1

White/Ethnic Minority Earnings and Employment Differentialsin Britain : Evidence from the LFS

D.H. Blackaby*D.G. Leslie**P.D. Murphy*N.C. O'Leary*

Revised December 2000

Abstract

It is twenty years since Britain passed legislation to combat racial discrimination. Despite this,evidence presented in this paper suggests that Britain’s non-white ethnic minorities still do notappear to face a level playing field in the UK labour market and their relative position does notappear to have improved since the 1970s. Native ethnic minorities also appear to be faring littlebetter than their parents. It is in gaining employment that the situation is particularly acute.

Keywords : Discrimination; Earnings; UnemploymentJEL classification : J71

* Economics Department ** Economics DepartmentUniversity of Wales Swansea Manchester Metropolitan UniversitySwansea ManchesterSA2 8PP M15 6BGUK UK

Acknowledgements

We are grateful to those taking part at the April 1998 European Science Foundation Conferenceon Migration and Development, Espinho, Portugal and to Stephen Wheatley Price for commentson an earlier copy of this paper. Helpful suggestions and comments have also been receivedfrom referees. All remaining errors are naturally our own. Material from the QLFS is CrownCopyright : has been made available by the Office for National Statistics in the UK through TheData Archive and has been used by permission. Neither the ONS nor the Data Archive bear anyresponsibility for the analysis or interpretation of the data reported here.

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I Introduction

In 1998 6.3 percent of the population of working age identified themselves as members of an

ethnic minority. This group represents a growing fraction of the workforce, according to the

1971 Census of Population the figure was approximately 2 percent, yet research on the labour

market experience of this group is relatively limited. Research into racial labour market

discrimination in the UK falls into two groups: that which has tended to be largely descriptive

of which the four Policy Studies Institute surveys (Daniel 1968, Smith 1977, Brown 1984 and

Modood et al 1997) are important contributors, and that which has used econometric techniques

in an attempt to directly measure labour market discrimination . Most of this latter research has

analysed racial wage differentials (see for example, Chiswick 1980, Stewart 1983, Dex 1986,

McNabb and Pascharopoulos 1981, and McCormick 1986). More recent research, however, has

attempted to measure both wage and employment discrimination and Blackaby, Clark, Leslie and

Murphy (BCLM 1994) using data pooled over two economic cycles in the 1970s and 1980s find

that insufficient employment opportunities appeared to be more of a problem than earnings

disadvantage. The ethnic wage gap increased from 7.3 percent in the 1970s to 12.1 percent in

the 1980s, whilst the unemployment differential grew from 2.6 percentage points to 10.9

percentage points.

A number of problems exist with the earlier econometric work analysing racial

discrimination, which the current paper seeks to address. Britain’s ethnic minority is a highly

diverse group, distinguished by a number of cultural differences such as nationality, language and

religion. Previous studies, because of small sample sizes, have tended to focus on ethnic

minorities as a broad aggregate.

3

Secondly, studies attempting to measure discrimination have not always drawn a

distinction between domestic schooling and experience and foreign schooling and experience.

This has probably led to an over-estimation of the degree of labour market discrimination.

Thirdly, previous work has analysed nominal rather than real wage differentials. The fact

that a higher proportion of the ethnic minorities live in the relatively prosperous South-East of

the country, where the cost of living is generally higher, means that an analysis of nominal wage

differentials may under estimate the degree of labour market discrimination.

Finally, the paper analyses the labour market experiences of members of the ethnic

minority population who were born in the UK. One explanation for the lower earnings and

higher unemployment experienced by Britain's ethnic minorities relative to whites is that they

may be less familiar with customs and institutions which may disadvantage them in the labour

market. It follows that as the proportion born in the UK increases, so their labour market position

should improve.

This suggests an inter-generation model with immigrants and their children facing different

advantages and disadvantages within the labour market. Although immigrants may have been

faced with disadvantages in the labour market as a result of discrimination, poor command of the

language, cultural isolation and overseas schooling, they may also have possessed a number of

advantages. Studies have consistently shown that immigrants are more highly motivated and are

of higher relative ability.1 Also, compared with the second generation, the majority of

immigrants arrived in this country when the demand for labour was relatively high. Second

1 See for example Kain and Persky (1967), Chiswick (1978) and Carliner (1980) and for a recent survey

Borjas (1994).

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generation ethnic minorities, however, being more integrated culturally within society, may have

benefitted from domestic schooling and from government anti-discrimination legislation.

Disadvantages relative to their parents may be faced by this group, in that they will have

regressed towards the mean in terms of ability and motivation. They may also not have derived

the full benefits of a domestic education in that they are more likely than whites to be found in

run-down areas where school facilities are poorest i.e. inner city education priority areas.

II Methodology

As is conventional in research on wage discrimination, a human capital approach is adopted and

separate earnings functions are estimated for each ethnic group. Given that the wage structure

is likely to be influenced by factors affecting whether individuals are working, a 'Heckman

correction' is undertaken to control for selectivity bias. Blinder (1973) and Oaxaca (1973)

initially set out the methodology by which earnings equations could be used to determine how

much of any wage differential is due to characteristic differences and how much is due to

coefficient differences. The latter difference is often attributed to discrimination. Subsequent

research has attempted to relate the empirical measure of discrimination more closely to that

proposed in the theoretical literature of Becker (1957) and Arrow (1972). The approach adopted

by Neumark (1988) and Oaxaca and Ransom (1994) is an attempt to estimate the competitive

wage structure that would exist in the absence of discrimination and use these as weights in the

decomposition of the wage gap. This approach leads to an unique solution and so avoids the

index number problem associated with the methodology of Blinder and Oaxaca as well as

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relating the empirical measure of discrimination more closely to that proposed in the theoretical

literature.2

More formally the wage offer gap can be decomposed as:

)]-(x-)-(x[+)]x-x([=)c-c(-E-E*ee*wwew*eewwew βββββλλ ˆˆˆˆˆlnln (1)

where E is real hourly earnings, e and w refer to ethnic minorities and whites respectively, x is

a row vector of characteristics, β is a vector of estimated coefficients, λ is the inverse Mills

ratio which controls for selectivity into employment and c its coefficient, and a bar signifies

a mean value. β* is an estimate of the non-discriminatory wage structure and is derived by using

the cross product matrices as weights from the earnings equation such that

βββ ˆˆˆ ew* )-(1+= ΩΩ (2)

where xx )xx +xx(= ww-1eeww ′′′Ω is the Oaxaca-Ransom weighting matrix.

2 Alternative approaches to the index number problem include Cotton (1988) who proposed a weighting

matrix based on employment shares and Reimers (1983), who suggested decomposing at the midpoint of thecoefficient and characteristics from the black and white equations.

The first term on the right-hand side of equation (1) represents the difference in wage

offers that is attributed to worker endowments of earnings related characteristics, proxying for

productivity. The second term represents that part of the wage offer differential due to

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differences in returns to these endowments usually ascribed to discrimination. Since the model

is linear and the least squares regression line goes through the sample means of the data, the

characteristic component can be further decomposed into its individual components. For

example, how much of the wage offer gap is due to differences in years of schooling. The

coefficient component cannot be further divided in this way as such individual decompositions

are arbitrary, being influenced by transformations of the data and choice of omitted categories

when using dummy variables (see Jones, 1983).

