One-digit industry wage structure in British Household Panel Survey
data (Waves One to Eleven; N = 63 000).
Average sample wage = £1163 per month (in real 1996 Pounds).
Other srv
BFinIns
TransCom
Dist Hot
Construction
Other mf
EngVehic
Exmetchm
NRG/Water
AgFF
0
500
1,000
1,500
2,000
Real Gross Monthly Wages by Industry
Standard Industrial Classification Divisions:
0
1
2
3
4
5
6
7
8
9
Agriculture, forestry & fishing (£902)
Energy & water supplies (£1758)
Extraction of minerals & ores other than fuels; manufacture of
metals, mineral products & chemicals (£1544)
Metal goods, engineering & vehicles industries (£1435)
Other manufacturing industries (£1124)
Construction (£1371)
Distribution, hotels & catering (repairs) (£717)
Transport & communication (£1347)
Banking, finance, insurance, business services & leasing (£1499)
Other services (£1144)
One-digit occupational wage structure in British Household Panel
Survey data (Waves One to Eleven; N = 63 000).
Average sample wage = £1163 per month (in real 1996 Pounds).
Other
Plt/mc op
Sales
Pers srv
Craft
Cler/Sec
Aprf/Tech
Prfnl
Mgr/Adm
0
500
1,000
1,500
2,000
Real Gross Monthly Wages by Occupation
Standard Occupational Classification Major Groups:
1
2
3
4
5
6
7
8
9
Managers & administrators (£1947)
Professional occupations (£1793)
Associate professional & technical occupations (£1457)
Clerical & secretarial occupations (£878)
Craft & related occupations (£1206)
Personal & protective service occupations (£728)
Sales occupations (£633)
Plant & machine operatives (£1131)
Other occupations (£647)
For comparison:
Non-union
(£1093)
Union
(£1377)
Female
Male
(£862)
(£1491)
LOOKING FOR LABOUR MARKET RENTS
WITH SUBJECTIVE DATA
Andrew E. Clark (PSE and IZA)
Observation:
There are industry and occupational wage differentials.
Question:
or:
Are these rents or compensating differentials?
Are high-wage jobs “better” than low-wage jobs?
Data:
Eleven waves of the British Household Panel Survey (BHPS).
Method: Two stages. Correlate the estimated occupational coefficients
from a wage equation with those from a utility (job satisfaction)
equation. A positive correlation implies that (inexplicably) high-wage
occupations are also (inexplicably) high satisfaction occupations, which
sounds like rents. The same approach for the industry coefficients.
Results:
OCCUPATION coefficients are
POSITIVELY AND SIGNIFICANTLY correlated:
especially for younger workers and for men.
However, there are NO SIGNIFICANT
CORRELATIONS at the INDUSTRY level.
This result holds for both level and panel first-stage
regressions.
Interpretation: Occupational wage differences are
partly rents; industry wage differences are not.
Supporting evidence:
Use spell data. How do individuals get to the high-rent
occupations?
*
From EMPLOYMENT (no surprise).
*
Via PROMOTION, rather than via voluntary mobility.
*
There is evidence of JOB-QUALITY LADDERS at
the firm level.
Conclusion:
There are occupational rents. They aren’t competed
away because firms control access to them, rather than
workers.
Why do firms allow rents to exist? Perhaps to incite
effort, as in tournament theory (evidence of job ladders)
Firms can only supply tournaments across occupations,
not across industries. The industry wage structure then
likely reflects other phenomena.
Wage and job satisfaction regressions.
The utility function of worker i in occupation o, Uio, is assumed to be
linear in wages, job disamenities, Do, and a raft of other individual and
job characteristics, Xi:
Uio = ’Xi + wio - Dio
(1)
The compensating differential offered by firms for Do will be just
enough to keep the worker on the same indifference curve: a unit of D
is compensated by extra income of / .
The wage of worker i in occupation o is argued, for simplicity, to
depend on the same X’s as does utility in (1), compensation for the
disamenities in that occupation, Do, and an occupation specific rent,
o:
wio = ’Xi + o + βDo
(2)
Note that worker homogeneity is assumed. From the utility function,
the compensating differential for D is β=/.
