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Projecting UK employment by ethnic group between 2012 and
2022 Paper presented to the British Society for
Population Studies annual conference, University of Leeds, 7th September 2015
David Owen, Anne Green, Lynn Gambin and Yuxin Li
Institute for Employment ResearchUniversity of Warwick
Background Challenge Project funded under Joseph
Rowntree Foundation “Poverty and Ethnicity” research programme – report at: http://www.jrf.org.uk/publications/projecting-employment-ethnic-group-2022
Access to employment is an important factor underlying poverty
The poorest ethnic groups also have the lowest employment rates.
Those in work are more likely to be in low-status and low-paying industries and occupations
How is this pattern likely to change over the medium-term future?
Projecting economic activity and employment by ethnic group
This has not previously been undertaken It becomes possible because:o detailed employment projections are
available from Working Futures 5 (CE/IER for UKCES)
o ETHPOP (Leeds University) – provide the first detailed projections of population by ethnic group
The analyses brought these two sets of projections together, and estimated future trends in labour market participation and employment rates
Aims of the research To identify patterns of change in economic activity and employment by
ethnic group from 1992 to 2012 for people aged 16 to 64 To project employment by ethnic group from 2012 to 2022 To examine the changing ethnic division of labour by sector and
occupation To demonstrate the historical and future impact of the changing
occupational composition of labour demand upon the probability of employment of different ethnic groups, and identify the implications for the incidence of poverty by ethnic group
To identify gender contrasts within ethnic groups To identify the likely differences in employment trends within the UK by
ethnicity over the period 2012 to 2022
The research had three elements1. Analysis of historical trends in economic activity and
employment by ethnic group – using Labour Force Survey data to create time-series data on labour market participation and employment by population group, ethnic group, region/nation, gender and age, together with the explanatory variables used to estimate regression models
2. Creating projections - of participation rates and employment by ethnic group, age and gender, for the nations and regions of the UK
3. Interpretation and dissemination – summarising the results of the projections and implications for poverty
Analysis of historical trends A time series of labour market participation data was created from LFS data -
variables extracted: ethnic group, nationality, age, gender, labour market status, industry and occupation of employment
Quarterly LFS data was available from Spring 1992 to Winter 2013, but not all variables are available for the first few years
A challenge for this exercise was the change in occupational and ethnic group classifications which occurred in 2001 and 2011
Therefore, an important part of this work was creating measures of occupation, industry and ethnic group which were consistent over time and across the nations of the UK
Time-series models of labour market participation rates and employment rates were estimated for ethnic group, age, gender and region
Alternative specifications of these models were experimented with. independent variables include individual characteristics which are key factors
influencing participation in the labour market, time, and region
Occupational trends, 1992-2013 (LFS)
19921994
19961998
20002002
20042006
20082010
20120.0
5.0
10.0
15.0
20.0
25.0
BME ethnic groups
Managers, directors and senior officialsProfessional occupationsAssociate professional and technicalAdministrative and secretarialSkilled trades occupationsCaring, leisure and other serviceSales and customer serviceProcess, plant and machine operativesElementary occupations
% o
f em
ploy
men
t1992
19941996
19982000
20022004
20062008
20102012
0.0
5.0
10.0
15.0
20.0
25.0
White ethnic groups
Managers, directors and senior officialsProfessional occupationsAssociate professional and technicalAdministrative and secretarialSkilled trades occupationsCaring, leisure and other serviceSales and customer serviceProcess, plant and machine operativesElementary occupations
% o
f em
ploy
men
t
Key patterns of occupational change, 1992-2013 for white and ethnic minority peopleGrowth in:
ProfessionalsAssociate professionalsCaring, leisure & other service
Decline in:Administrative & secretarialSkilled manual tradesSemi- and unskilled
occupationsStability in:
Managers and directors (white)Elementary occupations (BME)
Broad occupational trends common to white and ethnic minorities
Trends exaggerated for ethnic minorities
Relative loss of jobs in skilled manual trades and semi-skilled occupations greater for ethnic minorities
Share of elementary jobs declined for white people but not for ethnic minorities
Relative growth of professional jobs faster for ethnic minorities
Aggregate employment and population projections which the research is based upon
Working Futures 5 ETHPOP Projections of employment by industry,
occupation and region/nation (created for UKCES by IER and CE)
A detailed set of projections of the labour market for the UK and its constituent nations and regions from 2012 to 2022
Based on a macroeconomic model - includes estimates of labour market participation rates by gender
Employment projections are detailed, broken down by industry (22 categories), occupation (25 categories) and level of qualification (9 levels)
Estimates of employment are made for each year from 1992 to 2022
Employment is projected to grow by 5.8%t between 2012 and 2022
Projections of population by ethnic group from 2001 to 2050 were created by the Geography Dept. , Leeds University as part of the ESRC UPTAP research programme.
