www.skope.ox.ac.uk

Are UK labour markets polarising?

Craig Holmes

National Institute of Economic and Social Research, London,April 24th 2012

www.skope.ox.ac.uk

Introduction

• Part of SKOPE’s ESRC funded research programme (2008-13)• Main research question: what does the development of the

“hourglass” labour market mean for:– Earnings and job quality– Mobility and mobility barriers– Skills policy

• This presentation draws on this research• Main issues:

– Why is occupational polarisation not clearly observed in wage distributions?

– In what ways is the change in occupational structure actually important?

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Structure of talk

1. Polarisation in occupations and wage distributions2. Re-evaluation of theory3. Decomposition of changes to wage distributions4. Wage mobility

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Polarisation of occupations

• Routinisation hypothesis (Autor, Levy and Murnane, 2003):– Price of computer capital has fallen since late 1970s– Computer capital replaces labour engaged in routine tasks– Non-routine tasks may be complementary to computer capital (e.g.

management, skilled professionals)– Result: growth in non-routine occupations due to changes in demand

(complementarities) and supply (displaced routine workers)

• Polarisation hypothesis (Goos and Manning, 2007)– Routine occupations found in middle of income distribution– Non-routine occupations found at top and bottom of distribution

• Managers, skilled professionals at the top• Non-routine ‘service’ occupations at the bottom e.g. hairdressers, cleaners

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Polarisation and occupations

• Following Goos and Manning (2007), hourglass effect shown through changes in employment share of groups of occupations ranked by (initial) average wages – each of approx. 10% of labour supply.

• Data :– New Earnings Survey 1986 (ranking wage) – Labour Force Survey 1981-2008 (employment shares)– Hours rather than headcount

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Polarisation of occupations

• Growth in employment share, by ranked occupational group, 1981-2008

1 2 3 4 5 6 7 8 9 10

-10.00%

-5.00%

0.00%

5.00%

10.00%

15.00%

1986 1991 1995

1999 2004 2008

Occupational group

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Polarisation of occupations

• Similar evidence found:– US (Autor, Katz and Kearney, 2006; Autor, 2011) – Germany (Spitz-Oener, 2006; Oesch and Rodríguez Menés, 2011)– Spain and Switzerland (Oesch and Rodríguez Menés, 2011) and across

Europe (Goos, Manning and Salomons, 2009).

• What does this mean for wage distributions?

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Polarisation and wage distributions

• Wage distributions:– Rising upper and lower inequality (Machin and Van Reenen, 2007)– Increasing proportion below low-paid threshold (Lloyd, Mason and

Mayhew, 2008)

• Does this mean the middle of these distributions is disappearing?

• Density functions:– New Earnings Survey 1986-2002 – Labour Force Survey 1995-2008

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Polarisation and wage distributions

• New Earnings Survey:

-4 -2 0 2 4 6 80

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1986

1997

2002

Log gross hourly wage

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Polarisation and wage distributions

• Labour Force Survey – 1995-2008

-3 -2 -1 0 1 2 3 4 5 6 70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1995

2008

Log gross hourly wage

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Polarisation and wage distributions

• Has employment in the middle declined? • Using same datasets, look at changes in employment across

the distribution:– Log gross hourly wage distribution standardised (0.5th percentile up to

99.5th percentile)– Wage range divided into ten groups– Look for changes in employment at different wage levels on this scale– Polarisation would be reflected by growth in low-paying and high

paying jobs, and decline in middle-wage jobs

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Polarisation and wage distributions

• New Earnings Survey – (1) 1986-1997 and (2) 1997-2002

1 2 3 4 5 6 7 8 9 10

-6.00%

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

1986-1997

1997-2002

Wage percentile

Chan

ge in

em

ploy

men

t sha

re

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Polarisation and wage distributions

• Labour Force Survey – 1995-2008

1 2 3 4 5 6 7 8 9 10

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

1995-2008

Wage percentile

Chan

ge in

em

ploy

men

t sha

re

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Polarisation and wage distributions

• Family Expenditure Survey – 1987-2001

1 2 3 4 5 6 7 8 9 10

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

1987-2001

Wage percentile

Chan

ge in

em

ploy

men

t sha

re

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Discussion of results

• Why is there a difference between two approaches?• Goos and Manning (2007) demonstrated a compositional

effect– Leads to polarised wage distribution if wage structure of occupations

remains constant

• There may be wage effects:– Between-groups effects (Autor, Katz and Kearney, 2006)– Within-groups effects

