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Disability and pay: a decomposition of the pay gaps of disabled men in
the UK
Simonetta Longhi, Cheti Nicoletti and Lucinda Platt
ISER, University of Essex
Cambridge September 2009
Background
Disabled employees experience a major deficit in pay, compared to non-disabled: around 11% for men (a difference of c.£1.30 per hour) and 22% for women.(Compare though to c.16% for non-disabled women and 21-23% for Pakistani and Bangladeshi men)
Concern to measure the extent to which disabled people face employment discrimination – and whether that is changing (including in response to legislation)
DDA aimed to address discrimination against disabled people in employment more energetically than before
Employment discrimination can be at point of employment entry or within the labour market e.g. in pay.
But differences in pay among employed can stem from differences in qualifications, in types of occupation, and in productivity
Also vary substantially in average personal and employment characteristics compared to the overall labour force: older, less well qualified, higher rates of part-time work etc.; regional concentration, also some occupational segregation
Addressing pay gaps
Traditional approach to estimating discrimination in pay (e.g. for women, ethnic minorities): Decompose pay into the part explained by differences
in characteristics and the residual – unexplained part. Attribute residual fully to discrimination,; or be more cautious: residual includes discrimination
plus unmeasured characteristics of relevance; but still regarded prima facie as evidence of discrimination.
But in application to disabled person’s pay gaps there are both conceptual and methodological problems.
Conceptual / methodological issues
Disability different from sexIssues around ‘productivity’Issues about who is disabled –who is protected by legislation
Should we also be concerned about differences in ‘explained’ part?
Oaxaca popular but when groups compared different can end up with out of sample estimationfocus on mean but other parts of the distribution may be very relevant
Weighting decomposition approaches more robust and can explore different points of distribution but don’t give detailed decomposition
Definition of disability
For the definition adopted by the DDA, disability is defined as long term illness limiting daily activities.
Also possible to examine those with long-term illness which doesn’t limit activities (not covered by Act)
Previous research in the UK has used long-term illness alone to define disability and work limitations to define differences in productivity
Measuring limits on productivity
Condition limits amount of workCondition limits kind of workCo-morbidities (proxy for severity)Time off for sickness in any of the weeks
preceding an interview, versus no time off in any of the weeks preceding interview (utilises all interviews per individual not just one wave)
Added sequentially to evaluate impact on pay gap
Regression based decomposition:
• Oaxaca decomposition (see Blinder, 1973; Oaxaca, 1973) used to explain mean differences using linear regression models
• Advantage: it allows for a detailed decomposition of the pay gap
• Disadvantage: it can produce unreliable results if the linearity assumption is too restrictive and if the covariates for the two groups do not have common support so that the counterfactual mean estimation is based on out of the sample predictions (see Barsky et al 2002) and Nopo (2008).
Weighting based decomposition (DiNardo et al 1996)
• Using binary model to predict the probability of belonging to a particular group (propensity score) to compute weights .
• Counterfactual mean or quantiles are estimated by using the weights to equalize the distribution of the characteristics between groups with different personality traits
• Advantage: it does not impose a linearity assumption between log pay and covariates and does not require a common support for the explanatory variables but only for the propensity score
• Disadvantage: it does not allow for a detailed decomposition of the pay gap
Combined weighting and regression decomposition
Weighted estimation of linear regression (for the mean pay decomposition) and unconditional quantile regression (for the quantile differences decomposition) with weights based on the propensity score (predicted probability) of having high rather than low levels of a personality trait.
Advantage 1: This estimation is consistent if either the weights (i.e. the binary model) are correctly estimated or the regression models are correctly specified.