In order to examine the employment of different ethnic groups and to estimate selection

effects in the earnings equations, employment probits are first estimated. Using the same

methodology to decompose the earnings function, it is possible to decompose the employment

probits into an explained characteristic effect and an unexplained coefficient effect.3

Formally, the employment difference can be decomposed as follows:

)]x(P-)x(P[-)]x(P-)x(P[+)]x(P-)x(P[ = I-I e*eew*wwe*w*ew αααααα ˆˆˆˆˆˆˆˆ (3)

where Iwˆ and I

eˆ are the respective averages of the predicted employment probabilities for whites

and the comparison ethnic group, α* is the estimated non-discriminatory employment structure

and )x(P wwα is the average across the sample of the predicted probabilities when using the

white groups coefficients and characteristics and so on. The first term in square brackets

represents the portion of the difference in mean employment probabilities due to differences in

3 The specification of the employment equations used in the decomposition analysis follow the form

suggested by Nickell (1980). Although Nickell does not formally set out a structural model of the employmentdecision in this paper, nonetheless the specification adopted can be interpreted as a reduced form that wouldnormally be expected to arise from such a model.

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the characteristics that are included to predict employment. The remaining terms represent the

difference arising from the size of estimated coefficients associated with these characteristics.

Due to the non-linear property of a probit equation, it is not possible to unpack the

contribution of individual characteristics by simply multiplying coefficients by the difference in

characteristics, across ethnic groups, as was the case with the earnings function. However, Even

and Macpherson (1993), propose a linear decomposition of the probit model. The contribution

of any individual characteristic, k, explaining differences in employment across ethnic groups is

given by:

ααααˆˆˆˆ *ew

*kekwk*e*w

)x-x()x-x()]x(P-)x(P[ (4)

This method assigns to the kth characteristic a portion of the explained difference in

employment equal to that characteristic’s share of the overall difference in expected employment

propensities between white and ethnic minority workers, as estimated at the means of the data

and the non-discriminatory employment structure, α* .

III The Data

The data used are taken from the Office for National Statistics Labour Force Survey (LFS). It

is a survey of households living at private addresses in the UK. In 1992 the survey was

undertaken on a quarterly basis and the introduction of a question on earnings significantly

enhanced its usefulness. In this paper fourteen quarters of the LFS, 1993Q3 to 1996Q4, are used

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to analyse the relative labour market performance of ethnic minorities in Britain.4 Each

observation refers to a different individual.

The sample means for the micro data used in the subequent analysis are presented in

Table 1. The sample of those in employment is restricted to males who did not work in

agriculture, forestry or fishing.5 The self-employed are also excluded because there is no earning

information for this group. The final column presents information for those ethnic minorities

born in the UK. These native ethnic minorities are on average much younger than whites and so

in columns nine and ten data are presented for an adjusted random sample of native whites with

the same age structure as the native ethnic minorities.

The Table reveals a wide diversity in employment rates across ethnic groups. The black

and Pakistani groups experience substantially lower employment rates than other groups, at 62.7

and 63.8 percent respectively. The white group has the highest employment rate (80.5 percent),

which is marginally higher than the Indian group (78.2 percent). Native ethnic minorities are

also found to be twice as likely to be not working than a comparable sample of whites with a

similar age structure.

Not only is the employment rate of ethnic minority groups lower than whites, but their

earnings are also lower. White hourly earnings (in 1997 prices) over the period 1993 to 1996 are

4 The survey samples approximately 60,000 households every quarter and is significantly larger than the

General Household Survey which samples approximately 10,000 households annually and has been used in themajority of the earlier studies analysing racial discrimination in Britain.

5 Obviously it would have been of interest to have analysed female differentials, but the relatively lowparticipation rates for a number of ethnic groups results in very small samples. Blackaby, Drinkwater, Leslie andMurphy (1997), however, do analyse gender white/ethnic minority unemployment differentials using the much larger1991 Population Census. Unfortunately, however, this data set does not contain information on earnings.

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£9.08 which is substantially higher than that found for Pakistanis (£7.00) and blacks (£7.89).6

Indians, as in the case of employment, do better than other ethnic groups, but at £8.23 per hour

earnings are still significantly below those of whites. For native whites and native ethnic

minorities hourly earnings are similar, with only a 9 pence advantage in favour of whites.

It is important, however, to compare like with like. Table 1 shows that ethnic groups are

overly represented in the South-East and London where the cost-of-living is considerably higher.

For example, 34 percent of the black sample live in Inner London, whilst for the white sample

it is only 3 percent. Of the Indian sample 31 per cent live in Outer London, whilst for whites it

is only 6 percent.7 To make equivalent comparisons, hourly earnings need to be adjusted by a

regional cost of living index. Official estimates of the differences in living costs across regions

are unavailable in the UK. We have used, therefore, data published by Regional Reward Surveys

Ltd. (1997). By sampling prices in approximately 100 British towns, this organization estimates

the change of income required to hold constant the standard of living of individuals when moving

across regions. Table 1 shows that using this variable to deflate hourly earnings substantially

increases the size of white/ethnic group wage differentials. For example, failure to control for

cost-of-living differences would suggest a negligible earning difference (9 pence) between whites

and ethnic minorities born in the UK, whilst the real hourly earnings differential is relatively

large at 57 pence or 8 percent.8 The biggest increase in the differential is found between whites

6 We have followed the recommendation of Laux and Marshall (1994) and defined hourly earnings as

actual gross weekly earnings divided by total usual paid hours per week worked for the reasons given in theirpaper.

7 The settlement patterns of the ethnic minority population reflect the state of the labour market at the timeof their arrival. Ethnic minorities are under-represented in the Celtic fringe (defined here as Wales, Scotland, theSouth West and the North) where unemployment rates have traditionally been high and overly represented in theSouth-East where unemployment rates have been lower and earnings higher.

8 The deflator is the deviation of the region from the national cost of living index.

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and blacks (hourly earnings differential increases from £1.19 to £1.83), as blacks have the highest

proportion of employees living in the London area.

One possible reason why ethnic minorities experience lower employment and earnings

may be that they have fewer years of schooling and few qualifications. The Table reveals,

however, that whites, whether employed or out of work, have fewer years of formal schooling

than any other ethnic minority group. The labour market return to schooling, however, may be

lower if it is gained abroad.9 For blacks, approximately half of all formal schooling was gained

abroad, whilst for Indians and Pakistanis the majority of their education was gained in a foreign

country. In the case of qualifications, the percentage of those with degrees, (for those in

employment), is similar for whites, blacks and Pakistanis, but higher for Indians. However,

given that those out of work are significantly less likely to hold degrees and that blacks and

Pakistanis are overly represented in this group, a lower proportion of the black and Pakistani

populations have degrees. When it comes to having no qualifications the Pakistanis are found

to be overly represented in this group.

9 The LFS does not report the number of years educated in the UK and those gained abroad. The total

number of years spent in school is calculated as age left full time education minus 5. To gain estimates of the totalyears spent in education in the UK and those spent abroad for immigrants, we utilise information given on their yearof arrival into the UK, total years spent in education and their current age.

In the case of native whites and native ethnic minorities, the Table reveals that, for both

those in work and those out of work, that native ethnic minorities have spent more years in

school and have higher qualifications. This is consistent with the evidence of Gillborn and Gipps

(1996), Modood et al (1997) and Leslie and Drinkwater (1998), who all find that participation

in higher education is greater for all major ethnic groups than it is for whites. A number of

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factors may explain this finding, such as the motivational drive for betterment that migrants tend

to have for themselves and their families. The higher levels of unemployment found amongst

ethnic minorities is also likely to be an important factor in raising their demand for education.

The longer time spent in school is reflected in native ethnic minorities having a slightly higher

proportion with degrees and A-levels. However, the Higher Educational Statistics Agency

(HESA 1996) reported that generally ethnic minorities have lower A-level grades and lower class

degrees from less prestigious universities. Modood et al (1997), Berthand (1999) and Drew

(1995) also find that ethnic minorities are more likely to resit their exams and take longer to

achieve a given qualification. This may be explained by them beginning school at a later age,

being held back in classes due to poor English, or simply because they attended inferior schools.

