Changes in the Determinants of Employer-funded Training for Full-time Employees in Britain, 1984–94

OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 60, 2 (1998)0305-9049

CHANGES IN THE DETERMINANTS OF EMPLOYER-FUNDED TRAINING FOR

FULL-TIME EMPLOYEES IN BRITAIN, 1984–1994

Michael Shields†

I. INTRODUCTION

The last decade has witnessed a marked growth of interest in the relation-ships between vocational education, training and economic performance.In Britain, much of this interest has focused on youth training andtraining in early adulthood, and its effect upon the future wages andemployment prospects of participants.1 Fewer studies have examined thedeterminants of training throughout the life-cycle. What literature exists,however, has been quite consistent in a number of its findings — namely,that the probability of an individual receiving training is positively relatedto their qualification level, the size of their employer and their tradeunion membership status, and negatively related to age, part-timeworking and length of job tenure. Determinants which remain morecontentious are whether male employees receive more training thanequivalent females, whether firms in technology-led industries fund moretraining, whether public sector employees receive more training thanprivate sector workers, and whether the incidence of training moves pro-cyclically.

A re-examination of the determinants of employer-funded training,focusing on changes over the decade 1984 to 1994, is valuable for threereasons. Firstly, there has been a substantive shift in the characteristics ofthe British workforce (see Table A1 in the appendix). The ageing of theworkforce would be expected to lead to a decrease in the observedincidence of training. Conversely, the fall in the proportion of the work-

†The author would like to thank Martin Hoskins, Stephen Pudney, Kevin Lee, StephenWheatley Price and an anonymous referee for valuable discussion and comments. TheLabour Force Survey of the United Kingdom, Crown Copyright, is used with permission ofthe depositor (The Department of Education and Employment: Great Britain) and supplier(the Data Archive at the University of Essex). All figures were calculated with SPSS V.6 andExcel V.5. The usual disclaimer applies.

1 See Green et al. (1996a) and Mealli et al. (1996) for examples of the most recentcontributions.

189© Blackwell Publishers Ltd, 1998. Published by Blackwell Publishers, 108 Cowley Road, OxfordOX4 1JF, UK & 350 Main Street, Malden, MA 02148, USA.

force possessing no formal qualifications, and the rise in the proportionwith higher level qualifications, would suggest an increase. The size anddirection of the observed change in training incidence over the lastdecade will partly depend upon the conflicting effects of thesecharacteristcs.

Secondly, there have been considerable changes in the economicenvironment. Growing international competitive pressures, coupled withnotable advances in information and productive technology, have forcedmany firms to re-evaluate their organizational and productive strategiesand consequently, their skill needs. Increased unemployment and greaterjob insecurity have encouraged individuals to consider their stock oflabour market skills and hence their demand for education and training.The decade has seen significant industrial and labour market restructur-ing, with a reduction in the proportion of the workforce employed in theprimary and manufacturing sectors, increased female participation in thelabour force, increased part-time work and a shift towards the use ofmore skilled labour (Machin, 1996).

Thirdly, the Conservative government introduced many initiativesaimed at increasing the incidence of training at the workplace, such as theestablishment of the Training and Enterprise Councils and the introduc-tion of ‘Investors in People’ in 1991. At the core of these policies lies abelief that individuals and firms should take greater responsibility formaintaining and up-grading the skills of the workforce.2 Furthermore,other government policies such as the privatization and contracting-out ofpublic services, the weakening of the trade unions and the introduction ofquality and safety regulations at the workplace may have affected indivi-duals’ and employers’ demands for training.

This paper has three aims: firstly, to highlight what happened toemployer-funded training in Britain between 1984 and 1994; secondly, toidentify changes in the determinants of employer-funded training formale and female full-time employees; and thirdly, to examine the forcesthat have driven any increase in employer-funded training over theperiod.

Section II reviews the British literature on the determinants of training.Section III introduces the data and describes the changes in the incidenceand characteristics of employer-funded training. The econometric anddecomposition methodologies are described in Section IV. Section Vdiscusses the results and Section VI presents some conclusions.

II. THE LITERATURE ON THE DETERMINANTS OF TRAINING

Interest in the determinants of work-related training in Britain beganwith the seminal paper of Greenhalgh and Stewart (1987). Since then a

2 See Booth and Snower (1996) for a discussion of market failure in the training marketand recent government initiatives.

© Blackwell Publishers 1998

190 BULLETIN

number of studies have used either cross-sectional or cohort data fromsurveys, particularly focused on employment and training issues, or fromone of the large continuing government surveys.3 The findings of thesestudies are typically explained in terms of changes in the costs and/or theexpected benefits to the individual and firm of training, within the generalhuman capital framework; they are summarized in Table 1.

It is generally agreed that men have a higher probability of receivingtraining than women (Arulampalam and Booth, 1997; Booth, 1990, 1991;Green, 1993a; Greenhalgh and Stewart, 1987), but that this differentialdiminished throughout the 1980s (Greenhalgh and Mavrotas, 1993, 1996).Green (1993a) argues that between 8 and 45 percent of this differential isdue to gender discrimination, with younger women being especiallyprone.

The probability of training is found to be negatively related to age andpositively related to qualifications. Increased age reduces the expectedbenefits from training to the individual and firm, whilst prior qualifica-tions, viewed as a proxy for ability, are associated with a reduced cost oflearning and a higher likelihood of enhanced productivity from the appli-

3 Similar studies have been undertaken in the USA (e.g. Lynch 1992; Veum, 1996),Australia (e.g. Miller 1994), the Netherlands (e.g. Oosterbeek 1996) and Spain (e.g. Alba-Ramirez 1994).

TABLE 1The Main Findings from the British Literature on the Determinants of Training

Effect on theVariable probability of Level oftype Variable list training agreement

Personal Age µ HFemale µ ?Married + ?Dependent child(s) µ ?

Work-related Qualifications + HNew to job + HTrade union membership + HPart-time worker µ H

Employer Public sector + ?High technology industry + ?Small employer µ HHigh regional unemployment µ ?

Notes:1. A ‘H’ in the final column indicates a relationship which has a general consensus,

whilst a ‘?’ refers to relationships for which conflicting findings are observed.2. Table A2 in the appendix highlights the data sets used by the main studies.


191EMPLOYER-FUNDED TRAINING FOR FULL-TIME EMPLOYEES

cation of acquired skills. The age-training profile for men is steeper thanfor women, which is consistent with the hypothesis that employers aremore reluctant to train younger women, since they are more likely to quitfor child rearing (Green, 1991). The correlation between training andqualifications is most noticeable for female employees (Green, 1993a),and for workers who have received employer-provided training early intheir careers. In contrast, formal qualifications gained after school appearto have no significant effect (Blundell et al., 1996).

A common finding is that full-time employees undertake significantlymore training than those working part-time (e.g. Greenhalgh andMavrotas, 1996). Part-time work has a similar effect to age in that itreduces the time over which the benefits from training can be reaped. Inaddition, the probability of training is found to be highest within the firstsix or twelve months of job tenure, which reflects initial investment in theskills necessary for the job (Green, 1991; Green, 1994a; Greenhalgh andMavrotas, 1994). As expected, large firms invest in training to a greaterextent than small firms, since they can reap both economies of scale andbe more certain of retaining the trainee (Green, 1991, 1993a; Greenhalghand Mavrotas, 1994, 1996; Green et al., 1996b).4 Finally, trade unionmembership increases the likelihood of receiving training,5 since tradeunions provide a collective voice communicating and encouraging thetraining demands of workers (Booth, 1991; Green, 1993b, Greenhalghand Mavratos, 1994; Green et al., 1995, 1996b).6

There is no consensus concerning the impact of marital status on theprobability of training. Greenhalgh and Stewart (1987) find that marriedand post-married men receive more training than single men, whereasGreen (1993a) finds that marriage reduces the probability of training forwomen. Booth (1991) estimates no marital status impact for either sex.Several studies have found that the presence of children in the householdis associated with a significantly lower probability of training (Greenhalghand Stewart, 1987; Booth, 1991; Green, 1993a), with the disadvantagebeing considerably greater for married women than for married men.7

Three areas of disagreement concern the effects of sector of employ-ment, industry and region. Booth (1991) finds that training is higher for

4 Booth (1991) finds this to be true only for female employees. Alternatively, Deloitte et al.(1989) suggest that it is the enterprise size rather than establishment size which is theimportant determinant.

