DISABILITY, GENDER AND THE LABOUR MARKET IN WALES
Melanie K. Jones, Paul L. Latreille and Peter J. Sloane
WELMERC, Department of Economics, University of Wales Swansea
March 2004
ABSTRACT
Wales exhibits high rates of disability and inactivity, and a higher incidence of mental health
problems than other parts of Britain. Using data from the Welsh Local Labour Force Survey
2001, our results indicate that the low participation rate of the disabled in Wales is partly
attributable to their having fewer qualifications; marginal effects suggest education could be a
potent remedy for improving their labour market status. In terms of the pay differential
between disabled and non-disabled individuals, it would appear that disabled women in
Wales suffer disproportionately to disabled men.
JEL Classification: I1, J2, J3
Keywords: Disability, gender, employment, wage discrimination, Wales.
Acknowledgements
Material from the Quarterly Labour Force Surveys is Crown Copyright, has been made
available from the Office for National Statistics (ONS) through the UK Data Archive and has
been used by permission. Material from the Welsh Local Labour Force Survey is also Crown
Copyright and has been made available by the Welsh Assembly Government through the
ONS. The ONS, the Data Archive and the Welsh Assembly Government bear no
responsibility for the analysis or interpretation of the data reported here.

Corresponding author: Professor Peter J. Sloane, Department of Economics, University of Wales Swansea,
Singleton park, Swansea, SA2 8PP, U.K.. Tel: +44 (0)1792 295168. Fax: +44 (0)1792 295872. E-mail:
p.j.sloane@swansea.ac.uk
1.
Introduction
Increasing political attention has been focused on the disabled with the passing of the
Disability Discrimination Act (DDA) in 1995 and the subsequent formation of a Disability
Rights Commission, alongside other policy measures such as the the Disabled Person’s Tax
Credit and the New Deal for Disabled People. These measures reflect an awareness by
policy-makers of the problems faced by disabled people, who constitute a substantial,
important and increasing section of society. Using a broad definition of disability that
includes individuals with a long-term (12 months or more) health problem covered by the
DDA and/or that limits the kind or amount of work that an individual can do, and based on
the autumn quarter of the 2001 Labour Force Survey (LFS), Smith and Twomey (2002)
report that nearly one in five people of working age in Britain have a current disability. This
figure conceals substantial regional differences, with disability rates being highest in the
North West1 and in Wales (24.2% and 23.0% respectively) and lowest in the South East
(16.3%) (see Sly, 1996 for an earlier discussion of inter-regional variation).
In the present paper we adopt the narrower ‘work-limiting’ definition of disability (see
Section 2 for details), which is generally regarded as more appropriate in research
considering labour market issues. The effect of adopting this alternative definition is to
reduce the ‘headline’ disability rate by around 3½ percentage points to just under 16% of the
working age population2. As shown in Table 1, which also details the figures separately for
males and females, substantial regional variation is again evident using the work-limiting
disability measure. Thus, the incidence figure for the South East is just 13.19%, compared to
19.85% in Wales; a figure exceeded only by the North at 21.11%. The composition of
disability/health problems also exhibits some variance by region as shown in Table 2 (again
see Section 2 for data details), the most common disability/health problem in all regions
1
being that affecting limbs, followed by skin, breathing and organ problems. Especially
noteworthy is that mental health problems are more prominent in Wales and Scotland than
elsewhere, while the opposite is true in the South West.
As Smith and Twomey surmise:
“the reasons for regional variations in disabilities… are likely to be associated
with regional variation in: the distribution of industries; the availability of, and
access to healthcare and adequate housing; lifestyle and dietary behaviour;
levels of education; and the age distribution of the population.” (p. 418)
Whatever the cause, the consequences are profound.
As can be seen in Table 3, the
employment rate for the disabled across Great Britain as a whole is marginally below 40%,
and just half the non-disabled rate.
However, there is also substantial variation in the
percentage of the disabled who are in employment, from 26.65% in Wales to 49.78% in the
South-West.
These differences are much more marked than differences in ILO
unemployment rates. Part of the lower employment rates noted above can be attributed to
differences in activity rates: while fewer than half the disabled are inactive in three regions,
the figure exceeds 60% in four others, including Wales, where there are particularly high
levels of economic inactivity more generally3. Previous work has indicated that high levels
of disability are a contributory cause to this last phenomenon (Blackaby et al., 2003).
Furthermore, even when the disabled find employment, they are disproportionately
concentrated in less skilled work. This is reflected in substantial differences in relative pay
between the disabled and non-disabled. As can be seen in Table 4, disabled hourly pay as a
proportion of the non-disabled ranges from 82.4% in London and the South-East to 96.8% in
East Anglia for the workforce as a whole. When differentiated by gender, the data confirm
that earnings are typically higher for the non-disabled than for the disabled for both men and
women, and as might be expected, show also that male average pay typically exceeds that of
2
females in each region. They also indicate that, generally speaking, where earnings for the
non-disabled are higher relative to the Great Britain average, so too are those for the disabled,
reflecting the relative tightness of regional labour markets. It is unsurpising therefore, that
earnings in Wales are among the lowest in Great Britain for each of the sub-groups in Table
4, with the exception of disabled men. However, the figures for the disabled in Wales need to
be treated with circumspection (along with the corresponding ratios with the non-disabled),
being based on very small sample sizes (n=115 and 108 for males and females respectively) 4.
More reliable estimates can however be obtained using the Welsh ‘boost’ to the LFS, as
detailed below. To anticipate the results there, these latter data suggest that the hourly
earnings for disabled men (women) reported in Table 5 appear somewhat high (low), and that
the more conventional pattern of relative earnings noted above applies for this group also.
In this paper we focus on gender differences in the effects of disability on the labour market
focusing specifically on Wales, making use of the Welsh ‘boost’ to the LFS (see Section 2).
Since the relative position of women within the labour market in general is inferior to that of
men in terms of both occupational attainment and levels of earnings, it is possible that
disabled women are at a disadvantage not only relative to the non-disabled but also relative to
disabled men. Disaggregating by gender also enables us to distinguish different types of
disability and identify both within- and across-group differences.
As the preceding
discussion indicates, the Welsh labour market is characterised by, inter alia, high rates of
disability and inactivity (among both the disabled and non-disabled). These are issues of
considerable concern to policy-makers, both within Wales and more widely. To the extent
that issues relating to disability and the labour market in Wales are those of other parts of
Great Britain ‘writ large’, it is hoped the present paper may provide insights with wide
relevance.
3
The remainder of the paper is structured as follows. In Section 2 we discuss the data to be
employed in the estimation work, while section 3 discusses the estimation methodology itself.
Results are presented in Section 4, first in terms of the impact of disability on labour force
participation (employment), followed by earnings. The impact of different types of health
problem is the subject of sub-section 4.3, while decomposition results by both gender and
disability are discussed in the following sub-section. Finally, conclusions appear in Section
5.
2.
The Data
From March 2001 there has been a Welsh ‘boost’ to the Labour Force Survey, resulting in the
Welsh Local Labour Force Survey (WLLFS) dataset (see Hastings, 2003). The main LFS is
undertaken quarterly, with a 5-quarter rotation of the sample of private households; in any
quarter a fifth of respondents will be having their first interview (‘Wave 1’), while a fifth will
be experiencing their last prior to leaving the sample (‘Wave 5’). In contrast, the ‘boost’ is
undertaken annually, with households remaining in the sample for four years. The WLLFS
dataset contains households from Waves 1 and 5 of the main LFS sample for each quarter,
plus the ‘booster’ sample. For the former, the overlap from year to year is 50%, while for the
latter it is 75%5. The effect of boosting the sample in this manner is that while the main LFS
sample covers 4,600 households in Wales per year, the WLLFS contains 21,000 in total,
enabling disaggregation down to local, Unitary Authority level.
To elaborate on the definition of disability discussed previously, respondents in the LFS (and
the WLLFS) are asked first if they have any health problems or disabilities which would be
expected to last more than a year, and second, whether these would affect either the kind or
4
amount of paid work they can do. If positive answers are given to both of these questions we
classify individuals as disabled (i.e. the disability is ‘work-limiting’). A further question asks
about the type of health problem/disability, split into 17 categories. Where there are multiple
disabilities respondents are asked to state which of them is the main health problem/ability.
Because of problems of small cell sizes, we group these 17 types of disability into five main
categories (as in Table 2) in order to establish if there are significant differences among them
in terms of their impact on labour market outcomes.
The basic statistics on employment, unemployment and inactivity from the Welsh ‘boost’ are
contained in Table 5, which also shows the figures separately for men and women. As noted
previously, the substantial increase in sample size results in data that are more reliable than
those for Wales contained in the main Labour Force Survey and reported in Tables 1 – 4, and
suggest a slightly better outcome in terms of economic status for the disabled. Despite this
improvement however, it remains the case that activity (and accordingly employment) rates
for the disabled are very low, at just 36% (31%) and 29% (27%) for men and women
respectively. With regard to earnings, those for disabled men appear somewhat high in the
main LFS, while the reverse is true for women. For the non-disabled, the figures from the
two sources are much closer, reflecting the larger sample size and hence greater reliability of
the data.
Finally, it should be noted that in the remainder of the paper participation is defined as the
receipt by an employee of a positive wage. While this understates the degree of participation
by treating the unemployed as non-participants, as well as excluding those with missing wage
data, the self-employed6 and persons on government training schemes, such an approach is
5
standard in the literature, and necessitated by the nature of the data and the methodology
deployed, to which we now turn.
3.
Methodology
We adopt the standard labour force utility maximisation model in which individuals are
assumed to maximise their utility subject to budget and time constraints. Health enters the
model through the budget constraint, implying a lower wage offer on account of lower
productivity of disabled workers and the time constraint as illness leads to more absences and
less time for work.
An individual deciding whether or not to enter the labour market will compare the wage
offers of potential employers with his or her reservation wage. Low participation rates may
result from a combination of high reservation wages associated with certain types of
disability resulting from the extra time and energy required to participate in the labour market
and/or the presence of disability income transfers.
Let us assume there are two types of individual, the disabled (D) and the non-disabled (N).
For each of these types the wage offer equation is given by
WijO   j X ij  vij
( j  D, N )
[1]
6
where WijO represents the logarithm of the offer wage, X ij is a vector of the standard
productivity related characteristics in the human capital model for individual i of type j,  j is
the associated rate of return, and  ij the error term.
In turn, the reservation wage is given by:
WijR   j Z ij   ij
( j  D, N )
[2]
where WijR represents the reservation wage, the vector Z incorporates the conventional human
capital variables, with the addition of variables influencing the value of time (such as the
number of dependent children) and  ij is the error term.
The reservation wage is a latent variable, since it cannot be directly observed given the
absence of a relevant question in the LFS. Rather it is represented by an indicator variable I,
where I equals one if WijO  WijR and zero otherwise.
Thus, the probability that an individual works is:



Pr WijO  Wijr  0  Pr  j X ij   j Z ij   ij  vij

[3]
Assuming that the error terms v ij and  ij are normally distributed, the employment equation
may be estimated by a probit specification.
7
In estimating the wage equation given by [1] it is necessary to correct for sample selectivity,
since the employed are unlikely to be a random subset of the total population in terms of their
productive characteristics. Accordingly, we utilise a Heckman two-stage procedure in which
the probit estimates are utilised to derive the inverse Mills ratio, which is used as an
additional independent variable in the wage equation.
Next, we decompose the overall difference between the earnings of the non-disabled and the
disabled into explained and unexplained components, utilising a technique developed by
Reimers (1983) and applied to disability using US data by Lambrinos (1981) and Baldwin
and Johnson (1994), amongst others7. The difference in wage differs between non-disabled
(N) and disabled (D) employees can be decomposed as:
WN  WD  ( c N  N  c D  D ) 



( X N  X D ) ˆ N  ( 1   )ˆ D  X N ( 1   )  X D  ( ˆ N  ˆ D )
[4]
The left-hand side of the above equation can be interpreted as the difference in mean wage
offers of employers made to non-disabled and disabled employees respectively. The first
term on the right hand side of the equation represents that part of the difference in wage
offers which is attributable to differences in productivity (i.e. which is non-discriminatory),
while the second term represents that part of the wage difference which is unexplained (i.e.
which represents the difference in coefficients between the two groups). This will however,
only be discriminatory to the extent that there are no unobserved productivity differences
between the two groups as a result of types and degrees of severity of disability (for which
the number of health problems is included as a proxy). The term  is a vector representing
the relationship between the observed wage structure and the non-discriminatory norm.
8
Given the typical index number problem (see Oaxaca and Ransom, 1994)  can take values
varying from zero to one depending on which group is the frame of reference. In the tables
below we provide several frames of reference – using the non-disabled as a base (0), the
disabled (1), taking the mean of these two results (0.5), taking ratios given by the shares of
the non-disabled in the working population (column 4) and finally the figure obtained from a
pooled regression (*). It should be noted that were any discrimination to be eliminated, the
outcome is likely to be closer to the non-disabled norm given the relative importance of this
group in the total population.
Identification is obtained by including a variable for the number of children in the household
in the participation (employment) equation if the respondent is a head of household or his or
her spouse (otherwise zero) and by including a dummy variable indicating the presence of
income earner in the household apart from the respondent. Linear and quadratic terms for
age are included in the employment equation, as opposed to experience in the wage
equations. Six qualifications and twenty-two unitary authority dummies, together with ethnic
origin, type of household tenure and number of health problems appear in both employment
and wage equations. The latter also include occupational, industry, small establishment,
public sector and part-time dummies, together with a dummy variable for sickness absence in
the reference week and the aforementioned tenure variables. The hourly pay variable is
based on usual weekly pay divided by usual hours with a variable also included to adjust for
the amount of usual overtime, measured in hours. In addition to separate estimation by
reported disability status, all equations are estimated separately for men and women, thereby
allowing for the probability that some independent variables may have gender-specific
effects.
Further, we estimate employment and wage equations (for the disabled only)
augmented by five health type dummies derived from the 17 main health problems, as
9
outlined earlier, in order to assess the impact of disability types. This has been found to be of
considerable import in previous work in the UK, with mental health problems having
especially adverse effects (see Jones et al., 2003).
4.
