OCCUPATIONAL TASKS AND CHANGES IN THE WAGE STRUCTURE Sergio Firpo, EESP-FGV, Nicole Fortin, UBC Thomas Lemieux, UBC SOLE/EALE 3 rd World Conference June

OCCUPATIONAL TASKS AND CHANGES IN THE WAGE STRUCTURE

Sergio Firpo, EESP-FGV,Nicole Fortin, UBCThomas Lemieux, UBC

SOLE/EALE 3rd World ConferenceJune 17-19, 2010London, UK

2

Goal of this paper Assess whether occupation-based explanations can

account for some of the change in U.S. wage inequality and explain the U-shaped pattern of wage changes / polarization.

Follows the recent introduction of occupation-based explanations in the wage inequality literature, 1. Nuanced view of technological change (ALM, 2003, AKK,

2006, Goos and Manning, 2008), Based on routine vs. non-routine tasks

2. Trade vs. offshoring Trade in labor services lead to classification of occupations

as offshorable/non-offshorable by Blinder (2007) and Jensen and Kletzer (2007).

3. Top-end occupations Piketty and Saez (2003) and Gabaix and Landier (2008)

3

Changes in Wage Inequality

0 10 20 30 40 50 60 70 80 90 100-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

Figure 4: Change in Real Wages by Percentile, Men

1974 to 1989

1989 to 2004

Source: Lemieux (2008)

4

Occupation-based Explanations Limited Empirical Evidence? But despite recent surge of interest for occupational-based

explanations, there is only limited evidence of how they have contributed to changes in wage inequality.

Autor, Levy, and Murname (2003) show that “routine jobs” tend to be in the middle of the wage distribution. Suggests this is where wages should decline most rapidly.

Autor, Katz, and Kearney (2006) make this argument more explicitly and present evidence of a U-shaped change in wages (well-established) since the late 1980s that coincides with U-shaped changes in the distribution of occupations.

But, showing that shapes coincide is not like formally showing that computerization does account for a specific fraction of changes in inequality, and how they compete with traditional explanations.

5

Contribution We assess the role of occupational tasks in three steps: 1. Provide some motivating evidence by

i) presenting a simple skill pricing model at the occupational level,

ii) showing the changes in the level (between) and dispersion (within) of wages by occupation (2-digit occupations).

2. Look at whether changes in occupational wages (level and dis-persion) are linked to occupational tasks measured in the O*NET(technological content, offshorability, etc.)

3. Put all this together with other factors in a decomposition using reweighting and re-centered influence function regressions (3-digit occupations)

6

Wage Setting Model Linear skill pricing equation for worker i in occupation j at time

t:

Sik is a set of K cognitive, non-cognitive, manual skills, etc.

The key difference relative to “skill-based” studies of inequality

is that the rjkt vary across occupations because of the

“unbundling” problem (Rosen, Heckman and Scheinkman).

The returns to these skills rjkt change over time because of

technology, offshoring, and other factors, with implications for both the level of wages across occupations and dispersion of wages within occupations.

7

Regression Model One simple way of capturing both the level (between) and

dispersion (within) effect is to run regressions of wage changes on the base period wage for each percentile of the within-occupation wage distribution

We estimate the following regressions:

where q indicates percentiles of the wage distribution.

Comparing αj by occupations will show “between” occupation changes

Sign and magnitude of βj will tell us if dispersion is increasing “within” occupation

9

Data Processing We use the 1983-85 and 2000-02 Outgoing Rotation Group

(ORG) Supplements of the Current Population Survey. Years chosen to keep consistent occupation coding and

have union status. Focus on males age 16-64, non-allocated, weighted by

hours (see Lemieux (2006))

We extract the nine deciles of the within-occupation wage distribution, i.e. wjt

q for q=10,20,...,90

At this point, we work with 2-digit occupations (45 of them) to have precise enough estimates of each decile

