Does Changing Occupational Segregation Explain the Narrowing Racial Wage
Difference in the UK?
Hongliang Zhanga
Yu Zhub
Abstract
The past two decades has been a period in which the minorities in the UK have significantly
increased their share of employment in high-paying skilled occupations such as managerial,
professional, and technical occupations, relative to whites. By exploiting the largest possible
individual level data that is available, we focus on the role of occupational segregation on the
changes in the racial wage gap among UK born prime age male employees in this paper.
Following DiNardo, Fortin, and Lemieux (1996) and Lemieux (2002, 2006), we apply a
reweighting procedure to decompose the observed white-minority wage gap into the
explained portion attributable to the differences in the distribution of the observable
characteristics, and the unexplained portion, due to differential returns to the observable
characteristics. These differences are further decomposed into the corresponding between and
within occupational differences. To the best of our knowledge, this is the first study to apply
the reweighting procedure to examine the role of occupations in the context of racial wage
decomposition.
We start by documenting a substantial reduction in the observed racial wage differential
between 1993-98 and 1999-2007, in the order of 0.10 log points. However there was a slight
reversal of the trend during the Great Recession which began in 2008. Our results further
show that this dramatic narrowing of the racial wage gap in the UK is entirely accounted for
by the changes in the unexplained part, driven by the within occupational components. In
contrast, the improvement in the explained within occupation racial wage gap is largely offset
by a worsening of its between-occupational counterpart, thus leaving the total explained wage
gap almost constant over time.
Keywords: racial wage decomposition, between and within occupational differences, reweighting procedure
JEL classification: J31 (Wage Level and Structure; Wage Differentials); J71 (Discrimination)
a
b
Department of Economics, Chinese University of Hong Kong. E-mail: [email protected].
School of Economics, University of Kent. E-mail: [email protected].
1
1. Introduction
The economic studies of inequality have been growing very rapidly over the past quarter
century. Many researchers have attempted to extend the original Blinder-Oaxaca
decomposition methodologies (Blinder 1973, Oaxaca 1973) which usefully divide the
observed differentials in earnings or wages into an explained part, which can be attributed to
differences in observable characteristics, and an unexplained part, sometimes known as the
wage structure effect, which is due to “discrimination”. The role played by occupational
segregation has drawn particular attention of economists and sociologists, as Altonji and
Blank (1999, p3176) put it:
“A vast literature has emerged in sociology and economics that is concerned with the
fact that men and women and whites and blacks tend to work in different
occupations.”
However, it is not straight forward to allow for differences in occupational distributions
across two groups in the original Blinder-Oaxaca decomposition framework. To the extent
that occupational assignment itself could be subject to labour market discrimination, simply
adding occupational dummies to the regression is clearly not satisfactory, due to the implicit
assumption that occupations are exogenously determined. The Brown et al. approach (Brown
et al. (1980)) has been used successfully to assess the relative importance of within- and
between-occupation wage effects (see e.g. Liu et al. 2004). However, somewhat arbitrary
assumptions have to be made about the functional form of the occupational selection
equations. Moreover, finding plausible exclusion restrictions on how occupational
segregation interacts with wage determination could be challenging in empirical studies.
In this paper, we study the role of occupational segregation on the changes in the racial wage
gap among UK born male employees aged 25-49 in the pooled Labour Force Survey 19932012. This is a period in which the minorities have significantly increased their share of
employment in high-paying skilled occupations such as managerial, professional, and
technical occupations.
Following DiNardo, Fortin, and Lemieux (1996) and Lemieux (2002, 2006), we use the
methodology of applying the reweighting procedure to decompose the observed whiteminority wage gap into the explained portion, attributing to their differences in the
distribution of the observables X, and the unexplained portion, owing to their differences in
the returns to observable characteristics. These differences are further decomposed into the
2
corresponding between and within occupational differences. To the best of our knowledge,
this is the first study to apply the reweighting procedure to examine the role of occupations,
in the context of racial wage decomposition. Apart from the simple and economically
interpretable results, this reweighting approach also has the advantage of not requiring any
regression analysis or linearity assumptions, compared to the Brown et al. approach.
Our empirical analysis shows that the remarkable narrowing of the racial wage gap in the UK
over the past two decades is entirely accounted for by the changes in the unexplained part,
driven by the within occupational components. On the other hand, the improvements in the
explained within occupation racial wage gap is largely offset by a worsening of its betweenoccupational counterpart, thus leaving the total explained wage gap almost constant over
time.
