analysis of GDP growth incorporating regional variation and income classification as discrete variables. Region-specific and income classification–specific regression identifies the variations in scores and changes in importance of different allied sectors.

Resilience of the economy is related with its ability to cope with the challenges (e.g., economic and environmental crises). Economies need to be resilient as countries having resilient economy can enhance welfare of their people and achieve sustainable development (SD). Total factor productivity can be improved through smart technologies, smart workforce, and innovations. It requires holistic and systematic as well as strategic approach as it is directly related with the SD of the countries and welfare of their people. It is directly related with the policies covering all these main aspects with the long-term, systematic, and holistic view. Resilient economies need to enhance their resilience to keep them resilient despite of the changes which can act as threats in the future. Resilience of the economy necessitates transformation of the manufacturing industry (MI) into the sustainable, smart, circular, and global one. In this way MI's competitiveness can be enhanced. For this reason, based on an in-depth literature review, this chapter aims to examine ways for enhancing resilience of the global economy through smart, circular, and competitive MI. Specific focus is on the policies fostering the transformation of the MI into the sustainable, smart, circular, global, and innovative one. This chapter emphasizes the importance of and need for the productivity-based resilient economy. Policy makers, academics, and researchers in the relevant field can get benefit from this chapter.

The US-China trade war has brought forth the problem of balance of trade not only for them, but also for many other economies in the world. However, all the commodity segments are not equally affected and thus, the segment-wise trade analysis of commodities can bring up many valuable insights, vital for policy formulation process. Despite this, existing literature barely covered this aspect as a focal research. Therefore, this chapter has carried out segment-wise analysis of commodity classes popular in international trade discussions for the United States and China since the trade dispute intensified between them. In this chapter, we have built an argument around three commodity-segments which are popular in international trade studies namely, the raw material segment, semi-finished goods segment, and finished goods segments. While doing this analysis, we majorly focused on monopolistic power of economies in different commodity segments. We found that while in the segment of raw material, mostly cost is driving the trade, in the finished goods segment, variety and innovations are the key drivers that can boost trade by discovering new consumption spaces.

There is a rich literature which states that India did not suffer much from the impacts of the US financial crisis, but there is a school of thought which believes that the idea of India being insulated or decoupled from the contagion on account of limited integration into the world economy has been proved to be wrong. What is interesting is the focus has always been on the services sector and not on the manufacturing sector in India. In this background, this chapter tries to understand whether manufacturing sectors' productivity growth was one of the reasons that the crisis worsened in India or was it because of the crisis that India's manufacturing sector went into a deep recession. To look into the causality issue, the author estimates the productivity loss index (PLI) for the Indian industries during the period between July 2007 and July 2010 by estimating the fall in growth percentages in consecutive months for a total of 9,000 manufacturing, mining, and electricity industries. The data at monthly level have been retrieved from the Centre for Monitoring Indian Economy (CMIE) Prowess database. Based on the causality results, the chapter shows that it was because of the subprime crisis that India's manufacturing sector went into a deep recession. Using a probit model, the chapter also estimates the probability of the US subprime crisis being responsible for the productivity loss in India's manufacturing sector during the above-mentioned period.

In the present knowledge economy, intellectual capital (IC) is regarded as one of the significant determinants of efficiency, profitability, and ultimately value of a firm. This chapter empirically investigates the ramifications of the IC on the level of efficiency of the firm. In addition, exploration of the changing dynamics in the relationship between IC and firm level efficiency in the face of global economic crisis is of special interest of this chapter. In attaining the objectives of the study, a comprehensive database of 299 manufacturing firms (chosen randomly from a stratification of six BSE manufacturing industry subsectors) were utilized during the period from 1999–2000 to 2013–2014. Firm level efficiency scores and implications of IC (as measured by employing Pulic's Value Added Intellectual Capital Model) on the level of efficiency of the firms were examined simultaneously using Stochastic Frontier Analysis. Empirical results revealed that IC significantly determines the efficiency of the manufacturing firms during the period of study. However, the impact of financial crisis was not robust in changing the synergy between efficiency and IC. Size, age, and leverage were also found to be significant determinants of efficiency during the period of study.

With economic reforms increasing market competition, greater efficiency and productivity of factors of production, particularly of the workforce, have become important prerequisites for firms' growth and survival. Consequently, designing appropriate strategies to motivate the workforce in this direction appears as a critical aspect of human resource management. However, an important issue is if increase in wages, salaries, and other benefits can necessarily result in the desired outcomes. This chapter will examine this aspect. Estimating long-term trends in share of wages, salaries, and total emoluments in major industries, it is found that while the share of wages, salaries, and total emoluments has increased in recent years, such changes are not reflected in higher productivity. It is, therefore, suggested that, in addition to higher wages, salaries, and other benefits, enhancing efficiency and productivity of human resources requires adequate emphasis on human aspects as well.

