How has Income Inequality and Wage Disparity Between Native and Foreign Sub-populations in the UK Changed Since the Financial Crisis?

How has Income Inequality and Wage Disparity
Between Native and Foreign Sub-Populations in
the UK Changed Since the Financial Crisis?
BSc (Hons) Economics & Business Finance with Professional Development
College of Business, Arts and Social Sciences
Author- Dinal Shah
Word Count- 8004

College of Arts, Business and Social Science 1206695
1
Abstract
This study uses repeated cross-sectional data from 2007 to 2015 to examine in
detail what impact the 2008 recession had on income inequality and wage disparity
in the UK and on its sub-populations. The findings suggest inequality in the UK has
been decreasing since 2007, but that the foreign sub-population experiences a
greater degree of inequality than the native sub-population. Additionally, foreign and
native labour are imperfect substitutes where the wages of foreign workers are
greatly more susceptible to economic shocks effecting the UK’s labour market. To
the best of my knowledge this is the first paper to conduct research into income
inequality between native and foreign workers.
Acknowledgements
A very special thank you to my parents and brother for their unconditional love and
support.
I also wish to express my gratitude to Dr. Charles Grant for his guidance and
advice throughout the writing of this dissertation.

2
Contents
1. Introduction......................................................................................................... 3
2. Literature Review ............................................................................................... 5
2.1. Determinants of Inequality ........................................................................... 5
2.2. Changes in Income Inequality...................................................................... 6
2.3. Relationship Between Native and Foreign Workers..................................... 8
2.4. Economic Development, Growth and Income Inequality........................... 10
3. Data Analysis ................................................................................................... 13
3.1. The Quarterly Labour Force Survey (QLFS).............................................. 13
3.2. Defining the Sample................................................................................... 17
4. Methodology..................................................................................................... 18
4.1. Repeated Cross-Sectional to Time Series Data Transformation ............... 18
4.2. Measures of Inequality............................................................................... 20
4.2.1. Gini Coefficient.................................................................................... 20
4.2.2. 90:10 Ratio.......................................................................................... 21
4.3. Wage Movements...................................................................................... 22
4.3.1. The Median ......................................................................................... 22
4.3.2. The 10th and 90th Percentiles .............................................................. 23
4.4. Income Distribution- Frequency Density Polygon ...................................... 23
5. Results and Discussion .................................................................................... 24
5.1. The UK’s Income Distribution .................................................................... 24
5.2. Inequality in the UK.................................................................................... 25
5.3. Wage Movements in the UK ...................................................................... 26
5.4. Inequality within Sub-Populations and Education Groups.......................... 29
5.4.1. All Workforces ..................................................................................... 29
5.4.2. Highly Educated Workforce................................................................. 30
5.4.3. Intermediately Educated Workforce .................................................... 31
5.4.4. Lowly Educated Workforce.................................................................. 33
6. Conclusion........................................................................................................ 34
7. References....................................................................................................... 39
8. Appendix .......................................................................................................... 42
8.1. A ................................................................................................................ 42
8.2. B ................................................................................................................ 50
8.3. C ................................................................................................................ 56

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1. Introduction
It has been well documented that since the 1970’s income inequality increased
globally and in the United Kingdom (UK) (Mookherjee & Shorrocks, 1982). The latest
findings suggest that since the late 1990’s inequality around the world has been
decreasing, however there has been relatively little research conducted into how
income inequality is changing in the UK in the 21st century. Moreover, to the best of
my knowledge, there has been no previous research conducted into inequality
between native and foreign workers in the UK. As such, this dissertation was
inspired to concentrate its research on the UK between 2007 and 2015, in the hope
of contributing to the existing literature. The 9-year period of analysis also
encompasses the 2008 financial crisis, adding another intriguing dimension to this
study.
Over recent years there has been increasing concern over the levels of net migration
to the UK and the effect it is having on its economy, this criticism intensified during
the 2008 financial crisis. Various political parties, and the media in general, have
suggested that immigrants “are taking all our jobs and are lowering our wages” or
to put it more delicately; migrants lower the average income of native workers as a
result of increased competition in the labour market. This study investigates if this
claim has any standing in economic theory.
Additionally, there will be research conducted to determine if there is a disparity in
the degree of income inequality experienced by the native and foreign sub-
populations of the UK. Following on from this further analysis will look into the wage
gap between the sub-populations at various points in the UK’s income distribution.

4
This research topic is of particular significance currently due to the recent
announcement of the European Union (EU) referendum, due to take place in June
2016. The main argument for voting “out” of the EU is for better control of the UK’s
borders in order to stop the mass migration from Eastern European countries. It is
portrayed that this will improve job prospects for native workers, whether this is true
or not remains uncertain.
Repeated cross-sectional data from the Quarterly Labour Force Survey (QLFS) has
been combined with an array of techniques in order to map a complete picture of
the changes which have occurred in the UK over the 9 years of analysis. The income
distribution of the UK has been mapped using frequency density polygons, income
inequality has been quantified using the Gini coefficient and the 90:10 ratio, and
wage movements have been examined at the 10th, 50th, and 90th percentiles. This
analysis takes place for the UK and its sub-populations as well as in 3 derived
workforces, based on the educational attainments of workers. The 9 years of
analysis will also be split into 2 periods. Period 1 will be from 2007 to 2012, covering
the 2008 recession. Period 2 will be from 2012 to 2015, covering the recovery of the
UK economy.

5
2. Literature Review
There is an extensive body of published literature on income inequality, many
papers concentrate on its determinants and how best to quantify it. As the majority
of the literature is based on empirical analysis, the focus is mainly on how inequality
changed before and during the 20th century. This paper is unique in that it covers
the duration of 2008 financial crisis and compares the effect it had on inequality in
the UK and between its sub-populations; as opposed to populations between
countries.
2.1. Determinants of Inequality
The degree of income inequality experienced in an economy essentially is
determined by the demand and supply of labour. The social and economic factors
which affect this are widely discussed and debated.
Whilst technological changes, which mainly affected the demand for lowly skilled
workers, has been determined as the main contributor of the recent and drastic rise
in inequality (Autor, et al., 2008). The supply side determinants of inequality are less
clear, research has been conducted into the effects of immigration, trade
liberalization, education and the increase in female labour supply. Of these research
fields it is education, both the level and the distribution of it, which is uniformly
agreed to be the most significant supply side factor effecting the degree of inequality
experienced (Topel, 1997).
The theory presented indicates that as a result of rapid technological advancements
which automate and increase the efficacy of unskilled routine jobs, the demand for
unskilled labour has decreased. Simultaneously firms demand of highly skilled
workers to develop this technology, and in other sectors, has increased. This

6
resulted in the wage gap between highly educated and uneducated workers
increasing.
The only way to resolve this growing disparity is by reducing the oversupply of
unskilled workers by educating and evolving them into skilled workers. Thereby
increasing the supply of skilled workers in line with the increased demand for them;
thus overall reducing inequality (Machin, 1996).
Additionally, there has been evidence presented that the mobility of labour can be
particularly influential on changes in inequality (Kambourov & Manovskii, 2008). The
greater the mobility of labour in an economy the less susceptible the economy will
be to increases in inequality resulting from structural changes within it. This is
perhaps evident during the Thatcher period of governance in the UK when structural
changes in the UK economy, away from mining and manufacturing and toward
services, lead to a large increase in inequality in subsequent years. This was a result
of the high degree of immobility of unskilled labour in the mining and manufacturing
sectors.
2.2. Changes in Income Inequality
To gain a more informed perspective on how income inequality is currently
changing, an understanding of how inequality has changed since the 1980’s is
needed.
Income inequality rose at an astonishing rate during the last quarter of the 20th
century; in the UK (Jenkins, 1996) and globally (Jaumotte & Lall, 2013). However,
since the late 1990’s inequality has been fairly unchanged. This is because a relative
increase in pensions for the retired and social welfare support for the unemployed,
plus a reduction in income from investments for the wealthy, have acted to reduce

7
inequality. Somewhat unsurprisingly it was these same factors, moving in the
opposite directions, which contributed to the rapid rise in inequality during the 1970’s
and 1980’s (Brewer & Wren-Lewis, 2015).
Interestingly, Brewer et al. (2015) found that during 1980’s an unequal distribution
of education, combined with the increasing returns of wages to education,
accounted for a significant portion of the rise in income inequality. They also note
that their model does not account for around 50% of this, but that “a significant part
of it is due to inequalities between workers in different industries and occupations”
(Brewer & Wren-Lewis, 2015, p. 20). This statement is not extended to whether
workers are native or foreign, something this study will investigate.
In line with previous findings, the Institute of Fiscal Studies (IFS) recently reported
a sustained fall in income inequality since the 1990’s and additionally an increase in
the median income of the UK population since 2009 (Belfield, et al., 2014). This
report also cites changes in pension income and sustained welfare support as
contributing factors of reducing inequality. It should be noted that the findings of the
IFS report are based on income data at a household level whereas this study will
focus on individual’s income data.
The overall consensus, from the aforementioned literature, is that welfare support
for the unemployed, and relative changes in pension income, are significant
variables contributing to the reduction of inequality in the UK. This research is only
interested in observing the disparity in inequality between, the economically active
native and foreign working age sub-populations; and not the impact of fiscal policy
on inequality for the economically inactive. Therefore, the data in this study will be
formulated in a way such as to exclude these two variables.

