within occupation wage dispersion and task inequality

Within occupation wage inequality and task dispersion

Within occupation wage inequality and taskdispersion

Lucas van der Velde

GRAPEGroup for Research in APplied Economics

June 2016Warsaw International Economic Meeting


Table of contents

1 Motivation

2 Task content of occupations

3 Data description

4 Results

5 Conclusions


Motivation

Evolution of within occupation wage inequality

Notes: Figure taken from Kim andSakamoto (2008).

“Most of the growth in wageinequality has been withinoccupations, and the SBTC hypothesisdoes not explain this phenomenon.”


Motivation

Is the US case exceptional?

Figure: Wage inequality in selected European countries0

.05

.1.1

5.2

.25

Val

ue o

f the

inde

x

NOR SWE BEL FIN NLD ITA ESP SVK FRA POL EST GBR

Theil wage inequality decomposition

Within occupation Between occupation

Notes: Income corresponds to gross hourly wages.Data come from Structure of Earning Survey of the EU, 2010.


Motivation

Introduction

Goal: study the relation between the task content ofoccupations and within occupation inequality.

Hypotheses

1 Routine intensive occupations are associated with lower wagedispersion.

2 Dispersion of tasks within occupations is positively related towithin occupation wage inequality.


Task content of occupations

Theory

Types of tasks

1 Non-routine cognitive, eg. Interpreting data.

2 Non-routine interpersonal, eg. selling, motivating personnel.

3 Non-routine manual, eg. driving, repairing, cleaning.

4 Routine manual, eg. adjusting screws, supervising machines.

5 Routine cognitive, eg. proofreading, bookkeeping.



Theory II

Task content and income inequalityAutor et al. (2003), Acemoglu and Autor (2011), Goos andManning (2007), Michaels et al. (2014), Cortes (2016).

Task content and within occupation income inequalityHandel et al. (2013), De la Rica and Gortazar (2016)



Hypotheses

H1. Routine intensive occupations are associated withlower wage dispersion.

Borrowing from Handel et al. (2013), assume that (log) hourly wages inan occupation can be written as:

wj = α x1,j + (1 − α) x2,j + εj ; xi,j ∼ N(µi , σ2i ) (1)

where α is the fraction of time spent on non-routine tasks, and xi,j is theproductivity of worker j in performing task i (non-routine; routine).H1: σ2

1 > σ22



Hypotheses

H2. Dispersion of tasks within occupations is positivelyrelated to within occupation wage inequality.

wj = αj ∗ x1,j + (1 − αj) ∗ x2,j + εj ; xi,j ∼ N(µi , σ2i ) (2)

where α is the fraction of time spent on non-routine tasks by worker j.


Data description

Operationalizing the variables

The task content of occupations:

Derived from O*NET following Autor et al. (2003), Acemogluand Autor (2011), Goos and Manning (2007)

809 occupations in 2008, 954 occupations in 2014.

For example

Task Variables

- Analyzig data / infomationNon-Routine Cognitive - Thinking Creatively

- Interpreting information for others


Data description

Measures of wage inequality

American Community Survey 2008 and 2014 (> 1 m.observations in each year).

Net hourly wages derived from annual earnings.

Different occupation classification: 443 different occupationsin 2008, 456 in 2014.


Data description

Within occupation inequality

Figure: Between and within components of the Theil index

0 .2 .4 .6 .8 1

AgriculturalManagement

Sales and relatedTotal

Personal careBuilding cleaning

Fishig and huntingFood preparation

ProfessionalTransportation

Production occupationsHealthcare

Protective serviceConstruction

AdministrativeInstallation and repair

Within occupation Between occupations

Notes Theil index calculated for hourly wages in broadly defined occupational groups.

Groups are defined as 3 digit occupation codes (census).


Data description

From tasks to wages

Figure: Task content of occupations and wages (2014)−

1−

.50

.51

1.5

Inde

x va

lue

2 2.5 3 3.5 4 4.5Log hourly wage

Non−routine CognitiveNon−routine PersonalNon−routine Manual

Routine ManualRoutine cognitive

Task content and wages − ACS 2014


Results

Hypotheses

1 Routine intensive occupations are associated withlower wage dispersion.

2 Dispersion of tasks within occupations is positively relatedto within occupation wage inequality.


Results

Building some intuition

Figure: Routine content of occupation and income inequality

0.5

11.

