ethnic differences in the incidence and determinants of employer-funded training in britain

{Journals}sjpe/sjpe46-5/q125/q125.3d

Scottish Journal of Political Economy, Vol. 46, No. 5, November 1999# Scottish Economic Society 1999. Published by Blackwell Publishers Ltd, 108 Cowley Rd., Oxford OX4 1JF, UK and

350 Main St., Malden, MA 02148, USA

ETHNIC DIFFERENCES IN THE INCIDENCEAND DETERMINANTS OF EMPLOYER-

FUNDED TRAINING IN BRITAIN

Michael A. Shields and Stephen Wheatley Price�

ABSTRACT

Non-white full-time employees were offered, and undertook, less training than

whites in Britain in 1993±4, according to data from the Quarterly Labour Force

Survey. Estimates of the determinants of training outcomes and training offers

show a marked consistency across white and non-white, male and female,

employees. Over 90% of the average predicted training outcome differential, and

50%±60% of the difference in mean predicted training offers, cannot be explained

by differences in observable characteristics between white and non-white employ-

ees. These findings suggest that equal opportunities legislation has been

unsuccessful in eliminating unequal access to employer-funded training in Britain.

I INTRODUCTION

The role of work-related training in increasing the productivity of the labour

force has received considerable international attention over the last decade. In

particular, the economics of training literature has been concerned with how

much employer-provided training takes place and why some employees receive

training from their employers, whilst others do not (see Blundell et al., 1996;

Shields, 1998a).1 Concurrently, other research in the field of labour economics

has focused on ethnic differences in labour market performance, especially with

regard to unemployment, occupation and wages. The British literature, in this

area, has observed smaller returns to labour market experience amongst non-

white males and has suggested that differential access to work-related training

may be a cause (e.g. Blackaby et al., 1994; Shields and Wheatley Price, 1998).2

There has been little empirical research into ethnic differences in the incidence

and determinants of training, despite the fact that there are approximately

523

�University of Leicester

1 For a review of the international evidence see Ashton and Green (1996).2A second possibility is that ethnic minority workers receive lower returns from work-related

training (Duncan and Hoffman, 1979), due to restricted promotional opportunities(Arulampulam and Booth, 1997) or the increased likelihood of experiencing unemploymentspells (Schmidt and Zimmerman, 1996). We are unaware of any British study that hasinvestigated this aspect of the training experience for non-whites.


450,000 non-white males and 370,000 non-white females employed in Britain.3

However, there is some qualitative evidence suggesting that discrimination in the

provision of training takes place (e.g. Beishon et al., 1995; Palmer, 1992). This is

occurring despite the fact that discrimination in the provision of training

opportunities, on the grounds of race, has been outlawed since the Race

Relations Act of 1976. Furthermore, official government training policy, over

the last decade, has had little to say about issues of equity for disadvantaged

groups (Keep and Mayhew, 1996).

The only insight into this issue in Britain, in the economics literature,4 arises

from the inclusion of simple dummy variables for `non-white' or `ethnic' in

models of the determinants of training. This approach, however, does not

allow for potentially important heterogeneity in the key determinants of

training between whites and non-whites. This may arise if employers value

non-white work-related characteristics differently in the provision of training

opportunities. For instance, many non-white employees may possess

unfamiliar qualifications, since the majority were born abroad, which an

employer may not consider as valuable, for training purposes, as their UK

equivalents.

The potential importance of ethnic differences in the access to employer-

funded training goes beyond simply inequity in the training market. This is

because work-related training is associated with a reduced probability of

unemployment, greater promotional opportunities, higher occupational attain-

ment and increased wages (Blundell et al., 1996; Booth, 1991; Greenhalgh and

Stewart, 1987; Nickell, 1982; and Pudney and Shields, 1997). Therefore, if ethnic

minority groups experience difficulties obtaining work-related training, they

may be permanently impeded from future labour market success. Indeed, the

well-documented obstacles facing ethnic minorities in the British labour market,

namely increased probabilities of being unemployed (Blackaby et al., 1997),

reduced promotional opportunities (Pudney and Shields, 1997), limited

occupational attainment (Stewart, 1983) and lower wages (Blackaby et al.,

1994, 1998), may be partly a product of their lack of access to work-related

training.

The contribution of this paper is to examine ethnic differences in the incidence

and determinants of employer-funded training between white and non-white,

male and female, employees in Britain. We examine the determinants of

employer-funded training, undertaken in four weeks prior to interview, for each

of these four groups of full-time employees. We analyse data pooled from the

eight Quarterly Labour Force Surveys, undertaken between December 1992 and

November 1994. This is the only source of British data that allows statistically

3Figures derived from population weighted estimates from the 1993=4 Quarterly LabourForce Surveys.

4 Indeed only two studies, that the authors are aware of, have ever separately estimateddeterminants of training models for different ethnic groups. Duncan and Hoffman (1979) do sofor whites and non-whites in the US, but do not decompose the resulting coefficients, whilstVandenHeuvel and Wooden (1997) decompose the differential in training incidence betweenAustralian-born and non-English-speaking background workers in Australia.

524 MICHAEL A. SHIELDS AND STEPHEN WHEATLEY PRICE

# Scottish Economic Society 1999


reliable samples of non-whites to be obtained and which also provides

information on training. The resulting estimates are then decomposed in order

to identify the proportion of the ethnic difference in mean predicted probability

which is attributable to measured characteristic differences and that part due to

differences in the estimated coefficients. We also report the decomposition

results arising from trinomial logistic models of the determinants of on-the-job

and off-the-job training.

Differences in mean predicted probabilities, which cannot be explained by

differences in measured sample characteristics, are usually attributed to

discrimination. However, this is an assumption of the analysis rather than

proof of causality. In order to ascertain whether these unexplained differences in

training outcomes are due to the actions of employers or employees we also

estimate the determinants of training offers, ever made by the current employer,5

for each of our groups of interest. This enables us to examine the supply-side of

the employer-funded training market. Our comparison of the decomposition

results from the determinants of training offers, with those of training outcomes,

enables some inferences to be drawn about the sources of the unexplained ethnic

gap in training incidence in Britain.

The paper is set out as follows: Section II summarises the main findings of the

determinants of training literature, including the effect of ethnicity on training

outcomes, and discusses the theoretical reasons why we might observe ethnic

differences in the incidence and determinants of employer-funded training. The

source of our data, the actual incidence of training offers and outcomes, the

distribution of employer-funded training by location and length and the

training-related characteristics of our sample are described in Section III.

Section IV outlines the econometric and decomposition methodologies we

employ. Then the binary logistic estimates, of the determinants of recent training

outcomes and training offers, are presented in Section V. Section VI discusses

the results of the decomposition analysis in the light of current equal

opportunities policy. Lastly, our findings are summarised and conclusions

drawn in Section VII.

II LITERATURE REVIEW AND THEORETICAL CONSIDERATIONS

The determinants of training in Britain

Over the last decade there has been a steady growth in the number of studies

which have examined the incidence and characteristics of work-related training

in Britain (e.g. Blundell et al., 1996; Booth, 1991, 1993; Green, 1991, 1993;

Green et al., 1995, 1996; Greenhalgh and Mavrotas, 1994, 1996; Greenhalgh and

Stewart, 1987; and Shields, 1998a). Considering that these studies have used a

wide array of data sources and various definitions of training, there exists

substantial agreement about the key determinants of training for employees in

5The authors are unaware of any other study which has made use of this question in theLabour Force Survey.

ETHNIC DIFFERENCES IN EMPLOYER-FUNDED TRAINING 525



Britain (see Blundell et al., 1996; Shields, 1998a). These findings are typically

explained within the human capital framework and are highlighted below.

There is a general consensus that employers are more willing to provide

training, and employees are more likely to demand training, the younger and the

better educated the employee. The former finding arises because increased age

reduces the time over which the benefits of training can be reaped. The effect of

education on training occurs because qualifications, which act as a proxy for

ability, are associated with a lower cost of learning and a higher likelihood of

completing a training course. The age-training profile for men is steeper than for

women, which is consistent with the hypothesis that employers are more

reluctant to train younger female workers, since they are more likely to quit for

child rearing (Green, 1991). Moreover, the correlation between training and

qualifications is most noticeable for female employees (Green, 1993), and for

workers who have received employer-provided training early in their careers

(Blundell et al., 1996).

The probability of receiving training has also been found to be higher for

professional, associate professional and managerial occupations (Green, 1993;

Greenhalgh and Mavrotas, 1996), and in the first year of job tenure, reflecting

initial investment in the skills necessary for the job. Large firms typically provide

more training than small firms, due to the economies of scale they can reap and

because they can be more certain of retaining the services of the trainee.6 Trade

union membership has also been shown to increase the likelihood of training,

since trade unions provide a collective voice communicating and encouraging

the training demands of workers.7 Conversely, the probability of training has

been found to be significantly lower for part-time workers than for those

employed full-time. This is because part-time working has a similar effect to age

by reducing the time available to capture the benefits from training.