8
2.3. Relationship Between Native and Foreign Workers
Extensive research into the nature of the relationship between native and foreign
workers has found, contrary to the widely accepted but ill-informed belief, native and
foreign workers are not perfect substitutes. In other words, there is no constant ratio
of “x” foreign workers which equates to 1 native worker1. Thus, increasing the supply
of foreign labour will not directly increase the degree of competition for jobs
experienced by native workers. Nor will it necessarily apply downward pressure on
native wages. Although research generally agrees native and foreign workers are
not perfect substitutes, it does not agree on what the actual nature of this
relationship is.
An empirical study in the US found that foreign and native workers, in the computer
and technology industry, were compliments (Per, et al., 2014). The results of their
experiment showed that by restricting the supply of foreign labour the demand for
native labour consequently decreased. This resulted in productivity and employment
growth being impaired and could possibly cause the stagnation and decline of
wages for both native and foreign workers.
In contrast, a study based in the UK theorises that native and foreign workers are
imperfect substitutes (Manacorda, et al., 2010). Fascinatingly they find that even
when running their model with an elasticity of supply value of 20, an increase in the
supply of foreign labour will have very little impact on the wages of native workers.
Rather, an increase in foreign labour depresses the wages of other immigrants who
are already in the UK; due to increased competition in the foreign labour market.
1
This ratio is more commonly referred to as the marginal rate of substitution.

9
As the period of analysis in the Manacorda et al. (2010) paper extends from the
1970’s through to the 2000’s the conclusion drawn factors in the large scale net
migration the UK experienced over that time period. This is of particular significance
as this net migration to the UK continues over the time period covered in this
dissertation.
The supposition in (Dustmann, et al., 2013) paper draws an intriguing connection
between the findings presented by Per et al. (2014) and Manacorda et al. (2010).
Dustman et al. (2013) infer that that the effect of immigration on wages at any given
point in an income distribution depends on the density of immigrants at that point in
the distribution. If the density of immigrants exceeds the density of natives at any
point in the income distribution, immigration will have a negative effect on wages at
that point in the distribution. Conversely if the density of natives exceeds the density
of immigrants at any point in the distribution, immigration will increase the native’s
wages.
Extrapolating the theory and findings of these studies I can hypothesize that given
native and foreign labour supplies are not perfect substitutes, and can therefore be
treated as two distinct sub-populations, with differing factors effecting the supply and
demand of labour in each. The price of labour in each sub-population will be
different, thus wages will differ in each sub-population2. As wage distribution can
be used as a proxy for income distribution (Autor, et al., 2008), I expect there to be
2
Given that wages are the price of labour.

10
a marked difference between income inequality in the two sup-populations of the
UK.
2.4. Economic Development, Growth and Income Inequality
This study concentrates its analysis over a period of time where the UK experiences
both negative and positive economic growth. Understanding the relationship
between growth and inequality will be fundamental in correctly understanding and
interpreting my findings.
Nobel prize winning economist Simon Kuznets begins his 1955 thesis by
highlighting that determining the relationship between these variable is not simple.
He points out that there were a multitude of factors which economists had generally
steered clear of, for example political climates and behavioural economics. This was
done by restricting data sets or by making broad and sweeping assumptions in
empirical work. This resulted in conclusions being draw which were not truly
representative of the determinants of economic growth. In addition to this he also
had issues with the “looseness in definitions, unusual scarcity of data, and pressures
of strongly held opinions” (Kuznets, 1955) when conducting research into inequality.
Despite his initial frustration with the ambiguity and lack of completeness in this field
of research he then goes on to theorise a model which in his own words is “5%
empirical information, and 95% speculation, some of which is wishful thinking”
(Kuznets, 1955, p. 29). Ironically his inverted u-shaped hypothesis became the
centre piece of much of the research conducted into economic development and
growth in the following decades.
In essence his hypothesis stated that during the initial development stages of an
economy, inequality will increase drastically, mainly as the income of the self-

11
employed increases at a rate far out stripping those who are employed by these
entrepreneurs. It is only after the economy has stabilized and welfare and taxation
systems are put into place to redistribute the income of the wealthy, will inequality
start to decrease. After which inequality will continue on a downwards trend. Thus
giving an inverted u-shaped curve, with stages of growth on the x-axis and a
measure on inequality, such as the Gini coefficient, on the y- axis.
Fig.1- The Kuznets Curve
Unconventionally this paper does not conclude by explicitly presenting explanations
of the relationship between economic development and inequality, it instead
challenges other economists to recognise the issues with previous research in the
field and to evolve the speculative models developed by Kuznets in the body of the
paper to improve research going forward (Gregorio & Lee, 2002).
The second paper by (Aghion, et al., 1999) builds on Kuznets u-shaped hypothesis
and other papers in the field. It examines the relationship of inequality and economic
growth in both directions by analysing models and presenting economic theory. The
theory presented supports the notion that a reduction in inequality has a positive
effect on economic growth, given imperfect capital markets. However, they also

12
provide the counter argument that economic growth leading to technological
advancements, could increase inequality. The resulting reduction in the demand for
unskilled labour would therefore decrease their wages and thus increase income
distribution between unskilled and skilled workers. Aghion et al. (1999) also touch
briefly on how trade liberalization and the global mobility of labour have differing
impacts on inequality depending on the stage of development in the country being
studied.
These papers have been unable to identify a clear cut relationship between
economic growth and changes in inequality. Posing an intriguing question as to what
the results of our analysis will foster and if it fits in line with the models presented by
Kuznets (1955) and Aghion et al. (1999).

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3. Data Analysis
3.1. The Quarterly Labour Force Survey (QLFS)
The data for this paper has been obtained from the QLFS datasets which are
published by the Office of National Statistics (ONS)3. Since its conception in 1973
the methodology of the QLFS has changed multiple times. The most notable of
these reforms took place before 1994. By this time the QLFS had become an annual
survey which encompassed the entire UK and collected data in 5 successive
quarters, referred to as waves4. However, in 2006 to comply with EU regulations the
QLFS changed the way it defined its waves from seasonal to calendar quarters. As
a consequence of changing the way its sampling periods are defined, “comparisons
cannot be made between datasets of a calendar and seasonal nature” (ONS, 2015).
Additionally, substantial improvements were made to data collection with regard to
qualifications obtained outside of the UK in 2004. Prior to this, previous papers
resorted to basing the educational attainment of a respondent on the age they were
when they obtained their last qualification (Dustmann, et al., 2013). As my analysis
starts in 2007, this unreliable means of educational classification has been avoided,
and all datasets are comparable.
Two key factors determined the choice of the QLFS as a source of data. Firstly, the
methodology used and the sample size allow the data collected to be representative
of the UK population. Each quarter of the survey interviews 40,000 households,
which translates to a sample size in the region of 100,000 respondents per quarter
(ONS, 2015). Of the 5 datasets available each year, the first quarter, January-
3
The datasets were accessed via the UK Data Service.
4
5th
waves data is collected on the anniversary of the 1st
waves data.

