1

Explained and unexplained wage gaps across the main ethno-religious

groups in Great Britain

By Simonetta Longhi*, Cheti Nicoletti†, and Lucinda Platt‡

* Institute for Social and Economic Research, University of Essex, Colchester CO43YU; and IZA Bonn; email: slonghi@essex.ac.uk † Department of Economics and Related Studies, The University of York ‡ Centre for Longitudinal Studies, Institute of Education, University of London

We analyse the difference in average wages (the so called ‘wage gap’) of selected ethno-religious groups in Great Britain at the mean and over the wage distribution with the aim of explaining why such wage gaps differ across minority groups. We distinguish minorities not only by their ethno-religious background, but also by country (UK or abroad) in which people grew up and acquired their qualifications. We find that within all minority ethno-religious groups the second generation achieves higher wages than the first generation, but the amount that is explained by characteristics does not necessarily increase with generation. JEL classifications: C21, J32, J71

1. Introduction

Wage gaps of immigrants and ethnic minorities are well attested across a range of national

contexts, and have often been analysed through decomposition methods. The gap is usually

decomposed into an explained and an unexplained component, to ascertain the extent to

which minorities are disadvantaged by worse personal or job characteristics or by labour

market discrimination (e.g. Reimers, 1983; Schafgans, 1998; Elliott and Lindley, 2008).

Often, the unexplained part turns out to be rather large, and this is partly due to the focus on

minorities that include heterogeneous sub-groups. In the UK, for example, empirical studies

have aggregated all South Asian ethnic groups, which include minorities such as Indians,

who do relatively well compared to the majority, and Pakistanis, who face extremely low

average wages (Dustmann and Fabbri, 2005; Platt, 2006). Important distinctions have been

also found for immigrant minorities both according to the country of origin and to the length

of stay in the country of destination (Hum and Simpson, 1999; McCall, 2001; Card, 2005).

For the UK, research on ethnic disadvantage in access to the labour market has

dominated that on wages (e.g. Heath and McMahon, 1997; Berthoud, 2000; Wheatley Price,

2001; Kan and Heath, 2006); and analyses of wage gaps have focused on immigrants but

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tended to neglect ethnicity (e.g. Bell, 1997; Shields and Wheatley Price, 1998; Dustmann and

Fabbri, 2005). Most existing information on the sources of ethnic wage gaps in the UK

comes from a series of studies by Blackaby and colleagues, who often find that

characteristics of the minority only explain a very small part of a large gap (Blackaby et al.,

2002). These results prove to be very sensitive to the way ethnic groups are disaggregated:

the higher the level of aggregation the lower the proportion of the gap that can be explained

through characteristics. Elliott and Lindley (2008) use decomposition methods to analyse

wages of immigrants by broad ethnic groups: white, black, south Asian, and others. They

only partly distinguish between generations by also analysing the wages of UK-born non-

white workers and find that immigrants are overrepresented in occupations with lower and

higher wages, but that a significant wage gap remains for both the non-white who are UK

born and for non-white immigrants.

In contrast to the previous literature, we compare fairly homogeneous minority

groups, which we define by a combination of ethnic origin, religion, and age of arrival in the

UK. Groups defined in this way tend to summarise different cultures and migration histories

and display distinctive profiles across a range of characteristics. We focus on three groups

that have a relatively large presence in the UK, and which represent meaningful social

groups: Indian Hindus, Indian Muslims, and Pakistani Muslims.1

In all cases we separate people who grew up in the UK from those who grew up abroad. By

these means we hope to provide a more precise and more easily interpretable estimate of the

unexplained component of wage gaps, although this comes at the cost of reduced sample

sizes. Our reference group for the computation of the wage gaps is that of the majority: UK-

born White British Christian men.

Our analysis can be compared to that of Algan et al. (2009), who compare labour

market outcomes of first and second generation (defined by country of birth) ethnic

minorities to those of white UK born. However, they do not account for within-group

heterogeneity that can be proxied by differences in religious affiliation, and they identify

wage gaps simply by including dummies for ethnicity in the wage regressions, thus assuming

that returns to the other characteristics are the same across groups. We develop this analysis

1 It has to be noted that in this case religious denomination is likely to be a proxy for characteristics of the ethnic minority that are not observed in the data; hence, our results do not necessarily isolate the effects of religion per se (see also Section 2.1).

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by including religious denomination and by using decomposition methods to quantify and

analyse the wage gaps.

We decompose wage gaps using a combination of weighting and regression

approaches, as proposed by Firpo et al. (2007). The ‘recentered influence function

regression’ is used to extend the Oaxaca-Blinder decomposition from linear regression

models to quantile regressions, while weights are used to relax the linearity assumptions

imposed by regression models adopted in the Oaxaca-Blinder and the recentered influence

function approaches.

Our results suggest that Indian Hindus fare comparatively well in the UK labour

market, while Pakistani Muslims have the largest wage gaps. The experience of Indian

Muslims falls in between that of Indian Hindus and that of Pakistani Muslims, but seems

closer to that of Pakistanis. Unexplained differences in wages decline for second generations

but there are still large unexplained components for some groups. Looking across the

distribution, the results provide some suggestion of a glass ceiling for the most advantaged

minorities, but no evidence of a sticky floor for the least advantaged. Instead, we see that

wage gaps mostly come about through sorting into particular occupations and into part-time

work for less advantaged groups, while the more advantaged minorities are not doing as well

as their occupational distribution might suggest.

2. Previous literature and new contributions

2.1. Heterogeneity within ethnic groups

There is a large literature on wages of ethnic minorities. We focus here only on those studies

using decomposition methods to describe ethnic wage gaps in the UK. Most of the earlier

contributions on ethnic wage gaps in the UK derive from a series of studies by Blackaby and

colleagues. Blackaby et al. (1994) decomposed black/white wage gaps for 1973-1979 and

1983-1989, including immigrant status as one of the characteristics in the decomposition.

