The flow of knowledge is closely linked to proximity. While extensive works show that long-term geographic proximity affects work behavior, little is known about the effect of short-term colocation, such as conferences. Using participant data at Gordon Research Conferences, we estimate difference-in-differences and instrumental variable models, which show that attendees who have no prior within-conference collaborations are more likely to collaborate with other attendees, and that the researchers who have worked previously with other attendees are more likely to continue their collaborations. We also find that researchers who are junior, are located closer to the conference venue, and have established prior ties to the conference draw more collaborative benefits from temporary colocation across organizations. Thus, going to a conference alters the creation of collaborations.
Increased use of robots has roused concern about how robots and other new technologies change the world of work. Using numbers of robots shipped to primarily manufacturing industries as a supply shock to an industry labor market, we estimate that an additional robot reduces employment and wages in an industry by roughly as much as an additional 2 to 3 workers and by 3 to 4 workers in particular groups, which far exceed estimated effects of an additional immigrant on employment and wages. While the growth of robots in the 1996-2016 period of our data was too modest to be a major determinant of wages and employment, the estimated coefficients suggest that continued exponential growth of robots could disrupt job markets in the foreseeable future and thus merit attention from labor analysts.
Abstract This chapter will review the major studies undertaken in the twenty-first century to assess the changing nature of employee voice in the Anglo-American context. These studies are predominantly based on employee perceptions but also include employer surveys and multilevel analysis.
We use a residential sorting model incorporating migration disutility to recover the implicit value of clean air in China. The model is estimated using China Population Census Data along with PM2.5 satellite data. Our study provides new evidence on the willingness to pay for air quality improvements in developing countries and is the first application of an equilibrium sorting model to the valuation of non-market amenities in China. We employ two instrumental variables based on coal-fired electricity generation and wind direction to address the endogeneity of local air pollution. Results suggest important differences between the residential sorting model and a conventional hedonic model, highlighting the role of moving costs and the discreteness of the choice set. Our sorting results indicate that the economic value of air quality improvement associated with a one-unit decline in PM2.5 concentration is up to $8.83 billion for all Chinese households in 2005.
Firms often use non-linear incentive systems to motivate workers to achieve specified goals, such as paying bonuses to reach targets in sales, production, or cost reduction. Using administrative data from a major Chinese insurance firm that raised its sales targets and rewards for insurance agents greatly in 2015, we find that increased incentives induced agents to increase sales of the increasingly incentivized life insurance products, bunched around the new targets, albeit in part with some low quality sales that led to canceled contracts, while reducing sales of products outside the new incentive system. The greater non-linear incentives raised agent incomes and lowered turnover and substantially increased firm revenues net of the increase in payments to agents. The stock market reacted to the new system with a jump in the firms’ share price relative to its main competitor by 15-20% in the days surrounding introduction of the new system.
China’s advance to the forefront of scientific research is one of the 21st century’s most surprising developments, with implications for a world where knowledge is arguably “the one ring that rules them all.” This paper provides new estimates of China’s contribution to global science that far exceed estimates based on the proportion of papers with Chinese addresses in the Scopus database of international scientific journals. The standard address-based measure ignores two contributions from Chinese researchers: articles written by Chinese researchers with non-Chinese addresses and articles in Chinese language scientific journals not indexed in Scopus. Taking account of these contributions, we attribute 36 percent of the 2016 global scientific publications to China. In addition, we find that citations to Chinese-addressed articles have increased from far below the global average, which helped bring China’s share of global citations to approximately 37 percent of global citations to papers published in 2013. With a share of scientific publications and citations more than twice its share of global population or GDP, China has achieved a comparative advantage in knowledge that has implications for the division of labor and trade among countries and for the direction of research and of technological and economic development worldwide.
This paper seeks to convince you that the best response to the coming dominance of AI robots in the world of work is to expand both employee ownership of firms and citizen ownership of business capital more broadly. Section 1 analyzes the likely effects of advances in AI robot technologies on the comparative advantage of machines versus humans in high-value-added work and the consequences for wages and salaries and income inequality. Section 2 argues that the best way to assure that living standards increase for all in the age of AI robots is through enhanced employee ownership and greater citizens’ stake in business capital, distributing capital income far more widely than today.