14
March, QFLS dataset was used. This wave had the highest response rate, giving
access to the highest number of respondents.
Secondly, comprehensive datasets containing all the information required for
analysis, on the scale proposed, cannot be obtained from other national surveys.
For instance, the British Household Panel Survey has a sample size of just 10,000
respondents. Moreover, the QLFS provides access to particularly detailed income,
education and economic activity data. Having access to this ample dataset allowed
for cross examination of variables, to test for robustness and consistency when
allocating respondents to education bands and calculating individual respondents
net annual wages.
A limitation of using the QLFS as a data source is that since the early 2000’s there
has been a decline in response rates (ONS, 2015). Impacting on the degree to which
the QLFS is representative of the UK population. The number of respondents
qualifying to be in the sample per year, decreased by 20% over a 9-year period. The
largest sample of 10,538 respondents, was in 2008 and the smallest sample, of
8,171 respondents, was in 2013. The ONS has put the declining response rates
down to “factors such as interviewer training, media publicity concerning data
losses, as well as holidays, weather and even sporting events.” (Barnes, et al., 2008,
p. 2) Furthermore, the available sample of foreign respondents was considerably
smaller than the sample of native respondents5. On average, the foreign sub-
population made up just 12.5% of the overall qualifying sample each year (Table 4).
As a consequence of this much smaller sample, results derived from the foreign
5 In this paper the number of qualifying native/foreign respondents per year will be referred to as the
“native sub-population” and “foreign sub-population” respectively

15
sub-populations time series data may exhibit more variation and be more
susceptible to sampling error than the native sub-population.
Additionally, although the sampling method of the QLFS provides data
representative of the UK population, it does not guarantee to give a fair
representation of workforces from specific industries or sectors within the economy.
For example, in one particular year the income data of respondents in the
manufacturing sector may be under-represented and the income data of
respondents in the financial sector over-represented. There is no control allocated
for this in this study.
In order to conduct research into how inequality and wage movements differ at
points in the UK’s income distribution, the population will be divided into 3
workforces determined by the education level of respondents6. These being:
Table 1- Defining Education Groups
Education Group Qualification Levels
1- High Degree or higher
2- Intermediate Above GCSE but below Degree
3- Low Up to and including GCSE
As there is a positive correlation between education and income (Gregorio & Lee,
2002) the education variable is used as a proxy for points within the UK’s income
distribution. Education group 1 representing the top of the income distribution, group
2 the middle and group 3 the bottom of the distribution.
6
For more detail on how these education groups are derived and the qualifications which fall within
education bands see Appendix C

16
Fig.2- Income Distribution Curves of the UK Population by Education Groups in
2007
Proof of concept is observed in Fig.2. As education levels increase the income
distribution curves shift to the right, indicating the workforce earns a higher wage as
education levels increase. Consequently, placing each workforce at successively
higher intervals in the income distribution of the UK in any given year.
The UK population will also be categorised into subsets based on the respondent’s
place of birth. The “native” sub-population will be comprised of respondents born in
the England, Wales, Scotland and Northern Ireland. The “foreign” sub-population
will be comprised of all respondents born anywhere else in the world. This allows
for comparison between the two sub-populations in the UK.
In this study income will be defined as real net annual wages, as opposed to gross
annual wages. The net wage value gives a fairer representation of a respondents
“take home” income, as it represents income after tax and takes into consideration
welfare support. It does not however take into consideration housing costs or
autonomous consumption; therefore, net wages do not represent disposable
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
2.55 3.03 3.51 3.99 4.47 5.01 5.49
FrequencyDensity
Log Net Wages
High Intermediate Low

17
income. Disposable income takes into consideration housing costs and autonomous
consumption, such as food and clothing, which account for a higher percentage of
income for respondents who earn lower amounts. Making this a better measure for
income inequality than net wages. As the QLFS does not provide data on disposable
income this is a clear constraint of the data used when measuring income inequality.
3.2. Defining the Sample
Repeated cross-sectional data from the QLFS initially presented a sample size of
100,000 respondents per year, including respondents who were children, had retired
or were unemployed during the sampling week. For the purposes of this research,
respondents under 21 have been excluded from the sample, as they are likely to
still be in education and not full time work; thus negatively skewing the income
distribution. Similarly, respondents over 60 will be approaching the end of their
working careers and likely be working reduced hours or considering retirement;
therefore, they have also been excluded. Of the respondents aged 21-60 only those
in employment during the reference week are included in the sample7.
Respondents who met these criteria also needed to provide full and complete
answers to all survey questions relating to net income and qualifications. Failure to
answer one or more of the relevant questions, resulted in those respondents being
excluded from the sample due to an incomplete dataset. In the QLFS “earnings
questions are not addressed to respondents who are self-employed” (ONS, 2015,
p. 236) and hence self-employed workers were automatically excluded from our
sample, another limitation of the data used.
7
In accordance with the International Labour Organisations definition of (un)employment.

18
4. Methodology
4.1. Repeated Cross-Sectional to Time Series Data Transformation
Once the yearly samples were restricted to qualifying respondents, the net annual
wages of individual respondents were calculated by determining their usual nominal
net wages over a time period defined by the respondent and multiplying this value
by a relevant factor to estimate net annual wages. The estimates of nominal net
annual wages were then discounted using a discount factor derived from the UK’s
CPI index to return the real net annual wage figures8.
For robustness, the gross wages9 were also calculated to determine if the estimated
net wage is within a reasonable deviation, 0% to -30%10. In the majority of cases my
calculations satisfy this condition. Although a small percentage of respondents have
clearly given ill-informed answers to gross or net income variables. This is most
prominent with respondents reporting low incomes (below £1,000 annually) implying
there may be an issue regarding the accuracy of low income data collected from the
QLFS (Brewer & O'Dean, 2009). However, as these respondents gave valid
responses to all income questions asked during their QLFS interviews, their
calculated net wages have been treated as valid outcomes, despite being highly
implausible. This should not impact on our findings as these anomalies account for
an insignificant proportion of the overall sample.
At this stage, respondents were also allocated their education groups. An issue
arose when it was noticed a large proportion of the foreign sub-population
responded with the answer “other qualifications” in their interviews. Thus, initially it
8
All wage data is discounted to a base year of 2008. The term “wage” will be defined as the real annual
wage henceforth.
9
For further explanation on how net and gross wages are calculated from QLFS variables see Appendix C.
10
For rational behind these limits see Appendix C.

19
was not possible to allocate these respondents to education groups. This would
have eliminated them from the sample and drastically reducing the foreign sub-
populations, already small, sample size. Upon investigation it was determined that
they responded in this way, despite having qualifications below degree but above
GCSE level, because they could not determine which UK equivalent level of
qualification they held. Consequently, foreign respondents answering “other
qualification” where placed into the intermediately educated grouping. Similarly,
native respondents answering “other qualifications” did so, despite having a
comprehensive list of qualifications to choose from, because they had only partially
completed a basic qualification at the time of the interview. Hence they were
allocated to the lowly educated grouping11.
Once the net wages of all qualifying respondents had been calculated and education
groups allocated. Repeated cross-sectional data, from 2007-2015, was transformed
to give the following time series data:
 The Gini coefficient of the UK (Table 2)
 Median net wages of the UK (Table 3)
 Net wages at the 10th, 50th and 90th percentiles within education groups and
sub-populations (Table 6, 7 & 8)
 90:10 ratios within education groups and sub-populations (Table 9)
11
For more details on the rationale behind the group allocation of foreign and native respondents
answering “other qualifications” see Appendix C

20
4.2. Measures of Inequality
This study uses two well established methods of quantifying inequality. The Gini
coefficient and the 90:10 ratio will be used to measure the degree of inequality
experienced in the UK and between its sub-populations.
4.2.1. Gini Coefficient
The Gini coefficient is a numerical representation of the distribution of income within
a population. A coefficient of 0 represents perfect equality, where every person in
the population has the same proportion income. Whereas a coefficient of 1
represents perfect inequality, where one person in the population has 100% of the
total income12. In practice a coefficient near or at either one of these extremes is
highly unlikely.
Fig.3- Lorenz Curve of the UK Population 2007 and 2015
The Gini coefficient is often represented graphically via the Lorenz curve. The
coefficient is a ratio of areas alpha and beta (Fig.3). Where alpha is the area
12
The Gini coefficient can also be presented on a scale of 0-100. A coefficient of 100 representing perfect
inequality.
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
CumulativePercentageofNetWages
Cumulative Percentage of Population
Line of Absolute Equality Lorenz Curve 2007 Lorenz Curve 2015
α
β

21
between the line of absolute equality and the Lorenz curve, and beta is the area
under the Lorenz curve. The line of perfect equality (Gini=0) is drawn at a 45º angle
with the Lorenz curve plotted beneath. The closer the Lorenz curve is to the line of
equality the less inequality there is in the population. It is possible for the Lorenz
curve to exceed the line of equality in some uncommon cases when members of the
population receive a negative income13,14.
The Gini coefficient has been calculated for each year using the following
formulae:
𝛼 =
1
2
− 𝛽 𝛽 = ∑
𝑊𝑖 + 𝑊𝑖−1
2𝑛
𝑛
𝑖=2
𝐺𝑖𝑛𝑖(𝑝) =
𝛼
𝛼 + 𝛽
i = respondent number 15
W= cumulative percentage of total net wages 16
n = total number of respondents
p = population
4.2.2. 90:10 Ratio
This is the second measure of income inequality in this paper. It will be used to
calculate levels of inequality experienced by the native and foreign sub-populations
within the 3 education groups.
It is calculated using the following formula:
90: 10 𝑟𝑎𝑡𝑖𝑜 =
𝑤𝑎𝑔𝑒 𝑜𝑓 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑛𝑡 𝑎𝑡 90 𝑡ℎ
𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒
𝑤𝑎𝑔𝑒 𝑜𝑓 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑛𝑡 𝑎𝑡 10 𝑡ℎ 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒
13
Negative income would occur if a respondents only source of income was from government funded
welfare support.
14
In this study only income figures above 0 have been taken into consideration
15
Respondents are put in order of income, lowest to highest, before calculations are run.
16
Percentages are expressed as decimals. I.e. 25% of cumulative total net wages = 0.25