Substituting for wio and β in (1) yields
Uio = ’Xi + o
(3)
I estimate equations (2) and (3).
I have no information on o or Do: these are picked up by twodigit occupational and industry dummies. In the wage equation,
the estimated coefficients on these dummies will pick up both
rents and disamenities (o + βDo); in the utility (job satisfaction)
equation, the estimated coefficients will only reflect rents (o).
The empirical strategy is therefore to see if the systematic
differences in utility/job satisfaction across occupations are
correlated with their counterparts in a standard wage equation.
Correlate: the estimate of o + βDo with that of o.
Strong correlation => the rent component of wage differentials
is substantial.
Weak correlation => the rent element, o, is small.
Data
BHPS Waves 1 to 11.
Employees 16 to 65 only: 27 000 observations; 7000 different individuals.
[http://www.iser.essex.ac.uk/bhps]
The proxy utility measure is overall job satisfaction (which predicts quits,
absenteeism, and productivity). Measured on a one to seven scale:
BHPS: Overall Job Satisfaction
Not Satisfied at All
Completely Satisfied
Total
Value
Frequency
Percentage
1
2
3
4
5
6
7
521
772
1966
2177
5718
11595
4088
------
1.9%
2.9%
7.3%
8.1%
21.3%
43.2%
15.2%
--------
26837
100.0%
Table 1. Wage and Job Satisfaction Regressions.
Level Equations
Wages
Job
Satisfaction
0.048
-0.037
(.001)
(.004)
-0.571
0.540
(.017)
(.054)
Male
0.159
-0.158
(.006)
(.017)
Education: High
0.143
-0.219
(.007)
(.021)
Education: A/O/Nursing
0.044
-0.145
(.006)
(.019)
Union member
0.034
-0.091
(.006)
(.017)
Temporary contract
-0.059
-0.158
(.009)
(.028)
Ethnic group: African/Caribbean
-0.038
-0.255
(.022)
(.07)
Ethnic Group: Indian Subcontinent -0.064
0.036
(.019)
(.058)
Health: Excellent
0.038
0.362
(.006)
(.02)
Health: Good
0.013
0.138
(.006)
(.017)
Manager/Supervisor
0.129
0.031
(.005)
(.016)
Log hours
0.864
-0.246
(.006)
(.02)
Married
0.024
0.160
(.006)
(.019)
Separated
0.016
0.039
(.015)
(.048)
Divorced
0.002
0.140
(.009)
(.03)
Widowed
0.001
0.297
(.02)
(.064)
Job Tenure
0.038
-0.158
(.008)
(.025)
Job Tenure Squared
-0.001
0.003
(0)
(.001)
Firm Size: 1-24
-0.111
0.141
(.006)
(.019)
Firm Size: 25-199
-0.025
0.028
(.005)
(.017)
Renter
-0.077
0.099
(.006)
(.018)
Promotion Opportunities
0.041
0.278
(.005)
(.015)
Has second job
-0.047
-0.062
(.007)
(.022)
Organisation type dummies (7)
Yes
Yes
Work time: Mornings only
-0.143
0.119
(.011)
(.033)
Work time: Afternoons only
-0.128
0.183
(.018)
(.059)
Work time: Evenings only
-0.081
0.038
(.015)
(.047)
Work time: At night
0.070
-0.154
(.016)
(.049)
Work time: Both lunch/eves