Results
Descriptive statistics for the sample used in estimation8, are contained in Table 6. Disabled
men earned 89.8% of the figure for non-disabled men in our sample, with the corresponding
figure for women being 90.4%. These figures are similar to those for the boosted sample as a
whole (see Table 5), and in each case indicate that relative earnings for the disabled in Wales
compared to the non-disabled are not substantially at variance with the rest of Britain 9. In
contrast, the disabled men’s employment rate for the estimation sample was just 27.0% of
that of non-disabled men, with the corresponding figure for women being 31.8%. Both of
these figures are substantially lower than in the rest of Britain 10, where the disadvantage of
disabled men relative to non-disabled men is likewise greater than that of disabled women
relative to non-disabled women.
The share of disabled and non-disabled workers in overall employment in Wales varies
considerably across the 22 Welsh unitary authorities, with disabled men and women being
substantially over-represented in authorities such as Neath and Port Talbot and substantially
under-represented in authorities such Monmouth.
To some extent, this may reflect the
occupational and industrial composition of the unitary authorities, as under-and overrepresentation is reflected in these also.
10
In the case of both men and women, disabled persons in Wales are less well qualified than
their non-disabled counterparts and particularly so for higher qualifications including
degrees. Both disabled and non-disabled groups are less qualified than their counterparts in
the rest of Britain11, which is one factor contributing to their low participation rates. Disabled
persons also tend to be older (reflecting the age-related onset of many disabilities) and as
expected, suffer from a larger number of health problems than the non-disabled. In both
these cases the gap between the disabled and non-disabled is wider in Wales than in the rest
of Britain, with the disabled in Wales also being older and having more health problems than
their counterparts elsewhere in Britain. However, with the exception of disabled men, the
Welsh appear to have a lower incidence of sickness absence in the reference week, which
could itself be a consequence of a greater difficulty in finding work. In accordance with
expectations, the disabled in Wales are several orders of magnitude more likely to have had
time off during the reference week than the non-disabled.
There is also evidence of
differences in housing status, with a higher proportion of the disabled living in social housing
or owning their own home than the non-disabled, and correspondingly fewer having a
mortgage. They are also less likely to be in a household with another individual who has a
source of earned income. Finally, the disabled are more likely to be employed in small firms
and on a part-time basis, and typically work fewer overtime hours.
4.1
Labour Force Participation
The employment probit estimates are presented in Table 7 for men and Table 8 for women.
As can be seen in all cases likelihood ratio tests unambiguously reject the null hypothesis that
the coefficients in each regression are jointly insignificant, while the pseudo-R2 values are
also respectable.
11
On the whole, particular personal and other characteristics seem to have qualitatively similar
effects on the probability of employment for both the disabled and the non-disabled and these
effects are similar in Wales to the rest of Britain12. However, while qualitatively similar, Chisquared tests of parameter equality among the different comparator sub-groups
unambiguously reject the null of homogeneity in each case.
For disabled and non-disabled men and women, those with educational qualifications are
much more likely to be in employment than those without any qualifications with a marginal
effect which is much larger for the disabled, indicating the particular importance of gaining
qualifications for this group13. There are, in addition, strong age effects, with positive and
negative signs on the linear and quadratic terms respectively observed in all cases and
conforming to the conventional pattern, though in this case the marginal effects are larger for
the non-disabled. Married men, whether disabled or not are more likely to be employed than
single men, while the reverse applies to non-disabled women (albeit significance is confined
to the non-disabled group), perhaps reflecting traditional household roles. Likewise, the
presence of dependent children has a negative effect on participation, although this effect is
not significant for disabled men. The presence of an additional source of income by another
household member has a positive effect on employment participation for all groups, perhaps
contrary to prior expectations. It should be noted however, that this is not the conventional
measure of unearned income for an individual which would be expected to reduce labour
supply (see Kidd et al., 2000). Rather, it suggests that the measure reflects the increasing
polarisation of households as being either multiple earner or no earner categories (see, for
example, Dickens et al., 2000, Table 4). Possession of a mortgage also has a positive effect
on participation, although the effects of other forms of housing tenure are somewhat mixed.
12
For the disabled (both males and females), having more than one health problem has strong
negative effect on employment.
Turning to the Unitary Authority dummies, these are generally insignificant for disabled men,
apart from Merthyr (at the 10% level), although several are significant for non-disabled men
(the omitted category being Cardiff). In contrast, 10 unitary authorities have significantly
lower participation rates than Cardiff in the case of disabled women. What is perhaps
noteworthy however, is that with the exception of disabled women, after controlling for a
range of other factors impinging on participation, there is little evidence of an independent
Unitary Authority effect consistent with the notion of ‘inactivity hotspots’. A priori this
might have been anticipated in such areas as Blaenau Gwent, Ceredigion, Neath Port Talbot
and Rhondda, Cynon, Taff14.
4.2
Earnings
As with employment, it appears that earnings in Wales are determined in a qualitatively
similar manner for disabled and non-disabled men and women (Tables 9 and 10), although
parameter estimates are typically less precisely determined for the disabled, reflecting smaller
sample sizes. F tests of parameter equality are rejected in all cases.
Coefficient estimates are in general as expected. Thus, wages are higher for those with
qualifications relative to those without qualifications for each of the sub-groups, with the
coefficients generally increasing in magnitude for higher qualifications.
However, the
disabled do not obtain significantly higher earnings for lower qualifications compared to
those without any qualifications, while for disabled women the returns to higher
13
qualifications are greater than those for non-disabled women. There are some differences in
the returns to experience and tenure between the disabled and non-disabled. For men the
returns to tenure are linear, but higher for the disabled, while the returns to experience are
quadratic, but higher for the non-disabled. For women the returns to tenure are quadratic, but
insignificant for the disabled, while for both groups the returns to experience are quadratic
and initially higher for the non-disabled.
The occupational group dummies are generally negative and insignificant, which is
unsurprising given that managers and senior officials is the omitted category. However, in
the case of the disabled, only those in professional and associate professional and technical
groups do not earn significantly less that those in the omitted category. There are also fewer
significant differences in earnings across industries for the disabled (likely reflecting the large
number of coefficients being estimated relative to sample size).
Turning to other variables in these regressions, marriage only has a significant effect on
earnings for non-disabled men, while the number of health problems only impacts
(negatively) on earnings for non-disabled women. Being employed in a small firm (with
fewer than 20 employees) is associated with lower earnings for all sub-groups, although the
effect is somewhat smaller for the disabled, while the impact of overtime is restricted to nondisabled men’s earnings. The housing tenure variables are less important determinants of
earnings for the disabled relative to the non-disabled. Finally, in contrast to the case for the
non-disabled, there are no significant differences in earnings across the twenty-two unitary
authorities that are not captured by the included variables, although it is possible, as noted
previously, that this result is influenced by small sample size.
14
4.3
Employment Participation and Earnings by Type of Health Problem
In Tables 11 and 12 we repeat the preceding analysis, but focus on the disabled groups only,
incorporating information for the main types of health problem. Those with each type of
health problem are significantly more likely to be in employment than the omitted category of
mental health, confirming the findings in Jones et al. (2003), while individuals with multiple
health problems are also significantly less likely to be in paid work. Wales suffers relatively
to the rest of Britain by having a higher proportion of its population subject to mental health
problems as noted above (see Table 2) and also has a higher proportion with multiple health
problems.
Turning to the wage equations (Table 12), earnings are higher for disabled women with each
of the types of disability relative to the omitted category of mental health, but for disabled
men there are no significant earnings differences across types of disability15. For women at
least there is, therefore, a double penalty in terms of employment and earnings for those who
suffer from mental health problems. It is important however, to determine whether these
differences result from genuine productivity differences, misconceptions of the nature of
mental health problems and their impact on performance or pure prejudice; something
beyond the scope of the present analysis.
4.4
Gender and Disability Decompositions
A key feature of our analysis, given problems in interpreting subjective questions on
disability, is to decompose the differences between the disabled and non-disabled and
between genders. Thus we have two types of wage decomposition. The first of these
15
compares the disabled with the non-disabled separately for men and women, and of necessity
excludes types of disability (Table 13). For men the raw differential is small at 0.056,
compared to 0.221 for women (and 0.286 and 0.192 for men and women repectively in the
rest of Britain). Further, the larger part of it is due to differences in endowments rather than
differences in coefficients and using a pooled regression we can explain 96% of the
differential, leaving little scope for discrimination. Yet for Welsh women, roughly half of the
differential is unexplained using the pooled regression, while in the rest of Britain, 60% of
the differential for both genders remains unexplained.
It appears therefore, that qualifications and experience differences are the major explanations
for the lower earnings of disabled men in Wales relative to their non-disabled counterparts.
The results for women are consistent with discrimination being more substantial for disabled
women than disabled men, assuming a similar impact from omitted types of disability
variables.
Table 14 provides gender wage decompositions designed to show whether the disadvantage
of disabled women relative to disabled men is greater than, equal to or less than the
disadvantage of non-disabled women relative to non-disabled men. In this case the raw
differential is much larger in the case of the disabled and contrasts starkly with the situation
in the rest of Britain, while the percentage due to coefficient differences is also greater
relative to the non-disabled.
Whatever the basis of comparison used, the percentage ‘unexplained’ is always greater in the
disabled comparison, consistent with a discrimination story.
When the type of health
problem is controlled for in the gender decomposition (the lower panel of Table 14) there is
16
little change in the proportion of the wage gap explained or unexplained, indicating that there
is no gender difference in the impact of types of disability on earnings.
5.0
Conclusions
The proportions of the population experiencing disability is higher in Wales than elsewhere
in Britain, apart from the Northern region, while labour force participation rates for the
disabled are lower in Wales than in any other region. Wales suffers relatively to the rest of
Britain in having the highest proportion of the disabled with mental health and multiple
health problems, both of which adversely affect the probability of being in employment.
In Wales the disabled have fewer qualifications and tend to be older than elsewhere in
Britain.
The marginal effects of qualifications are greater for the disabled, making
improvements in the education of the disabled a potent remedy for improving their labour
market status. For disabled women, but not men, there are significant differences in the
probability of being in employment across unitary authorities after controlling for personal
and household characteristics.
The pay differential between non-disabled and disabled workers is lower for men in Wales
than elsewhere, while the reverse is true for women. Disabled men benefit more than other
groups from longer tenure, but almost all of the earnings gap between disabled and nondisabled men can be explained by differences in endowments of human capital, notably
qualifications and experience. For women the pay differential is larger, and half of the
difference is unexplained. Earnings for disabled women with mental health problems are
significantly lower than those of disabled women with other types of disability. The pay gap
17
between men and women is substantially higher for the disabled than for the non-disabled,
and a higher proportion of it is due to differences in coefficients. In general then, it would
appear that in terms of earnings, disabled women in Wales suffer disproportionately to
disabled men.
18
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19
TABLE 1: REGIONAL ANALYSIS OF DISABILITY INCIDENCE (%)
North
Yorkshire & Humberside
East Midlands
East Anglia
South East & London
South West
West Midlands
North West
Wales
Scotland
Great Britain
Males
Females
All
22.96
18.55
15.05
15.24
13.29
16.00
17.31
18.96
21.25
16.95
16.46
19.24
16.24
14.48
15.45
13.09
14.00
15.88
17.17
18.42
16.76
15.28
21.11
17.41
14.77
15.34
13.19
15.01
16.60
18.05
19.85
16.85
15.87
Notes: Data from the LFS, 2001. Working age population only.
20
TABLE 2: COMPOSITION OF HEALTH PROBLEMS BY REGION (%)
Main health problem
North
Yorks. &
Humb.
Limbs
Sight/hearing
Skin/breathing & organs
Mental health
Other
Total
39.9
4.2
32.4
13.4
10.3
100.0
39.8
3.9
30.4
11.0
15.0
100.0
West
Midlands
East
Anglia
39.0
4.3
29.7
12.5
14.6
100.0
40.2
5.3
30.5
12.6
13.4
100.0
South
East &
London
37.7
4.4
30.8
11.9
15.4
100.0
South
West
West
Midlands
North
West
Wales
Scotland
42.0
5.2
29.2
9.9
13.8
100.0
41.8
3.3
30.8
11.8
12.3
100.0
41.2
4.0
28.9
12.9
13.6
100.0
41.4
4.1
28.9
14.3
11.5
100.0
37.7
3.3
31.5
14.3
13.3
100.0
Notes: See notes to Table 1. Samples refer to the disabled only.
TABLE 3: ECONOMIC ACTIVITY & THE DISABLED
Employed
North
Yorks. & Humb.
East Midlands
East Anglia
South East & London
South West
West Midlands
North West
Wales
Scotland
Great Britain
Nondisabled
(1)
78.16
80.62
81.46
84.65
80.49
84.49
81.26
80.02
78.30
81.37
80.96
ILO Unemployed
Disabled
(2)
(2) as
percentage
of (1)
Nondisabled
(3)
31.76
38.83
41.92
48.52
46.53
49.78
40.66
33.39
26.65
32.38
39.89
(40.6)
(48.2)
(51.5)
(57.3)
(57.8)
(58.9)
(50.0)
(41.7)
(34.0)
(39.8)
(49.3)
5.52
3.59
3.47
2.68
3.47
2.70
3.80
3.56
4.18
5.16
3.72
Notes: See notes to Table 1.
21
Inactive
Disabled
(4)
(3) as
percentage
of (4)
Nondisabled
(5)
Disabled
(6)
3.39
4.59
4.77
5.24
4.33
4.80
4.68
3.63
4.38
4.94
4.40
(162.8)
(78.2)
(72.7)
(51.1)
(80.1)
(56.3)
(81.2)
(98.1)
(95.4)
(104.5)
(84.5)
16.32
15.79
15.06
12.67
16.04
12.81
14.94
16.42
17.52
13.47
15.32
64.86
56.58
53.30
46.24
49.14
45.41
54.65
62.98
68.97
62.68
55.71
(5) as
percent
age of
(6)
(25.2)
(27.9)
(28.3)
(27.4)
(32.6)
(28.2)
(27.3)
(26.1)
(25.4)
(21.5)
(27.5)
TABLE 4: REGIONAL PAY BY GENDER & DISABILITY (£ p.h.)
Male
Disabled
North
Yorkshire & Humberside
East Midlands
East Anglia
South East & London
South West
West Midlands
North West
Wales
Scotland
Great Britain
7.86
7.95
8.54
8.60
10.72
8.94
9.54
8.51
9.39
8.19
9.21
Nondisabled
9.17
9.36
9.78
10.17
13.19
10.27
9.87
9.86
9.28
10.03
10.88
Female
Disabled
pay as %
of nondisabled
pay
85.7
84.9
87.3
84.6
81.3
87.0
96.7
86.3
101.2
81.7
84.7
Disabled
6.66
6.37
7.12
8.85
8.11
7.21
7.16
7.91
5.78
6.74
7.40
Notes: See notes to Table 1.