22
Further explained, if the 90:10 ratio of a population is 3, the respondent at the 90th
percentile earns 3 times as much as the respondent at the 10th percentile.
Whilst the Gini coefficient will be used to determine the level of inequality in the UK
population, it is not necessarily the best method of measuring inequality in sub-
populations, which have an inherently smaller sample size. Former research has
revealed that when calculating the Gini coefficient, of datasets with small sample
sizes, the coefficient underestimates the degree of inequality (Deltas, 2000).
The 90:10 ratio makes for a more suitable measure when observing changes in
inequality of sub-populations with much smaller sample sizes as is not sensitive to
wage movements at the very extremes of the sub-populations17.
4.3. Wage Movements
4.3.1. The Median
The average wage of the UK and its sub-populations will be used to observe wage
movements over time. This study will define the average wage as the median wage
as opposed to the mean wage. The reason for this is that the mean wage figure is
influenced by respondents earning significantly more than the rest of the population,
the top 10%, thus positively skewing the average value. Alternatively, the median
wage value is defined as the wage the respondent in the middle of the income
distribution receives18. Thus, the median value is not influenced by the large
incomes received by high earners and gives a fairer representation of the wage
received by the average worker in a population (Blakely & Kawachi, 2001).
17
Below the 10th
percentile or above the 90th
percentile.
18
When respondents are ordered by income value, smallest to largest.

23
4.3.2. The 10th and 90th Percentiles
Whilst the median wage allows for comparisons of wages across populations,
observing movements in wages at the 10th and 90th percentiles within population
subsets allows for a more in-depth analysis of how wages are changing at particular
points in the UK’s income distribution. Moreover, it permits to better understand
where wage movements are occurring within sub-populations and thus a better
interpretation of the 90:10 ratio.
4.4. Income Distribution- Frequency Density Polygon
Cross-sectional datasets from 2007, 2012 and 2015 were also used to plot the
income distribution curves of the UK population and its subsets. Comparing the
shape and position of the curves will allow for comments on how the UK’s income
distribution has changed in periods 1, 2 and over the 9-years.
The distribution function is plotted using a frequency density polygon. The area
under the curve represents 100% of the population. Whilst the natural log value of
net wages is plotted on the x axis and the number of respondents receiving that
wage, as a fraction of the population, is plotted on the y axis. At any given point
along the curve the height of the curve represents the concentration of the
population receiving that log net wage value (Jenkins, 1996).

24
5. Results and Discussion
5.1. The UK’s Income Distribution
Over the course of period 1 the shape of the UK’s income distribution remains
unchanged (Fig.7), however there is a slight but distinct leftward shift in the
distribution curve. From this observation it appears that the recession has reduced
wages across the UK population by a uniform amount.
In period 2 there are transformations in both the shape and position of the
distribution (Fig.8). There is a clear rightward shift displayed, indicating wages have
increased amongst the population. In 2015 the distribution is also marginally steeper
and peaks higher in comparison with 2012. This possibly indicates a reduction in
inequality as there are more workers earning higher average wages and less
workers at the tails of the distribution.
When comparing income distributions of the UK population, pre-recession and post-
recovery (Fig.9), we see in 2015 the income distribution is slightly steeper at both
ends. Moreover, in 2007 and 2015 both curves peak at around the same log net
wage value, however in 2015 the peak of the curve is noticeably higher and wider
than in 2007. From this is can be inferred that by 2015 the wage received by the
average worker had returned to pre-crisis levels and that a higher proportion of
workers earn close to the average wage value. This could be interpreted as a
decrease in inequality.
Generally speaking, the income distribution curves of the UK are all positively
skewed, they are steep at the lower log wage value and less steep at the higher log
wage value. This indicates that less workers in the UK economy earn a low wage
and more workers earn at or around the median value.

25
5.2. Inequality in the UK
Over the last 9 years in the UK income inequality has been visibly declining. The
Gini coefficient reduced from 0.328 in 2007 to 0.321 in 2015 (Fig.10) representing
a decrease of 0.45% in period 1 and a further 2.08% in period 2. This equates to a
rate of reduction 4.5 times greater in the 3 years after 2012 than the 5 years before.
A possible explanation is that an increase in economic growth leads to a decrease
in unemployment and an increase in wages which in turn reduces inequality. On the
other hand, negative economic growth does not necessarily increase inequality.
When taking into account the finds of previous literature there is evidence of the UK
following Kuznets speculative curve (Fig.1). During the 1970’s-1990’s inequality
rose sharply in the UK, coinciding with the transition away from an industrial
economy and towards a services based economy. From the 1990’s-2012 inequality
remained relatively unchanged, and after 2012 my findings show inequality
decreasing at 4.5 times the rate as it was previously (Fig.10).
Despite a clear downwards trend in income inequality there is evidence of the
foreign sub-population experiencing a significantly higher degree of inequality than
the native sub-population (Fig.11). In 2007 inequality in the foreign sub-population
was 7.79% higher than in the native; and by 2015 this increased to 10.14%. It should
also be noted that movements in inequality between the UK’s sub-populations were
not consistent during the recession. In period 1, the correlation between the Gini’s
of the two sub-populations is very weak (0.21). The native sub-population
experienced a 0.87% decrease in inequality, as opposed to the foreign sub-
population which experienced a 1.11% increase.

26
Contrastingly in period 2, there is a very strong positive correlation (0.87) in the
movements of inequality; decreasing in the native and foreign sub-populations by
2.33% and 2.15% respectively. Over the 9-year period, this resulted in the native
sub-population experiencing a reduction in income inequality 2.12% greater than
the foreign sub-population.
This suggests that economic growth has had a uniformly negative impact on income
inequality but that economic instability induces opposing effects in the sub-
populations. These observations are in line with the hypothesis made earlier in this
study and furthermore they give the first indications of native and foreign labour not
being perfectly substitutable. If the relationship between these two types of labour
was that of perfect substitutes then inequality within the sub-populations should
react similarly to the same economic shocks, this was not the case during the
recession.
5.3. Wage Movements in the UK
The wage slump which occurred in the UK during the last recession (Cribb & Joyce,
2014) is evident in the data. During period 1, the wage of the average UK worker
decreased by 6.70% (Fig.12)
Examining the changes in the median wage of the sub-populations highlights the
inconsistency between the two (Fig.13). Although there is a moderately strong
positive correlation (0.67) between wage movements there is a significant difference
in the proportion of these changes. In 2007, the average wage of both sub-
populations was almost identical, at around £14,750. Over the course of period 1
the foreign sub-population experienced a decrease in median wages of £2,115
(14.29%), more than double the percentage decrease experienced by the native

27
sub-population (6.46%). Furthermore, in period 2 the median native wage seems to
be recovering faster than the median foreign wage; an increase of 7.11% for the
natives compared to a 5.08% increase in the foreign sub-population.
From being paid almost identical amounts in 2007, by 2015 the average foreign
worker is paid 9.44% less than their native counterpart. These observations show
that changes in economic growth cause native and foreign wages to move in the
same directions, but not in the same magnitude nor in a consistent ratio. Insinuating
that if an indifference curve which represented the trade-off between native and
foreign labour was drawn, it would have a negative gradient and be convex.
Therefore, the marginal rate of substitution between these two types of labour would
be continually changing19. If the two types of labour were perfect substitutes the
indifference curve would be linear and the marginal rate of substitution would be a
constant. This provides a strong case for the argument of native and foreign workers
being imperfect substitutes.
Previous research in this field (Blakely & Kawachi, 2001) found a negative
correlation between changes in median wages and income inequality. The results
from this analysis generally support their conclusion. In period 2, median wages in
the UK and its sub-populations are increasing and inequality is decreasing. This also
holds true for the foreign sub-population in period 1, where we have decreasing
median wages but increasing inequality.
19
Given the assumption that native and foreign labour are substitutable.

