Variation in Job Tasks: Measurement, Interpretation, and Relationship to Earnings

1. Variation in Job Tasks: Measurement, Interpretation, and Relationship to Earnings David H. AutorMIT and NBER Michael J. HandelNortheastern University Princeton-Cornell PDII Conference, October 3-4, 2008

2. Introduction: Measuring ‘Skill Demands’ • Economists usually measure skill with the following proxies: • Education • Potential (or actual) experience • Wages • These measures have clear conceptual limitations: • Education is a measure of potential productivity-related skills. • But education is not an input into a Prod’n function. • Using wages as a measure of skill simply assumes that whatever determines productivity also determines wages. • Claim: If we could measure tasks sufficiently well, education would be irrelevant to productivity and (perhaps) wages.

3. Introduction: Measuring Job Tasks • Potential value of job ‘tasks’ as measures of skill: • Theory building: • In place of abstract conceptions of skill, tangible information on actual job demands. • Microfoundation of labor demand: • In what sense(s) has work become more skilled? Hard to answer that question just using education as skill measure. • Specific applications: • Classifying job tasks may be especially helpful for understanding impacts of technical change, offshoring.

4. Education of U.S. Labor Force, 1870 – 2000 Katz & Goldin, 2007

5. College/High-School Relative Labor Supply: 1963 - 2003 Source: Autor, Katz and Kearney 2008

6. Trends in U.S. Task Input: 1960-2002Dictionary of Occupational Titles & U.S. Census + CPS Source: Autor, Levy and Murnane, 2003

7. Why the PDII? Existing Measures of Job Tasks in U.S. and International Data Sources • Existing data on job tasks • Dictionary of Occupational Titles (1937 to 1991) • Occupational Information Network: O*Net • Not fully populated until 2009, but already richer and more current than DOT • IAB ‘Pencils’ Data: W. Germany, 1979 to 1999 • Francis Green, Demanding Work • STAMP – Skills, Training and Management Practices (Handel, 2007, 2008)

8. Why the PDII when we have O*Net? • Why do we need person rather than (or in addition to) occupation level measures? • Heterogeneity of skills/tasks within occupations • Measurement error: Occupational assignment imprecise • Also: Vagueness of O*Net items:

9. Agenda • Conceptual model • How should job tasks ‘affect’ wages? • Validation • Descriptive summary, comparison to O*Net • Explaining variation in job tasks: • To what extent are differences in tasks explained by? • Measured education, experience, language (human capital) • Fixed occupation effects • Race and gender differences • Is variation in tasks ‘meaningful’? • Predictive of wages? • Net of education, occupation, race and gender? • What if we use O*Net instead? • Evidence for ‘Roy-model’ relationship btwn tasks and wages

10. Theory: How Should Tasks ‘Affect’ Wages? • We are used to thinking about Mincerian returns to education: • Return to education is a compensating differential for acquisition of human capital, foregone labor income. • If human capital is ‘unitary’ (one dimensional), a law of one price should prevail. • The economy wide price of human capital should not differ across occupations/industries. • A hedonic model of earnings is therefore natural:

11. Theory: How Should Tasks ‘Affect’ Wages? • Does the hedonic reasoning apply to the tasks as well? • That is, can we interpret ‘task returns’ in a wage regression like we interpret education returns? • The answer is no: • Tasks are not stocks of skills that must earn an equilibrium rate of return. • Tasks are applications of a worker’s skill endowment to a given set of activities. • Workers will choose the set of tasks (i.e., occupations) that maximize earnings or utility given their skill endowments. • This suggests a Roy model, not a compensating differentials (hedonic) model.

12. Theory: How Should Tasks ‘Affect’ Wages? • Consider the following stylized framework: • Occupations j produce output using a vector ofk tasks. • The demands for these tasks differ by occupation, and are represented by jk. • Concretely: The productivity of tasks differs among activities • Each worker i has a skill endowment that can be used to produce to ik units of each of the k tasks. • The production function for an occupation j is: • Assume the price of output of each occupation is unity. • A worker will therefore choose the occupation j in which he has the highest Yij.

14. Theory: How Should Tasks ‘Affect’ Wages? • What are the ‘return to tasks’ in this model? . Notice that the jk is occupation-specific. • Must ‘task returns’ (’s) be equalized across occupations (law of one price for tasks)? No. • Workers choose occupation that has the highest pay for their bundle of skills (task endowment), not for each skill separately. • Marginal worker in occupation j will be indifferent between j and his next best alternative occupation. • But infra-marginal workers will not be indifferent.

15. Theory: How Should Tasks ‘Affect’ Wages? • Does this model imply any restrictions on task prices? • No occupation j that has positive employment can be strictly dominated by another occupation. Rules out the existence of any occupation for which: • For this ‘non-dominance’ condition to be satisfied, it will generally have to be the case that: • Occupations that have a low return to one set of tasks must generally have a relatively high return to other tasks – otherwise, occupation likely to be dominated. • We provide some very preliminary evidence on this idea.

