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).
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Real Gross Monthly Wages by Industry
Standard Industrial Classification Divisions:
0 Agriculture, forestry & fishing (£902)1 Energy & water supplies (£1758)2 Extraction of minerals & ores other than fuels; manufacture of
metals, mineral products & chemicals (£1544)3 Metal goods, engineering & vehicles industries (£1435)4 Other manufacturing industries (£1124)5 Construction (£1371)6 Distribution, hotels & catering (repairs) (£717)7 Transport & communication (£1347)8 Banking, finance, insurance, business services & leasing (£1499)9 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).
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Real Gross Monthly Wages by Occupation
Standard Occupational Classification Major Groups:
1 Managers & administrators (£1947)2 Professional occupations (£1793)3 Associate professional & technical occupations (£1457)4 Clerical & secretarial occupations (£878)5 Craft & related occupations (£1206)6 Personal & protective service occupations (£728)7 Sales occupations (£633)8 Plant & machine operatives (£1131)9 Other occupations (£647)
For comparison:Non-union (£1093)Union (£1377)
Female (£862)Male (£1491)
LOOKING FOR LABOUR MARKET RENTS WITH SUBJECTIVE DATA
Andrew E. Clark (PSE and IZA)
Observation: There are industry and occupational wage differentials.
Question: Are these rents or compensating differentials? or: 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 two-digit 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
Value Frequency Percentage
Not Satisfied at All 1 521 1.9%2 772 2.9%3 1966 7.3%4 2177 8.1%5 5718 21.3%6 11595 43.2%
Completely Satisfied 7 4088 15.2%‑‑‑‑‑- ‑‑‑‑‑‑--
Total 26837 100.0%
Table 1. Wage and Job Satisfaction Regressions. Level Equations Panel Regressions
Wages Job Wages Job Satisfaction Satisfaction Age 0.048 -0.037 --- --- (.001) (.004) Age-squared/1000 -0.571 0.540 --- --- (.017) (.054) Male 0.159 -0.158 --- --- (.006) (.017) Education: High 0.143 -0.219 0.100 -0.389 (.007) (.021) (.017) (.197) Education: A/O/Nursing 0.044 -0.145 -0.009 -0.117 (.006) (.019) (.018) (.2) Union member 0.034 -0.091 0.025 -0.156 (.006) (.017) (.007) (.082) Temporary contract -0.059 -0.158 -0.086 -0.191 (.009) (.028) (.009) (.093) 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 -0.004 0.397 (.006) (.02) (.006) (.071) Health: Good 0.013 0.138 -0.004 0.177 (.006) (.017) (.005) (.056) Manager/Supervisor 0.129 0.031 0.061 0.151 (.005) (.016) (.005) (.059) Log hours 0.864 -0.246 0.785 -0.456 (.006) (.02) (.007) (.084) Married 0.024 0.160 0.023 -0.255 (.006) (.019) (.01) (.116) Separated 0.016 0.039 0.010 -0.206 (.015) (.048) (.018) (.201) Divorced 0.002 0.140 0.045 -0.535 (.009) (.03) (.016) (.185) Widowed 0.001 0.297 -0.002 0.417 (.02) (.064) (.035) (.435) Job Tenure 0.038 -0.158 0.016 -1.185 (.008) (.025) (.008) (.118) Job Tenure Squared -0.001 0.003 -0.001 0.021 (0) (.001) (0) (.005) Firm Size: 1-24 -0.111 0.141 -0.062 