28
However, this conclusion is violated when we examine the period 1 analysis of the
UK population and the native sub-population20. Here there is a decrease in both
median wages and inequality. A possible explanation of this is that during the
recession median wages reduced across all education groups equally (Fig.14), thus
reducing the median wage of the UK population but not increasing the overall
distribution of wages, instead shifting it down. Evidence supporting this movement
is present in Fig.7. During period 1, all three education groups experienced a
decrease in median wages of 8.6% ± 2.2%.
Thus far, we can conclude that there is a slight but distinct downward trend in
inequality within the UK (Dean & Platt, 2016, p. 145). Furthermore, the foreign sub-
population experiences a greater degree of income inequality than the native sub-
population and additionally, inequality has decreased more within the native sub-
population than the foreign. Moreover, the data suggests that the foreign sub-
populations wages were greatly more susceptible to the 2008 recession and have
been slower to recover post-recession. This supports findings in previous literature,
which indicates native and foreign labour are not perfect substitutes but there is in
fact some other relationship between these two types of labour.
20
As the sample size of the qualifying native respondents is 7-8 time greater than the sample size of the
qualifying foreign respondents. Any trends seen in the native sub-population are heavily reflected in the UK
population. See Table 4.

29
5.4. Inequality within Sub-Populations and Education Groups
Moving further, this study examines changes in income inequality and wages
experienced by similarly educated workers in the UK and between its sub-
populations.
5.4.1. All Workforces
It can be identified that the lowly educated workforce experiences a greater degree
of income inequality than intermediately educated workforce, which in turn
experiences more inequality than the highly educated workforce (
Fig.15). This shows a negative correlation between education and inequality
(Machin, 1996). Furthermore, inequality within the highly educated workforce is
significantly lower than within the intermediately and lowly educated workforces. It
therefore stands to reason, if average education levels in the UK rose and all
inhabitants of the UK had access to this higher level of education, income inequality
would decrease. This is in line with (Topel, 1997) findings.
An interesting discovery of this paper is that changes in inequality between the
highly educated and lowly educated workforces are strongly positively correlated
(0.79). Whereas changes in inequality in the intermediately educated workforce
moves independently of the other two workforces. This suggests that changes in
inequality at the two extremes of the income distribution, highest and lowest
educated workforces, move together.

30
Fig.4 Wage of the Native and Foreign Sub-Populations Within Education Groups
at the 50th Percentile (Median)
Line notation [High, P50, Native] reads as: highly educated workforce at the 50th percentile of the
native sub-population
5.4.2. Highly Educated Workforce
Within the highly educated UK workforce there are stark differences between
changes in the median wage and inequality in the sub-populations. During period 1
the highly educated foreign workforce experienced a dramatic 19.61% decline in
median wages, compared to a 5.02% decline experienced by the highly educated
native workforce (Fig.4). The foreign median wage also fluctuates more than the
native median wage21. It appears that during the recession the wages of the foreign
sub-population reacted more violently than those of the native sub-population.
Furthermore, within the highly educated workforce the foreign sub-population
experiences a significantly greater degree of inequality (Fig.16). In 2009, inequality
21
Though this could be due to sampling error. There is a much smaller available sample of highly educated
foreign respondents. see
Table 5
5,000
7,500
10,000
12,500
15,000
17,500
20,000
22,500
25,000
2007 2008 2009 2010 2011 2012 2013 2014 2015
RealNetWages(£'s)
Years
High P50 Native High P50 Foreign Inter P50 Native
Inter P50 Foreign Low P50 Native Low P50 Foreign

31
in the foreign sub-population peaked at a value of 6.2, 66% greater than the level of
inequality in the native sub-population at the same time.
However, it should be taken into account from 2007 to 2015 there is a prominent
declining trend in inequality for the foreign sub-population (-9.16%), whereas
inequality amongst the native sub-population has increased slightly (+1.91%). This
decline in inequality amongst the foreign sub-population can be explained by a
substantial 28.53% decline in wages at the 90th percentile during period 1 (Fig.17)
It is also apparent that foreign workers at the 90th percentile are consistently paid
more than native workers, although the difference between the two has markedly
decreased over time.
5.4.3. Intermediately Educated Workforce
Trends of a similar nature can be observed in the intermediately educated workforce
(Table 7). In 2007, the median wage of the native workforce is 10.11% greater than
in the foreign workforce. During period 1 the median wage of foreign workers fell to
£10,770 (-20.46%) a glaring contrast to the median wage of the native workforce,
£13,749 (-7.79%). This represents a 17.55% increase in the wage gap between
similarly educated workers in 6 years (Fig.4).
Intermediately educated foreign workers experienced a wage decrease to such an
extent that for the duration of period 2 they receive the same median wage as native
workers in the lower education group (Fig.4). One explanation of this phenomenon
is that during times of high unemployment, as seen in the UK post-recession, foreign
workers are willing to accept jobs for which they are overqualified for. Such
behaviour is known as downgrading (Dustmann, et al., 2013).

32
Inequality between the sub-populations fluctuates considerably during period 1 with
no discernible trend, however in period 2 we see the now familiar development of
inequality decreasing overall but being higher amongst the foreign sub-population
(Fig.18). The correlation between changes in inequality in period 2 is also
significantly higher (0.93).
Intriguingly, despite using the 90:10 ratio as a mean of measuring inequality within
education groups, this is strikingly similar to the trend observed when examining
changes in inequality of the UK population using the Gini coefficient in section 5.2.
of this study. This is most likely because the intermediately educated workforce has
the highest density of workers and the Gini coefficient factors population density into
its calculation of inequality. This results in any movements in inequality, observed in
areas of high density within the income distribution, being reflected in the Gini
coefficient.
The wage structure of the intermediately educated workforce (Fig.19) shows that at
the 10th, 50th, and 90th percentiles native workers are always paid more than foreign
workers. In addition to this, foreign workers at the 90th percentile experienced a
18.82% decrease in wages from 2007 to 2015, compared with a 7.47% decrease
experienced by native workers. Further analysis shows that at the 10th percentile
native wages have decreased by 2.47% whereas foreign wages have decreased
10.76%. It is evident that the foreign sub-population is paid less than their native
counterparts across all points of the intermediately educated UK workforce and that
they have also experienced a greater decline in their wages since 2007.

33
5.4.4. Lowly Educated Workforce
Once again the median wage of native workers is significantly higher than that of
foreign workers (Fig.4). It can be observed that foreign workers experienced a
greater decline in median wages in period 1, -10.62% compared to -6.02% for native
workers. The median wage of the foreign sub-population also recovered more slowly
post-recession, +4.37%, compared to +6.26% in native wages. The trends observed
here are similar to the trends observed in the highly and intermediately educated
workforces.
However, the notable difference within the lowly skilled workforce is that it is the
native sub-population which exhibits more inequality (Fig.20). Furthermore,
inequality amongst the foreign sub-population is clearly increasing (+9.86% in
period 2). On both counts this is the inverse of what has been seen in the previous
two education groups. The increase in inequality amongst the foreign sub-
population seems to be a product of an increase of 6.65% in wages at the 90th
percentile.
Despite the increase in wages during period 2 the foreign sub-population receives
a noticeably lower wage at the 90th and 50th percentiles over the 9-years.
Contrastingly, at the 10th percentile foreign and native workers receive almost
identical amounts, although this is possibly due to the minimum wage in the UK
(Fig.21).

34
6. Conclusion
This study finds that inequality in the UK has been decreasing since 2007 (Dean &
Platt, 2016) and that wages in the UK are recovering after the latest recession
(Stewart, 2015). When comparing native and foreign sub-populations it is found that
the foreign sub-population experiences a higher degree of inequality within the
upper and middle sections of the UK’s income distribution as well as in the UK
population as a whole.
In Comparison, in the lower part of the UK’s income distribution the native sub-
population experiences greater inequality. This is possibly because of the apparent
wage ceiling of lowly educated native workers being higher than that of lowly
educated foreign workers. The disparity in the wage ceilings, combined with the
minimum wage of the UK providing a “floor”, which the wages of the two sub-
populations cannot fall below, results in a much wider distribution of wages amongst
the native sub-population.
Puzzlingly, although the foreign sub-population is on average better educated than
the native sub-population (Fig.5) it experiences more inequality. Previous literature
(Gregorio & Lee, 2002) would suggest this should mean the foreign sub-population
would experience less inequality than the native sub-population, due to the proven
inverse relationship between inequality and education. This signifies that although
higher education is a significant factor in determining and reducing inequality. There
must be other variables which are causing the degree of inequality in the foreign
sub-population to be higher, despite the higher levels of education.
A possible explanation for the difference in inequality in the UK is that the distribution
of workers within the two sub-populations is fundamentally different.

