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

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

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.

Introduction: Measuring Job Tasks

• Potential value of job ‘tasks’ as measures of skill:

1. Theory building:

• In place of abstract conceptions of skill, tangible information on actual job demands.

2. Microfoundation of labor demand:

• In what sense(s) has work become more skilled? Hard to answer that question just using education as skill measure.

3. Specific applications:

• Classifying job tasks may be especially helpful for understanding impacts of technical change, offshoring.

Education of U.S. Labor Force, 1870 – 2000

Katz & Goldin, 2007

College/High-School Relative Labor Supply: 1963 - 2003Source

: Auto

r, Katz a

nd K

earn

ey 2

00

8Figure 6. Relative Supply of College Equivalent Labor 1963 - 2003 (March CPS)

-.9-.6

-.30

.3Lo

g Re

lativ

e Su

pply

Inde

x

1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003

Trends in U.S. Task Input: 1960-2002Dictionary of Occupational Titles & U.S. Census + CPS

40

45

50

55

60

65

1960 1970 1980 1990 2000

Task

Inpu

t (Pe

rcen

tile

s of 1

960

Task

Dis

trib

ution

)Abstract Tasks

Routine Tasks

Manual Tasks

Source

: Auto

r, Levy a

nd M

urn

ane, 2

00

3

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)

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:

Agenda

1. Conceptual model • How should job tasks ‘affect’ wages?

2. Validation • Descriptive summary, comparison to O*Net

3. Explaining variation in job tasks:• To what extent are differences in tasks explained by?

1. Measured education, experience, language (human capital)2. Fixed occupation effects3. Race and gender differences

4. Is variation in tasks ‘meaningful’?• Predictive of wages?• Net of education, occupation, race and gender?• What if we use O*Net instead?

5. Evidence for ‘Roy-model’ relationship btwn tasks and wages

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:

0 1ln i i i iw X H e


• 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.


• Consider the following stylized framework:

• Occupations j produce output using a vector of k 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.

ij j jk ki ik

Y


• 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.

jkk J j

w


• Does this model imply any restrictions on task prices?

1. No occupation j that has positive employment can be strictly dominated by another occupation. Rules out the existence of any occupation for which:

2. 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.

j andk jk jk j

Cov( ,jk j k ) 0

Agenda






5. Evidence for ‘Roy-Model’ relationship btwn tasks and wages

Task Measurement

• Task domains that we measure

1. ‘Data’ – Analytic tasks: Problem solving, reading, math, management

2. ‘People’ – Interpersonal tasks: Dealing with customers, suppliers, students, patients.

3. ‘Things’ – Physical, or repetitive cognitive, or mundane tasks

‘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?

‘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:

a) Customers or clients

b) Suppliers or contractors

c) Students or trainees

d) Patients

‘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?

Data (Analytic) Tasks by Occupation

Time on managing/supervisingAlmost all 49.8 17.2 17.5 11.5 21.1 19.0 13.8 17.9Half or more 16.0 13.5 11.6 5.3 10.6 8.5 5.1 4.8Less than half 34.2 69.3 70.9 83.2 68.3 72.5 81.1 77.3

Solve problems of 30+ minutesDaily 67.9 60.1 34.8 36.2 55.2 30.5 13.5 24.7Weekly 23.2 26.7 32.8 31.7 29.3 33.2 44.1 21.0Less than weekly 8.8 13.2 32.4 32.0 15.5 36.3 42.4 54.3

Use high-school+ mathDaily 17.4 18.5 16.0 12.8 27.5 18.5 22.8 3.8Weekly 15.3 13.4 7.3 5.3 21.8 11.5 3.0 4.4Less than weekly 67.4 68.1 76.6 81.9 50.7 70.0 74.2 91.8

Longest document typically read at job6 - 25 + pages 57.9 56.1 33.9 27.3 35.2 20.9 22.9 25.72-5 pages 28.5 34.0 35.6 29.5 33.8 21.0 17.6 20.51 or fewer 13.6 9.9 30.5 43.2 31.0 58.1 59.5 53.8

Pop share 10.6 25.0 11.7 13.3 8.3 7.1 6.5 15.8Observations 260 614 214 275 146 110 95 271

ManagerProf. Spec

Tech /Sales Clerical

Constr/ Repair

Product-ion

Trans-port

Service Occs

People (Interpersonal) Tasks by Occupation

Face to face contact (except coworkers)