22
Nondisabled
7.15
7.30
7.32
7.84
9.58
7.41
7.57
7.57
7.51
7.80
8.15
All
Disabled
pay as %
of nondisabled
pay
93.1
87.3
97.3
112.9
84.7
97.3
94.6
104.5
77.0
86.4
90.8
Disabled
7.29
7.21
7.84
8.72
9.38
8.12
8.37
8.22
7.62
7.46
8.31
Nondisabled
8.14
8.30
8.57
9.01
11.38
8.81
8.73
8.67
8.35
8.88
9.50
Disabled
pay as %
of nondisabled
pay
89.6
86.7
91.5
96.8
82.4
92.2
95.9
94.8
91.3
84.0
87.5
TABLE 5: WELSH BOOST STATISTICS
Non-disabled
Men
Women
Total
Disabled
Men
Women
Total
Employment
ILO Unemployment
Inactivity
Pay per
hour (£)
83.77
73.55
78.57
4.73
3.32
4.01
11.50
23.13
17.42
9.24
7.15
8.15
30.81
26.87
29.01
5.12
2.07
3.73
64.07
71.01
67.27
8.33
6.43
7.42
Notes: Data from the WLLFS, 2001. Working age population only.
23
TABLE 6: SUMMARY STATISTICS (MEANS)
Variable
Hourly Pay (£)
Proportion in employment
Anglesey
Gwynedd
Conwy
Denbighshire
Flintshire
Wrexham
Powys
Ceredigion
Pembrokeshire
Carmarthenshire
Swansea
Neath and Port Talbot
Bridgend
Vale of Glamorgan
Rhondda
Merthyr
Caerphilly
Blaenau Gwent
Torfaen
Monmouth
Newport
Occupation 2
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Ill in reference week
Married
Age
Age squared
Qual1
Qual2
Male
Disabled Non-disabled
8.360
9.305
0.205
0.758
0.035
0.038
0.053
0.050
0.032
0.034
0.028
0.035
0.036
0.047
0.041
0.045
0.025
0.032
0.023
0.029
0.053
0.053
0.055
0.043
0.044
0.048
0.090
0.060
0.049
0.053
0.032
0.053
0.064
0.050
0.054
0.034
0.060
0.051
0.058
0.042
0.053
0.046
0.036
0.055
0.040
0.050
0.100
0.124
0.131
0.132
0.066
0.052
0.157
0.182
0.057
0.022
0.049
0.043
0.186
0.184
0.155
0.127
0.009
0.009
0.017
0.028
0.330
0.305
0.076
0.102
0.142
0.151
0.080
0.078
0.081
0.081
0.216
0.199
0.089
0.019
0.609
0.552
48.228
37.606
2490.352
1599.370
0.044
0.127
0.067
0.143
24
Female
Disabled Non-disabled
6.480
7.168
0.213
0.669
0.033
0.037
0.039
0.052
0.033
0.035
0.035
0.035
0.035
0.046
0.038
0.045
0.027
0.036
0.023
0.034
0.060
0.058
0.054
0.047
0.042
0.041
0.085
0.061
0.048
0.052
0.038
0.050
0.071
0.053
0.055
0.033
0.061
0.048
0.053
0.041
0.058
0.043
0.037
0.050
0.039
0.048
0.073
0.097
0.083
0.132
0.181
0.228
0.028
0.023
0.166
0.141
0.164
0.129
0.075
0.043
0.162
0.141
0.002
0.003
0.002
0.005
0.116
0.107
0.012
0.010
0.256
0.222
0.034
0.028
0.075
0.110
0.444
0.465
0.087
0.034
0.616
0.559
44.304
36.677
2092.733
1493.086
0.041
0.112
0.081
0.137
Qual3
Qual4
Qual5
Small firm
Part-time
White
Tenure
Tenure squared
Experience
Experience squared
Public sector
Dependent children
Overtime
Social housing
Home owned
Home mortgaged
No of health problems
Other earner
0.248
0.090
0.142
0.280
0.117
0.991
9.278
174.998
26.816
859.131
0.259
0.413
3.759
0.294
0.300
0.319
3.151
0.392
0.286
0.174
0.122
0.248
0.066
0.987
9.025
168.154
21.371
609.009
0.236
0.649
3.860
0.117
0.198
0.607
0.209
0.674
0.110
0.184
0.133
0.355
0.503
0.986
7.432
113.842
25.174
766.793
0.381
0.638
1.813
0.320
0.237
0.361
3.129
0.458
Notes: Data from the WLLFS, 2001. Figures relate to the estimation samples used.
25
0.183
0.252
0.116
0.337
0.417
0.983
7.246
109.253
20.660
566.859
0.407
0.880
2.113
0.163
0.184
0.564
0.211
0.690
TABLE 7: MALE LABOUR FORCE PARTICIPATION PROBIT ESTIMATES
Constant
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Age
Age squared
Married
Anglesey
Gwynedd
Conwy
Denbighshire
Flintshire
Wrexham
Powys
Ceredigion
Pembrokeshire
Carmarthenshire
Swansea
Neath and Port Talbot
Bridgend
Vale of Glamorgan
Rhondda
Merthyr
Caerphilly
Blaenau Gwent
Torfaen
Monmouth
Newport
White
Dependent children
Other earner
Social housing
Home owned
Home mortgaged
No of health problems
No Obs
Log likelihood
χ2 (p-value)
Pseudo-R2
Disabled
Coefficient
t-stat
-3.795
-6.37
0.901
5.94
0.848
6.63
0.530
5.91
0.438
3.68
0.515
5.05
0.119
6.53
-0.002
-7.45
0.195
2.19
-0.624
-2.39
-0.372
-1.63
-0.687
-2.45
-0.044
-0.17
-0.211
-0.87
-0.019
-0.08
0.011
0.04
-0.378
-1.35
0.226
1.00
-0.227
-1.00
-0.008
-0.03
-0.108
-0.52
0.092
0.41
-0.052
-0.21
0.006
0.03
0.406
1.80
0.150
0.68
-0.068
-0.30
0.182
0.80
0.184
0.79
0.353
1.54
0.957
2.17
-0.043
-1.04
0.433
5.84
-0.175
-1.28
0.049
0.36
0.528
4.05
-0.213
10.09
2571
-939.570
731.73 (0.000)
0.280
***
***
***
***
***
***
***
***
**
**
**
*
**
***
***
***
Non-disabled
Coefficient
t-stat
-5.063
-21.38
0.313
4.14
0.437
6.05
0.284
4.88
0.219
3.42
0.306
4.33
0.274
28.35
-0.003
-27.92
0.354
6.20
-0.259
-2.08
-0.155
-1.32
0.007
0.05
0.137
1.00
0.036
0.29
0.065
0.52
0.418
2.86
-0.438
-3.31
-0.159
-1.37
-0.314
-2.59
-0.084
-0.70
-0.004
-0.04
0.105
0.86
0.053
0.44
0.018
0.14
-0.090
-0.68
-0.217
-1.82
-0.178
-1.41
0.211
1.65
0.017
0.14
-0.003
-0.02
0.292
1.90
-0.062
-2.37
0.395
9.22
-0.204
-2.63
0.066
0.88
0.593
8.78
0.047
1.54
7312
-2835.161
2425.64 (0.000)
0.300
***
***
***
***
***
***
***
***
***
**
***
***
***
*
*
*
**
***
***
***
Notes: All regressions include quarterly dummy variables. ***, ** and * denote significance at the 1%, 5% and
10% respectively. The χ2 statistic is a test that all slope coefficients are zero. Pseudo-R2 is McFadden’s measure,
defined as 1 minus the ratio of the maximized log-likelihood from the regression to that from a regression
including the optimal constant only (Maddala, 1983).
26
TABLE 8: FEMALE LABOUR FORCE PARTICIPATION PROBIT ESTIMATES
Constant
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Age
Age squared
Married
Anglesey
Gwynedd
Conwy
Denbighshire
Flintshire
Wrexham
Powys
Ceredigion
Pembrokeshire
Carmarthenshire
Swansea
Neath and Port Talbot
Bridgend
Vale of Glamorgan
Rhondda
Merthyr
Caerphilly
Blaenau Gwent
Torfaen
Monmouth
Newport
White
Dependent children
Other earner
Social housing
Home owned
Home mortgaged
No of health problems
No Obs
Log likelihood
χ2 (p-value)
Pseudo-R2
Disabled
Coefficient
t-stat
-2.309
-4.41
0.851
5.59
0.635
5.34
0.591
5.58
0.407
4.36
0.455
4.48
0.080
3.58
-0.001
-3.85
-0.009
-0.11
-0.729
-2.87
-0.475
-2.03
-0.379
-1.61
-0.241
-1.05
-0.190
-0.83
-0.542
-2.33
-0.180
-0.74
-0.510
-1.93
-0.155
-0.74
-0.415
-1.91
-0.275
-1.22
-0.675
-3.20
-0.621
-2.73
-0.247
-1.11
-0.368
-1.79
-0.533
-2.34
-0.275
-1.31
-0.463
-2.04
-0.035
-0.17
-0.189
-0.85
-0.200
-0.89
0.394
1.33
-0.121
-2.97
0.369
4.83
-0.165
-1.20
0.082
0.58
0.384
2.89
-0.154
-7.86
2310
-975.917
443.42 (0.000)
0.185
Notes: See notes to Table 7.
27
***
***
***
***
***
***
***
***
***
**
**
*
*
***
***
*
**
**
***
***
***
***
Non-disabled
Coefficient
t-stat
-5.341
-25.50
0.759
12.40
0.856
15.12
0.511
10.41
0.465
10.39
0.355
6.62
0.248
25.15
-0.003
-23.16
-0.148
-3.65
-0.068
-0.69
-0.008
-0.09
0.051
0.50
0.190
1.82
0.106
1.13
0.220
2.29
0.123
1.21
-0.177
-1.75
-0.016
-0.18
0.039
0.41
0.163
1.66
0.241
2.69
0.223
2.40
0.054
0.58
0.125
1.37
0.196
1.86
0.204
2.14
0.321
3.23
0.324
3.27
0.194
2.06
0.231
2.42
0.562
4.96
-0.313
-18.09
0.353
9.59
-0.015
-0.25
0.166
2.68
0.529
9.78
-0.014
-0.63
9129
-4674.3324
2246.29 (0.000)
0.194
***
***
***
***
***
***
***
***
***
*
**
*
*
***
**
*
**
***
***
**
**
***
***
***
***
***
TABLE 9: MALE SELECTIVITY CORRECTED WAGE EQUATION
Constant
Anglesey
Gwynedd
Conwy
Denbighshire
Flintshire
Wrexham
Powys
Ceredigion
Pembrokeshire
Carmarthenshire
Swansea
Neath and Port Talbot
Bridgend
Vale of Glamorgan
Rhondda
Merthyr
Caerphilly
Blaenau Gwent
Torfaen
Monmouth
Newport
Occupation 2
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Ill in reference week
Married
Experience
Experience squared
Qual 1
Qual 2
Qual 3
Disabled
Coefficient
t-stat
0.448
1.19
0.005
0.03
-0.103
-0.89
-0.046
-0.30
-0.198
-1.56
0.077
0.67
0.068
0.59
0.128
0.98
0.189
1.31
-0.025
-0.24
0.020
0.18
0.142
1.26
0.138
1.35
0.165
1.53
0.095
0.82
0.126
1.24
0.060
0.55
-0.022
-0.20
0.057
0.50
0.095
0.87
0.112
1.01
-0.070
-0.66
0.023
0.29
-0.028
-0.40
-0.350
-4.24
-0.262
-3.74
-0.420
-4.36
-0.503
-5.10
-0.334
-4.81
-0.415
-5.79
-0.119
-0.63
0.283
1.87
0.037
0.40
0.017
0.16
-0.045
-0.48
-0.134
-1.33
0.026
0.27
-0.067
-0.77
-0.207
-3.56
0.059
1.26
0.018
2.70
0.000
-2.03
0.380
3.80
0.218
2.46
0.166
2.57
28
***
***
***
***
***
***
*
***
***
**
***
**
**
Non-disabled
Coefficient
t-stat
1.603
20.62
-0.071
-2.03
-0.075
-2.31
-0.071
-2.01
-0.067
-1.95
0.018
0.58
-0.004
-0.11
-0.084
-2.41
-0.107
-2.59
-0.110
-3.45
-0.096
-2.76
-0.039
-1.22
-0.019
-0.63
0.005
0.17
0.027
0.87
-0.062
-1.97
-0.081
-2.27
-0.039
-1.21
-0.068
-2.01
-0.007
-0.22
0.034
1.11
0.012
0.39
-0.044
-2.03
-0.153
-7.45
-0.341
-12.68
-0.347
-17.79
-0.443
-11.48
-0.377
-12.52
-0.410
-20.33
-0.476
-21.74
-0.015
-0.26
0.269
6.86
0.171
6.31
0.159
5.48
-0.017
-0.58
0.092
3.04
0.180
5.98
0.118
4.29
-0.079
-2.14
0.072
5.23
0.027
10.23
0.000
-8.72
0.390
15.36
0.249
11.09
0.164
8.68
***
**
**
**
*
**
***
***
***
**
**
**
**
***
***
***
***
***
***
***
***
***
***
***
***
***
**
***
***
***
***
***
***
Qual 4
Qual 5
Small firm
Part time
White
Tenure
Tenure squared
Public sector
Overtime
No of health problems
Social housing
Home owned
Home mortgaged
Lambda
No Obs
RSS
F (p-value)
R2
0.164
0.023
-0.089
-0.042
1.083
0.018
0.000
0.000
0.000
0.006
0.039
0.061
0.136
-0.055
2.33
0.33
-2.20
-0.73
4.02
2.97
-0.77
0.00
-0.15
0.29
0.48
0.79
1.67
-0.51
528
66.920
9.77 (0.00)
0.504
**
**
***
***
*
0.097
0.088
-0.123
-0.027
0.011
0.010
0.000
0.008
0.003
-0.005
0.029
0.069
0.128
-0.026
4.71
4.15
-9.83
-1.15
0.21
5.38
-1.02
0.39
3.54
-0.67
1.05
2.81
5.42
-0.66
5541
766.264
94.09 (0.00)
0.506
***
***
***
***
***
***
***
Notes: All regressions include quarterly dummy variables. ***, ** and * denote significance at the 1%, 5% and
10% respectively. RSS denotes the residual sum of squares. The F statistic is a test that all slope coefficients are
zero.