There is less agreement about the effects of sector of employment and industry

on the probability of training. Booth (1991) found that training is higher for

employees in the public sector.8 However, several studies have since failed to find

this correlation (e.g. Green, 1993; Green et al., 1996). In terms of industry of

employment, we would expect those industries with the highest levels of

technological change, for example, to provide more training than low

6Alternatively, it may be due to a widening of the pay distribution in favour of employees inlarge firms. Increased pay raises the likelihood of workers remaining with the firm, whichincreases the benefits to the firm from training. In addition, employees may have a greaterdemand for firm-specific training if their probability of remaining with the firm is high.Furthermore, any divergence in the length of time horizon between large and small firms,adopted when evaluating the benefits from training, could lead to a greater willingness to trainon the part of large firms. Finally, large firms are subject to more quality and safety regulations,which require training, than small firms (Felstead and Green, 1996).

7 Trade union recognition at the workplace also tends to reduce employee turnover, thusraising the potential period over which the training investment can pay dividends (Green et al.,1995, 1996).

8 This may be because private sector firms are constrained by the need to make profits andmay be less willing to finance training, through fear of poaching. Alternatively, privatecompanies are more sensitive to the economic climate, making redundancies more probable andthus the investment in training is more likely to be lost.




technology industries. Using other data sources no uniform and consistent

relationship between industry and training intensity has emerged.9 However,

Greenhalgh and Mavrotas (1996) and Shields (1998a), using data from various

Labour Force Surveys since 1984, do reach some agreement. Both studies find

that training is highest in the `non-tradable' industries of health, education and

public administration and lowest in manufacturing and the service sector (e.g.

wholesale, retail, hotels and restaurants).

Training and ethnicity

In terms of the relationship between access to training and ethnicity, the only

British studies which have examined this issue have relied upon the findings

from simple dummy variables, indicating `non-white', in models of the

determinants of training. Table 1 provides a summary of the main British and

US studies. The British studies do not find a consistent effect of ethnicity of the

determinants of training. Interestingly, Green et al. (1996) found that

establishments with employees from ethnic minorities provide slightly more

training. However, in conclusion they note that there is little support for the

hypothesis of discrimination against ethnic minority workers in employer-

provided training in Britain. Surprisingly, a similar story of ambiguity emerges

for the US literature.

Differences between, otherwise observably equivalent, whites and non-whites

are most frequently attributed to discrimination on the part of the employer

(Becker, 1957). The actions of the employer may be motivated by racial

prejudice (the usual assumption) or involve the use of ethnicity as a rational

screening device (referred to as statistical discrimination). Discrimination, as

defined by the United Kingdoms' Race Relations Act of 1976, occurs when a

person is treated less favourably than other persons on racial grounds (Palmer,

1992, p. 86). In terms of training opportunities, discrimination may be defined

as occurring when white and non-white workers, having the same personal and

work-related characteristics, have an unequal chance of receiving employer-

funded training. This may be observed if employers undertake discriminatory

practices (have a taste for discrimination) in the allocation of training

opportunities or use ethnicity as a screening device for less productive workers.

Alternatively, they may be unwilling to employ ethnic minority workers in jobs

that involve large amounts of training (Duncan and Hoffman, 1979).

Discrimination may also indirectly affect training outcomes. Non-whites may

not put themselves forward for training if they anticipate lower returns to

9For example, Booth (1991) found that men employed in àgriculture and fishing' (which isgenerally accepted to be a low technology industry) and òther services' experienced asignificantly higher probability of training than other census-defined industrial sectors. Incontrast, Green (1993) found this to be only true for those employed in the ùtilities' and òtherservices'. Furthermore, Green et al. (1996) find substantial differences in training intensitybetween industrial sectors. In particular, workers in `distribution' and ùtilities' have asignificantly higher probability of training than the base group of òther services', whilstemployees in `transport and communications' and `metal goods, engineering and vehicles' aretypically less likely to be trained.




training (Duncan and Hoffman, 1979). A lower incidence of training could be

the result of non-whites believing they face discrimination in the access to higher

grades and better paid jobs (Arulampalam and Booth, 1997) or will experience

more unemployment spells in their subsequent working life (Schmidt and

Zimmermann, 1996).

Furthermore, there are a number of theoretical reasons why a rational, non-

prejudicial, employer might treat non-white employees differently in the

provision of training opportunities (i.e. exercise statistical discrimination).

Following the models of Lazear and Rosen (1990) and Barron et al. (1993), it

may be rational for a profit-maximising firm to provide less training to non-

whites, if they conclude that this group of employees are more likely to leave the

TABLE 1Empirical studies of the effect of ethnicity on training incidence

Data (year of data collection)Econometrictechnique Male Female

Main British StudiesGreenhalgh andStewart (1987)

The National Training Survey (1975) Logistic Ð �

Booth (1991) British Social Attitudes Survey (1987) Logistic NS NSBooth (1993) Survey of Graduates and Diplomats (1987) Logistic # � NSGreen et al. (1996) Employers' Manpower and Skills

Practices Survey (1991)Tobit � NS NS

Arulampalam andBooth (1997)

National Child Development Study (1991) Poisson # Ð NS

Dearden et al.(1997)

Quarterly Labour Force Survey(1992±1994)

Probit Ð Ð

Main US studiesDuncan andHoffman (1979)

Panel Study of Income Dynamics (1975) LinearProbability�

Ð Ð

Blakemore andLow (1983)

National Longitudinal Study of HighSchool Seniors (1976)

Probit�# Ð NA

Altonji andSpletzer (1991)

National Longitudinal Survey of the HighSchool Class of 1972 (1986)

Probit �# � �

Lynch (1992) National Longitudinal Survey of Youth(1980±1983)

Probit �# Ð Ð

Hill (1995) National Longitudinal Survey of LabourMarket Experience (1984)

Probit NA Ð

Veum (1995) National Longitudinal Survey of Youth(1986±1990)

Probit �# NS NS

Veum (1996) National Longitudinal Survey of Youth(1986±1991)

Probit �# NS NS

Loewenstein andSpletzer (1997)

Current Population Survey (1991) Probit � Ð Ð

National Longitudinal Survey of Youth(1988±1991)

Probit �# NS NS

Notes:1. All models are reduced-form; NS means not statistically significant; NA indicates that the group has not beenexamined in the study.2. `�' indicates that the estimates are from a pooled sample of males and females, with a female dummy variableincluded, `�' indicates that separate determinants of training models have been estimated for whites and non-whitesand `#' indicates that the study has examined young workers only (e.g. under 30 years of age).




firm. It is well known that in Britain non-whites are more likely to be

unemployed, or engaged in self-employment in order to avoid discrimination

and confinement to low status jobs (Clark and Drinkwater, 1998; Metcalf et al.,

1996). However, we are unaware of any studies concerning job transitions or

quit rates amongst non-whites.10

Using a specific sample from the Quarterly Labour Force Survey11 we have

calculated that non-white men are substantially more likely to report quitting

(21�7% of all reasons given, 13�0% for whites), for reasons other than family,

health, personal or early retirement, whilst their propensities to resign are the

same. Furthermore, all non-whites have an increased probability of being made

redundant (34�8% for men, 22�1% for women) compared to whites (29�4% for

men, 12�5% for women). Foreign born non-white men are also more likely to

have lost their job, through redundancy, or dismissal (46�7%) compared to their

native born counterparts (37�5%). Amongst non-white female immigrants,

19�4% gave a reason other than family, health, personal or early retirement for

leaving their last job, compared to only 6�4% of non-white native born females.

The evidence presented above tentatively suggests that, of those who have left

a full-time job in the past year, employers are more likely to have sacked non-

whites, or lost them because they are dissatisfied with their job, than whites. If

employers anticipate these losses they have an incentive to fund less training for

non-white workers. Employers may arrive at this decision over a period of time.

Their first non-white employees are likely to have been recent immigrants who,

because they lack knowledge about local labour market conditions, may be

unaware of where the most profitable job opportunities lie. Recent immigrants

are believed to engage in a variety of employment opportunities, in order to

acquire location-specific human capital, and are more likely to experience

unsuccessful job outcomes (Chiswick, 1982; see also Wheatley Price, 1998a,

1998b, for British evidence). This initial experience of non-white immigrants

may cloud employers' future judgements about non-white employees partially

explaining why they might engage in statistical discrimination.

In addition, since the majority of non-whites in Britain were born abroad,

many ethnic minority workers may not be fluent communicators in the English

language (Berthoud and Modood, 1997), reducing their likelihood of

successfully completing a training programme and diminishing their ability to

apply learnt skills. Similarly, employers may be uncertain as to the quality of any

education or experience received by immigrant employees in their native country

10 In the US there is considerable evidence suggesting that non-whites have a significantlyhigher probability of job separation (e.g. Lynch, 1991, Lowenstein and Spletzer, 1997).