The rest of the paper is organized as follows. Section two reviews the relevant literature.
Section three outlines the methodology of applying the reweighting procedure to decompose
the observed white-minority wage gap in the context of occupational segregation. Section
four describes and summarizes the data. Section five presents the empirical results. The last
section concludes.
2. Literature Review
The study of the native-immigrant wage gap and its change over time is not only of academic
interest, but also has great policy relevance. All major immigrant receiving countries have a
constant need of monitoring the native-immigrant gap, and the UK is no exception. Virtually
all recent studies on the immigrant-native wage differentials in the UK are based on the
Labour Force Survey (LFS), which is also used in this paper.1
Clark & Lindley (2009) use LFS pooled over 1993-2004. They distinguish between labour
market and education entrants, with the former directly entering the labour market upon
arrival while the latter typically entering the UK as a child and hence having received some
UK education. They find that the latter group is doing better than the former, and indeed
performing as well as white natives. However, they find no evidence to support the textbook
model of assimilation.
1
Wage or earning information was not available in the LFS until 1993. Earlier studies on the racial wage gap in
the UK were based on other data sources, such as the General Household Survey, see e.g. Chiswick (1980).
3
Hunt (2008) uses quantile regression to allow for heterogeneous effects. Using LFS pooled
over 1993-2005, she finds that there is more discrimination at the bottom while the positive
wage gap in favour of immigrants is attributed to those at higher quantiles.
Dustmann et al. (2010) analyse differential responses between immigrants and natives in the
cyclical pattern of employment and wages for Germany and the UK, with the latter based on
the LFS pooled over 1981-2005. While they find little evidence for differential wage
responses to economic shocks between natives and immigrants, there appear to be larger
differences in unemployment responses for immigrants relative to natives.
Longhi et al. (2013) study wage gaps of ethno-religious groups of male workers in Great
Britain using LFS pooled over 2002-2009 which contains religious affiliation. They include
in their decomposition analysis dummies for the one-digit Standard Occupation Classification
(SOC), as well as the top five occupations at the three-digit level in which certain minority
groups show relatively high concentrations. Their results suggest that there is a glass ceiling
for the most advantaged minorities, but little evidence of a sticky floor for the least
advantaged.
Liu et al. (2004 JDE) provides the first set of evidence on the intra- and inter-occupational
earnings differentials between immigrants and the native by studying Hong Kong 1996
census data. Following the Brown et al. approach, they show that the intra-occupational
earnings differential is largely unexplained and more important than its inter-occupational
counterpart. Moreover, there is also evidence of diminishing segregation with increased
residence in Hong Kong.
Demoussis et al. (2010) decompose native–immigrant wage differentials in Greece into inter
and intra occupation differences. It is shown that nearly half of the wage differential cannot
be explained by differences in characteristics, with asymmetrical occupational access by
native and immigrant workers being the dominant channel.
However, no UK study so far has attempted to distinguish the intra- and inter-occupational
earnings differentials between immigrants and the native. This is surprising given the
important policy relevance of the topic. In this paper we will fill in the gap.
4
3. Methodology
Following DiNardo, Fortin, and Lemieux (1996) and Lemieux (2002, 2006), we present in
this section the methodology of applying the reweighting procedure to decompose the
observed white-minority wage gap. For the ease of illustration, we present saturated version
of this reweighting procedure assuming the observables X can be classified into a limited
number of categories, i.e., coarse education-age cells in our case. In Section 3.1, we first
decompose the white-minority wage difference into the explained portion, attributing to their
differences in the distribution of the observables X, and the unexplained portion, owing to
their differences in the returns to observable characteristics. In Section 3.2, we further
separate both the explained and unexplained portions of white-minority wage difference into
the between and within occupational differences.
3.1 Decomposition into explained and unexplained difference
Let
and
denote the mean wage (in logarithm) of whites and minorities, respectively.
Thus, the observed white-minority wage difference is
. However, such difference is
at least in part attributable to racial differences in the distribution of observables X. To start
with, we assume the observables X can be fully saturated into J categories, i.e., coarse
education-by-age-by-region cells in our case, there are a total of K occupations. Define
as the counterfactual occupational structure of whites if they face the same conditional
occupational distribution as minorities, i.e.,
.