Blackaby et al. (1998) repeated the analysis for the 1990s, this time distinguishing between

ethnic groups, including Black, Indian and Pakistani; the wage decomposition included

length of stay in the UK as one of the explanatory factors. Blackaby et al. (2002) returned to

the analysis at more length and showed the importance of taking account of the differential

age structure between minorities and majority and whether or not the minorities were UK

born. However, while they distinguished the first generation according to ethnicity (Black,

Indian, and Pakistani), sample sizes restricted their analysis of UK born ethnic minorities to

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one aggregate group. In all cases Blackaby et al., found that characteristics only explained a

very small part of a large gap, though with some variation across groups (in this latter case

nearly half of the wage gap of Pakistanis was explained). The analysis was further extended

including more years of data and samples of reference and minority groups matched on age

and region, and concentrating on the UK born only, on the basis that they cannot be expected

to have the potential disadvantages associated with immigration (Blackaby et al., 2002). In

this case, characteristics were found to explain about half of the Black-White wage gap

(chiefly through differences in educational qualifications) but not to contribute at all to

Pakistani or Indian wage gaps. The variation in these results reveals sensitivity to how

immigrants versus UK born are treated and the extent to which ethnic groups are

disaggregated. Given the heterogeneity across ethnic groups, it is not surprising that the

higher the level of aggregation the less it is possible to explain these gaps through

characteristics.

In the UK, ethnic groups are rather heterogeneous and employment patterns vary in

distinctive ways within groups by both religious affiliation and by whether the minority

group member is first generation (immigrant) or second or subsequent generation (Clark and

Drinkwater, 2009). Generation has also been shown to be important in relation to wages

(Shields and Wheatley Price, 1998; Clark and Lindley, 2006), either because of lack of

familiarity with the context of the country of immigration, or through positive selection of

immigrants relative to the second generation, or because of differences in job-relevant skills

such as English language fluency between generations, or as a result of increasing rates of

qualifications across generations (Blackaby et al., 2002; Lindley, 2002a; Shields and

Wheatley Price, 2002; Dustmann and Fabbri, 2003; Dustmann and Theodoropoulos, 2010).

Though several analyses of labour market outcomes include controls for generation or for

time spent in the UK, it is not clear why we should expect the relationship between

generation and employment outcomes to be constant across groups. The immigrant

population shows huge polarisation in wages with some first generation groups (in particular

white migrants) faring extremely well, while for other groups being of the immigrant

generation is a source of labour market disadvantage; simply controlling for generation across

heterogeneous populations is therefore inappropriate and may over or under-state ethnic

group effects.

Increasing attention has been paid to differences in employment, including wages,

across religious groups. This has been explained in terms of religion impacting outcomes

either through the social network provided by religious affiliation or through discrimination

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based on religion (Clark and Drinkwater, 2007, 2009; Purdam et al., 2007). However,

because of the partial overlap between ethnicity and religious affiliation, analyses based on

religious affiliation alone are hard to interpret. For example, the majority of Hindus are

Indian, though under half of UK Indians are Hindus, 30% are Sikhs and 13% are Muslims.

By contrast, almost all British Pakistanis and Bangladeshis affiliate to Islam and between

them they make up two thirds of UK Muslims. Simply controlling for religious affiliation

may well be misleading, since what is being distinguished (or combined) through religious

differentiation is not clear. In the case of India, people with different religious denomination

are likely to come from different parts of the country: Indians coming from the North are

comparatively more likely to be Muslim, while those coming from the South are more likely

to be Hindu.2 A more precise way of distinguishing the outcomes of different minorities,

may be to examine the experience of specific ethno-religious groups (Brown, 2000; Lindley,

2002b), with discrete histories, and migration backgrounds.

In our analysis we distinguish our minority groups according not only to their

ethnicity, but also to their religious affiliation and whether they are first or second generation

in order to investigate more precisely differences in wage gaps for a set of UK minority

groups. We construct three ethno-religious minority groups: Indian Hindus, Indian Muslims

and Pakistani Muslims. We also distinguish each of these groups between first and second

generation. For the first generation there will be more factors that may be relevant to wages

that we cannot include in our models, thus leading to larger unexplained components; we

would also expect these factors to differ by group: for example, there are substantial

differences in English language fluency across ethnic groups.

2.2. Heterogeneity between ethnic groups and new decomposition method

A further practical issue relates to heterogeneity between groups. Most decomposition

analyses of the wage gap use the Blinder-Oaxaca method. However, because this method

relies on a linear regression assumption and on out-of-sample predictions, it can be applied

only to explain mean differences and can lead to misleading results if the range of possible

values of the characteristics differs between majority and minority (Barsky et al., 2002).

Nevertheless, since it allows a detailed decomposition of the wage gap into the contribution

of each specific covariate, the Blinder-Oaxaca method is still widely used.

2 We are grateful to one referee for pointing this out.

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Firpo et al. (2007) show how to generalise the Oaxaca decomposition of the mean gap

to quantiles, variance and other summary statistics by using the recentered influence function

(RIF) approach (Firpo et al., 2009). The RIF for a statistic (for example a quantile) is a

transformation of the outcome variable, in our case the log wage, such that its mean equals

the actual statistic. By assuming a linear relationship between the RIF and the explanatory

variables, we can then use the Oaxaca decomposition to explain differences in wages across

quantiles or other statistics. However, the computation of the counterfactual in this approach

is still based on a linearity assumption and possibly on out of the sample predictions.

A method which overcomes this limit is the weighting estimation (DiNardo et al.,

1996; Barsky et al., 2002). This involves the estimation of a model (e.g. logit) for the

probability of belonging to a minority rather than the majority using a set of explanatory

variables, and the use of its predictions to compute the weights, given by the ratio between

the probability of belonging to the majority and the probability of belonging to the minority.

Weighting methods require specifying and estimating a model only for the probability of

belonging to the minority, but do not provide a detailed decomposition.

By combining the RIF regression and weighting approaches we overcome the

limitations of each. We estimate the weighted linear regression of the RIF for each minority

group by using weights to equalise the empirical distributions of the covariates between

groups. This estimation is consistent if either the weights are correctly estimated or the

regression models are correctly specified. This means that we can be confident that our

estimates of explained and unexplained components are robust. The econometric details of

this approach are summarised in the Appendix.

2.3. Interpretation of the results of the decomposition

By using the combined weighting and regression approach, we are able to distinguish that

part of the wage gap which is explicable in terms of different characteristics, from that part

which stems from differences in returns to those characteristics and which is left unexplained.