• Observable differences (e.g. educational composition)• Unobservable differences (e.g. ability)

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A model of polarisation

Occupational group – area represents employment

share

wage

Composition + increased within -group inequality Change:Initial wage structure

= interquartile range

+ increased between -group inequality

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Discussion of results

• Wage distributions seem to exhibit an increase in spread in the upper half

• A small proportion have experienced very high wage growth.• By comparison, many good jobs earnings become relatively

closer to middle.• A growing lower end in the “lovely” jobs?• Polarisation of knowledge workers (Brown, Lauder and

Ashton, 2011)• Less increase in earnings variation at bottom end – minimum

wage effect?

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Decomposing wage distributions

• Given above patterns, would like to understand why wage distributions have changed as they have.

• Biggest issue with analysing changing distributions is separating out all effects:– Wage determination process:

• yt = gt(x)

– Composition effects come through changes to x – Wage effects come through changes to g– These may be different at different points in the wage distribution

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Decomposing wage distributionsChange Example 1

(routinisation)Example 2 (expansion of HE)

Example 3 (deunionisation)

Composition effect Growth in high wage and low wage non-routine occupations (Goos and Manning)

Growth in number of graduates

Decline of union membership

Between-groups effect

Increased productivity of non-routine occupations (Autor, Katz and Kearney)

Changing graduate premium

Decreasing union premium

Within-group effect

New employees in non-routine occupations have different unobserved characteristics (this paper)

Graduates/non-graduates have different unobserved characteristics

Union members/non-members have different unobserved characteristics

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Decomposing wage distributions• Number of approaches to measuring changing distributions,

usually involving some form of quantile regression:– Usually conditional on explanatory variables, like OLS regressions– We need to look at unconditional distributions– Conditional regressions do not aggregate to unconditional quantile

regressions, unlike OLS

• Firpo, Fortin and Lemieux (2007) – henceforth FFL:– Counterfactual distribution estimated by reweighting– Composition effects: initialcounterfactual– Wage effects: counterfactualfinal – Estimates individual contribution of covariates to both– Similar to Blinder-Oaxaxa decomposition of the mean

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Data• Family Expenditure Survey (1957-2001)

– Two surveys for sample: 1987 and 2001– Covers period of routinisation– Has wages and education attainment (unlike LFS and NES)

• Variables:– Hourly wage = gross weekly wage / (basic hours + usual overtime)– Age finished full-time education – convert this into dummies for

degree (21+), post-compulsory education (18-20) and high school education (16-17)

– Experience = age – age left FT education– Dummies for gender, union membership, type of work– No variables on racial background or industry.

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Data

• The 1987 survey also has a narrower occupational coding.– 351 groups– KOS (pre SOC90) classification

• The 2001 survey uses SOC2000 classification– 353 groups at 4-digit level– 81 groups at 3-digit level

• Manual conversion using 1987 descriptions into SOC2000 4-digit equivalent– Changed into 3 digit category – prevents losing 1987 occupations

which fit into two closely matched SOC2000 categories

• Used in this presentation

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Data

• Creates larger occupational groups:– Professional– Managerial– Intermediate– Admin– Manual routine– Manual non-routine– Service

High skill non-routine

Routine occupations

Low skill non-routine

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Results: reweighting

-0.5 0 0.5 1 1.5 2 2.5 3 3.50.00

0.20

0.40

0.60

0.80

1.00

1.20

InitialFinalCounterfactual

ln wage

Cumulative probability

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Results: reweighting• Change in log real gross hourly wage, 1987-2001

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Error Composition

Impact Total

Percentile

Chan

ge in

ln w

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Results: composition effects

10th Median 90th

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

Occupations Education Unions

Gender Employment status

Chan

ge in

ln w

• Estimated individual composition effects, 1987-2001

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Results: composition effects• Large impact of declining unionisation at bottom and middle.• Increased female participation has a negative effect (through

initial gender pay gap)• Increase in part-time work has small negative composition

effect• Expansion of higher education has impact even on low wage

jobs– Largest effect at top of distribution

• Occupational compositional effect negative at bottom and positive at top– Not as large as education or union at respective ends?