ETHPOP provides projections of population change by ethnic group, age, gender and geography (for regions and local authority districts in England, Wales, Scotland and Northern Ireland)
There is a ‘central’ projection representing the most likely outcome for population - and variants
For the population of economically active age over a 10-year period, the main influence upon variations in projections is assumptions made about international immigration and emigration
Creating the population data ETHPOP population projections underestimated the ethnic minority population in 2011, primarily because they
failed to replicate the high levels of international net migration experienced after 2001 The researchers who created these projections argue that the average of the TREND and UPTAP-ER variants
provide the best indication of the trend over time in the population of individual ethnic groups and this average comes closest to the 2011 Census of Population. They suggest adjusting the projections to match the Census data in 2011 (which is regarded as the most reliable estimate of the population by ethnic group) and then apply the projected trends in population to this base year
A database of projected population by ethnic group, gender, region/nation and five-year age group (within the age range 16 to 64) for each year from 2001 to 2022 was created from the average of the TREND and UPTAP-ER projections by:
Calculating the ratio of the projected population for year 2011+t (where t runs from -10 to +11) to the projected population in 2011
Multiplying this against the 2011 Census population gives an estimate of the population in year 2011+t (for each age group, gender and ethnic group). This step is repeated for each year in order to create a time-series for 2001 to 2022: the population for 2011 is thus the Census estimate
Some manipulation and estimation of data was necessary in order to create comparable data for all four nations, because the design of Census of Population 2011 Detailed Characteristics Table DC2101 differs between England and Wales , Scotland and Northern Ireland, mainly due to differences in ethnic classification between the three Censuses. In Scotland and Northern Ireland, there is only one group for people of mixed parentage, compared to four in England and Wales. Additionally, the White British and White Other ethnic groups are not distinguished in Northern Ireland. These population groups were estimated for Scotland and Northern Ireland and the white group in Northern Ireland.
Trends in UK working age population, 2012-2022
The working age population from ethnic minorities is projected to grow by 22 per cent between 2012 and 2022
The ethnic minority population is projected to grow in all age groups
The white population aged 40-49 is projected to decline
The slowest projected rates of increase amongst ethnic minorities in the working age population are projected for age groups from 16-34 years
The numbers in the immediate pre-retirement age group are larger in 2022, and more of this age group are from ethnic minorities
The share of people from ethnic minorities is projected to be largest for people aged from 25 to 44 in 2022, compared with the 20 to 34 age range in 2012
The working age population is projected to increase fastest in southern and eastern regions of England, but to decline in northern England and the devolved nations
Working Futures 5 UK projections, 2012-2022 Overall employment is projected to increase
by 5.8% between 2012 and 2022 The fastest rates of growth are projected for
Construction and Business and other services
The Primary and Manufacturing sectors are projected to lose 5.5% and 6.8% of employment respectively over the decade
Employment is projected to grow in Caring, leisure & other service occupations, Managerial, Professional and Associate professional & technical occupations and to decline in all other SOC major groups
Employment is projected to decline fastest in Administrative & secretarial and in Process, plant & machine operative occupations
Employment in London is projected to grow fastest, at 8%
Regression modelling of labour market participation and employment rates (planned) Using the LFS data base, a set of time-series regression models were estimated of the
probability of being in the labour market and the probability of being in employment The dependent variable was labour market status: employed, unemployed or
economically inactive Multinomial regression models were estimated, so the probabilities were of being
employed relative to being economically inactive, and the probability of being unemployed relative to that of being economically inactive
Labour market participation and employment probabilities could be calculated from the coefficients estimated
The independent variables were: Age, Age squared , Gender, Region, Highest qualification, whether born in the UK, whether educated in the UK, No. of dependent children and Marital status
Year dummies and interaction terms (e.g. gender*region) were also included. The intention was that the regression coefficients would be used to project labour
market and employment rates forward for the years 2013 to 2022
Results of regression modelling For females, the likelihood of being inactive is higher and the probability of being employed (or
unemployed) is lower than for males The probability of an individual being employed (or unemployed) relative to being inactive increases with
increasing age and then decreases The greater the number of dependent children, the greater the probability of being inactive and the lower