It is conventional to perceive the explained component as representing legitimate differences

in wages and to attribute the unexplained component to discrimination (e.g. Nielsen, 2000;

Blackaby et al., 2002; Green and Ferber, 2005; Bjerk, 2007). However, it is unlikely that the

wage equation is able to control for all those factors which are relevant to wages other than

employer discrimination. We treat the unexplained component more cautiously as

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comprising the effect of individual and labour market characteristics we have been unable to

measure directly, but which may include employer discrimination.3

Moreover, we argue that it is important to analyse the explained part of the

decomposition to understand to what extent wage gaps are related to systematic variation in

individual characteristics such as education or to sorting into particular types of occupation,

and the balance between them. If we argue that not all the unexplained component can be

interpreted as discrimination, we also suggest that the explained component is itself likely to

include the impact of discrimination within society as reflected in constraints on occupational

choices or lower educational outcomes in the second generation (Altonji and Blank, 1999).

Another way to look at the role of sorting and of the potential role of discrimination is

to analyse both the explained and the unexplained components at different parts of the wage

distribution. For example, unexplained disadvantage at the top of the distribution is

consistent with employer discrimination and a ‘glass ceiling’, while occupational sorting

among the lower paid fits with restricted employment opportunities or the development of

niche employment, where we would expect direct discrimination within the job to be less.

There is already some evidence of substantial variation in the wage distribution as well as in

the mean, with some groups having a much more concentrated distribution and others a more

extended one (Platt, 2006); however we still do not know the extent to which explanatory

factors and residuals differ across the distribution (though see Longhi and Platt, 2008 for a

preliminary investigation of this issue).

Looking across the distribution helps to determine the extent to which unexplained

differences in wages are more of an issue at the bottom (sticky floors) or the top (glass

ceilings) of the distribution, a distinction which has different policy implications. For these

reasons, we extend our decomposition analysis to consider the whole range of the wage

distribution. We analyse differences in mean gaps and at the 10th, 25th 50th, 75th and 90th

percentiles.

3. Data and descriptive statistics

The empirical analysis is based on 31 pooled quarters of the Labour Force Survey (LFS),

between the second quarter of 2002 (when religious affiliation was first asked) and the third

quarter of 2009. The LFS has a rotating panel design, in which respondents are interviewed

3 There is substantial evidence that employer discrimination does occur (Kan and Heath, 2006; Grewal et al., 2002; Wood et al., 2009), but to attempt to quantify its precise share of overall labour market disadvantage is likely to remain elusive.

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for up to five successive quarters; we select respondents at their first interviews only,

resulting in unique observations for any individual.4 We also exclude Northern Ireland,

where the ethnicity question is not comparable with the question asked for the rest of the UK.

We focus the analysis on men only.

We combine information on ethnic group categories, on categories for religious

affiliation, on country of birth, year of birth and time of arrival in the UK to identify seven

ethno-religious and generation groups. We analyse wage gaps of six minority groups: Indian

Hindus, Indian Muslims and Pakistani Muslims, breaking each down by whether they were

born in the UK or came while they were still young enough to participate in compulsory

education at age 10 or under (second generation) or whether they arrived in the UK aged 11

or older (first generation). Although this is not the traditional way of defining first and

second generation immigrants, we believe it is a better way to define groups that – in the

labour market – can be seen as relatively homogeneous. People born abroad but raised in the

UK are likely to be more similar to UK born people of the same ethnicity than to people born

and raised abroad. Nevertheless, our general conclusions do not change if we define first and

second generations simply by country of birth.

We use UK born White British Christian as the majority group of reference. Christian

affiliation does not necessarily imply practice among White UK born, but represents a

cultural majority. White British people who do not affiliate to any religion are, however,

very similar to those who claim a Christian religion.

Data on wages refer to hourly wages for those in employment; it is based on usual

hours including paid overtime and usual wage in the main job. We deflate hourly wages to

prices at first quarter 2008 across all waves using the consumer price index provided by the

UK Office for National Statistics (to account for other year-specific characteristics we also

include in the model year dummies). Since there is relatively high non-response on earnings

questions, in addition to those sample weights that aim to render the sample as a whole

representative of the population, the LFS provides separate weights for wage data, which take

into account both non-response and design effects. We adjust wages by these ‘income

weights’ in all the analyses.

To avoid including people who have not yet completed their education alongside

those who have, we focus only on men aged from 23 to 64. Rates of staying on in education

vary across ethnic groups, but by the age of 23 few are still in full-time education.

4 For more details on sample and survey methodology see the LFS User Guide, Vol. 1.

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As shown in Table 1, overall, average wages of first and second generation Indian

Hindu men are higher than wages of White British Christian men, while for Indian Muslim

and Pakistani Muslim men there are substantial average wage gaps for both the first and

second generations. For all groups, wages increase between the first and the second

generation; mean gaps of Pakistani Muslim men reduce considerably when moving from the

first to the second generation.

TABLE 1 ABOUT HERE

To reduce the possibility of out-of-sample predictions the explanatory variables (also

shown in Table 1) are defined so as to maximise the overlap between the minority and the

reference category. Hence, because the age ranges of our minority groups – especially for the

second generation – are different from those of the majority, rather than including age in

years we only use a dummy for whether aged over 34. Similarly, instead of including years

of job tenure, we use a dummy for those with five or more years of job tenure.

Among qualification levels, it has to be noted that for migrants ‘other qualifications’

often include those obtained abroad for which it is not always straightforward to identify a

UK equivalent. Possession of other qualifications may, therefore, proxy for a number of

aspects of migrant experience which can result in labour market disadvantage, whether

through lack of recognition (of e.g. experience or skills) or through lack of congruence

between jobs gained and skills, or lack of familiarity with the UK labour market.5

We include dummies for the 1-digit Standard Occupation Classification (SOC), as

well as five occupations at the 3-digit level in which certain minority groups show relatively

high concentrations.6 As shown in Table 1, minority groups appear to be more concentrated

in specific occupations than White British Christians are, though the second generations are

much less concentrated than first generations.

It can be argued that occupations might be endogenous to wages. However, this need

not be a problem for our analysis: our aim is to describe the relationship between wage gaps

and differences in characteristics between ethnic groups rather than to make inference on

causal relationships. The decomposition approach is useful to identify potential determinants 5 Since ‘other qualifications’ might include foreign qualifications of different levels, as a sensitivity analysis we have also estimated our models using years of education instead of qualifications. The explained part of the wage gaps is now slightly smaller, but the general patterns across ethno-religious groups and generations remain unchanged. 6 These have been identified using data for all men aged 16-64 in employment, whether or not their wage data are available.

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of the wage gap, but remains an exploratory analysis which does not take account of potential

endogeneity issues.