11

Descriptive Evidence: First step: U-Shaped Changes in Wages by Decile

AdminSup2

Serv_FoodFarm2

Serv_PersHand1Sales1Hand2Serv_Clean

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Produc2Secretairies

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Serv_Protect

Produc3

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Profes

EnginTech

Teachers

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SalesCommSalesFinan

Health2

AdminSup1

ManagersOtherTechProfes

Health1

AdminExecuNatSc

MathScEngineers

Lawyers

-.2

0.2

.4W

age

chan

ge

.5 1 1.5 2 2.5 3 3.5Base period wage

Wage change Quadratic fit

1983-85 to 2000-02 change for each decile

Figure 1: Change in wage by 2-digit occupation

Descriptive Evidence:First step: U-Shaped Changes in Wages by Decile

AdminExecuManagers

EngineersMathScNatSc

Health1

Health2

ProfesTeachers

Lawyers

Profes

Health3EnginTech

OtherTech

Supervi

SalesFinanSalesComm

Sales1

Sales1

AdminSup1

CompOpSecretairiesRecord

Mail

AdminS2

AdminSup2

Serv_Protect

Serv_Food

Serv_Healht

Serv_CleanServ_PersMechanics

Constr1Produc1

Produc2

Produc3

Transp1

Transp2

Constr2

Hand1

Hand2

Farm1

Farm2

Forest

-.2

0.2

.4W

age

chan

ge

.5 1 1.5 2 2.5 3 3.5Base period wage

1983-85 to 2000-02 change for each decileFigure 1: Fitted change in wage by 2-digit occupation

12

13

Occupational TasksNon-routine vs. Automated vs. Offshored

ON-

SITE

14

Occupational Tasks Using the O*NET We combine various “Work Activities” and “Work Context” elements from

the O*NET 13 to construct five measures of occupational tasks

Technology/Offshorability 1) Information Content: occupations with high information content that

are likely to be affected by ICT technologies; they could also be offshored as in Jensen and Kletzer (2007) (JK)

2) Automation: occupations with high degree of potential/actual automation of jobs and is similar in spirit to the manual routine index of Autor et al. (2003).

Non-Offshorability Designed to capture aspects of job making it unlikely to be offshored 3) Face-to-Face Contact: if a job requires face-to-face personal interactions

with clients and/or co-workers, it is unlikely to be offshored 4) On-Site Job: reflects the first criteria used by Blinder (B), does the job

need to be done at a U.S. work location? 5) Decision-Making: again, jobs where frequent decision-making is

required will be less likely to be offshored, and is similar in spirit to the non-routine analytical index of Autor et al. (2003).

15

Occupational Tasks Using the O*NET

Technological Change/Offshorability Information Content

4.A.1.a.1 Getting Information (JK) 4.A.2.a.2 Processing Information (JK) 4.A.2.a.4 Analyzing Data or Information (JK) 4.A.3.b.1 Interacting With Computers (JK) 4.A.3.b.6 Documenting/Recording Information (JK)

Automation/Routine 4.C.3.b.2 Degree of Automation 4.C.3.b.7 Importance of Repeating Same Tasks 4.C.3.b.8 Structured versus Unstructured Work (reverse) 4.C.3.d.3 Pace Determined by Speed of Equipment 4.C.2.d.1.i Spend Time Making Repetitive Motions

16


Non-Offshorability Face-to-Face Contact

4.C.1.a.2.l Face-to-Face Discussions 4.A.4.a.4 Establishing and Maintaining Interpersonal

Relationships (JK, B) 4.A.4.a.5 Assisting and Caring for Others (JK, B) 4.A.4.a.8 Performing for or Working Directly with the Public (JK, B) 4.A.4.b.5 Coaching and Developing Others (B)

On-Site Job 4.A.1.b.2 Inspecting Equipment, Structures, or Material (JK) 4.A.3.a.2 Handling and Moving Objects 4.A.3.a.3 Controlling Machines and Processes 4.A.3.a.4 Operating Vehicles, Mechanized Devices, or Equipment 4.A.3.b.4 Repairing and Maintaining Mechanical Equipment (*0.5) 4.A.3.b.5 Repairing and Maintaining Electronic Equipment (*0.5)