(1)
That is, we apply the conditional occupational structure of minorities to the distribution of
observables of whites. Let
be the counterfactual mean wage of whites if they
face the same conditional occupational structure and within each occupation the same wage
structure as minorities. Note that the subscript “M” in
denotes that the wage structure of
minorities is applied here. This counterfactual mean wage can be calculated as
(2)
5
In equation (2),
denotes the counterfactual employment share of occupation
for
whites if they had the same conditional occupational distribution as minorities,
denote the employment share of occupation C for minorities,
denotes the
proportion of whites in occupation C with observables X, and
denotes the
proportion of minorities in occupation C with observables X. That is, for each minority
worker, we first weight by the ratio of the representation of his observables among white
workers relative to minority workers in his own occupation, and then weight by the ratio of
the representation of his occupation among white workers had they faced the conditional
occupational structure of minorities relative to the representation of his occupation among
minority workers.
With this counterfactual mean wage,
, the observed white-minority wage gap
can be decomposed as follows
(3)
As
is the counterfactual mean wage of whites when they faced the same wage
structure in each occupation as minorities while holding constant both the conditional and
unconditional distribution of their observables, the first component in equation (3)
corresponds to the unexplained portion of the white-minority wage difference attributing to
their differences in labour market rewards (in both the occupational structure and withinoccupation wage structure), whereas the second component in equation (3) corresponds to the
explained portion of the white-minority wage difference owing to their differences in
observables (which can lead to differences in both the occupational structure and wages paid
within each occupation).
3.2 The further decomposition into the inter-occupational and intra-occupational
differences
In this subsection, we further consider the role of occupations in explaining the whiteminority wage gap, which can work through both the inter-occupational channel, i.e., whites
and minorities differ in their occupational distributions, and intra-occupational channel, i.e.
within the same occupation whites and minorities are paid differently. Moreover, both
6
channels can work through the explained and unexplained portions of white-minority wage
gap.
Let
be the counterfactual mean wage of whites had they faced the same wage
structure in each occupation as minorities while holding constant both their occupational
distribution and conditional distribution of observables within each occupation. This
counterfactual mean wage of whites can be calculated as follows
(4)
In contrast to
used in equation (2) that corresponds to the counterfactual
employment share of occupation C for whites,
in equation (4) is the actual
employment share of occupation C for whites. With this counterfactual mean wage,
, we can further decompose the unexplained portion of the white-minority
wage difference into the within and between-occupation differences as follows
(5)
The first component in equation (5),
, measures the
counterfactual white-minority wage difference even if they had the same occupational
structure and the same conditional distribution of observables within each occupation but
different wage structure. As it is entirely attributed to racial differences in wages to workers
with the same observables employed in the same occupation, we refer to this component as
“within-occupation unexplained” (WU) portion of the white-minority wage difference. The
second component in equation (5),
, compares the two
counterfactual wage of whites when they faced minorities’ wage structures. The two cases
differ in the conditional occupational structures: the former applies the conditional
occupational distribution of whites whereas the latter applies that of minorities. As the
difference is entirely attributable to white-minority differences in occupational distributions,
we thus refer to this component as “between-occupation unexplained” (BU) portion of the
white-minority wage difference.
7
We next consider the working of white-minority differences in observables X through the
between and within-occupation channels. Let
be the counterfactual mean
wage of minorities if they had the same conditional distribution of X as whites within each
occupation (i.e.,
the same (i.e.,
) while having their unconditional occupational distribution remaining
). This counterfactual mean wage of minorities can be calculated by
reweighting minorities’ wages to reflect the extent to which the observables X are relatively
represented among their white counterparts in the same occupation:
(6)
Note that this reweighting adjusts the conditional distribution X|C of minorities to be the
same as whites while keeping the occupational distribution of minorities. Therefore, the
explained portion of the white-minority wage difference can be further decomposed into the
between- and within-occupation channels as follows
(7)
Note that as
is predicted applying the occupational structure of minorities to the
distribution of observables of whites, the difference between
and
is due to white-
minority differences in observables. Thus, the first component in equation (7),
, measures the white-minority wage difference due to their
differences observables working through the inter-occupational channel only (as the
conditional distribution of observables in each occupation is held the same for the two racial
groups). We therefore refer to this component as “between-occupation explained” (BE)
portion of the white-minority wage difference. The second component in equation (7),
, is entirely attributable to white-minority differences in
conditional distribution in observables within each occupation and is therefore referred to as
the “within-occupation explained” (WE) portion of the white-minority wage difference.