0.159 (.006) (.019) (.007) (.075) Firm Size: 25-199 -0.025 0.028 -0.021 0.044 (.005) (.017) (.006) (.064) Renter -0.077 0.099 -0.024 -0.063 (.006) (.018) (.008) (.091) Promotion Opportunities 0.041 0.278 0.030 0.537 (.005) (.015) (.004) (.049) Has second job -0.047 -0.062 -0.043 -0.157 (.007) (.022) (.007) (.077) Organisation type dummies (7) Yes Yes Yes Yes Work time: Mornings only -0.143 0.119 -0.074 0.046 (.011) (.033) (.011) (.135) Work time: Afternoons only -0.128 0.183 -0.099 -0.101 (.018) (.059) (.018) (.213) Work time: Evenings only -0.081 0.038 -0.073 -0.409 (.015) (.047) (.015) (.17) Work time: At night 0.070 -0.154 0.070 -0.471 (.016) (.049) (.017) (.192) Work time: Both lunch/eves -0.037 -0.103 -0.016 -0.706 (.026) (.08) (.024) (.267) Work time: Other times/day -0.131 -0.008 -0.029 -0.119
(.04) (.127) (.032) (.371) Work time: Rotating shifts 0.057 -0.060 0.040 -0.181 (.008) (.025) (.009) (.099) Work time: Varies/no pattern -0.006 0.013 0.043 -0.109 (.012) (.036) (.011) (.123) Work time: Daytime and Evening 0.009 -0.023 0.012 -0.031 (.01) (.032) (.009) (.104) Work time: Other -0.068 -0.070 0.032 0.144 (.029) (.092) (.024) (.279) Incentive Payments 0.059 0.033 0.036 0.100 (.005) (.016) (.005) (.052) Trade Union Recognised 0.037 -0.043 0.063 0.011 (.006) (.019) (.006) (.073) Pension Member 0.114 -0.015 0.053 0.093 (.005) (.017) (.007) (.073) Region Dummies (17) Yes Yes Yes Yes Industry Dummies (53) Yes Yes Yes Yes Occupation Dummies (75) Yes Yes Yes Yes Wave Dummies (8) Yes Yes Yes Yes Constant 2.282 --- 3.806 --- (.038) (.039) Mu(1) --- -2.496 --- --- (.12) Mu(2) --- -2.139 --- --- (.12) Mu(3) --- 1.477 --- --- (.12) Mu(4) --- -0.141 --- --- (.119) Number of observations 27808 27808 27704 16997 Adjusted R-Squared 0.813 --- --- --- Log Likelihood --- -38259.56 --- -6447.35 Log Likelihood at Zero --- -39975.99 --- -6809.83
Table 2. Correlations between Estimated Coefficients in Wage and Job Satisfaction Regressions
Occupation Industry OLS Robust Spearman OLS Robust Spearman
Level Estimated Coefficients 1.82 2.07 0.21 0.32 -0.18 -0.04
(R2=.043) (p=.074) (R2=.002) (p=.77)
T-statistics 2.59 3.2 0.29 -1.16 -1.02 -0.05
(R2=.084) (p=.01) (R2=.026) (p=.72)
T-statistics 2.94 3.58 0.32 -1.09 -1.00 -0.09
(Huber-White) (R2=.106) (p=.005) (R2=.023) (p=.51)
Panel Estimated Coefficients 0.02 0.81 0.2 -0.63 2.01 0.12
(R2=.000) (p=.109) (R2=.008) (p=.41)
T-statistics 3.11 2.99 0.39 0.26 0.04 -0.03
(R2=.129) (p=.001) (R2=.001) (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
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Figure 1. The Relation between Estimated Coefficients in Wage and Job Satisfaction Regressions (Results for Men)
Industry T-statistics
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Figure 1. The Relation between Estimated Coefficients in Wage and Job Satisfaction Regressions (Results for Men)
Occupation Coefficients
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Figure 1. The Relation between Estimated Coefficients in Wage and Job Satisfaction Regressions (Results for Men)