the probability of being employed (or unemployed) People who were born in the UK have a significantly higher probability of employment (relative to being
inactive) for all ethnic groups except Asian-Bangladeshi, and a significantly higher probability of unemployment (relative to being inactive) for all ethnicities except for Chinese and Black-Caribbean people
The degree of education (both within and outside UK) increases the chance of being employed but does not necessarily lead to lower probability of unemployment
Being educated in the UK, compared to wholly non-UK education and partly non-UK education, does not have any advantages in employment and in fact tend to be associated with a lower probability of employment (or unemployment), except for the Chinese group
Married people are more likely to be employed compared to the unmarried, except for the Bangladeshi group, but are also more likely to be inactive rather than being unemployed, except for the Asian-Other and Black-African groups
The significance of other independent variables varies considerably between the “employed relative to inactive” and “unemployed relative to inactive” models and across ethnic groups
Compared to the other ethnic groups, the White group presents the most significant results for the variables considered in the multinomial regression, especially the region, qualification, year dummies and the interaction terms between gender and region
Creating aggregate projections The research generated a time series of estimated total labour
force and employment, by ethnic group, age and gender by multiplying the projected population in each breakdown (ethnic group, age and gender) by the corresponding: projected labour market participation rate projected employment rate
These projections were then adjusted to sum to aggregate projections from Working Futures 5
The aim was to create projections for all nations of the UK and regions of England
Main steps in producing projections of employment by industry/occupation Using LFS data from 2001 to 2013, the shares of employment within each industry
(occupation) for each gender by geographical area were calculated A logarithmic function was fitted to the time-series and this was used to extrapolate over
the period 2014 to 2022 Using the total employment figures for each ethnic group by gender in each geographical
area, the actual and projected shares of employment for each industry (occupation) was applied in order to produce a figure for employment for each industry (occupation)
The industry (occupation) employment figures were then used to calculate each ethnic group’s share of total employment in a particular industry (occupation) by gender and geography. The sum of all ethnic group shares of employment in industry (occupation) 1, for example, was equal to 100 per cent
These ethnic group shares were then applied to the employment figures for each industry (occupation) (by gender and geography) contained in the Working Futures projections. This provided an estimated figure for employment by industry (occupation) for each ethnic group, by gender and geography
For the analysis of employment by industry, rather than occupation, a moving average over 4 periods (3 in some cases where data did not permit use of four years) was used in order to extrapolate the shares to 2022 as the data did not exhibit any strong patterns of change over time
Labour market participation rates by gender and ethnic group, UK 2001-2022
Men For the UK, there was convergence over the period 2001
to 2013 as male ethnic minority participation rates increased, while those for White men stagnated or declined slightly.
Indian participation rates are projected to be higher than those for the White group by 2022.
For most other ethnic groups, a slow rate of increase is projected.
The main difference is for the Chinese group, whose participation rate is projected to decline (reflecting the importance of student migration for this ethnic group), more markedly for males than females. The participation rate for people of Mixed parentage is projected to fall slightly by 2022.
Women White women are projected to have the highest
participation rates, with Black, Indian and women of mixed parentage having the next highest rates.
The labour market participation rates of Bangladeshi and Pakistani women increased considerably between 2001 and 2013. Some further convergence is projected by 2022, but these rates are projected to remain much lower than those of other ethnic groups.
Employment rates by gender and ethnic group, UK 2001-2022
Between 2001 and 2013, ethnic minority employment rates increased, while White rates stagnated or declined slightly (males).
Men Indian employment rates overtook those of White men in
2012. The White employment rate fell in the recession and are not projected to recover to pre-recession levels.: the gap with the male Indian employment rate is projected to widen slightly.
For most other ethnic groups, a slow rate of increase is projected over the period 2012 to 2022, with Bangladeshi men experiencing the largest projected increase.
The relative position of ethnic groups is projected to remain stable. Chinese men are projected to experience the largest decline in employment rate (student migration).
Employment rates for men from the Black and Mixed parentage ethnic groups are also projected to decline slightly by 2022.
Women The employment rate for White women is projected to only
increase slightly, but to remain highest. The Indian, mixed parentage and Black ethnic groups display next highest rates.