11 The question on reasons for leaving your last job is only asked of individuals who had beenemployed in the last eight years and are currently not working. In order to eliminate the long-term unemployed or economically inactive and to approximate the time period of this study weselected only those individuals who reported being in full-time employment 12 months ago.However, this information is only available in the Spring Quarterly Labour Force Surveys (wepooled those from 1992, 1993 and 1994). Furthermore, 25±30% of interviewees did not respondto the question about reasons for leaving their last job. Given the peculiar sample and smallsample sizes (a total of 5 456 male and female, white and non-white, individuals) these figuresshould be treated with appropriate caution.




(Duncan and Hoffman, 1979).12 Thirdly, the unobserved characteristics of

foreign born employees may be systematically different from those of British

born workers, due to self-selection processes associated with migration (see

Borjas, 1994).

III DATA AND TRAINING CHARACTERISTICS

In this paper we focus on full-time male and female employees, of working age.

Eight consecutive quarters of the Quarterly Labour Force Survey (QLFS) of the

United Kingdom are pooled together (1992 Quarter 4±1994 Quarter 3),13 in

order to gain statistically reliable sample sizes of non-white employees (2 300

males and 2 143 females) for their separate analysis. The QLFS is the only

British data set that enables this to be done and which also asks all employees

about work-related training information. In particular, it allows us to identify

whether the individual reports having undertaken any work-related training in

the four weeks prior to interview, who funded the training spell and whether

their current employer has ever offered an employee training.14

Table 2 describes the incidence of training offers and outcomes experienced by

the white and non-white employees in our dataset. It shows that both non-white

men (43�7%) and non-white women (45�5%) are less likely to have been offered

training by their current employer than white men (49�8%) and women

(49�4%). There are also ethnic differences in the likelihood of having received

training in the four weeks prior to interview. The incidence of all training

amongst non-white males is 11�1%, compared with 13�1% for white males,

whilst 14�4% of non-white females, and 17�5% of white females, report having

undertaken training recently.

The employer funds the vast majority of all training, for all groups. As a

percentage of total training, non-white men self-fund their training activities

to a much greater extent (16�2%) than white men (7�6%), which is perhaps a

response to their lower incidence of employer-funded training (8�5%,

compared with 11�3% for white men). By contrast, whilst non-white women

have a lower incidence of employer-funded training (12�4%, compared to

14�4% for white women), this type of training represents a slightly higher

percentage of their total training (86�1%) than is the case for white females

12 See Shields and Wheatley Price (1998) for some British evidence that the returns, in termsof earnings, to education and experience acquired abroad is significantly different to thatobtained in the UK.

13We use only waves 1 and 5 from the Quarterly Labour Force Surveys 92Q4±93Q3, andonly wave 1 from the next four surveys so that no double-counting of individuals occurs. Thosestill in school, in other full-time education or working less than 30 hours per week were excludedfrom the samples.

14 In order to identify the recipients of employer-funded training we combine the responses tothe questions, `Over the last four weeks, have you taken part in any education or trainingconnected with your job, or a job that you might be able to do in the future?' and `Who paid thefees for this training?'. We use the measure of self-reported ethnicity to distinguish between thewhite and non-white samples. Unfortunately, no information concerning English languageability is available and questions on trade union membership are not asked in most of thequarters in our sample.




(82�2%). This may be due to the greater success white women have in securing

other forms of funding, or the fact that they undertake proportionally more

training for which there is no fee, since there is no difference in the percentage

of self-funding training undertaken by women according to ethnicity.

Table 3 describes the location and length of employer-funded training spells

for the different groups in our sample. As a proportion of the employer-funded

training that they do receive, non-white employees are more likely to be trained

on-the-job (with 70�3% taking place at the employers premises for men, 76�2%for females) in comparison to whites (65�2% and 68�9%, respectively).

Consequently, whites report receiving a greater proportion of their employer-

funded training off-the-job at other employers premises, in private training

centres, at home, in universities and other educational establishments. Since on-

the-job training is typically cheaper to provide and more firm-specific, whilst off-

the-job training is more costly and more general in nature (Green 1993; Lynch,

1992), non-white employees are also disadvantaged in the nature of the training

they receive.

Interestingly, although non-whites in Britain receive less employer-funded

training than whites, the training they do undergo is of longer duration (see

Table 3). Our dataset clearly shows that non-whites receive proportionately less

training lasting less than one week (41�0% for men, 41�2% for women) in

comparison to whites (49�0% and 48�2%, respectively). Since the duration of

training is greater for recent employees (Shields, 1998b), this finding may be due

to the fact that non-whites have been with their employer for less time, on

average, than whites (see Table A1 in the Appendix).

As has already been highlighted, straightforward inferences from the figures

discussed above are difficult due to the different work-related characteristics of

the samples, which are presented in Table A1. Non-whites are, on average, a

year younger and are more likely to have a child under nine years old, be foreign

TABLE 2The incidence of employee training offers and outcomes according to funding source

Males Females

Whites Non-whites Whites Non-whites

Incidence % Incidence % Incidence % Incidence %

Offered training 49�8 Ð 43�7 Ð 49�4 Ð 45�5 Ð

All training 13�1 100 11�1 100 17�5 100 14�4 100Employer-funded 11�3 86�3 8�5 76�6 14�4 82�2 12�4 86�1Self-funded 1�0 7�6 1�8 16�2 1�8 10�3 1�5 10�4Other=No fees 0�8 6�1 0�8 7�2 1�3 7�4 0�5 3�5

Sample size 58 846 2 300 34 738 2 143

Data Source:Authors' own calculations based on subsamples from the Quarterly Labour Force Surveys of the UnitedKingdom, Winter 1992ÐAutumn 1994.




born, possess no or other qualifications and be employed in lower occupational

categories (Clerical, Craft, Sales, Plant etc.) than whites. Interestingly, they are,

however, more likely to possess a higher qualification, be employed in the health

and education sectors and in larger firms than whites. Non-whites are also more

likely to be employed in the hotels=restaurants and transport sectors than

whites, with a lower proportion working in the primary industries. Male non-

white employees are more likely to be married and be employed in the

professions, whilst female non-whites are less likely to be married, and are more

concentrated in associate professional jobs and in the public sector, than their

respective white counterparts.

We now turn to an investigation of how much of the ethnic variation in the

observed incidence of training outcomes and offers can be explained by these

characteristic differences.

IV A MODEL OF TRAINING OUTCOMES AND OFFERS

Within the general human capital framework, we assume that the receipt of

training by an employee is the joint outcome of optimising behaviour on the part

of workers and employers.15 By focussing on employer-funded training, the vast

TABLE 3The location and length of employer-funded training

Males Females

Whites Non-whites Whites Non-whites

Percentage of training by locationEmployers premises 65�2 70�3 68�9 76�2Another employers premises 3�7 3�6 3�8 2�3Private training centre 6�6 6�2 5�4 5�7At home (OU, correspondence) 2�8 2�1 2�2 3�0Polytechnic, FE college, university 13�3 15�4 11�7 10�6Other educational institution 1�3 0�5 1�6 0�8Other 7�1 1�9 6�4 1�4

Percentage of training by lengthLess than 1 week 49�0 41�0 48�2 41�21 week less than 1 month 6�4 5�1 5�4 9�61 month less than 3 months 3�4 2�5 3�5 3�83 months less than 1 year 6�1 8�2 7�4 9�61 year less than 2 years 4�0 4�6 5�6 8�52 years less than 3 years 4�1 8�7 5�4 4�63 years or more 8�3 10�8 5�6 6�9Ongoing=no definite time limit 18�2 17�9 18�8 15�8Sample 6 650 196 5 003 266


15 See Bosworth et al. (1995), for an overview of models of the optimal training decisions ofindividuals and firms within the human capital framework.




majority of all training, our results can be interpreted more clearly. Given the

cross-sectional nature of the QLFS, we are unable to model the structural

framework of the training decision and separate employer and employee

demands for training. Consequently, we estimate reduced-form binomial logistic

regressions,16 which model the probability of an individual, with certain

characteristics, reporting to have received employer-funded training within the

four weeks prior to interview. Separate models are estimated for white (W) and

non-white (NW), male and female, employees, using the method of maximum

likelihood (see Greene, 1993, ch. 21 for details). This approach allows the

impact of independent variables on the probability of receiving employer-funded

training to differ between white and non-white, and male and female, employees.

If we let T� be the unobserved net benefit to the individual and employer fromproviding training, then if T�> 0, a training spell will be recorded by the

individual at interview. Hence, the model used is:

T �W � �WXW � �W (1)

T �NW � �NWXNW � �NW (2)

Tp � 1 iff T � > 0

Tp � 0 iff T � Æ 0

where Tp is a dummy variable indicating receipt of training, X is a vector of the

determinants of training and � is an error term.