29
TABLE 10: FEMALE SELECTIVITY CORRECTED WAGE EQUATION
Constant
Anglesey
Gwynedd
Conwy
Denbighshire
Flintshire
Wrexham
Powys
Ceredigion
Pembrokeshire
Carmarthenshire
Swansea
Neath and Port Talbot
Bridgend
Vale of Glamorgan
Rhondda
Merthyr
Caerphilly
Blaenau Gwent
Torfaen
Monmouth
Newport
Occupation 2
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Ill in reference week
Married
Experience
Experience squared
Qual 1
Qual 2
Qual 3
Disabled
Coefficient
t-stat
1.403
4.97
-0.199
-1.41
0.051
0.44
0.051
0.45
0.057
0.53
-0.086
-0.83
-0.003
-0.03
-0.040
-0.36
0.070
0.53
-0.002
-0.02
0.015
0.14
-0.127
-1.18
-0.104
-0.88
-0.060
-0.47
0.036
0.35
0.111
1.14
-0.203
-1.67
-0.116
-1.17
-0.131
-1.15
-0.027
-0.30
0.002
0.02
-0.018
-0.18
0.170
1.81
0.007
0.08
-0.183
-2.50
-0.512
-4.46
-0.319
-4.03
-0.397
-4.86
-0.277
-2.81
-0.364
-4.59
0.393
1.07
0.617
1.74
0.181
1.92
0.139
0.89
0.152
1.90
0.340
3.10
0.209
2.28
0.131
1.79
-0.032
-0.58
0.008
0.20
0.029
4.43
-0.001
-4.09
0.423
3.54
0.250
2.67
0.136
1.73
30
***
*
*
**
***
***
***
***
***
*
*
*
***
**
*
***
***
***
***
*
Non-disabled
Coefficient
t-stat
1.586
23.16
-0.125
-3.96
-0.081
-2.90
-0.113
-3.67
-0.082
-2.73
-0.011
-0.40
-0.091
-3.24
-0.098
-3.29
-0.119
-3.60
-0.079
-2.90
-0.096
-3.37
-0.105
-3.72
-0.084
-3.18
-0.078
-2.86
-0.064
-2.35
-0.078
-2.88
-0.081
-2.60
-0.056
-2.00
-0.045
-1.55
-0.055
-1.93
-0.049
-1.83
-0.017
-0.64
0.147
6.08
-0.068
-3.15
-0.316
-15.80
-0.471
-13.88
-0.404
-18.11
-0.420
-18.18
-0.417
-13.57
-0.497
-21.73
0.215
2.67
0.395
6.27
0.184
6.89
0.167
3.54
0.057
2.37
0.242
7.36
0.227
9.17
0.106
4.83
-0.038
-1.57
-0.005
-0.47
0.020
11.13
0.000
-11.28
0.332
13.30
0.211
9.78
0.099
5.38
***
***
***
***
***
***
***
***
***
***
***
***
***
**
***
***
**
*
*
***
***
***
***
***
***
***
***
***
***
***
***
**
***
***
***
***
***
***
***
***
Qual 4
Qual 5
Small firm
Part time
White
Tenure
Tenure squared
Public sector
Overtime
No of health problems
Social housing
Home owned
Home mortgaged
Lambda
No Obs
RSS
F (p-value)
R2
0.082
-0.006
-0.067
0.007
-0.171
0.006
0.000
0.095
0.002
-0.074
-0.038
0.027
0.108
0.132
1.26
-0.09
-1.83 *
0.18
-1.04
0.93
0.53
1.98 **
0.46
-0.92
-2.01 **
0.35
1.34
1.07
493
54.709
8.05 (0.00)
0.467
Notes: See notes to Table 9.
31
0.072
0.060
-0.071
-0.011
0.016
0.016
0.000
0.096
0.001
-0.043
-0.012
-0.002
0.035
0.066
4.24
3.23
-7.13
-1.12
0.38
9.31
-3.34
6.78
0.69
-1.92
-1.66
-0.08
1.77
2.40
6105
697.359
113.90 (0.00)
0.530
***
***
***
***
***
***
*
*
*
**
TABLE 11: DISABLED LABOUR FORCE PARTICIPATION PROBITS
Male
Constant
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Age
Age squared
Married
Anglesey
Gwynedd
Conwy
Denbighshire
Flintshire
Wrexham
Powys
Ceredigion
Pembrokeshire
Carmarthenshire
Swansea
Neath and Port Talbot
Bridgend
Vale of Glamorgan
Rhondda
Merthyr
Caerphilly
Blaenau Gwent
Torfean
Monmouth
Newport
White
Health 1
Health 2
Health 3
Health 5
Dependent children
Other earner
Social housing
Home owned
Home mortgaged
No of health problems
No Obs
Log likelihood
χ2 (p-value)
Pseudo-R2
t-stat
Coefficient
-4.563
-7.31
0.954
6.14
0.834
6.38
0.509
5.56
0.380
3.10
0.482
4.63
0.131
6.97
-0.002
-8.05
0.109
1.20
-0.611
-2.28
-0.372
-1.58
-0.722
-2.48
-0.067
-0.25
-0.209
-0.84
0.012
0.05
0.075
0.28
-0.448
-1.55
0.229
0.98
-0.253
-1.07
-0.037
-0.15
-0.120
-0.55
0.066
0.29
-0.102
-0.40
-0.027
-0.12
0.419
1.79
0.127
0.56
-0.059
-0.25
0.129
0.55
0.190
0.79
0.360
1.54
0.902
2.00
0.783
6.21
0.835
4.32
0.968
7.48
0.544
3.44
-0.073
-1.74
0.423
5.60
-0.173
-1.24
0.072
0.51
0.531
3.96
-0.206
-9.61
2553
-903.09657
785.58 (0.000)
0.3031
Notes: See notes to Table 7.
32
***
***
***
***
***
***
***
***
**
**
*
**
***
***
***
***
*
***
***
***
Female
Coefficient
t-stat
-3.110
-5.65
0.835
5.33
0.626
5.16
0.538
4.99
0.393
4.12
0.455
4.41
0.095
4.13
-0.001
-4.51
-0.021
-0.25
-0.731
-2.81
-0.510
-2.13
-0.369
-1.53
-0.227
-0.96
-0.205
-0.88
-0.506
-2.11
-0.139
-0.56
-0.465
-1.72
-0.144
-0.68
-0.368
-1.64
-0.213
-0.93
-0.657
-3.02
-0.580
-2.48
-0.214
-0.94
-0.382
-1.81
-0.524
-2.24
-0.252
-1.17
-0.452
-1.94
-0.074
-0.34
-0.148
-0.65
-0.130
-0.57
0.449
1.53
0.693
5.89
0.639
3.43
0.839
6.82
0.522
3.83
-0.143
-3.39
0.324
4.18
-0.161
-1.14
0.036
0.25
0.340
2.51
-0.162
-8.08
2299
-943.984
494.17 (0.000)
0.2074
***
***
***
***
***
***
***
***
***
**
**
*
***
**
*
**
*
***
***
***
***
***
***
**
***
TABLE 12: DISABLED SELECTIVITY CORRECTED WAGE EQUATION
Constant
Anglesey
Gwynedd
Conwy
Denbighshire
Flintshire
Wrexham
Powys
Ceredigion
Pembrokeshire
Carmarthenshire
Swansea
Neath and Port Talbot
Bridgend
Vale of Glamorgan
Rhondda
Merthyr
Caerphilly
Blaenau Gwent
Torfean
Monmouth
Newport
Occupation 2
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Ill in reference week
Married
Experience
Experience squared
Qual 1
Qual 2
Qual 3
Male
Coefficient
t-stat
0.380
0.93
0.028
0.18
-0.090
-0.78
-0.050
-0.32
-0.179
-1.41
0.101
0.87
0.077
0.67
0.155
1.18
0.188
1.30
-0.012
-0.11
0.036
0.32
0.169
1.49
0.137
1.34
0.172
1.60
0.110
0.93
0.126
1.23
0.065
0.59
-0.014
-0.13
0.063
0.55
0.106
0.97
0.125
1.12
-0.061
-0.57
0.036
0.45
-0.030
-0.44
-0.351
-4.25
-0.250
-3.57
-0.419
-4.35
-0.509
-5.15
-0.343
-4.95
-0.413
-5.72
-0.130
-0.68
0.282
1.85
0.045
0.48
0.032
0.31
-0.048
-0.50
-0.122
-1.19
0.019
0.19
-0.070
-0.78
-0.210
-3.62
0.055
1.27
0.018
2.58
0.000
-1.95
0.381
3.84
0.216
2.56
0.159
2.57
33
***
***
***
***
***
***
*
***
***
*
***
**
**
Female
Coefficient
t-stat
1.217
3.62
-0.202
-1.42
0.044
0.38
0.061
0.54
0.065
0.60
-0.080
-0.76
0.014
0.11
-0.038
-0.34
0.084
0.63
0.009
0.10
0.023
0.21
-0.099
-0.93
-0.098
-0.82
-0.052
-0.42
0.045
0.45
0.097
0.99
-0.207
-1.71
-0.112
-1.13
-0.107
-0.94
-0.029
-0.31
0.023
0.23
0.006
0.06
0.172
1.83
0.017
0.19
-0.186
-2.53
-0.529
-4.61
-0.320
-4.03
-0.421
-5.12
-0.287
-2.92
-0.371
-4.67
0.403
1.10
0.602
1.70
0.200
2.12
0.144
0.91
0.172
2.14
0.334
3.03
0.221
2.37
0.137
1.87
-0.027
-0.48
-0.006
-0.14
0.031
4.52
-0.001
-4.15
0.414
3.51
0.247
2.65
0.121
1.59
***
*
*
**
***
***
***
***
***
*
**
**
***
**
*
***
***
***
***
Qual 4
Qual 5
Small firm
Part-time
White
Tenure
Tenure squared
Public sector
Overtime
Social housing
No of health problems
Home owned
Home mortgaged
Health 1
Health 2
Health 3
Health 5
Lambda
No Obs
RSS
F (p-value)
R2
0.148
0.012
-0.093
-0.032
1.084
0.019
0.000
0.004
-0.001
0.043
0.013
0.064
0.133
0.046
0.091
0.078
-0.052
-0.060
2.19
0.18
-2.27
-0.54
4.05
3.13
-1.01
0.07
-0.25
0.52
0.64
0.82
1.65
0.46
0.76
0.73
-0.50
-0.58
524
65.720
9.22 (0.00)
0.505
Notes: See notes to Table 9.
34
**
**
***
***
*
0.082
-0.004
-0.070
0.007
-0.174
0.006
0.000
0.100
0.002
-0.075
-0.038
0.008
0.085
0.207
0.218
0.230
0.157
0.138
1.27
-0.05
-1.90
0.20
-1.05
0.81
0.62
2.08
0.34
-0.91
-1.97
0.10
1.09
2.11
1.85
2.14
1.65
1.09
490
53.890
7.58 (0.00)
0.467
*
**
**
**
*
**
*
TABLE 13: DISABLED AND NON-DISABLED WAGE DECOMPOSITION
Mean prediction non-disabled
Mean prediction disabled
Raw differential
- due to endowments
- due to coefficients
- due to interaction
D
Unexplained
Explained
% unexplained
% explained
Differential due to selection variable
Male
2.089
2.033
0.056
0.034
0.005
0.017
0
1
0.5 0.913 *
0
1
0.022 0.005 0.014 0.007 0.002 0.080 0.125
0.034 0.051 0.043 0.050 0.054 0.142 0.096
39.8 8.9 24.3 11.6 4.1 36.0 56.7
60.2 91.1 75.7 88.4 95.9 64.0 43.3
0.047
35
Female
1.813
1.592
0.221
0.142
0.125
-0.046
0.5
0.102
0.119
46.3
53.7
-0.118
0.925
0.122
0.099
55.1
44.9
*
0.110
0.111
49.8
50.2
TABLE 14: GENDER WAGE DECOMPOSITION
Non-disabled
Mean prediction males
2.089
Mean prediction females
1.813
Raw differential
0.276
- due to endowments
0.066
- due to coefficients
0.162
- due to interaction
0.048
D
0
1
0.5 0.476 *
Unexplained
0.210 0.162 0.186 0.187 0.125
0.066 0.114 0.090 0.089 0.151
Explained
% unexplained
76.1 58.8 67.4 67.9 45.2
23.9 41.2 32.6 32.1 54.8
% explained
Differential due to selection variable
-0.036
Mean prediction males
Mean prediction females
Raw differential
- due to endowments
- due to coefficients
- due to interaction
Disabled
2.033
1.592
0.441
0.059
0.273
0.109
0
1
0.5 0.517 *
0.382 0.273 0.327 0.325 0.283
0.059 0.168 0.113 0.115 0.158
86.6 61.9 74.2 73.8 64.2
13.4 38.1 25.8 26.2 35.8
-0.200
Disabled with types of
health problem included
2.035
1.589
0.446
0.060
0.275
0.111
D
Unexplained
Explained
% unexplained
% explained
0
1
0.5 0.517
0.386 0.275 0.331 0.329
0.060 0.171 0.115 0.117
86.6 61.7 74.2 73.8
13.4 38.3 25.8 26.2
Differential due to selection variable
-0.206
36
*
0.288
0.158
64.5
35.5
VARIABLE DEFINITIONS
Dependent variables
Hourly pay
Gross weekly earnings divided by usual hours worked per week
Employment participation Dummy variable equal to 1 if individual has a positive hourly
wage, 0 else
Human capital variables
Experience
Qual 6
Years of (potential) labour market experience (age minus
school-leaving age)
Years in present job
Dummy variable, equals 1 if highest qualification is university
degree or higher degree
Dummy variable, equals 1 if highest qualification is other degree
Dummy variable, equals 1 if highest qualification is A level
Dummy variable, equals 1 if highest qualification is O level
Dummy variable, equals 1 if highest qualification is other
qualification
Dummy variable, equals 1 if no qualifications (base)
Industry variables
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Industry 9 and 10
Agriculture and fishing
Energy and water
Manufacturing
Construction
Distribution, hotels etc
Transport communication etc
Banking and finance
Public administration
Other (base)
Occupation variables
Occupation 1
Occupation 2
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Managers and senior officials (base)
Professional occupations
Associate professional and technical
Administrative and secretarial
Skilled trades
Personal service occupations
Sales and customer service occupations
Process, plant and machine operatives
Elementary occupations
Tenure
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Health variables
Ill in reference week
No of health problems
Health 1
Health 2
Health 3
Health 4
Dummy variable, equals 1 if ill in reference week
Number of health problems reported
Dummy variable, equals 1 if main health problem affects limbs
Dummy variable, equals 1 if main health problem affects
sight/hearing
Dummy variable, equals 1 if main health problem affects skin,
breathing and organs
Dummy variable, equals 1 if main health problem is mental
health (base)
37
Health 5
Dummy variable, equals 1 if main health problem is other
Housing status variables
Social housing
Home owned
Home mortgaged
Private rent
Dummy variable, equals 1 if renting from non-private sector
Dummy variable, equals 1 if home owned outright
Dummy variable, equals 1 if home mortgaged
Dummy variable, equals 1 if renting from private sector (base)
Other variables
Age
Married
Dependent children
Other earner
White
Small firm
Public sector
Part-time
Overtime
Age (years)
Dummy variable denoting marital status, equals 1 if married
Number of dependent children in household if head of household
or spouse (0 else)
Dummy variable, equals 1 if another individual in household has
a labour market income
Dummy variable denoting ethnic group, equals 1 if white
Dummy variable denoting marital status, equals 1 if less than 20
employees in firm
Dummy variable, equals 1 if individual is employed in the public
sector
Dummy variable, equals 1 if employed part time
Amount of usual overtime (hours)
1
The absence of a similarly sized boost to the LFS in regions other than Wales makes it less feasible to conduct
a corresponding analysis in these cases.