The skill profiles of ethnic groups have also led them to seek employment in particular

industries. Ballard (1996) has shown, for example, that Pakistanis were attracted to the textile

industry. The early Caribbean immigrants, however, were overly represented in transport, where

relatively low wages at a time of relatively high employment made the industry unattractive to

whites (see Brooks 1981). Table 1 reveals that all minority ethnic groups are overly represented

in 'distribution, hotels and catering' and blacks and Indians in 'other manufacturing' which

includes textiles.

Diversity is also revealed in the propensity to marry and divorce and obviously cultural

and religious beliefs play an important role here. Traditionally, Asians marry at an earlier age.

Whilst under specific situations divorce is permitted under Islamic law and within Hindu and

Sikh culture, it is clearly discouraged (see Berrington 1996). These influences are clearly

reflected in the data on family formation presented in Table 1. Indians and Pakistanis have

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higher marriage and lower divorce rates and larger families than whites whilst the reverse is the

case for blacks.

Ethnic minority groups are also more likely to work part-time and have shorter job

tenure.10 Both of these are factors that are likely to lead to relatively lower earnings, although

Indians again appear to be an exception in these two respects. It is unlikely that the higher

propensity to work part-time arises from choice or tradition, but is rather a function of the more

restricted job opportunities. For example, Modood et al (1997) finds that for women, where part-

time employment may be chosen to fit in with family responsibilities, part-time work is much

lower amongst minority ethnic groups than whites.

10 Shorter tenure may also reflect disrupted labour market experience. As the work experience variable

is measured as age minus age left school, such disruptions will not be picked up.

Diversity is also revealed in the level of public sector attachment across ethnic groups.

Twenty four percent of whites are found to work in the public sector whilst the figure for blacks

is substantially higher at 37 percent. However, Indians and Pakistanis are less likely to be

working in the public sector than whites. A similar pattern is also revealed in the case of union

membership. For whites 40 percent are union members, whilst for blacks, Indians and Pakistanis

the figures are 48, 39 and 34 percent respectively. Hirsch and Addison (1986) report that the

majority of studies find that ethnic minorities are more likely to be union members or vote for

union recognition, which may arise from the perception that unions attempt to reduce labour

market discrimination. Indeed the union/non-union markup is generally found to be larger for

ethnic minorities than for whites. When it comes to plant-size, Pakistanis are found to work in

larger plants than whites, whilst the opposite is the case for Indians. Table 1, therefore, reveals

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wide diversity across ethnic groups in the characteristics which have been found to be important

in predicting both the probability of employment and the level of earnings.

IV Employment

Probit equations of employment for whites and the major ethnic minority groups shown in Table1

found are presented in Table 2.11 A number of personal characteristics are found to be important

in predicting employment. For whites, having controlled for qualifications, time spent in

education, whether it is gained in the UK or abroad, significantly increases the probability of

employment. The effects of schooling, having controlled for qualifications, are weaker for

minority ethnic groups. Whilst all of the coefficients except one are positive, none are significant

at the 5 percent level. The presence of qualifications, however, is found to be significant in

increasing the probability of employment for all groups.

The probability of being in work is also clearly related to an individuals age, being

lower for the young and old. Marital status is also found to be an important predictor of

employment. Being single, widowed or divorced significantly increases the probability of

whites and Indians being out of work, though for Pakistanis being single and for blacks being

widowed or divorced is not found to be important in predicting employment. The presence of

three or more children also increases the probability of being out of work for whites. This is

usually attributed to the work disincentive effects induced by the benefit system that links benefit

entitlement to family size, though this effect is not found to be present for the minority ethnic

groups. As in other studies (see Nickell 1980 and Hughes and McCormick 1987), housing tenure

11 A likelihood ratio test rejects the null hypothesis of parameter equally across equations. The pooling

of whites with all ethnic groups is rejected 269[55] = )39(2χ . The pooling of all ethnic minorities is rejected

119[55] = )39(2χ as is the pooling of native whites with native ethnic minorities 151[50] = )35(2χ .

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is found to be important in predicting whether an individual is out of work. For all groups, living

in a council house or privately renting accommodation significantly increases the probability of

not working. The only other variable found to be as significant is an assessment made by the

individual of their health. Those in bad health are universally less likely to be in employment.

Table 2 indicates substantial regional diversity in the probability of employment across

ethnic groups. As shown by Bradley and Taylor (1994), the well established North/South divide

in unemployment practically disappeared in the 1990-1992 recession and has only slowly begun

to reappear. For whites, after controlling for characteristics, only Inner London and the North

West have lower probabilities of employment than individuals living in the Celtic Fringe and

these are relatively small. For the minority ethnic groups, the reverse is generally the case, as the

probability of being in employment is higher in the Celtic Fringe. The higher probability of

employment amongst ethnic minorities in the Celtic Fringe may arise because those who moved

to these areas may have had employment to go to. They will also be moving to a region where

the ethnic minority population is small and are likely to be living outside of an ethnic community.

To do this they are unlikely to have English language problems which Modood et al (1997) have

shown increases the probability of unemployment. In addition, recent immigrants, who have a

higher probability of unemployment, are found to be attracted to those areas and communities

which have a strong ethnic presence. As shown in Table 1, ethnic minority populations are

overly concentrated in the Greater London area and the Metropolitan Counties in the North and

Midlands, with more than half living in Greater London and the South-East. An increasing

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concentration in the larger cities and inner urban areas may be important in explaining the lower

rates of employment experienced by ethnic minority groups.12

12 Obviously standard regions are a less than ideal geographical unit of measurement to analyse labour

markets. Much important information is obviously lost in this degree of aggregation. However, the included housingtenure may also act as a proxy for local market conditions.

For whites, being an immigrant who arrived after 1970 is associated with a significantly lower

probability of employment. For the ethnic minority groups no clear picture emerges - the

majority of the year of arrival dummies are not significant.

When comparing a sample of ethnic minorities born in the UK with a sample of native

whites with a similar age structure, a number of differences and similarities arise. First, having

controlled for qualifications, the length of time spent in education for native ethnic minorities is

negatively associated with employment, while the reverse is true for whites. If schooling

provides characteristics other than qualifications which are valued in the labour market then a

positive relationship between time spent in education and the probability of employment would

be expected. If, however, qualifications are used primarily in the labour market as a screening

device for worker quality, then taking a longer time to get a given qualification one would expect

to be negatively associated with employment. Thus, this variable may be proxying other aspects

of worker quality. Earlier it was noted that individuals from the ethnic minority populations

generally take longer to obtain a given qualification. The effect of qualifications (having

controlled for years of schooling) in reducing the probability of being out of work is stronger for

this group than for whites. This result shows the importance of qualifications rather than years

of schooling in enabling ethnic minorities to gain employment. However, Connor et al (1996)

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find evidence that native ethnic minority graduates are discriminated against by employers and

this results in lower levels of employment. There is, however, much less divergence in

employment probabilities for a number of the other variables included within the analysis, such

as marital status and general health.

Given the relatively small sample of ethnic minorities born in the UK, separate equations

for each individual ethnic group have not been estimated. However, to shed light on whether

ethnic differences exist, separate ethnic intercept dummies are introduced into the employment

probit estimated on a sample of native whites and ethnic minorities. This reveals that blacks,

Indians and Pakistanis are still significantly more likely to be out of work. Relative to a baseline

white, being Indian decreases an individuals employment probability by 10 percentage points,

being black decreases it by 12 percentage points and being Pakistani decreases it by 24

percentage points.

V Employment Decompositions

Taken together Tables 1 and 2 show that both characteristics and the coefficients associated with

these characteristics vary across ethnic groups. To examine the importance of characteristics and

coefficients differences in explaining employment differences across ethnic groups, Table 3

presents the results of probit decompositions using the methodology described in Section II.