Advantage 2: The closeness of the generalized Oaxaca decomposition and combined decomposition results tells us the confidence with which we can use the detailed results for the contribution of different characteristics deriving from the generalized Oaxaca decomposition
Note that generalized Oaxaca can be applied to decompose quantile differences (Firpo et al 2007) using unconditional quantile regressions (see Firpo et al 2009). It is similar to the Oaxaca method except for the fact that the dependent variable is given by the recentered influence function
Contribution of this paper
• More precise definition of disability Also looks at ‘non-disabled’ with a long term health condition
• Better operationalisation of productivity – in stages; and• Differentiate
where those not limited in productivity are similar to non-disabled
Where characteristics ‘mop-up’ the pay gap Where residual gap which is not accounted for by
characteristic• Distinction between types of disability where discrimination
may be differentially associated with type physical long-term conditions and long-term mental health
conditions• Decompose pay gaps across the distribution of pay• Produce robust estimates of explained and unexplained
components using combined regression and weighting approach
• Consider explained as well as unexplained components
Data: UK Labour Force Survey 1997-2008
• Quarterly survey, semi panel (respondents followed for five waves), nationally representative unclustered probability sample of c. 50,000 households per quarter, with information on responding adults. Earnings information collected in waves 1&5
• We use 47 quarters, wave 1 responses to produce a sample of men aged between 23 and 64, living in the UK and in paid employment (excluding self-employed). We restrict our sample to those who are White British and UK born. Our total sample is 120,835 cases
• Compare pysically and mentally disabled and those with a physical/mental non-activity limiting long-term health condition, according to whether work-limited, severity of condition, and lack of sickness absences, with those with no long-term health condition.
• Log hourly wage (from pay and hours information) • Wage determinants: age & age squared, job tenure and
square education level, part-time job, private sector, firm size, region, occupation
• Logit (for weighting by propensity to belong to group) also includes dummies for marital status and children (<5 and 5-15)
Summary of groups analysed
1. Non activity limiting long term physical health condition2. Physically disabled (activity limiting condition)3. Non activity limiting long term mental health condition4. Mentally disabled (activity limiting condition)5. Reference group: no long term health condition
Within 1-4, look at all and then successive subsets of thosea. Where the condition doesn’t limit the amount of workb. (a)+where the condition doesn’t limit the kind of workc. (b)+no comorbiditiesd. (c)+no days off sick in any waves observed
Rates of Disability
In the population aged 16 and over, 64.7 percent of people do not have any long term health condition
15 percent have a along term health condition that does not limit activity;
the remaining 20.3 percent have a long term condition that also limits activity (disability).
Among those with non activity limiting long term health condition, 84.3 percent have a physical disability as their main health problem, while for 3.5 percent the main health problem is a mental condition.
Among those disabled, for 76 percent the main condition is a physical condition, while for 9.1 percent the main condition is a mental health problem.
Among those with a long term physical condition, the condition limits activity for 33.6% of cases, it limits the amount of work for 23.4% of cases and it limits the kind of work for 36.1% of cases
Among those with a long term mental health problem, the condition limits activity for 55.0% of cases, it limits amount of work for 38.5% of cases and it limits the kind of work for 53.0% of cases
Results: decomposition at the mean: 1. Non activity limiting physical
condition
MeanGap
Composition effect
(Combined)
Residual effect
(Combined)
Composition effect (Oaxaca)
1a) All -0.050* -0.023 -0.026 -0.020
1b) 1a + does not affect amount of work -0.030* -0.010 -0.020 -0.008
1c) 1b + does not affect kind of work -0.012* 0.001 -0.013 0.005
1d) 1c + no other conditions -0.008 0.005 -0.012 0.007
1e) 1d + no days of sickness leave 0.003 0.000 0.003 0.005
Results: decomposition at the mean:
2. Physical disability
MeanGap
Composition effect
(Combined)
Residual effect
(Combined)
Composition effect (Oaxaca)
2a) All -0.141* -0.061 -0.080 -0.058