2
The 2001 data include observations from waves 1 and 5 from each of the four quarters, and thus repeated
observations are excluded from the analysis.
3
Note that Wales has the highest inactivity rate among both the disabled and the non-disabled.
4
Similar caution is required for the East Midlands, where the corresponding sample sizes are 109 and 101.
5
The initial ‘booster’ contains the equivalent of four years of observations, with 25% being replaced in each of
the next three years, such that only a quarter of these will be surveyed for the full four years .
6
The issue of relative self-employment rates between the disabled and non-disabled is an area for future
research.
7
The same technique has also been used in the British disability context by Kidd et al. (2000) and by the present
authors (Jones et al., 2003).
8
In addition to the exclusions noted previously, this sample comprises only individuals who have complete
information with regard to the variables used in the analysis.
9
The apparent conflict with the figures from the main LFS in Table 4 derives from the small cell sizes for the
disabled there which, as discussed previously, render the data much less reliable than those from the WLLFS.
10
Given the criteria for inclusion in the estimation sample, these figures are not commensurable with those in
the preceding tables. Employment rates for the rest of Britain calculated on the same basis are 0.309 and 0.790
for the disabled and non-disabled respectively, while those for women are 0.304 and 0.690.
11
These are available in the appendix. The same is true for all comparisons made in the text.
12
It should be noted however, that these are not strictly comparable to the results here in that they include
regional rather than Unitary Authority dummies, and as such do not control for intra-regional heterogeneity.
13
In terms of the highest qualification type (Qual1), the marginal effect is 0.27 for disabled men compared to
0.07 for non-disabled men, the corresponding figures for women being 0.28 and 0.22 respectively. A full set of
marginal effects is available on request.
14
Running regressions for each of the four sub-groups excluding all personal characteristics (i.e. including a
constant and 21 Unitary Authority dummies only) indicates that more significant variation exists in raw
employment (participation) rates, albeit only marginally so for disabled men. Supply-side factors (most notably
qualifications) therefore appear to explain much of the variation in the present sample. Although not splitting
the sample by disability status, Blackaby et al. (2003) report that the variation in inactivity rates within Wales is
substantially compressed when controlling for both supply- and demand-side factors (see especially Sect. 4.2.6).
38
15 This is in contrast to the situation in the rest of Britain. DISABILITY, GENDER
AND THE LABOUR MARKET
Melanie K. Jones, Paul L. Latreille and Peter J. Sloane
WELMERC, Department of Economics, University of Wales Swansea
November 2003
ABSTRACT
Using data from the 2002 LFS, we examine the impact of disability on
labour market outcomes by gender. Our results indicate that substantial
differences in both the likelihood of employment and levels of earnings
exist, despite several years of operation of the Disability Discrimination
Act. Significant heterogeneity within the disabled group is identified: those
suffering from mental health forms of disability fare particularly badly.
Wage decompositions suggest the ‘penalty’ for disability is greater for
women than for men. Using the Baldwin and Johnson (1992) methodology,
we find the employment effects associated with wage discrimination against
the disabled are very small.
JEL Classification: I1, J2, J3
Keywords: Disability, gender, employment, wage discrimination, decomposition analysis.
1.
INTRODUCTION
The economic analysis of disabled workers with respect to the labour market has been
surprisingly neglected in the UK, especially given the numerical size of this group 15. Using
the 2001 Labour Force Survey (LFS) Smith and Twomey (2002) note that nearly one in five
people of working age had a current long term disability in the UK; this amounts to some 3.7
million men and 3.4 million women. As the European Foundation (2003) notes, although
cultural factors may operate both across and within countries to influence the incidence of
reported disability, only Finland has a higher percentage of the working age population
39
reporting chronic illness or disability than the UK15. The contrast in labour market outcomes
for disabled and non-disabled persons is stark: the employment rate for the disabled is just
48%, compared to a rate of 81% for the non-disabled, while for those disabled people in
employment, average earnings are substantially lower than for their non-disabled
counterparts.
The above figures are especially striking when considered in the context of legislative and
other reforms over the last few years aimed at securing improvements in the labour market
position of disabled individuals. The major legal change in this regard was the passing of the
Disability Discrimination Act (DDA) in 1995, which was designed to protect the disabled
against discrimination and to facilitate and enhance their access to employment by imposing
obligations on employers (with 15 or more employees) to make reasonable adjustment to
their premises and/or employment arrangements15. In addition, a Disability Rights
Commission provides advice and information, supports disabled persons in securing their
rights under the DDA, and campaigns on behalf of this group. The Government has also
improved incentives to work via the tax and benefit system and more particularly through the
Disabled Person’s Tax Credit, while the New Deal for Disabled People (NDPP) introduced in
July 2001 further attempts to help those out of employment to get back into work. This last
policy measure is a voluntary programme whereby disabled people have access to a network
of Jobs Brokers whose role is essentially to provide advice about the local labour market and
to support individuals in finding and retaining work.
A key issue for policymakers is to determine the extent to which such reforms have achieved
their objectives. However, estimation of the impact of legislation and other policy measures
in this area is hazardous for a number of reasons. In this regard work in the US is more
advanced, and a number of studies has attempted to estimate the employment effects of the
40
Americans with Disabilities Act (ADA). Thus, De Leire (2000) found that on average over
the post ADA period, employment of men with disabilities was 7.2% lower than before the
Act was passed. Similar results were obtained by Acemoglu and Angrist (2001), who point
out that although the number of disability transfer payments went up, this cannot on its own
explain the decline in employment. Consistent with ADA being the explanation, the impact
was greater in larger firms (smaller firms being exempt) and in States with more ADA-related
discrimination charges. The implication of these results is that the legislation reduced the
demand for disabled workers by raising the costs of employing such workers by more than
the increase in demand brought about by any reduction in discrimination. However, these
results have been questioned on the grounds that the work disability measure used may not
accurately reflect coverage under the ADA. Legislation may, by removing the stigma of
disability, encourage more individuals to report a disability. Further, some who previously
reported a disability prior to the legislation may not do so subsequent to its introduction if
improvements to the workplace mean they are no longer limited in their work15. As Kruse
and Schur (2003) conclude, the analysis of the employment effects of disability legislation is
confounded by changes in the composition of those reporting disabilities, the role of
disability income and the relative effects of business cycles on workers with and without
disabilities.
In the UK, no comparable studies exist that attempt to examine the impact of the DDA.
Indeed, to our knowledge there are very few extant economic studies of the labour market
outcomes of the disabled. Blackaby et al. (1999) is a comprehensive report prepared for the
then Department for Education and Employment (DfEE) using data from the 1991 Census,
1992-4 Quarterly LFS data and the General Household Survey (GHS). Irrespective of data
source, the findings indicate that the unemployment probabilities of the disabled/those with
long-term health problems are higher than for the non-disabled/those without long-term
41
health problems, while their earnings are lower. Differences in characteristics (productivity)
account for a maximum of around half of the differences, the employment differential being
perceived as the more substantial (confirming the figures above).
The only study published in an economics journal to date, is that by Kidd et al. (2000) which
uses data from the 1996 LFS, but restricts the analysis to males only. These authors again
find that human capital/productivity characteristics differences between the disabled and nondisabled explain around 50% of the wage and participation rate differentials between the two
groups. They therefore conclude that, notwithstanding difficulties in interpretation, the size of
the residual or unexplained element of the difference (in wages) suggests that it “may, in part,
be addressed by the implementation of the 1995 Disability Act” (2000: 979).
The present paper in large part adopts the approach in Kidd et al. using more recent data from
the LFS. Importantly however, we do not attempt a formal evaluation of the impact of the
DDA using the results of Kidd et al. as a base or benchmark against which to gauge progress.
This is in part due to the fact that similar problems apply to those experienced by US
researchers examining the ADA. However, these difficulties are compounded in the UK
context by a change in the order of the disability questions in the LFS. More specifically,
until the Winter of 1997 individuals were asked:
1) if they had health problems which would affect any kind of paid work they might
do; and
2) if the health problem would be expected to last more than a year.
From Spring 1997 the order in which these questions were asked was reversed (and an
additional question was asked about the amount of paid work the disabled can do). As
Cousins, Jenkins and Laux (1998) note, this simple change identified 24% fewer respondents
in the UK reporting a long-term disability which affected the kind of work they might do, and
42
of those it did identify a greater proportion were economically inactive. This makes any
attempt to estimate the employment effects of the DDA using the LFS hazardous 15, although
we do attempt to offer some insights into its likely impact. In the light of the above
difficulties of interpretation concerning pre- and post-legislative changes, both generally and
more specifically using the LFS, the present paper focuses instead on gender differences in
disability effects in the labour market. Since the relative position of women in the labour
market in general is inferior to that of men, at least in terms of earnings, it is clearly of
interest to ascertain whether disabled women are similarly disadvantaged relative to disabled
men15. However, long-term illness affects manual workers disproportionately and men are
heavily concentrated in these jobs relative to women, so this is an empirical issue. Further,
comparing men and women overcomes many of the difficulties outlined above. The disability
rates for men and women of working age are very similar and there is no evidence of
differential reporting bias according to gender. Given that the results in Kidd et al. (2000)
were restricted to males only, we believe extending the analysis to consider both sexes
constitutes an important and original contribution to the UK literature.
In addition, while most studies of discrimination focus on between-group differences in
economic outcomes, we also identify within-group differences. Disability varies both in type
and intensity, leading to the possibility of omitted variable bias when differences in
functional capabilities are excluded. The problem is that it is generally not possible to
incorporate these into the analysis of between group differences, since the non-disabled, by
definition, do not possess such disabilities. However, we can compare the case of disabled
men and women, including functional limitations in both equations (see Salkever and
Domino, 2000)15. To anticipate our results somewhat, it is clear that significant differences do
exist between types of disability. This is most notable for individuals with mental health
problems, whose labour market position appears especially adversely affected. This has
43
potentially important implications for the design of policy, which has hitherto largely focused
on physical impairment and adaptation.
The remainder of the paper is structured as follows. In Section 2 we set out the empirical
methodology employed, followed in Section 3 by a brief description of the data. Results
appear in Section 4, together with a discussion of the implications deriving from these, while
conclusions follow in Section 5.
2.
METHODOLOGY
The standard labour economics model assumes that individuals select that
combination of consumption and hours of work which maximises their
utility, subject to budget and time constraints. Health may be incorporated
into the standard model, either through the budget constraint (via a lower
wage offer), the time constraint (via more absences lowering time available
for work) or through the utility function itself if poor health reduces utility
(see Ettner, 2000).
We follow the traditional labour force participation model in assuming that an individual
decides upon whether or not to enter the labour market on the basis of a comparison between
the employer’s wage offer and his or her reservation wage. Low employment rates 15 could be
due in part to high reservation wages associated with certain types of disability as a
consequence of disability income transfers and the extra demands on time and energy
required to participate in the labour force. Low employment rates might also be due to low
market wage rates offered to the disabled as a consequence of lower levels of productivity
and/or employer discrimination (Kruse and Schur, 2003).
There are two types of individual: the disabled, represented by D and the non-disabled by N.
For both of these types the wage offer equation is given by:
WijO   j X ij  vij
( j  D, N )
(1)
44
where WijO denotes the logarithm of the (offer) wage, X ij is a vector of productivity related
characteristics for individual i of type j and  j the associated rates of return, making the
normal assumptions of the human capital model. The reservation wage is given by:
WijR   j Z ij   ij
( j  D, N )
(2)
where the vector Z incorporates the conventional human capital variables, with the addition
of factors influencing the value of time (such as the number of dependent children). We do
not directly observe the reservation wage, which is a latent variable, but rather the indicator
variable I, where I = 1 if WijO  WijR and 0 otherwise. Thus, the probability that an individual
works is:



Pr WijO  Wijr  0  Pr  j X ij   j Z ij   i j  vi j

Assuming that v ij and  i j are normally distributed the labour force participation
(employment) equation may be estimated by a probit specification.
In estimating the wage equation (1), it is important to correct for sample selection, given that
the disabled in particular are unlikely to be a random sub-set of the population as a whole.
Indeed, if wage discrimination against disabled workers is substantial and leads to those
subject to significant discrimination exiting from the labour force, the estimate of true wage
discrimination would be below its true level. Thus, we utilise a Heckman two-stage
procedure in which the probit estimates are used to derive the inverse Mills ratio, which is
used as an additional regressor in the wage equation.