None 5.5 9.5 5.3 29.3 10.2 25.6 15.6 7.0

Customers/clients 42.9 51.4 81.1 36.3 40.6 18.9 50.3 63.2

Suppliers/contractors 19.1 8.6 14.3 12.3 24.1 5.5 20.3 6.0

Students/trainees 14.4 31.8 21.9 15.2 15.1 9.8 12.5 22.9

Patients 4.5 16.6 12.1 8.7 0.3 3.1 1.3 20.8

Population share 10.6 25.0 11.7 13.3 8.3 7.1 6.5 15.8Observations 260 614 214 275 146 110 95 271

ManagerProf. Spec


Constr/ Repair

Product-ion

Trans-port

Service Occs

Physical/Repetitive Tasks by Occupation

Time on physical tasksAlmost all 16.7 21.5 39.3 30.9 74.4 72.1 75.8 76.6Half or more 6.4 14.8 19.2 9.3 17.1 13.7 16.8 5.5Less than half 76.9 63.7 41.5 59.8 8.5 14.3 7.3 18.0

Time on repetitive tasksAlmost all 6.8 13.5 33.1 44.2 20.3 50.3 57.1 53.3Half or more 10.2 13.0 17.1 21.6 24.6 17.9 19.4 18.7Less than half 83.0 73.6 49.9 34.2 55.2 31.9 23.5 28.0

Population share 10.6 25.0 11.7 13.3 8.3 7.1 6.5 15.8Observations 260 614 214 275 146 110 95 271

ManagerProf. Spec


Constr/ Repair

Product-ion

Trans-port

Service Occs

Correlations among PDII Variables (+ Education)

Data People Things

1. Data 1.00

2. People 0.06 1.00

3. Things -0.37 0.12 1.00

4. Yrs Education 0.39 0.07 -0.48

Comparison of PDII Measures with O*Net Scales

Individual Occupation

1. Manage 0.44 0.742. Problem solve 0.44 0.733. Math 0.28 0.464. Read 0.41 0.655. Routine tasks a. manual 0.37 0.59 b. cognitive -0.27 -0.386. Physical 0.63 0.817. Customer 0.33 0.488. Suppliers 0.06 0.009. Train 0.27 0.4010. Patient 0.52 0.63

11. Data 0.56 0.8112. People 0.23 0.2713. Things 0.58 0.78

PDI task measurement level

B. Composite Scales

A. Individual Variables

Agenda






5. Evidence for ‘Roy model’ relationship btwn tasks and wages

Explaining Variation in Tasks: Data (Analytic) Tasks(1) (2) (3) (4) (5) (6) (7)

Less than HS 0.01 0.00 0.06 0.04(0.11) (0.10) (0.10) (0.10)

Some college 0.27 ** 0.30 ** 0.03 0.02(0.07) (0.07) (0.06) (0.06)

College degree 0.66 ** 0.66 ** 0.09 0.09(0.07) (0.07) (0.07) (0.07)

Post college 0.90 ** 0.89 ** 0.23 * 0.23 *(0.08) (0.08) (0.09) (0.09)

Experience 0.04 ** 0.05 ** 0.01 * 0.02 *(0.01) (0.01) (0.01) (0.01)

Experience2 /100 -0.10 ** -0.11 ** -0.05 ** -0.05 **(0.02) (0.02) (0.02) (0.02)

Spanish language -0.69 ** -0.66 ** -0.36 ** -0.51 **(0.13) (0.15) (0.12) (0.14)

Female -0.28 ** -0.27 ** -0.04 0.00(0.05) (0.05) (0.05) (0.05)

Black -0.18 * -0.12 0.13 ~ 0.07(0.09) (0.08) (0.08) (0.08)

Hispanic -0.30 0.02 0.13 ~ 0.22 **(0.30) (0.08) (0.07) (0.07)

Asian 0.29 ~ 0.00 0.04 -0.05(0.16) (0.15) (0.14) (0.14)

130 occ dummies No No Yes No Yes Yes Yes

R-Squared 0.181 0.035 0.484 0.200 0.502 0.487 0.506

Explaining Variation in Tasks: Data (Analytic) Tasks

(1) (2) (3) (4) (5) (6) (7)

Less than HS 0.01 0.00 0.06 0.04

Some college 0.27 ** 0.30 ** 0.03 0.02

College degree 0.66 ** 0.66 ** 0.09 0.09

Post college 0.90 ** 0.89 ** 0.23 * 0.23 *

Experience 0.04 ** 0.05 ** 0.01 * 0.02 *

Experience2 /100 -0.10 ** -0.11 ** -0.05 ** -0.05 **

Spanish language -0.69 ** -0.66 ** -0.36 ** -0.51 **

Female -0.28 ** -0.27 ** -0.04 0.00

Black -0.18 * -0.12 0.13 ~ 0.07

Hispanic -0.30 0.02 0.13 ~ 0.22 **

Asian 0.29 ~ 0.00 0.04 -0.05


R-Squared 0.181 0.035 0.484 0.200 0.502 0.487 0.506

ijk k i j iT X e

Explaining Variation in Tasks: People (Interpersonal) Tasks(1) (2) (3) (4) (5) (6) (7)