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Results: wage effects

• Marked fluctuations in occupational premia (relative to administrative occupations):

Professional Managerial Intermediate Routine Manual Routine Non Manual

Service

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

10th 25th Median 75th 90th

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Results: wage effects

• Stable graduate premium over majority of distribution:

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

-0.02

0.00

0.02

0.04

Graduate premium

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Results: wage effects

• Other effects– Small effects through changing union premia and wage penalties to

part-time work– Large positive wage effects through narrowing gender pay gap –

(between 5% and 7% increase in wages across percentiles except at top decile of distribution)

– General ‘shift’ very high at bottom end – possibly the result of minumum wage introduced in 2001.

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Within-group effects

• Earnings within growing occupations should become more varied.

• May reflect differences in educational attainment.– If educational attainment has increased too, may reflect varying wage

premia across the distribution

• May reflect unobservable differences:– General productive ability– Specific skills in certain occupations

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Within-group effects

• NCDS earnings data on managerial workers, based on occupation five years before:

0 - 5

0

50 -

100

100

- 150

150

- 200

200

- 250

250

- 300

300

- 350

350

- 400

400

- 450

450

- 500

500

- 550

550

- 600

600

- 650

650

- 700

700

- 750

750

- 800

800

- 850

850

- 900

900

- 950

950

- 100

0

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Managerial Routine

Gross weekly wage

Empl

oym

ent s

hare

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Within-group effects

• NCDS earnings data on intermediate workers, based on occupation five years before:

0 - 5

0

50 -

100

100

- 150

150

- 200

200

- 250

250

- 300

300

- 350

350

- 400

400

- 450

450

- 500

500

- 550

550

- 600

600

- 650

650

- 700

700

- 750

750

- 800

800

- 850

850

- 900

900

- 950

950

- 100

0

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Intermediate Routine

Gross weekly wage

Empl

oym

ent s

hare

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Within-group effects

• Is this the result of observable differences between the two?• Ordered logit model:

– Dependent variable, Y – earnings group– Y = 1,…,20– Include qualifications and demographics– Observed wages in 1991, 1999 and 2004. Observed occupations five

years before each date.Managerial Intermediate

Occupation of employment five years before

PROFESSIONAL 0.246 0.616 **MANAGERIAL Ref. 0.574 ***INTERMEDIATE -0.290 Ref.ROUTINE -0.498 *** -0.982 ***MANUAL -1.212 ** -0.586SERVICE -1.240 *** -1.339 ***UNEMP -1.941 *** -1.495 ***NONEMP -1.110 *** -1.098 ***

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Conclusion• Little previous work on evidence of polarisation in UK earnings

distributions.• We define middle relative to overall spectrum of wages• In some cases, evidence that the middle has expanded –

although with different occupational titles• Occupational polarisation wage polarisation if there are no

wage effects– Other determinants of earnings have changed as well as occupational

stucture– May be unobservable within-group effects

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Contact Details

Craig HolmesESRC Centre on Skills, Knowledge and Organisational

Performance (SKOPE), Department of Education,

Norham Gardens,Oxford

Email: craig.holmes@education.ox.ac.uk

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Appendix

• Methodology slides for FFL• Managerial wage distributions

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Decomposing wage distributions

• Data:– N observations, N0 from initial distribution, N1 from final distribution

– Ti = 1 if from final distribution, i = 1,...,N. Pr(Ti) = p

– Yi and Xi observed

– Yi = Yi0 (1 – Ti) + Yi1 Ti

where Yit = gt(Xi, ei), t = 0,1

• Data can be reweighted to find the (unobserved) counterfactual distribution.– Counterfactual is wage distribution that would have arisen given initial

wage determination process but final explanatory variables

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Decomposing wage distributions

• Reweighting:

– where p(X) = Pr (T=1 | x = X)

• Calculate p(X) using logistical regression• This counterfactual can be used to decompose wage and

composition effects of a distributional statistic:– Let this statistic be represented by functional v(F) – e.g. percentile– Δv(F) = ΔvW + ΔvC