Similarly, we also model whether their current employer has ever offered an

employee training. If we assume that an employer would be willing to fund any

training offer that they make, estimates of the determinants of training offers for

whites and non-whites, and their subsequent decomposition, enable us to

determine more accurately the supply-side of the employer-funded training

market (see Oosterbeek, 1998). We assume that an employer will make a

training offer to an employee if the unobserved expected net benefit to the

employer from the training spell is greater than zero. If this is the case an

employee will report having received an offer of training.

We include, as explanatory variables in the models, a number of worker and

firm characteristics which are suggested by theory to affect the training decision

of employers and individuals, and that have been found to be important in

earlier studies (e.g. Green, 1993; Greenhalgh and Mavrotas, 1996; Shields,

1998a). These are the employee's age, their marital status, whether they have a

young dependant child, their highest qualification level, occupational status,

length of job tenure and whether the job is considered permanent or temporary.

Employer characteristics of industry and size of firm are also included, together

with seven regional, one year and three seasonal dummy variables. Given the

16 Since there is no theoretical reason for choosing a logistic rather than a probit model wefollow the other British studies in estimating logistic models (see Table 1). This is preferable tonon-parametric models such as MSCORE which generally suffer from imprecisely estimatedcoefficients making interpretation of the results difficult. However, we have estimated ourmodels using such techniques and the signs of the resulting coefficients are generally the same asthose from our logistic models.




theoretical reasons why a rational employer might be less willing to fund

training for immigrant workers, and in the absence of information on language

ability or the quality of education, we include a dummy variable indicating

foreign born individuals in our models. This is an attempt to capture the

expected reduced probability of training for these workers.

One problem with non-linear discrete models is that the estimated coefficients

and their associated marginal effects are difficult to interpret. This is especially

so if dummy variables are used (Greene 1993, p. 641). For this reason, the

predicted probabilities (PP) of each category variable have been simulated,

whilst holding the other variables at their respective sample mean values.

However, differences in the predicted probabilities between white and non-white

groups are a composite of differences due to different average group

characteristics and those arising from the different estimated coefficients. We

can simulate the effect of treating non-whites as if they were whites by applying

the white coefficient structure to average non-white characteristics and

calculating the resulting predicted probabilities (denoted PP�). Hence we can

identify the differences, for each categorical variable, that are due to different

characteristics (by comparing the white sample PP with PP�) and those due to

different coefficients (non-white PP with PP�).We examine the hypothesis that whites and non-whites, possessing the same

personal and work-related characteristics, have an unequal chance of receiving,

or being offered, employer-funded training by decomposing the logistic

estimates, using a familiar format (e.g. Green, 1993; Shields, 1998a). Following

Gomulka and Stern (1990), the following estimate is obtained:

TÃWÿTÃNW� [ :::P ( Ã�W,XW)ÿ :::P ( Ã�W,XNW)]

� [ :::P ( Ã�W,XNW)ÿ :::P ( Ã�NW,XNW)] (3)

where Ã� is the vector of coefficient estimates from the appropriate logistic

regression and X is a vector of the mean characteristics of the sample. TÃW and

TÃNW are the respective average of the predicted training probabilities for whites

and non-whites.:::P ( Ã�w,Xw) is the predicted probability of undertaking (or being

offered) employer-funded training obtained using the coefficients estimated for

white employees and their average sample characteristics. The other terms have

similar meanings. The first bracketed term provides an estimate of the difference

in the mean training outcome (or offer) probability, between whites and non-

whites, due to differences in their average sample characteristics, and the second

term in brackets indicates that part which is due to differences in their estimated

coefficient structures.

Equation (3) (and the calculation of PP�) assumes that the underlying

association between the measured characteristics and employer-funded training,

which would exist in the absence of any differential behaviour on the part of

individuals and employers, is represented by the white coefficient structure.

There are a number of ways to derive a pooled non-discriminatory coefficient

structure, using both white and non-white coefficients. However, we believe that

using the white coefficients as the non-discriminatory base is a reasonable




assumption given that non-whites represent less than 4% of our combined

sample (see also Blackaby et al., 1997).

Before we discuss our results it is important to note two potential problemsÐ

selection (see Green, 1993) and endogeneity (see Blundell et al., 1996)Ðboth of

which can lead to biased coefficient estimates. In the training context, selection

bias is generated through the existence of unobservable determinants of training

which are correlated with unobservable determinants of current employment.

This effect may be different for males and females, whites and non-whites. This

implies that the determinants of training analysis may be improved by

incorporating a sample selection correction procedure, which estimates the

training probabilities conditional on individuals' employment. This would

require modelling the employment decision as a choice between employment,

self-employment, unemployment and various inactive states. However, the

QLFS does not enable us to empirically distinguish between these outcomes.17

Perhaps the more important potential bias is due to the possible endogeneity

of some of the explanatory variables. In the context of training, endogeneity

arises if there exists some common unobservable factors (e.g. ability or

motivation) that influence training participation and one or more of the

explanatory variables in the determinants of training model (Blundell et al.,

1996). It might be the case, for example, that the individuals who are most

motivated for training are more likely to change jobs in order to access

employer-funded training opportunities. This would suggest that the coefficient

on increased job tenure would be downwardly biased in our determinants of

training model. Alternatively, it may be the case that employers are more likely

to train the most motivated workers in order to retain their services. This, of

course, would have a converse effect on the estimated job tenure parameters.

Similarly, if it is the more able who obtain higher qualifications, and are more

likely to successfully complete training spells, then the coefficients on the

qualification variables in our models will over-estimate their importance. Other

variables, which might be affected by similar endogeneity concerns, are

occupational status and firm size, in the training outcomes model, as well as

the job tenure and permanent job dummy variables, in the training offers

model.18

17 Furthermore, it is questionable whether the incorporation of such a selectivity model wouldresult in more reliable estimates of the white= non-white training differential since theemployment outcome may itself be associated with the same type of discriminatory process thatis examined in this paper (Green, 1993).

18 In principle the method of instrumental variables (IV) could be used to control forpotential endogeneity if there were suitable instruments (variables which are significantlycorrelated with the endogenous variables but not with training outcomes or offers) in the QLFS.Alternatively, we could construct separate equations for each of our potentially endogenousvariables, with the unobservable heterogeneity terms linking these variables to the trainingdecision being accommodated using simulated maximum likelihood (see Pudney and Shields,1997, for an example of this method). This approach requires both a formal model for each ofthe endogenous variables and the covariates to identify each equation. Unfortunately, theQLFS does not contain enough suitable information for either approach to be attempted.




V THE DETERMINANTS OF EMPLOYER-FUNDED TRAINING

Recent training outcomes

The binomial logistic regression results, which estimate the determinants of an

individual reporting to have undertaken employer-funded training in the

previous four weeks, are presented in Table A2 for males and in Table A3 for

females.19 The estimates of the training model, for working age employees in

Britain in 1993±4, are in substantial agreement with previous studies and show a

marked consistency across all groups.20 In particular, regardless of ethnicity or

sex, an individual is more likely to have received employer-funded training the

younger and the better qualified they are. In addition, higher occupational

status, working in larger firms and being employed in the finance and real estate,

or health and education, sector significantly increases the likelihood of an

employee being trained by their employer. By contrast, marital status and the

presence of young dependent children in the household do not significantly

affect the probability of receiving such training.

Uniquely amongst men, white employees are significantly less likely to have

undertaken employer-funded training recently if they are employed in the

wholesale and retail sector. However, if they are employed in the transport

sector or have spent less than two years with the current employer they are

significantly more likely to have done so. Only amongst non-white males is the

foreign born dummy variable statistically significant. Lack of English language

fluency, and the possession of unfamiliar qualifications, may cause these non-

white immigrants to receive less training than their native born counterparts.

White females who work in the transport or public administration sectors,

who were recruited within the previous two years or who possess other

qualifications, are significantly more likely to have received employer-funded

training recently, compared to those in the respective base groups. By contrast,

white women employed in the craft and related sector and in small firms are less

likely to have received such training. No such differences are present amongst

non-white females. However, non-white females who are employed in the

primary industries are significantly more likely to be trained than those in the

manufacturing sector.

The predicted probabilities (PP), calculated for a person with the average

characteristics of each group, reveal the separate effect of each characteristic on

the likelihood of having received employer-funded training in the previous four

weeks. It is immediately noticeable that a non-white male, with average

characteristics, is predicted to receive less training than an average white male in

every case, except for the Health and Education sector. Similarly, for females,

non-whites are predicted to receive less training in every category, except the

19 For all the female groups, due to their differing occupational and industrial distribution, aslightly different model specification is used.

20As in the training literature (e.g. Green, 1993; Lynch, 1992; Royalty, 1996) we also estimatetrinomial logistic models of the determinants of on-the-job and off-the-job training and findconsiderable consistency across the training types.




four highest occupational groups and in primary industries where they have

predicted probabilities of training 3±6 percentage points higher than white

employees. However, as mentioned earlier, such comparisons compound two

separate effects.