-0.037
-0.103
(.026)
(.08)
Work time: Other times/day -0.131 -0.008 -0.029 -0.119
Panel Regressions
Wages
Job
Satisfaction
Age
---
---
Age-squared/1000
---
---
---
---
0.100
(.017)
-0.009
(.018)
0.025
(.007)
-0.086
(.009)
---
-0.389
(.197)
-0.117
(.2)
-0.156
(.082)
-0.191
(.093)
---
---
---
-0.004
(.006)
-0.004
(.005)
0.061
(.005)
0.785
(.007)
0.023
(.01)
0.010
(.018)
0.045
(.016)
-0.002
(.035)
0.016
(.008)
-0.001
(0)
-0.062
(.007)
-0.021
(.006)
-0.024
(.008)
0.030
(.004)
-0.043
(.007)
Yes
-0.074
(.011)
-0.099
(.018)
-0.073
(.015)
0.070
(.017)
-0.016
(.024)
0.397
(.071)
0.177
(.056)
0.151
(.059)
-0.456
(.084)
-0.255
(.116)
-0.206
(.201)
-0.535
(.185)
0.417
(.435)
-1.185
(.118)
0.021
(.005)
0.159
(.075)
0.044
(.064)
-0.063
(.091)
0.537
(.049)
-0.157
(.077)
Yes
0.046
(.135)
-0.101
(.213)
-0.409
(.17)
-0.471
(.192)
-0.706
(.267)
Work time: Rotating shifts
Work time: Varies/no pattern
Work time: Daytime and Evening
Work time: Other
Incentive Payments
Trade Union Recognised
Pension Member
Region Dummies (17)
Industry
Dummies (53)
Occupation
Dummies (75)
Wave Dummies (8)
Constant
(.04)
0.057
(.008)
-0.006
(.012)
0.009
(.01)
-0.068
(.029)
0.059
(.005)
0.037
(.006)
0.114
(.005)
Yes
(.127)
-0.060
(.025)
0.013
(.036)
-0.023
(.032)
-0.070
(.092)
0.033
(.016)
-0.043
(.019)
-0.015
(.017)
Yes
(.032)
0.040
(.009)
0.043
(.011)
0.012
(.009)
0.032
(.024)
0.036
(.005)
0.063
(.006)
0.053
(.007)
Yes
(.371)
-0.181
(.099)
-0.109
(.123)
-0.031
(.104)
0.144
(.279)
0.100
(.052)
0.011
(.073)
0.093
(.073)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Mu(1)
Yes
2.282
(.038)
---
Mu(2)
---
Mu(3)
---
Mu(4)
---
Number of observations
Adjusted R-Squared
Log Likelihood
Log Likelihood at Zero
27808
0.813
-----
Yes
---
-2.496
(.12)
-2.139
(.12)
1.477
(.12)
-0.141
(.119)
27808
---38259.56
-39975.99
Yes
3.806
(.039)
---
Yes
-----
---
---
---
---
---
---
27704
-------
16997
---6447.35
-6809.83
Table 2. Correlations between Estimated Coefficients in Wage
and Job Satisfaction Regressions
Occupation
Level
Estimated Coefficients
OLS
Robust
Spearman
OLS
Robust Spearman
1.82
2.07
0.21
0.32
-0.18
(R2=.043)
T-statistics
2.59
(p=.074) (R2=.002)
3.2
(R2=.084)
T-statistics
(Huber-White)
Panel
Estimated Coefficients
2.94
3.58
(R2=.106)
0.02
3.11
(R2=.129)
0.29
-1.16
0.32
-1.09
-1.02
0.2
-0.63
-1.00
0.39
0.26
(p=.001) (R2=.001)
-0.09
(p=.51)
2.01
(p=.109) (R2=.008)
2.99
-0.05
(p=.72)
(p=.005) (R2=.023)
0.81
-0.04
(p=.77)
(p=.01) (R2=.026)
(R2=.000)
T-statistics
Industry
0.12
(p=.41)
0.04
-0.03
(p=.83)
Note: Bold = significant at the five per cent level; Italic = significant at the ten per cent level.
