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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.

This paper explores the pattern of technical change in the Korean economy from 1970 to 2013 and investigates its determinants. We use the Classical growth-distribution schedule to show that the labor-saving and capital-using pattern has predominated. For the rationale behind this Marx-biased technical change, we focus on the relationship between technical change and real wage growth via the evolution of labor and capital productivity, and verify the historical direction of technical change against the rise and fall of the working class. Furthermore, we find that the deviation during the post-crisis period from the long-run trend of Marx-biased technical change is not attributable to the vitality of new technological innovations, but rather the reflection of class dynamics over extracting productivity under weaker capital deepening. The results suggest that the recent deterioration of labor share and labor unions in Korea is closely associated with low incentive for technological progress, which contributes to prolonged stagnation.

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.

Air pollution affects labour productivity and these effects arise in both indoor and outdoor environments and at varying levels of worker skill. They also arise at levels of air pollution generally considered to be within existing air quality standards and guidelines. Although the damage per individual is small when compared to more extreme events, such as mortality and hospitalisations, the effects are more widespread and may thus represent a significant cost to society. Labour is an essential element in every nation’s economy serving as one of the primary factors of production and India not an exception. Investing in human capital is viewed as a key source of sustained increase in labour productivity and economic growth. On the other hand, environmental regulations are typically considered to be a struggle on the economy. However, improved environmental quality may actually enhance productivity by creating a healthier workforce. At the same time, air pollution may affect labour productivity and can reduce the productivity of workers in physically demanding occupations. This chapter may be an attempt to provide comprehensive estimates of the major air pollutants in different states of India and also tries to identify the linkage between air pollution and labour productivity in case of Indian manufacturing sector.

This study attempts to analyze energy intensity, capacity utilization (CU), output and productivity growth of aggregate manufacturing sector in India during the period 1980–1981 to 2016–2017. A decadal analysis as well as a comparison between pre- and post-liberalization period of productivity growth is also made. Total factor productivity growth (TFPG) is also adjusted with CU to obtain adjusted TFPG. The trend in energy intensity is also analyzed to answer the question of sustainability. Results shows that TFPG declined in the post-reform period, highlighting the fact that liberalization process has its adverse impact on productivity growth. From the study it is observed that a declining trend in adjusted TFPG in the post-reform period, but the rate of decline is higher. Energy intensity and CU of the Indian manufacturing industries is found to be increasing over the study period. Increasing energy intensity, quite significantly, would increase the level of pollution generated by the manufacturing industries. So, interestingly enough, this may lead to conclude that the growth of the manufacturing industries is not in line with the basic essence of sustainable development.

Inequality in the distribution of family income in the U.S., which had remained virtually unchanged since the end of World War II until 1968, has increased sharply since then. In contrast, schooling and skill inequality has declined rather steadily over the postwar period. Another notable change over the past 30 years or so has been the widespread diffusion of computers. Using aggregate time-series data for the 1947–2000 period, I find that the largest effects on inequality come from office, computing and accounting equipment (OCA) investment, which accounted for about half of the rise in inequality between 1968 and 2000. The unionization rate is second in importance, and its decline over this period explains about 40 percent of the increase in inequality. The decline in the dispersion of schooling, on the other hand, plays almost no role in explaining the rise in inequality. On the basis of pooled time series, industry regressions for the 1970–2000 period, I also find that investment in OCA is positively related to changes in skill inequality, while changes in the unionization rate are negatively related.

Purpose – This study examines how managerial ability relates to employee productivity using a broad and generalized sample of US firms.

Methodology – This study employs a generalized sample of firm-years from all industries between 1980 and 2013.

Findings – By contending that managers differ in their ability to synchronize management processes and human capital in ways that enhance employee productivity, the authors provide evidence showing that more-able managers are associated with higher employee productivity. In addition, the authors find that high-ability managers moderate the negative relation between uncertain environments (high-technology firms) and employee productivity. Furthermore, the authors decompose employee productivity into employee efficiency components and employee cost components. The authors find a significant positive association between managerial ability and the employee efficiency component, but do not see a significant association between managerial ability and the employee cost component.

Value – The results contribute to the understanding of employee productivity by showing the relation between managerial ability and employee productivity.