Polarization in access to work-related training in Great Britain

Abstract

Policy-makers across industrialized nations have sought to increase participation in work-related training as a route to improved competitiveness. However, research conducted in Britain during the 1990s identified significant differences in participation, suggesting that processes of labour market polarization were being played out in unequal access to training. This article updates and builds upon this work through an analysis of British Labour Force Survey data. The analysis sought to assess continuing inequalities in work-related training, comparing the experiences of samples of public and private sector employees, in order to identify evidence of polarized access to skills development opportunities. The study also sought to establish if union representation increased participation and reduced inequalities in access to training. It was found that older workers, the lower skilled and unqualified, part-timers and temporary workers (among others) remained disadvantaged. Trade union presence had powerful positive effects on participation and reduced some inequalities, especially in the private sector.

Keywords

Employability equal opportunities human capital trade unions training

Introduction

Policy-makers across industrialized nations have spoken of the need to increase participation in training and embed a culture of learning at work as a route to improved productivity and competitiveness (BERR, 2008; European Commission, 2010). In Britain, concerns have also emerged that unequal access to training may be contributing to a ‘low skills equilibrium’ that has negative impacts on organizational efficiency and individual occupational performance (Wilson and Hogarth, 2003). Accordingly, government has argued that ‘a dynamic economy can only be achieved by giving everyone an equal chance to develop their skills and maximize their potential’ and that ‘equal access to vocational training means greater employability for workers and enhanced workplace satisfaction’ (DTI, 2006: 1).

However, efforts to promote increased, and more equal, access to training have taken place within a labour market context that is increasingly characterized by processes of polarization. Labour market polarization has seen a rise in higher skilled, well-paid employment, but also many more low-skilled jobs that offer few opportunities for progression (Goos et al., 2009). There is some evidence that these processes have also played out in access to training. A number of studies undertaken during the 1990s identified significant inequalities in access to work-related training, with lower participation rates among, for example, older workers (Taylor and Urwin, 2001), the unqualified (Blundell et al., 1996) and those working in lower skilled occupations (Arulampalam and Booth, 2001). Conversely, there was some evidence that public sector workers and those working in unionized workplaces were more likely to receive training.

There is a need to update and build upon this evidence in order to gauge the extent to which inequality in access to training remains a key dimension of polarization in the workplace. This article presents an analysis of British Labour Force Survey (LFS) data from 2007 to assess whether a range of individual and workplace factors predicted if workers had:¹ (a) received work-related training during the preceding three months; (b) not received training during that time, but had previously been offered training; or (c) never been offered training by their current employer. (By using data from the final quarter of 2007, we can avoid any distortive effects associated with the recession of 2008–2009 and its aftermath.)

The article also specifically focuses on the extent to which trade union presence/recognition was a predictor of access to training. Our particular interest in the potential for a ‘union effect’ is rooted in the generally positive evidence from the 1990s on the association between union recognition and access to training (Green et al., 1999), along with the increasing role of trade unions in promoting learning at work in Britain since 1998 (Heyes, 2007). Our aims are therefore to identify if there is evidence of polarization in access to training, and then to assess any positive union effect in mitigating such inequalities. Given the well-established differences in access to training in public and private sector workplaces (see next section for discussion), and the much higher level of trade union penetration in the former, we provide separate analyses for each of these sectors.

Following this introduction, part two of the article discusses the context for our research – previous evidence of inequalities in access to work-related training, the broader debate about skills and job polarization and the potential for a union effect in improving participation rates. The third and fourth parts describe the methodology and results of our analysis. The article concludes with a discussion of how inequalities in access to training reflected processes of polarization, the extent to which there was a union effect and the potential for unions to play a continuing role in promoting participation in skills development.

Context: Polarization, access to work-related training and the role of trade unions

Polarization and access to work-related training in Britain

British policy-makers’ expressed desire to promote ‘equal access to vocational training’ was faced with the reality of severe inequalities in participation rates during the 1990s, with significant disadvantage apparently faced by both older workers and younger women with caring roles, those with few qualifications, low-skilled workers, part-timers and temporary staff (see, for example, Arulampalam and Booth, 2001; Blundell et al., 1996; Böheim and Booth, 2004; Harris, 1999). Indeed, unequal access to training formed one theme of concerns regarding the apparent emergence of an increasingly polarized labour market during the 1980s (Gallie, 1991). These concerns deepened during the 1990s as evidence grew of ‘increased differentiation of labour market experiences . . . [for those in] higher level occupations, there has been a marked rise in skill levels, whereas for those in lower manual occupations skill development has been limited’ (Gallie, 1999: 245). An emerging literature identified evidence of an ‘hourglass economy’ (Brown et al., 2001; O’Leary and Oakley, 2008), defined by the polarization of a large group of skilled/qualified workers with access to continuous development, and growing numbers of unskilled people ‘at the bottom’ facing deskilling or stagnation (Ellingsæter, 2000; Goos et al., 2009).