In the next section we summarise the extent to which wage gaps can be explained by

differences in job, worker and firm characteristics across minorities and the White British

Christian majority. The signs have been set so that wage gaps are expressed as positive and

wage advantages (or the reverse of gaps) are expressed as negative. We show differences in

wages at the mean and five quantiles, the amount that is explained according to the combined

weighting and regression approach, the amount unexplained (i.e. the gap minus the

explained), and the amount that is explained according to a generalised Oaxaca

decomposition. Where the wage gap itself is statistically significant and the amount

explained by characteristics is similar for both the combined weighting and regression

approach and the generalised Oaxaca, we use the generalised Oaxaca decomposition to

explore what characteristics are contributing to the explained component of the gap. For

reasons of space we only show the summary results; full tables are available on request.

4. Results

4.1. Decomposition at the mean

From now onwards we consider wages in logs so that wage gaps are approximately equal to

relative rather than the absolute changes in wage. Table 2 summarises the results of the

decomposition at the mean for all the ethno-religious groups, and shows the gap at the mean

in the first column. The second column shows the amount of the gap that is explained

according to the combined weighting and regression decomposition approach; this is the

difference between the counterfactual (the wage that the minority would have if they had the

same characteristics as the majority) and the actual mean wage. The third column shows the

unexplained part, which is the difference between the majority and the counterfactual mean

wage. If differences in the distribution of characteristics were the only cause of the gap, these

two mean wages would coincide since the counterfactual mean equalises the distribution of

characteristics across the minority and the majority. The explained and unexplained

components sum up to the observed mean wage gap. Column (4) shows the explained

component of the wage gap according to the generalized Oaxaca decomposition. When the

two explained components – columns (2) and (4) – are close, it is appropriate to derive the

contribution of particular characteristics to the explained component from the Oaxaca

decomposition, since in such cases the Oaxaca can be seen to correspond to a robust estimate

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of the contribution of differences in characteristics to wage differences. In general, the

overall decomposition provided by the combined weighting and regression decomposition

and by the generalized Oaxaca are quite close. The divergence is biggest for Pakistani

Muslim first generation, but this is also where the overall gap is largest.

Table 2 shows that while Indian Hindus have an advantage in terms of mean wages

compared to the White British Christian majority, their individual and job characteristics

imply that their wage should be even higher, as their characteristics over-explain their wage

advantage. The detailed decomposition in Table 3 shows the contribution of the individual

and job characteristics to the explained part of the gap. The table shows that it is

occupational distribution that mostly contributes to the ‘over-explanation’ of the wage

advantage: Indian Hindus are over-represented in professional occupations, especially health

professionals, and under-represented in skilled trades. Thus, it would appear that this group,

though making their way into the more highly paid occupations and demonstrating

achievement in absolute terms, are not fully obtaining the levels of wage that might be

expected from those professional occupations. Because occupational distribution and

concentration in fact explains much more than the small wage advantage experienced by this

group, it leaves a substantial unexplained component.

The second generation Indian Hindus experienced a substantial, and statistically

significant, mean wage advantage. Similarly to the first generation, this advantage is over-

explained by their characteristics, though the resulting unexplained element is small,

amounting to less than 2% of the reference wage. From Table 3 we see that qualifications and

occupational distribution account for the majority of the explained part of the wage

advantage. Second generation Indian Hindus tend to have high levels of education and to be

over-represented among health professionals, and under-represented in part-time work.

TABLES 2 AND 3 ABOUT HERE

The remaining four ethno-religious groups all experience wage gaps rather than

advantages. The experiences of the Indian group vary significantly by religious affiliation:

the absolute wage distribution of Indian Muslims is closer to that of Pakistani Muslims than

to that of Indian Hindus. First generation Indian Muslims experience a gap of 13% of the

majority mean wage, and less than half of this gaps is explain by characteristics. Once again

the most important characteristics to explain the wage gap are related to the occupational

distribution: this group is over-represented among health professionals, but also among sales

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and customer service occupations, and in part-time jobs. As with Indian Hindus,

occupational distribution plays a bigger role than educational qualifications, suggesting that it

is the sorts of jobs that Indian Muslims end up in – given their qualifications – that are more

pertinent to their wage disadvantage than simply their average differences in qualifications.

There is some indication, then, that lower wages may stem from lower access to suitable full-

time work opportunities and some sorting or selection into lower paid occupations. The

second generation experiences a smaller absolute wage gap which is not statistically

significant but which seems to be over-explained by characteristics. We should note that the

sample size for this group is very small, which may impact not only on the statistical

significance but also on the ability to ‘explain’ the gap. Interestingly, however, for second

generation Indian Muslims, by contrast with the first generation, the critical factor in the

explained part of the mean wage gap is qualifications, with occupation counteracting this

impact to a certain extent. Though they have higher qualifications on average than the first

generation, the wage gap of second generation Indian Muslims, relative to the majority, can

be explained by relatively lower qualifications; however, within occupations, second

generation Indian Muslims are not getting the returns that they might expect.

Finally, Pakistani Muslim men experience the largest wage gaps compared to White

British Christians. The decomposition indicates that a substantial proportion of this gap can

be explained by job and individual characteristics. However, there is still a substantial

unexplained component. For the first generation, the most important characteristics that

contribute to the explanation of the gap are over-representation in part-time work, possession

of ‘other’ or ‘no’ qualifications relative to higher qualifications, and over-representation in

less skilled occupations such as process plant and machinery, and elementary occupations.

Even after the extensive explanatory role played by these characteristics, first generation

Pakistani Muslims face an unexplained wage gap amounting to 15% of the reference wage.

This might suggest lack of recognition of foreign qualifications (Hudson et al., 2006) and that

first generation minorities may end up in lower skilled occupations and be constrained to

work part-time.

Pakistani Muslim men from the second generation have a substantially lower mean

wage gap than the first generation, though a lower proportion of it is explained by

characteristics. Concentration in part-time work and over-representation in sales and

administrative and secretarial occupations contribute to the explained part of the gap, but

their qualifications would imply higher wages. The biggest factor contributing to lower

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wages is the concentration of younger workers in this group, which may suggest that some of

the disadvantage will dissipate with time.