17


Non-Offshorability Decision-Making

4.A.2.b.1 Making Decisions and Solving Problems (JK) 4.A.2.b.2 Thinking Creatively (JK) 4.A.2.b.4 Developing Objectives and Strategies 4.C.1.c.2 Responsibility for Outcomes and Results 4.C.3.a.2. Frequency of Decision Making

For each occupation, O*NET provides information on the “importance” and “level” of required work activity and on the “frequency” of five categorical levels of work context.

We follow Blinder (2007) in arbitrarily assigning a Cobb-Douglas weight of two thirds to the “importance” and one third to the “level” in a weighed sum for work activities. For work contexts, we simply multiply the frequency by the value of the level.

22

Occupational TasksSecond-Step: Regressions of Tasks on αj and βj

Call these five measures of task content (in each occupation j) TCjh, for h = 1, .. 5.

The second step regressions are

and

23

Occupational TasksSecond-Step: Regressions of Tasks on αj and βj

Table 2- Estimated Effect of Task Requirements on Intercept and Slope of Wage Change Regressions by 2-digit Occupation

(1) (2) (3) (4) (5) (6) (7) (8)TechnologyInformation content 0.010 0.027 0.015 0.010 0.017 0.040 0.046 0.030

(0.012) (0.011) (0.017) (0.018) (0.008) (0.012) (0.010) (0.015)Automation -0.035 -0.043 -0.025 -0.023 -0.046 -0.056 -0.055 -0.036

(0.013) (0.011) (0.017) (0.018) (0.009) (0.010) (0.015) (0.015)OffshorabilityNo Face-to-Face -0.040 -0.013 0.044 0.037 -0.052 -0.057 -0.043 -0.024

(0.017) (0.016) (0.023) (0.026) (0.011) (0.012) (0.019) (0.017)No On-Site Job -0.001 0.001 0.024 0.024 0.017 0.019 0.031 0.024

(0.007) (0.006) (0.009) (0.009) (0.005) (0.005) (0.007) (0.006)No Decision making -0.025 -0.008 -0.032 -0.025 -0.025 -0.055 -0.053 -0.020

(0.018) (0.018) (0.025) (0.029) (0.011) (0.015) (0.015) (0.021)Base wage no yes no yes no yes no yesR-square 0.400 0.550 0.450 0.440

Intercept Slope Intercept SlopeTasks included together Tasks included separately

25

Occupational TasksWithin and Between Changes and Automation

CompOpProcess

Mail

Serv_Healht

Produc1Produc2

Produc3

Opera1

Opera2

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-.2-.1

0.1

Wag

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ange

.5 1 1.5 2 2.5 3 3.5Base period wage

1983-85 to 2000-02 change for each decileFigure 2b: F itted change in wage: Top-10 Auto

27

Occupational Tasks Within and Between Changes and On-Site

ManagersMathSc

Professor

Lawyers

ProfesSalesFinanSalesComm

Sales2

CompOpSecretairiesRecord

-.20

.2.4

Wag

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ange

.5 1 1.5 2 2.5 3 3.5Base period wage

1983-85 to 2000-02 change for each decileFigure 2d. F itted change in wage: Bottom-10 On-Site

29

Main FindingsSecond-Step Regressions Occupational task requirements correlate in a reasonable

way with slopes and intercepts Occupation-specific slopes very important for fitting the

model/capturing curvature. Reflects heterogeneity in changes in within-occupation

inequality

But leaves two questions open: 1) What is the precise quantitative contribution of

occupations to changes in overall inequality? 2) What happens when we control for other factors

(education, union status, etc.)?