5 Green (1993b) finds this to be true only for small establishments. In contrast, Arulampa-lam et al. (1996) finds trade union membership to be negatively correlated with trainingincidence, and therefore cast doubt upon the adequacy of the Quarterly Labour ForceSurvey’s trade union membership question.

6 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 etal., 1996).

7 Green (1993a) proposes a number of explanations for this. On the demand-side,employers may judge women with family responsibilities to be less worth investing in (morerisky), or on the supply-size, women themselves may feel obliged to forfeit training oppor-tunities owing to other commitments.


192 BULLETIN

employees in the public sector and suggests that this is firstly, becauseprivate sector firms are more constrained by the need to make profits andmay be less willing to finance training, through fear of poaching, andsecondly, private companies are more subject to the economic climate,making redundancies more probable and therefore more expensive.Green (1993a) and Green et al. (1996), however, fail to find this correla-tion. Booth (1991) and Green (1993a) estimate that the industry in whichthe individual is employed has no impact for women, but men employedin ‘agriculture, forestry or fishing’ or ‘other services’ (Booth), and ‘utili-ties’ or ‘other services’ (Green), have a significantly higher training prob-ability than other census-defined industries. Green et al. (1996b) findsubstantial differences in training intensity between industrial sectors. Inparticular, workers in ‘distribution’ and ‘utilities’ have a significantlyhigher probability of training than the base group of ‘other services’,whilst employees in ‘transport and communications’ and ‘metal goods,engineering and vehicles’ are typically less likely to be trained. Green-halgh and Mavrotas (1996) find that training is highest in ‘non-tradable’industries (e.g. health, education and national and local government) andlowest in ‘medium technology manufacturing’ (e.g. foods, drinks, clothing,timber). Those employed in industries actively engaged in research anddevelopment were also found to have a higher probability of receivingtraining.

Finally, with regard to region of employment, Booth (1991), using 1987data, found no significant effects for men, but women working inScotland, the North or London were less likely to receive training thanwomen in the South of England. In contrast, Green et al. (1996b), foundno consistency in the regional estimates, concluding ‘that the region inwhich the establishment is located has very little influence on the trainingintensity of the workers’ (p. 14). Similarly, several studies (Arulampalamet al., 1996; Blundell et al., 1996; Green et al., 1996b) have found nosignificant effect of local unemployment on the probability of training.

III. THE DATA

The data used in this study are drawn from the UK’s Labour ForceSurveys (LFS) of 1984, 1989, and 1994.8 These three years were chosen toprovide the longest spell of compatible training information from theLFS.9 All three surveys permit us to identify whether the individual hasundertaken any work-related training in the four weeks prior to inter-

8 The first quarter of 1994, collected in the spring, is used for compatibility with the twoearlier surveys (both collected in the spring), to eliminate any problems due to seasonaldifferences.

9 Training data from the Quarterly Labour Force Survey for the summer of 1994 andonwards are not comparable to those for earlier quarters/years due to changes in thequestionnaire.



view.10 Additionally, the location of training, sources of finance andduration of the training spell can be identified. Two samples wereextracted from each survey, that of male (MFT) and female full-timeemployees (FFT). This gives a sample of approximately 28,000 male and14,000 female employees of working age in each of the three years.11

One potential problem with compatibility between the LFS and othersurveys used to examine the determinants of training arises because thereis no general agreement on what constitutes training (see Campanelli andChannell, 1994; Shackleton, 1995). Surveys may differ in their estimatesof training incidence and other training-related variables as the exactquestions asked, and the degree of memory prompting and similarfactors, vary.12 Moreover, using surveys to examine changes over time islikely to exacerbate this problem. An individual’s reply to the trainingparticipation question is likely to be conditional on the year of interview.This might be particularly important in recent years given the politicalheightening of issues surrounding education and training. Nevertheless,the LFS remains the only large scale data source capable of revealingtrends in training incidence whilst permitting adequate sample sizes forimportant sub-groups of the working population. However, the reliabilityof the estimates is clearly open to debate.13

The salient features of the samples are first presented, highlighting thetraining experiences of men and women over the decade.14 Table 2 showsthat there has been a significant increase in the percentage of the work-force experiencing training. The majority of this increase occurred in thefirst half of the decade, between 1984 and 1989. Interestingly, femalesbenefited from both a higher growth rate (68 percent increase in trainingbetween 1984 and 1994, compared to 49 percent for men) and a higherincidence of training than males throughout the 10-year period.

The decade also saw a notable shift in the financing of training. By1994, employer-funded training accounted for 86.5 percent and 81.7percent of total training, respectively, for full-time males and females,increasing from 76.4 percent and 72.4 percent in 1984. Conversely, therewas a decline in the percentage (and incidence) of training funded by the

10 The question asked of respondents was, ‘Over the last four weeks, have you taken part inany education or training connected with your job, or a job that you might be able to do inthe future?’. This information was combined with the response to the question, ‘Who paidthe fees for this training?’, to identify the recipients of employer-funded training.

11 Aged between 16–65 for men and 16–60 for women. A full-time employee is defined asusually working 30 hours or more per week. Those still at school or in other full-timeeducation were excluded from the samples. In addition, immigrant and ethnic minoritygroups were excluded since these groups may have different training profiles to native-bornwhites.

12 See Arulampalam et al. (1996) for a discussion of ‘recall error’, and how it applies to theLFS.

13 See Felstead et al. (1977) for a discussion of the reliability of survey data on training.14 All tables in this section are the author’s own calculations, based on the corresponding

LFS.


194 BULLETIN

government, and for men, the period also saw a significant fall in thepercentage of training which was self-funded.

With regard to the location of employer-funded training, the LFSshows that the percentage taking place at the workplace (on-the-job) roseconsiderably between 1989 and 1994. The most significant increase wasfor male employees (55 percent of all employer-funded training in 1989,rising to 65 percent by 1994), compared to females (65 percent, rising to68 percent). By contrast, there was a decline in the proportion of trainingtaking place off-the-job in colleges for further education, universities andother educational establishments (22 percent to 14.5 percent for males,and 18 percent to 14 percent for females). Moreover, there has been asteady increase in the amount of training taking place at anotheremployer’s premises, and an increased use of the Open University andcorrespondence courses by British employers.