There may be a number of different, but interrelated, drivers of polarization. It has been suggested that in ‘liberal’ economies such as Britain and the US, persistent wage inequality has resulted in increasing demand for low-level service work, i.e. a low-skilled, low-paid ‘service class’ of workers is increasingly required to provide the services demanded by high-skilled workers with a substantial disposable income to spend (Manning, 2004). Crucially, it has been suggested that a key impact of investments in new technologies has been the replacement of routinized, medium-skilled employment (especially in manufacturing) so that labour markets are increasingly dominated by high-skilled and low-skilled jobs, which for different reasons cannot easily be automated (Autor, 2010). The pervasiveness of polarization across different national/regional labour markets appears to suggest that this relative decline of routinized jobs in the ‘middle’ is indeed a key explanation (Goos et al., 2009). Certainly, British LFS data for the 1990s and 2000s suggest a clear decline in middle-ranked jobs in terms of pay and skills, while lower skilled service jobs saw substantial growth – a pattern that fits with the ‘routinization’ thesis. For Oesch and Rodriguez Menes (2011: 528) ‘the polarized pattern of occupational upgrading observed for Britain . . . is thus consistent with the idea that technology is a better substitute for the routine tasks typical of mid-range production and office jobs than for the non-routine tasks characteristic of low-paid interpersonal service jobs’.

Much of the research on polarization has focused on the occupational skills content and wage level of jobs. However, as noted above, one of the most troubling aspects of processes of polarization is that those at the bottom of the labour market may face becoming trapped in low-paid jobs that lack the training and progression routes that previously allowed people to move on to better employment – and this has also emerged as a theme for labour market studies (Gallie, 2007; Tregaskis and Brewster, 1998). For example, Dieckhoff et al.’s (2007) analysis of British Household Panel Survey (BHPS) data from the early 2000s found increasing polarization when comparing the training experiences of managers/professionals and low-/semi-skilled workers; and noted significant inequalities between full-time and part-time workers.

As noted earlier, employment sector may also matter. Specifically, previous research has suggested a public–private split, with significantly improved chances of accessing training for many public sector workers. Studies using the longitudinal National Child Development Survey (Blundell et al., 1996) and LFS data (Green et al., 1999) found significant positive effects associated with working in the public sector during the 1990s. These differences have been confirmed by analyses of BHPS data (Sousounis and Bladen-Hovell, 2010) and employer skills surveys (Dickerson and Wilson, 2009). As we discuss later, recent LFS data also suggest that, in general, participation rates remain higher in the public sector.

Trade unions and the ‘union effect’

The 1990s and 2000s also saw increasing interest in the potential role of unions in addressing inequalities in access to training at work. During this period, support for training emerged as a key element of ‘new unionism’ in Britain – ‘a lifeline for embattled trade unionists’ (Oxenbridge and Brown, 2002: 262) – following a period of retrenchment and decline during the 1980s. Union campaigns have since played an important role in everything from helping entry-level workers to develop literacy skills and identify promotion routes (Munro and Rainbird, 2004) to supporting re-skilling for those threatened with redundancy (Heyes, 2007). During the 2000s, centre-left administrations (at UK level and in the devolved national governments of Scotland and Wales) also provided funding for ‘Union Learning Representatives’ to facilitate access to training for excluded groups, with some positive results (Findlay et al., 2006; Stuart et al., 2010).

More generally, the evidence on the impact of union presence/recognition on access to work-related training is mixed, but on the whole positive. Hoque and Bacon’s (2008) analysis of the Workplace Employee Relations Survey (WERS) of 2004 found that trade union presence (and even the recognition of Union Learning Representatives) had only a weak influence on the take-up of training. However, there is earlier (if now somewhat dated) evidence of a stronger union effect. Böheim and Booth (2004) used WERS data from the late 1990s to identify an increased probability of training for individuals in unionized workplaces, supporting the conclusions of Booth et al.’s (2003) analysis of BHPS data from the early part of that decade. Green et al.’s (1999) research with LFS data from the early 1990s again suggested a higher probability of accessing training in union-recognized workplaces.

Combating polarization in access to training has been a stated priority for both government and the trade union movement in Britain. But both government and unions have faced a British workplace that, at least according to research conducted during the 1990s, has become defined by multiple, significant inequalities in access to training. We now report on an analysis of LFS data from 2007, identifying whether significant inequalities in participation in work-related training remained in place, and considering the extent of any union effect in promoting access to skills development opportunities.

Methodology

The data used for the analysis were taken from the fourth quarter (October–December) of LFS 2007 (Office for National Statistics, 2009). As noted earlier, the use of pre-2008 data means that results are unlikely to have been affected by the influence of the economic crisis that started in that year. The dataset contained information on adults under the state retirement age (16–59 for women, 16–64 for men) and employed in Britain during the reference week (excluding the self-employed, those on government training schemes and unpaid family workers).

Our analysis also focused on those individuals who had either recently engaged in work-related training (i.e. within the preceding three months);² or those who had not participated during that period and had never been offered training by their current employer. The middle group, those who answered that they had been offered training by their current employer at some time, but had not trained during the preceding three months, were included in the preliminary analysis and multinomial logistic model, but results are not reported in this article. Our reasons for this were two-fold. First, we were particularly interested in differences between those who reported having ready access to work-related training and those who had been denied training. Those who had participated during the preceding quarter, or conversely had never been offered training at any time, therefore represented clear counterpoints allowing us to highlight significantly different experiences in the workplace. Second, the middle group of LFS respondents answered positively when asked if their ‘current employer [had] ever offered any training or education’ and therefore represent an amalgam that includes those receiving substantial training relatively recently (for example, within the preceding six months), those whose only training may have been a short induction experienced many years previously and those who were offered training at some point but did not participate. Such an aggregated group is arguably so broad-based that it offers limited insights on inequalities in access to work-related training (this was confirmed by an initial analysis and subsequent results from the multinomial modelling process).