8
A full version of our decomposition is summarized in the following equation:
(8)
4. Data
This analysis is based on the UK Labour Force Survey (LFS), the largest regular household
survey in the UK. The LFS is intended to be representative of the whole UK population, with
about 60,000 responding households in each quarter. Since 1992, the LFS has had a rotating
panel design where each household participates for five consecutive quarterly waves.
However, earnings information for employees is only available in the fifth (outgoing) wave.
In this paper, we pool Wave 5 data from the survey years 1993 to 2012.2
To focus attention to the white-minority wage gap, we exclude from our sample all non UKborn immigrants to avoid issues related to immigration selection (see, e.g., Clark & Lindley
2009) and English language proficiency (see, e.g., Miranda & Zhu 2013). We also drop
individuals under 25 because they are likely to be still in education and individuals over 50
because of having too few minority observations in this group. Our white sample includes all
individuals in the LFS identifying themselves as ethnic whites in answering the ethnic origin
question, while the remaining individuals (i.e., non-whites) are classified as minorities. After
exclusion of observations with missing values on key variables, including log real gross
hourly wage, we get an analytical sample containing 136,915 observations, of which 2,742 or
2.0% are minorities.
In the interest of consistency and clarity, we classify qualifications using the original National
Qualifications Framework (NQF).3 Specifically, qualifications are classified into five “levels”
2
The earliest wave 5 with earnings is spring 1993.
3
See Ofqual’s webpage: http://ofqual.gov.uk/qualifications-and-assessments/qualification-frameworks/levels-
of-qualifications/).
9
where (1) “Level 1” denotes a sub-GCSE4 level qualification, (2) “Level 2” denotes a GCSElevel qualification,5 (3) “Level 3” denotes an A-level6 or equivalent qualification, (4) “Level
4” denotes a first degree or equivalent qualification, and (5) “Level 5” denotes a higher
degree at postgraduate level.
The sparse observations for minorities (2% of the sample) necessitate a parsimonious
specification for the application of our proposed method (the discrete version). The
observable characteristics consist of educational qualifications (3 types), age range (two
types) and region of residence (2 types). So the qualifications are classified as high (NQF
Levels 4-5), medium (Levels 2-3) and low (Levels 0-1). For our sample of 25-49 year olds,
we only distinguish between aged 40 and above, or below 40. As for region, we distinguish
between the Southeast region including London, and the rest of the UK.
Table 1 about here
Table 1 shows summary statistics for the pooled sample, broken down by ethnicity. The final
column shows the gap in the means. The raw white-minority wage gap for the whole sample
period is 0.043 log points, which is relatively small but statistically significant at 1%.
Minorities are significantly over-represented at higher level of qualifications. For example,
minorities are 10.5 percentage points (or 27% in relative terms) more likely to hold a degree
level qualification. In contrast, whites are 2.4 percentage points (12% in relative terms) more
likely to hold low level qualifications and 8.1 percentage points (28% in relative terms) more
likely to hold medium level qualifications. Moreover, whites are 18.3 percentage points
(87%) more likely to be above 40, compared to minorities. It is worth noting that minorities
are 24 percentage points (82%) more likely to live in the Southeast region (including
London).
Using a three-group classification of occupations, we find that there is no significant
difference in the occupational distribution by race. However, this might mask changes in
composition over time, which are of more interest from a policy perspective.
4
GCSE stands for General Certificate of Secondary Education.
5
The difference between Level 1 and Level 2 is in the quality rather than the quantity, as the latter requires
achieving 5 or more GCSEs at Grade C or above, which is the government’s “gold standard” for leavers of
compulsory education at age 16.
6
A-Level is shorthand for the General Certificate of Education Advanced Level.
10
2.35
2.4
2.45
2.5
2.55
2.6
2.65
2.7
2.75
Fig 1: Mean male log real hourly wage, by minority status and year
93/94 95/96 97/98 99/00 01/02 03/04 05/06 07/08 09/10 11/12
Whites
Minorities
Figure 1 visually presents the changes in the racial wage gap over the 20 year sample period,
broken down by ten two-year intervals. The effect of the Great Recession which started in
2008 is highly visible, with a notable decrease in real hourly wages for both groups. The first
15 years, on the other hand, represents the longest spell of uninterrupted economic growth in
the UK since the Second World War, as evidenced by the steady growth in wages for whites.