In order to eliminate the effect of average group characteristic differences,21

we will compare the simulated non-white predicted probabilities with those

calculated assuming non-whites have been rewarded, in terms of recent

employer-funded training, as if they were whites (PP�). For males the differencesin these predicted probabilities is particularly striking for those with degree or

higher qualifications (non-white PP� 0�076, PP� � 0�144), those employed in

sales and personal occupations (0�062, PP� � 0�130) and workers in the Other

industrial sector (0�029, PP� � 0�102). Larger than average differences (>0�044),due to coefficients, are also evident in the predicted probabilities associated with

being foreign born, possessing `O'-level=GSCE highest qualifications and

working in the manufacturing, hotel and restaurant or finance and real estate

sectors.

Non-white male employees are predicted to receive marginally more employ-

er-funded training, than if they were treated as whites, in the health and

education sector, with those possessing A-levels or equivalent highest qualifica-

tions predicted to receive only slightly less. Small (< 0�025) differences in

predicted training outcomes, which are attributable to coefficients, are also

found amongst those men with higher vocational qualifications, those working

in the primary industries and employees in jobs which are not permanent.

Non-white female employees are predicted to receive substantially less

employer-funded training, than if they were treated as whites, if they work in

the public administration sector (non-white PP� 0�070, PP� � 0�161), have beenwith their current firm for less than one year (0�082, PP� � 0�135) or only 1±2years (0�069, PP� � 0�134). The former finding is particularly interesting since

equal opportunities policies are supposed to be rigorously enforced in the public

sector. Similar predicted training gaps (�0�05) are evident for non-white femaleswho have children under the age of nine years old, those who are native born,

possess degree or higher qualifications or who are employed in the transport

sector.

Interestingly, non-white females in the four highest occupational categories,

and those employed in the primary industries, are predicted to be much more

likely to receive employer-funded training when treated as non-whites rather

than as whites. This is particularly true of those who are in managerial positions

(non-white PP� 0�26, PP� � 0�148). Furthermore, there are only small (Å0�02)differences in the predicted probability of training, due to differences in

coefficients, for non-white females who possess highest qualifications cate-

gorised as `A'-levels or equivalent, secondary vocational or no qualifications. In

addition, non-white women who are foreign born, working in the finance and

21 In most cases characteristic differences account for very little of the ethnic differences inpredicted probabilities (compare the white sample PP with PP�). This is not surprising given thesimilarity of the group means (Table A1).




real estate or health and education sectors, employed in small firms or who have

been with their current employer for at least five years are predicted to receive

similar amounts of recent training as whites.

Offers of training by the employer

The binomial logistic regression estimates of the determinants of ever having

been offered training by the current employer are presented in Table A4 for

males and in Table A5 for females. Using the same explanatory variables as

before,22 our results indicate that the significant effects of age, highest

qualification, occupational status and firm size on the probability of receiving

a training offer are similar (in terms of sign) to their effect on training outcomes.

These findings can be explained with reference to the supply (employer) side

theories concerning the determinants of training (see the first sub-section of

Section II). Furthermore, being married significantly increases the likelihood of

having received an offer of training for whites, but not for non-whites. As with

recent training outcomes, the coefficient on the dummy variable indicating the

presence of young dependent children is not statistically significant for any of

the samples.

Interestingly, the coefficient on the foreign born dummy variable is now

uniformly negative and, for males, statistically significant. Evidently, a lack of

English language ability, poor or unfamiliar foreign qualifications or the fear of

higher quit rates make employers inclined to offer less training to immigrant

workers in Britain. All male employees working in the wholesale and retail,

hotels and restaurants and other industrial sectors, and also non-white men in

the health and education sector, are significantly less likely to have been offered

training by their current employer, compared to their counterparts in the

manufacturing sector. In addition, white men employed in the transport and

public administration sectors are significantly more likely to have been the

subject of a training offer, which is not the case for non-whites.

Amongst females the results are more consistent. Offers of training are

significantly more probable for women employees working in the transport,

finance and real estate, public administration and health and education sectors,

regardless of ethnicity. Not surprisingly, for all employees, being in a non-

permanent job, or having been recently recruited, significantly reduces the

likelihood of having been offered training by the current employer. Straightfor-

ward comparison of the predicted probabilities for each group indicates than an

average non-white is less likely to have been offered training by their current

employer, compared to an average white employee, according to virtually every

category variable. The notable exceptions are having job tenure of less than one

22 In order to reduce possible endogeneity concerns we also estimated a reduced specificationof the model (see, for example, Booth, 1993; Blundell et al., 1996). Therefore we dropped thejob tenure, not permanent, married and child (ren) under 9 years dummy variables obtainingsimilar estimates of the determinants of training offers. The resulting decompositions are verysimilar with, for both men and women, a fractionally larger predicted gap in training offers andthe proportion explained by coefficients rising by approximately 10%.




year (less than two years for women) and, for females only, possession of higher

vocational qualifications or working at the managerial level.

A comparison of the non-white predicted probability with that of PP� revealsthat non-white men are much more likely to have been offered training by their

current employer if they are working in an associate professional job (non-white

PP� 0�560, PP� � 0�506) or have been recruited to the firm in the last year

(0�337, PP� � 0�291), compared to if they had been treated as white men.

Conversely, non-white men with A-level or equivalent or other highest

qualifications or those working in clerical or sales and personal occupations

are predicted to be at least 5% less likely to have been offered training than if

they had been treated as their white colleagues were. The same is true of non-

white men employed in the finance and real estate, health and education or other

sectors, in small firms or with one to two years or more than five years of job

experience with their current firm.

Non-white females who possess higher vocational qualifications (non-white

PP� 0�500, PP� � 0�475), work in managerial positions (0�551, PP� � 0�510) orhave been with their current firm for less than two years (0�389, PP� � 0�355 forless than one year and 0�463, PP� � 0�435 for one to two years) are more likely tohave been offered training, than if they were white. However, the greatest

disadvantage in terms of training offers (over 5% predicted probability

differential) occurs for those non-white women who possess secondary vocational,

other or no qualifications, who work in sales and personal jobs, are employed in

the manufacturing sector or who have been with their firm for at least five years.

VI DISCUSSION OF DECOMPOSITION RESULTS

The results from the decomposition analysis, of both binomial and trinomial

logistic regression estimates of the determinants of recent training outcomes23

and that of training offers made by the current employer, are provided in

Table 4. The difference between an average white and an average non-white

employee's predicted probability of receiving employer-funded training is 0�044for males and 0�034 for females, whilst the ethnic differences in offers are 0�067and 0�045, respectively.Decomposing these differences in mean predicted probability, we find that the

component attributable to differences in work-related characteristics explains

only 9% of the gap in the provision of all employer-funded training for males

and just 3% of that for females. Differences due to coefficients account for 97%

of the disadvantage experienced by non-white female employees in all recent

training outcomes, 96% of the on-the-job training gap and 94% of that for off-

the-job training; 91% of the male ethnic differential in all recent training and

67% of the male off-the-job training gap is accounted for by coefficient

differences. In the case of on-the-job training, non-white males are found to have

23 The trinomial estimates are not reported here. For men the relevant tables can be found inShields and Wheatley Price (1997) and the results for females are available from the authors onrequest.




characteristics that suggest they should receive more of such training than

whites. To put these findings in a UK context, if there were no coefficient

differences between white and non-whites, 18 000 more non-white male

employees and over 12 000 more non-white females would have reported

receiving employer-funded training in our data-set.24

These findings strongly suggest white and non-white workers, who possess the

same observable personal and work-related characteristics, do not have an equal

chance of receiving employer-funded training. However, as we suggested in the

second sub-section of Section II, part of the unexplained difference in mean

predicted training probability may arise if employers believe that non-whites are

more likely to quit the firm. In addition, employers may anticipate that non-white

employees are less likely to successfully complete a training spell, perhaps due to

poor English language ability or uncertainty about the quality of their foreign

qualifications. Alternatively, non-whites may be less likely to put themselves

forward for training if they seek general, rather than firm-specific, labour market

skills or if they believe they will receive a poor return to training because of

discrimination elsewhere in the labour market. The final possible explanation for

unexplained ethnic differences, and the usual conclusion to similar analyses of other

labour market outcomes (e.g. Blackaby et al., 1997), is that employers discriminate,

according to ethnicity, in their willingness to fund work-related training.