In summary, ethno-religious groups experience very distinctive patterns of wages and

wage gaps. As we would expect, the unexplained component is higher for first than second

generation for Indian Hindus and Pakistani Muslims, but this is not the case for Indian

Muslims. Differences in qualifications, which might be thought to be most directly related to

wages, contribute only little to the explanation of wage gaps, while occupational clustering

and, in some cases, over-representation in part-time work have a larger contribution. The

way the labour market is organised and the sorts of jobs people have access to – or select

into, for whatever reason – are very important in determining wages, and the results are

consistent with the existence of some within employment discrimination that impinges on

wages among the disadvantaged and the advantaged. Focusing on the mean, employer

discrimination may play a role in providing or limiting access to certain jobs, but it appears to

have less of a role in specifically reducing wages. However, there is greater indication of

unexplained downward pressure on wages among the more advantaged groups. This may

suggest that it is at higher levels of wages that employer discrimination is revealed, while at

lower wage levels it is the particular jobs that can be accessed that determines wage gaps. It

is of interest, then, to analyse gaps across the wage distribution, to which we now turn.

4.2. Decomposition over the wage distribution

Tables 4, 6 and 8 show the results of the decomposition across the wage distribution for

Indian Hindu men, Indian Muslim men, and Pakistani Muslim men, separately by generation;

the headings are the same as in Table 2. Tables 5, 7, and 9 show the relative contribution of

the individual and job characteristics for the same groups.

Table 4 shows that the wage advantage of Indian Hindu men is statistically significant

from the 50th percentile upward for the first generation and at the 25th and 50th percentile for

the second generation. For both generations it is occupational distribution – and the same

occupations – that contribute to the explanation of the wage advantage, namely over-

representation among health professionals and under-representation among skilled trades

occupations (see Table 5). For the second generation at the 25th percentile, we see from

Table 5 that only under-representation in part time work contributes significantly to the wage

advantage; while at the 50th percentile qualifications are more important. Hence, there is

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some evidence that qualifications gained from one generation to the next can increase wages

other than through determining occupation, at least around median earnings.

TABLES 4 AND 5 ABOUT HERE

Turning to Indian Muslims and Pakistani Muslims, we find statistically significant

wage gaps at the 25th percentile for Indian Muslims. We find significant gaps from the 25th

percentile upward for first generation Pakistani Muslims; and at the 25th, 50th and 75th

percentiles for second generation Pakistani Muslims (see Tables 6 and 8). The sizes of the

actual gaps show some variation across the quantiles, ranging from 10% to 19% of wages of

the reference group for second generation Pakistani Muslims to between 40% and 55% for

first generation Pakistani Muslims. Indian Muslims have smaller gaps, which are generally

not statistically significant. The proportions explained are high across the board: in most

cases between 60-90% is explained, with the exceptions of the 90th percentile for the first

generation Pakistani Muslims, and the 75th percentile for second generation Pakistani

Muslims, where only 1/3 and 1/4 of the gap, respectively, is accounted for. What can the

decompositions tell us about the differences that are contributing to these wage gaps?

For first generation Indian Muslims, the most important contributions to the wage gap

at the 25th percentile (the only one statistically significant in the top part of Table 6) come

from over-representation in sales and customer service occupations and relatively low-level

qualifications. For second generation Indian Muslims, instead, it is qualifications, and

specifically low rates of level 2 and level 3 qualifications that play the most important role in

determining the explained part of the wage gap (see Table 7). For this group occupational

distribution (under-representation in skilled trades and in process plant and machine

operatives) offsets some of the wage gap.

TABLES 6 AND 7 ABOUT HERE

For first generation Pakistani Muslims the large gap at the 25th percentile (top part of

Table 8) is largely explained, but the only variables to contribute significantly to the gap are

age, and working in smaller firms (see Table 9). Occupational distribution appears to play a

role in the determination of the wage gap, but no particular occupation makes a statistically

significant contribution. At the median of the wage distribution the biggest factors in the

explanation of the gap are possession of ‘other’ and of ‘no’ qualifications, over-

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representation in part-time work and in particular occupations (process, plant and machine

operatives and elementary occupations). The results are very similar at the 75th percentile.

At the 90th percentile, a much smaller proportion of the gap is explained, leaving an

unexplained component of nearly 30 percent. Under-representation in the public sector

contributes to that part of the gap that is explained, together with under-representation among

functional managers.

For second generation Pakistani Muslims (bottom part of Table 8) the majority of the

gap at the 25th and 50th percentiles is explained by characteristics, and the absolute size of the

gap is not as big as for the first generation. As Table 9 shows, it is, however, harder to point

to individual characteristics that contribute to the explanation of the gap, other than age (or

youth, in this case). As the effects of youth should disappear with age, this is a positive

message. At the 25th percentile over-representation in part-time work also has an effect in

reducing wages. For this group, neither education nor occupational distribution make a

significant contribution to accounting for the wage gap.

TABLES 8 AND 9 ABOUT HERE

In summary, for the explanation of the wage gaps of Indian Muslim men and

Pakistani Muslim men occupational distribution is far more salient for the first than second

generation. Part-time work plays a part in explaining wage differences particularly at the

bottom of the distribution for the second generation as well as for the first. Educational

qualifications matter for wages of first generation Pakistani Muslims, while age (or rather

youth) contributes to the wage gaps for both the first and second generation: this may reflect

both the overall age distribution of the populations but also the extent to which older

members of the first generation leave or are excluded from the labour market altogether.

5. Summary and conclusions

In this paper we analyse wage gaps of ethno-religious groups of male workers in the UK. We

focus on three of the largest UK minorities which also represent meaningful social groups:

Indian Hindu, Indian Muslim, and Pakistani Muslim men, distinguishing between first and

second generation, using White British Christian men as a reference group. The results show

striking differences in wages between ethno-religious groups: Indian Hindus have the highest

wages and Pakistani Muslims the lowest; Indian Muslims fare better than Pakistani Muslims

16

but worse than Indians who are Hindus. Furthermore, the experience of Indian Muslims

appears to be somewhat closer to that of Pakistani Muslims than to Indian Hindus, on

average, thus suggesting the distinctiveness of each ethno-religious group.

As already mentioned, it has to be noted that in the case of India, people with different

religious denominations are likely to come from different parts of the country: while Muslim

Indians are likely to come from the North, Hindu Indians are likely to come from the South.

Hence, the difference in performance between the Indian groups compared might not be due

to religion per se, but to other factors related to the country of origin that we cannot

disentangle from religious denomination. The difference in experience between the two

Indian groups is not necessarily a consequence of religious discrimination, but stems from

other factors that make Indian Muslims a specific group. Indeed, the fact that the experience

of Indian and Pakistani Muslims differs challenges accounts of an overarching Muslim

experience of disadvantage.

