Within occupation wage dispersion and the task content of jobs

Within occupation wage dispersion and the task content of jobs
Within occupation wage dispersion and the task
content of jobs
Lucas van der Velde
GRAPE
Group for Research in APplied Economics
April 2017
Workshop on Labour Economics
van der Velde GRAPE Group for Research in APplied Economics

Introduction
Motivation
A First test on models’ assumption
(Autor et al. 2003, 2006, Acemoglu and Autor 2011, Jung and Mercenier 2014)
B Circular reasoning
C Wage dispersion within occupation is greater than between.
D Within occupation wage dispersion can increase in future

Introduction
Assumption of task content models
Task productivity and individual ability
(Jung and Mercenier 2014, Cortes 2016):
0 <
∂ lnφm(z)
∂z
<
∂ lnφr (z)
∂z
<
∂ lnφnr (z)
∂z
,
where
→ z indicates worker ability
→ φ(z)i worker’s productivity in task i
Variations over this subject oﬀered in Autor et al. (2003, 2006), Beaudry et al. (2016)

Introduction
Importance of within occupation wage dispersion
Figure: Sources of wage inequality in selected European countries
0 .1 .2 .3
Value of the index
BGR
LVA
GBR
LTU
EST
POL
CYP
FRA
SVK
CZE
LUX
0 .1 .2 .3
Value of the index
ROM
PRT
DEU
ESP
ITA
HUN
NLD
GRC
BEL
FIN
NOR
SWE
Within occupations Between occupations
Source: Structure of Earning Survey of the EU, 2010.

Introduction
Hypothesis
Hypothesis
Within occupation wage dispersion is positively correlated with the
non-routine task content of the occupation, ceteris paribus

Data and method
Data: O*NET
Task content of occupations
Occupational Network (O*NET) data from 2010
Data collected from US workers, available at occupation level
Information on importance and frequency of tasks

Data and method
Data: O*NET
Five tasks (as in e.g. Autor et al. 2003, Acemoglu and Autor 2011)
Routine: cognitive and manual
Non-routine: cognitive, interpersonal and manual
Routine task intensity index RTI = routine − non-routine

Data and method
Data: O*NET
Five tasks (as in e.g. Autor et al. 2003, Acemoglu and Autor 2011)
Routine: cognitive and manual
Non-routine: cognitive, interpersonal and manual
Routine task intensity index RTI = routine − non-routine
Applied to EU countries before (e.g. Goos and Manning 2007, Goos et al.
2014, Lewandowski et al. 2016)
Independent measure for each country.

Data and method
Data: EU-SES
Structure of Earnings Survey
⇒ Employee-employer matched data: years 2002, 2006 and 2010

Data and method
Data: EU-SES
⇒ Why...?

Data and method
Data: EU-SES
⇒ Why...?
1. Large sample sizes
2. Reliable information on hourly wages and occupations.
3. Comparable information for EU countries.
Descriptive statistics

Data and method
Empirical approach
Specification
wage dispersionj,t = α0 + βRTIj,t + γD +
where
Wage dispersion in occupation j in t→ e.g. 90th to 10th percentiles
β is the coefficient of interest → H1: β is negative and significant
D represents a set of controls

Results
Wage dispersion and RTI
Measure of dispersion 90/10 90/50 50/10

Results
Unconditional
Wages

Results
Unconditional
Wages
Conditional
Wages

Results
Unconditional β -0.09***
Wages R2
0.48
Conditional β -0.07***
Wages R2
0.54

Results
Unconditional β -0.09*** -0.04*** -0.04***
Wages R2
0.48 0.42 0.39
Conditional β -0.07*** -0.04*** -0.03**
Wages R2
0.54 0.48 0.47

Results
Which tasks are driving the results?
wage dispersionj,t = α0 +
5
i=1
βi Ti,j,t + γD +

Results
5
i=1
βi Ti,j,t + γD +
90/10 90/50 50/10
Uncond. Cond. Uncond. Cond. Uncond. Cond.
NR Cog. 0 0.07*** -0.01 0.04*** 0.01 0.04***

Results
5
i=1
βi Ti,j,t + γD +
90/10 90/50 50/10
NR Cog. 0 0.07*** -0.01 0.04*** 0.01 0.04***
NR. Per. 0.07*** 0.04*** 0.04*** 0.02*** 0.03*** 0.02***

Results
5
i=1
βi Ti,j,t + γD +
90/10 90/50 50/10
NR Cog. 0 0.07*** -0.01 0.04*** 0.01 0.04***
NR. Per. 0.07*** 0.04*** 0.04*** 0.02*** 0.03*** 0.02***
NR. Man. -0.02 -0.01 -0.01 -0.01 -0.01 0.00

Results
5
i=1
βi Ti,j,t + γD +
90/10 90/50 50/10
NR Cog. 0 0.07*** -0.01 0.04*** 0.01 0.04***
NR. Per. 0.07*** 0.04*** 0.04*** 0.02*** 0.03*** 0.02***
NR. Man. -0.02 -0.01 -0.01 -0.01 -0.01 0.00
R. Cog. 0.05*** 0.01** 0.01*** 0 0.03*** 0.01***

