OCCUPATIONAL TASKS AND CHANGES IN THE WAGE STRUCTURE

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

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

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.  2 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)

Changes in Wage Inequality

Figure 4: Change in Real Wages by Percentile, Men

0,20 0,15 0,10 0,05 0,00 -0,05 -0,10 -0,15 3 -0,20 0 20 Source: Lemieux (2008) 40 1989 to 2004 1974 to 1989 60 80 100

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.

4

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) 5

Wage Setting Model

 Linear skill pricing equation for worker i in occupation j at time t:  S ik is a set of K cognitive, non-cognitive, manual skills, etc.

 The key difference relative to “skill-based” studies of inequality is that the

r jkt

vary across occupations because of the “unbundling” problem (Rosen, Heckman and Scheinkman).

 The returns to these skills

r jkt

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.

6

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 α j by occupations will show “between” occupation changes Sign and magnitude of β j “within” occupation will tell us if dispersion is increasing 7

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. w

jt 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 9

Descriptive Evidence:

First step: U-Shaped Changes in Wages by Decile

Figure 1: Change in wage by 2-digit occupation 1983-85 to 2000-02 change for each decile Health1 .5

1 AdminSup2 Health2 Lawyers Hand1 Serv_Pers Transp1 Profes SalesComm Serv_Protect EnginTech Engineers Serv_Healht Constr2 CompOp Constr1 Produc1 Mail AdminSup1 1.5

2 Base period wage Wage change 2.5

Quadratic fit 3 3.5

11

Descriptive Evidence:

First step: U-Shaped Changes in Wages by Decile

Figure 1: Fitted change in wage by 2-digit occupation 1983-85 to 2000-02 change for each decile Health1 .5

1 AdminSup2 Health2 Lawyers Hand1 Forest Teachers Profes Managers Hand2 Transp1 EnginTech CompOp Constr2 Produc2 Produc1 Mail AdminSup1 1.5

2 Base period wage 2.5

3 3.5

12

Occupational Tasks

Non-routine vs. Automated vs. Offshored

13

Occupational Tasks

Using the O*NET

 We combine various “Work A ctivities” and “Work C ontext” 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).

14

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  15

Occupational Tasks

Using the O*NET

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

4.A.4.a.8

4.A.4.b.5

Assisting and Caring for Others (JK, B) Performing for or Working Directly with the Public Coaching and Developing Others (B) (JK, B)  On-Site Job  4.A.1.b.2

     4.A.3.a.2

4.A.3.a.3

4.A.3.a.4

4.A.3.b.4

4.A.3.b.5

Inspecting Equipment, Structures, or Material (JK) Handling and Moving Objects Controlling Machines and Processes Operating Vehicles, Mechanized Devices, or Equipment Repairing and Maintaining Mechanical Equipment (*0.5) Repairing and Maintaining Electronic Equipment (*0.5) 16

Occupational Tasks

Using the O*NET

Non-Offshorability

 Decision-Making      4.A.2.b.1

4.A.2.b.2

4.A.2.b.4

4.C.1.c.2

4.C.3.a.2. Making Decisions and Solving Problems (JK) Thinking Creatively (JK) Developing Objectives and Strategies Responsibility for Outcomes and Results 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.

17

Occupational Tasks

Second-Step: Regressions of Tasks on

α

j

and

β

j

  Call these five measures of task content (in each occupation j) TC

jh

, for h = 1, .. 5. The second step regressions are  and 22

Occupational Tasks

Second-Step: Regressions of Tasks on

α

j

and

β

j

 Table 2- Estimated Effect of Task Requirements on Intercept and Slope of Wage Change Regressions by 2-digit Occupation (1) Tasks included together Intercept (2) (3) Slope (4) (5) Tasks included separately Intercept (6) (7) Slope (8) Technology Information content Automation 0.010

(0.012) -0.035

(0.013) 0.027

(0.011) -0.043

(0.011) 0.015

(0.017) -0.025

(0.017) 0.010

(0.018) -0.023

(0.018) 0.017

(0.008) -0.046

(0.009) 0.040

(0.012) -0.056

(0.010) 0.046

(0.010) -0.055

(0.015) 0.030

(0.015) -0.036

(0.015) Offshorability No Face-to-Face No On-Site Job No Decision making Base wage R-square 23 -0.040

(0.017) -0.001

(0.007) -0.025

(0.018) no 0.400

-0.013

(0.016) 0.001

(0.006) -0.008

(0.018) yes 0.550

0.044

(0.023) 0.024

(0.009) -0.032

(0.025) no 0.450

0.037

(0.026) 0.024

(0.009) -0.025

(0.029) yes 0.440

-0.052

(0.011) 0.017

(0.005) -0.025

(0.011) no -0.057

(0.012) 0.019

(0.005) -0.055

(0.015) yes -0.043

(0.019) 0.031

(0.007) -0.053

(0.015) no -0.024

(0.017) 0.024

(0.006) -0.020

(0.021) yes

Occupational Tasks

Within and Between Changes and Automation

Figure 2b: Fitted change in wage: Top-10 Auto 1983-85 to 2000-02 change for each decile Hand1 Opera1 Serv_Healht Process Produc2 Produc1 Mail .5