Less than HS -0.15 -0.14 -0.16 -0.16(0.11) (0.11) (0.11) (0.11)

Some college 0.03 0.01 -0.03 -0.04(0.07) (0.07) (0.07) (0.07)

College degree -0.07 -0.05 -0.09 -0.07(0.07) (0.07) (0.08) (0.08)

Post college -0.16 ~ -0.12 -0.31 ** -0.29 **(0.09) (0.09) (0.10) (0.10)

Experience -0.02 * -0.02 * -0.01 ~ -0.01 ~(0.01) (0.01) (0.01) (0.01)

Experience2 /100 0.01 0.01 0.01 0.01(0.02) (0.02) (0.02) (0.02)

Spanish language -0.77 ** -0.79 ** -0.74 ** -0.78 **(0.14) (0.16) (0.14) (0.15)

Female 0.21 ** 0.23 ** -0.15 * -0.08(0.05) (0.05) (0.06) (0.06)

Black 0.26 ** 0.21 * 0.25 ** 0.19 *(0.09) (0.09) (0.08) (0.08)

Hispanic 0.19 * -0.02 -0.08 0.10(0.08) (0.08) (0.07) (0.08)

Asian -0.24 -0.30 ~ -0.24 -0.24(0.16) (0.16) (0.15) (0.15)


R-Squared 0.054 0.024 0.361 0.074 0.398 0.370 0.403

Explaining Variation in Tasks: People (Interpersonal) Tasks

(1) (2) (3) (4) (5) (6) (7)

Less than HS -0.15 -0.14 -0.16 -0.16

Some college 0.03 0.01 -0.03 -0.04

College degree -0.07 -0.05 -0.09 -0.07

Post college -0.16 ~ -0.12 -0.31 ** -0.29 **

Experience -0.02 * -0.02 * -0.01 ~ -0.01 ~

Experience2 /100 0.01 0.01 0.01 0.01

Spanish language -0.77 ** -0.79 ** -0.74 ** -0.78 **

Female 0.21 ** 0.23 ** -0.15 * -0.08

Black 0.26 ** 0.21 * 0.25 ** 0.19 *

Hispanic 0.19 * -0.02 -0.08 0.10

Asian -0.24 -0.30 ~ -0.24 -0.24


R-Squared 0.054 0.024 0.361 0.074 0.398 0.370 0.403

ijk k i j iT X e

Explaining Variation in Tasks: Things (Physical/Repetitive)(1) (2) (3) (4) (5) (6) (7)

Less than HS -0.04 -0.05 0.00 -0.02(0.10) (0.10) (0.10) (0.10)

Some college -0.34 ** -0.34 ** -0.07 -0.06(0.06) (0.06) (0.06) (0.06)

College degree -0.99 ** -0.99 ** -0.38 ** -0.39 **(0.06) (0.06) (0.07) (0.07)

Post college -1.27 ** -1.27 ** -0.50 ** -0.52 **(0.08) (0.08) (0.09) (0.09)

Experience -0.03 ** -0.03 ** -0.01 ~ -0.01 *(0.01) (0.01) (0.01) (0.01)

Experience2 /100 0.04 ** 0.04 ** 0.02 0.02(0.02) (0.02) (0.01) (0.01)

Spanish language 0.59 ** 0.49 ** 0.31 ** 0.33 **(0.13) (0.14) (0.12) (0.13)

Female 0.13 * 0.13 ** 0.04 0.04(0.05) (0.05) (0.05) (0.05)

Black 0.33 ** 0.15 ~ 0.13 ~ 0.11(0.09) (0.08) (0.07) (0.07)

Hispanic 0.48 ** 0.13 ~ 0.11 ~ 0.01(0.08) (0.07) (0.06) (0.07)

Asian -0.10 0.23 ~ 0.21 0.31 *(0.16) (0.14) (0.13) (0.13)


R-Squared 0.295 0.042 0.543 0.304 0.567 0.546 0.570

Explaining Variation in Tasks: Things (Physical/Repetitive)

(1) (2) (3) (4) (5) (6) (7)