The chapter examines the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries as well as total manufacturing industry of Gujarat during the period from 1981–82 to 2010–11, using a stochastic frontier approach. The empirical finding clearly states that although factor accumulations as well as resource allocations in most of the 2-digit manufacturing industries of the state have been improved during the postreform period, technological progress (TP) and technical efficiency change (TEC) of the same have deteriorated in most industries of the state during that period. As a result TFPG in the major manufacturing industries as well as total manufacturing industry of the state have declined because the combined effect of their improvement in scale effect (SC) and allocation efficiency effect (AEC) could not offset the declining effect of both the TP and TEC of the same during that period. In this context, the government should take some policy initiatives to improve productive efficiency of the organized manufacturing industries in Gujarat. Once efficiency increases, it enhances competitiveness, thereby increasing productivity growth and its different sources of organized manufacturing industries of the state.

This study is an attempt to estimate the output growth and total factor productivity growth (TFPG) in Indian manufacturing industry. Most of the developed nations have been facing economic depression in its output and employment growth frequently which led to several worldwide recessions and developing nations have also been affected by these. Our objective is to examine a possible way of mitigating economic recession in the light of Indian experience. In these connections we have tried to establish a link between TFPG and indicators of economic growth like export, GDP, employment, etc. We have computed TFPG of Indian manufacturing industry for last 30 years. On an average TFPG of Indian manufacturing industry has been found negative with a declining trend. Time series analysis of our study reveals that all the variables are stationary at first difference. They are also found to be co-integrated. On the basis of volumes of India's exports of manufacturing products in 2017, we have selected top 27 destination countries. More than 60% of Indian's manufacturing products were exported to these countries in this year. We have conducted panel data analysis to find the relation between TFPG and growth of India's export. The result shows that there has been a positive and significant relation between these two. This study also found that there has been a positive relation between growth of gross value added and employment growth in Indian manufacturing industry. So, TFPG may be a useful way to mitigate economic recession.

World economies including India have been moving toward recession. To combat this recession more employment generation through investment is required in a highly populated economy like India. Since unorganized manufacturing enterprises (UMEs) provide employment to a huge mass in India, therefore its growth and productivity is a matter of concern in the Indian economy. The present study analyzes the growth and productivity of UMEs on the basis of the latest two rounds of NSSO unit level data incorporating all states and union territories (UTs) of India. It reveals that the growth of UMEs, employment, gross value added (GVA) and fixed assets widely varied across states/UTs, and these growth rates were substantially high in a number of states during 2010–11 and 2015–16. In most of the states/UTs the labor productivity of UMEs has increased significantly but not the capital productivity. Our analysis supports the theoretical relationship among growth of employment, GVA, and labor productivity. Therefore, the government has to make deliberate attempts to increase the growth of UMEs on one side and raise productivities of UMEs through skill developments on the other side to overcome the problem of unemployment in particular and expedite the growth of the Indian economy in general to combat the global economic recession.

This chapter measures total factor productivity growth (TFPG) using Malmquist productivity index (MPI) and the growth of MPI of Indian Textile Industry employing nonparametric data envelopment analysis (DEA), during 1995–2016, exploring company (firm) level Center for Monitoring of Indian Economy (CMIE) Prowess data; examines whether TFPG has improved after the withdrawal of multifiber trade agreement (MFA) since 2005; decomposes TFPG into technical change (TC), technical efficiency change (TEC), and scale efficiency change (SEC); and explains the factors behind the movement of TFPG, considering the effect of R&D (RD), exports (EX), marketing expenditures (MKTs) advertisement expenditures (ADVs), imports (IMP), using second-stage panel regression. Empirical evidence supports fluctuating pattern of TFPG during 1995–2016, with a marginal declining tendency. TFPG has increased in 1999–2000, 2000–01, 2009–10, and 2012–13. After dismantling MFA, MPI level has significantly declined, with an increase in its growth rate, but the increase is not statistically significant. The effect of EX, RD, ADV are nonlinear, U-shaped, and IMP and MKT are inverted U-shaped, implying that the sign effect of any variable depends on its size. There are joint interaction effects of (a) RD and EX; RD and MKT which are positive, (b) ADV and MKT as represented by the ratio (ADV/MKT), having nonlinear inverted U-shaped relation. The joint interaction effect supports that the impact of one variable depends on the magnitude of other. The marginal effect of EX, IMP, and ADV are positive; increase in these variables promotes TFPG. The greater role of ADV over MKT is evident. The marginal effect of RD is negative; the average level of RD is too low to generate positive effects, and, thus, there is an urgency of increasing RD. The promising part of the decomposition analysis is that highest contribution to growth rate of TFPG is the growth rate of TEC followed by growth rate of TC, and thus by increasing TEC and TC, higher growth rate of TFPG is achieved and may be beneficial in the long run and may lead to absorption of economic shocks for an economy facing recession in its output growth. Some policy suggestions are made for boosting up TFPG.

