Understanding pay gaps
Speech given by
Andrew G Haldane
Chief Economist
Bank of England
Co-authors: Zahid Amadxarif, Marilena Angeli and Gabija Zemaityte
Joint Bank of England, Federal Reserve Bank and European Central Bank conference on
Gender and Career Progression
Frankfurt
21 October 2019
The views expressed here are not necessarily those of the Bank of England or the Monetary Policy
Committee. I would like to thank Will Abel, Shiv Chowla, Julia Giese, Brian Hallissey, Sam Juthani,
Tomas Key, Clare Macallan, Jen Nemeth, Doug Rendle and Ratidzo Starkey for their comments and
contributions.
This work contains statistical data from ONS which is Crown Copyright. The use of the ONS statistical
data in this work does not imply the endorsement of the ONS in relation to the interpretation or
analysis of the statistical data. This work uses research datasets which may not exactly reproduce
National Statistics aggregates.
1
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Section 1: Introduction
"Pay gaps" measure the difference in pay between people with different demographic characteristics doing
identical jobs. They are considered to be a good approximation of inequality in workplace rewards (EHRC
(2018)). Under the Equality Act 2010, it is against the law to discriminate on the basis of protected personal
characteristics. When it comes to pay, this means people should earn the same wage for the same work,
irrespective of their gender, race, religion, disability, or other protected characteristics. In other words, no
"pay gap" should exist across any of these characteristics.
Since 2017, it has been compulsory for companies in Great Britain with over 250 employees to report gender
pay gaps each financial year. Data for 2018 published earlier this year shows a gender pay gap of around
10% across reporting UK organisations.1 The UK government has also begun a consultation on requiring
companies to publish ethnicity pay gaps on an annual basis.2 While 63% of employers monitor ethnicity pay
gaps, only 31% of employers currently publish them.3 The Bank of England has published data on its gender
pay gap since 2017 and on its ethnicity pay gap since 2018.
While it is widely recognised that these pay gap measures may be imperfect proxies for pay inequality,
greater transparency about pay gaps can serve as an important incentive device. Publishing companies' pay
gaps encourages them to explain and, with time, close these gaps. The benefits of doing so, for individual
companies and the economy at large, have been found to be large. For example, closing the gender pay
gap has been estimated to add £600 billion (or 27% of GDP) to UK GDP by 2025 (McKinsey (2016)), while
full representation of BAME individuals across the labour market, through improved participation and
progression, could add an additional £24 billion (or over 1% of GDP) per year (BEIS (2017)).
Internationally, similar moves to improve pay gap reporting are underway. Iceland has since January 2018
required external auditors to assess pay inequalities among companies with more than 25 employees.4,5
The Danish Equal Pay Act 2014 requires companies with a minimum of 10 employees to make available
gender-disaggregated pay statistics. A number of other EU member states have implemented new reporting
standards, including recently in Germany and France. In the US, all employers with more than 100 workers
must disclose pay information to the Equal Employment Opportunity Commission.
In this paper, we provide a detailed empirical examination of gender and ethnicity pay gaps in the UK using
micro-level survey data. As well as describing the evolution of these pay gaps over time, we identify some
of the key explanations and drivers of them over time. This decomposition is important when accurately
1
Latest ONS LFS 2019Q1 release.
2
https://www.gov.uk/government/consultations/ethnicity-pay-reporting
3
Business in the Community (2019).
4
Pay in Iceland is assessed using an Equal Pay Standard which highlights four main criteria: expertise, responsibility, effort, and work
environment.
5
Wagner, I (2018), 'Certified Equality: The Icelandic Equal Pay Standard'
https://www.researchgate.net/publication/329371051_Certified_Equality_The_Icelandic_Equal_Pay_Standard
2
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interpreting data on pay gaps and when deciding where action might best be taken - by companies and/or
public policymakers - to close these gaps and harvest some of the benefits this would bring.
Section 2: Literature Review
Gender and ethnicity pay gaps have been studied fairly extensively using US data. In the US, the
female/male earnings ratio has increased steadily since the 1980s (Blau and Kahn (2017)). This is typically
attributed to improvements in women's levels of education and experience (Stanley and Jarrell (1998), Jarrell
and Stanley (2004), Weichselbaumer and Winter-Ebmer (2005)). Alkadry and Tower (2006) find human
capital explains over 90% of the gender pay gap. Some studies have also found occupational representation
and segregation has been a factor explaining gender pay gaps (Lewis and Soo Oh (2009)).