Within all three minority ethno-religious groups the second generation achieves

higher wages than the first generation, but the amount that is explained by characteristics

does not necessarily increase with generation. Statistically significant wage gaps are found

for most groups around the middle of the wage distribution, and there is, in general, little

evidence of wage gaps – or advantage – at the extremes of the distribution. In most cases the

majority of the gap can be explained by characteristics, and specifically by sorting into

particular occupations. A portion of the wage advantage for Indian Hindus is explained by

their over-representation in some highly paid occupations such as professionals (especially

health professionals) and under-representation in part-time work; whereas the wage gap for

both Indian Muslims and Pakistani Muslims is partly explained by their concentration in low

paid occupations like sales and customer service, and in part-time work. It is also interesting

that, contrary to expectations, qualifications are not noticeably more important in explaining

differences in wages for the first compared to the second generation. The types of jobs that

people are employed in, given their qualifications, seem more important in accounting for

wage gaps.

Restricting attention to statistically significant wage gaps, the unexplained

components are always below 10% of White British Christians wages with the exception of

first generation Pakistani Muslims, for whom there are substantial unexplained components,

particularly towards the top of the distribution. Once we control for individual

characteristics, the wage advantage of Indian Hindus disappears, revealing a wage penalty,

which is reflected in positive unexplained components. Apart from the substantial

17

unexplained component at the top of the distribution for second generation Indian Hindu

men, there is not compelling evidence of the unexplained component being a ‘glass ceiling’,

as it does not generally concentrate towards the top of the distribution.

Acknowledgements

We would like to thank the editor and two anonymous referees for comments on an earlier

version of this paper. Data on the LFS is available from the Data Archive at the University of

Essex (www.data-archive.ac.uk).

Funding

Economic and Social Research Council through the Research Centre on Micro-Social Change (RES-518-28-001); European Commission Marie Curie Fellowship (234845-TRANSID to L.P.).

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20

Tables Table 1: Descriptive statistics of the weighted estimation sample White

British Christian

Indian Hindu 1st gen

Indian Hindu 2nd gen

Indian Muslim 1st gen

Indian Muslim 2nd gen

Pakistani Muslim 1st gen

Pakistani Muslim 2nd gen

No of observations 57660 594 271 121 95 404 293 Hourly wage (£) 13.47 14.63 14.99 12.09 13.31 8.41 12.06 Aged 34-64 (Ref: 23-33)

77.0 59.2 51.4 63.6 46.5 58.9 38.1

Working part-time (ref: full-time)

5.0 4.9 2.8 15.6 8.1 24.3 12.0

Public sector (ref: private sector)

21.6 23.8 17.8 24.8 20.2 10.5 18.7

Job tenure > 5 yrs 60.6 39.4 43.0 49.9 51.9 38.4 41.8 Qualifications (NVQ) Level 4 or more 33.0 47.8 61.4 35.4 53.2 22.7 47.4 Level 3 16.9 4.7 13.8 5.7 7.4 3.9 13.5 Level 2 22.2 4.9 13.2 5.4 7.0 5.6 13.1 Less than 2 12.3 1.6 7.5 2.8 15.4 5.1 10.5 Other qualifications 6.3 31.8 1.5 32.2 6.3 39.5 4.0 No qualifications 9.3 9.3 2.6 18.6 10.7 23.2 11.5 Occupations: 1. Managers & senior officials

22.1 14.5 23.6 13.2 25.6 7.8 12.1

2. Professional Occupations

14.1 38.6 27.1 20.4 25.3 10.9 20.1

3. Associate prof. & technical

14.6 10.0 15.5 6.6 9.4 5.8 14.8

4. Administrative & secretarial

5.1 6.9 9.6 7.3 9.0 5.9 7.5

5. Skilled trades Occupations

15.2 5.3 6.9 3.8 1.6 10.0 7.9

6. Personal service Occupations

2.6 1.4 0.7 5.2 4.5 2.3 3.0

7. Sales & customer service occupation

2.9 3.7 5.6 12.2 11.6 11.5 11.9

8. Process, plant & machine operat.

13.5 9.0 5.6 17.5 6.4 25.1 10.3

9. Elementary Occupations

10.1 10.7 5.4 13.8 6.5 20.7 12.5

113. Functional Managers

7.6 5.6 10.9 3.5 10.3 2.3 5.8

213. Information comm. tech. prof.

2.2 15.5 7.9 1.7 2.4 0.9 5.7

221. Health Professionals

0.6 13.6 5.0 10.2 2.7 4.8 2.7

711. Sales assist. retail cashiers

1.5 3.6 3.4 8.3 7.5 10.6 8.0

821. Transport drivers & operat.

5.9 2.6 1.0 4.7 3.1 10.4 4.7

Firm size: 0-25 29.6 21.4 29.7 32.7 41.8 42.5 34.0 Firm size: 26-250 40.7 31.5 32.8 32.2 29.6 33.6 35.8

21

Firm size: 250+ 29.6 47.1 37.5 35.0 28.6 23.9 30.2 Scotland & North of England

25.5 9.8 2.4 14.1 15.8 24.6 24.9

Wales and Mid England

38.1 26.8 35.3 44.7 48.8 32.6 42.3

London and the South

36.4 63.4 62.3 41.1 35.4 42.8 32.8

Included only in the logit model: Whether health prob 26.4 18.6 16.9 24.7 17.6 17.5 13.3 With children 0-4 7.1 12.3 9.4 14.4 20.8 16.5 16.1 With children 5-15 28.2 26.7 31.8 44.4 38.4 42.8 45.5 Married/cohabiting 77.8 78.6 66.1 90.2 73.9 86.6 79.4 Note: all variables in percentages

22

Table 2: Mean wage gaps and their decomposition by ethno-religious group Ethno-religious group

(1) Mean gap

(2) Explained

(3) Unexplained

(4) Explained Oaxaca

Indian Hindu 1st gen -0.060* -0.152 0.092 -0.158 Indian Hindu 2nd gen -0.105* -0.117 0.012 -0.096 Indian Muslim 1st gen 0.127* 0.050 0.077 0.034 Indian Muslim 2nd gen 0.080 -0.068 0.148 -0.057 Pakistani Muslim 1st gen 0.459* 0.311 0.148 0.241 Pakistani Muslim 2nd gen 0.138* 0.058 0.080 0.076 Mean wage gaps that are statistically significant at the 5% level are indicated with * Table 3: Detailed decomposition at the mean by ethno-religious group