35
Fig.5- Composition of Sub-Populations by Level of Education
The native sub-population has an almost equal distribution of workers between its
3 education groups, one third in each with a slight bias towards the lowly educated
workforce. Whereas the foreign sub-population has a drastically uneven
distribution of workers. Somewhere in the ratio of 2:3:122 in 2007 and moving
towards the ratio of 3:3:1 by 2015. Since there is an uneven distribution of workers
in the foreign sub-population, this will directly result in the distribution of wages
being more uneven. Especially since the distribution is heavily biased towards the
highly educated workforce.
The analysis conducted on wage movements presents a striking disparity between
native and foreign sub-populations. At almost all points within the UKs income
distribution native workers are paid more than their foreign counter parts. Foreign
workers were also the hardest hit by the 2008 recession, experiencing far greater
declines in their wages within all education groups than native workers. In addition
22
Of 5 workers, 2 are highly educated, 3 intermediately, 1 lowly educated
0%
20%
40%
60%
80%
100%
Native
Foreign
Native
Foreign
Native
Foreign
Native
Foreign
Native
Foreign
Native
Foreign
Native
Foreign
Native
Foreign
Native
Foreign
2007 2008 2009 2010 2011 2012 2013 2014 2015
PercentageofSub-Population
Years and Sub-Populations
Low Intermediate High

36
to this, relative to their native equivalents the rate at which foreign worker’s wages
have recovered post-recession is much slower.
Building on the work of Manacorda et al. (2010), this study presents evidence
supporting their conclusion of native and foreign workforces being imperfect
substitutes, and to an extent the foreign workforce being treated as inferior when
compared with the native workforce. In that, similarly educated and experienced
workers, who are in their working prime are paid significantly different amounts and
are subject to relatively greater negative impacts to the same economic shocks,
based solely on their country of birth, ceteris paribus.
Fig.6- Percentage of Population Composed of Foreign Workers: The UK and its
workforces
The only exceptions to this are the most highly paid, highly educated foreign workers
who receive a higher wage than their native equivalents. A possible explanation for
this is that a supply shortage of highly skilled native workers in the engineering and
medical sectors of the UK economy is driving the recruitment of highly skilled foreign
labour from abroad (gov.uk, 2015). In order to incentivise foreign workers to relocate
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
2007 2008 2009 2010 2011 2012 2013 2014 2015
PercentageComposition
Years
High Intermediate Low Foreign Population Over All

37
to the UK, firms are willing to pay a wage premium. This is possible in the 21st
century due to the increased global mobility of labour for highly skilled workers
(Mahroum, 2000). Evidence supporting this theory is present in Fig.6. where it can
be observed that there has been a sharp increase in the percentage composition of
foreign workers in the highly skilled workforce during period 1.
Using the Keynesian demand and supply framework I now present a possible
alternative explanation for foreign wages experiencing a disproportionately large
decline during the recession and slower recovery post-recession.
From 2007 to 2012 the demand for labour in the UK economy decreased in line with
the shrinking economy. This resulted in both native and foreign sub-populations
experiencing a fall in wages as a result of decreased demand for their labour.
However, the proportion of the UK population which is comprised of foreign workers
has been steadily increasing, from 10.54% in 2007 to 14.86% in 2015 (Fig.6). In
accordance with the Keynesian framework (Fig.22&Fig.23) this would mean the
foreign sub-population experienced downward pressure on its wages from a
decrease in demand as well as an increase in supply. The native sup-population on
the other hand only experienced a decrease in demand and therefore relatively less
downward pressure on its wages (Per, et al., 2014). Similarly, after the recession
the demand for labour started to increase, this caused wages to also increase.
However, the foreign sub-population continues to experience an increasing supply
of labour (Fig.25&Fig.24). This results in foreign wages increasing at a slower rate
than native wages as the native sub-population does not experience the slight
downward pressure on wages which occurs from the increase in supply.

38
The argument is made that as native and foreign labour are imperfect substitutes
the effect of an increasing supply of foreign labour is not transferable to the native
sub-population. Therefore, any changes which only affect the foreign labour market
have little to no impact of the native sub-populations wages.
In closing, the research shows income inequality in the UK has been decreasing
since 2007, and this decline accelerated substantially after 2012. Additionally, in the
UK the foreign sub-population experiences a higher degree of income inequality
than the native sub-population.
It is also evident that the native sub-population, on average, receives a higher net
wage than their foreign equivalents at the majority of points within the UK’s income
distribution. Furthermore, since 2012 net wages have been recovering faster in the
native sub-population than in the foreign.
This study presents a case for the inverted u-shaped relationship between economic
development and inequality as well as for native and foreign workers being imperfect
substitutes. However, I believe a combination of the financial crisis in 2008 and high
levels of net migration to the UK during the period of analysis have resulted in the
extent of the wage bias being exaggerated. Further research would need to be
conducted to confirm these findings.

39
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42
8. Appendix
8.1. A
Fig.7- Income Distribution of the UK Population in Period 1: 2007-2012
Fig.8- Income Distribution of the UK Population in period 2: 2012-2015
Fig.9- Income Distribution of the UK Population 2007-2015
The UK population is defined as economicaly active respondents aged 21-60
Log Net Wage refers to the natural log value of the real annual net wages of respondents.
0
0.02
0.04
0.06
0.08
0.1
0.12
2.49 3.03 3.51 3.99 4.53 5.01 5.49
FrequencyDensity
Log Net Wages2007 2012
0
0.02
0.04
0.06
0.08
0.1
0.12
2.49 3.03 3.51 3.99 4.53 5.01 5.49
FrequencyDensity
0
0.02
0.04
0.06
0.08
0.1
0.12
2.49 3.03 3.51 3.99 4.53 5.01 5.49
FrequencyDensity

43
Fig.10- Gini Coefficient of the UK Population
Fig.11- Gini Coefficients of Native and Foreign Sub-Populations
The Gini coefficiant is calcualted on the real annual net wages of the UK population.
The native sub-population is defined as respondents who were born in the UK, the foreign sub-
population is defined as respondents born anywhere else in the world.
The polynomial trend line in Fig.10 represents potential evidence in this study of the UK following
Kuznets curve. See section 5.2. of this dissertation for more information
0.315
0.320
0.325
0.330
0.335
0.340
0.345
2007 2008 2009 2010 2011 2012 2013 2014 2015
GiniCoefficiant
year
UK Kuznets Curve
0.31
0.32
0.33
0.34
0.35
0.36
0.37
0.38
2007 2008 2009 2010 2011 2012 2013 2014 2015
GiniCoefficiant
Year
Native Foreign

44
Fig.12- Median Real Net Wage of the UK Population
Fig.13- Median Real Net Wage of Native and Foreign Sub-Populations
population is defined as respondents born anywhere else in the world.
2014 foreign wage data exhibits an outlying value which has been highlighted above and a linier
trend line joins 2013 and 2015 data points.
13,600
13,800
14,000
14,200
14,400
14,600
14,800
2007 2008 2009 2010 2011 2012 2013 2014 2015
RealNetWages(£'s)
Year
UK
12,500
13,000
13,500
14,000
14,500
15,000
2007 2008 2009 2010 2011 2012 2013 2014 2015
RealNetWages(£'s)
Year
Native Foreign
Foreign
Outlier

45
Fig.14- Wage of the UK population Within Education Groups at the 50th Percentile
(Median)
Fig.15- 90:10 Ratios of the UK Population within Education Groups
Line notation [High, P50, UK] reads as: highly educated workforce at the 50th percentile of the UK
population
The high education group is defined as respondents with a degree level education or above. The
intermediate education group is defined as respondents with a level of education above GCSE but
below degree. The low education group is defined as respondents with a level of education up to
and including GCSE’s
The UK population is defined as economicaly active respondents aged 21-60.
-
5,000
10,000
15,000
20,000
25,000
2007 2008 2009 2010 2011 2012 2013 2014 2015
RealNetWages(£'s)
Years
High P50 UK Inter P50 UK Low P50 UK
3.4
3.6
3.8
4.0
4.2
4.4
4.6
2007 2008 2009 2010 2011 2012 2013 2014 2015
90:10Ratio
Years
High UK Intermediate UK low UK

46
Fig.16- 90:10 Ratios of the Highly Educated Native and Foreign Sub-Populations
Fig.17- Wage of Highly Educated Native and Foreign Sup-Populations at 10th, 50th,
90th Percentiles
Line notation [High, P90, Native] reads as: highly educated workforce at the 90th percentile of the
native sub-population
The high education group is defined as respondents with a degree level education or above.
population is defined as respondents born anywhere else in the world
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
2007 2008 2009 2010 2011 2012 2013 2014 2015
90:10Ratio
Years
High Native High Foreign OLS High Native OLS High Foreign
-
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
45,000
50,000
2007 2008 2009 2010 2011 2012 2013 2014 2015
RealNetWages(£'s)
Years
High P90 Native High P90 Foreign High P50 Native
High P50 Foreign High P10 Native High P10 Foreign