Following an exploratory analysis of the data, a number of modelling approaches were tested (including probit, generalized and multi-level models) before we decided on using a multinomial logistic regression model. This multinomial logistic approach was applied to two different models, for workers in the public sector and in the private sector. This was justified given our interest in whether inequalities in access to skills, and the role of any union effects, differed within public sector and private sector workplaces. The public and private sectors also provide contrasting settings. As already noted, research conducted during the 1990s suggested that training participation rates were then higher in the public sector. Trade union membership rates are also significantly higher in public sector workplaces. During the period covered by our research, only 15.9% of private sector employees were union members compared to 58.6% of their public sector counterparts (BERR, 2007). As outlined below, the public and private sectors were combined and a dummy variable used to indicate sector and to create interaction terms to reflect the effect of being employed in the private sector.

Our analysis deployed these models to account for the effects of union presence/ recognition alongside a range of other variables that previous research suggested may influence participation in training, including (see above and for a fuller literature review, Johnson et al., 2009):

Educational attainment (see, for example, Blundell et al., 1996);

Occupational skill levels (Arulampalam and Booth, 1998);

Length of tenure in employment (Harris, 1999);

Whether employment is full-time and/or permanent (Böheim and Booth, 2004);

Individuals’ family/caring responsibilities (Arulampalam and Booth, 2001);

Size of workplace and sector (Taylor and Urwin, 2001).

We also included control variables for age, gender, ethnicity and region of residence, taking ‘Scotland’ as the baseline.³ Our analysis therefore identified the effects of these different individual, workplace and other factors (within the limits of LFS data), as well as assessing the importance of trade union presence as a predictor of access to, and the chances of being offered, work-related training. LFS data cannot offer longitudinal insights into the long-term impacts of participating in (or being denied) training, but the size of the dataset and broad range of control variables that it provides are helpful in exploring factors influencing access to training. Given these benefits, LFS data have often been used in previous studies of work-related training (Harris, 1999; Newton et al., 2005; Taylor and Urwin, 2001), and provide the basis for what follows.

Findings from an analysis of the LFS

About the sample

Our data were drawn from the LFS for October–December 2007. During this quarter, the LFS suggested that 41.9% of public sector employees reported participating in training during the preceding three months, compared to just 25.3% of private sector workers (the national average was 30.0%). Our dataset contained 43,178 cases of which 12,180 were public sector employees and 30,998 were private sector employees. Table 1 provides information on the makeup of the public and private sector samples. While the ethnic balance of the samples was similar and broadly reflected the British labour force, it is notable that women made up more than two-thirds of public sector interviewees but fewer than 44% of those in our private sector group. Young people made up a much larger proportion of our private sector sample, with more than 16% of private employees aged under 25.

Table 1.

Demographic profile of public and private sector samples.

	Private		Public
	Count	%	Count	%
Training not offered	11,125	35.9%	1787	14.7%
Training offered	12,032	38.8%	5294	43.5%
Received training	7841	25.3%	5099	41.9%
Female	13,496	43.5%	8074	66.3%
Male	17,502	56.5%	4106	33.7%
Aged 16–24	5184	16.7%	655	5.4%
Aged 25–39	11,022	35.6%	4021	33.0%
Aged 40–49	7918	25.5%	4004	32.9%
Aged 50–64	6874	22.2%	3500	28.7%
Ethnic minority	2490	8.0%	925	7.6%
White	28,504	92.0%	11,252	92.4%
Degree or equivalent	5790	18.8%	4381	36.1%
Higher education	2624	8.5%	1891	15.6%
GCE A level or equiv.	7847	25.5%	2159	17.8%
Other qualifications	4306	14.0%	951	7.8%
No qualification	2649	8.6%	496	4.1%
GCSE grades A–C or equiv.	7560	24.6%	2261	18.6%
Managers, admin, professional and ass.	11,717	37.8%	6940	57.0%
Skilled trades occupations	3259	10.5%	225	1.8%
Personal service occupations	1962	6.3%	1756	14.4%
Elementary occupations	4206	13.6%	1013	8.3%
Other	9841	31.8%	2236	18.4%
50 or more employees	14,497	47.2%	8221	67.8%
Under 50 employees	16,249	52.8%	3909	32.2%
North East	1359	4.4%	655	5.4%
North West (inc. Merseyside)	3539	11.4%	1477	12.1%
Yorkshire and Humberside	2860	9.2%	1183	9.7%
East Midlands	2627	8.5%	864	7.1%
West Midlands	2620	8.5%	1058	8.7%
Eastern	3173	10.2%	1062	8.7%
London	3062	9.9%	1096	9.0%
South East	4928	15.9%	1683	13.8%
South West	2809	9.1%	1098	9.0%
Wales	1344	4.3%	706	5.8%
Scotland	2677	8.6%	1298	10.7%
Married or cohabiting	15,638	50.4%	7443	61.1%
Else	15,360	49.6%	4737	38.9%
Fewer than 6 months	3782	12.2%	755	6.2%
6 months but fewer than 12	2706	8.8%	522	4.3%
1 year but fewer than 2	3969	12.8%	985	8.1%
2 years but fewer than 5	7172	23.2%	2462	20.2%
More than 5 years	13,268	42.9%	7441	61.2%
Part-time job	6878	22.2%	3564	29.3%
Full-time job	24,116	77.8%	8614	70.7%
Not permanent job in some way	1526	4.9%	816	6.7%
Permanent	29,467	95.1%	11,364	93.3%
Pay conditions affected by agreements between trade union and employer	5004	20.5%	7181	71.6%
Not affected	19,378	79.5%	2843	28.4%
One or more children under 5 years old	4446	14.3%	1587	13.0%
No children under 5 years old	26,552	85.7%	10,593	87.0%
A–B: Agriculture & fishing	307	1.0%	23	0.2%
C,E: Energy & water	548	1.8%	36	0.3%
D: Manufacturing	5937	19.2%	81	0.7%
F: Construction	2325	7.5%	174	1.4%
G–H: Distribution, hotels & restaurants	8057	26.0%	126	1.0%
I: Transport & communication	2560	8.3%	351	2.9%
J–K: Banking, finance & insurance, etc.	6451	20.8%	341	2.8%
L–N: Public admin, educ. & health	3209	10.4%	10,460	85.9%
O–Q: Other services	1564	5.1%	578	4.7%