The raw racial wage gap in 1993-94 was highly significant, at 0.142 log points, and peaked in
1995-96 at 0.165 log points. This implies that a UK-born ethnic minority male would earn
one-sixth less than his white counterpart in the mid-1990s. However, over the next decade of
1997-2006 this gap was dramatically narrowed, so much so that by 2005-06 the gap was
slightly in favour of minorities, although statistically insignificant. With the onset of the
Great Recession in 2008, minorities were disproportionately affected, with the gap jumping
to 0.077 log points for 2007-08. By the end of the sample period, the gap was narrowed again
to 0.027 log points for 2011-12.
In Table 2, we focus on the temporal changes in the wage differentials and the corresponding
differences in mean characteristics across the two groups. It is clear that between 1993-98
11
and 1999-2007,7 the raw racial wage differential was reduced by over 0.10 log points. Even
with the slight increase in the recession period, the gap was still under 0.04 log points for the
2008-12 period as a whole.
In the first sample period (1993-98), minorities were significantly less likely to hold medium
level qualifications, while more likely to have both low (sub-GCSE) and high level (degree or
above) qualification, compared to whites. However, minorities’ lead in higher qualifications
was not significant even at the 10% level. Over the next decade and a half, minorities
substantially widened their lead over whites in high level qualification, at the expense of both
low and medium level qualifications. The leap in the excess share of minorities holding
degree level qualifications was over 9 percentage points, making minorities 26% more likely
to hold high level qualifications by 2008-12.
Unsurprisingly, this trend in qualifications is reflected in the changing occupational
distribution. Over the sample period, whites’ excess share in high-skilled occupations, i.e.
managerial & senior administrator as well as professional occupations was reduced from
0.079 to -0.018, although the latter was not statistically significant. There was a
corresponding 8.4 percentage points swing in the opposite direction in the excess share of
medium skilled occupations. As for the share of low-skilled occupation, there was no
significant change in the white-minority differential over time. Therefore the main story
seems to be a significant occupational upgrading over time for minorities relative to their
white counterparts, mostly from mid-skilled to high-skilled jobs.
5. Empirical Results
5.1. Wage equation as benchmarks
We start our empirical analysis by presenting in Table 3 wage equations on a “saturated
model” with all possible combinations of the three categorical variables for qualifications,
age and region, by sample periods and for whites and minorities separately. We assume that
these three dimensions capture all the relevant observable characteristics, for the purpose of
7
The first period predates the introduction of the National Minimum Wage in April 1999 and
the enactment of the Race Relations (Amendment) Act 2000.
12
our study of the racial wage gap. In Table 4, we add dummies for occupations to show that
the effect of allowing for differences in occupational segregation.
Looking across the columns in Table 3, we find that the returns to the observables are by and
large constant over time for whites. On the other hand, the returns to medium and high level
qualifications for minorities have increased substantially over time. When we control for
broad occupation groups in Table 4, the returns to medium and high qualifications trend
downwards for whites, while the corresponding positive trends for minorities are maintained.
In the standard Blinder-Oaxaca decomposition framework, the changes in the observed racial
wage gap over time are explained by the changes in the means of observables, or the changes
in the (relative) returns, or the interactions. However, this does not allow for changes in the
relative occupational distributions which in turn might be determined by the observables in
some unknown functional form.
Compared to the weighting method we have developed, the regression approach (even the
standard Blinder-Oaxaca decomposition or the Brown et al. (1980) approach allowing
occupations to be determined by observables) has to impose arbitrary functional forms and/or
exclusion restriction assumptions on how occupational segregation interact with wage
determination.
5.2. Decomposition, by sample periods and pooled
Table 5 presents the results of the new decomposition for the 3 subsample periods separately
in columns 1-3, as well as for the full sample period in the last column. The first row reports
the observed mean total log wage differential. This is first decomposed into the total
explained and the total unexplained parts in rows 2 and 3. In rows 4-5, the observed racial
wage differential is alternatively decomposed into the between-occupation and the withinoccupation parts, which are in turn attributed to the respective explained and the unexplained
components. So the total explained part is the sum of the explained inter-occupational wage
gap and the explained intra-occupational wage gap, whereas the total unexplained part is
derived by adding the unexplained between and within-occupational wage gap.
The first thing to note is that the 0.099 (i.e. 0.138-0.039) log points change in the observed
racial wage gap over time can be fully accounted by the changes in the unexplained part. In
terms of the total explained, there is a 0.03 log point improvement between the first and the
13
second period. However, by the time of the recession period, the contribution of the total
explained part is back to where it started.