In estimating the size of the discriminatory wage differential which may exist between
disabled and non-disabled employees we follow earlier studies by Lambrinos (1991) and
Baldwin and Johnson (1994), based on a technique developed by Reimers (1983). The
45
(3)
difference in wage offers between non-disabled (N) and disabled (D) employees can be
decomposed as:
W N  W D  (c N  N  c D  D )  ( X N  X D )
ˆ
N

 (1   ) ˆ D  X N (1   )  X D  ( ˆ N  ˆ D )
The left-hand side of equation (4) then represents the difference in mean wage offers between
non-disabled and disabled employees. The first term on the right-hand side represents that
part of the difference in wage offers which is attributable to differences in productivity, while
the second term represents that part of the wage difference which is unexplained. The latter is
conventionally interpreted as discrimination, but here we are dependent on the types and
degrees of disability captured in our measures of these to control for unobserved productivity
differences.  is a vector representing the relationship between the observed wage structure
and the non-discriminatory norm. It takes values ranging from zero to one depending on
which group is the frame of reference given the typical index number problem (see Oaxaca
and Ransom, 1994). We provide results using the non-disabled as the base (0), the disabled
(1), taking the mean of these two results (0.5), taking ratios given by the shares of the nondisabled and disabled in the working population and finally the figure obtained from a pooled
regression (*).
It has been argued that health and employment may be endogenous. Thus
in the case of mental health disability, employment may have a positive
effect by increasing opportunities for social networking and role
satisfaction, but also a negative effect if it increases occupational stress. In
the case of physical health, positive effects may arise from the ability of
higher income from work to be invested in health improvements, but
negative effects from occupational hazards or stress from work overload.
In such cases health may be correlated, either positively or negatively with
the error term in the participation equations. Such evidence has been found
by Ettner (2000) using 1993 US data. Two-thirds of her sample reported
either positive or negative effects (more cases being positive than negative).
However, using a two-step instrumental variable approach she finds that
the effects of health on labour market outcomes are not particularly
sensitive to reverse causality. For this reason, and because of the difficulty
46
(4)
of finding appropriate instruments in our data set, no attempt is made here
to deal with potential problems of endogeneity.
3.
DATA
We utilise individuals in waves 1 or 5 from each of the four quarters of the
2002 LFS, so as to exclude repeated observations on the same individual
(by design individuals remain in the survey for five consecutive quarters).
Thus there is no longitudinal element in our sample. The disabled are
defined as individuals who have a long-term illness (twelve months or
more) which limits the type or amount of work they can do, with all other
individuals classified as non-disabled. As noted earlier, labour market
activity equals one if the individual is an employee with a positive wage,
and otherwise is zero15.
As Baldwin and Johnson note, in theory all variables in the wage equation
should also be included in the employment equation, but clearly some of
these variables will not be observed for those not in employment. This
could adversely influence the correction for selectivity bias in our equation.
Identification is obtained by including a spline variable for the number of
children in the household in the employment equation if the respondent is
the head of household or their spouse (zero otherwise). In addition to this,
we also incorporate a dummy indicating the presence of a labour market
income earner in the household in the participation equation. Finally, we
use experience and its square in the wage equation, but linear and
quadratic terms in age in the employment equation. Qualifications
dummies and regional dummies, together with ethnic origin, type of
household tenure and number of health problems appear in both
employment and wage equations. The latter also includes occupational and
industry dummies, the number of days off sick in the reference week, a
small establishment dummy, a public sector dummy, a part-time dummy
and tenure variables. The hourly pay variable is based on usual weekly pay
divided by usual hours, with a dummy variable included also for the
amount of usual overtime. In addition to separate estimation by reported
disability status, all these equations are estimated separately for men and
women, thereby allowing for the possibility that some of the independent
variables may have gender specific effects.
In addition, we estimate employment and wage equations for the disabled only augmented by
five health type dummies derived from the 17 main health problems identified in the LFS. It
was necessary to merge some of these for estimation purposes because of problems of small
cell sizes. It should be noted that only just under a quarter of those reporting a health problem
47
claim sickness or disability benefit15, but this figure is higher for men (26%) than for women
(21%)15. There is also substantial variation in the percentage of those with different types of
health problem claiming sickness/disability benefits, ranging from 3.2% in the case of skin
conditions/allergies to 62.1% in the case of mental illness/phobia. Similar variability occurs
in relation to ILO unemployment and inactivity by reported health problem (cf. disability).
The former ranges from 1.3% in the case of ‘other’ progressive illness to 8.7% in the case of
learning difficulties and the latter from 20.1% in the case of skin conditions/allergies to
80.1% in the case of mental illness/phobia. Therefore there is a very wide variation in the
extent to which various types of health problem hamper job prospects, with mental illness
having the most severe effects. This last statistic confirms the particular difficulties faced by
persons with mental illness identified in previous research (see Meager et al., 1998; Bunt et
al., 2001).
4.
RESULTS
The summary mean statistics for the estimation sample in Table 1 show
that the disabled men’s (employment) participation rate in 2002 was just
39.1% of that of non-disabled men, with the corresponding figure for
women at 44.1%. Disabled men earned 83.1% of the non-disabled men’s
level, with the corresponding figure for women at 88.4%. Thus the
disadvantage of disabled men relative to non-disabled men is greater than
that of disabled women relative to non-disabled women (but even so, both
groups of men earn more than even non-disabled women on average).
Comparing these figures to those for males reported in Kidd et al. (2000),
who used the 1996 LFS (and subject to the caveat given above concerning
comparisons over time using this dataset), it would seem that the earnings
differential in favour of non-disabled men may have widened (the premium
at that time being 14%). The difference in employment participation rates
also appears to have widened, with these rates falling for both non-disabled
and disabled men. There is no prima facie evidence therefore, at least on
the basis of these data, that the relative position of the disabled has
improved over the six years since the introduction of the DDA (but note the
caveats in Section 2).
48
Turning to the other variables in Table 1, a few important differences among the sub-groups
are especially worthy of note. In large part these conform to expectations. Thus, for both men
and women, disabled persons are on average, less well qualified than their non-disabled
counterparts, with the disparity being most acute for the higher qualifications such as degrees.
Disabled persons are also typically older (reflecting the fact that many disabilities exhibit
age-related onset), and entirely unsurprisingly, suffer from a larger number of health
problems than non-disabled individuals. For this reason this group is also more likely to own
their own home; they are also however, more likely to be in public housing. Both male and
female disabled groups also, on average, are less likely to be in a household where another
individual has a source of earned income (for a discussion of which, see below), suggesting
that they cannot rely on this as a means to ameliorate their own disadvantage in the labour
market.
For those who are in employment, there are also substantial differences both between the
disabled and non-disabled, and also between males and females. These differences include
not only the proportions working in particular occupational groups, the public sector and
small firms. Men typically work more overtime hours than women, and the non-disabled
more than the disabled; this is inversely correlated with the proportions working part-time, as
would be expected. Finally, it is especially interesting to note that disabled males and females
have longer average tenure than their corresponding non-disabled comparator group.
4.1 Employment participation
The employment participation probit estimates are presented in Tables 2
and 3 for men and women respectively. As can be seen, in all cases,
Likelihood Ratio tests unambiguously reject the null hypothesis that the
coefficients in each regression are jointly insignificant.
Turning to the coefficient estimates, most findings are in accordance with
expectations. Thus the results show that both men and women with
49
educational qualifications are significantly more likely to be in employment
than those without any qualifications; a finding that applies both for the
disabled as well as the non-disabled. However, the marginal effect of each
qualification is stronger for the disabled, indicating the particular
importance of obtaining qualifications among this group.15 There are, in
addition, strong age effects, with positive and negative signs on the linear
and quadratic terms respectively observed in all cases, conforming to the
usual pattern. Married men, whether disabled or not are more likely to be
employed than single men, while the reverse applies to women, reflecting
conventional household roles. In a similar vein, the presence of children
generally has a negative effect on participation, although this effect is not
significant for disabled men. The presence of an earned source of income
by another household member has a positive effect on employment
participation, as does possession of a mortgage, while habitation of social
housing has the opposite effect. Outright home ownership reduces the
likelihood of employment for non-disabled men, but increases it for
disabled men. The income variable is especially noteworthy. In particular it
should be noted that this is not the conventional measure of unearned
income for an individual, which would be expected to reduce labour supply
(as found in Kidd et al. 2000). Given the sign of its parameter estimate in
Tables 2 and 3, it seems likely that our measure is instead capturing the
polarisation of households as being either dual income or no income types
(see for example Dickens et al. 2000, Table 4).
For the disabled, having a number of health problems reduces the
likelihood of employment. There are also significant regional effects, with
lower employment participation rates in regions with slacker labour
markets compared to the omitted region (the South-East and London). In
contrast to non-disabled men, disabled men have a significantly lower
participation rate in East Anglia, but a significantly higher participation
rate in the South West. In the case of women, regional differences between
the non-disabled and disabled are more marked. In the North-West and
Scotland participation is significantly lower for the disabled, but
significantly higher for the non-disabled. In other regions there are
significant differences for either group, but not the other. On the whole,
therefore, particular personal and other characteristics appear to have
similar qualitative effects on the probability of employment for both the
non-disabled and disabled, although there are some notable exceptions.
However, while qualitatively similar, χ2 tests of parameter equality among
the different comparator sub-groups unambiguously reject the null of
homogeneity in each case15.
4.2 Earnings
50
In general, it seems to be the case also that earnings are determined in a qualitatively similar
fashion for disabled and non-disabled persons (Tables 4 and 5), although F tests of parameter
equality are rejected in all cases, and more comprehensively so when comparing men and
women than disabled and non-disabled.
In terms of specific coefficient estimates, these are once again largely in accordance with the
usual predictions. Thus, wages are higher for those with qualifications relative to those
without qualifications in each of the sub-group regressions, with the coefficients generally
increasing in magnitude as one progresses up the qualifications hierarchy. Other human
capital variables such as (maximum potential) experience and tenure with the current
employer are always significant at better than the 1 per cent level, and in both cases there is
evidence of the conventional decreasing returns. So far as occupation is concerned, the
occupational group dummies are generally significantly negative and of plausible relative
magnitudes given the omitted category of managers and senior officials; the only notable
exception is females in professional occupations, whose earnings are higher than the base
group.
Turning to other variables in these regressions, in conformity with a number of previous
studies (see for example Blackaby et al. 1998), wages are higher for married men than for
single men, irrespective of whether they are disabled, while the reverse is true for women
(albeit this effect is only statistically significant for the non-disabled). Being employed in a
small firm (fewer than 20 employees) is associated with lower earnings for all of our subgroups, while for all except disabled females, the number of health problems and wages are
negatively and significantly related. For the housing status variables, these are largely in
accordance with priors: being in social housing is negatively related to earnings for all
51
groups, while the reverse is true for those in possession of a mortgage; no relationship is
evident for those who own their home outright.
As might be expected a priori given the omitted category (London and the South East), all
regional dummies exhibit negative coefficient signs in each of the four sub-group regressions.
These are significant with just one exception, namely disabled males in East Anglia. The
industry dummies have a fairly consistent effect across the groups, with higher earnings in
banking and finance, energy and water, and manufacturing. For males, being employed in
agriculture and fishing or distribution and hotels has a significant negative effect for the nondisabled only. Similarly, for females, being employed in distribution and hotels, construction
and public administration only affects the wage of the non-disabled group. Interestingly,
being employed in the public sector confers a wage advantage for women only. Finally, the
selectivity correction term (lambda) is only significant (with positive sign) for disabled
women.
4.3 Earnings and employment participation and type of health problem
In Tables 6 and 7 we repeat the preceding analysis, but focus on the disabled groups only,
incorporating information for each individual concerning their main type of health problem15.
Those with each of the broad types of included health problem/disability are significantly
more likely to be in employment than the omitted category of mental health, while
individuals with multiple health problems are significantly less likely to be in employment. 15
The earnings equations also show that those with all types of disability apart from the “other”
category earn significantly more than those with mental health problems, with the exception
of women with sight/hearing problems. This is in contrast with the earlier work of Kidd et al.,
where mental health was associated with a lower employment probability only. Using the
2002 data suggests therefore, that of the various disability types, mental health therefore is
52
more problematical both for gaining entry into the labour market and in obtaining earnings
comparable to those of other workers. This is an important finding, confirming as it does the
findings of inter alia, Bunt et al. (2001) and Meager et al. (1998) concerning the especially
acute nature of the labour market disadvantage suffered by those with problems of this type.
The reasons for the acuity of the problem faced by those with mental health problems are
difficult to determine but two factors seem likely to be important. The first is that employers
may for various reasons, be more reluctant to hire those with mental health problems than
with other forms of disability, and consequently when this group do find work, they do so at a
lower wage. This reluctance (to hire) is of course a form of discrimination15, and precisely the
phenomenon that the DDA was designed to address. However, it should be noted that the
discrimination may in many cases reflect not prejudice, but rather a lack of knowledge
concerning, and misconceptions of, the nature of mental health problems and the
consequences of and limitations imposed thereby (Brook 200315). The second is that
employers may have a tendency to interpret disability in terms of “physically obvious, or
particularly severe, impairments” (Aston et al. 2003: 5), and hence to focus on the physical
adaptations to premises required under the DDA, rather than adjustments to working
arrangements15. This implies that employers may therefore, inadvertently, not be as
accommodating to the needs of those with mental health problems. There is also evidence to
suggest that they are less likely to make adaptations for new hires (Goldstone with Meager
2002)15; the high inactivity rates of those with mental health problems may therefore make
this especially problematic for this group when they attempt to (re) join the labour market.
4.4 Gender and disability decompositions
A key feature of our analysis is to decompose the differences between the disabled and nondisabled and between genders. Thus we have two types of wage decomposition15. The first
53
compares the disabled with the non-disabled separately for men and women, and of necessity
excludes types of disability (Table 8). For men, endowments/characteristics are a larger
component in explaining the raw differential, which is of the same magnitude for each
gender, than are differences in coefficients, while the reverse is the case for women.
Whichever basis of comparison is used, the ‘unexplained’ percentage is always greater for
women than for men. This contrasts with the findings of Blackaby et al. (1999), but is
consistent with discrimination being more substantial for disabled women than for disabled
men, assuming the same impact from omitted types of disability variables.