Finally, as discussed elsewhere (e.g. Felstead and Green, 1996; Green-halgh and Mavrotas, 1993) the data reveal a trend towards shorter spellsof training in Britain. For instance, there was a substantial increase in theproportion of training lasting less than one week (38 percent and 40percent of male and female training in 1989, rising to 53 percent and 51percent by 1994), and a corresponding decline of training lasting at leastsix months. This may indicate that as the proportion of employer-fundedtraining provided at the workplace increased, there was some trade-off in

TABLE 2The Percentage of Employees Receiving Training in the Last Four Weeks by Funding

Source

MFT FFT

1984 1989 1994 1984 1989 1994

All training 9.6 13.9 14.3 11.4 17.6 19.1Employer-funded 7.3 11.8 12.4 8.3 14.1 15.6

(76.4) (85.0) (86.5) (72.4) (80.1) (81.7)Self-funded 0.9 1.1 1.0 1.2 1.9 2.1

(9.6) (8.2) (7.1) (10.9) (11.0) (11.0)Government-funded 0.6 0.2 0.3 0.9 0.6 0.6

(6.3) (1.6) (2.3) (8.1) (3.5) (3.1)

Note:1. The figures in brackets give the percentage of all training accounted for by each

funding source.2. Employer-funded includes training funded by the current employer or a potential

employer; self-funded includes training funded by the individual, their family and relatives;and government-funded training includes training funded by local authorities, TECs andother government agencies.



length, and perhaps quality, of averaging training received.15 In particular,there appears to be some evidence that qualifications are now often beingawarded for short courses which have little depth (see Felstead andGreen, 1996, for a full discussion). In terms of training expenditure,however, evidence from the CBI’s Industrial Trends Quarterly Survey ismore encouraging. This suggests that for each year since 1989 a greaternumber of British firms have expected to authorize more, rather thanless, expenditure on training in the next 12 months than in the previous12 months.16

IV. A MODEL OF TRAINING INCIDENCE

It is assumed that the receipt of training by an employee is the jointoutcome of optimizing behaviour on the part of workers and employers.Given the cross-sectional nature of the LFS and data limitations, it is notpossible to model the structural framework of the training decision andseparate employer and employee demands for training and skilled labour.Consequently, reduced form binary logit models are used to estimate theprobability of an individual with certain characteristics, reporting to havereceived employer-funded training within the four weeks prior to inter-view. The focus on employer-funded training is based on the fact thatover 80 percent of training is financed principally by employers, andallows a clearer interpretation of the results. Separate regressions areestimated for male and female full-time employees for 1984, 1989 and1994.

Let T* be the unobserved net benefit to the individual and employerfrom providing training. Assuming an efficient bargain has been struck, atraining spell will be recorded by the individual at interview if T*a0.Therefore, the model can be denoted by:

T*M\aMXM+mM (1)

T*F\aFXF+mF (2)

Tp\1 iff T*a0

Tp\0 iff T*R0

15 Unfortunately, the LFS is unable to identify whether total training time increased ordecreased over the period. This is because of the discrete nature of the training lengthquestion and the inclusion, in 1989 and 1994, of a category for ‘ongoing/no definite limit’training (representing around 16 percent of total employer-funded training), which providesno training length information. The author is unaware of any other data source which allowsfor the reliable calculation of total training time over the period.

16 The survey asks whether employers ‘expect to authorize more or less expenditure in thenext twelve months than you authorized in the past twelve months on training and retrain-ing?’ A simple balance of opinion is constructed by subtracting the negative answers from thepositive ones. The balance has remained positive since the training question was added to thesurvey in 1989.


196 BULLETIN

where Tp is a dummy variable indicating receipt of training, X, representsa vector of individual and employer characteristics, m denotes an errorterm and the superscripts M and F refer to male and female full-timeemployees respectively.

The explanatory variables used include a number of individual and firmcharacteristics which have been found to be important in earlier studies(e.g. Greenhalgh and Mavrotas 1996), and which are suggested by theoryto affect the training decision of individuals and firms.17 These are age,highest qualification level, marital status, dependent children, job tenureand whether the job is permanent or temporary.18 Employer character-istics — region, industry and size of firm — are additionally included.19

The regional dummies attempt to capture the effects of regional differ-ences in economic conditions20 and the continuous variables, of age andjob tenure have been transformed into spline dummies to avoid theimposition of restrictive functional forms.

The coefficients and the associated marginal effects estimated fromnon-linear discrete models are difficult to interpret and is especially thecase where dummy variables are used (Greene, 1993, p. 641). For thisreason, the predicted probabilities (PP) of each category variable havebeen simulated, whilst holding other category variables at their meanvalues.

The changing training prospects for each employee group are examinedby decomposing the logit equations. The format followed is one whichhas been used in a number of recent papers in the UK aimed at explain-ing the differences in a binary outcome between two groups (e.g.Blackaby and Murphy, 1995; Green, 1993a). However, instead of decom-posing differences between groups, the aim here is to examine differencesin one group over two time-periods. Following the methodologydeveloped by Gomulka and Stern (1990), the following estimate isobtained:

TNewµTOld\[P(aN, XN)µP(aO, XN)]+[P(aO, XN)µP(aO, XO)] (3)

where a is the estimate of the coefficients from the logistic equation, TN

and TO are the respective average of the predicted training probabilitiesfor the newest year (e.g. 1994) and the more distant year (e.g. 1984), and

17 A number of data comparability problems have needed to be tackled. Firstly, the changefrom the 1980 SIC industry classification to the 1992 SIC definitions needed to be mapped(see p. 163 of the Quarterly Labour Force Survey, March, 1993 to November, 1994, UserGuide, vol. 3). Secondly, the introduction of many new vocational qualifications over theperiod, required matching into pre-existing categories (see Shields, 1997).

18 Unfortunately, the trade union membership question was not asked in the Spring of 1994.19 Size of firm is derived from the question, ‘How many employees were there at the place

where you worked (last week)’. The dummy variable used her for size of firm, indicates ananswer of 25 or more, and is restricted to that definition by the discrete classes used by theLFS.

20 Regional dummies might additionally capture the differential success of TECs and otherregional initiatives.



P(aN, XN) is the average predicted probabilities across the samples usingthe newest data coefficients and sample characteristics, say both 1994.The other terms have obvious meanings. The first bracketed termprovides an estimate of the change in the mean training probabilitybetween two periods due to differences in the coefficients, and the secondbracketed term gives the change attributable to differences in the under-lying characteristics of the sample.

Having identified the percentage growth in the average training prob-ability attributable to the changing characteristics of the samples, it wouldbe valuable to be able to distinguish which groups of characteristics hadthe largest effects. This allows the driving forces behind the increase intraining to be specified in greater detail, and provides an idea of how theincidence of training may change in the future owing to continuing shiftsin the age distribution of employees, the underlying qualification base andthe industrial structure. To achieve this, the vector of mean characteristicsare partitioned into K sets:

XA\(Xage, Xrest), XI\(Xindustry, Xrest), XQ\(Xqualifications, Xrest)

and the second term in brackets on the RHS of (3) is decomposed asfollows:

P(aO, XN)µP(aO, XO)\[P(aO, XKN)µP(aO, XO)]+[P(aO, XN)µP(aO, XKN)]

where aKN represents the vector of mean characteristics made up of theset K from the most recent year, N, and the remainder from year O. Thisprovides an estimate of how much additional training, given the 1984coefficient structure, would be observed in 1989 and 1994 solely due toeach of these structural changes in turn.

V. EMPIRICAL RESULTS

(a) Male Full-Time Employees

The estimates for the determinants of training for male employees areprovided in Table 3. As expected from theory, age and qualifications playan important role in the training decision of individuals and employers.The probability of training declines sharply for men after the age of 19,with employees aged 30–34, for example, receiving 16 percent in 1984and 31 percent in 1994, of the training probability for men under 20. Therelative increase in willingness by employers to invest in older workersrepresents a significant flattening of the age-training profile for maleemployees.

