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.

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.

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.