Mark Doms Federal Reserve Bank of San Francisco

1. Education, TechnologyAdoption, and Wage Growth: Evidence and Trends from American Cities, 1980-2005 Mark Doms Federal Reserve Bank of San Francisco “The Economics of Geography: Cities, Growth, and Economic Development,” Federal Reserve Bank of Cleveland April 3-4, 2008

2. Special thanks to, • Tim Dunne and the organizers of this conference • My colleagues at the SF Fed • My coauthors • My research assistants

3. Today’s talk draws upon Labor Supply and Personal Computer AdoptionMark Doms, FRB-San FranciscoEthan Lewis, Dartmouth College The IT Revolution at the City Level: Testing a Model of Endogenous Biased Technology AdoptionPaul Beaudry, University of British ColombiaMark Doms, FRB-San FranciscoEthan Lewis, Dartmouth College Technology and Changes in the Male-Female Wage GapPaul Beaudry, University of British ColombiaMark Doms, FRB-San FranciscoEthan Lewis, Dartmouth College

4. Synopsis of today’s talk • Adoption of IT across cities • The most important factor in explaining the differences across cities is the education levels of their labor forces • What happened to wages in highly educated/high IT cities? • Above average wage growth • Above average widening of relative wages (college relative to high school) • Above average shrinking of male-female wage gaps

5. Synopsis of today’s talk, cont’d • Why are some cities more highly educated than others? • Industry mix • Colleges and universities • Immigration 4. Summary and some of the implications of these findings

6. But first, a word from our data 230 “cities”\“CMSAs”\“metro areas” are the units of observation Technology data at the city level • Private technology marketing survey, 1990-2002 • ~80,000 establishments per year • Compute mean PCs/worker by city • Adjust for industry and establishment size • PCs/worker highly correlated with other IT measures

7. But first, a word from our data, cont’d Education (“skills”) and wage data • “Skill share” or “skill supply” or “college equivalent share” or “education” • share of workers with a college degree + ½*those with some post high school education • Commonly used definition • Results robust to other definitions • Wages of full-time workers • College graduates and high school graduates • Always in logs • Public use censuses of 1980, 1990, and 2000 (and 2005 ACS)

8. 1. Adoption of IT across cities

9. 1. Adoption of IT across cities We tested numerous possible explanations for the wide disparity in IT adoption across cities, including…

10. 1. Adoption of IT across cities: Explanations the size of local IT sector, and … Austin, TX Seattle, WA San Francisco, CA Denver, CO

11. 1. Adoption of IT across cities: Explanations the quality of university IT/EE departments, and … University of Michigan Ann Arbor Massachussetts Institute of Technology Boston Carnegie Mellon Pittsburgh UC Berkeley San Francisco

12. 1. Adoption of IT across cities: Explanations …and many more. Bottom line: The dominant factor appears to be education of the workforce • Other factors have, at best, marginal effects relative to education • Conducted tests on causality • In the technology/skills literature, identification is a big problem—correlation does not imply causation

13. 1. Adoption of IT across cities: Explanations

14. 1. Adoption of IT across cities: Explanations

15. 1. Adoption of IT across cities: Explanations

16. One reason for interest in technology adoption stems from the effects technology adoption has on productivity and wages.

17. 2. What happened to wages in highly educated/ high IT cities? • Wages of college graduates • Wages of high school graduates • Wages of men and women • How these wages have changed over time

18. 2. What happened to wages in highly educated/ high IT cities? Average wages of college grads

19. 2. What happened to wages in highly educated/ high IT cities? Changes in wages of college grads

20. 2. What happened to wages in highly educated/ high IT cities?Average wages of high school grads

21. 2. What happened to wages in highly educated/ high IT cities? High school graduates, changes

22. 2. What happened to wages in highly educated/ high IT cities? • IT was adopted the most aggressively in cities where educated workers were relatively abundant. • Wages of college educated workers increased faster than for lower educated workers. • The education wage gap increased the most in highly educated cities. • Technical details are in the first Beaudry/Doms/Lewis paper.