The employment rates of Bangladeshi and Pakistani women increased considerably between 2001 and 2013, and are projected to increase more than for other ethnic minority groups by 2022, but remain much lower than those of other ethnic groups.
Method for projecting employment by ethnic group Using annual data from the LFS, employment rates (the share of individuals in employment) for each ethnic
group (by gender and geography) were calculated. This can be denoted for a particular ethnic group (eth) and gender (sex) in a particular region/area (geog) at time t as: .
A logarithmic function was fitted to the time series of employment probabilities. This predicted the employment rate for a particular ethnic group (eth) and gender (sex) at time t. In order to smooth the series, the logarithm function was fitted for the period 2001 to 2022.
The appropriate employment rate for each ethnic group and gender in each geography was applied to the projection of working age population for the particular group ( for each period t) as below:
Where represents the estimated working population of ethnic group eth and gender sex at time t and is the estimated number of people between the ages of 16 and 64 years in employment within ethnic group eth and of gender sex at time t.
In order to ensure the projections of employment by ethnic group are constrained within the overall projections set out in Working Futures an adjustment was made to the employment figures generated in the previous step. The total employment across all ethnic groups within a region and for each gender was calculated was calculated as:
for each gender within each geography at time t and the share of this total employment by ethnic group (), by gender and ethnicity, for each year was
calculated as: for each gender within each geography. These shares were then applied to the respective Working Futures total employment figure for each gender
within each geographical area in order to produce a figure of employment for each ethnic group (by gender and geography) in each year from 2001 to 2022.
Headline results for employment For the UK as a whole, males and females in all ethnic groups are projected to share in the
increase in employment over this period. the largest relative increases in projected employment over the period from 2012 to 2022 are for people of Mixed parentage, followed by people from an Other ethnic group.
For the UK, the ethnic minority groups with the smallest projected relative increases in employment – the Indian, Chinese and Black groups – are all estimated to see an increase in employment of between a fifth and a quarter, while a 3% increase in employment is projected for the White group, which accounts for just over 40% of the growth in total employment
The share of UK total employment accounted for by White people is projected to fall to 86% in 2022, down from 89% in 2012
In England, a small decline in employment amongst the White British group is projected over the period from 2012 to 2022, with this decline most evident in London (where employment amongst the White British group is projected to decline by 6%); an increase in employment of around a quarter is projected amongst Other White groups
Outside England the projected increase in employment for the White group is lower than for ethnic minorities (defined here as all non-White ethnic groups)
In London, 40 per cent of individuals in employment in 2022 are projected to be from the White British group (down from 46% in 2012) and over 21% are projected to be from the Other White group (up from just under 19% in 2012), compared with just over 83% (down from 86% in 2012) and over 6% (up from 5% in 2012), respectively, in England outside London.
Geographical contrasts(inner circle is 2012; outer circle is 2022)
89%
86%
WhiteMixed parentageIndianPakistaniBangladeshiChineseAny other AsianBlackOther ethnic group
All UK46%
40%
White British
Other White groups
Mixed parentage
Indian
Pakistani
Bangladeshi
Chinese
Any other Asian
Black Caribbean
Black African
Black Other
Other ethnic group
London
86%
82%
White British
Other White groups
Mixed parentage
Indian
Pakistani
Bangladeshi
Chinese
Any other Asian
Black Car-ibbean
Black African
Black Other
Other ethnic group
Rest of England
UK projected male employment by sector, 2022 Ethnic minorities are over-represented
in (the relatively low paid) Trade, accommodation & transport (most notably those from the Bangladeshi, Pakistani and Other Asian group) sector
These latter groups are under-represented in (the highly-paid) Business & other services (e.g. financial services, legal services, etc), but the Indian, Mixed parentage, Chinese, Black and Other ethnic group are over-represented in this broad sector
Black males are easily the most disproportionately concentrated in Non-market services of any ethnic group. Other ethnic groups with a higher than average share of employment accounted for by this broad sector are the Mixed parentage, Chinese, Any other Asian and Other ethnic groups.
