1) Technology often automates specific tasks within jobs rather than completely replacing jobs, leading to partial automation and changes in the composition of jobs. 2) While automation may reduce some jobs, it can also create new jobs in related industries to meet increased demand, resulting in job churn rather than widespread unemployment. 3) When machines and humans complement each other's skills, it can lead to occupational substitution as some jobs are replaced while new occupations emerge that combine work with machines. This has contributed to job polarization.

1. TECHNOLOGY & INEQUALITY
James Bessen
Technology & Policy Research Initiative, BU School of Law

2. Thinking about future tech…
• This time IS different
• Identify what is different
• Understand what is similar
• Propose:
• Look at past
• Recent
• History
• Speculate…

3. 3 STORIES

4. 1. MACHINES REPLACE HUMANS
 UNEMPLOYMENT

5. Tasks v. Jobs
• Automation has largely automated specific tasks
• Only rarely completely automating jobs
• Jobs involve many tasks requiring diverse skills

6. Partial Automation
• Of 270 occupations in 1950 US Census:

8. Machine Learning: Better than humans?
• Reading lips
• Playing chess, Go, Jeopardy
• Reading X-rays

9. AI & Complete Automation
• Machine Learning = “Idiot Savant”
• Individual tasks
• Large, labelled data
• No model of the world, common sense, meaning
• Basic method around since 1980s
• Big Data, Big Hardware
• “General Artificial Intelligence”
• AI as marketing

10. Accountants &
auditors Loan Officers
Paralegals &
legal assistants
First computer
automation
1954 1956; AI 1987 1963
What is being
developed?
Scan, categorize, &
enter data from paper;
retrieve contract terms
Automatic loan
processing; fraud
detection
Legal text retrieval;
e-discovery
Task ML can't do
Evaluate and advise re
organizational controls
Monitor business
performance evaluating
non-standardized
information
Evaluate retrieved
documents for legal
relevance to case
Job growth, 2000-16 10% -- 13%
Predicted, 2016-26
(BLS)
10% 11% 15%
Examples: Partial automation

11. Partial automation  Job loss??
Technology

12. Common argument:
• Automation reduces jobs in affected industries
•
• Automation sometimes creates jobs in affected industries

15. What
changed?

16. Demand
Less labor / yard

Lower price

More yards consumed

17. Demand

18. Demand
1 set
clothing
Full
closet

19. Today?
• Bar code scanners => MORE cashiers
• E-commerce => MORE jobs (distribution)
• E-discovery => MORE paralegals

20. ATMs and bank tellers

21. Industry job growth from IT

22. Winners & Losers from IT
Satiated
demand

23. Churn, not so much unemployment

24. Churn, not so much unemployment
Overstates impact:
• “Non-employed”
includes self-employed
• Ignores hiring

25. Policy need:
• Understand patterns of demand
• Help workers transition to new industries

26. 2. MACHINES + HUMANS
REPLACE HUMANS

28. 0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Number of Typesetters & Compositors

29. Substitution

30. 0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Typesetter and compositor jobs
Graphic design jobs

31. Occupation Growth and Computer Use
-0.6%
-0.4%
-0.2%
0.0%
0.2%
0.4%
0.6%
0.8%
Own computer use Substitution Net

32. Computer use by occupation
0%
20%
40%
60%
80%
Low wage Mid wage High wage

33. Job polarization

34. Computer use

35. Policy need:
• Understand occupational substitution
• Help workers transitions to new occupations

36. 3. CLOSED KNOWLEDGE

37. Growing productivity gap (OECD)

38. IT Productivity Gap

39. Closed knowledge
• Proprietary systems
• Walmart logistics
• Large bank credit card systems
• Boeing/Airbus design systems
• Why hard to imitate?
• Economies of scale, network effects
• Proprietary knowledge
• Hiring
• Training
• Learning on the job

40. Closed Knowledge & Wages
• Weaker labor markets for technology-specific skills
• Limited alternative employment
• Weaker incentives to invest in skills
• Lower wage growth

41. Knowledge & wages historically
• Automation of tasks
• Increases value of skill on remaining tasks
•  Greater demand for skill
• Demand  higher wages
• BUT ONLY with robust labor markets

42. Cotton Textiles, value-added share
Automation increases demand for skill

43. Cotton Textiles, value-added share
Puzzle #1

44. Cotton Textiles, value-added share
Puzzle #2

45. Growth: Productivity v. Wage
0%
1%
2%
3%
4%
1850 - 1950 1958 - 2011
Productivity Real Wage

46. Textile Wages
• 1850 – 1950
• Automation increased skill demand
• Robust labor markets bid up wages
• 1950+
• Automation increased skill demand
• Labor market incentives weak
• Declining employment
• Short career prospects
• Declining bargaining power

47. Policy need:
• Robust labor market in new skills

48. POLICY

49. Policy needs
• Transitioning to new industries, occupations, locations
• Training
• Work-study, apprenticeship
• Certification of new skills
• Temporary support
• Encouraging labor markets in new skills
• Employee mobility
• Non-compete agreements
• Expanded trade secrecy laws
• Occupational licensing
• Standardized knowledge
• Open standards
• Government knowledge sharing