Figure 1. The Relation between Estimated Coefficients in
Wage and Job Satisfaction Regressions (Results for Men)
Industry Coefficients
0.8
Job satisfaction
coefficients
0.6
0.4
0.2
0
-0.4
-0.2
-0.2
0
0.2
-0.4
-0.6
Wage coefficients
0.4
0.6
Figure 1. The Relation between Estimated Coefficients in
Wage and Job Satisfaction Regressions (Results for Men)
Industry T-statistics
4
Job satisfaction t-stats
3
2
1
0
-10
-5
-1
0
-2
-3
-4
Wage t-stats
5
10
Figure 1. The Relation between Estimated Coefficients in
Wage and Job Satisfaction Regressions (Results for Men)
Occupation Coefficients
0.9
0.8
Job satisfaction
coefficients
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.5
-0.1 0
0.5
Wage coefficients
1
Figure 1. The Relation between Estimated Coefficients in
Wage and Job Satisfaction Regressions (Results for Men)
Occupation T-Statistics
10
Job satisfaction t-stats
8
6
4
2
0
-5
0
5
10
-2
Wage t-stats
15
20
25
Table 3. Correlations between Estimated Coefficients in Wage
and Job Satisfaction Regressions: Demographic Groups
Occupation
OLS
Robust Spearman
Women
Estimated Coefficients
T-statistics
Men
Estimated Coefficients
T-statistics
Young
Estimated Coefficients
T-statistics
Old
Estimated Coefficients
T-statistics
Young
Men
0.29
(R =.002)
-0.13
(R2=0)
3.17
2
(R =.137)
4.71
2
(R =.261)
1.74
2
(R =.043)
2.77
2
(R =.103)
1.11
(R2=.02)
0.95
2
(R =.015)
3.70
2
(R =.191)
5.50
2
(R =.343)
0.33
2
Estimated Coefficients
T-statistics
0.15
2.96
4.65
3.87
3.79
1.16
1.41
4.02
5.20
-0.09
(p=0.51)
-0.04
(p=0.78)
0.37
(p=0.00)
0.49
(p=0.00)
0.28
(p=0.02)
0.36
(p=0.00)
0.14
(p=0.28)
0.12
(p=0.36)
0.46
(p=0.00)
0.60
(p=0.00)
OLS
0.15
(R =.001)
-0.96
(R2=.021)
0.39
2
(R =.003)
-0.39
(R2=.003)
-0.23
(R2=.001)
-1.43
(R2=.041)
0.13
(R2=0)
-1.03
(R2=.023)
-0.96
(R2=.021)
-1.37
(R2=.043)
Industry
Robust Spearman
0.22
2
-0.82
0.19
-0.29
-1.29
-1.37
-0.64
-1.02
-0.73
-1.27
-0.08
(p=0.61)
-0.09
(p=0.56)
-0.01
(p=0.97)
-0.07
(p=0.64)
-0.22
(p=0.12)
-0.21
(p=0.15)
-0.01
(p=0.97)
-0.07
(p=0.62)
-0.18
(p=0.25)
-0.23
(p=0.13)
Estimated Coefficients
HighEducated
2.17
(R2=.08)
T-statistics
3.05
2
(R =.147)
0.94
Not High- Estimated Coefficients
2
(R =.113)
Educated
T-statistics
1.23
2
(R =.178)
Estimated Coefficients
1.49
Union
2
(R =.036)
T-statistics
1.13
2
(R =.021)
Estimated Coefficients
1.51
Non2
(R =.034)
union
T-statistics
2.27
2
(R =.074)
2.15
3.17
1.04
2.42
1.34
1.78
1.66
2.31
0.3
(p=0.02)
0.41
(p=0.00)
0.4
(p=0.29)
0.67
(p=0.05)
0.13
(p=0.31)
0.14
(p=0.28)
0.17
(p=0.17)
0.2
(p=0.11)
-0.93
(R2=.019)
-1.92
(R2=.077)
0.68
2
(R =.009)
-0.14
(R2=0)
-0.39
(R2=.003)
-1.23
(R2=.034)
2.17
2
(R =.096)
-0.02
(R2=0)
Note: Bold = significant at the five per cent
level; Italic = significant at the ten per cent
level.
-1.28
-0.2
(p=0.18))
-1.83
-0.26
(p=0.08)
-0.88
0.02
(p=0.88)
0
0.06
(p=0.66)
-0.47
-0.07
(p=0.67)
-1.1
-0.13
(p=0.41)
1.75
0.22
(p=0.14)
0.81
0.08
(p=0.61)
INTERPRETATIONS
Omitted variables (ability, unemployment rate etc)
 The same results are found in both panel and level
regressions
 Controlling for the local unemployment rate
doesn’t change anything.
 Controlling for thirteen-level education doesn’t
either.
INTERPRETATIONS
Endogenous choice of occupation/heterogeneity
• Panel results are the same as level results.
• If there is sorting, we’d expect higher correlations for older workers
(who have already sorted): we find the opposite.
• Try and control for tastes for income and hard work:
• marital status, number and ages of children, spouse’s labour force status,
spouse’s income.
• Parents’ labour force status, parents’ occupation.