Results
5
i=1
βi Ti,j,t + γD +
90/10 90/50 50/10
NR Cog. 0 0.07*** -0.01 0.04*** 0.01 0.04***
NR. Per. 0.07*** 0.04*** 0.04*** 0.02*** 0.03*** 0.02***
NR. Man. -0.02 -0.01 -0.01 -0.01 -0.01 0.00
R. Cog. 0.05*** 0.01** 0.01*** 0 0.03*** 0.01***
R. Man. -0.01 0 -0.01 0.01 0 -0.01
N 1862 1862 1862 1862 1862 1862
R2 0.65 0.59 0.55 0.51 0.55 0.52

Results
Alternative explanations I
Reallocation induced wage dispersion
Autor et al. (2003), Acemoglu and Autor (2011), Jung and Mercenier (2014)

Results
wage dispersionj,t = α0 + β1RTIj,t + β2Occ changet,t−5 + γD +

Results
wage dispersionj,t = α0 + β1RTIj,t + β2Occ changet,t−5 + γD +
Unconditional
Conditional
−.03 −.02 −.01 0 .01 .02
90/10 90/50 50/10
RTI
Unconditional
Conditional
−1 0 1 2 3
90/10 90/50 50/10
Emp. Change

Results
Alternative explanations II
Winner-takes-all markets and RTI
Brynjolfsson and McAfee (2016)

Results
Alternative explanations II
Winner-takes-all markets and RTI
Brynjolfsson and McAfee (2016)
wage dispersionj,t = α0 + β1RTIj,t + β2S1 S10 + γD +
Unconditional
Conditional
−.1 −.08 −.06 −.04 −.02
90/10 90/50 50/10
RTI
Unconditional
Conditional
.005 .01 .015 .02 .025
90/10 90/50 50/10
Share ratio

Results
Conclusions
1. New test of RBTC hypothesis predictions
Within occupation wage dispersion is greater in occupations with
more non-routine content
2. Confirmed and robust
Alternative channels do not affect conclusions
Other robustness checks: nonlinearities in RTI, more disaggregated
occupations, alternative definitions of occupational change, task
content over regions...

Results
Conclusions
1. New test of RBTC hypothesis predictions
Within occupation wage dispersion is greater in occupations with
more non-routine content
2. Confirmed and robust
Alternative channels do not affect conclusions
Other robustness checks: nonlinearities in RTI, more disaggregated
occupations, alternative definitions of occupational change, task
content over regions...
Implications for theory
→ Data confirm characterization of routine/ non-routine tasks

Results
Thank you for your attention

Appendix
Acemoglu, D. and Autor, D.: 2011, Skills, tasks and technologies: Implications for
employment and earnings, Handbook of Labor Economics 4, 1043–1171.
Autor, D., Katz, L. F. and Kearney, M. S.: 2006, The polarization of the US labor
market, American Economic Review 96(2), 189–194.
Autor, D., Levy, F. and Murnane, R. J.: 2003, The skill content of recent
technological change: An empirical exploration, Quarterly Journal of Economics
118(4), 1279–1333.
Beaudry, P., Green, D. A. and Sand, B. M.: 2016, The great reversal in the demand
for skill and cognitive tasks, Journal of Labor Economics 34(S1), S199–S247.
Cortes, G. M.: 2016, Where have the middle-wage workers gone? A study of
polarization using panel data, Journal of Labor Economics 34(1), 63–105.
Goos, M. and Manning, A.: 2007, Lousy and lovely jobs: The rising polarization of
work in Britain, Review of Economics and Statistics 89(1), 118–133.
Goos, M., Manning, A. and Salomons, A.: 2014, Explaining job polarization:
Routine-biased technological change and oﬀshoring, American Economic Review
104(8), 2509–2526.
Jung, J. and Mercenier, J.: 2014, Routinization-biased technical change and
globalization: Understanding labor market polarization, Economic Inquiry
52(4), 1446–1465.
Lewandowski, P., Hardy, W. and Keister, R.: 2016, Technology or upskilling? Trends
in the task tomposition of jobs in Central and Eastern Europe, IBS Working Papers
(01/2016).

Appendix
Descriptive statistics: EU-SES
Sample description
Year 2002 2006 2010
Avg. Nocc 11 747 (18 421) 18 876 (51 498) 12 491 (23 744)
Median Nocc 5 120 6 627 4 926
- Wage inequality
p90/p10 3.73 (1.13) 3.72 (1.14) 3.61 (0.92)
p90/p50 2.10 (0.41) 2.07 (0.35) 2.06 (0.33)
p50/p10 1.75 (0.3) 1.77 (0.3) 1.73 (0.26)
Notes: Standard deviations presented in parentheses. Sample includes all EU members except
Cyprus and Luxembourg.

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