Goldin (2014) provides one of the most comprehensive studies of the gender pay gap in the US across
different age groups. She finds that, while the gap has narrowed for most age groups, the aggregate gap
has remained fairly stable. By looking across the life-cycle, she finds that as workers get older the size of the
gender pay gap tends to rise. It also differs significantly by occupation, with business occupations having the
largest gender pay gaps and technology and science-related occupations the lowest.
Like others, Goldin finds that the narrowing of the gap across age groups is largely due to a rise in the
human capital of women relative to men, due to factors such as improved levels of education, experience,
and labour force participation. The remaining pay difference can partly be explained by higher-paying
occupations in the corporate, financial and legal worlds, especially at later career stages. This is
disadvantageous for women who are more likely to work part-time or work fewer hours due to raising
children.
The US literature on ethnic minority pay has tended to focus on wage differentials between white and black
ethnic groups, the two largest ethnic groups in the US. Studies have tended to find a large wage differential
between black and white men, although this does appear to have reduced over time with the black/white
earnings ratio increasing from just under 60% in 1967 to around 80% by 2009 (Lang and Lehmann (2012)).
This broadly mirrors trends in the gender pay gap. Studies looking at wage gaps over the life-cycle find that
there is little evidence of an ethnicity pay gap during early working life, but that this grows to around 14% by
age 40 (Tomaskovic-Devey et al (2005)).
Evidence on the role of education on the size of the ethnicity pay gap is mixed. Some studies find that,
among young men, the black/white pay differential appears, in part, to be explained by education (O'Neill
(1990), Rodgers and Spriggs (1996) and Carneiro, Heckman, and Masterov (2005)). For example, the pay
differential decreases when controlling for performance on the Armed Forces Qualifying Test (Neal and
Johnson (1996)). Studies also find that, among college-educated men, there is no difference in pay between
black and white males (Black et al (2006)). This might suggest the ethnicity pay gap can largely be
3
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explained by human capital differences in white-collar sectors, but not in blue-collar jobs (Bjerk (2007)).
Other studies, however, have found that human capital differences increase the pay differential (Lang and
Manove (2011)).
Wage differentials between black and white women have historically been considerably lower than between
men (Lang (2007)) and at times have even reversed. However, Neal (2004) demonstrates that this finding
partially reflects the differential selection of black and white women into the labour force. For example, black
women face a lower household income than white women, which provides a stronger incentive for them to
enter the workforce in the first place.
For the UK, although evidence is more partial, it suggests that both gender and ethnicity pay gaps have
fallen over time (ONS (2018), ONS (2019)). For gender, the factors determining these gaps include age,
educational background, sector, occupation and part time work (Olsen and Walby (2004), Manning and
Swaffield (2005), Brynin (2017), ONS (2018)). For ethnicity, it includes occupation, being born in the UK and
education (ONS (2019), Brynin and Guveli (2012)). As in the US, the UK ethnicity gap appears to be larger
for males than females (Longhi and Brynin (2017)).
When it comes to explaining the sources of pay gaps, three main approaches have typically been used.
Regression techniques estimate pay gaps controlling for various characteristics of the individual or job
(Goldin (2014), Brynin and Guveli (2012)). Oaxaca-Blinder decompositions account for differences in pay
between two groups using an independent set of factors. This is useful for capturing the effects of
compositional differences in the characteristics of groups (O'Donnell et al (2008)). Finally, a rich literature
uses experiments to assess bias in hiring, promotion and bargaining between groups (Neumark (2018)).
In this paper, we use the first two approaches - regression-based techniques and decompositions - to
analyse gender and ethnicity pay gaps in the UK. In the next section, we describe the dataset, summarise
key trends in the UK labour market over the past 25 years and discuss unconditional pay gaps by gender
and ethnicity. In the following section, we provide an explanation of these pay gaps using factor-
decomposition and regression-based methods. Section 5 summarises our key findings and policy
recommendations.
Section 3: Trends in the UK Labour Market
This paper uses quarterly returns from the UK Labour Force Survey (LFS) from 1994 Q1 to 2019 Q1. This
gives us a sample of 1,154,759 observations of employed individuals, reduced to 563,353 observations
when incorporating pay variables, as there are fewer observations for respondents on pay.6 There are two
6
Throughout our analysis we only use wave 1 respondents in order to ensure that we are not counting multiple responses from the
same individual. In addition to this, we only include workers aged 25 and over in order to remove noise from the data. Most workers
under 25 are earning in the minimum wage range which is subject to regulation, and may also particularly represent part-time
employment.