Less than HS -0.04 -0.05 0.00 -0.02

Some college -0.34 ** -0.34 ** -0.07 -0.06

College degree -0.99 ** -0.99 ** -0.38 ** -0.39 **

Post college -1.27 ** -1.27 ** -0.50 ** -0.52 **

Experience -0.03 ** -0.03 ** -0.01 ~ -0.01 *

Experience2 /100 0.04 ** 0.04 ** 0.02 0.02

Spanish language 0.59 ** 0.49 ** 0.31 ** 0.33 **

Female 0.13 * 0.13 ** 0.04 0.04

Black 0.33 ** 0.15 ~ 0.13 ~ 0.11

Hispanic 0.48 ** 0.13 ~ 0.11 ~ 0.01

Asian -0.10 0.23 ~ 0.21 0.31 *


R-Squared 0.295 0.042 0.543 0.304 0.567 0.546 0.570

ijk k i j iT X e

Agenda






5. Evidence for ‘Roy Model’ relationship btwn tasks and wages

(1) (2) (3) (4) (5) (6) (7) (8)

Data 0.20 * 0.07 * 0.16 * 0.18 ** 0.13 ** 0.06 **(0.02) (0.02) (0.02) (0.02) (0.02) 0.019

People -0.03 * -0.03 -0.03 ~ -0.04 * -0.03 ~ -0.02(0.02) (0.02) (0.02) (0.02) (0.02) 0.018

Things -0.25 ** -0.14 ** -0.15 ** -0.23 ** -0.13 ** -0.11 **(0.02) (0.02) (0.02) (0.02) (0.02) 0.021

Female -0.27 ** -0.08 * -0.17 ** -0.21 ** -0.07 ~(0.03) (0.04) (0.03) (0.03) 0.038

Black -0.20 ** -0.15 ** -0.18 ** -0.17 ** -0.15 **(0.05) (0.05) (0.05) (0.05) 0.05

Hispanic -0.12 * -0.03 -0.16 ** -0.10 * -0.05(0.05) (0.05) (0.05) (0.05) 0.05

Asian 0.15 0.12 0.30 * 0.25 * 0.174(0.11) (0.10) (0.12) (0.11) 0.113

Spanish language -0.54 ** -0.39 ** -0.30 ** -0.42 ** -0.32 **(0.09) (0.09) (0.09) (0.09) (0.09)

Constant 2.91 2.91 ** 2.47 ** 2.70 ** 2.53 ** 3.05 ** 2.62 ** 2.74 **(0.02) (0.01) (0.05) (0.05) (0.05) (0.02) (0.05) (0.05)

Education, Exp No No Yes Yes Yes No Yes Yes130 occ dummies No Yes No Yes No No No Yes

R-squared 0.33 0.61 0.39 0.62 0.41 0.38 0.46 0.64

Hourly Wage Regressions with PDII Task Measuresln ij i k ik j i

k

w X T e

(1) (2) (3) (4) (5) (6) (7) (8)

Data 0.20 * 0.07 * 0.16 * 0.18 ** 0.13 ** 0.06 **

People -0.03 * -0.03 -0.03 ~ -0.04 * -0.03 ~ -0.02

Things -0.25 ** -0.14 ** -0.15 ** -0.23 ** -0.13 ** -0.11 **

Female -0.27 ** -0.08 * -0.17 ** -0.21 ** -0.07 ~

Black -0.20 ** -0.15 ** -0.18 ** -0.17 ** -0.15 **

Hispanic -0.12 * -0.03 -0.16 ** -0.10 * -0.05

Asian 0.15 0.12 0.30 * 0.25 * 0.174

Spanish lang -0.54 ** -0.39 ** -0.30 ** -0.42 ** -0.32 **

Constant 2.91 2.91 ** 2.47 ** 2.70 ** 2.53 ** 3.05 ** 2.62 ** 2.74 **

Education, Exp No No Yes Yes Yes No Yes Yes130 occ dummies No Yes No Yes No No No Yes

R-squared 0.33 0.61 0.39 0.62 0.41 0.38 0.46 0.64

Hourly Wage Regressions with PDII Task Measuresln ij i k ik j i

k

w X T e

Hourly Wage Regressions with PDII and O*Net Task Measures

(1) (2) (3) (4) (5) (6) (7) (8) (9)

PDI Data (person level) 0.20 * 0.07 * 0.06 * 0.12 * 0.12 * 0.06 *(0.02) (0.02) (0.02) (0.05) (0.05) (0.02)

PDI People (person level) -0.03 * -0.03 -0.01 0.02 0.02 -0.01(0.02) (0.02) (0.02) (0.03) (0.03) (0.02)