2b) 2a+ does not affect amount of work -0.051* -0.009 -0.042 -0.008
2c) 2b + does not affect kind of work -0.018+ 0.013 -0.031 0.012
2d) 2c + no other conditions -0.003 0.021 -0.024 0.019
2e) 2d + no days of sickness leave 0.005 0.020 -0.014 0.024
Results: decomposition at the mean: 3. Non activity limiting mental health
condition
MeanGap
Composition effect
(Combined)
Residual effect
(Combined)
Composition effect (Oaxaca)
3a) All -0.131* -0.084 -0.047 -0.076
3b) 3a+ does not affect amount of work -0.103* -0.062 -0.041 -0.063
3c) 3b + does not affect kind of work -0.067 -0.032 -0.034 -0.033
3d) 3c + no other conditions -0.054 -0.021 -0.033 -0.017
3e) 3d + no days of sickness leave -0.001 0.001 -0.002 -0.001
Results: decomposition at the mean:
4. Mental disability
MeanGap
Composition effect
(Combined)
Residual effect
(Combined)
Composition effect (Oaxaca)
4a) All -0.297* -0.130 -0.168 -0.093
4b) 4a+ does not affect amount of work -0.184* -0.071 -0.113 -0.053
4c) 4b + does not affect kind of work -0.151* -0.044 -0.108 -0.051
4d) 4c + no other conditions -0.166* -0.062 -0.105 -0.052
4e) 4d + no days of sickness leave -0.164* -0.145 -0.019 -0.141
Decomposition across the pay distribution: physically disabled
- all
Gap
Composition effect
(Combined)
Residual effect
(Combined)
Composition effect
(Oaxaca)
10th percentile -0.117* -0.040 -0.077 -0.029
25th percentile -0.125* -0.066 -0.059 -0.065
50th percentile -0.135* -0.085 -0.050 -0.080
75th percentile -0.137* -0.062 -0.075 -0.065
90th percentile -0.140* -0.050 -0.089 -0.048
Decomposition across the pay distribution: physically disabled no
productivity limitations
Gap
Composition effect
(Combined)
Residual effect
(Combined)
Composition effect
(Oaxaca)
10th percentile 0.003 0.013 -0.010 0.018
25th percentile 0.000 0.019 -0.020 0.016
50th percentile 0.004 0.005 -0.001 0.012
75th percentile 0.011 0.020 -0.009 0.018
90th percentile -0.007 0.022 -0.030 0.033
Decomposition across the pay distribution: mentally disabled -
all
Gap
Composition effect
(Combined)
Residual effect
(Combined)
Composition effect
(Oaxaca)
10th percentile -0.223* -0.078 -0.145 -0.049
25th percentile -0.244* -0.104 -0.140 -0.097
50th percentile -0.294* -0.151 -0.143 -0.106
75th percentile -0.301* -0.152 -0.149 -0.085
90th percentile -0.242* -0.154 -0.088 -0.087
Decomposition across the pay distribution: mentally disabled – no
productivity limitations
Gap
Composition effect
(Combined)
Residual effect
(Combined)
Composition effect
(Oaxaca)
10th percentile -0.025 -0.026 0.001 -0.084
25th percentile -0.059 -0.054 -0.004 -0.034
50th percentile -0.130+ -0.103 -0.027 -0.123
75th percentile -0.283* -0.127 -0.156 -0.171
90th percentile -0.261* -0.101 -0.161 -0.193
Detailed decomposition: mentally disabled with no productivity
limitations Mean P10 P25 P50 P75 P90
Pay gap -0.164* -0.025 -0.059 -0.130+ -0.283* -0.261*
Explained -0.141 -0.084 -0.034 -0.123 -0.171 -0.193 Residual -0.023 0.059 -0.025 -0.007 -0.113 -0.068 Combined contribution of: Education -0.028 -0.067 0.011 -0.036 -0.015 -0.027 Occupation -0.118 0.028 -0.041 -0.118 -0.159 -0.199 Age & Job tenure -0.006 -0.002 -0.014 -0.008 -0.007 -0.005 Firm characteristics -0.008 -0.015 -0.021 -0.017 -0.012 0.003 Part-time -0.007 -0.011 -0.008 -0.003 -0.007 -0.006 Year 0.027 -0.018 0.036 0.064 0.046 0.040 Region 0.000 0.001 0.003 -0.006 -0.015 0.000
Conclusions (1): the good newsWe find little or no evidence of discrimination as
most of the gap can be explained in terms of reduced productivity of workers with a long term illness.
Those without apparent productivity differences are no different in pay – or in pay-relevant characteristics – from non-disabled
There is no evidence that those who have a long-term health condition but do not fall under the DDA are subject to discrimination
Conclusions (2): But
• For disabled people with a mental condition that affects daily activity an unexplained pay gap remains, but only at the top of the wage distribution.
• For those with a mental health disability where the difference at the mean is explained by characteristics, the characteristics themselves, particularly occupation –which plays the largest role - may also be shaped by discrimination Are those with mental health conditions who are relatively
well qualified selecting into lower paying occupations which ‘accommodate’ them?
• Approach assumes that ‘less productive’ workers are not also subject to discrimination on account of their condition / its severity / its impact on their performance, which may be a strong assumption to make (they may differ in their experience of workplace and employers from those with no work-related limitations).