Column 1 reveals that the employment rate for ethnic minorities is 11 percentage points lower

than that for whites. Half of this difference is explained by differences in characteristics across

the two groups. The characteristic difference found to be most important is immigrant status.

The fact that across the sample immigrants are much more likely to be out of work and that the

ethnic minorities are much more likely to be immigrants explains 30 percent of the difference in

employment rates. The housing tenure variable is also found to be important in explaining the

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employment difference.13 The model predicts that if the housing tenure of the ethnic minorities

were similar to those of whites their employment rate would rise by over one percentage point.

Subsequent columns analyse the employment difference from whites for individual ethnic

groups. Blacks seem to have the lowest employment rate of all the ethnic groups, 18 percentage

points lower than for whites. Just over a half of this difference is explained by the diversity in

characteristics across the two groups. Given that blacks tended to arrive earlier than other groups

and that a lower proportion are immigrants, it follows that immigrant status will be less important

in explaining the difference in employment rates.14 For blacks, the most important factor is found

to be housing tenure. This factor explains a quarter of the difference in employment rates. In the

case of Indians, the employment difference is much lower, only two percentage points, and none

of this difference is explained by characteristics. The contribution of individual characteristics

is not very informative here as the overall employment difference is relatively small. This is

most definitely not the case with Pakistanis, where the employment rate is 17 percentage points

lower than for whites. Again half of this difference is explained by characteristics. Education,

family size, health and immigrant status are all found to make an important contribution.

The final column reveals that native ethnic minorities have made no progress in closing

the employment gap. When making a comparison of a native white sample with the same age

structure it is found that their employment rate is 16 percentage points lower. In this instance,

only approximately a third of this difference is explained by the characteristics. By construction,

immigrant status is ruled out as an explanation. The most important explanatory variables are

housing tenure and region of residence. However, the vast majority of the significant difference

13 Housing tenure picks up unmeasured worker quality effects as well as acting (as noted earlier) as a

proxy local labour market indicator.

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in employment rates is not explained by the model and therefore labour market discrimination

against this group cannot be ruled out.

VI Earnings

Table 4 presents the selectivity adjusted earnings equations for different ethnic groups. The

negative and significant selectivity term for all groups suggests that if those out of work were to

find work they would have higher earnings than individuals with similar characteristics already

in jobs. This result is compatible with such individuals setting higher reservation wages and is

consistent with their lower employment probability.

For whites, earnings are found to be positively associated with job tenure, working in

large plants, being in good health, married and having arrived in the UK after 1960. These

findings are fairly well established in the literature, but Table 4 reveals that a number of these

relationships cannot be confirmed for individual ethnic minorities. For example, being married

is not found to be associated with higher earnings for any ethnic group, whilst higher earnings

for the most recent immigrants are only found to be significant for Pakistanis. Pakistanis, are the

only group who do not appear to gain significant benefits from longer job tenure. All groups,

however, are found to have higher earnings when working in larger plants and being in good

health, although the magnitude differs across ethnic groups. Consistent with the evidence cited

by Lewis (1990) for the US, ethnic minorities experience a larger mark-up when working in the

public sector than whites. Whites working in the public sector are found to have 2 percent mark-

up over those in the private sector. For blacks, Indians and Pakistanis the figure is 8, 6 and 39

percent respectively, although only in the latter case is the figure significant. Being a union

14 See Hatton and Wheatley Price (1998) for a comprehensive discussion of immigration trends and

policies in the UK.

19

member and covered by a collective agreement is also found to be more important in raising the

earnings of ethnic minorities than for whites, ranging from 7 percent for Pakistanis to 13 percent

for blacks, although only in the latter case is the mark-up significant.15

For whites, the rate of return to education is positive whether it is gained in the UK or

abroad. Given that educational qualifications are also included in the equation, this suggests that

education increases earnings potential through means other than providing an individual solely

with qualifications. For blacks and Pakistanis, UK education is again positively related to an

individuals earnings, although the coefficient is insignificantly different from zero for Indians,

and for native ethnic minorities, so the negative effect of additional schooling, given

qualifications, seen with employment for native ethnic minorities does not apply to earnings.

To analyse in more detail the rates of return to schooling and qualification we turn to

Table 5. This Table shows the returns to schooling and educational qualifications when they are

entered separately into an earnings equation, in contrast to Table 4. This enables us to draw

comparisons with the earlier literature. Row 1 of the Table shows that the rate of return to

schooling is 7.4 percent for whites which is double that of blacks and nearly double that of

Indians.16 Earlier studies have consistently shown a lower return to education for the ethnic

minorities. For example, McNabb and Psacharopoulos (1981) find a rate of return of 8.5 percent

for whites and only 6.1 percent for the ethnic minorities using data from 1972. Using the same

15 Again this is consistent with the results found in the US. Lewis (1986), in his extensive survey finds

the union/non-union wage gap to be generally between 5 and 10 percentage points larger for blacks than for whites.16 Pakistanis are found to have the highest rate of return to schooling of all groups. Caution is required

in interpreting these results as the Pakistani estimates are based on a relatively small sample of 167 observations.Also Blackaby, Leslie, Murphy and O’Leary (1998) find using the LFS over a slightly earlier period 1992Q4 to1995Q4 a lower rate of return to Pakistanis than Whites but still higher than Indians and blacks. The finding ofhigher returns to Asians than blacks, however, is consistent with the work of Chevalier and Walker (1999) using datafrom the Family Resources Survey.

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data set but a different specification, Chiswick (1980) finds a 7.5 percent return for whites and

a 4.6 percent return for the ethnic minorities. Blackaby (1986), using data from 1975 finds a 5.7

percent return for whites and only a 3 percent return for ethnic minorities. BCLM (1994), using

data pooled from the 1970s and 1980s and controlling for selectivity into employment, finds a

5.4 percent return for whites in the earlier period falling slightly to 5.2 percent in the later period.

For the ethnic minorities the figure rises from 2.3 percent in the 1970s to 2.7 percent in the

1980s.

Obviously, the lumping together of foreign and domestic schooling is likely to be

inappropriate.17 For example, if immigrants are primarily from agricultural countries, the content

of their schooling and curriculum followed will mean that any given level of schooling will be

valued less in the labour market than a similar amount gained by natives. In addition, employers

may find it difficult to evaluate an immigrant's education, qualifications and previous labour

market experience. Thus the perceived quality of education and past experience may differ by

race and as a result continue to exert an impact on current earnings even in a non-discriminatory

setting. Support for these hypotheses are found in Table 5. For blacks and Pakistanis the rate

of return on domestic schooling ( rUK ) is higher than that gained on foreign schooling ( rFor ).

This would suggest that the finding of lower rates of return to education for ethnic minorities

reported in earlier studies may not be the result of discrimination but may be due to the use of

inappropriate measures of human capital. However, when comparing native whites with native

ethnic minorities, where these problems are eliminated, the differences in return to schooling for

whites and ethnic minorities are generally reduced but still evident at 8.3 percent and 6.2percent

17 Chiswick (1980) noted the problem in his study but had too few observations to deal with it adequately.

21

respectively. 18 In the case of qualifications, all groups generally appear to gain substantial

benefits, especially from higher level qualifications. For example, having a degree rather than

no qualifications raises the earnings of native whites by 82 percent (calculated as [100*(e0.60-1)]),

the earnings of native ethnic minorities by 103 percent.