5lo

g(90

/50)

−2 −1 0 1 2 3Routine Task Index

Correlation:−.14, p−val<.05

0.5

11.

5lo

g(50

/10)

−2 −1 0 1 2 3Routine Task Index

Correlation:−.05, p−val>.1


Results

Testing hypothesis 1

We estimate the following regression:

log wage dispersion = α + Tasks β + ε

where:

- j represent different occupations;

- wage dispersion refers to 90/50 and 50/10 ratios for hourlywages and residual hourly wages;


Results


Task Index Hourly wages Residual hourly wages90/50 50/10 90/50 50/10

Non-Routine Cognitive 0.03** 0.03** 0.03** 0.02(0.01) (0.01) (0.01) -0.01

Non-Routine Personal -0.02* -0.03*** -0.03** -0.03***(0.01) (0.01) (0.01) (0.01)

Routine Cognitive -0.02** -0.02** -0.02** -0.02*(0.01) (0.01) (0.01) (0.01)

Routine Manual -0.04*** 0.00 -0.02 0(0.01) (0.02) (0.02) (0.01)

Non-Routine Physical 0.01 0.01 0.02 0.03**(0.02) (0.02) (0.02) (0.02)

Observations 455 455 455 455R-squared 0.73 0.64 0.61 0.62


Results

Hypotheses

1 Routine intensive occupations are associated with lowerwage dispersion.

2 Dispersion of tasks within occupations is positivelyrelated to within occupation wage inequality.


Results


We estimate the following regressions:

log wage dispersion = α + var(Tasksj)β + ε

where:

- j represent different occupations;

- wage dispersion refers to 90/50 and 50/10 ratios for hourly wagesand residual hourly wages;


Results


Hourly wage Residual hourly wage90/50 50/10 90/50 50/10

Non-Routine Cognitive 0.22*** 0.07 0.19*** 0.05(0.07) (0.07) (0.06) (0.06)

Non-Routine Personal -0.04 -0.03 -0.06 -0.02(0.05) (0.05) (0.04) (0.04)

Routine Cognitive -0.02 -0.00 -0.02 -0.00(0.06) (0.05) (0.05) (0.05)

Routine Manual 0.07 0.07 0.02 0.07(0.08) (0.08) (0.07) (0.07)

Non-Routine Physical -0.08 0.03 -0.05 -0.00(0.11) (0.10) (0.09) (0.09)

Observations 98 98 98 98R-squared 0.59 0.27 0.51 0.33


Conclusions

What can we say

We employ tools from the task content literature to analyzewage inequality within occupations.

Evidence supports hypothesis 1: occupations with moreroutine content present lower wage inequality.

Evidence in favor of hypothesis 2 is less conclusive. Onlydispersion in non-routine seems to matter.


Conclusions

What needs to be done

Formalize the intuitions.

Extend the analysis to countries in the EU.

Analyze dynamics of income inequality, eg. changes in thedispersion vs. changes in task content.


Conclusions

Goodbye frame

Thank you for your attention


Conclusions

Acemoglu, D. and Autor, D.: 2011, Skills, tasks and technologies: Implicationsfor employment and earnings, Handbook of labor economics 4, 1043–1171.

Autor, D., Levy, F. and Murnane, R. J.: 2003, The skill content of recenttechnological change: An empirical exploration., Quarterly Journal ofEconomics 118(4).

Cortes, G. M.: 2016, Where have the middle-wage workers gone? a study ofpolarization using panel data, Journal of Labor Economics 34(1), 63–105.

De la Rica, S. and Gortazar, L.: 2016, Differences in job de-routinization inOECD countries: Evidence from PIAAC, IZA Discussion Paper 9736.

Goos, M. and Manning, A.: 2007, Lousy and lovely jobs: The rising polarizationof work in britain, The review of economics and statistics 89(1), 118–133.

Handel, M. J. et al.: 2013, Putting tasks to the test: Human capital, job tasks,and wages, Journal of labor Economics 31(2 Part 2), S59–S96.

Kim, C. and Sakamoto, A.: 2008, The rise of intra-occupational wageinequality in the united states, 1983 to 2002, American Sociological Review73(1), 129–157.

Michaels, G., Natraj, A. and Van Reenen, J.: 2014, Has ict polarized skilldemand? evidence from eleven countries over twenty-five years, Review ofEconomics and Statistics 96(1), 60–77.

within occupation wage dispersion and task inequality

Economy & Finance