47
Fig.18- 90:10 Ratios of Intermediately Educated Native and Foreign Sub-
Populations
Fig.19- Wage of Intermediately Educated Native and Foreign Sup-Populations at
10th, 50th, 90th Percentiles
Line notation [Inter, P50, Native] reads as: Intermediately educated workforce at the 90th percentile
of the native sub-population
The intermediate education group is defined as respondents with a level of education above GCSE
but below degree.
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
5.0
5.2
2007 2008 2009 2010 2011 2012 2013 2014 2015
90:10Ratio
Years
Intermediate Native Intermediate Foreign
-
5,000
10,000
15,000
20,000
25,000
30,000
2007 2008 2009 2010 2011 2012 2013 2014 2015
RealNetWages(£'s)
Years
Inter P90 Native Inter P90 Foreign Inter P50 Native
Inter P50 Foreign Inter P10 Native Inter P10 Foreign

48
Fig.20- 90:10 Ratios of Lowly Educated Native and Foreign Sub-Populations
Fig.21- Wage of Lowly Educated Native and Foreign Sup-Populations at 10th, 50th,
90th Percentiles
The low education group is defined as respondents with a level of education up to and including
GCSE’s
3.0
3.2
3.4
3.6
3.8
4.0
4.2
4.4
4.6
4.8
2007 2008 2009 2010 2011 2012 2013 2014 2015
90:10Ratio
Years
low Native low Foreign
-
5,000
10,000
15,000
20,000
25,000
2007 2008 2009 2010 2011 2012 2013 2014 2015
RealNetWages(£'s)
Years
Low P90 Native Low P90 Foreign Low P50 Native
Low P50 Foreign Low P10 Native Low P10 Foreign

49
Fig.23- Foreign Labour Market
Movement: 2007-2012
k
nnnnnnnnbbb
Fig.22- Native Labour Market
Movement: 2007-2012
Fig.24- Foreign Labour Market
Movement: 2012-2015
Fig.25- Native Labour Market
Movement: 2012-2015
During the recession the foreign labour market (Fig.23) experiences a decrease in demand as
well as an increase in supply, as a result of net migration to the UK. This causes a larger fall in
the price of labour (wages) in the foreign labour market than in the native labour market (Fig.22).
As the native labour market only experiences a fall in demand.
During the economic recovery of the UK, wages grow faster in the native labour market as the
supply of native labour is fixed and demand increases (Fig.25). However, in the foreign labour
market there is an increase in supply as well as demand (Fig.24), so although price (wages) is
increasing, the increase is slower than the increase in the native labour market.

8.2. B
Table 2- Gini Coefficients of UK Population
Population 2007 2008 2009 2010 2011 2012 2013 2014 2015
UK 0.32899 0.33774 0.33258 0.34068 0.33212 0.32750 0.32864 0.33699 0.32067
Native 0.32604 0.33242 0.32902 0.33812 0.32649 0.32321 0.32531 0.33282 0.31568
Foreign 0.35146 0.37474 0.35803 0.35631 0.36695 0.35535 0.35021 0.36109 0.34772
Table 2 represents the time series Gini Coefficients calculated from the repeated cross-sectional data sets
Table 3- Median Wages of UK Population (£’s)
Population 2007 2008 2009 2010 2011 2012 2013 2014 2015
UK 14,736.56 14,560.00 14,622.98 14,381.95 14,146.90 13,749.43 13,865.00 14,229.60 14,360.04
Native 14,699.28 14,560.00 14,670.90 14,582.80 14,146.90 13,749.43 14,023.22 14,229.60 14,727.64
Foreign 14,806.97 14,400.00 14,084.06 13,794.54 13,738.81 12,691.78 12,898.48 14,229.60 13,336.86
Table 3 represents the time series median wages of the UK population calculated from the repeated cross-sectional data sets

51
Table 4- Total Number of Qualifying Respondents
2007 2008 2009 2010 2011 2012 2013 2014 2015
UK 10,446 10,538 9,741 9,434 8,462 8,635 8,171 8,217 8,323
Native 9,345 9,317 8,618 8,314 7,379 7,530 7,121 7,076 7,086
Foreign 1,101 1,221 1,123 1,120 1,083 1,105 1,050 1,141 1,237
Table 5- Number of Qualifying Respondents by Education Groups
education
group
Popu-
lation
2007 2008 2009 2010 2011 2012 2013 2014 2015
1
UK 2,733 2,813 2,660 2,742 2,620 2,782 2,776 2,904 3,000
Native 2,401 2,446 2,314 2,336 2,139 2,258 2,296 2,355 2,444
Foreign 332 367 346 406 481 524 480 549 556
2
UK 3,758 3,745 3,493 3,359 2,959 3,017 2,879 2,929 2,947
Native 3,178 3,105 2,915 2,843 2,527 2,609 2,473 2,478 2,432
Foreign 580 640 578 516 432 408 406 451 515
3
UK 3,955 3,980 3,588 3,333 2,883 2,836 2,516 2,384 2,376
Native 3,766 3,766 3,389 3,135 2,713 2,663 2,352 2,243 2,210
Foreign 189 214 199 198 170 173 164 141 166
Tables 4&5 show the number of qualifying respondents in each iteration of the repeated cross-sectional data, and the break down by education groups.
The high education group is defined as respondents with a degree level education or above. The intermediate education group is defined as respondents with
a level of education above GCSE but below degree. The low education group is defined as respondents with a level of education up to and including GCSE’s.

52
Table 6- Workers in Education Group 1 (£’s)
Percen
tiles
Popu
lation
2007 2008 2009 2010 2011 2012 2013 2014 2015
90th
UK 39,155.01 39,600.00 39,904.85 38,655.03 36,999.57 34,902.39 35,312.93 35,574.00 35,574.00
Nat 38,208.19 39,099.60 39,102.84 37,518.12 36,274.09 34,902.39 35,083.86 34,557.60 34,557.60
For 49,328.95 48,000.00 44,881.22 44,680.67 38,087.79 35,254.94 36,115.74 38,623.20 37,606.80
70th
UK 27,171.93 26,499.20 26,994.46 26,148.99 25,029.12 24,854.73 23,790.98 24,224.20 24,384.45
Nat 26,816.56 26,400.00 26,931.86 26,148.99 25,029.12 25,207.28 24,249.14 23,885.40 23,865.07
For 29,821.03 27,600.00 28,138.79 27,378.00 25,354.68 24,325.91 22,701.32 25,410.00 24,776.44
50th
UK 21,123.23 21,600.00 21,126.10 21,032.89 19,816.54 19,390.22 19,605.69 19,311.60 19,108.32
Nat 21,123.23 21,600.00 21,173.04 20,843.40 20,132.12 20,062.71 19,605.69 19,311.60 19,311.60
For 22,365.77 20,400.00 21,085.02 21,737.77 19,125.51 17,980.02 18,558.33 19,819.80 18,464.60
30th
UK 16,153.06 16,560.00 15,844.57 15,916.78 15,224.24 14,807.08 14,446.29 14,976.65 14,229.60
Nat 16,153.06 16,800.00 15,961.94 15,916.78 15,235.12 14,807.08 14,446.29 15,122.00 14,600.59
For 16,153.06 15,592.80 15,257.74 16,106.26 13,771.46 12,691.78 13,414.42 14,490.48 13,213.20
10th
UK 10,372.75 10,192.00 10,124.78 9,447.74 8,903.84 8,886.36 8,770.96 9,147.60 9,147.60
Nat 10,499.49 10,400.00 10,445.68 9,663.76 9,358.72 9,518.83 9,286.90 9,401.70 9,318.69
For 9,616.45 8,400.00 7,221.21 8,593.17 6,567.42 7,403.54 7,221.60 8,131.20 8,070.22
UK= United Kingdom, Nat= native, For=foreign
The UK population is defined as economicaly active respondents aged 21-60. The native sub-population is defined as respondents who were born in the UK,
the foreign sub-population is defined as respondents born anywhere else in the world.
The high education group is defined as respondents with a degree level education or above.