The lower return to labour market experience found for native ethnic minorities is

consistent with the work of Shields and Wheatley Price (1999a) who find that equal opportunities

legislation has not been successful in enabling ethnic minorities to gain equal access to employer-

funded training. The finding of a lower return to labour market experience for ethnic minorities

is also consistent with that found by Lazear (1976) in his work on the reaction of firms in the US

to government affirmative action legislation. He finds that the ethnic wage differentials did

decline following the introduction of the legislation, but the amount of on-the-job training given

to young ethnic minorities fell, which he predicted would lead to an increase in wage differentials

later in life. Shields and Wheatley Price (1999a) note that both the 1976 Race Relation Act and

the 1984 Commission for Racial Equality Code of Practice in Employment, in Britain, outlawed

discriminatory practices in the provision of training opportunities, yet very few firms had a plan

by which this was to be achieved or monitored.

18 This figure is similar to that found by Shields and Wheatley Price (1998) when estimating an earnings

equation for native ethnic minorities using the same data set and a similar estimation technique but over the shorterperiod 1992Q4 to 1994Q3.

22

VII Earnings Decompositions

To analyse in detail whether it is differences in mean characteristics or differences in rewards to

these characteristics that determine differences in earnings across ethnic groups, ethnic earnings

decompositions are presented in Table 6 using the framework outlined in Section II. Column 1

reveals that the nominal wage differential between whites and all ethnic groups is 10 percent.

Correcting for selectivity bias results in a wage offer differential of 11 percent. The

decomposition shows that the characteristics of ethnic minorities in no way explain the lower

earnings of this group. The characteristic endowment of ethnic minorities actually suggests their

earnings should be higher than whites.19 The most important individual component in

contributing to the wage differential is region of residence. This characteristic alone suggests that

ethnic minorities earnings should be 7 percent higher than whites as they are overly represented

in the relatively prosperous South-East. Column 2 reveals that deflating nominal earnings by a

regional cost-of-living index increases the offered wage differential from 11 to 17 percent, with

characteristic differences now explaining 15 percent of the wage differential.

19 This was also the finding of McNabb and Psacharopoulos (1981), Blackaby (1986) and BCLM (1994).

The Table shows wide diversity in the white/ethnic minorities geometric mean and

offered wage differential across the individual ethnic groups. In the case of the geometric mean

wage differential, the white/Pakistani differential is largest at 31 percent followed by the

white/black (17 percent) with the white/Indian differential being the smallest at 15 percent. The

effect of correcting for selectivity bias on the wage differentials depends on the mean values of

λ and the size of its coefficient. The mean values of λ are found to be inversely related to the

sample unemployment rates. For whites, blacks, Indians and Pakistanis the figures are 0.55,

1.03, 0.73 and 1.33 respectively. The difference in the mean value of λ between whites and

23

individual ethnic minority groups is larger than the difference in the selectivity effect and

explains why the predicted wage offer differential for individual ethnic minority groups decreases

relative to whites. This suggests that in terms of unobservable traits the sample of ethnic

minorities not working are less similar to ethnic minorities in employment than is the case for

whites.

This finding may arise from the fact that we have not been able to measure proficiency

in English. Carliner (1995) for example states that immigrants into the US who speak little or

no English find it more difficult to get jobs and especially well paid jobs outside of immigrant

enclaves. Shields and Price (1999b) show that English language fluency is an important

determinant of occupational success within the UK. Modood et al. (1997) note that it is Pakistani

and Bangladeshis amongst the larger ethnic minority groups who are least likely to be fluent in

English. This appears to arise from the fact that they are more recent immigrants and are more

likely to live in ethnic enclaves. Lazear (1995) has shown, again for the US, that the size and

cohesion of ethnic enclaves influences progress in language development after entry.

Decomposing the offered wage differential for individual ethnic groups reveals that for

blacks and Indians coefficient differences explain the majority of the offered wage differential.

Indeed for Indians, coefficient differences account for the 92 percent of the difference in the wage

offer differential and would suggest substantial discrimination against this group. In the case of

Pakistanis, characteristics explain over 62 percent of the wage offer differential but

discrimination is still found to reduce their wages by 7.6 percent. When looking at the

contribution of individual characteristics in explaining differences in wage offers across ethnic

groups, some uniformity is revealed. Job tenure, employment status, potential experience20,

education21 and qualifications are all factors which would point to whites having higher earnings

than blacks, Indians and Pakistanis.

The only characteristic that consistently favours the ethnic minorities is the year of arrival

variable. This variable predicts that earnings should be higher for ethnic minorities than whites.

This effect arises from the fact that for whites, who dominate the sample, immigrants usually

earn more than natives (this effect is not consistently observed for the ethnic minority group) and

a higher proportion of the ethnic minority population are immigrants.

It is possible, however, that white immigrants and ethnic minority immigrants may differ

in ways that may not be captured by the controls included in the model – English language

fluency for example. If this is the case the contribution of coefficients in explaining the earnings

differentials is increased and may lead to the degree of measured labour market discrimination

20 It is important to appreciate that potential experience is likely to be an over-estimate for ethnic minoritieswhen compared to whites because of their increased propensity to be unemployed. This will lead to a decreasein the amount of the earnings gap attributed to characteristics and increase that due to discrimination.

21 The positive role of education in explaining the higher wages of Whites over ethnic minorities is insharp contrast to the findings of McNabb and Psacharopoulos (1981), Blackaby, Leslie, Murphy and O’Leary (1998)and arises from the failure of those earlier studies to distinguish between education gained in the UK and that gainedabroad.

being exaggerated. The problem of unmeasured differences of this type should be smaller for

the native groups. Limiting a comparison between these two groups gives greater robustness to

any conclusion concerning discrimination. The last two columns of Table 6 make this

comparison. The age adjusted native ethnic/white minority real earnings differential is 6

percent.22 Again the effect of selectivity correction increases the size of the offer wage

differential relative to the geometric mean wage differential. The decomposition reveals that

characteristic differences again explain only a small proportion of the offered wage differential

between these two groups. Obviously, in this case immigrant status is not playing a role and the

fact that native ethnic minorities have more years of schooling and are shown on the whole to be

better qualified would suggest that their earnings should be higher. These positive factors are

offset particularly by whites having been in their jobs longer and a more favourable industry

afilliation.23 Overall, however, the results suggest that 97 percent of the 7 percent offer wage

differential that is found to exist between native whites and native ethnic minorities cannot be

explained by factors included in our model and may be due to discriminatory practices.

The unexplained component of the wage differential (at 6.8 percentage points), however,

is smaller than that faced by blacks, Indians and Pakistanis. Does this suggest that discrimination

faced by native ethnic minorities is less than that faced by their parents? This proposition should

be considered with caution given that the native ethnic minority population is relatively young.

If discrimination on the basis of colour takes place because whites do not wish to be in a

subservient position to ‘non-whites’, then even though ethnic minorities may be in occupations

with favourable earnings profiles they may not get promoted as quickly and so fully benefit from

the earnings profiles found in these occupations

22 The nominal wage differential is actually zero and this shows the importance of controlling for cost-of-

living influences when analysing white/ethnic minorities wage differentials.

23 The fact that this may arise from discriminating behaviour is not an issue that is addressed in this paper.

The finding of a smaller white/ethnic minority wage differential for younger workers is

consistent with earlier research. When Greenhalgh (1980) included an ethnic minority dummy

in an earnings equation using a sample of single men and women under 30, she finds it is

insignificant and concludes that discrimination against this group has now abated. Dex (1986)

compares the progress of a matched sample of whites and West Indians six years after leaving

school and finds that the earnings functions are not significantly different and that the long term

prospects of West Indians looks bright. Chiswick (1980), using data from 1972, concludes that

because the native-born male earnings disadvantage is smaller than the foreign-born earnings

disadvantage for ethnic minorities, earnings differences will narrow and may even disappear as

an increasing proportion of the ‘non-white’ population is native-born. There is little evidence,

though, to support this proposition. BCLM (1994) finds that the nominal white/ethnic minorities

earnings differential increased from 7.3 percent in the 1970s to 12.1 percent in the 1980s. In this

paper we find that the differential has fallen slightly in the 1990s to 10 percent, which is exactly

the same figure found by Blackaby (1986) for 1975. This work suggests a more sanguine attitude

towards the earnings prospects of native ethnic minorities may be necessary.