PDI Things (person level) -0.25 ** -0.14 ** -0.09 ** 0.07 * 0.08 * -0.09 **(0.02) (0.02) (0.02) (0.03) (0.03) (0.02)

PDI Data (occ mean) 0.24 * 0.19 * 0.12 ** 0.19 ** 0.14 ** 0.09 *(0.02) (0.03) (0.03) (0.03) (0.03) (0.03)

PDI People (occ mean) 0.01 0.03 0.00 0.00 0.02 -0.01(0.02) (0.02) (0.02) (0.02) (0.03) (0.02)

PDI Things (occ mean) -0.20 ** -0.09 ** -0.04 -0.18 ** -0.07 * -0.02(0.02) (0.03) (0.03) (0.03) (0.04) (0.04)

ONet Data (occ mean) 0.40 ** 0.19 ** 0.12 * 0.12 ** 0.05(0.03) (0.03) (0.05) (0.05) (0.04)

ONet People (occ mean) 0.00 0.00 0.02 0.02 0.02(0.03) (0.02) (0.03) (0.03) (0.03)

ONet Things (occ level) 0.03 -0.18 ** 0.07 * 0.08 * 0.03(0.03) (0.03) (0.03) (0.03) (0.03)

Constant 2.91 2.91 ** 2.91 ** 2.65 ** 2.91 ** 2.91 ** 2.91 ** 2.91 ** 2.66 **(0.02) (0.02) (0.02) (0.05) (0.02) (0.02) (0.02) (0.02) (0.05)

Demos + Human cap vars No No No Yes No No No No Yes

R-Squared 0.331 0.358 0.385 0.478 0.315 0.366 0.366 0.392 0.479

Hourly Wage Regressions with PDII and O*Net Task Measures

(1) (2) (3) (4) (5) (6) (7) (8) (9)

PDI Data (person level) 0.20 * 0.07 * 0.06 * 0.12 * 0.12 * 0.06 *

PDI People (person level) -0.03 * -0.03 -0.01 0.02 0.02 -0.01

PDI Things (person level) -0.25 ** -0.14 ** -0.09 ** 0.07 * 0.08 * -0.09 **

PDI Data (occ mean) 0.24 * 0.19 * 0.12 ** 0.19 ** 0.14 ** 0.09 *

PDI People (occ mean) 0.01 0.03 0.00 0.00 0.02 -0.01

PDI Things (occ mean) -0.20 ** -0.09 ** -0.04 -0.18 ** -0.07 * -0.02

ONet Data (occ mean) 0.40 ** 0.19 ** 0.12 * 0.12 ** 0.05

ONet People (occ mean) 0.00 0.00 0.02 0.02 0.02

ONet Things (occ level) 0.03 -0.18 ** 0.07 * 0.08 * 0.03

Constant 2.91 2.91 ** 2.91 ** 2.65 ** 2.91 ** 2.91 ** 2.91 ** 2.91 ** 2.66 **

Demos + Human cap vars No No No Yes No No No No Yes

R-Squared 0.331 0.358 0.385 0.478 0.315 0.366 0.366 0.392 0.479

*ln w PDI b PDI o O Netij i k ik k jk k jk j i

k k k

w X T T T e

Agenda






5. Evidence for ‘Roy-model’ relationship btwn tasks and wages

Testing the Roy (Self-Selection) Model (a Bit)

• Procedure:

1. For each occupation j with 4+ wage observations (of which there are 127), estimate by OLS:

2. 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.

1 2 3ij j j ij j ij j ij ijw Data People Things e

1 0 2j j j

Data vs. Things Coefficients Across Occupations-1

-.5

0.5

1

-1.5 -1 -.5 0 .5Coef on Physical/Routine Tasks in Occ[j]

Coef on Data Tasks in Occ[j] 95% CIFitted values

Data vs. People Coefficients Across Occupations-.

50

.51

-1 -.5 0 .5Coef on People Tasks in Occ[j]

Coef on Data Tasks in Occ[j] 95% CIFitted values

People vs. Things Coefficients Across Occupations

-.5

0.5

-1 -.5 0 .5Coef on Physical/Routine Tasks in Occ[j]

Coef on People Tasks in Occ[j] 95% CIFitted values

Conclusions

1. Substantial variation in tasks between and within occupations:• Btwn occ component has high ‘convergent’ validity w/O*Net

2. 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.

3. 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

4. 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.

Next Steps

1. Refinement of survey questions for measuring tasks:

• Struck out on distinguishing ‘routine’ in the sense of repetitive from routine in the sense of mundane.

2. 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’?

3. Long-term intellectual objective:

• Using tasks as a microfoundation to understand relationship between human capital, productivity, and wages.

Documents

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