The total unexplained part, which is associated with “discrimination”, has reduced by 60%
(from 0.176 to 0.070 log points) in absolute value over time. However, in terms of its relative
contributions, it is becoming more important.
As for the changes in occupational segregation, we find a much larger reduction in the
within-occupational gap (0.070 log points) than in the between-occupational gap (0.028 log
points) over time. However, due to its much smaller initial value, the smaller reduction in the
between-occupational gap has been sufficient to reverse the sign from being favourable to
whites in period one to being slightly in favour of minorities by the time of the second period.
This means that the within gap can more than fully account for the observed wage gap since
the second period.
Among the explained components, the within and the between-occupation wage differentials
have moved in opposite direction, with the former making a slightly larger contribution to the
change in the total. In contrast, among the unexplained components, between-occupation
discrimination has barely changed while within-occupation discrimination decreases
dramatically, by 0.117 log points over the whole sample period.
To sum up, the remarkable narrowing of the racial wage gap over the past two decades is
entirely accounted for by the unexplained part, driven by the within occupational
components. The improvements in the explained within occupation racial wage gap is largely
offset by a worsening of its between-occupational counterpart, thus leaving the total
explained wage gap almost constant over time.
6. Concluding Remarks
The past two decades has been a period in which the minorities in the UK have significantly
increased their share of employment in high-paying skilled occupations such as managerial,
professional, and technical occupations, relative to whites. By exploiting the largest possible
individual level data that is available, we focus on the role of occupational segregation on the
changes in the racial wage gap among UK born prime age male employees in this paper.
Following DiNardo, Fortin, and Lemieux (1996) and Lemieux (2002, 2006), we apply a
reweighting procedure to decompose the observed white-minority wage gap into the
14
explained portion attributable to the differences in the distribution of the observable
characteristics, and the unexplained portion, due to differential returns to the observable
characteristics. These differences are further decomposed into the corresponding between and
within occupational differences. To the best of our knowledge, this is the first study to apply
the reweighting procedure to examine the role of occupations, in the context of racial wage
decomposition.
We start by documenting a substantial reduction in the observed racial wage differential
between 1993-98 and 1999-2007, in the order of 0.10 log points. However there was a slight
reversal of the trend during the Great Recession which began in 2008. Our results further
show that this dramatic narrowing of the racial wage gap in the UK is entirely accounted for
by the changes in the unexplained part, driven by the within occupational components. In
contrast, the improvements in the explained within occupation racial wage gap is largely
offset by a worsening of its between-occupational counterpart, thus leaving the total
explained wage gap almost constant over time.
Our findings suggest that there has been significant progress in reducing the labour market
discrimination against ethnic minorities in the UK over the last two decades, perhaps partly
as a result of the National Minimum Wage and better enforcement of racial equality
legislations. However, this process is far from complete, as evidenced by the 0.07 log point
unexplained wage differential towards the end of the sample period. Moreover, given the
strong persistence of the unexplained between-occupation wage gap, the government should
now make more effort to facilitate occupational upgrading by the disadvantage minorities in
the first place, by for instance providing better career advice and offering language courses
where needed.
15
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17
Tables
Table 1: Summary Statistics
Whites
Minorities
Difference
Log real gross hourly wage (Apr 2012 constant prices)
2.630
2.588
0.043***
Low qualification (NQF Level 0-1)
0.232
0.208
0.024***
Medium qualification (NQF Level 2-3)
0.374
0.293
0.081***
High qualification (NQF Level 4-5)
0.394
0.499
-0.105***
Aged 40-49 (reference: Aged 25-39)
0.394
0.210
0.183***
Southeast Region inc. London (reference: rest of UK)
0.292
0.532
-0.240***
High-skilled occupations
0.397
0.393
0.004
Mid-skilled occupations
0.344
0.346
-0.003
Low-skilled occupations
0.260
0.261
-0.001
Observations
132,918
2,742
(%)
(98.0%)
(2.0%)
Note: SOC description based on SOC 2000. ***, ** and * indicate statistical significance at 1%, 5% and 10%
respectively. High-skilled occupations include managers, senior administrators and professional occupations.
Mid-skilled occupations include associate professional and technical; administrative and secretarial; and skilled
trades occupations. Low-skilled occupations include personal service; sales and customer service; process, plant
and machine operatives, and elementary occupations.