As with the probit equations, due to relatively small samples it is not possible to estimate

native earnings equations for each minority ethnic group. However, it is possible to shed light

on the relative performance of individual ethnic groups by including ethnic intercept dummies

in a pooled earnings equation containing the native ethnic minorities and the age adjusted sample

of native whites. This again reveals diversity in earnings across ethnic groups. Pakistanis are

found to have the lowest earnings, being 13 percent below those of whites, whilst the figure for

blacks and Indians is 5 percent and 8 percent respectively. A fairly consistent picture appears to

follow from the analysis of both the earnings and employment. Pakistanis appear the most

disadvantaged group in the labour market and native ethnic minorities are consistently doing

worse than native whites. These observations appear to support the finding of Borjas (1992) who

stated for the US that 'ethnicity matters and it matters for a very long time'. This arises from the

fact that the way in which an individual is raised affects an individual's behaviour and

opportunities and therefore their future labour market success.

Conclusions

Evidence presented in this paper suggests that ethnic minorities do not appear to face a level

playing field in the UK labour market and their relative position does not appear to have

improved since the 1970s. Native ethnic minorities also appear to be faring little better than their

parents. Our findings imply that ethnic differences in labour market remuneration cannot be

explained as a characteristic problem - such as poor qualifications, and an unfavourable regional

and industrial distribution. Such generalizations about attributes of the ethnic minority

populations can reinforce stereotyping, which may result in these populations being excluded

from the most remunerative jobs and excuse government from taking appropriate policy

initiatives in this area.24 At the beginning of the twenty first century discrimination should have

no place in restricting an individuals opportunities. Discrimination undermines those beliefs

that are central to a fair and democratic society and places a high penalty on those ethnic

minorities whose life chances are limited.

24 Darity and Mason (1998) have shown that in the US anti-discrimination laws have played an important

role in reducing discriminatory practices.

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Table 1Means Values of Dependent and Explanatory Variables for Different Ethnic Groups

White Black Indian Pakistani White NativeNative

Eth.MinVariableNW Em NW Em NW Em NW Em NW Em NW Em

Employment Status 19.5 80.5 37.3 62.7 21.8 78.2 36.2 63.8 16.5 83.5 33.0 67.0Nominal hourly earn.(1997 prices) 9.08 7.89 8.23 7.00 7.49 7.40Real hourly earnings(1997 prices) 9.03 7.20 7.73 6.74 7.45 6.88Years Educ. UK 11.6 12.8 6.0 7.4 4.3 6.6 5.0 6.1 12.6 13.4 13.6 14.1Years Educ. For. 0.4 0.3 7.6 6.8 9.8 8.7 8.3 8.2 0 0 0 0Years Exper. UK 28.7 22.0 16.2 17.7 18.3 16.4 16.4 14.4 9.8 10.9 8.1 10.7Years Exper. For. 0.2 0.1 3.7 2.1 6.1 2.3 4.5 1.7 0 0 0 0Degree 7 18 6 17 10 22 7 16 6 16 9 19HND or equivalent 5 9 5 10 3 10 1 6 3 9 5 8OND or equivalent 12 17 13 14 7 9 4 7 13 17 14 17A levels or equivalent 4 6 4 6 5 4 3 4 7 9 7 10O levels or equivalent 16 23 15 20 11 14 11 20 31 34 28 30Other qualification 19 13 26 18 29 26 30 22 12 8 11 8No qualification 37 14 31 15 35 15 44 25 28 7 26 8Work limiting illness 46 6 29 3 39 5 43 5 26 5 22 2Scotland, Wales,S.West, North 34 29 3 5 4 4 8 7 33 30 6 7York/Humberside 9 9 4 3 4 4 19 15 9 10 9 5East Midlands 7 8 2 5 14 10 2 3 6 8 5 5East Anglia 3 4 1 2 1 1 1 4 3 4 2 1Inner London 4 3 41 30 14 6 10 8 4 2 27 20Outer London 6 6 23 26 24 33 12 16 5 6 16 22South East 15 21 7 14 8 16 11 15 15 20 6 15West Midlands 9 9 12 12 23 17 19 20 9 9 18 17North West 14 11 6 3 9 8 19 12 14 11 10 8Born in UK 96 97 38 41 20 18 22 19 100 100 100 100Arr. Before 60 2 1 9 7 8 4 4 1 0 0 0 0Arr. Between 1960-69 1 1 24 27 32 33 34 35 0 0 0 0Arr. After 1970-79 1 1 7 11 23 31 15 20 0 0 0 0Arr. After 1980 1 1 22 15 16 13 24 25 0 0 0 0Married 59 75 40 62 71 83 72 82 32 56 24 50Single 29 20 50 29 23 15 21 14 64 41 74 48Divorced/Widowed 12 5 11 9 6 2 6 4 4 2 2 2Job ten. < 1 year 18 21 22 27 27 34Job ten. ≥≥≥≥1 & ≤≤≤≤ 5 yrs 25 35 29 31 34 35Job ten. > 5 years 56 44 49 42 39 30Plant Size ≤≤≤≤ 25 empl. 25 25 23 28 28 30Part-time 3 8 4 8 3 8

Industry 1 3 1 1 1 2 1Industry 2 5 2 2 3 4 1Industry 3 16 15 22 20 15 16Industry 4 11 6 12 18 11 6Industry 5 7 3 3 3 7 6Industry 6 14 16 18 20 19 21Industry 7 10 17 15 7 9 15Industry 8 12 8 11 14 14 11Industry 9 23 32 17 14 18 23Public Sector 24 37 20 14 19 26Non mem – notcovered

48 44 52 55 56 54

Mem – not covered 4 5 5 5 3 7Non mem – covered 12 8 10 11 12 10Mem – covered 36 43 34 29 28 29Age ≤≤≤≤ 24 14 10 20 8 15 7 24 14 49 30 50 25Age ≥≥≥≥ 25 & ≤≤≤≤ 39 23 42 44 57 29 51 30 54 46 65 46 71Age ≥≥≥≥ 40 & ≤≤≤≤ 49 16 27 10 17 18 28 19 25 2 3 2 2Age > 49 47 21 26 18 39 13 27 7 4 2 1 2Mortgaged Property 30 72 21 55 42 74 40 67 31 72 30 57House owned outright 25 12 7 8 30 17 23 17 11 8 13 14Council housing 33 9 49 23 13 3 16 7 39 10 35 17Private rented 12 7 23 14 14 6 20 8 18 10 22 12No children 74 56 71 53 52 37 32 22 61 60 60 59One child 11 18 11 23 17 20 14 17 17 19 15 20Two children 9 20 9 18 16 29 19 29 12 16 12 14Three or more child. 6 7 10 6 15 14 34 32 9 5 12 7Child under 4 years 10 17 15 21 18 25 31 41 19 23 19 22Sample Size 20048 44985 473 392 323 489 279 167 4733 13079 384 361

Notes : NW refers to ‘those not working’, Em ‘in employment’, means for the year dummies are not presented.The sample of native whites is drawn so as to have the same age structure as native ethnic minorities.

Table 2Male Employment Probits 1993Q3 to 1996Q4 for Different Ethnic Groups

Variable White Black Indian Pakistani NativeWhite

NativeEth.Min.