The complementary relationship between qualifications and the prob-ability of receiving training strengthened considerably between 1984 and1989 for male workers, with the probability differential betweenemployees with high and medium level (e.g. degree and ‘A’ level or


198 BULLETIN

TABLE 3The Determinants of Employer-funded Training for Male Employees: Logistic Estimates

1984 1989 1994

Variable a SE PP a SE PP a SE PP

16–19 — — 0.315 — — 0.318 — — 0.35520–24 µ1.3688 0.0847* 0.105 µ0.9762 0.0804* 0.162 µ1.0992 0.1127* 0.15525–29 µ1.9261 0.1052* 0.063 µ1.4256 0.0911* 0.110 µ1.2676 0.1156* 0.13430–34 µ2.1486 0.1172* 0.051 µ1.5270 0.0993* 0.100 µ1.3535 0.1213* 0.12435–39 µ2.1682 0.1213* 0.050 µ1.6095 0.1040* 0.093 µ1.5024 0.1264* 0.10940–44 µ2.3403 0.1317* 0.042 µ1.7502 0.1059* 0.082 µ1.5875 0.1289* 0.10145–49 µ2.4615 0.1417* 0.038 µ1.8417 0.1133* 0.075 µ1.7337 0.1316* 0.08850–54 µ2.7433 0.1559* 0.029 µ2.0787 0.1244* 0.060 µ1.8824 0.1414* 0.07755+ µ3.2585 0.1684* 0.019 µ2.5565 0.1304* 0.038 µ2.3083 0.1496* 0.052

Single — — 0.047 — — 0.081 — — 0.095Married 0.1161 0.0774 0.052 0.1962 0.0598* 0.097 0.1616 0.0627* 0.110W/D/S µ0.2522 0.1759 0.037 0.1430 0.1234 0.092 0.0641 0.1182 0.100

No. child s9 years — — 0.050 — — 0.095 — — 0.107Child(s) under 9 years 0.0406 0.0635 0.052 µ0.1170 0.0523H 0.085 µ0.0453 0.0519 0.103

Higher degree 0.1810 0.1569 0.089 0.5599 0.1232* 0.167 0.4063 0.1138* 0.158First degree 0.6353 0.0949* 0.133 0.8168 0.0763* 0.206 0.5215 0.0768* 0.174Other Degree 0.2608 0.1406 0.095 0.6954 0.1148* 0.186 0.5574 0.1130* 0.179Higher vocational 0.0957 0.1197 0.082 0.7040 0.0871* 0.188 0.4373 0.0687* 0.162Teaching qualification 0.6397 0.2175* 0.133 1.3581 0.1675* 0.308 0.9532 0.2111* 0.245Nursing qualification µ0.2661 0.3916 0.058 0.2732 0.2873 0.131 0.7644 0.2291* 0.211‘A’ level or equivalent 0.0425 0.0992 0.078 0.3963 0.0822* 0.145 0.2421 0.0864* 0.137Secondary vocational 0.1704 0.0742H 0.088 0.3228 0.0614* 0.136 µ0.2001 0.0732* 0.093‘O’ level or equivalent — — 0.075 — — 0.103 — — 0.111GCSE µ0.7948 0.1108* 0.035 µ0.6122 0.1045* 0.058 µ0.3769 0.1135* 0.079

© B

lackwell Publishers 1998

199E

MPL

OY

ER

-FU

ND

ED

TR

AIN

ING

FO

R F

UL

L-T

IME

EM

PLO

YE

ES

TABLE 3 — contd

1984 1989 1994


Other µ0.5037 0.1678* 0.047 µ0.2378 0.0934H 0.083 µ0.3500 0.1046* 0.081No qualifications µ1.0579 0.0804* 0.027 µ0.9102 0.0746* 0.044 µ0.9643 0.1025* 0.045

North 0.0125 0.1083 0.053 µ0.0566 0.0886 0.100 0.0667 0.0934 0.120Yorkshire/Humberside µ0.0865 0.0923 0.050 µ0.1573 0.0761H 0.091 0.0750 0.0761 0.121East Midlands µ0.0435 0.0977 0.052 0.0319 0.0786 0.108 µ0.1624 0.0864 0.098East Anglia µ0.2047 0.1362 0.045 µ0.3438 0.1118* 0.077 µ0.1155 0.1117 0.102London 0.0951 0.0850 0.059 µ0.0941 0.0698 0.097 µ0.0884 0.0775 0.105Rest of South East — — 0.054 — — 0.105 — — 0.113South West 0.0124 0.0931 0.055 µ0.1135 0.0759 0.095 µ0.0776 0.0798 0.106West Midlands Central µ0.1967 0.1325 0.045 µ0.1443 0.1003 0.093 µ0.2761 0.1162H 0.088Rest of West Midlands µ0.1635 0.1226 0.046 µ0.1389 0.0971 0.093 µ0.0261 0.0946 0.110Manchester+Merseyside µ0.1648 0.1054 0.046 µ0.3900 0.0915* 0.074 µ0.1586 0.0930 0.098Rest of North West µ0.1997 0.1284 0.047 µ0.1775 0.1031 0.090 µ0.0126 0.0980 0.112Wales µ0.2790 0.1279H 0.042 µ0.3578 0.1117* 0.076 µ0.1030 0.1021 0.103Scotland µ0.2210 0.0893H 0.044 µ0.3682 0.0762* 0.075 µ0.2362 0.0783* 0.091

Agriculture and fishing µ0.7230 0.2551* 0.023 µ0.4633 0.2135H 0.055 µ0.9644 0.2906* 0.041Mining and quarrying 0.3554 0.1448* 0.066 µ0.0589 0.1548 0.081 µ0.1802 0.2320 0.085Manufacturing — — 0.047 — — 0.085 — — 0.100Electricity/gas/water 0.3907 0.1435* 0.068 0.1863 0.1286 0.101 0.5369 0.1315* 0.160Construction µ0.1392 0.0918 0.041 µ0.0683 0.0780 0.080 µ0.0643 0.0847 0.095Wholesale and retail µ0.4311 0.0936* 0.031 µ0.1749 0.0725H 0.073 µ0.2173 0.0759* 0.082Hotel and restaurants µ0.7726 0.2609* 0.022 µ0.1673 0.1720 0.074 µ0.2314 0.1721 0.081Transport etc. 0.2582 0.0924* 0.060 0.0973 0.0758 0.093 µ0.2110 0.0847H 0.083

© B


200B

UL

LE

TIN

TABLE 3 — contd

1984 1989 1994


Finance 0.5672 0.1120* 0.075 0.4330 0.0889* 0.126 0.4962 0.0877* 0.155Real estate etc. 0.1961 0.1067 0.057 0.1140 0.0819 0.095 0.1138 0.0785 0.111Public administration 0.6096 0.0826* 0.083 0.6015 0.0681* 0.145 0.6261 0.0698* 0.173Education 0.4376 0.1114* 0.071 0.5871 0.0935* 0.144 0.3805 0.0947* 0.140Health and social work 0.6662 0.1284* 0.088 0.7434 0.1121* 0.164 0.5501 0.1046* 0.162Other µ0.0996 0.1507 0.043 0.0331 0.1197 0.088 0.1949 0.1141 0.119

a25 workers — — 0.053 — — 0.100 — — 0.114s25 workers µ0.2438 0.0595* 0.042 µ0.3160 0.0494* 0.074 µ0.3260 0.0518* 0.085

s1 year with firm 0.0794 0.0761 0.051 µ0.0179 0.0608 0.090 0.1333 0.0940 0.1161–2 years 0.1770 0.0873H 0.056 µ0.0081 0.0686 0.087 µ0.0549 0.0696 0.0982–5 years 0.2320 0.0645* 0.059 0.0553 0.0544 0.103 0.1281 0.0516H 0.116a5 years — — 0.047 — — 0.092 — — 0.103

Permanent job — — 0.050 — — 0.093 — — 0.107Not permanent µ0.1555 0.0935 0.043 µ0.4728 0.1529* 0.084 µ0.2864 0.1102* 0.082

Constant µ0.4188* µ0.5232* µ0.5720*Sample size 28079 27881 24354µ2LL 13432 18608 17245µ2LL (slopes\0) 15731 20801 18640Model x2 2298* 2193* 1394*(52 d.f.)