Discriminatory practices in the provision of training opportunities for ethnic

minority workers have been specifically outlawed in the United Kingdom since

the 1976 Race Relations Act, introduced nearly two decades before the data we

have used in this study was collected. It is also prohibited in the Commission for

Racial Equality Code of Practice in Employment (approved by parliament in

TABLE 4Decomposition of the ethnic employer-funded training differential

Males Females

Recent trainingoutcomes Offers

Recent trainingoutcomes Offers

All On Off All On Off

Mean predicted probabilityWhites 0�108 0�069 0�041 0�495 0�124 0�092 0�035 0�496Non-Whites 0�064 0�038 0�021 0�428 0�090 0�065 0�018 0�451Difference In mean probability 0�044 0�031 0�020 0�067 0�034 0�027 0�017 0�045Tw ÿ TNW

Difference due to coefficients 0�040 0�035 0�014 0�040 0�033 0�026 0�016 0�021{:::P ( Ã�W,XNW)ÿ :::

P ( Ã�NW,XNW)} (91%) (112%) (67%) (59%) (97%) (96%) (94%) (48%)Difference due to characteristics 0.004 ÿ0.04 0.06 0.027 0.001 0.001 0.001 0.024{:::P ( Ã�W,XW)ÿ :::

P ( Ã�W,XNW)} (9%) (ÿ12%) (33%) (41%) (3%) (4%) (6%) (52%)

Data Source:

24Assuming a population of 450, 000 male and 370, 000 female non-white British employees,aged 16±64 (footnote 3).




1984). Furthermore, European Community law states that the principle of equal

treatment should apply to vocational training. In addition, by the end of 1991,

all the 82 Training and Enterprise Councils in England and Wales and the 22

Local Enterprise Companies in Scotland had a contractual responsibility to

ensure equality of opportunity in their training provision with regard to race and

gender (Commission for Racial Equality, 1991).

However, the legal requirements outlined above are not being fulfilled since

only 51% of large firms, and 13% of their subsidiaries, had regular ethnic

monitoring in 1994 and only 45% and 13%, respectively, had implemented an

action plan to realise their racial equality policies. Furthermore, there is a legal

allowance for employers to take `positive action' in order to meet the training

needs of any particular racial group, including the provision of English language

instruction and communication skills, in the Race Relations Act of 1976

(Palmer, 1992). Nevertheless, only 12% of large companies, and 3% of their

subsidiaries, provided training under the auspices of positive action (Commis-

sion for Racial Equality, 1995).

The investigation of offers of training, by the current employer, attempts to

capture the supply side of the employer-funded training market. Of the 0�067difference in predicted probability of having received a training offer from the

current employer for males, only 41% can be explained by differences in average

characteristics between whites and non-whites. Amongst females the predicted

gap is 0�045, of which 52% is explained by ethnic differences in characteristics.

Therefore, 59% of the male difference in training offers, and 48% of the female

gap, are attributable to differential treatment of white and non-white employees,

by their employees. This may be due to their discriminatory attitudes or result

from beliefs about relative quit rates or the relative probabilities of successfully

completing a training spell.

It follows, that if all the training offers ever made by the current employer had

been accepted by the employees, and if the period over which we have observed

training outcomes (i.e. the four weeks prior to interview) is a random typical

sample of all employer-funded training outcomes, we would expect that only

59% of the difference in the mean predicted probability of recent training

outcomes for males, and 48% of that for females, would be due to coefficients

differences. In other words differential treatment of whites and non-whites by

employers (on the supply side) would account for all the unexplained differences

in the incidence of employer-funded training. However, our results suggest that

this is not the case. Non-whites (on the demand side) appear to be less willing to

undertake employer-funded training than whites. This factor may account for as

much as one-third of the ethnic training gap for men, and nearly half that for

women, which would usually be ascribed to discrimination on the part of

employers.

VII CONCLUSIONS

This is the first paper to examine the incidence and determinants of employer-

funded training for full-time white and non-white employees in Britain, using




data from the Quarterly Labour Force of the United Kingdom. We have

investigated recent training outcomes and training offers made by the current

employer using logistic models. Decomposition analysis has allowed us to

attribute differences in the mean predicted probabilities to differences due to

employee characteristics and that proportion due to variations in the coefficient

structure (usually ascribed to discrimination). Our main findings are that:

(i) Non-white employees are less likely to report having undertaken an

employer-funded training spell, in the four weeks prior to interview, than

whites.

(ii) Non-white males and females are less likely to have ever been offered

training by their current employer, compared to whites.

(iii) The main determinants of recent employer-funded training outcomes, and

of training offers made by employers, are substantially the same for white

and non-white, male and female, employees in Britain.

(iv) Less than 9% of the ethnic differences in the mean predicted probability of

recent training outcomes, for both male and female employees, can be

explained by differences in average group characteristics.

(v) A substantial proportion of the unexplained component of the mean

predicted training differential (upto one-third for males, approximately

one-half for females) is attributable to differences in the demand for

training by white and non-white employees. The remainder arises from the

differential treatment of employees, in terms of offering training, by

employers according to ethnicity. This latter factor may be due to racially

motivated discrimination or statistical discrimination, which may arise

from rationally held beliefs about higher quit rates amongst non-white

employees or their abilities to successfully complete training spells.

Ethnic differences in training opportunities have not been as apparent, or

received as much public attention, as the disadvantage experienced by Britain's

ethnic minorities in their employment, promotion and wage outcomes. Indeed,

most firms in Britain do not have written criteria by which training

opportunities are allocated, in contrast to the case of employment. They also

do not provide equal opportunities training for the key decision-makers within

the firm that are involved in personnel matters (Commission for Racial

Equality, 1987). As a result, the equal opportunities monitoring of training

outcomes has been difficult to undertake.

Our findings suggest that there is unequal access to employer-funded training

in Britain. This finding may be explained partly by the poorer response of non-

whites to training opportunities, partly by employers beliefs about the lower

average returns from training non-white employees and the remainder by

discrimination on the part of employers. Nevertheless, because of the established

links between work-related training and reduced unemployment probabilities,

improved promotional opportunities, more favourable occupational attainment

and higher wages, ethnic minorities may be permanently disadvantaged in the

British labour market as a result of undertaking less employer-funded training.

However, irrespective of the original sources of labour market disadvantage, a




vicious circle may have emerged, amongst Britains' ethnic minorities, linking

higher unemployment and quit rates with a lower incidence of employer-funded

training, poorer promotional opportunities, reduced occupational status and

lower wages. Tackling the deficiency in employer-funded training, for non-

whites, may provide an opportunity to break this pattern.

ACKNOWLEDGEMENTS

The authors are grateful to the editor Robert Hart, Martin Hoskins, Steve

Pudney and two anonymous referees for valuable comments, which have greatly

improved the final version of this paper. The Labour Force Survey of the United

Kingdom, Crown Copyright, is used with permission of the depositor (The

Office for National Statistics) and supplier (the Data Archive at the University

of Essex). The usual disclaimer applies.

APPENDIX

TABLE A1Employee sample characteristics

Male Female

Variable White Non-white White Non-white

Age 38�7 37�6 36�2 35�5Single 0�266 0�240 0�355 0�388Married 0�734 0�760 0�645 0�612No child < 9 years 0�839 0�748 0�921 0�830Child (ren) under 9 years 0�161 0�252 0�079 0�170Native born 0�964 0�218 0�953 0�431Foreign born 0�036 0�782 0�047 0�569Degree or higher 0�152 0�194 0�129 0�143Higher Vocational 0�142 0�103 0�157 0�175`A' level or equivalent 0�064 0�043 0�080 0�062Secondary Vocational 0�230 0�125 0�114 0�083`O' level=GCSE 0�171 0�120 0�285 0�212Other 0�073 0�170 0�054 0�113No Qualifications 0�168 0�245 0�181 0�212Managerial 0�200 0�148 0�146 0�098Professional 0�111 0�130 0�114 0�104Associate Professional 0�084 0�075 0�119 0�143Clerical 0�078 0�094 0�310 0�282Craft and Related 0�195 0�158 0�038 0�062Sales and Personal 0�110 0�140 0�174 0�178Plant and Machinery 0�222 0�255 0�099 0�133Primary industries 0�127 0�043 0�032 0�015Manufacturing 0�323 0�315 0�178 0�173Wholesale and Retail 0�125 0�122 0�127 0�109Hotels and Restaurants 0�018 0�082 0�035 0�048Transport 0�097 0�121 0�042 0�054Finance and Real Estate 0�119 0�120 0�165 0�141Public Administration 0�079 0�052 0�086 0�116

(continued)




TABLE A1Continued

Male Female

Variable White Non-white White Non-white

Health and Education 0�076 0�116 0�291 0�303Other 0�036 0�029 0�045 0�041>25 workers 0�606 0�641 0�565 0�623< 25 workers 0�394 0�359 0�435 0�377< 1 year with firm 0�180 0�210 0�213 0�2041±2 years 0�072 0�079 0�089 0�1082±5 years 0�201 0�271 0�256 0�265> 5 years 0�547 0�440 0�442 0�423Permanent job 0�959 0�943 0�940 0�930Not permanent 0�041 0�057 0�060 0�070North 0�149 0�076 0�144 0�044East Midlands 0�121 0�091 0�113 0�065London 0�090 0�428 0�112 0�414Rest of South East 0�204 0�140 0�208 0�110South West 0�083 0�022 0�080 0�014West Midlands 0�095 0�139 0�090 0�098North West 0�112 0�070 0�118 0�037Wales and Scotland 0�146 0�034 0�135 0�013