Coeff t Coeff t Coeff t Coeff t Coeff t Coeff tConstant 0.85 (16.83) -0.00 (0.00) 0.92 (2.38) 0.80 (1.77) 0.91 (8.84) 1.22 (3.25)Years educ. UK 0.03 (8.54) 0.02 (1.21) 0.01 (0.56) -0.02 (0.58) 0.02 (2.00) -0.06 (2.41)Years educ. For. 0.04 (6.54) 0.00 (0.12) -0.01 (0.39) 0.03 (1.45) - - - -Degree 0.22 (8.13) 0.50 (2.63) 0.54 (2.89) 0.60 (2.26) 0.53 (9.43) 1.01 (4.56)HND or equiv. 0.24 (9.60) 0.43 (2.30) 0.79 (3.37) 0.36 (0.98) 0.65 (12.52) 0.74 (3.44)OND or equiv. 0.29 (16.52) 0.33 (2.21) 0.22 (1.21) 0.74 (2.53) 0.48 (14.34) 0.54 (3.46)A levels or equiv. 0.13 (4.99) 0.23 (1.10) 0.22 (0.96) 0.57 (1.73) 0.34 (7.82) 0.72 (3.63)O levels of equiv. 0.27 (16.16) 0.40 (2.80) 0.33 (2.11) 0.53 (2.82) 0.49 (17.49) 0.50 (3.84)Other Quals. 0.22 (13.48) 0.15 (1.12) 0.40 (3.16) 0.17 (1.14) 0.34 (9.17) 0.41 (2.40)Age ≥≥≥≥25 & ≤≤≤≤ 39 0.27 (14.40) 0.64 4.74 0.27 (1.59) 0.32 (1.68) 0.22 (9.69) 0.44 (4.56)Age ≥≥≥≥ 40 & ≤≤≤≤49 0.28 (12.78) 0.76 (3.62) 0.36 (1.61) -0.21 (0.74) 0.14 (1.96) 0.15 (0.47)Age ≥≥≥≥ 50 -0.27 (11.95) 0.46 (1.76) -0.42 (1.69) -1.04 (3.00) -0.48 (6.88) -0.09 (0.26)Single -0.37 (22.14) -0.45 (3.64) -0.58 (3.62) -0.27 (1.26) -0.50 (17.63) -0.65 (5.20)Div/Wid -0.41 (20.33) 0.22 (1.41) -0.69 (2.88) -0.93 (3.39) -0.59 (10.04) -0.46 (1.48)York/Humberside 0.05 (2.57) -0.16 (0.53) 0.02 (0.07) -0.27 (1.17) 0.04 (1.23) -0.25 (1.12)East Midlands 0.12 (5.74) 0.33 (0.95) -0.21 (0.94) -0.56 (1.39) 0.09 (2.43) 0.17 (0.69)East Anglia 0.24 (8.28) 0.25 (0.63) -0.32 (0.73) 0.31 (0.69) 0.18 (3.49) -0.34 (0.88)Inner London -0.03 (1.06) -0.06 (0.29) -0.32 (1.37) -0.41 (1.45) -0.09 (1.74) -0.04 (0.21)Outer London 0.03 (1.58) -0.02 (0.07) 0.02 (0.12) -0.21 (0.84) 0.03 (0.77) 0.12 (0.62)South East 0.17 (10.82) -0.16 (0.47) 0.13 (0.56) -0.33 (1.33) 0.17 (5.82) 0.25 (1.13)West Midlands 0.11 (5.69) -0.10 (0.42) -0.39 (1.82) 0.03 (0.13) 0.12 (3.43) -0.13 (0.67)North West -0.05 (2.78) -0.25 (0.90) -0.28 (1.16) -0.17 (0.75) -0.12 (3.89) -0.07 (0.32)House ownedoutright -0.45 (31.60) 0.02 (0.10) -0.30 (2.80) -0.30 (2.13) -0.19 (5.93) -0.09 (0.67)Council Housing -0.88 (60.44) -0.71 (6.62) -0.67 (3.99) -0.68 (3.75) -0.82 (33.51) -0.58 (5.12)Private rented -0.55 (31.23) -0.69 (5.52) -0.63 (4.48) -0.85 (5.05) -0.59 (21.40) -0.67 (5.30)One Child 0.03 (1.65) 0.26 (1.84) -0.50 (0.37) 0.01 (0.04) -0.04 (1.53) 0.15 (1.11)Two Children 0.04 (2.28) 0.28 (1.75) -0.13 (0.91) 0.04 (0.21) -0.07 (2.00) -0.04 (0.25)Three or moreChild. -0.21 (8.40) -0.06 (0.35) -0.13 (0.86) -0.09 (0.53) -0.34 (7.82) -0.08 (0.45)Youngest Child <4 -0.12 (5.83) -0.19 (1.28) -0.07 (0.50) -0.12 (0.79) -0.16 (4.61) 0.42 (2.66)Arrived before1960 -0.09 (1.71) -0.07 (0.27) 0.06 (0.22) 0.58 (1.32)Arr. between1960-69 0.01 (0.08) 0.06 (0.33) 0.19 (1.07) 0.68 (2.94)Arr. between1970-79 -0.25 (3.62) -0.17 (0.71) 0.25 (1.34) 0.27 (1.28)Arrived after1980 -0.40 (4.90) 0.02 (0.08) 0.10 (0.45) -0.17 (0.62)Work limitingillness

-1.30 (98.97) -1.47 (10.57) -1.24 (10.68) -1.47 (9.86) -0.97 (34.33) -1.02 (7.12)

2R 0.28 0.24 0.26 0.28 0.19 0.20Log Likelihood -36519 -638 -577 -361 -11365 -592Sample Size 102584 1269 1485 771 31401 1163

Notes : The default is an over 50 year old married male, with no children, living in the North, SouthWest, Wales or Scotland as an owner occupier with a mortgage, in good health and having no

qualifications in 1992. t statistics are in parentheses. Years dummy coefficients are notpresented.

Table 3White/Ethnic Minority Employment Probit Decompositions

Labour Force Survey 1993Q3 – 1996Q4

Whites vsWhite/All

EthnicMinorit.

White/Black

White/Indian

White/Pakistani

NativeWhite/

NativeEthnic

Minorit.

NativeWhite/*

NativeEthnic

Minorit.Differences in means

ew II ˆˆ −]55[)39(2 =χ 5% level

0.11269

0.18165

0.0282

0.17131

0.14192

0.16151

Difference due to coefficients %

)]*ˆ()ˆ([)]*ˆ()ˆ([ eeewww xPxPxPxaP ααα −−−50 46 106 50 83 66

Difference due to characteristics %

= )*ˆ()*ˆ( ew xPxP αα −50 54 -6 50 17 34

Age -8 -4 8 -5 -8 0Education 5 2 -2 9 -3 2Marital Status 1 9 7 -4 14 7Region 5 5 -1 2 6 9Housing Tenure 13 25 9 3 14 13Children 6 2 -4 12 2 2Health -1 -2 3 11 -7 3Immigrant Status 30 15 -24 22 - -Year 0 0 -0 -0 0 -1

Notes : 2x tests for differences in coefficients in the white and ethnic minorities employment equations. * Sample of native whites selected to have the same age structure of native ethnic minorities.

Table 4Male Employee Earnings Function : LFS 1993Q3 – 1996Q4

Variable White Black Indian Pakistani Native*White

NativeEth.Min.