Europe and North America have witnessed consistent decline in the manufacturing sector over a period of time. It is also evident from the existing literature that shows growth of Indian manufacturing industries is not at all satisfactory. The objective of the chapter is to analyze the manufacturing sector in India and also to highlight key factors related to the growth of manufacturing industry with special emphasis on Eastern India.

For the estimation, cluster sampling was used to collect data from 166 respondents in India. We initially sent the questionnaire to 200 entrepreneurs out of which 34 respondents did not retrograde. As a result the total sample size was 166. The scales were made operational by using 5-point Likert scales (1 = “Strongly Disagree” to 5 = “Strongly Agree”). We also followed recommended sample size for conducting multinomial logistic regression.

It is found that liberalized foreign direct investment policy, focus on export, focus on increasing rural consumption, delicensing of industries, and financial sector liberalization significantly influence sustainable economic development.

Indian textiles have a history of fine craftsmanship, and because of that, it has its own global appeal and international demand. India has its own extensive base of raw material and manufacturing. This makes India the second-largest textile exporter in the world after China. India's share in the global textiles and apparels trade was approximately 5% in 2017. In 2017, India's textile industry contributes 7% of total industry output and employs about 45 million people directly. Its contribution to the country's GDP and export earnings in the same year was 2% and 15%, respectively. With this backdrop, the present chapter aims to investigate twin objectives. Initially, the changing growth pattern of India's readymade garment export is investigated. Along with this, the impacts of trade openness on India's readymade garment export are also scrutinized. The entire study is conducted based on the secondary data, compiled from the various issues of “Handbook of Statistics on Indian Economy,” published by “Reverse Bank of India.” The data are compiled for the period 1987–1988 to 2018–2019. The investigation of the first objective is facilitated by the “Poirer's Spline function approach.” On the contrary, for the exploration of the second objective, we have calculated the “trade openness index.” It is measured as the sum of export and import as a percentage of GDP. The study concludes that the Indian readymade garment industry shows a declining growth rate and the industry is benefited from trade openness. The study ends with suitable policy prescriptions.

The present chapter examined the behavior of relative wage rate, productivity of labor, and total factor productivity growth (TFPG) and also attempted to explore the causal relationship between relative wage rate and productivity of labor as well as relative wage rate and TFPG in food and beverage industry in India over the period 1980–1981 to 2017–2018. The result of Sen (2003) approach of endogenous structural break suggests that the series of relative wage rate, productivity of labor, and TFPG follows trend stationary process. A significant break is found for all the three aforementioned variables, being 1984–1985 for relative wage rate and productivity of labor whereas 2007–2008 for TFPG. For the three variables, growth rate has increased after the break. Bidirectional causality between relative wage rate and productivity of labor as well as relative wage rate and TFPG is evident.

Global recession is a serious issue to both the developed and developing economies. Reports published by the Ministry of Statistics and Programme Implementation (2019–20) have revealed that the growth of gross domestic products (GDPs) has shrunk significantly in the last few quarters. Due to such recession productions by many, manufacturing industries have reduced significantly, and a large number of people have lost their work, and scope of new job creations has also decreased. Food sector has also been affected by global recession (Agbedeyi & Adigwe, 2018). Food Processing Industry (FPI) is India's one of the most sunshine manufacturing industries and ranks fifth among the Indian industries in terms of production, consumption, and exports. The country ranks second in global ranking in terms of producing food products next to China. Despite the global recession, the FPIs helped the Indian economy to maintain the growth of the GDP and have created new job opportunities. Around 70 lakh persons are employed in both registered and unregistered food processing units in India. The value of food exported in the year 2018–19 was US $35.30 billion which was 10.69% of India's total export (i.e. US$330.67 billion) (MoFPI report, 2018–19). In this backdrop, the present chapter will try to find out the role of FPI in the Indian economy and will also highlight the prospects of this industry in the coming years.

The contribution of the Indian Automobile industry in the economic growth of the country is significantly high. Besides catering to a large domestic market, the automobile industry in India has also captured market shares in many foreign countries successfully in the last few decades. Not only is it an important export-oriented industry of the nation but also the fourth largest exporter of automobiles in Asia. However, in the recent years (2018–2019), it has faced an unprecedented slump. This chapter captures this fact by calculating the growth of car selling for the four quarters of the period 2018–2019 across the Indian states. It primarily tries to find out whether the variation in income and tax levied on petrol and diesel has an impact on the variation in the car selling across the states for the abovementioned time period. It has been proven from our study that higher income of a state has a positive impact, whereas higher tax on petrol and diesel which varies across the states has a negative impact on car selling. Apart from this, this study then distinctively tries to find out whether there exists any neighborhood impact on growth rate of car selling and different tax rate on petrol and diesel on the basis of Moran's Index. It is witnessed that there exists a high level of spatial autocorrelation among the different states in case of growth of a car selling and tax imposition on diesel as well as on petrol. This fact necessitates some degree of regional orientation in formulating an effective policy to revive the automobile industry on the part of the Government.