• A number of these attract significant estimates, but the correlation
between the occupation coefficients in wage and job satisfaction
regressions stays the same, as does that for industry coefficients.
I think that the occupational differences reflect rents.....
Here’s why:
Table 3. Getting to the Good Jobs: Occupations
Use BHPS Spell data to see how individuals get to not high and
high-quality jobs (as defined by negative or insignificant, and
positive significant occupation dummy estimates in Table 1's job
satisfaction regressions respectively).
WHERE DO THEY COME FROM? Job Quality by Previous
Labour Force Status:
Job Quality
Not High High N
Previous LF status
Employed/self-employed 65.2
34.8
9599
Unemployed
77.4
22.6
3564
Looking after family
70.6
29.4
1304
F-T education
78.0
22.0
1137
Something else
69.8
30.2
1037
Total
69.4
30.6
16641
2(4) = 227.9
WHY DID THEY LEAVE THEIR LAST JOB?
Job Quality
Not High High
N
 Occupational  Occupational job
wage coeff*100 satisfaction coeff*100
Reason last job ended
Promoted
Left for better job
Made redundant
Dismissed or sacked
Temporary job ended
Other reason
Total
2
 (5) = 164.6
55.4
67.6
74.4
84.3
70.6
67.1
65.3
44.6
32.4
25.6
15.7
29.4
32.9
34.7
2412
3238
644
108
795
2061
9258
3.26
2.08
-1.74
-0.91
0.52
-1.16
1.32
1.54
0.76
0.38
-1.23
-0.37
0.08
0.72
Occupation and status scores (Chan and Goldthorpe)
1 HP
Chartered accountants, clergy, medical practitioners, probation
officers, solicitors
2 SM
Company treasurers, financial managers, computer systems
managers, personnel managers
3 TPE
College lecturers, education officers and inspectors, school teachers
4 API
Computer analysts and programmers, graphic designers, investment
analysts, quantity surveyors
5 SET
civil and structural engineers, clinical biochemists, industrial
chemists, planning engineers, software engineers
6 GMA Bank and building society managers, general managers in industry,
national and local government officers
7 APH Community workers, nurses, occupational therapists, youth workers
8 AOA Accounts assistants, clerical officers in national and local
government, library assistants, record clerks
9 SEC
Personal assistants, receptionists, secretaries, word processor
operators
10 BSR buyers and purchasing officers, technical sales representatives,
wholesale representatives
11 PDM Clerks of works, farm managers, maintenance managers, transport
managers, works managers
12 RCW Commercial and clerical assistants, despatchers, ¯ling clerks stock
and storekeepers
13 MPS Catering managers, hoteliers, publicans, shopkeepers and managers
14 HCA Dental nurses, educational assistants, nursery nurses, nursing
auxiliaries
15 SW
Cash desk and check-out operators, sales and shop assistants,
window dressers
16 PSP
Fire service and police officers, security guards
17 PSW Caretakers and housekeepers, hairdressers and beauticians, travel
attendants, undertakers
18 RWS Car park attendants, cleaners, counter-hands, couriers and
messengers, hotel porters, postal workers
19 CW
Bar staff, chefs, cooks, waiters and waitresses
20 SMO Gardeners and groundsmen, printers, textile workers, woodworkers
21 TO
Bus and coach drivers, lorry and van drivers, taxi drivers
22 SMC Bricklayers, electricians, painters and decorators, plasterers, roofers,
telephone repairmen
23 SMM Fitters, setters, setter-operators, sheet metal workers, turners,
welders
24 PMO Assemblers, canners, fillers and packers, food processors, moulders
and extruders, routine inspectors and testers
25
GL
Agricultural workers, labourers, goods porters, refuse collectors
Table 4. Occupational Wage Rents and Social Status
Bivariate correlations with social status
Spearman rank
correlation coefficient
Occupational
part of wages
Non-occupational
part of wages
Residual
part of wages
t-statistic
0.679 (0.1%)
3.42
0.429 (5.3%)
1.65
0.276 (28%)
1.33
Multivariate regression of social status on wages
Occupational
part of wages
Non-occupational
part of wages
Residual
part of wages
Constant
4.591
(1.878)
0.099
(.934)
-3.200
(18.2)
-0.689
(6.392)
N
21