Inequalities in access to work-related training

Both private sector and (to a somewhat lesser extent) public sector workforces were characterized by multiple, significant inequalities in access to work-related training. Tables 2 and 3 present the findings for first public sector and then private sector employees. (These tables contrast those ‘never offered training’ and ‘receiving training in the preceding three months’. The contrast between ‘never offered training’ and ‘offered training at some time’ is not shown but is available from the authors.)

Table 2.

Multinomial logistic regression: participated in training in previous 13 weeks vs never offered training (public sector).

	Coef.	SE	Sig.	Odds ratio	95.0% CI for Exp(B)
					Lower	Upper
Female	0.302	0.083	0.000	1.353	1.150	1.592
Reference: male
Aged 16–24	−0.056	0.150	0.707	0.945	0.704	1.268
Aged 40–49	−0.047	0.090	0.602	0.954	0.800	1.138
Aged 50–64	−0.130	0.099	0.192	0.878	0.723	1.067
Reference: aged 25–39
Ethnic minority	−0.275	0.128	0.031	0.760	0.591	0.975
Reference: white
Degree or equivalent	0.291	0.110	0.008	1.337	1.078	1.659
Higher education	0.608	0.128	0.000	1.837	1.430	2.361
GCE A level or equiv.	0.209	0.111	0.059	1.233	0.992	1.531
Other qualifications	−0.305	0.135	0.024	0.737	0.565	0.961
No qualification	−1.298	0.188	0.000	0.273	0.189	0.395
Reference: GCSE grades A–C or equiv.
Managers, admin, professional and ass.	1.012	0.099	0.000	2.751	2.266	3.340
Skilled trades occupations	0.085	0.237	0.720	1.089	0.684	1.733
Personal service occupations	0.748	0.117	0.000	2.114	1.681	2.658
Elementary occupations	−0.765	0.143	0.000	0.465	0.351	0.616
Reference: other
50 or more employees	−0.188	0.075	0.012	0.829	0.716	0.960
Reference: under 50 employees
North East	0.247	0.176	0.161	1.280	0.906	1.809
North West (inc. Merseyside)	0.205	0.136	0.132	1.227	0.940	1.602
Yorkshire and Humberside	0.246	0.146	0.093	1.279	0.960	1.705
East Midlands	0.341	0.162	0.035	1.407	1.024	1.932
West Midlands	0.037	0.148	0.803	1.038	0.776	1.388
Eastern	0.378	0.154	0.014	1.459	1.079	1.972
London	−0.006	0.153	0.969	0.994	0.737	1.341
South East	0.253	0.135	0.061	1.288	0.988	1.680
South West	0.595	0.156	0.000	1.813	1.336	2.461
Wales	0.407	0.179	0.023	1.502	1.059	2.132
Reference: Scotland
Married or cohabiting	0.061	0.076	0.420	1.063	0.917	1.232
Reference: else
Fewer than 6 months	−0.497	0.135	0.000	0.608	0.467	0.793
6 months but fewer than 12	−0.151	0.165	0.359	0.860	0.622	1.187
1 year but fewer than 2	−0.135	0.134	0.316	0.874	0.671	1.137
More than 5 years	0.172	0.090	0.056	1.188	0.996	1.417
Reference: 2 years but fewer than 5
Part-time job	−0.476	0.082	0.000	0.621	0.529	0.730
Reference: full-time job
Not permanent job in some way	−0.404	0.127	0.001	0.667	0.520	0.856
Reference: permanent
Pay conditions affected by agreements between trade union and employer	0.476	0.073	0.000	1.610	1.395	1.857
Reference: not affected
One or more children under 5 years old	0.233	0.179	0.192	1.262	0.890	1.791
Reference: no children under 5 years old
A–B: Agriculture & fishing	−0.171	0.942	0.856	0.843	0.133	5.338
C,E: Energy & water	−0.489	0.495	0.323	0.613	0.233	1.618
D: Manufacturing	−0.396	0.409	0.333	0.673	0.302	1.501
F: Construction	0.098	0.310	0.752	1.103	0.601	2.026
G–H: Distribution, hotels & restaurants	0.157	0.329	0.632	1.170	0.615	2.229
I: Transport & communication	−0.627	0.229	0.006	0.534	0.341	0.838
J–K: Banking, finance & insurance, etc.	0.672	0.262	0.010	1.959	1.171	3.276
L–N: Public admin, educ. & health	0.431	0.147	0.003	1.539	1.153	2.054
Reference: O–Q: Other services
Interaction term between child under 5 and gender	−0.582	0.216	0.007	0.559	0.366	0.854
Constant	−0.381	0.229	0.097
Valid sample size	9941
Missing	2239
Total	12,180
Cox and Snell	0.139
Nagelkerke	0.161
−2 Log likelihood	16040.76

Table 3.

Multinomial logistic regression: participated in training in previous 13 weeks vs never offered training (private sector).

