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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?
www.skope.ox.ac.uk
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: [email protected]
www.skope.ox.ac.uk
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
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
% 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
www.skope.ox.ac.uk
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
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
% 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
www.skope.ox.ac.uk
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
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 e
mp
loym
ent s
hare
bel
ow
th
resh
old
wa
ge
Threshold gross weekly wage
1995-2002
2002-2008
www.skope.ox.ac.uk
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
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
% 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
www.skope.ox.ac.uk
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
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
1900
2000
% 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