30

Reweighted RIF-Regression Decomposition:Third-Step Methodology:

We use a decomposition similar in spirit to Oaxaca-Blinder mean decomposition between time period 1 and time period 0,

ΔμO = E [Y|D = 1] - E [Y|D = 0]

= E [X|D = 1](β1-β0) + (E [X|D = 1] -E [X|D = 0]) β0

= ΔνS + Δν

X

wage structure effect composition effect but that works with other distributional statistic ν besides the mean i) the outcome variable, Y, has been replaced by by the recentered

influence function RIF(y; ν) of the statistics of interest ν (Firpo, Fortin, and Lemieux, 2009) For example, for the τ-th quantile of the distribution, qτ , we have :

RIF(y;)= qτ + IF(Y; qτ) = qτ + [τ –1(Y≤ qτ) ]/f(qτ)

Using the coefficients γν from a regression of RIF(y; ν) on X,

ΔνS = E [X|D = 1](γν

1- γν0) and Δν

X = (E [X|D = 1] -E [X|D = 0]) γν

0

31

Reweighted RIF-Regression Decomposition:Third-Step Methodology:

ii) Even in the case of the mean, if the true conditional expectation is not linear, the OB decomposition is biased (Barsky et al., 2002). Almost surely the case for other distributional statistics.

We address this issue using the modified decomposition:

ΔνS = E [X|D = 1](γν

1- γν01)

and

ΔνX = (E [X|D = 1] -E [X|D = 0]) γν

0+ Rν

where γν01 are the period 1 coefficients estimated in sample 0

reweighted to look like period 1, and the approximation error R is the difference between composition effects estimated by reweighting and RIF regressions.

iii) We address the omitted group issue by trying to pick a reasonable base group: HS graduates, non-union, 15-19 years of experience, and one standard deviation below the mean of the task measures.

32

RIF-Regressions Results The γν

0 and γν1

Table 1. Unconditional Quantile Regression Coefficients on Log Wages

Years: 1983/85 2000/02Quantiles: 10 50 90 10 50 90Explanatory VariablesUnion covered 0.208 0.406 -0.055 0.112 0.278 -0.073

(0.003) (0.004) (0.004) (0.003) (0.005) (0.006)Non-white -0.090 -0.140 -0.055 -0.040 -0.131 -0.071

(0.006) (0.004) (0.004) (0.006) (0.004) (0.006)Non-Married -0.162 -0.122 -0.015 -0.072 -0.111 -0.066

(0.004) (0.004) (0.004) (0.004) (0.004) (0.005)Education ( High School omitted) Primary -0.278 -0.392 -0.156 -0.390 -0.378 -0.070

(0.008) (0.007) (0.004) 0.012 (0.009) (0.005) Some HS -0.301 -0.146 -0.023 -0.396 -0.188 0.034

(0.007) (0.004) (0.004) (0.01) (0.005) (0.004) Some College 0.045 0.129 0.086 0.031 0.118 0.053

(0.005) (0.005) (0.004) (0.004) (0.004) (0.004) College 0.142 0.316 0.375 0.102 0.386 0.561

(0.004) (0.006) (0.007) (0.004) (0.005) (0.011) Post-grad 0.088 0.337 0.559 0.066 0.403 1.025

(0.005) (0.006) (0.011) (0.004) (0.006) (0.018)O*NET MeasuresInformation Content 0.048 0.072 0.015 0.038 0.061 0.030

(0.002) (0.002) (0.002) (0.002) (0.002) (0.003)Automation 0.021 -0.025 -0.047 0.009 -0.053 -0.035

(0.002) (0.002) (0.002) (0.002) (0.002) (0.003)No Face-to-Face 0.109 0.132 0.121 0.083 0.110 0.107

(0.003) (0.002) (0.003) (0.002) (0.002) (0.004)Non On-Site Job -0.007 0.021 0.037 -0.016 0.014 0.051

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)No Decision-Making -0.123 -0.127 -0.125 -0.109 -0.135 -0.118

(0.003) (0.003) (0.003) (0.003) (0.003) (0.004)Number of obs. 274,625 252,397

Note: 6 Experience classes also included in the regressions. Boostrapped standard errors (100 reps) are in parentheses.