	Coef.	SE	Sig.	Odds ratio	95.0% CI for Exp(B)
					Lower	Upper
Female	0.116	0.045	0.009	1.123	1.029	1.226
Reference: male
Aged 16–24	0.520	0.060	0.000	1.683	1.497	1.891
Aged 40–49	−0.175	0.050	0.000	0.840	0.761	0.927
Aged 50–64	−0.502	0.057	0.000	0.606	0.542	0.677
Reference: aged 25–39
Ethnic minority	−0.070	0.071	0.322	0.932	0.812	1.071
Reference: white
Degree or equivalent	0.389	0.060	0.000	1.475	1.310	1.660
Higher education	0.448	0.072	0.000	1.566	1.359	1.804
GCE A level or equiv.	0.066	0.051	0.193	1.069	0.967	1.181
Other qualifications	−0.316	0.065	0.000	0.729	0.642	0.828
No qualification	−1.053	0.087	0.000	0.349	0.295	0.413
Reference: GCSE grades A–C or equiv.
Managers, admin, professional and ass.	0.598	0.051	0.000	1.819	1.647	2.009
Skilled trades occupations	0.207	0.070	0.003	1.230	1.072	1.412
Personal service occupations	0.777	0.093	0.000	2.174	1.812	2.608
Elementary occupations	−0.328	0.064	0.000	0.721	0.635	0.817
Reference: other
50 or more employees	0.553	0.039	0.000	1.739	1.610	1.878
Reference: under 50 employees
North East	−0.052	0.106	0.624	0.949	0.771	1.169
North West (inc. Merseyside)	−0.241	0.081	0.003	0.786	0.670	0.921
Yorkshire and Humberside	−0.236	0.086	0.006	0.789	0.668	0.934
East Midlands	−0.126	0.087	0.148	0.881	0.743	1.046
West Midlands	−0.108	0.087	0.215	0.898	0.758	1.065
Eastern	−0.084	0.083	0.313	0.920	0.782	1.082
London	−0.454	0.087	0.000	0.635	0.536	0.753
South East	−0.238	0.075	0.002	0.788	0.680	0.914
South West	−0.167	0.085	0.050	0.847	0.717	1.000
Wales	−0.041	0.103	0.689	0.959	0.783	1.175
Reference: Scotland
Married or cohabiting	−0.110	0.043	0.010	0.896	0.824	0.974
Reference: else
Fewer than 6 months	0.047	0.063	0.454	1.048	0.927	1.185
6 months but fewer than 12	−0.082	0.071	0.250	0.921	0.801	1.059
1 year but fewer than 2	−0.073	0.062	0.239	0.929	0.822	1.050
More than 5 years	0.158	0.049	0.001	1.171	1.064	1.289
Reference: 2 years but fewer than 5
Part-time job	−0.244	0.051	0.000	0.783	0.708	0.866
Reference: full-time job
Not permanent job in some way	−0.305	0.086	0.000	0.737	0.623	0.873
Reference: permanent
Pay conditions affected by agreements between trade union and employer	0.765	0.050	0.000	2.150	1.949	2.371
Reference: not affected
One or more children under 5 years old	0.070	0.070	0.318	1.073	0.935	1.231
Reference: no children under 5 years old
A–B: Agriculture & fishing	−0.375	0.215	0.081	0.687	0.451	1.047
C,E: Energy & water	0.850	0.175	0.000	2.338	1.659	3.296
D: Manufacturing	−0.138	0.094	0.144	0.871	0.724	1.048
F: Construction	0.262	0.107	0.014	1.299	1.054	1.601
G–H: Distribution, hotels & restaurants	0.096	0.090	0.282	1.101	0.924	1.313
I: Transport & communication	0.029	0.106	0.782	1.030	0.837	1.268
J–K: Banking, finance & insurance, etc.	0.436	0.091	0.000	1.547	1.295	1.847
L–N: Public admin, educ. & health	1.439	0.100	0.000	4.217	3.463	5.134
Reference: O–Q: Other services
Interaction term between child under 5 and gender	−0.482	0.108	0.000	0.618	0.500	0.763
Constant	−0.973	0.123	0.000
Valid sample size	24,039
Missing	6959
Total	30,998
Cox and Snell	0.176
Nagelkerke	0.199
−2 Log likelihood	44302.14

The human capital factors (educational attainment and occupational skills) identified as significant by previous studies again appeared to be important. Higher qualified employees were significantly more likely to report participating in training, while the lowest qualified were disadvantaged.⁴ For example, both public and private sector employees holding ‘higher education’ qualifications were more than one and a half times more likely than our ‘low–medium qualified’ reference group (those qualified to the equivalent of English GCSE, the main school qualification taken by 14- to 16-year-olds) to report receiving training (an odds ratio of approximately 1.57–1.84 in the two sectors). Employees educated to degree level were also significantly more likely to have engaged in training (1.34 and 1.48 times more likely than the reference group for the public and private sectors respectively). Conversely, both public and private sector employees with no qualifications (or ‘other’ low level qualifications) were significantly less likely to have participated.

In both the public and private sectors, those in the lowest skilled ‘elementary occupations’ were significantly less likely to report participating in training, even though we used the medium-skilled, broad categorization of ‘other occupations’ as our reference value.⁵ In this case, negative effects were stronger in the public sector (a decrease in the odds ratio of 54% among public workers in elementary jobs compared to only 28% for their private sector counterparts). Meanwhile, managers, professionals and administrators were significantly more likely than the reference group to have participated in training (by 2.75 and 1.82 times in the public and private sectors respectively). It is notable that inequalities defining access to training for both the lowest and highest skilled employees were more pronounced in the public sector.

