Technology and the Future of Work

Transcription

1 Technology and the Future of Work Alan B. Princeton University & NBER June 1, 2018 Trento Festival of Economics

2 Overview Waves of technological change have transformed economies over time, caused short-term disruptions, and ultimately raised living standards Robots, AI, and Big Data have the potential to disrupt work and raise productivity, but evidence of faster productivity growth so far has been weak Jobs change but work does not disappear Main worry is impact of technological progress on inequality and opportunity We can either prepare for the future or try to hold it back May

3 Economists have predicted that technology will destroy jobs for decades and have been wrong We are being afflicted with a new disease of which some readers may not have heard the name, but of which they will hear a great deal in the years to come namely, technological unemployment. -- John Maynard Keyes, 1930 Labor will become less and less important...more and more workers will be replaced by machines. I do not see that new industries can employ everybody who wants a job. -- Wassily Leontief, 1952

4 Employment Increased 500% in U.S. Since 1939

5 But Hours Worked Declined as Society Grew Richer

6 Quick Tour of Literature: Three Generations Generation I (1990s) Skill-Biased Technological Change - Bound & Johnson (1992); Katz & Murphy (1992); Krueger (1993); Berman, Bound & Griliches (1994); Goldin & Katz (1996); Autor, Katz & Krueger (1998) Generation II (2000s) Task-Biased Technological Change - Autor, Levy & Murnane (2003); Goos & Manning (2007); Schmitt, Shierholz, & Mishel (2013) Generation III (2010s) Assess the Robots and AI - Acemoglu & Restrepo (2016); Frey & Osborne (2017); McKinsey Global (2017); OECD Study by Nedelkoska and Quintini (2018) Institute

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8 Quick Tour of Literature: Three Generations Generation I (1990s) Skill-Biased Technological Change - Bound & Johnson (1992); Katz & Murphy (1992); Krueger (1993); Berman, Bound & Griliches (1994); Goldin & Katz (1996); Autor, Katz & Krueger (1998) Generation II (2000s) Task-Biased Technological Change - Autor, Levy & Murnane (2003); Goos & Manning (2007); Schmitt, Shierholz, & Mishel (2013) Generation III (2010s) Assess the Robots and AI - Acemoglu & Restrepo (2016); Frey & Osborne (2017); McKinsey Global (2017); OECD Study by Nedelkoska and Quintini (2018) Institute

9 Current Wave of Tech: Robots, AI, and Big Data

10 Sizing Jobs at Risk from Automation Method: Expert Panels evaluate job tasks and technical bottlenecks for AI and Robots to determine which jobs/tasks are at risk. For example, AI not good at complex problem solving, influencing others, unstructured communication. AI good at solving simple problems, selling, rote tasks. Studies suggest 25% to 50% of U.S. jobs are at risk of automation. In many cases, however, only certain parts of jobs are at risks, so those jobs may continue to exist but evolve. Estimated share of jobs at risk varies across countries, and is roughly similar in Italy and U.S. Note: Blinder and Krueger (2009) did similar exercise for jobs at risk for outsourcing: 25% of jobs at risk.

11 OECD Estimates of Automation Risk of Jobs

12 OECD Estimates: Percent of Jobs at Risk of Automation

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14 Source: McKinsie Global Institute (2017)

15 Low Wage & Low Skill Jobs More Likely to be Automated U.S. Estimates

16 Don t Panic This is Not New and IS Manageable Jobs have been changing for long time Help Wanted Ads from New York Times in 1950

17 New Occupations Account for 44% of Net Job Growth from 2000 to 2017 in USA Millions of Jobs New Occupations Old Occupations

18 Average Job in New Occupations Pays 80% More than Average Job in Old Occupations Average Hourly Wage $45 $44.76 $40 $35 $30 $25 $22.36 $20 $15 $10 $5 $0 New Occupations Old Occupations

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20 May 2018 Note: Labor productivity is defined as output per hour worked. Source: The Conference Board. 20

21 Related Changes to Job Market Pose More Challenges than Disappearance of Jobs Disruption to older workers from automation and trade a major concern hard to transition to new sectors Effect of automation on wages and quality of jobs more important than effect on quantity of jobs Loss of worker bargaining power Rise in self-employment and Gig work partly due to technology (e.g., dititally-enabled platform economy)

22 UberX has Fueled Uber s Exponential Growth Source: Hall and Krueger (2015) 22

23 Italy US IRS UK US Hhd Source: OECD and IFS 23

24 How many self-employed and gig-workers? ~3 Source: The data on self-employed as % of WAPOP are from OECD for Italy and UK, and from IRS and OECD for US. The data on gig-workers are from frdb Survey (Italy), LSE-CEP survey (UK) and Princeton Self-employment Survey (US). 24

25 Self-Employed in US want Help with Health Insurance Rankings of Various Benefits US Retirement Savings UI Sick Leave Source: Princeton Self-Employment Survey Health Ins. Life Ins. Workers' Comp Family Leave Disability Ins. 25

26 Self-Employed in Italy want Help with Retirement Savings Ranking of various benefits - Percent Italy Source: frdb Survey 26

27 Policy Can Go in Two Possible Directions Prevent change (tax robots/anti-science/force demand for old jobs/block online platforms/anti-trade/tarriffs) Embrace new technology and improve jobs of the future (invest more in education/life long learning accounts/expand social protection to self-employed and gig workers/help displaced workers transition to new jobs/income support for the older displaced)