1. Educational Mismatch among Ph.D.s: Determinants and Consequences Keith A. Bender and John S. Heywood Department of Economics and Graduate Program in Human Resources and Labor Relations, UW-Milwaukee Presentation to SEWP Conference 19 October, 2005

2. Motivation What is an ‘educational mismatch’? Why should it matter? –Worker costs –Employer costs –Social costs Why scientists with PhD’s? –Homogeneity of sample –Key for innovation and R&D –Concern that trained scientists are leaving science (Preston 2004)

3. Some Literature How many are mismatched? –Approximately 40% in national samples Why does mismatch persist in equilibrium? –Govt subsidization causes an oversupply of the highly educated (Groot and Maassen van den Brink 2000a) –Educational signals imperfectly correlated with worker productivity, which is difficult/costly to detect (Tsang and Levin 1985) –Internal Labor Markets force maintenance of pay hierarchies (Thurow 1975)

4. Literature Continued How is educational mismatch measured? –External job analysis –Worker perceptions of requirements Effects on earnings? –Falls by approx 14% (Groot and Maassen van den Brink 2000b; Borgans et al. 2000; Allen and van der Velden 2001) Effects on job satisfaction? –No (Buchel 2002) or slightly negative effect (Solomon et al. 1981, Allen and van der Velden 2001; Moshavi and Terborg 2002) Effects on turnover? –Generally increased (Solomon et al. 1981; Allen and van der Velden 2001)

5. Data and Methods 1997 Survey of Doctorate Recipients Primary Measure of Mismatch: –Thinking about the relationship between your work and your education, to what extent is your work related to your doctoral degree? ‘Closely related’ (69.3%) ‘Somewhat related’ (23.4%) ‘Not at all related’ (7.3%)

6. Data and Methods con’d Outcome Measures –Annual Earnings –Job Satisfaction (four point scale) –Job Change Other variables –Sector, Demographic, Job characteristics, Current Discipline

7. Table 2: Educational Mismatch, Earnings, Job Satisfaction and Turnover

8. Consequences of Mismatch Log Earnings Regressions Job Satisfaction

9. Consequences con’d Changing Jobs

10. Consequences con’d Influence of Age Possible that since accumulation of and return to human capital takes time, consequences may grow over time –Age-Earnings profiles by Mismatch –Age-Satisfaction profiles by Mismatch

11. Age-Earnings Profiles At age 28: 4.5% and 8.6% decrease At age 62: Over 10% decrease At age 28: under 2% decrease At age 62: 12.1% and 20.9% percentage point decrease

12. Age-Satisfaction Profiles At age 28: 6.7 and 17.5 percentage point decrease At age 62: 13.3 and 27.2 percentage point decrease At age 28: 11.4 and 16.9 percentage point decrease At age 62: 15.3 and 19.9 percentage point decrease

13. Reasons for Mismatch People who report their job not being closely related to their education are asked why.

14. Effects of Reasons

15. Determinants of Mismatch

16. Conclusions Educational Mismatch occurs in the highly educated sector – approx 30%, but more in the nonacademic sector, 43% Mismatch results in adverse outcomes –6-12% lower earnings –10-18% reduction in probability of being satisfied in job –4-7% more likely to change jobs Who are the mismatched? –Nonacademics, older workers, those not doing teaching or research, ‘hard’ scientists –No appreciable difference between genders or races