)(1

)(1

)1&Pr()1|Pr( 0

0

Xp

Xp

p

TEF

p

TyYTyYF

yYC

C

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Data

• Descriptive statistics (mean values):Variable T=0 T=1

UNION 28.36% 15.30% PART-TIME 22.61% 23.65% GENDER 46.80% 50.34%

PROFESSIONAL 11.86% 13.35% MANAGER 6.49% 11.49% INTERMEDIATE 7.90% 11.88% MANUAL ROUTINE 33.17% 24.61% ADMIN 22.39% 10.61% MANUAL NON-ROUTINE 2.76% 2.59% SERVICE 15.44% 24.73%

DEGREE 9.99% 16.86% POST COMP 10.55% 16.77% HIGH SCHOOL 41.94% 44.04% NO ED 35.24% 18.55%

EXPERIENCE 20.68 21.91

N 6368 5908

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Results: reweighting Time (1987=0, 2001=1 Individual UNION -0.7602 *** -(15.25) PART-TIME -0.3328 *** -(6.23) GENDER 0.3050 *** (6.52) Occupation MANAGER -0.0833 -(0.92) PROFESSIONAL -0.8067 *** -(9.24) ADMIN -1.2336 *** -(15.18) SERVICE 0.2273 *** (2.89) MANUAL ROUTINE -0.2946 *** -(4.03) MANUAL NON-ROUTINE -0.0955 -(0.72) Education DEGREE 0.7308 *** (10.03) POST COMP 0.5068 *** (8.17) NO ED -1.3260 *** -(23.16) EXPERIENCE 0.0399 *** (21.24) CONSTANT -0.2935 *** -(3.08) N 12276

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Results: reweightingPercentile v(F0) v(F1) v(FC) ΔvW ΔvC

0.05 0.4148 0.5488 0.4055 0.1433 -0.0093 0.10 0.6403 0.7445 0.6316 0.1129 -0.0087 0.15 0.7340 0.8476 0.7236 0.1240 -0.0104 0.20 0.8141 0.9433 0.8079 0.1354 -0.0062 0.25 0.8921 1.0283 0.8952 0.1332 0.0030 0.30 0.9581 1.1048 0.9676 0.1372 0.0094 0.35 1.0321 1.1779 1.0471 0.1308 0.0150 0.40 1.0986 1.2564 1.1260 0.1305 0.0273 0.45 1.1634 1.3239 1.2065 0.1174 0.0431 0.50 1.2251 1.3957 1.2915 0.1042 0.0664 0.55 1.2873 1.4669 1.3795 0.0874 0.0922 0.60 1.3640 1.5415 1.4663 0.0752 0.1023 0.65 1.4398 1.6291 1.5557 0.0734 0.1159 0.70 1.5183 1.7137 1.6489 0.0648 0.1306 0.75 1.6046 1.8163 1.7491 0.0673 0.1445 0.80 1.7046 1.9232 1.8517 0.0716 0.1471 0.85 1.8239 2.0377 1.9889 0.0487 0.1650 0.90 1.9706 2.1813 2.1214 0.0599 0.1508 0.95 2.1675 2.4138 2.3376 0.0762 0.1700

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A quantile regression approach

• FFL’s second contribution is to find a linear approximation of each distributional functional, conditional on the explanatory variables– An influence function, IF, of v(F) is a measure of sensitivity to outliers,

where E(IF) = 0– A recentered influence function, RIF = v(F) + IF, so E(RIF) = v(F)– RIF’s can be conditional on X– Assume a linear projection of RIF onto X:

– where j = {0, C, 1}

vttj

vj

vj XRIF

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A quantile regression approach• FFL show that:

– ΔvC = E(X|T=1) γC - E(X|T=0) γ0

– ΔvW = E(X|T=1) (γ1 – γC)

• Moreover, if expectation of RIF is linear, γC = γ0.– Composition effects are sum of change in composition of each

explanatory variable, multiplied by wage return in initial distribution– Wage effect is sum of change in wage returns between counterfactual

and final distribution, multiplied by final composition of each explanatory variable.

• This is a more general case of the Blinder-Oaxaca decomposition, where v(F) is the mean.

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A quantile regression approach• Our approach looks at quantiles across distribution

– j = {0, C, 1}– τ = 0.05, 0.1, 0.15,...,0.95

• Estimate fi(qτ) using kernel density methods

qf

IFvyRIF

yFyqFv

j

qyq

j

|inf

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A quantile regression approach

• FFL’s second contribution is to break wage and composition effects into individual components e.g. occupation, education etc.