34

Figure 1. Unconditional Quantile Regression Coefficients: 1983/85-2000/02

-.2

0.2

.4

0 .2 .4 .6 .8 1Qu a n tile

2 0 0 0 -0 2

1 9 8 3 -8 5

Union

-.2-.1

5-.1

-.05

0

0 .2 .4 .6 .8 1Qu a n tile

Non- W hit e

-.5-.2

50

.25

.5.7

51

0 .2 .4 .6 .8 1Qu a n tile

Elem ent ar y

-.5-.2

50

.25

.5.7

51

0 .2 .4 .6 .8 1Qu a n tile

Dr op- O ut-.5

-.25

0.2

5.5

.75

1

0 .2 .4 .6 .8 1Qu a n tile

Som e College-.5

-.25

0.2

5.5

.75

1

0 .2 .4 .6 .8 1Qu a n tile

College

-.5-.2

50

.25

.5.7

51

0 .2 .4 .6 .8 1Qu a n tile

Post - G r aduat e

0.0

2.0

4.0

6.0

8.1

0 .2 .4 .6 .8 1Qu a n tile

I nf or m at ion

-.08

-.06

-.04

-.02

0.0

2

0 .2 .4 .6 .8 1Qu a n tile

Aut om at ion

.04

.06

.08

.1.1

2.1

4

0 .2 .4 .6 .8 1Qu a n tile

No Face2Face-.0

4-.0

20

.02

.04

.06

0 .2 .4 .6 .8 1Qu a n tile

No O n- Sit e

-.16

-.14

-.12

-.1-.0

8-.0

6

0 .2 .4 .6 .8 1Qu a n tile

No Decision- M aking

35


-.2

0.2

.4

0 .2 .4 .6 .8 1Quantile

2000-02

1983-85

Union

36


-.2

-.15

-.1-.0

50

.05

0 .2 .4 .6 .8 1Quantile

Non-Married

37


-.2

-.15

-.1-.0

50

0 .2 .4 .6 .8 1Quantile

Non-White

38


-.7

-.6-.5

-.4-.3

-.2-.1

0 .2 .4 .6 .8 1Quantile

Experience < 5

39


0

.02

.04

.06

.08

.1

0 .2 .4 .6 .8 1Quantile

Information

40


-.0

8-.0

6-.0

4-.0

20

.02

0 .2 .4 .6 .8 1Quantile

Automation

41


.0

4.0

6.0

8.1

.12

.14

0 .2 .4 .6 .8 1Quantile

No Face2Face

42


-.0

4-.0

20

.02

.04

.06

0 .2 .4 .6 .8 1Quantile

No On-Site

43


-.1

6-.1

4-.1

2-.1

-.08

-.06

0 .2 .4 .6 .8 1Quantile

No Decision-Making

44

Decomposition Results Δν

O = ΔνS+ Δ ν

X and ΔνX = (E [X|D = 1] -E [X|D = 0]) γν

0

Wage structure effects essential to account for decreasing inequality at the bottom and increasing inequality at the top

For composition effects, only de-unionization has the right signs

Table 3. Decomposition Results 1983/85-2000/02 90-10 50-10 90-50 Variance GiniTotal Change 0.0622 -0.0830 0.1452 0.0443 0.0085

(0.0149) (0.0147) (0.0034) (0.0013) (0.0004)Wage Structure -0.0208 -0.1287 0.1079 0.0170 0.0041

(0.0114) (0.0113) (0.0043) (0.0015) (0.0004)Composition 0.0829 0.0457 0.0373 0.0273 0.0043

(0.0055) (0.0051) (0.0031) (0.0008) (0.0003)Composition Effects:Union 0.0215 -0.0159 0.0375 0.0080 0.0046