Coeff t Coeff t Coeff t Coeff t Coeff t Coeff tConstant 1.08 (42.89) 1.40 (4.70) 1.55 (4.74) 0.46 (0.68) 0.91 (18.13) 1.27 (3.73)Educ. UK x10 0.34 (26.89) 0.27 (2.57) 0.03 (0.30) 0.76 (3.41) 0.42 (16.68) 0.16 (1.12)Educ. For.x10 0.27 (11.83) 0.12 (1.35) 0.14 (1.58) 0.42 (2.15)Exper. UK x10 0.34 (43.99) 0.40 (4.11) 0.21 (1.66) 0.53 (2.64) 0.53 (34.30) 0.35 (2.96)Exper.2 UK x 100 -0.05 (32.79) -0.07 (2.86) -0.05 (1.77) -0.02 (0.30) -0.10 (23.98) -0.05 (1.56)Exper. For. x10 0.24 (4.52) 0.15 (0.90) -0.04 (0.33) 0.32 (0.89)Exper.2 For. x100 -0.03 (1.30) -0.05 (0.49) -0.05 (0.12) -0.13 (0.47)Degree 0.49 (47.21) 0.36 (3.73) 0.55 (5.22) 0.40 (2.35) 0.40 (18.14) 0.63 (4.34)HND or equiv. 0.35 (36.78) 0.18 (1.95) 0.07 (0.60) 0.22 (1.10) 0.28 (14.02) 0.28 (2.23)OND or equiv. 0.16 (19.30) 0.11 (1.33) 0.07 (0.74) -0.28 (1.63) 0.12 (6.94) 0.11 (1.11)A Level or equiv. 0.33 (30.88) 0.17 (1.66) 0.19 (1.42) 0.05 (0.25) 0.24 (12.46) 0.30 (2.47)O Level or equiv. 0.16 (20.07) 0.05 (0.62) 0.08 (0.92) -0.23 (1.89) 0.08 (4.83) 0.11 (1.08)Other qualification 0.05 (5.93) 0.07 (0.96) 0.12 (1.48) -0.07 (0.57) 0.03 (1.72) 0.23 (1.97)Single -0.09 (14.76) -0.03 (0.51) -0.05 (0.56) 0.31 (1.90) -0.06 (6.42) -0.04 (0.68)Divorced/Widowed -0.01 (1.13) -0.17 (2.27) 0.17 (1.11) 0.15 (0.66) 0.00 (0.16) -0.12 (0.66)Work Limitingillness

0.12 (9.97) 0.01 (0.54) -0.02 (0.14) 0.46 (2.36) 0.00 (0.10) 0.10 (0.63)

Part-Time -0.14 (12.09) -0.04 (0.51) -0.05 0.43 -0.19 (1.20) -0.07 (3.50) -0.10 (1.12)Job ten. ≥≥≥≥ 1 & ≤≤≤≤5Years 0.10 (16.81) 0.08 (1.39) 0.16 (2.64) 0.06 (0.55) 0.11 (12.85) 0.13 (2.43)Job ten. >5 years 0.22 (36.68) 0.14 (2.35) 0.25 (4.02) -0.02 (0.20) 0.17 (17.35) 0.16 (2.29)Plant Size ≤≤≤≤ 25 -0.14 (29.24) -0.11 (2.16) -0.26 (4.74) -0.35 (3.54) -0.12 (15.51) -0.14 (2.67)Public Sector 0.02 (2.89) 0.08 (1.35) 0.06 (0.75) 0.39 (2.47) 0.02 (1.33) 0.00 (0.05)coveredMem – not covered -0.00 (0.46) 0.07 (0.81) 0.10 (0.93) 0.22 (1.08) 0.04 (1.85) 0.15 (1.54)Non mem –covered 0.01 (1.51) 0.04 (0.56) 0.10 (1.26) -0.10 (0.68) 0.02 (1.50) 0.08 (1.03)Mem – covered 0.01 (2.70) 0.12 (2.19) 0.08 (1.35) 0.07 (0.64) 0.05 (5.46) 0.05 (0.70)Arrived before1960 0.01 (0.35) -0.11 (0.91) 0.15 (0.94) -0.57 (1.49)Arr. between1960-69 -0.01 (0.29) -0.03 (0.44) -0.11 (1.30) -0.19 (1.16)Arr. between1970-79 0.05 (1.90) 0.00 (0.03) -0.15 (1.67) 0.23 (1.51)Arrived after 1980 0.34 (8.88) 0.16 (1.10) -0.07 (0.50) 0.47 (2.04)Selectivity -0.31 (26.99) -0.19 (2.25) -0.26 (1.98) -0.34 (2.49) -0.25 (12.79) -0.14 (1.35)

2R 0.46 0.38 0.41 0.59 0.50 0.46Sample Size 44985 392 489 167 13079 317

Notes : The default is a married male born in UK, living in the Celtic fringe, in good health, working full-timefor less than 1 year in a plant in the public sector employing more than 25 workers in the energy and watersupply industry, having no formal qualifications in 1992 and not a member of a union or covered by acollective agreement. t-statistics in parentheses are derived using heteroscedastic-consistent estimatesof the standard errors. Industry, regional and year dummy coefficients are not presented. (The Celticfringe is defined as: Scotland, Wales, the North and South West.)

Table 5Rates of Return to Education and Qualifications Across Ethnic Groups

White EthnicMin.

Black Indian Pakistani NativeEthnic min.

NativeWhites +

r 0.074 0.050 0.036 0.039 0.079 0.062 0.083rUK 0.076 0.056 0.052 0.036 0.102rFor 0.059 0.047 0.028 0.040 0.076Degree 0.65 0.62 0.47 0.63 0.57 0.71 0.60HND orequivalent

0.41 0.32 0.23 0.14* 0.39 0.30 0.33

OND orequivalent

0.17 0.16 0.12* 0.10* -0.21* 0.14* 0.14

A Levels 0.39 0.27 0.22 0.22 0.12* 0.33 0.31O Levels 0.16 0.11 0.05* 0.10* -0.20* 0.11* 0.08Otherqualification

0.05 0.19 0.06* 0.18 -0.00* 0.24 0.04

Notes : r is the rate of return to schooling.+ signifies that the sample has been adjusted to have the same age structure as the native ethnic minorities sample (see Table 3)* signifies that the coefficients are not significant at the 5% level.

Table 6

Decomposition of the Male Employee Ethnic Offer Wage GapCorrected for Selectivity (Heckman) : LFS 1993Q3 – 1996Q4

White/All

Ethnic**Minorit.

White/All

EthnicMinorit.

White/Black

White/Indian

White/Pakistani

White/NativeEthnic

Minorit.

White*/NativeEthnic

Minorit.Approximate Geometric MeanWage Differential= ew EE )ln()ln( −

0.10 0.16 0.17 0.15 0.31 0.23 0.06

Component due to Selectivity= )ˆ()ˆ( ecww cc λλ − -0.01 -0.01 0.01 0.01 0.11 -0.03 -0.01Predicted Population Wage OfferDifferential= )]ˆˆ()ˆˆ[()ln()ln( eewwew ccEE λλ −−−

0.11 0.17 0.16 0.14 0.20 0.26 0.07

Difference due to Coefficients= *)ˆˆ(*)ˆˆ( ββββ −−− eeww xx 15.3 14.5 11.2 12.9 7.6 7.5 6.8

Difference due to Coefficients (%)= *)ˆˆ(*)ˆˆ( ββββ −−− eeww xx 139 85 70 92 38 29 97

Difference due toCharacteristics (%)= )(*ˆ ew xx −β

-39 15 30 8 62 71 3

Components of CharacteristicEffect (%)Education 13 8 16 5 17 -13 -39Qualifications 3 2 9 2 22 -9 -13Experience 18 11 2 7 30 56 9Marital Status 1 1 5 -4 -3 10 5Health 2 1 2 1 1 2 1Employment Status 5 3 4 1 3 3 8Plant Size 2 1 -1 -2 2 2 3Job Tenure 18 11 10 9 13 18 23Year of Arrival -40 -24 -18 -14 -27 - -Industry 13 8 12 6 0 6 18Region -73 5 -11 -3 1 -4 -9Year -0 0 0 -0 -0 0 -0Public Sector -0 -0 -2 1 1 -0 -2Union 1 0 -0 0 1 0 -2

Notes : * age adjusted (see notes to Table 3)** nominal earnings