• Method found in final paper, omitted here for time.• Idea is to find a linear approximation of each statistic in each

distribution using explanatory variables:– Composition effects are sum of change in composition of each

explanatory variable, multiplied by wage return in initial distribution– Wage is sum of change in wage returns between counterfactual and

final distribution, multiplied by final composition of each explanatory variable.

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Results: individual contributions

• Decomposition by wage and composition

10th percentile Median 90th percentile

Composition Wage Composition Wage Composition Wage

UNION -0.0232 0.0061 -0.0244 -0.0071 0.0040† -0.0003 PART TIME -0.0024 0.0062 -0.0023 0.0039 0.0007 0.0053 GENDER -0.0012† 0.0163† -0.0094 0.0654 -0.0092 0.0107 DEGREE 0.0158 0.0056 0.0196 -0.0023 0.0301 0.0119 POST COMP 0.0078 0.0104 0.0091 -0.0032 0.0112 0.0005 NO ED -0.0022† 0.0001† 0.0160 -0.0005 0.0301 -0.0075 EXP 0.0110 -0.0361 0.0094 0.0258 0.0126 0.0774 PROF -0.0005† 0.0012† 0.0005† 0.0154 0.0045 0.0553 MANAGER -0.0038† 0.0066 -0.0038‡ 0.0255 0.0126 0.0511 MANUAL ROUTINE 0.0156 0.0134 0.0414 0.0587 0.0444 0.0383 ADMIN 0.0059† 0.0036 0.0372 0.0184 0.0455 0.0204 MANUAL NON ROUTINE 0.0002 0.0017 0.0007 0.0067 0.0009 0.0068 SERVICE -0.0432 0.0164 -0.0582 0.0838 -0.0442 0.0356 CONSTANT 0.0000 0.0741 0.0000 -0.1607 0.0000 -0.2248

TOTAL -0.0202 0.1256 0.0358 0.1299 0.1434 0.0808

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Wages at the top

• Gross weekly earnings data from UK Labour Force Survey• 1990s:

– Increased employment in higher wage jobs across all good, non-routine occupations

– Long tail: some of growth occurred a long way from the median

• 2000s:– Some increase in low wage employment – despite increasing

graduatisation– Some increase in very high wage employment A hollowing out of the

middle of the distribution– Differences by sector of employment (manufacturing, retail, financial

intermediation and real estate/business activity)

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Wages at the top

• Managerial occupations:

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%100

200

300

400

500

600

700

800

900

1000

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1300

1400

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1700

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% c

hang

e in

em

plo

yment s

hare

belo

w t

hre

shold

wag

e

Threshold gross weekly earning, £

1995-2002 2002-2008

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Wages at the top

• Professional occupations:

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

100

200

300

400

500

600

700

800

900

100

0

110

0

120

0

130

0

140

0

150

0

160

0

170

0

180

0

190

0

200

0

% c

han

ge in

em

plo

ymen

t sha

re b

elo

w t

hre

sho

ld w

ag

e

Threshold gross weekly earning, £

1995-2002 2002-2008

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Wages at the top

• Example: managerial occupations in real esate, renting and business activities

-30.0%

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%10

0

200

300

400

500

600

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800

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1000

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1200

1300

1400

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1600

1700

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% c

hang

e in

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ploy

men

t sha

re b

elow

thr

esho

ld w

age

Threshold gross weekly wage

1995-2002

2002-2008

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Wages at the top

• Example: managerial occupations in manufacturing

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%1

00

200

300

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100

0

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% c

han

ge

in e

mp

loym

ent s

hare

bel

ow

th

resh

old

wa

ge

Threshold gross weekly wage

1995-2002

2002-2008

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Wages at the top

• Example: managerial occupations in retail and wholesale

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

100

200

300

400

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600

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1000

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1300

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1600

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1800

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% c

hang

e in

em

ploy

men

t sha

re b

elow

thr

esho

ld w

age

Threshold gross weekly wage

1995-2002

2002-2008

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Wages at the top

• Example: managerial occupations in financial intermediation

-25.0%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%10

0

200

300

400

500

600

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800

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1000

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1200

1300

1400

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1900

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% c

hang

e in

em

ploy

men

t sha

re b

elow

thr

esho

ld w

age

Threshold gross weekly wage

1995-2002

2002-2008