A number of workplace and employment factors were also significant. In many cases, there were consistent trends across public and private sectors, but again with stronger effects in the former. For example, working in a part-time job decreased the odds of participating in training (by 37.9% and 21.7% among public sector and private sector employees respectively). Similar inequalities existed between temporary and permanent employees in both sectors, while in relation to length of tenure, those who had been employed for more than five years were significantly more likely to have engaged in work-related training (an odds ratio of 1.19 and 1.17 in the public and private sectors) compared to our reference category of those with tenures of two to five years. Among public sector employees there were also negative effects associated with having tenure of fewer than six months.

Previous studies have suggested that ‘size matters’, and that employees in larger workplaces have better access to skills provision. Our analysis of LFS data supports this argument, and especially in the private sector, where employees based in workplaces with more than 50 staff were 1.74 times more likely to have received training than their counterparts in smaller companies. As a final workplace factor, we included a Standard Industry Classification variable (reflecting a range of sub-sectors within both the public and private sectors). In line with previous studies, we found significant positive effects associated with working in certain industries within the private sector (such as the construction and energy sectors and financial services) where there are well-established and highly formalized training and professional development structures (Spilsbury, 2003). As already noted, participation rates were generally much higher in the public sector, so it is perhaps understandable that there was a strong significant effect associated with being a private sector employee working in more public-oriented areas of the economy (for example, working for private organizations engaged in health-related activities).

Finally, a number of our individual-level control variables were significant. Previous studies have suggested that, in the 1990s, older workers were significantly less likely to be offered training opportunities (Taylor and Urwin, 2001). These inequalities remained in place in 2007, but were only significant for private sector employees. Significant age-based differences were also present throughout the private sector sample, with all employees in older age groups significantly less likely to have participated in training, and large positive effects associated with falling into the youngest age group (employees aged 16–24 were more than one and a half times more likely to report training compared to the reference group).

The official data included in our models offered only a binary ‘white/non-white’ variable covering workers’ ethnic origin. We included this variable in both models, and being ‘non-white’ had a significant negative effect in the public sector. However, we should acknowledge the need for further research to identify the complex differences within and between specific minority groups. For example, a previous study has suggested that employees of Bangladeshi ethnicity were most likely to report never having been offered training by their employer, whereas those describing themselves as ‘Black British’ were least likely to have experienced such disadvantage (TUC, 2005).

Female employees were significantly more likely to report having participated in training (being female increased the odds of training by 1.35 times for public sector and 1.12 for the private sector). An indicator for employees having children aged under five at first appeared to be the only variable that was not significant in both models. However, following exploratory analysis we found that the effects of having children aged under five were different for men and women, and we therefore included an interaction term between the ‘gender’ and ‘children’ variables. In both models, this produced a significant negative effect, suggesting that women with young children were less likely to participate in training than all other groups.

In summary, these findings suggest that in 2007 there remained significant inequalities in access to work-related training. Across both the private and public sectors there was evidence of a continuing polarization in access, with those older, low-skilled and unqualified workers less likely to have participated in training and development opportunities (and, crucially, more likely to report never having been offered training by their employer). Those who have raised concerns about a broader polarization of opportunities in Britain’s ‘hourglass economy’ (O’Leary and Oakley, 2008) appear to be supported by this evidence, while our analysis also suggests that workers excluded from a primary labour market of permanent, full-time positions within larger organizations (along with women with childcare responsibilities) may also have fewer opportunities for learning and occupational mobility.

It is also of considerable interest that some of these inequalities were of a greater magnitude in the public sector sample. This is not to say that public sector workers were less likely in general to participate in work-related training. As noted already, at the time of the research, the LFS found higher participation rates among public employees, more than two-fifths of whom reported training in the preceding three months, compared to only one-quarter of their private sector counterparts. However, while public sector workers were generally much more likely to participate in work-related training, there were sometimes greater inequalities, as represented by strength of effects, between different groups. To investigate the nature of the differences between the two sectors interaction terms were included in a combined dataset of private and public sector respondents. The interaction term was used to indicate if an individual was a private sector employee. This showed that men, those aged 16–24, those employed for more than six months, part-timers and those in elementary occupations were significantly more likely to have received training if they worked in the private (rather than public) sector. Those employed in the private sector aged 50–64, married/cohabiting or who were employed as managers were less likely to have received training. In the private sector the interaction between pay and conditions affected by agreements between trade union and employer showed a significant positive association with the likelihood of receiving training so reinforcing the value of union presence.

Was there a union effect?

Our analysis of predictors of participation in training also found that there were strong, significant positive effects associated with trade union presence/recognition (defined by whether ‘agreements between trade unions and employers affected pay and conditions’ in respondents’ workplaces).⁶

Within the public sector sample, those employed in workplaces where trade unions were recognized in agreeing pay and conditions through collective bargaining were significantly more likely to report having recently participated in training, with an odds ratio of 1.61 (i.e. public sector employees working in union-recognized environments were more than one and a half times more likely to report having received training, compared to their counterparts in non-recognized workplaces). Among private sector employees the effect was even stronger, with employees in union-recognized workplaces more than twice as likely (odds ratio: 2.15) to have recently participated in training.