Occupation T-Statistics
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Table 3. Correlations between Estimated Coefficients in Wage and Job Satisfaction Regressions: Demographic Groups
Occupation Industry OLS Robust Spearman OLS Robust Spearman
Women Estimated Coefficients 0.29 0.33 -0.09 0.15 0.22 -0.08 (R2=.002) (p=0.51) (R2=.001) (p=0.61) T-statistics -0.13 0.15 -0.04 -0.96 -0.82 -0.09 (R2=0) (p=0.78) (R2=.021) (p=0.56)
Men Estimated Coefficients 3.17 2.96 0.37 0.39 0.19 -0.01 (R2=.137) (p=0.00) (R2=.003) (p=0.97)
T-statistics 4.71 4.65 0.49 -0.39 -0.29 -0.07 (R2=.261) (p=0.00) (R2=.003) (p=0.64)
Young Estimated Coefficients 1.74 3.87 0.28 -0.23 -1.29 -0.22 (R2=.043) (p=0.02) (R2=.001) (p=0.12) T-statistics 2.77 3.79 0.36 -1.43 -1.37 -0.21 (R2=.103) (p=0.00) (R2=.041) (p=0.15)
Old Estimated Coefficients 1.11 1.16 0.14 0.13 -0.64 -0.01 (R2=.02) (p=0.28) (R2=0) (p=0.97) T-statistics 0.95 1.41 0.12 -1.03 -1.02 -0.07 (R2=.015) (p=0.36) (R2=.023) (p=0.62)
Young Estimated Coefficients 3.70 4.02 0.46 -0.96 -0.73 -0.18 Men (R2=.191) (p=0.00) (R2=.021) (p=0.25)
T-statistics 5.50 5.20 0.60 -1.37 -1.27 -0.23 (R2=.343) (p=0.00) (R2=.043) (p=0.13)
High- Estimated Coefficients 2.17 2.15 0.3 -0.93 -1.28 -0.2 Educated (R2=.08) (p=0.02) (R2=.019) (p=0.18))
T-statistics 3.05 3.17 0.41 -1.92 -1.83 -0.26 (R2=.147) (p=0.00) (R2=.077) (p=0.08)
Not High- Estimated Coefficients 0.94 1.04 0.4 0.68 -0.88 0.02 Educated (R2=.113) (p=0.29) (R2=.009) (p=0.88)
T-statistics 1.23 2.42 0.67 -0.14 0 0.06 (R2=.178) (p=0.05) (R2=0) (p=0.66)
Union Estimated Coefficients 1.49 1.34 0.13 -0.39 -0.47 -0.07 (R2=.036) (p=0.31) (R2=.003) (p=0.67) T-statistics 1.13 1.78 0.14 -1.23 -1.1 -0.13 (R2=.021) (p=0.28) (R2=.034) (p=0.41)
Non- Estimated Coefficients 1.51 1.66 0.17 2.17 1.75 0.22 union (R2=.034) (p=0.17) (R2=.096) (p=0.14)
T-statistics 2.27 2.31 0.2 -0.02 0.81 0.08 (R2=.074) (p=0.11) (R2=0) (p=0.61)
Note: Bold = significant at the five per cent level; Italic = significant at the ten per cent level.
INTERPRETATIONSOmitted 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.
INTERPRETATIONSEndogenous 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 Occupational Occupational job Not High High N wage coeff*100 satisfaction coeff*100 Reason last job ended
Promoted 55.4 44.6 2412 3.26 1.54
Left for better job 67.6 32.4 3238 2.08 0.76
Made redundant 74.4 25.6 644 -1.74 0.38
Dismissed or sacked 84.3 15.7 108 -0.91 -1.23
Temporary job ended 70.6 29.4 795 0.52 -0.37
Other reason 67.1 32.9 2061 -1.16 0.08
Total 65.3 34.7 9258 1.32 0.72
2(5) = 164.6
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 t-statistic correlation coefficient
Occupational 0.679 (0.1%) 3.42 part of wages Non-occupational 0.429 (5.3%) 1.65 part of wages Residual 0.276 (28%) 1.33 part of wages
Multivariate regression of social status on wages
Occupational 4.591 part of wages (1.878) Non-occupational 0.099 part of wages (.934) Residual -3.200 part of wages (18.2) Constant -0.689 (6.392) N 21