Note:1. ‘*’ indicates significant at the 1 percent level; ‘H’ indicates significant at the 5 percent level; ‘—’ shows the base group.2. W/D/S\widowed, divorced or separated.

© B


201E

MPL

OY

ER

-FU

ND

ED

TR

AIN

ING

FO

R F

UL

L-T

IME

EM

PLO

YE

ES

equivalent) qualifications and those lacking qualifications wideningsubstantially. The largest differentials were seen in the boom year of1989, which suggests a relationship between the economic climate and thetraining returns from qualifications, with the differential between quali-fied and non-qualified workers increasing in times of economic upturn.Given that training in Britain is associated with increased promotionalopportunities and high wages (see, for example, Blundell et al., 1996;Booth, 1991; Greenhalgh and Stewart, 1987; and Nickell, 1982), thisrelationship could help to explain the widening of the pay distribution inBritain over the 1980s (see Machin, 1996).21

Marital status and family responsibilities have increased in importanceas determinants of training. In contrast to 1984, married men in 1989 and1994 were significantly more likely to receive employer-funded trainingthan single males. Two possible explanations could be that increased jobmobility and growing fears of poaching have led employers to use maritalstatus as a signal to indicate a higher level of commitment to the firm, orthat married men in 1989 and 1994 had a greater demand for trainingrelated to the higher likelihood of their spouse being employed.22 Thenegative effect on training from having a young dependent child orchildren was found to be significant only in the favourable economicclimate of 1989.

Industry plays a key role in determining whether male workers receiveemployer-funded training. The decade saw a marked consistency in thisrelationship, with workers in the ‘publicly-oriented service’ — publicadministration, education and health and social work — and those infinance and the utilities, receiving significantly more training than manu-facturing employees. Men employed in agriculture and fishing, and whole-sale and retail experienced the lowest probability of receiving trainingthroughout the decade.

The explanations for higher training in the public sector given by Booth(1991) suggest that the privatization of the utilities (electric, gas, water)and the increased cost pressures on public administration would havereduced any positive training differential experienced by workers in thesesectors by 1994. In fact, the training probability differential betweenpublic administration and manufacturing, for example, increased between1984 and 1994 (0.036 to 0.073). A similar increased differential overmanufacturing was experienced by men working in the utilities sector(0.021 and 0.060).

Scottish males consistently experienced a lower probability of receivingtraining than those in the South of England. A similar disadvantage wasfound for Welsh employees in 1984 and 1989. This could be due to

21 See Constantine and Neumark (1996) for an investigation of the link between trainingand the growth of wage inequality in the USA over the 1980s.

22 Married men with working wives may have a greater psychological incentive to train andmove up the occupational ladder.


202 BULLETIN

relatively high levels of unemployment in these regions.23 By 1994, men inthe central West Midlands region were also found to have a significantlylower probability of receiving employer-funded training, which does notappear to be explained by higher unemployment in that region.24 A tenta-tive explanation could be that government initiatives such as the estab-lishment of Training and Enterprise Councils and the take-up of‘Investors in People’, have been less successful in this region.

Small employers funded significantly less training than their largercounterparts throughout the decade. The differential between large andsmall firms, however, increased after 1984. Given that the overall inci-dence of employer-funded training rose significantly after the mid-1980s,large firms may be able to spread the fixed costs of training betweenmany more employees, and benefit from increased economies of scale(Green et al., 1996b). Moreover, the recent introduction of strict qualityand safety regulations requires additional investments in training, whichare likely to apply to a greater extent to large than to small firms(Felstead and Green, 1996).25

In contrast to previous studies, no consistent relationship was foundbetween job tenure and training probability for male employees. Thissuggests that any need for employers to provide initial job training to newrecruits is counteracted by uncertainty regarding new employees commit-ment to the firm. Those employed in a temporary post are found toreceive significantly less employer-funded training throughout the period.

(b) Female Full-Time Employees

The comparable logit estimates for female employees are provided inTable 4. The predicted probability of receiving employer-funded trainingfor young females under 20 is considerably lower than for young men. Aswith males there has been a flattening of the age-training profile, witholder workers benefiting from an increased relative training probability.The shape of the age-training profile for females appears to exhibit asignificantly negative slope until age 30, then an increased training prob-

23 In times of high unemployment employers might provide less training because employeescan make fewer training demands as there is a large pool of employees waiting to take upjobs (Green et al., 1996). Moreover, high unemployment may reduce the power of tradeunions to bargain for training for their members. Finally, firms might fear that they may haveto make redundancies, which reduces their willingness to train.

24 In an earlier specification, regional dummies were replaced with regional unemploymentrates. As with previous studies, these were found to be insignificant for each group, in eachof the three years.

25 Other possible explanations include a widening of the pay distribution in favour ofemployees in large firms. Increased pay raises the likelihood of workers remaining with thefirm, which increases the benefits to the firm from training (Green et al., 1996). Similarly,employees may have a greater demand for firm-specific training if their probability of remain-ing with the firm is high. Furthermore, any divergence in the length of time horizon betweenlarge and small firms, adopted when evaluating the benefits from training, could lead to agreater willingness to train on the part of large firms.



TABLE 4The Determinants of Employer-funded Training for Female Employees: Logistic Estimates

1984 1989 1994


16–19 — — 0.132 — — 0.165 — — 0.21120–24 µ0.5806 0.1105* 0.079 µ0.2537 0.0981* 0.133 µ0.3172 0.1315H 0.16325–29 µ0.9746 0.1424* 0.054 µ0.4352 0.1126* 0.113 µ0.4041 0.1372* 0.15230–34 µ0.5923 0.1534* 0.078 µ0.5123 0.1273* 0.106 µ0.5699 0.1461* 0.13135–39 µ0.6518 0.1577* 0.074 µ0.1211 0.1279 0.149 µ0.3837 0.1477* 0.15440–44 µ0.7316 0.1675* 0.068 µ0.4389 0.1302* 0.113 µ0.3689 0.1494H 0.15645–49 µ0.9476 0.1800* 0.056 µ0.3600 0.1383* 0.121 µ0.6363 0.1528* 0.12450–54 µ1.2812 0.2073* 0.041 µ0.5869 0.1497* 0.099 µ0.8258 0.1662* 0.10555+ µ1.8400 0.2550* 0.024 µ0.8246 0.1754* 0.080 µ0.7255 0.1780* 0.114

Single — — 0.061 — — 0.118 — — 0.133Married 0.0957 0.0877 0.067 0.0443 0.0654 0.123 0.0878 0.0632 0.144W/D/S 0.1699 0.1471 0.072 0.0253 0.1073 0.120 0.2012 0.0979H 0.158

No. child s9 years — — 0.065 — — 0.121 — — 0.144Child(s) under 9 years 0.0056 0.1145 0.066 0.0052 0.0900 0.121 µ0.1351 0.0723 0.128