It has been suggested that an additional positive union effect may be that unionized workplaces tend to be characterized by less inequality in access to training (Hoque and Bacon, 2008). To further investigate this issue, we computed models using interaction terms for employees working in unionized and non-unionized workplaces in each of the public and private sectors. The significance of the coefficients of the variables was then compared, and there were generally fewer significant differences between employees in unionized environments, but with more consistent findings for our private sector sample. That said, some key aspects of polarization remained among private sector employees in both unionized and non-unionized environments. For example, those occupying managerial, professional and skilled positions were significantly more likely to report participating in training, while elementary/unskilled workers remained less likely to have trained (although the strength of these negative effects was somewhat reduced for unionized workplaces). Yet other significant inequalities appeared to be eliminated where unions were present. Among those in non-unionized workplaces, part-timers, those with job tenures of fewer than five years and those working for smaller employers were less likely to report training, but these groups were not significantly disadvantaged in unionized workplaces. Among the non-unionized group, those with higher level qualifications were more likely to have participated in training. There were again no such statistically significant advantages for higher qualified employees in our unionized private sector sample, suggesting that inequality in access to training between the high-qualified and ‘the rest’ was alleviated where unions were present. (However, there appeared to be a very limited union effect for the unqualified, who remained significantly less likely to have participated in training irrespective of union coverage.)

Within our public sector sample, there was less clear evidence of union presence alleviating inequalities in access to skills. In some cases working in unionized workplaces had a positive effect in reducing significant inequalities (for example, for women with children aged under five). But there were also examples where unions appeared irrelevant (unqualified workers remained significantly disadvantaged in both unionized and non-unionized workplaces); and for some groups (for example, part-timers) inequalities were greater where unions were present. There is a need for further research on the relationship between union effects and access to training in the public sector. The high level of union penetration in the public sector may mean that those working in non-unionized workplaces may be concentrated within a relatively small number of specialist organizations/occupations (where there are specific factors driving differences in participation).

In summary, while union effects were not consistent across all the inequalities in access to skills discussed above, there is evidence that trade union presence alleviated some of the disadvantage experienced by the most vulnerable groups, at least in the private sector. Of course, as we note above, being employed in a unionized workplace in itself generally increased workers’ chances of participating in training in both public and private sectors.

Discussion

Our research raises a number of important issues. First, we have demonstrated that inequalities in access to work-related training remain present in the public as well as private sectors. Furthermore, while public sector workers were in general more likely to report participating in training than their private sector counterparts, differences in participation between employees within the public sector were sometimes greater. This reminds us that, despite generally higher participation rates in the public sector, there remains a need to promote development opportunities for employees at all levels, including those in entry-level/lower skilled positions and older workers. Our findings also suggest that employers’ failure to offer training was in itself a key barrier to participation for many disadvantaged workers – it is important to recall that the LFS specifically identifies people who ‘have never been offered training by their current employer’ – again raising questions about the potential need for more interventionist approaches to promoting greater (and more equitable) investment in human capital.

More generally, as already noted, this study has broader value in contributing to the continuing debate around the polarization of opportunities in the labour market. A growing literature has identified how changes in the structure of opportunities have led to an increase in relatively well-paid, skilled jobs but also more low-paid, unskilled jobs characterized by few opportunities for progression. Concerns over such processes of polarization, through which the job market’s ‘middle is disappearing’, have informed research in Britain and elsewhere (Goos et al., 2009). Crucially, it has also been suggested that processes of polarization are reflected in access to work-related training, and our findings broadly support this thesis. Polarization in access to training is particularly important, because it means that higher skilled people are consistently able to increase their advantage vis-a-vis the less skilled, embedding and reinforcing inequalities in the labour market (Whitfield, 2000). This has the potential to feed into wage inequalities, with further negative consequences for social and occupational mobility (O’Leary and Oakley, 2008). We have demonstrated that LFS data suggest that those at the bottom end of the labour market – lower skilled and unqualified workers – are much more likely to report receiving no offer of training from their employers, while the highest skilled and qualified are among those most likely to participate in training. We are also able to add to the evidence of a continuing polarization in training participation between those in stable, permanent, full-time employment and those in less secure, part-time work on the margins of the labour market.

What role can trade unions play in addressing these issues? LFS data certainly point to a positive union effect – union members and people in unionized workplaces tend to be more likely to have access to work-related training. There is also some suggestion that, in general, union presence may lessen polarization in access to training across different groups of workers (although, as noted, further research is required on sectoral and workplace differences in the extent of union effects in diminishing training inequalities). Our findings appear to complement recent qualitative research highlighting the potentially important role of unions in facilitating access to training, especially for disadvantaged groups – we have noted that government initiatives designed to support union officers to facilitate access to training have produced some positive results (Stuart et al., 2010). The centre-right coalition that came to power in Britain in 2010 has supported the principle of unions continuing to facilitate access to workplace learning (Cable, 2010), but the resources available to maintain this work are likely to be severely limited. For our part, the analysis in this article adds to the evidence that unions have the potential to play a positive role in combating inequalities.

We should acknowledge the limitations of this study. Further research is required to gain a better understanding of the content, quality and utility of training offered, geographical differences in opportunities, and long-term returns in terms of qualifications and/or improved earnings. A more detailed exploration of polarization in training participation using longitudinal data may also allow us to examine any longer-term negative impacts on career progression experienced by those denied opportunities for skills development. There is also clear scope for further cross-national research. Processes of labour market polarization appear to be consistent across a range of different national contexts (Goos et al., 2009), but it is clear that employment regimes and labour market institutions play some role in explaining substantial international differences (Oesch and Rodriguez Menes, 2011). There remains work to do in defining and understanding the extent to which different institutional and labour market contexts contribute to processes of polarization and unequal access to opportunities to develop skills.

