In this study, we analyze the impacts of minimum wages on firms’ robot adoption using novel panel data related to robots imported by firms in China from 2001 to 2012, a period when most of China’s robots are imported. We find that minimum wages raise firms’ robot adoption significantly. The effects of minimum wages on robot adoption are larger for firms in routine-intensive industries, for firms in labor-intensive industries, and for large firms. Employing robots significantly increases firms’ value added, labor employment, labor productivity, total factor productivity, and average wages. The firm-level production function estimations show that on average, one robot replaces about 15 workers in China.

This study examines the contribution of Chinese diaspora researchers – those born in China but working outside the country – to China's catching up in global science to become a world leader in research publications and citations. Using a novel name-based way to identify Chinese diaspora authors of scientific papers, we show that these researchers produce a large proportion of global scientific papers of high quality, gaining about twice as many citations as other papers of the same vintage. Our analysis also shows that diaspora researchers are a critical node in the co-authorship and citation networks that connect scientific discovery in China with the rest of the world. In co-authorship, diaspora researchers are over-represented on international collaborations with China-addressed authors. In citations, a paper with a diaspora author is more likely to cite China-addressed papers than a non-China addressed paper without a diaspora author; and, commensurately, China-addressed papers are more likely to cite a non-China addressed paper with a diaspora author than a non-China paper without a diaspora author. Through those pathways, diaspora research contributed to China’s 2000-2015 catch-up in science and to global science writ large, consistent with ethnic network models of knowledge transfer, and contrary to brain drain fears that the emigration of researchers harms the source country.

Using a panel survey, we investigate how the welfare of rural-urban migrant workers in China is affected by trade union presence at the workplace. Controlling for individual fixed-effects, we find the following. Relative to workers from workplaces without union presence or with inactive unions, both union-covered non-members and union members in workplaces with active unions earn higher monthly income, are more likely to have a written contract, be covered by social insurances, receive fringe benefits, express work-related grievances through official channels, feel more satisfied with their lives, and are less likely to have mental health problems.

What, if anything, can the United States do to reverse the upward trend in inequality and the danger that it will lead to populist despotism or a corrupt oligarchy with laws made for the few, not for the many?

I propose two sets of policies. The first requires reforms in labor laws and regulations to better enable workers to organize and bargain collectively with employers. The second requires tax and procurement policies to encourage firms to develop employment ownership programs so that workers own some of the capital that employs them and additional policies that increase worker investments in capital more broadly. By operating on ownership of both labor and capital, the policies can modernize American economic institutions to fit the coming world of artificial intelligence (AI) robotics and avoid Madison’s Scylla and Charybdis choice between anarchy and corruption.

This paper uses U.S. Census Bureau panel data that link firm software investment to worker earnings.  We regress the log of earnings of workers by age group on the software investment by their employing firm.  To unpack the potential causal factors for differential software effects by age group we extend the AKM framework by including job-spell fixed effects that allow for a correlation between the worker-firm match and age and by including time-varying firm effects that allow for a correlation between wage-enhancing productivity shocks and software investments. Within job-spell, software capital raises earnings at a rate that declines post age 50 to about zero after age 65. By contrast, the effects of non-IT equipment investment on earnings increase for workers post age 50.  The difference between the software and non-IT equipment effects suggests that our results are attributable to the technology rather than to age-related bargaining power. Our data further show that software capital increases the earnings of high-wage workers relative to low-wage workers and the earnings in high-wage firms relative to low-wage firms, and may thus widen earnings inequality within and across firms.

Recent analyses of the potential effects of advanced technology on jobs has tended to focus on possible reductions in routine cognitive white-collar jobs due to computer algorithms and in blue-collar jobs due to robots and factory automation. This paper provides a different perspective on the possible future of work by: (1) measuring changes in job attributes/tasks from 2005 to 2015, straddling the boundary between the pre-AI and AI eras; and (2) decomposing those changes via a shift-share analysis into the changes that occurred within occupations and changes in the shares of employment between occupations with different characteristics.  Our primary source of information on job characteristics over time is the Occupational Information Network (O*NET) database developed by U.S. Department of Labor's Employment and Training Administration.  While prior research has used O*NET data cross-sectionally, we create a new panel dataset that allows us to analyze changes over time for 170 job characteristics from four O*NET questionnaires completed consistently by workers (job incumbents) since 2003. Per our title, we find that within-occupation changes dominate, raising doubts about the ability of projections based on expected changes in the occupational composition of employment to capture the likely future of work. Indeed, our data show only weak relationships between automatability, repetitiveness, and other job attributes and changes in occupational employment. The results suggest that analysts give greater attention to within-occupation impacts of technology in assessing the future of work.

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.

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.