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 persons pay gaps there are both conceptual and methodological problems.

Conceptual / methodological issues Disability different from sex Issues 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 estimation focus 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 dont 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 doesnt 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 work Condition limits kind of work Co-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 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 condition 2.Physically disabled (activity limiting condition) 3.Non activity limiting long term mental health condition 4.Mentally disabled (activity limiting condition) 5.Reference group: no long term health condition Within 1-4, look at all and then successive subsets of those a.Where the condition doesnt limit the amount of work b.(a)+where the condition doesnt limit the kind of work c.(b)+no comorbidities d.(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 Mean Gap Composition effect (Combined) Residual effect (Combined) Composition effect (Oaxaca) 1a) All-0.050* b) 1a + does not affect amount of work-0.030* c) 1b + does not affect kind of work-0.012* d) 1c + no other conditions e) 1d + no days of sickness leave

Results: decomposition at the mean: 2. Physical disability Mean Gap Composition effect (Combined) Residual effect (Combined) Composition effect (Oaxaca) 2a) All-0.141* b) 2a+ does not affect amount of work-0.051* c) 2b + does not affect kind of work d) 2c + no other conditions e) 2d + no days of sickness leave

Results: decomposition at the mean: 3. Non activity limiting mental health condition Mean Gap Composition effect (Combined) Residual effect (Combined) Composition effect (Oaxaca) 3a) All-0.131* b) 3a+ does not affect amount of work-0.103* c) 3b + does not affect kind of work d) 3c + no other conditions e) 3d + no days of sickness leave

Results: decomposition at the mean: 4. Mental disability Mean Gap Composition effect (Combined) Residual effect (Combined) Composition effect (Oaxaca) 4a) All-0.297* b) 4a+ does not affect amount of work-0.184* c) 4b + does not affect kind of work-0.151* d) 4c + no other conditions-0.166* e) 4d + no days of sickness leave-0.164*

Decomposition across the pay distribution: physically disabled - all Gap Composition effect (Combined) Residual effect (Combined) Composition effect (Oaxaca) 10 th percentile * th percentile * th percentile * th percentile * th percentile *

Decomposition across the pay distribution: physically disabled no productivity limitations Gap Composition effect (Combined) Residual effect (Combined) Composition effect (Oaxaca) 10 th percentile th percentile th percentile th percentile th percentile

Decomposition across the pay distribution: mentally disabled - all Gap Composition effect (Combined) Residual effect (Combined) Composition effect (Oaxaca) 10 th percentile * th percentile * th percentile * th percentile * th percentile *

Decomposition across the pay distribution: mentally disabled – no productivity limitations Gap Composition effect (Combined) Residual effect (Combined) Composition effect (Oaxaca) 10 th percentile th percentile th percentile th percentile * th percentile *

Detailed decomposition: mentally disabled with no productivity limitations

Conclusions (1): the good news We 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).

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