Table 9 considers gender wage decompositions to consider whether the
disadvantage of disabled women relative to disabled men is greater or less
than the disadvantage of non-disabled women to non-disabled men. Again
the raw differential is of comparable size in the two cases. While the part of
the raw differential explained by endowments and coefficients in the nondisabled comparison is roughly equal, in the disabled comparison the
difference in coefficients dominates the difference in endowments. Similarly
to Table 8, whatever the basis for comparison used, the percentage
‘unexplained’ is always greater in the disabled comparison, which is again
consistent with the discrimination story. When the type of health problem
is controlled for in the gender decomposition (the lower panel of Table 9),
the unexplained wage gap increases, indicating there is a gender difference
in the impact of types of disability on earnings.
The last aspect of our analysis is to examine the employment implications of the wage
discrimination for both men and women. This is undertaken using the Baldwin and Johnson
(1992) methodology, deployed in Kidd et al. (2002). The results of this procedure are set out
in Table 10. The top part of the table sets out the predicted employment participation
probabilities for the disabled and non-disabled in the presence and absence of discrimination,
with the non-discriminatory wage structure being a weighted average of the disabled and
non-disabled returns for the gender group under consideration, with the weights being the
proportions of each group in the relevant populations (male and female). Predictably, male
54
employment participation rates are higher than for females, and for the non-disabled
compared to the disabled. As can be seen, the employment effects of changing to the
alternative wage structure are in all cases small, particularly for the non-disabled. The group
with the largest employment effect is perhaps not surprisingly disabled men, although even
here it is scarcely overwhelming. As Kidd et al. (2000: 977-978) indicate, “This is important
from a policy viewpoint – it suggests that wage discrimination per se may be important but
the implied employment effect associated with the discriminatory wage reduction is very
small”. In elasticity terms however, our results for men suggest a significantly higher
responsiveness to wages for men than found in the previous work of both Kidd et al. for the
UK and Baldwin and Johnson in terms of ethnicity for the US. This would appear to indicate
that although discrimination in wages may have a small impact overall, disabled men have
become more sensitive to wage variations since the passing of the DDA.
5.
CONCLUSIONS
In this paper, while recognising the difficulties in identifying the impact of
disability on labour market outcomes, we compare the effect of disability
by gender. The evidence suggests that substantial differences in both
likelihood of employment and levels of earnings remain, even after several
years of operation of the Disability Discrimination Act. Significant
heterogeneity within the disabled group is also identified, with the type of
health problem having an important influence on employment and
earnings. As with ethnicity, it becomes important to differentiate between
the sub-groups to identify those who face the greatest labour market
disadvantage. The evidence suggests that those suffering from mental
health forms of disability fare particularly badly, and indicates that future
efforts may need to be directed towards assisting this particular group.
Although our data do not allow us to investigate the reasons for the
particularly extreme degree of disadvantage faced by this group, it would
be surprising if at least part of this did not result from some form of
discrimination (and most notably for those (re) joining the labour market).
As such, part of the answer may reside in improving employers’ access to
information concerning the various types of mental illness and their
implications for work. It may also be helpful to emphasise the ‘reasonable
adjustments’ that can be made for workers with this type of disability; the
55
popular conception of such adjustments perhaps being more with physical
environment.
Our wage decompositions suggest the ‘penalty’ for disability is greater for
women than for men, consistent with the presence of discrimination,
although we must note that it is possible that our controls for productivity
differences may be imperfect. Finally, we find little evidence using the
Baldwin and Johnson (1992) methodology that the employment effects
associated with discrimination in wages against the disabled are
substantial. However, there is a suggestion that the male disabled may be
becoming more sensitive to earnings than in the period prior to the
implementation of the DDA.
Table 1. Basic statistics
Variable
Hourly pay (£)
Proportion in employment
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Age
Age squared
Married
Region 1
Region 2
Region 3
Region 4
Region 6
Region 7
Region 8
Region 9
Region 10
White
Dependent children
Other earner
Social housing
Home owned
Male
Disabled
Non-disabled
9.307
11.207
0.309
0.790
0.065
0.172
0.075
0.131
0.282
0.310
0.102
0.152
0.156
0.124
46.837
38.129
2372.635
1634.930
0.564
0.543
0.084
0.053
0.100
0.095
0.072
0.076
0.029
0.034
0.080
0.091
0.093
0.091
0.116
0.100
0.075
0.047
0.106
0.095
0.931
0.925
0.444
0.609
0.427
0.670
0.339
0.113
0.238
0.169
56
Female
Disabled
Non-disabled
7.494
8.465
0.304
0.690
0.059
0.141
0.098
0.139
0.155
0.201
0.168
0.231
0.159
0.138
43.745
36.969
2049.125
1510.239
0.556
0.561
0.069
0.053
0.096
0.095
0.073
0.075
0.033
0.033
0.082
0.088
0.097
0.091
0.122
0.102
0.063
0.050
0.102
0.094
0.913
0.918
0.638
0.856
0.501
0.714
0.350
0.155
0.193
0.150
Home mortgaged
No. of health problems
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Occupation 2
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Experience
Experience squared
Overtime
Tenure
Tenure squared
Public sector
Small firm
Part time
Days illness
0.338
2.695
0.012
0.017
0.245
0.079
0.184
0.107
0.124
0.184
0.096
0.121
0.071
0.171
0.032
0.044
0.156
0.172
26.471
866.337
3.842
9.235
177.289
0.207
0.265
0.121
0.239
0.611
0.238
0.010
0.021
0.248
0.084
0.167
0.099
0.154
0.173
0.137
0.142
0.054
0.158
0.021
0.044
0.127
0.124
21.496
623.744
4.291
8.379
151.856
0.197
0.235
0.075
0.054
0.370
2.673
0.004
0.004
0.082
0.008
0.241
0.037
0.122
0.443
0.077
0.124
0.218
0.024
0.141
0.138
0.032
0.171
24.807
757.532
2.269
6.786
100.885
0.368
0.318
0.498
0.218
0.588
0.231
0.004
0.005
0.088
0.015
0.221
0.038
0.150
0.426
0.109
0.143
0.237
0.016
0.128
0.124
0.028
0.122
20.634
571.938
2.468
6.616
94.299
0.362
0.294
0.426
0.074
Notes: In all cases figures relate to the estimation samples used.
Table 2. Male labour force participation probit estimates
Male
Constant
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Age
Age squared
Married
Region 1
Region 2
Region 3
Disabled
Coefficient
t-stat
-3.215
-16.14 ***
0.784
11.06 ***
0.855
12.75 ***
0.559
11.92 ***
0.520
8.56 ***
0.525
9.77 ***
0.125
13.89 ***
-0.002
-15.38 ***
0.270
6.02 ***
-0.446
-6.20 ***
-0.049
-0.79
-0.036
-0.52
57
Non-disabled
Coefficient
t-stat
-4.926
-55.61 ***
0.383
11.12 ***
0.447
12.42 ***
0.336
11.60 ***
0.325
9.83 ***
0.323
9.45 ***
0.254
56.67 ***
-0.003
-55.26 ***
0.217
8.46 ***
-0.248
-6.11 ***
-0.048
-1.46
0.006
0.15
Region 4
Region 6
Region 7
Region 8
Region 9
Region 10
White
Dependent children
Other earner
Social housing
Home owned
Home mortgaged
No. of health problems
No obs
Log likelihood
χ2 (p-value)
Pseudo-R2
-0.264
0.139
0.033
-0.374
-0.406
-0.240
0.469
-0.009
0.387
-0.310
0.138
0.549
-0.236
-2.69 ***
2.13 **
0.52
-6.11 ***
-5.43 ***
-3.70 ***
6.51 ***
-0.44
10.41 ***
-4.74 ***
2.03 **
8.73 ***
-20.46 ***
-0.074
0.046
0.015
-0.186
-0.158
-0.093
0.624
-0.051
0.396
-0.287
-0.106
0.393
0.036
8349
-3673.015
2976.96 (0.000)
0.288
-1.45
1.33
0.43
-5.86 ***
-3.67 ***
-2.81 ***
20.07 ***
-4.25 ***
19.82 ***
-8.47 ***
-3.19 ***
13.76 ***
2.47 **
33781
-12797.960
9114.74 (0.000)
0.263
Notes: Regressions also include dummy variables for the quarter in which the individual was surveyed. ***, **
and * denote significance at the 1%, 5% and 10% level respectively. The χ2 statistic is a test that all
slope coefficients are zero. Pseudo-R2 is McFadden’s measure, defined as 1 minus the ratio of the
maximised log-likelihood from the regression to that from a regression including the optimal constant
only (Maddala, 1983).
Table 3. Female labour force participation probit estimates
Female
Constant
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Age
Age squared
Married
Region 1
Region 2
Region 3
Region 4
Region 6
Region 7
Region 8
Region 9
Disabled
Coefficient
t-stat
-3.095
-14.29 ***
1.052
14.80 ***
0.938
16.15 ***
0.720
13.97 ***
0.618
12.30 ***
0.523
10.19 ***
0.114
10.62 ***
-0.001
-10.83 ***
-0.137
-3.39 ***
-0.132
-1.88 *
-0.047
-0.77
0.023
0.34
-0.071
-0.77
0.139
2.23 **
0.076
1.26
-0.257
-4.35 ***
-0.229
-3.00 ***
58
Non-disabled
Coefficient
t-stat
-4.562
-53.49 ***
0.660
23.25 ***
0.833
29.56 ***
0.481
19.66 ***
0.502
21.17 ***
0.375
14.48 ***
0.223
47.24 ***
-0.003
-42.98 ***
-0.261
-13.38 ***
-0.017
-0.52
0.056
2.13 **
0.104
3.58 ***
-0.004
-0.10
0.157
5.68 ***
0.036
1.35
0.064
2.47 **
-0.004
-0.11
Region 10
White
Dependent children
Other earner
Social housing
Home owned
Home mortgaged
No. of health problems
No obs
Log likelihood
χ2 (p-value)
Pseudo-R2
-0.247
0.435
-0.162
0.402
-0.223
0.025
0.352
-0.172
-3.92 ***
6.62 ***
-8.09 ***
10.44 ***
-3.50 ***
0.37
5.72 ***
-16.49 ***
8200
-3947.932
2172.63 (0.000)
0.216
0.136
0.495
-0.364
0.322
-0.072
-0.008
0.427
0.014
5.02 ***
19.09 ***
-43.92 ***
17.96 ***
-2.60 ***
-0.27
17.80 ***
1.25
40427
-20446.192
9144.57 (0.000)
0.183
Notes: See notes to Table 2.
Table 4. Male selectivity corrected wage equation.
Constant
Region 1
Region 2
Region 3
Region 4
Region 6
Region 7
Region 8
Region 9
Region 10
Occupation 2
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Days illness
Male
Disabled
Non-disabled
Coefficient
t-stat
Coefficient
t-stat
1.716
14.75 ***
1.932
62.87 ***
-0.184
-4.65 ***
-0.189
-16.33 ***
-0.126
-4.41 ***
-0.199
-22.40 ***
-0.166
-5.28 ***
-0.172
-17.89 ***
-0.070
-1.50
-0.149
-10.94 ***
-0.104
-3.69 ***
-0.148
-16.64 ***
-0.132
-4.71 ***
-0.150
-16.65 ***
-0.129
-3.99 ***
-0.163
-18.22 ***
-0.262
-6.65 ***
-0.184
-15.27 ***
-0.157
-4.73 ***
-0.161
-17.94 ***
-0.068
-1.96 **
-0.062
-6.94 ***
-0.184
-5.87 ***
-0.190
-21.71 ***
-0.413
-11.13 ***
-0.403
-33.64 ***
-0.388
-12.97 ***
-0.413
-47.19 ***
-0.489
-9.42 ***
-0.542
-29.77 ***
-0.453
-10.01 ***
-0.483
-34.95 ***
-0.478
-15.10 ***
-0.511
-53.22 ***
-0.535
-17.28 ***
-0.593
-59.90 ***
-0.060
-0.75
-0.073
-2.71 ***
0.247
3.50 ***
0.217
10.64 ***
0.146
3.51 ***
0.098
7.49 ***
0.205
4.44 ***
0.129
8.90 ***
0.005
0.12
-0.034
-2.50 **
0.148
3.35 ***
0.090
6.38 ***
0.203
4.76 ***
0.201
15.02 ***
0.102
2.44 **
0.017
1.23
-0.014
-1.86 *
-0.015
-2.75 ***
59
Married
Experience
Exp Squared
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Small Firm
Part-time
White
Tenure
Ten squared
Public Sector
Overtime
No. of health problems
Social housing
Home owned
Home mortgaged
Lambda
No obs
RSS
F (p-value)
R2
0.086
0.026
0.000
0.463
0.262
0.157
0.179
0.073
-0.115
-0.088
0.076
0.009
0.000
0.023
0.001
-0.042
-0.105
0.034
0.100
0.083
3.74 ***
8.33 ***
-7.13 ***
10.01 ***
5.87 ***
4.64 ***
4.85 ***
2.03 **
-6.15 ***
-3.31 ***
1.78 *
3.59 ***
-0.64
0.80
0.60
-3.82 ***
-2.73 ***
0.98
2.78 ***
1.48
0.067
0.032
-0.001
0.377
0.202
0.115
0.082
0.041
-0.131
-0.033
0.043
0.009
0.000
0.007
0.004
-0.099
-0.010
0.015
0.051
0.024
2579
388.924
52.67 (0.000)
0.496
10.95 ***
27.29 ***
-24.24 ***
30.79 ***
17.14 ***
11.30 ***
7.41 ***
3.67 ***
-22.19 ***
-3.17 ***
3.65 ***
10.94 ***
-4.32 ***
0.68
11.02 ***
-8.12 ***
-2.51 **
1.39
5.80 ***
1.15
26692
4003.678
662.97 (0.000)
0.549
Notes: Regressions also include dummy variables for the quarter in which the individual was surveyed. ***, **
and * denote significance at the 1%, 5% and 10% level respectively. RSS denotes the residual sum of
squares. The F statistic is a test that all slope coefficients are zero.
Table 5. Female selectivity corrected wage equation.