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main advantages to using the LFS relative to other surveys, such as the British Household Panel Survey
(BHPS) and the Annual Survey of Hours and Earnings (ASHE).7
First, the LFS has the longest time-series of pay and other work characteristics at a quarterly frequency in
the UK. Using it allows us to track the evolution of pay gaps over time. Second, the LFS dataset offers a
rich set of information on the characteristics of both workers and their jobs. These controls improve the
robustness of the analysis, statistically, and the ability to explain movements in pay gaps behaviourally, using
various individual and work-related characteristics.
There have been a number of important changes in the employed LFS survey population over time. First,
the share of ethnic minorities in the workforce has more than doubled, from 4% in 1994 to 10% in 2019.
Second, the share of the population in higher education or with a degree has also increased significantly,
from 22% to 43%. Third, the share of the population not born in the UK has almost tripled, from 6% in 1994
to 17% in 2019. These trends are representative of the composition of the UK population, as reported in the
2011 Census.
(a) Participation Trends
Female participation in the workforce has been on a broadly upward trend in the UK. Elsewhere across the
G7, trends have been more disparate (Chart 1). Based on OECD Labour Force Statistics, UK rates of
female participation are second only to Canada across the G7, having recently overtaken the US.
Chart 1: G7 female participation rates
Per cent
70
60
50
40
30
20
1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016
Canada France Germany Italy Japan UK US
Sources: ONS Labour Force Survey, OECD Labour Force Statistics and Bank of England calculations.
7
This paper does not aim to produce better estimates of the gender and ethnicity pay gaps than the official ones published by the ONS.
ASHE data is the best source for estimating the overall gaps, but the LFS has a rich set of individual and job characteristics allowing the
type of analysis showcased here.
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The increase in female participation has been accompanied by an opposite trend among the male
population, which has fallen from 70% to 65% over the sample. In other words, the gender "participation
gap" between males and females in the UK has fallen by around 10 percentage points over the past 25
years, though it remains positive at around 8 percentage points.
The participation of ethnic minorities in the UK labour force has also been on an upward trend since the
1990s, reaching around 64% in 2019. Labour participation by the white population was, by contrast, on a
slightly downward trend prior to the GFC, after which it has flattened off. That leaves participation rates for
ethnic minorities today slightly above the levels of the white population.
There have been some striking gender-related shifts in the pattern of work. The share of part-time
employment among females has been declining, with a corresponding larger share of females working
full-time. The opposite is true among males, with the share of men working part-time increasing by 5
percentage points over the sample. For ethnic minorities, part-time employment has steadily increased, with
a pronounced pick up following the crisis.
Next, we look at three variables which are often used to capture aspects of "human capital": the highest
qualification achieved, the occupation of a person, and the sector in which they are employed. For
illustrative purposes, we split the sample into four sub-groups - white men, white women, ethnic minority
men, and ethnic minority women - though we also consider more granular sub-sets of these groups.
Chart 2 compares the highest qualification attained among the four sub-samples in 1994 and 2019. The
main trend is the same across the board: the share of people with degrees or higher education has almost
doubled since 1994. The increases are, however, most pronounced among women and ethnic minorities.
For example, ethnic minority females have seen a 31 percentage point increase between 1994 and 2019. In
2019, around 60% had a degree or a higher education qualification.
Conversely, the shares of people without a qualification have decreased significantly across the four groups,
with the trend for UK-born ethnic minority women reducing at a faster pace than for non-UK born ethnic
minorities. Both UK-born and non-UK born workers entering the labour force market in the UK are materially
more qualified than in the past.
6
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Chart 2: Highest qualification attained
Per cent
100
80
60
40
20
0
1994 2019 1994 2019 1994 2019 1994 2019
White male White female Ethnic minority male Ethnic minority female
Degree Higher education GCE/A-Level GCSE Other No qualification
Source: ONS Labour Force Survey and Bank of England calculations.
Chart 3 shows the composition of the labour force by occupation across the four sub-groups. Occupational
participation differs strikingly by gender, less so by ethnicity. There is a higher share of managers,
professional, and associate occupations among men than women. Compared to men, women have a larger
representation in administrative, and personal, sales and customer service occupations, while men have a
larger representation in skilled trades and process and plant occupations. This holds true across ethnicities.