Higher degree 0.7545 0.2928* 0.150 0.8500 0.2002* 0.244 0.4038 0.1488* 0.203First degree 0.9617 0.1329* 0.179 1.0347 0.0947* 0.279 0.6594 0.0827* 0.248Other Degree 0.7020 0.2878H 0.144 0.8762 0.2078* 0.248 0.7993 0.1773* 0.275Higher vocational 0.8437 0.2660* 0.162 0.8686 0.1769* 0.245 0.3732 0.0877* 0.198Teaching qualification 0.7369 0.1743* 0.148 1.3750 0.1300* 0.353 0.6949 0.1311* 0.255Nursing qualification 0.2821 0.1492 0.099 0.5159 0.1109* 0.188 0.4739 0.1067* 0.215‘A’ level or equivalent 0.4037 0.1166* 0.111 0.5924 0.0865* 0.200 0.2957 0.0892* 0.186Secondary vocational 0.5581 0.1328* 0.127 0.2616 0.0993* 0.152 µ0.1582 0.0897 0.127‘O’ level or equivalent — — 0.077 — — 0.121 — — 0.146GCSE µ0.6087 0.1477* 0.043 µ0.5444 0.1359* 0.074 µ0.4406 0.1365* 0.099

© B


204B

UL

LE

TIN

TABLE 4 — contd

1984 1989 1994


Other µ0.1719 0.1870 0.065 0.0978 0.1149 0.132 µ0.1216 0.1182 0.131No qualifications µ0.8222 0.1127* 0.035 µ0.7753 0.0929* 0.060 µ1.0469 0.1127* 0.056

North µ0.1149 0.1613 0.064 µ0.2511 0.1200H 0.112 µ0.1296 0.1222 0.139Yorkshire/Humberside µ0.1290 0.1311 0.063 µ0.0968 0.0993 0.129 0.1198 0.0900 0.172East Midlands 0.0906 0.1339 0.078 µ0.0277 0.1068 0.137 µ0.2439 0.1047H 0.126East Anglia µ0.2694 0.1940 0.055 0.1141 0.1300 0.154 0.0014 0.1279 0.156London 0.0861 0.1105 0.077 µ0.1469 0.0856 0.118 µ0.0497 0.0845 0.149Rest of South East — — 0.071 — — 0.140 — — 0.156South West 0.0385 0.1286 0.074 µ0.2462 0.0998H 0.113 µ0.1243 0.0952 0.140West Midlands Central µ0.1536 0.1760 0.062 µ0.0496 0.1253 0.134 µ0.2828 0.1347H 0.122Rest of West Midlands µ0.1244 0.1701 0.064 µ0.0829 0.1309 0.130 µ0.2413 0.1235H 0.126Manchester+Merseyside µ0.2773 0.1539 0.055 µ0.2404 0.1128H 0.113 µ0.2034 0.1056 0.131Rest of North West µ0.0931 0.1713 0.065 µ0.1573 0.1298 0.122 0.1076 0.1118 0.170Wales µ0.2674 0.1526 0.056 µ0.2155 0.1331 0.116 µ0.1644 0.1203 0.135Scotland µ0.3674 0.1267* 0.050 µ0.6479 0.0991* 0.078 µ0.3505 0.0917* 0.115

Agriculture and fishing µ0.3761 0.4129 0.065 0.0135 0.4385 0.088 µ0.1418 0.4400 0.093Mining and quarrying 0.6085 0.4231 0.080 µ0.1518 0.5422 0.076 0.0963 0.6301 0.115Manufacturing — — 0.045 — — 0.087 — — 0.106Electricity/gas/water 0.9275 0.2702* 0.107 0.8952 0.2315* 0.189 0.6962 0.2489* 0.192Construction 0.1468 0.2957 0.052 0.3541 0.2155 0.120 µ0.0182 0.2118 0.104Wholesale and retail 0.3678 0.1266* 0.064 0.1010 0.1015 0.095 0.0173 0.1055 0.107Hotel and restaurants 0.0552 0.2166 0.048 µ0.1280 0.1827 0.077 µ0.0875 0.1735 0.098Transport etc. 0.4927 0.1822* 0.072 0.3038 0.1356H 0.114 0.0337 0.1487 0.109

© B


205E

MPL

OY

ER

-FU

ND

ED

TR

AIN

ING

FO

R F

UL

L-T

IME

EM

PLO

YE

ES

TABLE 4 — contd

1984 1989 1994


Finance 0.5548 0.1466* 0.076 0.5327 0.1062* 0.140 0.5260 0.1060* 0.167Real estate etc. 0.1924 0.1589 0.054 0.2079 0.1111 0.105 0.1246 0.1087 0.118Public administration 0.5217 0.1410* 0.074 0.5578 0.1062* 0.143 0.6661 0.0996* 0.187Education 0.5367 0.1495* 0.075 0.8753 0.1086* 0.186 0.7229 0.1016* 0.196Health and social work 0.8914 0.1210* 0.104 0.9363 0.0936* 0.196 0.7367 0.0910* 0.198Other µ0.4177 0.1801H 0.067 0.2492 0.1497 0.109 0.1961 0.1366 0.126

a25 workers — — 0.067 — — 0.128 — — 0.148s25 workers µ0.1112 0.0734 0.061 µ0.2028 0.0569* 0.107 µ0.1539 0.0549* 0.130

s1 year with firm 0.4380 0.0999* 0.086 0.2064 0.0766* 0.135 0.3444 0.1070* 0.1801–2 years 0.3743 0.1134* 0.081 0.1325 0.0834 0.127 0.1166 0.0759 0.1492–5 years 0.0935 0.0902 0.062 0.0661 0.0686 0.120 0.1141 0.0582H 0.149a5 years — — 0.057 — — 0.113 — — 0.135

Permanent job — — 0.066 — — 0.122 — — 0.143Not permanent µ0.4177 0.1515* 0.044 µ0.4723 0.1432* 0.080 µ0.1670 0.1003 0.124

Constant µ2.151* µ1.839* µ1.589*Sample size 12996 14649 14474µ2LL 7019 11107 12072µ2LL (slopes\0) 7734 12359 12986Model x2 715* 1251* 914*(52 d.f.)

Note:1. ‘*’ indicates significant at the 1 percent level; ‘H’ indicates significant at the 5 percent level; ‘—’ shows the base group.2. W/D/S\widowed, divorced or separated.

© B


206B

UL

LE

TIN

ability between 30 and 40 years of age, and a significant decline there-after. The low training probability between 20 and 30 is probably relatedto child-bearing age: employers will be cautious about investing intraining for females in the light of future child-birth (Green, 1993a). Theincreased likelihood of training observed in the 30–40 age range can beexplained by higher initial training requirements for women returning tothe labour market. The period 1984 to 1989 saw a subtle shift in the age-training profile, with the ‘child-bearing kink’ appearing at around the 30thyear in 1984 and the 35th year in 1989 and 1994. This may reflect amovement towards (or the expectation of) later child-bearing.

The complementary relationship between training and qualifications ismore pronounced for females than for males in all three years. Theaverage training probability differential between a degree and no qualifi-cations, for example, was 0.144 (0.106 for men) in 1984, increasing to0.219 (0.162 for men) in 1989, and declining to 0.192 (0.129 for men) by1994. As in the case of men, the increase in the differential between 1984and 1989, and the decline between 1989 and 1994, suggests a relationshipwith the economic climate.

Marital status and family responsibilities are found to play no signifi-cant role in determining training for female full-time workers. The effectof having a young dependent child or children is likely to be a moreimportant determinant of training for women working part-time. Anyincreased uncertainty employers might have concerning the labour marketcommitment of female returnees might be compensated by an above-average level of motivation (and willingness to train) for those womenchoosing to return to full-time working.

The relationship between the probability of receiving employer-fundedtraining and industry of employment is similar for females and for males.Women employed in public administration, education and health, andfinance and the utilities, have significantly higher training probabilitiesthan those working in manufacturing. As in the case of men, the trainingprobability differential between these industries increased substantiallyafter 1984. The training differential between the high training industriesand manufacturing is greater for females, and the gender gap increasedsignificantly between 1984 and 1989. As with males, we find an increase inthe probability differential between those employed in public administra-tion and the utilities, on the one hand, and other industrial sectors, on theother.