Female
Constant
Region 1
Region 2
Region 3
Region 4
Region 6
Region 7
Region 8
Region 9
Region 10
Occupation 2
Occupation 3
Occupation 4
Disabled
Coefficient
t-stat
1.738
15.75 ***
-0.210
-6.06 ***
-0.172
-6.01 ***
-0.116
-3.79 ***
-0.166
-3.99 ***
-0.122
-4.56 ***
-0.146
-5.34 ***
-0.151
-4.88 ***
-0.153
-3.91 ***
-0.210
-6.45 ***
0.112
2.69 ***
-0.056
-1.55
-0.277
-8.46 ***
60
Non-disabled
Coefficient
t-stat
1.930
73.70 ***
-0.169
-15.98 ***
-0.160
-19.40 ***
-0.152
-16.95 ***
-0.131
-10.22 ***
-0.140
-17.05 ***
-0.137
-16.20 ***
-0.146
-18.15 ***
-0.141
-12.97 ***
-0.139
-17.05 ***
0.054
4.99 ***
-0.131
-13.61 ***
-0.331
-36.86 ***
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Days illness
Married
Experience
Exp squared
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Small firm
Part-time
White
Tenure
Ten squared
Public sector
Overtime
No. of health problems
Social housing
Home owned
Home mortgaged
Lambda
No obs
RSS
F (p-value)
R2
-0.411
-0.449
-0.436
-0.446
-0.495
-0.057
0.365
0.149
0.104
-0.004
0.094
0.228
0.050
-0.017
-0.020
0.016
0.000
0.390
0.247
0.177
0.138
0.083
-0.059
-0.058
-0.035
0.015
0.000
0.112
0.005
-0.009
-0.024
0.004
0.080
0.135
-7.33 ***
-12.45 ***
-11.47 ***
-8.08 ***
-13.78 ***
-0.47
2.84 ***
3.38 ***
1.20
-0.12
1.89 *
5.93 ***
1.47
-2.02 **
-1.18
5.76 ***
-5.51 ***
7.41 ***
5.45 ***
4.53 ***
3.81 ***
2.39 **
-3.44 ***
-3.41 ***
-0.89
5.02 ***
-2.45 **
4.91 ***
2.51 **
-0.26
-2.97 ***
0.13
2.51 **
2.58 ***
2490
334.748
47.74 (0.000)
0.479
-0.493
-0.490
-0.463
-0.555
-0.551
0.005
0.222
0.132
0.162
-0.026
0.172
0.191
0.029
-0.009
-0.012
0.021
0.000
0.351
0.200
0.101
0.058
0.047
-0.071
-0.030
-0.006
0.015
0.000
0.054
0.004
-0.029
-0.007
-0.004
0.026
-0.011
-25.71 ***
-46.55 ***
-42.56 ***
-33.85 ***
-50.72 ***
0.14
7.01 ***
10.10 ***
7.80 ***
-2.24 **
11.49 ***
16.61 ***
2.72 ***
-1.95 *
-2.24 **
25.63 ***
-24.58 ***
28.93 ***
17.94 ***
10.46 ***
6.26 ***
4.78 ***
-13.95 ***
-5.68 ***
-0.55
15.84 ***
-5.99 ***
7.63 ***
7.71 ***
-2.71 ***
-1.87 *
-0.36
3.05 ***
-0.77
27907
3742.959
628.06 (0.000)
0.524
Notes: See notes to Table 4.
Table 6. Disabled labour force participation probits
Male
Constant
Qual 1
Qual 2
Coefficient
-3.878
0.763
0.825
t-stat
-18.48 ***
10.53 ***
12.09 ***
61
Female
Coefficient
-3.929
1.076
0.900
t-stat
-17.07 ***
14.68 ***
15.19 ***
Qual 3
Qual 4
Qual 5
Age
Age squared
Married
Region 1
Region 2
Region 3
Region 4
Region 6
Region 7
Region 8
Region 9
Region 10
White
Dependent children
Other earner
Social housing
Home owned
Home mortgaged
No. of health problems
Health 1
Health 2
Health 3
Health 5
No obs
Log likelihood
χ2 (p-value)
Pseudo-R2
0.497
0.474
0.482
0.133
-0.002
0.218
-0.469
-0.070
-0.048
-0.275
0.110
0.004
-0.371
-0.417
-0.263
0.520
-0.028
0.394
-0.283
0.161
0.556
-0.233
0.719
0.815
0.836
0.375
10.42 ***
7.62 ***
8.84 ***
14.39 ***
-16.25 ***
4.80 ***
-6.40 ***
-1.11
-0.67
-2.75 ***
1.66 *
0.06
-5.97 ***
-5.46 ***
-3.99 ***
7.11 ***
-1.36
10.44 ***
-4.26 ***
2.32 **
8.68 ***
-19.98 ***
11.93 ***
8.61 ***
13.63 ***
5.17 ***
8321
-3543.526
3205.58 (0.000)
0.311
0.693
0.605
0.507
0.127
-0.002
-0.157
-0.149
-0.082
0.014
-0.083
0.132
0.063
-0.268
-0.255
-0.240
0.501
-0.184
0.369
-0.223
0.032
0.351
-0.169
0.816
0.720
0.924
0.497
13.15 ***
11.78 ***
9.70 ***
11.41 ***
-11.94 ***
-3.82 ***
-2.08 **
-1.32
0.20
-0.89
2.08 **
1.02
-4.46 ***
-3.27 ***
-3.72 ***
7.47 ***
-8.98 ***
9.38 ***
-3.41 ***
0.46
5.57 ***
-15.83 ***
13.79 ***
7.08 ***
14.91 ***
7.38 ***
8163
-3.792.864
2442.70 (0.000)
0.244
Notes: See notes to Table 2.
Table 7. Disabled selectivity corrected wage equation
Constant
Region 1
Region 2
Region 3
Region 4
Region 6
Region 7
Region 8
Region 9
Region 10
Occupation 2
Male
Coefficient
1.673
-0.179
-0.123
-0.163
-0.062
-0.106
-0.136
-0.120
-0.256
-0.154
-0.067
Female
t-stat
Coefficient
12.79 ***
1.620
-4.58 ***
-0.213
-4.32 ***
-0.176
-5.21 ***
-0.118
-1.32
-0.166
-3.78 ***
-0.126
-4.84 ***
-0.148
-3.76 ***
-0.154
-6.56 ***
-0.155
-4.67 ***
-0.209
-1.93 *
0.114
62
t-stat
11.95 ***
-6.13 ***
-6.10 ***
-3.84 ***
-3.99 ***
-4.67 ***
-5.39 ***
-4.95 ***
-3.96 ***
-6.47 ***
2.72 ***
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Industry 7
Industry 8
Days illness
Married
Experience
Exp squared
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
Small firm
Part-time
White
Tenure
Ten squared
Public sector
Overtime
No. of health problems
Social housing
Home owned
Home mortgaged
Lambda
Health1
Health2
Health3
Health5
No obs
RSS
F (p-value)
R2
-0.181
-0.413
-0.386
-0.489
-0.457
-0.479
-0.533
-0.068
0.227
0.133
0.194
-0.005
0.136
0.194
0.094
-0.014
0.077
0.025
0.000
0.447
0.248
0.139
0.160
0.058
-0.116
-0.079
0.072
0.010
0.000
0.018
0.001
-0.103
-0.036
0.028
0.087
0.058
0.117
0.146
0.121
0.057
-5.76 ***
-11.11 ***
-12.86 ***
-9.43 ***
-10.11 ***
-15.12 ***
-17.21 ***
-0.84
3.21 ***
3.20 ***
4.19 ***
-0.12
3.09 ***
4.52 ***
2.25 **
-1.76 *
3.61 ***
8.03 ***
-6.83 ***
10.03 ***
5.82 ***
4.39 ***
4.49 ***
1.70 *
-6.20 ***
-2.95 ***
1.69 *
3.72 ***
-0.73
0.62
0.45
-2.71 ***
-3.46 ***
0.82
2.47 **
1.10
2.80 ***
2.80 ***
2.74 ***
1.34
2573
386.103
49.03 (0.000)
0.497
Notes: See notes to Table 4.
63
-0.054
-0.277
-0.414
-0.452
-0.439
-0.449
-0.496
-0.054
0.359
0.147
0.095
-0.007
0.093
0.227
0.050
-0.017
-0.024
0.016
0.000
0.392
0.243
0.174
0.137
0.081
-0.058
-0.056
-0.027
0.015
0.000
0.108
0.004
-0.010
-0.024
0.006
0.079
0.141
0.138
0.084
0.143
0.069
-1.50
-8.43 ***
-7.33 ***
-12.50 ***
-11.51 ***
-8.12 ***
-13.76 ***
-0.44
2.78 ***
3.31 ***
1.09
-0.18
1.87 *
5.91 ***
1.46
-1.97 **
-1.36
5.82 ***
-5.65 ***
7.45 ***
5.49 ***
4.55 ***
3.82 ***
2.36 **
-3.35 ***
-3.29 ***
-0.66
5.03 ***
-2.43 **
4.73 ***
2.46 **
-0.27
-2.99 ***
0.18
2.50 **
2.66 ***
3.07 ***
1.47
3.01 ***
1.62
2482
333.495
43.92 (0.000)
0.478
Table 8. Disabled and non-disabled wage decomposition
Male
Female
Mean prediction non-disabled
2.238
1.990
Mean prediction disabled
2.010
1.760
Raw differential
0.228
0.230
- due to endowments
0.162
0.118
- due to coefficients
0.119
0.152
- due to interaction
-0.053
-0.040
D:
0
1
0.5 0.912 *
0
1
0.5 0.918 *
Unexplained
0.066 0.119 0.092 0.114 0.104 0.113 0.152 0.132 0.149 0.145
Explained
0.162 0.109 0.135 0.114 0.123 0.118 0.078 0.098 0.081 0.085
% unexplained
29 52.1 40.6 50.1 45.8 48.9 66.1 57.5 64.7 63.1
% explained
71 47.9 59.4 49.9 54.2 51.1 33.9 42.5 35.3 36.9
Differential due to selection variable
-0.060
-0.125
Table 9. Gender wage decomposition
Non-disabled
Disabled
Mean prediction males
2.238
2.010
Mean prediction females
1.990
1.760
Raw differential
0.248
0.250
- due to endowments
0.101
0.071
- due to coefficients
0.110
0.133
- due to interaction
0.036
0.046
D:
0
1
0.5 0.489 *
0
1
0.5 0.509 *
Unexplained
0.147 0.110 0.128 0.129 0.080 0.179 0.133 0.156 0.156 0.113
Explained
0.101 0.137 0.119 0.119 0.168 0.071 0.117 0.094 0.094 0.137
% unexplained
59.3 44.5 51.9 52.1 32.3 71.7 53.2 62.5 62.3 45.1
% explained
40.7 55.5 48.1 47.9 67.7 28.3 46.8 37.5 37.7 54.9
Differential due to selection variable
0.011
-0.055
Disabled with controls
for type of health problem
2.031
1.759
0.271
0.070
0.156
0.046
0
1
0.5 0.509 *
0.202 0.156 0.179 0.179 0.134
0.070 0.115 0.092 0.093 0.138
Mean prediction males
Mean prediction females
Raw differential
- due to endowments
- due to coefficients
- due to interaction
D:
Unexplained
Explained
64
% unexplained
% explained
Differential due to selection variable
74.3 57.6 65.9 65.8 49.2
25.7 42.4 34.1 34.2 50.8
-0.076
Table 10. Employment effects of wage differences
Employment probability
- Non-disabled discriminatory
- Non-disabled non-discriminatory
- Disabled discriminatory
- Disabled non-discriminatory
Employment elasticities
- Non-disabled
- Disabled
Male
Female
0.8513
0.8509
0.2343
0.2456
0.7202
0.7200
0.2494
0.2498
0.212
1.514
0.109
0.149
VARIABLE DEFINITIONS
Dependent variables
(Log) hourly wages
Employment
participation
Human capital variables
Experience
Gross weekly earnings divided by usual hours worked per week
Dummy variable equal to 1 if individual has a positive hourly
wage, 0 else
Qual 6
Years of (potential) labour market experience (age minus
school-leaving age)
Years in present job
Dummy variable, equals 1 if highest qualification is university
degree or higher degree
Dummy variable, equals 1 if highest qualification is other degree
Dummy variable, equals 1 if highest qualification is A level
Dummy variable, equals 1 if highest qualification is O level
Dummy variable, equals 1 if highest qualification is other
qualification
Dummy variable, equals 1 if no qualifications (base)
Industry variables
Industry 1
Industry 2
Industry 3
Industry 4
Industry 5
Industry 6
Agriculture and fishing
Energy and water
Manufacturing
Construction
Distribution, hotels etc
Transport communication etc
Tenure
Qual 1
Qual 2
Qual 3
Qual 4
Qual 5
65
Industry 7
Industry 8
Industry 9 and 10
Banking and finance
Public administration
Other (base)
Occupation variables
Occupation 1
Occupation 2
Occupation 3
Occupation 4
Occupation 5
Occupation 6
Occupation 7
Occupation 8
Occupation 9
Managers and senior officials (base)
Professional occupations
Associate professional and technical
Administrative and secretarial
Skilled trades
Personal service occupations
Sales and customer service occupations
Process, plant and machine operatives
Elementary occupations
Region variables
Region 1
Region 2
Region 3
Region 4
Region 5
Region 6
Region 7
Region 8
Region 9
Region 10
North
Yorkshire and Humberside
East Midlands
East Anglia
South East and London (base)
South West
West Midlands
North West
Wales
Scotland
Health variables
Days illness
No of health problems
Health 1
Health 2
Health 5
Number of days off sick in the reference week (0-7)
Number of health problems reported
Dummy variable, equals 1 if main health problem affects limbs
Dummy variable, equals 1 if main health problem affects
sight/hearing
Dummy variable, equals 1 if main health problem affects skin,
breathing and organs
Dummy variable, equals 1 if main health problem is mental
health (base)
Dummy variable, equals 1 if main health problem is other
Housing status variables
Social housing
Home owned
Home mortgaged
Private rent
Dummy variable, equals 1 if renting from non-private sector
Dummy variable, equals 1 if home owned outright
Dummy variable, equals 1 if home mortgaged
Dummy variable, equals 1 if renting from private sector (base)
Other variables
Age
Married
Age (years)
Dummy variable denoting marital status, equals 1 if married
Health 3
Health 4
Dependent children
Number of dependent children in household if head
of household or spouse (0 else)
66
Other earner
White
Small firm
Public
Part-time
Overtime
Dummy variable, equals 1if there is another
individual in household has a labour market income
Dummy variable denoting ethnic group, equals 1 if white
Dummy variable denoting marital status, equals 1 if less than 20
employees in firm
Dummy variable, equals 1 if individual is employed in the
public sector
Dummy variable, equals 1 if employed part time
Amount of usual overtime (hours)
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