18
Table 2: White-minority differentials, by sample period
Time Period
1993-1998
1999-2007
2008-2012
Pooled
Log real gross hourly wage
(Apr 2012 constant prices)
Low qualification (NQF Level
0-1)
Medium qualification (NQF
Level 2-3)
High qualification (NQF Level
4-5)
Aged 40-49 (reference: Aged
25-39)
Southeast Region inc. London
(reference: rest of UK)
High-skilled occupations
0.137
(0.022)***
-0.031
(0.017)*
0.058
(0.019)***
-0.027
(0.019)
0.296
(0.019)***
-0.255
(0.018)***
0.079
(0.019)***
-0.049
(0.018)***
-0.030
(0.018)*
0.030
(0.015)**
0.034
(0.012)***
0.075
(0.013)***
-0.109
(0.014)***
0.192
(0.014)***
-0.246
(0.013)***
-0.010
(0.014)
0.004
(0.013)
0.006
(0.012)
0.039
(0.020)*
0.015
(0.014)
0.105
(0.018)***
-0.119
(0.018)***
0.119
(0.019)***
-0.221
(0.017)***
-0.018
(0.018)
0.035
(0.018)**
-0.017
(0.016)
0.043
(0.011)***
0.024
(0.008)***
0.081
(0.009)***
-0.105
(0.009)***
0.183
(0.009)***
-0.240
(0.009)***
0.004
(0.009)
-0.003
(0.009)
-0.001
(0.008)
53,233
(38.9%)
60,781
(44.4%)
22,901
(16.7%)
136,915
(100.0%)
Mid-skilled occupations
Low-skilled occupations
Obs
(%)
Note: The table reports the white-minority difference in the sample means for each variable denoted by the row
heading for each period denoted by the column heading. SOC description based on SOC 2000. Standard errors
in parentheses. Standard errors in parentheses. ***, ** and * indicate statistical significance at 1%, 5% and 10%
respectively.
19
Table 3: Wage equations, by ethnicity and sample period, without occupation controls
Periods
Medium qualification
(NQF Level 2-3)
High qualification (NQF
Level 4-5)
Aged 40+
Southeast
Medium qualification *
(Aged 40+)
High qualification *
(Aged 40+)
Medium qualification *
Southeast
High qualification *
Southeast
(Aged 40+) * Southeast
Medium qualification *
(Aged 40+) * Southeast
High qualification *
(Aged 40+) * Southeast
Constant
19931998
0.238
(0.008)
0.550
(0.008)
0.111
(0.010)
0.196
(0.012)
0.013
(0.013)
0.122
(0.014)
0.044
(0.015)
0.019
(0.015)
-0.004
(0.020)
0.015
(0.026)
-0.027
(0.026)
2.158
(0.006)
52563
0.239
Obs
R2
Note: Standard errors in parentheses.
Whites
19992007
0.204
(0.008)
0.516
(0.008)
0.086
(0.010)
0.196
(0.012)
0.061
(0.013)
0.112
(0.013)
0.051
(0.015)
0.062
(0.015)
-0.011
(0.019)
-0.010
(0.024)
-0.031
(0.024)
2.256
(0.006)
59458
0.234
20
20082012
0.167
(0.016)
0.504
(0.016)
0.097
(0.019)
0.172
(0.028)
0.065
(0.023)
0.116
(0.022)
0.035
(0.033)
0.061
(0.032)
0.037
(0.038)
0.002
(0.045)
-0.023
(0.043)
2.242
(0.014)
22152
0.224
19931998
-0.051
(0.070)
0.271
(0.066)
-0.076
(0.241)
-0.002
(0.070)
0.290
(0.313)
0.332
(0.324)
0.289
(0.097)
0.216
(0.093)
0.006
(0.304)
-0.488
(0.396)
-0.052
(0.407)
2.236
(0.046)
670
0.164
Minorities
19992007
0.174
(0.062)
0.491
(0.055)
0.260
(0.104)
0.088
(0.069)
-0.143
(0.134)
-0.120
(0.128)
0.124
(0.088)
0.161
(0.080)
-0.045
(0.138)
0.063
(0.181)
0.100
(0.168)
2.186
(0.046)
1323
0.235
20082012
0.269
(0.112)
0.632
(0.098)
0.219
(0.129)
0.461
(0.157)
-0.118
(0.168)
-0.056
(0.153)
-0.363
(0.187)
-0.316
(0.168)
-0.326
(0.208)
0.472
(0.259)
0.288
(0.240)
2.070
(0.086)
749
0.172
Table 4: Wage equations, by ethnicity and sample period, with occupation controls
Periods
Medium qualification
(NQF Level 2-3)
High qualification (NQF
Level 4-5)
Aged 40+
Southeast
Medium qualification *
(Aged 40+)
High qualification *
(Aged 40+)
Medium qualification *
Southeast
High qualification *
Southeast
(Aged 40+) * Southeast
Medium qualification *
(Aged 40+) * Southeast
High qualification *
(Aged 40+) * Southeast
Mid-skilled occupations
Low-skilled occupations
Constant
19931998
0.187
(0.008)
0.381
(0.008)
0.095
(0.010)
0.164
(0.011)
-0.000
(0.013)
0.096
(0.013)
0.043
(0.015)
0.035
(0.015)
-0.003
(0.019)
0.016
(0.025)
-0.024
(0.025)
-0.247
(0.005)
-0.354
(0.006)
2.441
(0.007)
52563
0.292
Obs
R2
Note: Standard errors in parentheses.