Indian Hindu 1st gen

Indian Hindu 2nd gen

Indian Muslim 1st gen

Indian Muslim 2nd gen

Pakistani Muslim 1st gen

Pakistani Muslim 2nd gen

Explained -0.158 -0.096 0.034 -0.057 0.241 0.076 Part time 0.000 -0.006 0.014 0.002 0.015 0.009 Public sector 0.000 0.001 0.000 -0.010 0.012 -0.001 Qualifications -0.003 -0.013 0.002 0.049 0.085 -0.022 Other job / employee characteristics

0.014

0.009

0.015

0.009

0.025

0.015

Age 0.025 0.043 0.004 0.015 0.030 0.059 Occupation -0.151 -0.094 0.024 -0.109 0.083 0.014 Region -0.035 -0.038 -0.011 -0.008 -0.010 0.005

23

Table 4: Decomposition across the wage distribution: Indian Hindu men Quantile (1)

Quantile gap (2)

Explained (3)

Unexplained (4)

Explained Oaxaca

1st Generation Indian Hindu men 10th p 0.062 -0.088 0.150 -0.168 25th p 0.014 -0.039 0.053 -0.117 50th p -0.068* -0.170 0.102 -0.080 75th p -0.166* -0.164 -0.002 -0.160 90th p -0.132* -0.214 0.082 -0.186 2nd Generation Indian Hindu men 10th p 0.008 -0.035 0.043 -0.112 25th p -0.091* -0.108 0.016 -0.089 50th p -0.126* -0.192 0.066 -0.110 75th p -0.108 -0.134 0.026 -0.114 90th p -0.156 -0.321 0.164 -0.100 Quantile gaps that are statistically significant at the 5% level are indicated with * Table 5: Detailed decomposition across the wage distribution: Indian Hindu men 10th p 25th p 50th p 75th p 90th p 1st Generation Indian Hindu men Explained -0.168 -0.117 -0.080 -0.160 -0.186 Part time 0.002 0.002 0.000 -0.001 -0.002 Public sector -0.002 -0.003 -0.001 0.003 0.007 Qualifications -0.040 -0.016 0.026 -0.031 -0.061 Other job / employee characteristics 0.044 0.018 0.024 -0.003 -0.056 Age -0.022 -0.005 0.015 0.067 0.157 Occupation -0.057 -0.057 -0.133 -0.173 -0.219 Region -0.078 -0.043 -0.015 -0.021 0.019 2nd Generation Indian Hindu men Explained -0.112 -0.089 -0.110 -0.114 -0.100 Part time 0.001 -0.014 -0.003 -0.004 -0.007 Public sector -0.004 0.001 0.003 0.004 0.002 Qualifications -0.055 0.006 -0.024 0.000 0.041 Other job employee characteristics -0.001 0.009 0.036 0.009 -0.021 Age 0.046 0.035 0.041 0.062 0.083 Occupation -0.068 -0.105 -0.108 -0.154 -0.123 Region -0.034 -0.020 -0.058 -0.036 -0.076

24

Table 6: Decomposition across the wage distribution: Indian Muslim men Quantile (1)

Quantile gap (2)

Explained (3)

Unexplained (4)

Explained Oaxaca

1st Generation Indian Muslim men 10th p 0.180 0.254 -0.073 0.141 25th p 0.290* 0.308 -0.018 0.072 50th p 0.070 -0.034 0.103 0.115 75th p 0.091 0.118 -0.026 0.011 90th p -0.012 0.008 -0.004 0.001 2nd Generation Indian Muslim men 10th p 0.128 0.155 -0.027 0.155 25th p 0.170 0.030 0.140 -0.100 50th p 0.133 0.280 -0.147 0.010 75th p -0.044 -0.197 0.153 0.002 90th p -0.007 -0.090 0.082 -0.321 Quantile gaps that are statistically significant at the 5% level are indicated with * Table 7: Detailed decomposition across the wage distribution: Indian Muslim men 10th p 25th p 50th p 75th p 90th p 1st Generation Indian Muslim men Explained 0.141 0.072 0.115 0.011 0.001 Part time 0.089 0.050 0.010 -0.001 0.008 Public sector -0.012 -0.001 0.004 0.000 0.009 Qualifications 0.317 0.074 0.071 -0.068 -0.103 Other job / employee characteristics 0.011 0.066 0.009 0.003 -0.014 Age 0.009 -0.009 -0.007 0.026 0.031 Occupation -0.114 -0.041 0.071 0.033 0.052 Region -0.031 -0.001 -0.028 -0.008 -0.013 2nd Generation Indian Muslim men Explained 0.155 -0.100 0.010 0.002 -0.321 Part time 0.037 0.021 0.003 0.000 -0.033 Public sector -0.008 -0.008 -0.005 -0.019 -0.009 Qualifications 0.002 0.025 0.091 0.064 -0.007 Other job characteristics 0.101 0.045 0.018 0.025 -0.060 Age -0.121 -0.017 -0.008 0.085 0.111 Occupation 0.284 -0.132 -0.096 -0.147 -0.373 Region -0.051 -0.036 0.002 -0.030 0.025

25

Table 8: Decomposition across the wage distribution: Pakistani Muslim men Quantile (1)

Quantile gap (2)

Explained (3)

Unexplained (4)

Explained Oaxaca

1st Generation Pakistani Muslim men 10th p 0.291 -0.071 0.362 0.566 25th p 0.419* 0.458 -0.040 0.452 50th p 0.530* 0.534 -0.004 0.424 75th p 0.534* 0.432 0.102 0.282 90th p 0.428* 0.139 0.289 0.145 2nd Generation Pakistani Muslim men 10th p 0.175 0.115 0.061 0.180 25th p 0.167* 0.194 -0.026 0.144 50th p 0.194* 0.126 0.068 0.096 75th p 0.106* 0.027 0.079 0.034 90th p 0.061 0.040 0.021 0.041 Quantile gaps that are statistically significant at the 5% level are indicated with * Table 9: Detailed decomposition across the wage distribution: Pakistani Muslim men 10th p 25th p 50th p 75th p 90th p 1st Generation Pakistani Muslim men Explained 0.566 0.452 0.424 0.282 0.145 Part time -0.073 0.065 0.077 0.012 0.003 Public sector -0.049 -0.020 -0.023 0.000 0.034 Qualifications 0.427 0.091 0.135 0.089 0.012 Other job characteristics 0.290 0.108 0.065 0.021 -0.012 Age 0.106 0.110 0.046 0.030 0.021 Occupation -0.080 0.141 0.152 0.142 0.096 Region -0.022 -0.028 -0.036 -0.010 -0.003 2nd Generation Pakistani Muslim men Explained 0.180 0.144 0.096 0.034 0.041 Part time 0.051 0.038 0.006 -0.001 -0.008 Public sector 0.004 0.010 0.004 -0.005 -0.015 Qualifications 0.020 0.001 -0.035 -0.031 -0.016 Other job characteristics 0.028 -0.024 0.012 0.026 0.054 Age 0.038 0.088 0.079 0.032 0.009 Occupation 0.036 0.036 0.028 0.004 -0.011 Region 0.005 0.001 0.003 0.003 0.005