1 1.5

2 Base period wage 2.5

3 3.5

25

Occupational Tasks

Within and Between Changes and On-Site

Figure 2d. Fitted change in wage: Bottom-10 On-Site 1983-85 to 2000-02 change for each decile .5

1 Lawyers SalesFinan Managers MathSc Sales2 1.5

2 Base period wage 2.5

3 3.5

27

Main Findings

Second-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.)?

29

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, Δ μ O = E [Y|D = 1] - E [Y|D = 0] = E [X|D = 1](β 1 -β 0 ) + (E [X|D = 1] -E [X|D = 0]) β 0 = Δ

ν

S + Δ

ν

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, q τ , we have : RIF (y;)= q τ + IF(Y; q τ ) = q τ Using the coefficients γ

ν

+ [ τ –1(Y≤ q τ ) ]/f(q τ ) from a regression of RIF (y; ν) on X, Δ

ν

S = E [X|D = 1](γ

ν

1 - γ

ν

0 ) and Δ

ν

X = (E [X|D = 1] -E [X|D = 0]) γ

ν

0 30

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: Δ

ν

S = E [X|D = 1](γ

ν

1 - γ

ν

01 ) and Δ

ν

X = (E [X|D = 1] -E [X|D = 0]) γ

ν

0 + R

ν

where γ

ν

01 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.

31



RIF-Regressions Results

The

γ

ν

0 and

γ

ν

1

32 Table 1. Unconditional Quantile Regression Coefficients on Log Wages Years: Quantiles: Explanatory Variables Union covered 10 1983/85 50 90 10 2000/02 50 Non-white Non-Married 0.208

(0.003) -0.090

(0.006) -0.162

0.406

(0.004) -0.140

(0.004) -0.122

-0.055

(0.004) -0.055

(0.004) -0.015

0.112

(0.003) -0.040

(0.006) -0.072

0.278

(0.005) -0.131

(0.004) -0.111

90 -0.073

(0.006) -0.071

(0.006) -0.066

(0.004) (0.004) (0.004) (0.004) (0.005) (0.004) Education ( High School omitted) Primary -0.278

(0.008) Some HS Some College College -0.301

(0.007) 0.045

(0.005) 0.142

-0.392

(0.007) -0.146

(0.004) 0.129

(0.005) 0.316

-0.156

(0.004) -0.023

(0.004) 0.086

(0.004) 0.375

-0.390

0.012

-0.396

(0.01) 0.031

(0.004) 0.102

-0.378

(0.009) -0.188

(0.005) 0.118

(0.004) 0.386

-0.070

(0.005) 0.034

(0.004) 0.053

(0.004) 0.561

Post-grad (0.004) 0.088

(0.005) (0.006) 0.337

(0.006) (0.007) 0.559

(0.011) (0.004) 0.066

(0.004) (0.005) 0.403

(0.006) (0.011) 1.025

(0.018) O*NET Measures Information Content Automation No Face-to-Face Non On-Site Job No Decision-Making Number of obs.