53
Percen
tiles
Popu
lation
2007 2008 2009 2010 2011 2012 2013 2014 2015
90th
UK 27,335.94 27,476.10 26,994.46 26,148.99 25,753.88 25,383.56 24,481.31 24,664.64 25,410.00
Nat 27,460.20 27,478.80 26,994.46 26,641.66 26,117.34 26,441.21 24,765.08 25,410.00 25,410.00
For 27,294.52 27,540.00 25,468.68 22,789.42 22,834.54 21,152.97 21,119.11 21,168.22 22,157.52
70th
UK 18,845.23 19,200.00 18,954.80 18,474.83 18,499.79 17,451.20 17,405.21 17,278.80 17,617.60
Nat 19,246.99 19,228.80 19,561.20 19,327.52 18,499.79 17,980.02 17,616.91 18,114.28 18,295.20
For 17,395.60 18,000.00 17,491.23 15,720.09 16,323.34 13,749.43 13,851.24 15,246.00 14,737.80
50th
UK 14,910.51 15,000.00 15,117.87 14,211.41 14,146.90 13,291.11 13,300.91 13,213.20 13,721.40
Nat 14,910.51 15,288.00 15,257.74 14,779.87 14,146.90 13,749.43 13,414.42 13,721.40 14,229.60
For 13,541.65 13,468.00 12,910.39 12,506.04 11,970.45 10,770.39 10,827.84 11,180.40 11,851.22
30th
UK 11,319.56 11,481.10 11,619.35 11,141.74 10,664.58 10,259.19 10,060.81 10,164.00 10,342.89
Nat 11,684.04 11,760.00 11,736.72 11,369.13 10,882.23 10,576.48 10,318.78 10,417.08 10,898.35
For 10,021.11 10,400.00 10,210.95 9,853.24 9,568.02 8,461.19 8,605.86 8,808.80 9,228.91
10th
UK 6,336.97 6,256.80 6,455.20 6,139.33 5,889.10 5,749.38 5,551.50 5,895.12 6,098.40
Nat 6,461.22 6,600.00 6,807.30 6,253.02 5,905.78 5,921.77 5,812.91 6,098.40 6,301.68
For 5,922.79 5,486.00 5,277.22 5,847.90 5,441.11 4,583.14 4,643.45 4,797.41 5,285.28
The native sub-population is defined as respondents who were born in the UK, the foreign sub-population is defined as respondents born anywhere else in the
world.
The intermediate education group is defined as respondents with a level of education above GCSE but below degree.

54
Percen
tiles
Popu
lation
2007 2008 2009 2010 2011 2012 2013 2014 2015
90th
UK 21,129.44 21,000.00 21,126.10 21,460.37 20,676.23 20,095.32 20,121.62 20,328.00 20,639.02
Nat 21,396.17 21,175.20 21,126.10 21,601.34 20,676.23 20,442.23 20,415.71 20,328.00 20,768.10
For 18,638.14 19,200.00 19,189.54 18,986.44 16,599.02 17,662.73 16,879.81 18,701.76 18,837.28
70th
UK 14,910.51 14,560.00 14,553.53 14,533.53 14,146.90 13,749.43 13,868.44 13,818.81 14,529.44
Nat 14,910.51 14,603.60 14,670.90 14,730.60 14,146.90 13,749.43 14,085.14 14,045.29 14,734.75
For 13,667.97 13,872.80 13,172.51 13,794.54 12,003.10 12,205.26 12,098.77 12,196.80 12,629.62
50th
UK 11,646.25 11,676.00 11,678.04 11,369.13 10,882.23 10,899.07 11,262.95 11,011.00 11,453.13
Nat 11,704.75 11,700.00 11,713.25 11,369.13 11,069.04 10,999.54 11,350.66 11,180.40 11,688.60
For 10,768.70 10,800.00 10,705.85 10,937.10 9,431.26 9,624.60 10,318.78 9,029.02 10,045.42
30th
UK 8,614.96 8,723.00 8,661.70 8,526.85 8,161.67 8,249.66 8,255.03 8,097.32 8,698.86
Nat 8,614.96 8,800.40 8,727.43 8,526.09 8,161.67 8,362.47 8,255.03 8,131.20 8,764.76
For 8,884.18 7,800.00 7,980.97 9,095.30 7,073.45 7,403.54 7,223.15 6,637.09 7,145.29
10th
UK 4,845.92 4,800.00 4,835.53 4,737.14 4,527.01 4,583.14 4,551.61 4,560.67 4,624.62
Nat 4,845.92 4,800.00 4,871.91 4,729.56 4,527.01 4,583.14 4,616.97 4,576.51 4,782.16
For 4,617.08 4,728.00 4,636.00 4,926.62 4,470.42 4,509.81 4,127.51 4,245.84 4,377.97
world.
The low education group is defined as respondents with a level of education up to and including GCSE’s

55
Table 9- 90:10 Ratios of Populations and Education Groups
Education
Group
Population 2007 2008 2009 2010 2011 2012 2013 2014 2015
1
UK 3.7748 3.8854 3.9413 4.0915 4.1555 3.9276 4.0261 3.8889 3.8889
Native 3.6391 3.7596 3.7434 3.8824 3.8760 3.6667 3.7778 3.6757 3.7084
Foreign 5.1296 5.7143 6.2152 5.1996 5.7995 4.7619 5.0011 4.7500 4.6599
2
UK 4.3137 4.3914 4.1818 4.2593 4.3731 4.4150 4.4099 4.1839 4.1667
Native 4.2500 4.1635 3.9655 4.2606 4.4223 4.4651 4.2604 4.1667 4.0323
Foreign 4.6084 5.0201 4.8262 3.8970 4.1967 4.6154 4.5481 4.4124 4.1923
3
UK 4.3603 4.3750 4.3689 4.5302 4.5673 4.3846 4.4208 4.4572 4.4629
Native 4.4153 4.4115 4.3363 4.5673 4.5673 4.4603 4.0896 4.4418 4.3428
Foreign 4.0368 4.0609 4.1392 3.8538 3.7131 3.9165 4.0896 4.4047 4.3027
world.
The high education group is defined as respondents with a degree level education or above. The intermediate education group is defined as respondents with
a level of education above GCSE but below degree. The low education group is defined as respondents with a level of education up to and including GCSE’s

56
8.3. C
Wage Calculations
Real Net Annual Wage= {NETPRD x [NET99 if USNET99=yes, USUNPAY if USNET99=no]} x CPI Deflator
Real Gross Annual Wage= {GRSPRD x GROSS99} x CPI Deflator
Data Strength Test: IF {[Gross Wage x 0.7] <Net Wage< Gross Wage]} = PASS
 NETPRD and GRSPRD represent their derived factors of multiplication. E.g. If the time period expressed by the respondent is wages for 1 week, the
multiplication factor is 52 to estimate annual wages. Similarly, if the period is 3 months the factor is 4.
 USNET99 is the variable which determines if the answer given to NET99 is the usual amount the respondent is paid for a given time period. If the answer
is no, the respondents answers USUNPAY with the usual amount they are paid for that time period
 Strength test checks if net wage value given by the respondent is within a 0%-30% deviation of gross wage value. This is based on a respondent earning
£100,000 gross wages yielding £70,000 net wage when calculated by UK income tax brackets (30%). And a respondent with no tax obligations (0%),
earning less than £10,000. The pass rate of this test varies from 64-70% between years.
 The CIP deflator is calculated with 2008 as the base year and figures of the CPI Index are obtained from the Office of National Statistics
 Full Details on the variables use can be obtained from the QLFS accompanying document called “Variable Details [year]”. Variable names and definitions
are subject to slight changes over 2007-2015 but the methodology used remains robust.

57
Education Groups and Robustness Test
Bands of education as derived from HIQUALD
Derived
Education Band
QLFS Code Description
QLFS
Code
1 Degree or equivalent 1
2
Higher education 2
GCE, A-level or equivalent 3
3
GCSE grades A*-C or equivalent 4
No qualification 6
2 or 3 Other qualifications 5
-9
Don’t know 7
No Answer -9
TEST: HIQUAL derived education group = LEVQUAL derived education group
 2 possible outcomes: exclude respondents from sample or reallocate respondents answering “other qualification”
 Excluding these respondents reduce the total sample size by,11.8%; and more notably the foreign sample size by 34%.
 The test had a pass rate of 72.01% when the respondents were excluded and a pass rate of 72.99% after the respondents were reallocated, (foreign
respondents answering other qualification to group 2 and native respondents to group 3)
 HIQUALD is derived from HIQUAL variable, details of this can be found in the QLFS document called “Variable Details [year]”.
 HIQUALD is the education variable from which education groups in this paper are derived. LEVQUAL is only used to check for the robustness of splitting
respondents who answer “other qualification” into education groups 2 and 3
Test data from 2008 quoted
level of education as derived from LEVQUAL
Derived Education
Band
QLFS Code Description
QLFS
Code
1 NVQ level 4 and above 1
2
NVQ level 3 2
Trade apprenticeships 3
NVQ level 2 4
3
Below NVQ level 2 5
No qualifications 7
2 or 3 Other qualifications 6
-9 no answer -9

How has Income Inequality and Wage Disparity Between Native and Foreign Sub-populations in the UK Changed Since the Financial Crisis?

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