Sample Size 58 847 2 300 34 738 2 143


TABLE A2The determinants of employer-funded training for white and non-white male employees: binary logisticestimates

White Non-White

Variable � S.E. PP � S.E. PP PP�

Age ÿ0�0324 0�0015� Ð 0�0381 0�010� Ð ÐSingle Ð Ð 0�109 Ð Ð 0�069 0�106Married ÿ0�0170 0�0325 0�108 ÿ0�0962 0�1823 0�063 0�104No child <9 years Ð Ð 0�110 Ð Ð 0�065 0�106Child (ren) under 9 years ÿ0�458 0�0353 0�105 ÿ0�0341 0�1889 0�063 0�102Native born Ð Ð 0�108 Ð Ð 0�077 0�103Foreign born 0�0240 0�0677 0�110 ÿ0�2916 0�1415# 0�059 0�105Degree or higher 0�9094 0�0617� 0�140 0�9388 0�3650� 0�076 0�144Higher Vocational 1�0016 0�0584� 0�151 1�5383 0�3595� 0�131 0�155`A' level or equivalent 0�8970 0�0669� 0�138 1�6019 0�4138� 0�138 0�142Secondary Vocational 0�5834 0�0567� 0�105 0�9825 0�3771� 0�080 0�108`O' level=GCSE 0�6508 0�0578� 0�111 0�7758 0�3820# 0�066 0�115Other 0�4626 0�0767� 0�094 0�7513 0�3772# 0�064 0�097

(continued)




TABLE A2Continued

White Non-White


No Qualifications Ð Ð 0�061 Ð Ð 0�031 0�063Managerial 0�8075 0�0519� 0�131 1�1199 0�3471� 0�085 0�129Professional 0�9896 0�0592� 0�153 1�5072 0�3603� 0�120 0�151Associate Professional 0�8972 0�0577� 0�141 1�3266 0�3618� 0�103 0�139Clerical 0�5585 0�0606� 0�105 0�9306 0�3565� 0�071 0�103Craft and Related 0�5780 0�0508� 0�107 1�0114 0�3392� 0�077 0�105Sales and Personal 0�8200 0�0564� 0�132 0�7782 0�3745# 0�062 0�130Plant and Machinery Ð Ð 0�063 Ð Ð 0�029 0�062Primary industries 0�0680 0�0453 0�103 0�5587 0�3596 0�082 0�099Manufacturing Ð Ð 0�097 Ð Ð 0�049 0�094Wholesale and Retail ÿ0�1675 0�0494� 0�083 ÿ0�0465 0�3109 0�046 0�080Hotels and Restaurants ÿ0�0498 0�1022 0�093 ÿ0�1112 0�4555 0�044 0�089Transport 0�2713 0�0509� 0�123 0�4723 0�2958 0�076 0�119Finance and Real Estate 0�2586 0�0433� 0�122 0�5131 0�2589# 0�079 0�118Public Administration 0�5066 0�0483� 0�151 0�8494 0�3206� 0�107 0�146Health and Education 0�4619 0�0492� 0�146 1�2160 0�2709� 0�147 0�141Other 0�0914 0�0748 0�105 ÿ0�5199 0�5709 0�029 0�102>25 workers Ð Ð 0�121 Ð Ð 0�076 0�116<25 workers ÿ0�3140 0�0284� 0�091 ÿ0�5114 0�1737� 0�047 0�087<1 year with firm 0�1191 0�0379� 0�116 0�2084 0�2253 0�070 0�1121±2 years 0�1307 0�0489� 0�117 0�2145 0�2784 0�070 0�1132±5 years 0�0314 0�0343 0�108 0�2321 0�1931 0�071 0�104>5 years Ð Ð 0�105 Ð Ð 0�057 0�101Permanent job Ð Ð 0�108 Ð Ð 0�063 0�105Not permanent 0�0209 0�0621 0�110 0�2968 0�2584 0�083 0�107Constant ÿ1�9677 ÿ2�4213Average Individual 0�108 0�064 0�105

Sample size 58 847 2 300ÿ2LL ÿ21 138 ÿ655ÿ2LL (slopes� 0) ÿ22 824 ÿ803Model �2 (40 d.f ) 3 371� ÿ294�

Notes:1 �Indicates significant at the 1% level, # significant at the 5% level;Ðshows the omitted category.2 Seven regional, three seasonal and one year dummies are also included in each model.3 PP� is the predicted probability of a training offer, for an average non-white, calculated using the white coefficientstructure i.e. ( Ã�W,XNW).Data Source:Authors' own calculations based on subsamples from the Quarterly Labour Force Surveys of the United Kingdom,Winter 1992ÐAutumn 1994.




TABLE A3The determinants of employer training offers for white and non-white female employees: binary logisticestimates

White Non-White


Age ÿ0�0132 0�0018� Ð ÿ0�0266 0�0099� Ð ÐSingle Ð Ð 0�121 Ð Ð 0�096 0�119Married 0�0496 0�0355 0�126 ÿ0�1187 0�1503 0�086 0�125No child< 9 years Ð Ð 0�123 Ð Ð 0�091 0�120Child (ren) under 9 years 0�1333 0�0556# 0�138 ÿ0�1036 0�1952 0�083 0�135Native born Ð Ð 0�125 Ð Ð 0�079 0�128Foreign born ÿ0�0925 0�0779 0�115 0�2434 0�1748 0�099 0�118Degree or higher 1�0331 0�0826� 0�165 0�9533 0�3607� 0�116 0�166Higher Vocational 1�0556 0�0773� 0�168 1�1073 0�3439� 0�133 0�169`A' level or equivalent 0�9600 0�0852� 0�155 1�1559 0�3965� 0�139 0�156Secondary Vocational 0�7389 0�0821� 0�128 1�0026 0�3878� 0�122 0�129`O' level=GCSE 0�7413 0�0723� 0�129 0�6468 0�3264# 0�088 0�130Other 0�5906 0�1017� 0�113 0�3676 0�4054 0�068 0�113No Qualifications Ð Ð 0�066 Ð Ð 0�048 0�066Managerial 0�3856 0�0592� 0�147 0�9083 0�2871� 0�226 0�148Professional 0�7010 0�0672� 0�191 0�9325 0�2977� 0�230 0�193Associate Professional 0�6397 0�0607� 0�182 0�9325 0�2772� 0�230 0�183Clerical ÿ0�140 0�0537 0�104 0�2256 0�2631 0�128 0�104Craft and Related ÿ0�5330 0�1473� 0�064 ÿ0�4604 0�6464 0�042 0�065Sales and Personal Ð Ð 0�105 Ð Ð 0�065 0�106Primary industries 0�2045 0�1072 0�115 1�0223 0�5015# 0�143 0�112Manufacturing Ð Ð 0�096 Ð Ð 0�057 0�093Wholesale=Retail=Hotels etc 0�0145 0�0688 0�097 0�1562 0�3715 0�065 0�094Transport 0�2418 0�0950� 0�119 0�1624 0�4507 0�066 0�116Finance and Real Estate 0�3051 0�0640� 0�126 0�8784 0�3411� 0�126 0�122Public Administration 0�6264 0�0691� 0�166 0�2208 0�3693 0�070 0�161Health and Education 0�5339 0�0594� 0�153 0�9197 0�3237� 0�131 0�149>25 workers Ð Ð 0�135 Ð Ð 0�090 0�132<25 workers ÿ0�2178 0�346� 0�111 ÿ0�222 0�1525 0�089 0�109<1 year with firm 0�1735 0�0474� 0�137 ÿ0�2197 0�2208 0�082 0�1351±2 years 0�1632 0�0595� 0�136 ÿ0�4106 0�2653 0�069 0�1342±5 years 0�0332 0�0420 0�121 ÿ0�1231 0�1846 0�09 0�120>5 years Ð Ð 0�118 Ð Ð 0�100 0�116Permanent job Ð Ð 0�125 Ð Ð 0�090 0�123Not permanent ÿ0�0767 0�0699 0�117 ÿ0�0609 0�2879 0�085 0�115Constant ÿ2�5048 ÿ2�8422Average Individual 0�124 0�090 0�123

Sample size 33 177 2 049ÿ2LL ÿ12 839 ÿ683ÿ2LL (slopes� 0) ÿ13 816 ÿ775Model �2 (36 d.f ) 1 955� ÿ183�

Notes:1 � indicates significant at the 1% level, # significant at the 5% level;Ðshows the omitted category.2 Seven regional, three seasonal and one year dummies are also included in each model.3 PP� is the predicted probability of a training offer, for an average non-white, calculated using the white coefficientstructure i.e. ( Ã�W,XNW).Data Source:Authors' own calculations based on subsamples from the Quarterly Labour Force Surveys of the United Kingdom,Winter 1992ÐAutumn 1994.