Length of job tenure with the current firm is an important determinantof employer-funded training for female full-time employees. Females, ifrecently employed, have a higher probability of training. Training may beparticularly important for women returning to jobs after spending timeout of the labour force (Green, 1993a). Moreover, Scottish femaleemployees experienced the lowest probability of training throughout thedecade. By 1994, female employees working throughout the Midlands



region were receiving significantly less employer-funded training thanthose in other areas in England. Female employees in large firms experi-enced a significantly higher training probability than those employed insmall firms in 1989 and 1994 but not in 1984.

(c) Decomposition Results

Tables 5 and 6 provide the decomposition results. For males and females,respectively, the average predicted probability of training is estimated to

TABLE 5Decomposition of the Growth in Employer-funded Training for Male Full-time

Employees

1984–1989 1989–1994 1984–1994

Difference in mean probabilityTNµTO 0.042 0.014 0.056

Difference in coefficients{P(aN, XN)µP(aO, XN)} 0.036 (85%) µ0.008 (µ57%) 0.041 (74%)

Difference in characteristics{P(aO, XN)µP(aO, XO)} 0.006 (15%) 0.022 (157%) 0.015 (26%)

Principal components ofcharacteristic differences

Age structure µ0.0001 µ0.0040 µ0.0030Qualification base +0.0060 +0.0253 +0.0184Industrial structure +0.0014 +0.0014 +0.0003

TABLE 6Decomposition of the Growth in Employer-funded Training for Female Full-time

Employees

1984–1989 1989–1994 1984–1994

Difference in mean probabilityTNµTO 0.056 0.021 0.077

Difference in coefficients{P(aN, XN)µP(aO, XN)} 0.046 (81%) µ0.001 (µ7%) 0.058 (75%)

Difference in characteristics{P(aO, XN)µP(aO, XO)} 0.010 (19%) 0.023 (107%) 0.019 (25%)

Principal components ofcharacteristic differences

Age structure µ0.0000 µ0.0032 µ0.0035Qualification base +0.0073 +0.0232 +0.0225Industrial structure +0.0003 +0.0059 +0.0027


208 BULLETIN

have increased by 0.056 and 0.077 between 1984 and 1994. The majorityof this increase, around 75 percent and 73 percent, occurred between1984 and 1989, and can be attributed to changes in the demand fortraining and skilled labour by individuals and employers (the coefficients).In contrast, the growth in training between 1989 and 1994 was, on thewhole, driven by changes in the average work-related characteristics ofthe workforce.26 The important change was a general improvement in thequalification base of the workforce rather than shifts in the age or indus-trial structures. Consequently, male and female employees would havereceived a 0.025 and 0.023 higher average probability of training, respec-tively, in 1994 than in 1989, solely on account of their improved qualifica-tion levels. As a result, had the educational levels of the workforceremained at their 1989 level, 1994 would have witnessed a fall in therecorded incidence of training rather than the increase actually observed.

VI. CONCLUSION

This paper has examined the incidence and determinants of employer-funded training over the decade from 1984 to 1994, using three compar-able cross-sections from the British Labour Force Survey. The resultsconfirm that age and prior qualifications are key determinants of trainingfor full-time employees, irrespective of the year in focus. The estimatesdo, however, indicate other consistencies and also some notable changesin the determinants of training over the period. These are:

(i) The effect of industry on the probability of receiving employer-funded training was remarkably consistent throughout the decade, and ofthe same magnitude as that of age and education. Those employed inpublic administration, education, health and financial services had ahigher probability of training than those employed in other industries.

(ii) The age-training profile flattened significantly over the period, withthe percentage growth in training probability being considerably greaterfor older workers than for younger workers.

(iii) There was a significant training differential between large andsmall firms throughout the decade, and the size of this differentialincreased significantly after 1984.

(iv) The complementary nature of prior qualifications and the prob-ability of training strengthened between 1984 and 1989, with the trainingdifferential between those with qualifications and those without wideningsubstantially.

Furthermore, decompositions of the growth in employer-fundedtraining between 1984 and 1989 suggest that changes in individuals’ and

26 This contrasts to the finding for part-time females, where the training growth was fullyexplained in both sub-periods by coefficient changes (see Shields, 1996).



employers’ demands for training and skilled labour, respectively, changedsignificantly, accounting for nearly all of the growth in training. Thisfinding contrasts sharply to that for 1989 to 1994, where significantimprovements in the qualification base of the workforce drove theincrease in training incidence. The estimates suggest that had the educa-tional levels of the workforce remained at their 1989 figures, the period1989–1994 would have observed an actual decline in the average trainingincidence.

Taking into account the finding of Blundell et al. (1996, p. 45) thatformal qualifications gained after school appear to have no significanteffect on the probability of receiving employer-provided training, thispaper finds that one key to increased training at the workplace lies in acontinually improving education system, resulting in fewer youngstersleaving school with low-level qualifications or none.

Department of EconomicsUniversity of Leicester

Data of Receipt of Final Manuscript: September 1997

REFERENCES

Alba-Ramirez, A. (1994). ‘Formal Training, Temporary Contracts, Productivityand Wages in Spain’, BULLETIN, Vol. 56, pp. 161–70.

Arulampalam, W., Booth, A. and Elias, P. (1996). The Incidence and Duration ofWorkplace Training in the UK, University of Warwick, mimeo.

Arulampalam, W. and Booth, A. (1997). ‘Who Gets Over the Training Hurdle: AStudy of the Training Experiences of Young Men and Women in Britain’,Journal of Population Economics, Vol. 10, pp. 197–217.

Blackaby, D. and Murphy, P. (1995). ‘Earnings, Unemployment and Britain’sNorth-South Divide: Real or Imaginary?’ BULLETIN, Vol. 57, pp. 487–512.

Blundell, R., Dearden, L. and Meghir, C. (1996). The Determinants and Effects ofWork-Related Training in Britain. The Institute for Fiscal Studies.

Booth, A. (1990). Earnings and Learning: What Price Firm Specific Training?Birkbeck College, mimeo.

Booth, A. (1991). ‘Job-Related Formal Training: Who Receives It and What Is ItWorth?’, BULLETIN, Vol. 53, pp. 281–94.

Booth, A. and Snower, D. (eds.) (1996). Acquiring Skills: Market Failures, TheirSymptoms and Policy Responses. Cambridge University Press.

Campanelli, P. and Channell, J. (1994). Training: An Exploration of the Word andthe Concept with an Analysis of the Implications for Survey Design. EmploymentDepartment.

Constantine, J. and Neumark, D. (1996). ‘Training and the Growth of WageInequality’, Industrial Relations, Vol. 35, pp. 491–510.

Deloitte, Haskins and Sells (1989). Training in Britain. A Study of Funding, Activityand Attitudes. Employer’ Activities. HMSO, London.


210 BULLETIN

Felstead, A. and Green, F. (1996). ‘Training Implications of RegulationCompliance and Business Cycles’, in Booth, A. and Snower, D. (eds.),Acquiring Skills: Market Failures, Their Symptoms and Policy Responses.Cambridge University Press.

Felstead, A., Green, F. and Mayhew, K. (1997). Getting the Measure of Training.Centre For Industrial Policy and Performance, University of Leeds.

Finegold, D. (1992). ‘The Changing International Economy and its Impact onEducation and Training’, Oxford Studies in Comparative Education, Vol. 2, pp.57–81.

Gallie, D. and White, M. (1993). Employee Commitment and the Skills Revolution:First Findings from the Employment in Britain Survey. Policy Studies Institute,London.