From 2000 to 2016 China increased its scientific publications in the international journals indexed by Scopus to become the largest contributor to global science, accounting for about 23% of journal articles adjusted for the Chinese share of addresses or names on publications. Publications with all-China addresses contributed the most to the increase, followed by cross-country collaborations and papers by Chinese-named researchers outside the country. The same period also saw a huge increase in scientific publications in Chinese language journals not indexed in Scopus. We estimate that while Chinese language papers gain about 1/5th as many citations as non-Chinese (largely English) papers in Scopus they are so numerous that even valued as making 1/5th the contribution of a Scopus paper, China accounts for 36% of global scientific papers defined as Scopus papers and China language equivalent papers and for 37% of citations to those papers. China's move to the forefront of scientific inquiry makes it a key driver of the direction of scientific and technological progress and of the knowledge-based economies of the foreseeable future.
In this article, the authors assess the credibility of research that has tested the theoretical contests between the monopoly and the collective voice model of unions developed by Freeman and Medoff in What Do Unions Do? The authors go beyond prior analyses by examining more than 2,000 estimates that consider the effects of unions on a broad range of organizational and individual outcomes, including productivity, productivity growth, capital investment, profits, and job satisfaction. They advance our understanding of the current empirical findings and credibility of this research by using metastatistical analysis to evaluate research quality, publication selection bias, statistical power, and heterogeneity. The authors conclude that compared to other areas of economics, research on union effects has lower bias but larger problems of statistical power. They argue that Freeman and Medoff’s monopoly–collective voice model helped produce more credible results, and they suggest ways to reduce the power and heterogeneity problems in existing research.
The labor market for specialists in STEM jobs is a complex and controversial topic for economists, labor market researchers, and policy makers. U.S. Engineering in a Global Economy continues a long tradition of research by the NBER into both the supply and demand sides of the engineering job market, while also expanding the scope beyond the United States to consider the practice of engineering and innovation in a global economy. Contributors draw on the most up-to-date data on engineering education and practice to explore the challenges of developing an engineering workforce that can contribute substantially to the innovation driving modern economic growth. These authors highlight what economists and labor market researchers have learned and identify issues that might be addressed in future research, including a labor market that is not optimally employing STEM qualified workers in their field of training, and the ways in which US students, firms, and educational institutions are responding to increased competition in the global economy.
This book examines both the demand and supply side of the engineering job market in the United States and the practice of engineering and innovation in a global economy. The authors provide assessments of engineering education, engineering practice, and careers which can inform science and engineering educational institutions, funding agencies, and policy makers about the challenges facing the U.S. in developing its engineering workforce in the global economy.
This paper uses linked establishment-firm-employee data to examine the relationship between the scientists and engineers proportion (SEP) of employment, and productivity and labor earnings. We show that: (1) most scientists and engineers in industry are employed in establishments producing goods or services, and do not perform research and development (R&D); (2) productivity is higher in manufacturing establishments with higher SEP, and increases with increases in SEP; (3) employee earnings are higher in manufacturing establishments with higher SEP, and increase substantially for employees who move to establishments with higher SEP, but only modestly for employees within an establishment when SEP increases in the establishment. The results suggest that the work of scientists and engineers in goods and services producing establishments is an important pathway for increasing productivity and earnings, separate and distinct from the work of scientists and engineers who perform R&D.
We augment standard log earnings eq1uations for workers in US manufacturing with variables reflecting measured and unmeasured attributes of their employer. Using panel employee-establishment data, we find that establishment-level employment, education of co-workers, capital equipment per worker, and firm-level R&D intensity affects earnings substantially. Unobserved characteristics of employers captured by employer fixed effects also contribute to the variance of log earnings, although less than unobserved characteristics of individuals captured by individual fixed effects. The observed and unobserved measures of employers mediate the effects of individual characteristics on earnings and increase earnings inequality through sorting of workers among establishments.