TABLE A4The determinants of employer training offers for white and non-white male employees: binary logisticestimates

White Non-White


Age ÿ0�0116 0�0009� Ð 0�0063 0�0054 Ð ÐSingle Ð Ð 0�470 Ð Ð 0�436 0�430Married 0�1376 0�0227� 0�504 ÿ0�0459 0�1216 0�425 0�464No child< 9 years Ð Ð 0�495 Ð Ð 0�429 0�455Child (ren) under 9 years 0�0024 0�0246 0�496 0�0294 0�1095 0�422 0�456Native born Ð Ð 0�496 Ð Ð 0�477 0�475Foreign born ÿ0�1032 0�0476# 0�470 ÿ0�2552 0�1296# 0�414 0�449Degree or higher 0�5266 0�0395� 0�532 0�6169 0�1839� 0�489 0�500Higher Vocational 0�4583 0�0328� 0�515 0�4893 0�1728� 0�457 0�479`A' level or equivalent 0�3970 0�0431� 0�499 0�3114 0�2508 0�413 0�463Secondary Vocational 0�3925 0�0288� 0�498 0�5239 0�1628� 0�466 0�462`O' level=GCSE 0�4550 0�0318� 0�514 0�5423 0�1730� 0�470 0�478Other 0�5598 0�0387� 0�540 0�3278 0�1512# 0�417 0�504No Qualifications Ð Ð 0�401 Ð Ð 0�340 0�367Managerial 0�3666 0�0303� 0�520 0�4797 0�1723� 0�459 0�482Professional 0�4660 0�0399� 0�544 0�5606 0�2085� 0�480 0�507Associate Professional 0�4643 0�0386� 0�544 0�8814 0�2114� 0�560 0�506Clerical 0�3502 0�0375� 0�516 0�2889 0�1851 0�413 0�478Craft and Related 0�2044 0�0284� 0�479 0�4404 0�1497� 0�450 0�441Sales and Personal 0�3322 0�0354� 0�511 0�3117 0�1840 0�418 0�473Plant and Machinery Ð Ð 0�428 Ð Ð 0�345 0�392Primary industries ÿ0�0013 0�0290 0�483 ÿ0�2786 0�2337 0�395 0�446Manufacturing Ð Ð 0�484 Ð Ð 0�463 0�446Wholesale and Retail ÿ0�0260 0�0303 0�478 ÿ0�2757 0�1682 0�396 0�440Hotels and Restaurants ÿ0�1032 0�0696 0�458 ÿ0�6899 0�2318� 0�302 0�421Transport 0�1177 0�0322� 0�513 0�1707 0�1558 0�506 0�480Finance and Real Estate 0�0676 0�315# 0�500 ÿ0�2492 0�1702 0�402 0�463Public Administration 0�3526 0�0376� 0�571 0�3460 0�2286 0�550 0�534Health and Education 0�0942 0�0382# 0�507 ÿ0�3453 0�1771# 0�379 0�470Other ÿ0�1077 0�0489# 0�457 ÿ0�3957 0�2875 0�368 0�419>25 workers Ð Ð 0�537 Ð Ð 0�480 0�494<25 workers ÿ0�4262 0�0187� 0�431 ÿ0�5866 0�1028� 0�339 0�389<1 year with firm ÿ1�0824 0�0271� 0�312 ÿ0�6011 0�1329� 0�337 0�2911 2 years ÿ0�6185 0�0348� 0�419 ÿ0�6511 0�1882� 0�326 0�3952±5 years ÿ0�3517 0�0232� 0�484 ÿ0�1367 0�1131 0�447 0�460>5 years Ð Ð 0�572 Ð Ð 0�481 0�548Permanent job Ð Ð 0�501 Ð Ð 0�439 0�464Not permanent ÿ0�6413 0�0509� 0�346 ÿ0�8219 0�2293� 0�256 0�314Constant 0�2292 ÿ0�3931Average Individual 0�495 0�428 0�455

Sample size 58 847 2 300ÿ2LL 76 379 2 899ÿ2LL (slopes� 0) 81 576 3 152Model �2 (46 d.f ) 5 197� 252�

Notes:1 �Indicates significant at the 1% level, # significant at the 5% level;Ðshows the omitted category.2 Seven regional, three seasonal and one year dummies are also included in each model.3 PP� is the predicted probability of a training offer, for an average non-white, calculated using the white coefficientstructure i.e. ( Ã�W,XNW).Data Source:Authors' own calculations based on subsamples from the Quarterly Labour Force Surveys of the United Kingdom,Winter 1992ÐAutumn 1994.




TABLE A5The determinants of employer training offers for white and non-white female employees: binarylogistic estimates

White Non-White


Age ÿ0�0024 0�0012# Ð 0�0121 0�0061# Ð ÐSingle Ð Ð 0�483 Ð Ð 0�456 0�463Married 0�0799 0�0244� 0�503 ÿ0�0345 0�1014 0�448 0�483No child <9 years Ð Ð 0�497 Ð Ð 0�446 0�478Child (ren) under 9 years ÿ0�0681 0�0415 0�480 0�1228 0�1268 0�476 0�460Native born Ð Ð 0�497 Ð Ð 0�476 0�491Foreign born ÿ0�1177 0�0600# 0�470 ÿ0�1788 0�1216 0�432 0�462Degree or higher 0�3896 0�0530� 0�507 0�7386 0�2115� 0�497 0�484Higher Vocational 0�3537 0�0444� 0�498 0�7507 0�1877� 0�500 0�475`A' level or equivalent 0�4619 0�0513� 0�525 0�7157 0�2369� 0�491 0�502Secondary Vocational 0�3664 0�0432� 0�501 0�4323 0�2070# 0�421 0�478`O' level= GCSE 0�4252 0�0375� 0�516 0�7032 0�1719� 0�488 0�493Other 0�7593 0�0558� 0�598 0�7648 0�1825� 0�503 0�576No Qualifications Ð Ð 0�410 Ð Ð 0�320 0�388Managerial 0�1979 0�0385� 0�525 0�6905 0�1844� 0�551 0�510Professional 0�1844 0�0502� 0�522 0�4863 0�2075# 0�500 0�506Associate Professional 0�0749 0�0440 0�494 0�3187 0�1833 0�458 0�478Clerical 0�1096 0�0325� 0�503 0�4802 0�1444� 0�498 0�487Craft and Related ÿ0�3158 0�0653� 0�398 ÿ0�1722 0�2406 0�341 0�383Sales and Personal Ð Ð 0�476 Ð Ð 0�381 0�459Primary industries 0�0312 0�0654 0�461 0�0466 0�3812 0�387 0�436Manufacturing Ð Ð 0�453 Ð Ð 0�376 0�428Wholesale=Retail=Hotels etc ÿ0�0453 0�0370 0�442 0�0536 0�1711 0�389 0�417Transport 0�3417 0�0594� 0�538 0�4081 0�2028# 0�478 0�513Finance and Real Estate 0�2385 0�0385� 0�512 0�3171 0�1529# 0�453 0�487Public Administration 0�4691 0�0475� 0�570 0�6997 0�1855� 0�548 0�545Health and Education 0�2651 0�0357� 0�519 0�4593 0�1537� 0�488 0�494>25 workers Ð Ð 0�526 Ð Ð 0�474 0�501<25 workers ÿ0�2816 0�0236� 0�456 ÿ0�2462 0�0999# 0�413 0�431<1 year with firm ÿ0�7421 0�0328� 0�374 ÿ0�3155 0�1444# 0�389 0�3551±2 years ÿ0�4074 0�0415� 0�455 ÿ0�0141 0�1640 0�463 0�4352±5 years ÿ0�1923 0�0282� 0�508 0�0149 0�1206 0�470 0�488> 5 years Ð Ð 0�556 Ð Ð 0�466 0�536Permanent job Ð Ð 0�491 Ð Ð 0�470 0�490Not permanent ÿ0�9412 0�0539� 0�288 ÿ1.1641 0�2200� 0�217 0�273Constant ÿ0�0148 ÿ1.5175Average Individual 0�496 0�451 0�475

Sample size 33 177 2049ÿ2LL 46 605 2681ÿ2LL (slopes� 0) 48 958 2934Model �2 (36 d.f ) 2 353� 252�

Notes:1 �Indicates significant at the 1% level, # significant at the 5% level;Ðshows the omitted category.2 Seven regional, three seasonal and one year dummies are also included in each model.3 PP� is the predicted probability of a training offer, for an average non-white, calculated using the whitecoefficient structure i.e. ( Ã�W,XNW).Data Source:Authors' own calculations based on subsamples from the Quarterly Labour Force Surveys of the UnitedKingdom, Winter 1992ÐAutumn 1994.




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Date of receipt of final manuscript: 3 March 1999.



ethnic differences in the incidence and determinants of employer-funded training in britain

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