0.048

(0.002) 0.021

(0.002) 0.109

(0.003) -0.007

(0.001) -0.123

(0.003) 0.072

(0.002) -0.025

(0.002) 0.132

(0.002) 0.021

(0.001) -0.127

(0.003) 274,625 0.015

(0.002) -0.047

(0.002) 0.121

(0.003) 0.037

(0.001) -0.125

(0.003) 0.038

(0.002) 0.009

(0.002) 0.083

(0.002) -0.016

(0.001) -0.109

(0.003) 0.061

(0.002) -0.053

(0.002) 0.110

(0.002) 0.014

(0.001) -0.135

(0.003) 252,397 0.030

(0.003) -0.035

(0.003) 0.107

(0.004) 0.051

(0.001) -0.118

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

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

 Union Non-White Elementary Drop-Out 0 2000-02 1983-85 .2

.4

Quantile .6

Some College .8

1 0 .2

.4

Quantile .6

College .8

1 0 .2

.4

Quantile .6

Post-Graduate .8

1 0 .2

.4

Quantile .6

Information .8

1 0 .2

.4

Quantile .6

Automation .8

1 0 .2

.4

Quantile .6

No Face2Face .8

1 0 .2

.4

Quantile .6

No On-Site .8

1 0 .2

.4

Quantile .6

No Decision-Making .8

1 34 0 .2

.4

Quantile .6

.8

1 0 .2

.4

Quantile .6

.8

1 0 .2

.4

Quantile .6

.8

1 0 .2

.4

Quantile .6

.8

1

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

 Union 35 0 2000-02 1983-85 .2

.4

Quantile .6

.8

1

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

 Non-Married 36 0 .2

.4

Quantile .6

.8

1

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

 Non-White 37 0 .2

.4

Quantile .6

.8

1

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

 Experience < 5 38 0 .2

.4

Quantile .6

.8

1

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

 Information 39 0 .2

.4

Quantile .6

.8

1

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

 Automation 40 0 .2

.4

Quantile .6

.8

1

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

 No Face2Face 41 0 .2

.4

Quantile .6

.8

1

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

 No On-Site 42 0 .2

.4

Quantile .6

.8

1

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

 No Decision-Making 43 0 .2

.4

Quantile .6

.8

1

Decomposition Results

Δ

ν

O =

Δ

ν

S

+ Δ

ν

X and

Δ

ν

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

γ

ν

0

Table 3. Decomposition Results 1983/85-2000/02 90-10 50-10 90-50 Variance Total Change 0.0622

-0.0830

0.1452

0.0443

Wage Structure Composition (0.0149) -0.0208

(0.0114) 0.0829

Composition Effects: Union (0.0055) 0.0215

(0.0147) -0.1287

(0.0113) 0.0457

(0.0051) -0.0159

(0.0034) 0.1079

(0.0043) 0.0373

(0.0031) 0.0375

(0.0013) 0.0170

(0.0015) 0.0273

(0.0008) 0.0080

Education Experience Technology Offshorability Other (0.0039) -0.0009

(0.0022) 0.0141

(0.0011) 0.0030

(0.0022) 0.0073

(0.0008) 0.0103

(0.0016) (0.0036) 0.0111

(0.0018) 0.0186

(0.0009) 0.0073

(0.0018) 0.0033

(0.0007) 0.0009

(0.0014) (0.0007) -0.0120

(0.0018) -0.0045

(0.0009) -0.0043

(0.0018) 0.0040

(0.0007) 0.0093

(0.0016) (0.0002) -0.0019

(0.0003) 0.0032

(0.0003) 0.0430

(0.0002) -0.0204

(0.0002) 0.0048

(0.0002) Gini 0.0085

(0.0004) 0.0041

(0.0004) 0.0043

(0.0003)  0.0046

(0.0001) -0.0041

(0.0001) -0.0022

(0.0001)  0.0182

(0.0001) -0.0054

(0.0001) 0.0025

(0.0001) 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 44

Decomposition Results

Δ

ν

S = E [X|D = 1](

γ

ν

1 -

γ

ν

01 )

Table 3. Decomposition Results 1983/85-2000/02 Union 90-10 0.0111

50-10 -0.0048

90-50 0.0160

Variance Gini Wage Structure Effects: Total -0.0208

-0.1287

0.1079

0.017

0.0041

(0.0114) (0.0113) (0.0043) (0.0015) (0.0004) 0.0040

0.0031

(0.0031) (0.0025) (0.0021) (0.0006) (0.0001) Education Experience 0.0890

(0.0029) (0.0024) (0.0025) (0.0019) (0.0005) -0.0337

(0.009) 0.0169

-0.0081

0.0721

-0.0257

0.0324

-0.0097

(0.0098) (0.0092) (0.0037) 0.0072

-0.0052

(0.001) Technology Offshorability Other Residual 0.0484

-0.0322

0.0806

0.4520

0.2067

(0.0178) (0.0154) (0.0176) (0.0027) (0.0008) 0.0500

-0.0218

0.0718

0.3334

0.0888

(0.0115) (0.0131) (0.0113) (0.0024) (0.0007) -0.0516

-0.0274

-0.0242

-0.0213

-0.0074

(0.0112) (0.0094) (0.0079) (0.0017) (0.0004) -0.1339

-0.0511

-0.0828

-0.7736

-0.2890

(0.0059) (0.0054) (0.0048) (0.0054) (0.0015) 45  For wage structure effects, both technology and offshorability have the right signs and sizeable comparable “magnitude”

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

Change in Log Wages 2000/02-1983/85 Total Change Wage Structure Composition 46 0 .2

.4

Quantile .6

.8

1

Figure 6. Decomposition of Composition Effects

Composition Effects Union Education Experience Technology Offshorability 48 0 .2

.4

Quantile .6

.8

1

Figure 7. Decomposition of Wage Structure Effects

Wage Structure Effects Union Education Experience Technology Offshorability 50 0 .2

.4

Quantile .6

.8

1

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

 Total Effects Union Education Experience Technology Offshorability 51 0 .2

.4

Quantile .6

.8

1

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!

52

THANK YOU!