Chart 3: Occupational representation
Per cent
100
80
60
40
20
0
1994 2019 1994 2019 1994 2019 1994 2019
White male White female Ethnic minority male Ethnic minority female
Managers, professional and associates Administrative
Skilled trade Personal, sales and customer
Process and plant Elementary
Source: ONS Labour Force Survey and Bank of England calculations.
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Chart 4 shows the composition of the UK labour market by sector. The vast majority of men across both
ethnic sub-samples work in the 'industry' sector (including manufacturing, utilities and mining) and trade and
transport sectors. The latter is particularly important for ethnic minority males, where close to 40% of this
sub-group works. On the other hand, the public sector is dominated by females of both ethnicity
backgrounds. The share of people working in professional services and finance is similar across the board.
Chart 4: Sectoral representation
Per cent
100
80
60
40
20
0
1994 2019 1994 2019 1994 2019 1994 2019
White male White female Ethnic minority male Ethnic minority female
Industry Public Trade and transport Professional Finance Other
Source: ONS Labour Force Survey and Bank of England calculations.
Pay Trends
(b)
Table 1 shows summary statistics of the distribution of earnings across gender and ethnicity. It shows that
white men have the highest mean and median hourly earnings, as well as higher earnings across every
percentile of the pay distribution. Males also have higher earnings than females across the pay distribution
for both ethnic groups. Ethnic minority females have higher earnings than white females across the
distribution. All distributions have a strong positive skew, though white males have the longest upper tail.
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Table 1: Summary statistics for hourly earnings (£) across gender and ethnicity groups
Statistic White male White female Ethnic minority Ethnic minority
male female
Mean 13.6 10.4 12.9 11.1
25th percentile 7.6 6.0 6.9 6.4
Median 10.9 8.35 10 9.1
75th percentile 16.4 12.7 15.7 13.6
Standard deviation 14.4 8.6 11.1 8.4
Skewness 46.3 20.4 13.9 16
Source: ONS Labour Force Survey and Bank of England calculations.
Charts 5 plots the distribution of hourly pay in £ for males, females and ethnic minorities separately on two
dates: 1997 and 2018. It shows that there is a higher peak of low-paid females in 1997 and, to a lesser
extent, in 2018. Put differently, more of the distribution of pay among males is skewed towards the upper tail
of higher pay rates. Pay distributions between whites and ethnic minorities show fewer differences.
Chart 5: Distribution of hourly pay by gender and ethnicity
Source: ONS Labour Force Survey and Bank of England calculations.
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Chart 6 plots the mean hourly pay by decile for different ethnicity and gender groups. In 1997, white males
earned more than all other groups, on average, across the entire distribution. The same is broadly true in
2018, except that ethnic minority males now earn more in the top decile. Females continue to earn less than
males right across the distribution. The difference between white females and ethnic minority females is
relatively small across the distribution.
Chart 6: Mean hourly pay by decile for ethnicity and gender interaction groups
Source: ONS Labour Force Survey and Bank of England calculations.
(c) Unconditional gender and ethnicity pay gaps
Unconditional gender and ethnicity pay gaps are calculated as the difference between median male (white)
gross hourly earnings and median female (non-white) earnings, divided by median male (white) earnings.8
As with published pay gaps for companies, these measures make no attempt, at this stage, to control for
factors driving or explaining these gaps; they are unconditional pay gaps.
The unconditional gender pay gap is large over the sample, averaging just over 22% (Chart 7). This gap
has shrunk over the past 25 years, from around 30% in the mid-1990s to around 20% in 2018. This
downward trend is particularly pronounced in the first part of the sample, when the pay gap was falling by, on
average, around half a percentage point per year. Since the GFC, however, the gender pay gap has
flattened-off and has been essentially unchanged since 2007.
At the same time gender pay gaps have been falling, female participation in the workforce has been steadily
rising (Chart 7). Increased female participation may have contributed to the shrinking of the gender pay gap
8
Unconditional gaps are calculated using raw pay data from the LFS and will not attempt to account for individual or job characteristics.
10
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during the 1990s and early 2000s, as female representation in certain sectors and occupations expanded.9
But there is little evidence of these effects over the past decade, during which female participation in the
workplace has, if anything, picked up pace but the gender pay gap has been sticky.
Chart 7: Female participation rate and unconditional gender pay gap
Gender pay gap (per cent)
31
29
1994-2009 2010-2019
27
25
23
21
19
17
15
52 53 54 55 56 57 58
Female participation (per cent)
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive gender pay gap in this chart means that women earn less than men.