26

Appendix: Quantile and mean decomposition of the wage gap

We decompose wage gaps using the method proposed by Firpo et al. (2007), which allows a

detailed decomposition at the mean and at different percentiles of the wage distribution (see

also Fortin et al., 2011). Firpo et al.’s method combines the use of weights to equalize the

empirical distributions of the explanatory variables between the two subpopulations

compared (DiNardo et al., 1996) and of ‘recentered influence function’ regressions (Firpo et

al., 2009).

The recentered influence function (RIF) for the τ-quantile (qτ) of a variable y, in our

case the log wage, is given by

RIF(y,qτ)=qτ+[τ-dτ]/fY(qτ), (1)

where fy(qτ) is the density distribution function of y computed at the quantile qτ, and dτ is a

dummy variable taking value one if y≤qτ and zero otherwise.7 The RIF(y,qτ) satisfies the

following properties:

a) its mean is equal to the actual τ-quantile, Ey[RIF(y,qτ)]= qτ;

b) the mean of its expectation conditional on a vector of variables X, Ey[RIF(y,qτ)|X], is

again equal to the actual statistic qτ, i.e. Ex[Ey[RIF(y,qτ)|X]]=qτ.

The conditional expectation Ey[RIF(y,qτ)|X] is a function of X and is what Firpo et al.,

(2009) define as the unconditional quantile regression. Assuming a linear relationship

between RIF(y,qτ) and X for both the minority group and White British Christian people, we

can estimate Ey[RIF(yj,qτ)|Xj] using a linear regression:

RIF(yj,qτ)=Xj βj(qτ)+uj, (2)

7 In our empirical application, we estimate RIF(y,qτ) by replacing qτ with its sample estimate and computing the density distribution by using a nonparametric kernel estimation. For the estimation of the RIF we use the Stata ado file rifreg written by Firpo et al. (2009) and downloadable from http://faculty.arts.ubc.ca/nfortin/datahead.html

27

where j is a group indicator (0 for White British Christian people and 1 for the minority

group), Xj is a vector of K explanatory variables including the constant, βj(qτ) is the

corresponding vector of coefficients for the τ-quantile, and uj is an error term. Given the

properties (a) and (b), it is easy to prove that the difference between the τ-quantile for group 1

and 0 is

q1τ – q0τ = Ex Ey[RIF(y1, qτ)|X1]- Ex Ey[RIF(y0, qτ)|X0]

= )q()q( 0011 ττ ββ XX − , (3)

where jX is the mean of X for group j. The counterfactual τ-quantile for the disabled people,

as if they had the same characteristics of the non disabled people, can be computed as

)q(10 τβX . By subtracting and adding this counterfactual τ-quantile from the right hand side

of the above equation, we can decompose the difference in the quantile into two additive

components, the explained component and the residual component,

q1τ – q0τ ( ) ( ))()()( 010101 τττ βββ qqXqXX −+−= . (4)

This decomposition, which we call the generalized Oaxaca, is equivalent to the Oaxaca

method with the only difference that the dependent variable in the regression model is the

RIF rather than y. Since the RIF of the mean is equal to y, the generalized Oaxaca includes

the standard Oaxaca decomposition as a special case.

The generalized Oaxaca decomposition allows us to produce a detailed decomposition to

evaluate the contribution of each variable,

q1τ – q0τ ( ) ( ))()()( 110

1,1,0,1 τττ βββ qqXqxx

K

kkkk −+−= ∑

=, (5)

28

where kjx , is the k-th component of the vector of variables jX and β1,k is the corresponding

coefficient for the comparison group. However, the estimation of the counterfactual is based

on a linearity assumption and possibly on out of the sample predictions as in the case of the

Oaxaca decomposition. To overcome this limit, we combine the use of weights and the RIF

regression, i.e. we use weighted least squares estimation of the linear regression of the RIF

for the minority group

RIF(y1, τq )=X1 )(1 τβ qWR + u1, (6)

with weights given by

w(X)= [Pr(d=0|X)Pr(d=1)]/[Pr(d=1|X)Pr(d=0)], (7)

where d is a dummy taking value 1 for disabled people and 0 for non disabled, and Pr(d=1|X)

is the conditional probability of belonging to the minority group. We assume that the logit

transformation of Pr(d=1|X) is linear in X, i.e. we assume a logit model. The estimation of

the weighted regression is consistent if either the weights (i.e. the logit model) are correctly

estimated or the linear regression model is correctly specified. The counterfactual mean or

quantile are computed as in the Oaxaca decomposition, but considering the coefficients

estimated using the weighted regression (RIF) model instead of the simple mean regression

model. From comparing the counterfactual with the quantile wage of White British Christian

men, we can again decompose the wage gap into the part explained by differences in the

distribution of explanatory variables and the residual ‘unexplained’ part,

))].()(([)]()([ 0111101 ττττττ ββββ qqXqXqXqq WRo

WRo −+−=− (8)

We can further decompose the explained component into two parts:

29

( ) ( )[ ],)()()(

)()(

1101

,1,0,1

111

τττ

ττ

βββ

ββ

qqXqxx

qXqX

WRK

kkkk

WRo

−+

−=

−

∑=

(9)

with the first part in square brackets equal to the explained component based on the

generalized Oaxaca approach, and the second part equal to the difference between the

explained component in the generalized Oaxaca and in the combined weighting and

regression based approach. The smaller the difference in the explained component between

the two decomposition approaches, the higher the confidence with which we can use the

detailed results for the contribution of different characteristics derived from the generalized

Oaxaca decomposition (see Firpo et al., 2007 for more details).