16. Agenda • Conceptual model • How should job tasks ‘affect’ wages? • Validation • Descriptive summary, comparison to O*Net • Explaining variation in job tasks: • To what extent are differences in tasks explained by? • Measured education, experience, language (human capital) • Fixed occupation effects • Race and gender differences • Is variation in tasks ‘meaningful’? • Predictive of wages? • Net of education, occupation, race and gender? • What if we use O*Net instead? • Evidence for ‘Roy-Model’ relationship btwn tasks and wages

17. Task Measurement • Task domains that we measure • ‘Data’ – Analytic tasks: Problem solving, reading, math, management • ‘People’ – Interpersonal tasks: Dealing with customers, suppliers, students, patients. • ‘Things’ – Physical, or repetitive cognitive, or mundane tasks

18. ‘Data’ (Analytic) Tasks • Q25d. How much of your workday (involves/involved) managing or supervising other workers?   • Q25g. The next question is about the “problem solving” tasks you (do/did) at your job. Think of “problem solving” as what happens when you are faced with a new or difficult situation where you have to think for a while about what to do next. How often (do/did) you have to solve problems at your job that take at least 30 minutes to find a good solution? • Q25h. How often (do/did) you solve problems at your jobs using advanced mathematics such as algebra, geometry, trigonometry, probability, or calculus? • Q25i. What (is/was) the longest document that you typically read as part of your job?

19. ‘People’ (Interpersonal) Tasks • Q29. To what extent does the work you (do/did) on your main job involve face-to-face contact with people other than your co-workers or supervisors? Would you say a lot, a moderate amount, a little, or none at all? • Q30. I am going to read a list of the types of people with whom you may have face-to-face contact on your job. As I read each one, please tell me whether you (have/had) a lot of face-to-face contact with this type of person, some contact, or no contact at all: • Customers or clients • Suppliers or contractors • Students or trainees • Patients

20. ‘Things’ (Physical/Repetitive) Tasks • Q25b. How much of your workday (involves/involved) carrying out short, repetitive tasks?   • Q25c. How much of your workday (involves/involved) doing physical tasks such as standing, handling objects, operating machinery or vehicles, or making or fixing things with your hands?

21. Data (Analytic) Tasks by Occupation

22. People (Interpersonal) Tasks by Occupation

23. Physical/Repetitive Tasks by Occupation

24. Correlations among PDII Variables (+ Education)

25. Comparison of PDII Measures with O*Net Scales

26. Agenda • Conceptual model • How should job tasks ‘affect’ wages? • Validation • Descriptive summary, comparison to O*Net • Explaining variation in job tasks: • To what extent are differences in tasks explained by? • Measured education, experience, language (human capital) • Fixed occupation effects • Race and gender differences • Is variation in tasks ‘meaningful’? • Predictive of wages? • Net of education, occupation, race and gender? • What if we use O*Net instead? • Evidence for ‘Roy model’ relationship btwn tasks and wages

27. Explaining Variation in Tasks: Data (Analytic) Tasks

28. Explaining Variation in Tasks: Data (Analytic) Tasks

29. Explaining Variation in Tasks: People (Interpersonal) Tasks

30. Explaining Variation in Tasks: People (Interpersonal) Tasks

31. Explaining Variation in Tasks: Things (Physical/Repetitive)

32. Explaining Variation in Tasks: Things (Physical/Repetitive)

33. Agenda • Conceptual model • How should job tasks ‘affect’ wages? • Validation • Descriptive summary, comparison to O*Net • Explaining variation in job tasks: • To what extent are differences in tasks explained by? • Measured education, experience, language (human capital) • Fixed occupation effects • Race and gender differences • Is variation in tasks ‘meaningful’? • Predictive of wages? • Net of education, occupation, race and gender? • What if we use O*Net instead? • Evidence for ‘Roy Model’ relationship btwn tasks and wages

34. Hourly Wage Regressions with PDII Task Measures

35. Hourly Wage Regressions with PDII Task Measures

36. Hourly Wage Regressions with PDII and O*Net Task Measures

37. Hourly Wage Regressions with PDII and O*Net Task Measures

38. Agenda • Conceptual model • How should job tasks ‘affect’ wages? • Validation • Descriptive summary, comparison to O*Net • Explaining variation in job tasks: • To what extent are differences in tasks explained by? • Measured education, experience, language (human capital) • Fixed occupation effects • Race and gender differences • Is variation in tasks ‘meaningful’? • Predictive of wages? • Net of education, occupation, race and gender? • What if we use O*Net instead? • Evidence for ‘Roy-model’ relationship btwn tasks and wages

39. Testing the Roy (Self-Selection) Model (a Bit) • Procedure: • For each occupation j with 4+ wage observations (of which there are 127), estimate by OLS: • Regress ’s on one another to look for negative relationship: • Note that estimates are very noisy due to very small sample sizes in most occupations. • We trim the top and bottom 5 percent of ’s.

40. Data vs. Things Coefficients Across Occupations

41. Data vs. People Coefficients Across Occupations

42. People vs. Things Coefficients Across Occupations

43. Conclusions • Substantial variation in tasks between and within occupations: • Btwn occ component has high ‘convergent’ validity w/O*Net • Substantial differences in job tasks btwn education, gender and race groups: • But most (not all) of observed education, race and gender diffs in job tasks are due to between-occupation differences. • Tasks are highly predictive of wages • True conditional on education, experience, race and gender • Also true conditional on occupation dummies • Also true conditional on O*Net occupation means • Initial evidence for a Roy model of wage determination • Occupations that have high returns to analytic skills, have low returns to physical skills and v.v.

44. Next Steps • Refinement of survey questions for measuring tasks: • Struck out on distinguishing ‘routine’ in the sense of repetitive from routine in the sense of mundane. • Longer term data improvement objectives: • Would be invaluable to supplement task data with direct skill measures – e.g., AFQT NAEP – to understand selection into tasks. • Should task measures be collected in standard labor force survey data to follow trends in task use and task ‘returns’? • Long-term intellectual objective: • Using tasks as a microfoundation to understand relationship between human capital, productivity, and wages.