If we assess by qualification, the largest gender pay gaps are observed for people with GCSE or no
qualifications, while the lowest gaps are for workers with a degree or equivalent (Chart 8). Generally
speaking, the higher the qualifications, the lower the gender pay gap.
Location also appears to play an important role. The gender pay gap outside London is consistently higher
than in London, across all levels of qualification. The largest gap between London and the rest of the UK is
among workers who have no or "other" qualifications. This might be explained by the fact that "other"
qualifications includes degrees from outside the UK which may get less recognition.
9
Olivetti, C and Petrongolo, B (2016) show that structural change of the economy becoming more service based, can explain at least
half of the overall variation in female hours, both over time and across countries.
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Chart 8: Median gender pay gap by qualification, sample average
Gender pay gap, per cent
25
London Other
20
15
10
5
0
Degree or Higher educ GCE A Level or GCSE grades A-C or Other qualifications No qualification
equivalent equiv equiv
Male Female
1 2
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive gender pay gap in this chart indicates that women earn less than men.
By occupation, consistent with qualifications, the gender pay gap is smaller for professional and
administrative occupations than for skilled trades, customer service and plant operatives (Chart 9). For
almost all occupations, the gender pay gap is higher outside London. In 2018, the gender pay gap outside
London remained higher for managers, skilled trades and elementary occupations. For the rest of the
occupations, the pay gap in London overtook the respective gap outside London.
Broadly speaking, occupations with median hourly pay of more than £10 (managers, professional, and
associate occupations) have lower gender pay gaps than occupations where the median hourly pay is less
than £6.50 (sales and customer services, and elementary occupations). Relatedly, there is a clear inverse
relationship between the gender pay gap and female-dominated professions. Occupations with a lower
share of females tend to be characterised by higher gender pay gaps and vice versa, consistent with
increased female representation shrinking gender pay gaps cross-sectionally, if not always over time.
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Chart 9: Median gender pay gap by occupation, sample average
Gender pay gap, per cent
35
London Other 30
25
20
15
10
5
0
Managers Professional Associate Administrative Skilled trades Personal Sales and Process and Elementary
professionals service customer plants
Male Female
male female
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive gender pay gap in this chart indicates that women earn less than men.
Finally, if we look at unconditional gender pay gaps by sector (Chart 10), these gaps are broadly-based
across sectors. The largest gaps are in the finance sector, both in London and the rest of the UK. These
average close to 40%. This is materially larger than the next-worst sector, ICT, which averages around
30%. The evolution of the gender pay gap in finance suggests it has fallen only modestly, from over 45% in
the mid-1990s to around 38% in 2018.
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Chart 10: Median gender pay gap by sector, sample average
Gender pay gap, per cent
45
40
London Other
35
30
25
20
15
10
5
0
Agriculture Industry Construction Trade and ICT Finance Real Estate Professional Public sector Other
transport
Male Female
male female
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive gender pay gap in this chart indicates that women earn less than men.
The unconditional ethnicity pay gap exhibits a rather different pattern than the gender pay gap. This gap has
also been materially lower, averaging around 4% over the sample. As the gender gap fell sharply in the
decade to 2005, the ethnicity pay gap was broadly stable at around 2% (yellow dots in Chart 11). The
ethnicity pay gap then began increasing steadily between 2006 and 2014 (red dots), averaging 6% over the
period. Recently, it has begun to fall (green dots), reaching just under 4% in 2018.
Chart 11 shows that higher ethnic minority participation in the workplace has only been associated with
lower pay gaps very recently. This, and the relatively volatile pattern in the ethnicity pay gap over time,
presents something of a puzzle. We might instead expect pay disparities to decline as ethnic minorities
become better represented in the labour market through higher participation.
14
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Chart 11: Ethnic minority participation and unconditional pay gaps
Ethnicity pay gap (per cent)
12
2006 - 2014
10
2015 - 2019
8
6
1997 - 2005
4
2
0
-2
-4
55 56 57 58 59 60 61 62 63 64 65
Ethnicity participation (per cent)
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive ethnicity pay gap in this chart indicates that ethnic minorities earn less than their white counterparts, whereas a
negative ethnicity pay gap indicates that ethnic minorities earn more than their white counterparts.
Looked at by qualification, white workers earn more than ethnic minorities for most levels of qualifications.