Whites
19992007
0.114
(0.008)
0.281
(0.008)
0.079
(0.009)
0.141
(0.011)
0.044
(0.012)
0.084
(0.012)
0.055
(0.014)
0.088
(0.014)
-0.010
(0.018)
-0.009
(0.023)
-0.024
(0.022)
-0.286
(0.004)
-0.496
(0.005)
2.627
(0.007)
59458
0.332
21
20082012
0.073
(0.015)
0.251
(0.016)
0.080
(0.017)
0.129
(0.026)
0.061
(0.021)
0.107
(0.021)
0.035
(0.031)
0.085
(0.029)
0.027
(0.035)
-0.001
(0.042)
-0.015
(0.040)
-0.258
(0.007)
-0.532
(0.009)
2.626
(0.015)
22152
0.328
19931998
-0.092
(0.068)
0.133
(0.068)
-0.140
(0.234)
-0.020
(0.068)
0.321
(0.304)
0.414
(0.315)
0.298
(0.095)
0.241
(0.091)
0.136
(0.297)
-0.563
(0.385)
-0.166
(0.396)
-0.218
(0.045)
-0.312
(0.051)
2.487
(0.060)
670
0.213
Minorities
19992007
0.099
(0.058)
0.231
(0.054)
0.217
(0.096)
0.075
(0.064)
-0.180
(0.124)
-0.093
(0.119)
0.057
(0.082)
0.157
(0.074)
-0.023
(0.128)
0.106
(0.169)
0.066
(0.157)
-0.284
(0.029)
-0.511
(0.036)
2.588
(0.051)
1323
0.342
20082012
0.167
(0.103)
0.373
(0.093)
0.187
(0.118)
0.421
(0.144)
-0.124
(0.154)
-0.095
(0.140)
-0.387
(0.171)
-0.361
(0.155)
-0.330
(0.191)
0.536
(0.237)
0.372
(0.220)
-0.349
(0.043)
-0.599
(0.052)
2.550
(0.089)
749
0.307
Table 5: Decomposition of log wage differentials between whites and minorities, by sub-periods
Time Period
Observed total differential
Total explained (%)
Total unexplained (%)
Total between occupation (%)
Total within occupation (%)
between occupation explained (BE) (%)
between occupation unexplained (BU) (%)
within occupation explained (WE) (%)
within occupation unexplained (WU) (%)
1993-1998
0.1375
1999-2007
0.0304
2008-2012
0.0389
Pooled
0.0426
-0.0383
(-27.9%)
0.1757
(127.9%)
0.0276
(20.1%)
0.1098
(79.9%)
0.0075
(5.5%)
0.0201
(14.6%)
-0.0459
(33.4%)
0.1557
(113.2%)
-0.0665
(-218.8%)
0.0969
(118.8%)
-0.0055
(-18.1%)
0.0359
(118.1%)
-0.0212
(-69.7%)
0.0156
(51.3%)
-0.0453
(-149.0%)
0.0812
(267.0%)
-0.0307
(-78.9%)
0.0696
(178.9%)
-0.0014
(-3.6%)
0.0403
(103.6%)
-0.0323
(-83.0%)
0.0309
(79.4%)
0.0017
(4.4%)
0.0387
(99.5%)
-0.0438
(-102.8%)
0.0865
(202.8%)
0.0018
(4.2%)
0.0408
(95.8%)
-0.0203
(-47.7%)
0.0220
(51.6%)
-0.0236
(-55.4%)
0.0644
(151.2%)
22