UK projected female employment by sector, 2022
Relative to the average, White females are slightly over-represented in the Primary sector, Manufacturing, Construction and Non-market services
Bangladeshi, Pakistani, Chinese and Any other Asian females are markedly over-represented in Trade, accommodation and transport in 2022, which represents a slight intensification in the position in 2012
In broad terms females from these ethnic groups in this broad sector might be expected to be at greater than average risk of low earnings
In contrast, females from the Mixed parentage, Indian and Chinese ethnic groups are over-represented in the Business & other services sector, as are (to a less marked extent) females from Black and Other ethnic groups
Black females are projected to maintain their over-representation in Non-market services in 2022
Occupational profile of UK male employment, 2022
Occupations were grouped into high, intermediate and low pay occupations.
White males are over-represented in High pay and Intermediate pay occupations.
Indian males, Chinese males and males of Mixed parentage are also over-represented amongst High pay occupations.
Pakistani and Bangladeshi males are over-represented amongst Intermediate pay occupations.
Males from the Any other Asian, Bangladeshi and Black groups are particularly likely to be employed in Low pay occupations.
Males in nearly all ethnic groups are expected to share in the projected increase in employment in High pay occupations.
A larger share of projected total employment for males in all ethnic groups is accounted for by Low pay occupations in 2022 than in 2012, with the exception of the Chinese group, where the share of male employment in such occupations is projected to decrease very slightly.
Males from the Black group and the White group display amongst the smallest projected increases in the share of employment in Low pay occupations, while males from the Bangladeshi, Mixed parentage and Other ethnic groups display the largest projected increases in the share of employment in Low pay occupations
Occupational profile of UK female employment, 2022
Low pay occupations account for a greater share of female employment than of male employment.
With the exception of the Other ethnic group, females in all ethnic groups are expected to share in the projected increase in employment in High pay occupations.
The increase in the share of employment accounted for by High pay occupations is greater for females than males in all ethnic groups.
The Chinese, Indian and Bangladeshi groups have the largest increases in the share of female employment in High pay occupations, followed by the White group.
The ethnic groups with the highest shares of female employment in High Pay occupations are the Chinese and Indian groups.
There is a decrease in the projected share of employment in Intermediate pay occupations for females in all ethnic groups. The projected decreases in employment shares are most marked for the Indian, Mixed parentage and Pakistani groups.
There is a projected reduction in the share of female employment accounted for by Low pay occupations over the period from 2012 to 2022. The White, Indian, Bangladeshi, Chinese and Black groups all share in this reduction.
For the Mixed parentage, Pakistani and Any other Asian groups polarisation in employment is evident with an increase in the share of employment for both Low pay and High pay occupations.
Implications of projections Many of the existing ethnic inequalities in the profile of male employment are likely to
persist over the medium term In the UK, ethnic minorities are projected to account for 14%, 12% and 22% of total male
employment in high, intermediate and low pay occupations respectively in 2022. Indian males, Chinese males and males of Mixed parentage are also disproportionately concentrated amongst High pay occupations. Pakistani and Bangladeshi males are over-represented amongst Intermediate pay occupations.
Males from all ethnic minority groups other than the Chinese group are projected to be concentrated in Low pay occupations in 2022, particularly those from the Other Asian, Bangladeshi and Black groups.
Although females are more concentrated in Low pay occupations (especially those from the Bangladeshi, Any other Asian and Pakistani groups) than males they are also set to benefit more than males from the projected increase in High pay occupations between 2012 and 2022, particularly in London. This is the case for most ethnic minorities.
Polarisation of employment structures is more evident in the Rest of England and here the maintenance of the advantage of the White British group remains more apparent. Polarisation of employment is also more evident for males than for females. Relative to other ethnic groups, the White group tends to be more concentrated in Intermediate occupations.
Challenges encountered The research was quite ambitious, being the first known attempt to produce detailed
employment projections by ethnic group in the UK; the availability of relatively robust projections of the population by ethnic group made this possible
The database for estimation of labour market participation and employment rates proved not be adequate for the initial ambitions of the project to be met
Changes in occupation, industry and ethnic group classifications over time and differences between the nations of the UK meant that it proved necessary to aggregate ethnic groups
The sample size available for regression modelling proved to be too small for statistically significant results to be obtained for all nations and regions of the UK
Because of this, the original methodology was substituted by a simpler and less data-intensive method for projecting labour market participation and employment rates
Consequently, it proved necessary to aggregate regions In England, it was possible to distinguish London from the rest of England, but
projections could not be made for individual regions For Wales, Scotland and Northern Ireland, only projections for white people and
minority ethnic groups as a whole could be produced