(0.0039) (0.0036) (0.0007) (0.0002) (0.0001)Education -0.0009 0.0111 -0.0120 -0.0019 -0.0041

(0.0022) (0.0018) (0.0018) (0.0003) (0.0001)Experience 0.0141 0.0186 -0.0045 0.0032 -0.0022

(0.0011) (0.0009) (0.0009) (0.0003) (0.0001)Technology 0.0030 0.0073 -0.0043 0.0430 0.0182

(0.0022) (0.0018) (0.0018) (0.0002) (0.0001)Offshorability 0.0073 0.0033 0.0040 -0.0204 -0.0054

(0.0008) (0.0007) (0.0007) (0.0002) (0.0001)Other 0.0103 0.0009 0.0093 0.0048 0.0025

(0.0016) (0.0014) (0.0016) (0.0002) (0.0001)

45

Decomposition ResultsΔν

S = E [X|D = 1](γν1- γν

01)

For wage structure effects, both technology and offshorability have the right signs and sizeable comparable “magnitude”

Table 3. Decomposition Results 1983/85-2000/02 90-10 50-10 90-50 Variance GiniWage Structure Effects:Total -0.0208 -0.1287 0.1079 0.017 0.0041

(0.0114) (0.0113) (0.0043) (0.0015) (0.0004)Union 0.0111 -0.0048 0.0160 0.0040 0.0031

(0.0031) (0.0025) (0.0021) (0.0006) (0.0001)Education 0.0890 0.0169 0.0721 0.0324 0.0072

(0.0029) (0.0024) (0.0025) (0.0019) (0.0005)Experience -0.0337 -0.0081 -0.0257 -0.0097 -0.0052

(0.009) (0.0098) (0.0092) (0.0037) (0.001)Technology 0.0484 -0.0322 0.0806 0.4520 0.2067

(0.0178) (0.0154) (0.0176) (0.0027) (0.0008)Offshorability 0.0500 -0.0218 0.0718 0.3334 0.0888

(0.0115) (0.0131) (0.0113) (0.0024) (0.0007)Other -0.0516 -0.0274 -0.0242 -0.0213 -0.0074

(0.0112) (0.0094) (0.0079) (0.0017) (0.0004)Residual -0.1339 -0.0511 -0.0828 -0.7736 -0.2890

(0.0059) (0.0054) (0.0048) (0.0054) (0.0015)

46

Figure 5. Decomposition of Total Change into Composition and Wage Structure Effects

-.05

0.0

5.1

.15

Log

Wag

e C

hang

e

0 .2 .4 .6 .8 1Quantile

Total ChangeWage StructureComposition

Change in Log Wages 2000/02-1983/85

48

Figure 6. Decomposition of Composition Effects

-.05

0.0

5.1

.15

Log

Wag

e C

hang

e

0 .2 .4 .6 .8 1Quantile

UnionEducationExperienceTechnologyOffshorability

Composition Effects

50

Figure 7. Decomposition of Wage Structure Effects

-.05

0.0

5.1

.15

Log

Wag

e C

hang

e

0 .2 .4 .6 .8 1Quantile


Wage Structure Effects

51

Figure 5. Decomposition of Total Change into Composition and Wage Structure Effects

-.0

50

.05

.1.1

5Lo

g W

age

Cha

nge

0 .2 .4 .6 .8 1Quantile


Total Effects

52

Conclusion Yes, occupational tasks help explain the U-shape feature of

changes in the wage distribution

But this is not just technology related. Offshoring works just as well, and so do

i) union wage effects, an ii) de-unionization (composition effect) iii) education wage effects, iv) demographics wage effects (non-married, non-white,

experience< 5)

Rare case where we seem to be able to explain all that needs to be explained!

THANK YOU!

Documents

OCCUPATIONAL TASKS AND CHANGES IN THE WAGE STRUCTURE Sergio Firpo, EESP-FGV, Nicole Fortin, UBC Thomas Lemieux, UBC SOLE/EALE 3 rd World Conference June