The exception are those with "other" or no qualifications living outside London. Perhaps surprisingly given
the significant share of ethnic minorities in the workforce, ethnicity pay gaps are larger in London than
outside London, for all qualifications (Chart 12). These gaps are larger for those with higher levels of
qualification, such as a degree. This presents a second puzzle in the unconditional pay gap data.
Recent data do not change this picture. In the latest calendar year (2018), ethnicity pay gaps are positive
across all qualifications, averaging 8%. They remain larger in London than elsewhere, with the highest
ethnicity pay gaps in degree and A-level qualification categories. Unconditional ethnicity pay gaps are still
consistently smaller than unconditional gender pay gaps.
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Ethnicity pay gap, per cent
20
London Other
15
10
5
0
-5
Degree or Higher educ GCE A Level or GCSE grades A-C or Other qualifications No qualification
equivalent equiv equiv
Ethnic minority White
ethnic minority white
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive ethnicity pay gap in this chart indicates that ethnic minorities earn less than their white counterparts, whereas a
negative ethnicity pay gap indicates that ethnic minorities earn more than their white counterparts.
Ethnicity pay gaps differ significantly by occupation and sector. They are largest in managerial, professional
and skilled occupations and lowest, or even negative, in elementary, personal service and sales and
customer occupations (Chart 13). This is the mirror-image of gender, where pay gaps were largest among
the lowest-paid occupations. By sector, pay gaps are largest in the professional and finance sectors (Chart
14). In other words, ethnicity pay gaps are largest among the highest-paid.
16
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Ethnicity pay gap, per cent
20
London Other
15
10
5
0
-5
-10
Managers Professional Associate Administrative Skilled trades Personal Sales and Process and Elementary
professionals service customer plants
White
Ethnic minority
ethnic minority white
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive ethnicity pay gap in this chart indicates that ethnic minorities earn less than their white counterparts, whereas a
negative ethnicity pay gap indicates that ethnic minorities earn more than their white counterparts.
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Chart 14: Median ethnicity pay gap by sector, sample average
Ethnicity pay gap, per cent
20
London Other
15
10
5
0
-5
-10
-15
Agriculture Industry Construction Trade and ICT Finance Real Estate Professional Public sector Other
transport
Ethnic minority White
ethnic minority white
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive ethnicity pay gap in this chart indicates that ethnic minorities earn less than their white counterparts, whereas a
negative ethnicity pay gap indicates that ethnic minorities earn more than their white counterparts.
If we look at ethnic minority workers at a more granular level, some interesting cohort effects emerge. There
is a very wide dispersion of pay among different ethnic minority cohorts, with some having negative and
others positive pay gaps compared with their white counterparts (Chart 15). Median hourly pay is highest for
those of Chinese ethnicity, at just over £11, or around 15% higher than for whites. There are also negative
pay gaps, though smaller ones, for workers from a mixed/multiple ethnic and Indian background.
At the other end of the spectrum, there are significantly positive pay gaps for other ethnic minority cohorts,
including black and Afro-Caribbean workers. The largest pay gaps are for workers from a Pakistani and
Bangladeshi background, which average 13% and 20%, respectively (Chart 15). These large pay gap
differences between different ethnic minority cohorts beg the question of whether it is useful to think about a
single "ethnicity pay gap".
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Chart 15: Granular ethnicity pay gaps
Ethnicity groups
Mixed/Multiple ethnic groups
Indian
Pakistani
Bangladeshi
Chinese
Any other asian background
Black/African/Caribbean/Black British
Other ethnic group
-40 -30 -20 -10 0 10 20 30
Ethnicity pay gap, per cent
2018 Sample average
Source: ONS Labour Force Survey and Bank of England calculations.
Note: A positive ethnicity pay gap in this chart indicates that ethnic minorities earn less than their white counterparts, whereas a
negative ethnicity pay gap indicates that ethnic minorities earn more than their white counterparts.
Whether an ethnic minority individual is born in the UK or not also plays an important role in their pay.
Chart 16 shows that ethnic minorities born in the UK (apart from those from Bangladeshi and Pakistani
backgrounds) earn more than UK-born white people. Educational attainment is part of the explanation here.
Although ethnic minorities born in the UK are a small part of our sample (2%), they are almost twice as likely
to have a degree relative to white UK-born workers (40% versus 21%).
In stark contrast, most ethnic minorities born outside the UK earn considerably less than white people born
outside the UK. This in part