23. 2. What happened to wages in highly educated/ high IT cities? Historical analogy • Female and child labor were relatively abundant in the Northeast prior to the industrial revolution. • That is one of the reasons why more factories were initially located there. • Wages of women and children increased the fastest. • Technology, in this case, was “biased” towards unskilled labor.

24. 2. What happened to wages in highly educated/ high IT cities? Male-female Male-female wage gaps • Narrowed considerably from 1980 (though still large). • As with college and high school wages, we examine changes across cities over time. • The logic is that IT has helped level the playing field—a brain over brawn argument.

25. 2. What happened to wages in highly educated/ high IT cities? Changes in male-female wages

26. 2. What happened to wages in highly educated/ high IT cities? Changes in male-female wages

27. 2. What happened to wages in highly educated/ high IT cities? Changes in male-female wages

28. Since highly educated cities enjoyed above average wage growth, why are some cities more educated than others?

29. 3. Why are some cities more educated than others? There are many, many reasons, including, • Industry mix • Colleges and universities • Immigration

30. 3. Why are some cities more educated than others? Industry mix Industry mix: • Industries vary in their skill mix • For instance, the financial securities industry has a higher skill mix (.76) than carwashes (.16) • Cities vary in their mix of industries • Jeremy’s discussion of IT centers • Therefore, we expect some cities to have higher skilled workforces because of their industry mix

31. 3. Why are some cities more educated than others? Industry mix Average education of a city = Education “predicted” by industry mix + Education NOT predicted by industry composition (“Surplus”)

32. 3. Why are some cities more educated than others? Industry mix Example: Seattle vs. Scranton, PA

33. 3. Why are some cities more educated than others? Industry mix Example for 2000

34. 3. Why are some cities more educated than others? Industry mix

35. 3. Why are some cities more educated than others? Change in industry composition Quick, but important, tangent: • What are the factors behind a city’s increase in the “predicted” skill share? • That is, why do high-skill industries (which tend to be “good job” industries) locate in one city instead of another? • “Surplus” skill • Immigration • Manufacturing’s importance

36. 3. Why are some cities more educated than others? Change in industry composition

37. 3. Why are some cities more educated than others? Change in industry composition Other factors that appear to be important in the change in the “predicted” skill level are: • Immigration • The immigrant group that had notable effects on the skill level of cities from 1980-2005 was from Latin/South America • Tend to be very low skilled • Tend to concentrate in cities in California and Texas • Comparative advantage of low skilled cities is to specialize in low-skill industries • Immigrants from Asia tend to be highly skilled, but relatively small in number

38. 3. Why are some cities more educated than others? Change in industry composition Other factors that appear to be important in the change in the “predicted” skill level are: • Manufacturing’s importance • Productivity and trade played havoc with the work forces & populations in industrial cities, such as Cleveland, Flint, and Detroit. Returning back from this tangent to understanding why some cities are more educated than others, …

39. 3. Why are some cities more educated than others? Colleges and Universities Abundance of colleges and universities • Local college presence lowers the cost of attending college for area’s residents • Finding jobs locally for recent college graduates are low • Cities with universities may have amenities valued by college graduates

40. 3. Why are some cities more educated than others? Colleges and Universities

41. 3. Why are some cities more educated than others? Colleges and Universities • Robust to other measures of university presence as well • In terms of industry mix • University cities have a higher skill industry mix than other cites. • Unclear as to why

42. 4. Summary and implications • PCs were adopted most aggressively in cities which had more college-educated workers. • Wages increased most rapidly for in these highly educated cities, especially for highly educated workers. • Begs the question of why are some cities more educated than some others?

43. 4. Summary and some implications • May be obvious but, • When thinking about attracting industries, should specific industries be targeted, or should resources be spent in making a fertile environment for business formation? • “Build it and they will come” • Why did Microsoft settle in Seattle?

44. 4. Summary and some implications • What are the attributes of cities that make them appealing to the highly educated? Cultural amenities? Outdoor amenities? Quality of public schools? Political environment? Weather?

45. 4. Summary and some implications Out of time?