Gomulka, J. and Stern, N. (1990). ‘The Employment of Married Women in theUnited Kingdom 1970–83’, Economica, Vol. 57, pp. 171-99.

Green, F. (1991). ‘Sex Discrimination in Access to Job-Related Training’, BritishJournal of Industrial Relations, Vol. 29, pp. 295–304.

Green, F. (1993a). ‘The Determinants of Training of Male and FemaleEmployees in Britain’, BULLETIN, Vol. 55, pp. 103–22.

Green, F. (1993b). ‘The Impact of Trade Union Membership on Training inBritain’, Applied Economics, Vol. 25, pp. 1033–43.

Green, F., Hoskins, M. and Montgomery, S. (1996a). ‘The Effects of CompanyTraining, Further Education and the Youth Training Scheme on the Earningsof Young Employees’, BULLETIN, Vol. 58, pp. 469–88.

Green, F., Machin, S. and Wilkinson, D. (1995). Unions and Training: An Analysisof Training Practices in Unionised and Non-Unionised Workplaces. University ofLeeds, School of Business Studies Discussion Paper.

Green, F., Machin, S. and Wilkinson, D. (1996b). The Determinants of WorkplaceTraining. University of Leeds, School of Business Studies Discussion Paper.

Greene, W. (1993). Econometric Analysis: Second Edition. Macmillan.Greenhalgh, C. and Mavrotas, G. (1993). ‘Workforce Training in the Thatcher

Era — Market Forces and Market Failures’, International Journal of Manpower,Vol. 14, pp. 17–32.

Greenhalgh, C. and Mavrotas, G. (1994). ‘The Role of Career Aspirations andFinancial Constraints in Individual Access to Vocational Training’, OxfordEconomic Papers, Vol. 46, pp. 579–604.

Greenhalgh, C. and Mavrotas, G. (1996). ‘Job Training, New Technology andLabour Turnover’, British Journal of Industrial Relations, Vol. 34, pp. 131–50.

Greenhalgh, C. and Stewart, M. (1987). ‘The Effects and Determinants ofTraining’, BULLETIN, Vol. 49, pp. 171–90.

Lynch, L. (1992). ‘Private Sector Training and the Earnings of Young Workers’,American Economic Review, Vol. 82, pp. 299–312.

Machin, S. (1996). ‘Changes in the Relative Demand for Skills’, In Booth, A. andSnower, D. (eds.), op cit.

Mealli, F., Pudney, S. and Thomas, J. (1996). ‘Training Duration and Post-Training Outcomes: A Duration-limited Competing Risks Model’, EconomicJournal, Vol. 106, pp. 422–33.

Miller, P. (1994). ‘Gender Discrimination in Training: An Australian Perspective’,British Journal of Industrial Relations, Vol. 32, pp. 539–59.



Nickell, S. (1982). ‘The Determinants of Occupational Success in Britain’, Reviewof Economic Studies, Vol. 49, pp. 43–53.

Oosterbeek, H. (1996). ‘A Decomposition of Training Probabilities’, AppliedEconomics, Vol. 28, pp. 799--805.

Rigg, M. (1989). Training in Britain: A Study of Funding, Activity and Attitudes:Individual’s Perspectives, Policy Studies Institute, London.

Shackleton, J. (1995). Training for Employment in Western Europe and the UnitedStates. Edward Elgar.

Shields, M. (1996). Changes in the Determinants of Work-Related Training: GreatBritain, 1984–1994. University of Leicester, Discussion Paper in Economics.

Shields, M. (1997). Changes in the Determinants of Employer-Funded Training forFull-Time Employees in Britain, 1984–1994. University of Leicester, DiscussionPaper in Economics.

Veum, J. (1996). ‘Gender and Race Differences in Company Training’. IndustrialRelations, Vol. 35, pp. 32–44.


212 BULLETIN

TABLE A1Changes in the Characteristics of British Employees

MFT FFT

Percentage 1984 1989 1994 1984 1989 1994

16–1920–2425–2930–3435–3940–4445–4950–5455+

6.111.912.112.513.110.710.09.5

14.1

5.411.913.112.412.313.310.48.7

12.5

2.79.7

14.014.412.712.613.6

9.311.0

11.420.312.9

8.810.410.0

9.78.77.8

9.419.2

144.710.310.012.6

9.88.25.8

3.615.316.712.511.112.213.6

9.06.0

Highest qualificationHigher degreeFirst degreeOther degreeHigher vocationalTeaching qualificationNursing qualification‘A’ level or equivalentSecondary vocational‘O’ level or equivalentCCSEOtherNo qualifications

2.05.62.73.90.80.35.2

15.912.24.63.8

43.0

2.16.82.44.50.80.35.5

18.714.74.99.3

30.0

3.19.72.9

15.50.60.56.4

21.313.2

4.58.2

14.1

0.74.80.80.84.05.06.74.6

26.27.44.6

34.4

1.06.50.91.33.35.18.27.1

27.65.87.4

25.8

2.110.2

1.28.23.15.48.1

11.723.2

4.95.9

16.0

Social class (defined by occupation)ProfessionalIntermediateSkilled non-manualSkilled manualPartly skilledUnskilled

7.023.011.737.014.55.2

6.925.712.735.214.43.9

8.132.511.630.113.4

3.2

1.425.643.5

9.817.6

1.8

2.229.942.7

9.114.5

1.5

3.437.735.6

8.712.4

2.0

IndustryAgriculture and fishingMining and quarryingManufacturingElectricity/gas/waterConstructionWholesale and retailHotels and restaurantsTransport, storage and communicationsFinanceReal estate, renting and businessPublic administrationEducationHealth and social workOther community, social and personal

1.93.0

34.92.5

10.010.91.49.23.34.67.54.72.73.4

1.72.0

34.22.39.2

12.01.59.73.86.17.83.92.53.3

1.41.0

31.11.88.2

13.11.79.74.68.07.94.53.43.6

0.70.5

24.71.21.5

14.94.14.06.56.07.89.4

14.93.8

0.50.3

21.50.91.6

15.33.74.87.88.47.99.3

14.13.9

0.50.2

17.30.71.7

12.73.34.07.89.28.9

11.217.8

4.7

Notes:1. Figures derived from the corresponding Labour Force Survey.



TABLE A2The British Determinants of Training Literature

Main Studies Data set (year of data collection)

All workersGreenhalgh and Stewart (1987)Rigg (1989)*Booth (1990)Booth (1991)Green (1991)Gallie and White (1993)*Green (1993a)Green (1993b)Greenhalgh and Mavrotas

(1993)*Greenhalgh and Mavrotas

(1994)Green et al. (1995)Arulampalam et al. (1996)Green et al. (1996b)Green and Felstead (1996)Greenhalgh and Mavrotas

(1996)

National Training Survey (1975)Training in Britain Survey (1987)Survey of Graduates and Diplomats (1987)British Social Attitudes Survey (1987)Labour Force Survey (1984)Employment in Britain Survey (1992)General Household Survey (1987)Labour Force Survey (1989)Labour Force Survey (1979, 1984, 1989)Training in Britain Survey (1987)Employers Manpower and Skills Practices

Survey (1990-91)Quarterly Labour Force Survey (1994, 1994)Employers Manpower and Skills Practices

Survey (1999–91)Labour Force Survey (1990, 1992)Labour Force Survey (1984, 1989)

Young workers onlyBlundell et al. (1996)Arulampalam and Booth (9197)

National Child Development Study(1981–1991 cohort data)

National Child Development Study(1981–1991 cohort data)

Note:1. ‘*’ indicates a descriptive rather than an econometric analysis.


214 BULLETIN

Documents

Changes in the Determinants of Employer-funded Training for Full-time Employees in Britain, 1984–94