Conclusions

Policy-makers have expressed a commitment to combating inequalities in access to work-related training. However, our analysis of British LFS data has demonstrated that severe inequalities remained in place in 2007. Older workers, those with lower educational attainment and working in low-skilled jobs, temporary and part-time employees and women with young children were significantly disadvantaged. Once again ‘the evidence is that skills development provision is often accessed by many of the most able, rather than those most in need, who are in fact often least likely to have access to opportunities’ (Johnson et al., 2009: 54). The evidence suggests that processes of labour market polarization continue to be reflected in access to training, and that these problems remain relevant in both the public and private sectors. There is some evidence that union effects – especially in the private sector – may offer some protection against the polarization of training opportunities.

In terms of public policy recommendations, the preceding analysis points to the need for more interventionist strategies to promote access to training and combat polarization in access to skills development and progression. The evidence also supports policies to ensure a role for unions in facilitating access to training for those currently excluded. There remains a clear need for policy-makers to work with employers and trade unions to promote more, and more equitable, work-related training in both the private and public sectors. In the absence of urgent policy action, processes of polarization – which risk denying training and progression to those outside a core of more skilled, stable jobs – will continue to trap the disadvantaged at the bottom end of the labour market. Such inequality and limited mobility is likely to have negative impacts on both economic productivity and well-being.

Footnotes

Acknowledgements

The authors are grateful for the helpful comments of two anonymous referees.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

Author biographies

Colin Lindsay is senior lecturer at the Department of Human Resource Management, University of Strathclyde. He has published research on skills issues in journals including Work, Employment and Society and the Human Resource Management Journal. He also recently co-edited special issues of the journals Policy Studies and Environment and Planning C: Government and Policy, both focusing on employability and labour market policies.

Jesus Canduela is an applied statistician and research fellow at the Employment Research Institute, Edinburgh Napier University. He has published research on employment and training issues in journals including Work, Employment and Society, Journal of Education and Work and Economic and Industrial Democracy.

Robert Raeside is an applied statistician who has research interests in demography, evaluation of interventions in social systems and in social network analysis. He publishes in these areas and supervises a number of research students.

References

Arulampalam

Booth

(1998) Training and labour market flexibility: Is there a trade-off? British Journal of Industrial Relations 36(4): 521–536.

Arulampalam

Booth

(2001) Learning and earning: Do multiple training events pay? A decade of evidence from a cohort of young British men. Economica 68: 379–400.

Autor

(2010) The Polarization of Job Opportunities in the US Job Market. Washington, DC: Center for American Progress.

BERR (Department for Business, Enterprise and Regulatory Reform) (2007) Trade Union Membership. London: BERR.

BERR (Department for Business, Enterprise and Regulatory Reform) (2008) It’s Time to Talk Training: How to Develop a Dialogue on Skills at the Workplace. London: BERR.

Blundell

Dearden

Meghir

(1996) The Determinants and Effects of Work-related Training in Britain. London: Institute for Fiscal Studies.

Böheim

Booth

(2004) Trade union presence and employer-provided training in Britain. Industrial Relations 43(3): 520–545.

Booth

Francesconi

Zoega

(2003) Unions, work-related training and wages: Evidence for British men. Industrial and Labour Relations Review 57(1): 520–545.

Brown

Green

Lauder

(2001) High Skills: Globalization, Competitiveness and Skill Formation. Oxford: Oxford University Press.

10.

Cable

(2010) Remarks by the Secretary of State for Business, Innovation and Skills to the Unionlearn Annual Conference. 12 July. Available at: www.unionlearningfund.org.uk/news/news.cfm/newsid/51 (accessed 4 August 2010).

11.

Dickerson

Wilson

(2009) The Incidence and Intensity of Employer-provided Training. Sheffield: Sheffield Economic Research Papers.

12.

Dieckhoff

Jungblut

J-M

Connell

(2007) Job-related training in Europe: Do institutions matter? In: Gallie

(ed.) Employment Regimes and the Quality of Work. Oxford: Oxford University Press, pp. 77–103.

13.

DTI (Department for Trade and Industry) (2006) Age Legislation Fact Sheet 4: Vocational Training. London: DTI.

14.

Ellingsæter

(2000) Scandinavian transformations: Labour markets, politics and gender division. Economic and Industrial Democracy 21(3): 335–359.

15.

European Commission (2010) New Skills for New Jobs: Action Now. Brussels: European Commission.

16.

Findlay

Stewart

Dutton

. (2006) Evaluation of the Scottish Union Learning Fund. Edinburgh: Scottish Executive.

17.

Gallie

(1991) Patterns of skill change: Upskilling, deskilling or the polarisation of skills? Work, Employment and Society 5(3): 319–351.

18.

Gallie

(1999) The polarisation of the labour market and exclusion of vulnerable groups. In: Isaksson

Hogstedt

Eriksson

al.

(eds) Health Effects of the New Labour Market. New York: Kluwer, pp. 245–266.

19.

Gallie

(2007) Production regimes and the quality of employment in Europe. Annual Review of Sociology 33: 85–104.

20.

Goos

Manning

Salomons

(2009) Job polarisation in Europe. American Economic Review Papers and Proceedings 99(2): 58–63.

21.

Green

Machin

Wilkinson

(1999) Trade unions and training practices in British workplaces. Industrial and Labour Relations Review 52(2): 179–195.

22.

Harris

(1999) The determinants of work-related training in Britain in 1995 and the implications of employer size. Applied Economics 31(4): 451–463.

23.

Heyes

(2007) Training, social dialogue and collective bargaining in Western Europe. Economic and Industrial Democracy 28(2): 239–258.

24.

Hoque

Bacon

(2008) Trade unions, union learning representatives and employer-provided training in Britain. British Journal of Industrial Relations 46(4): 702–731.

25.

Johnson

Sawicki

Pearson

. (2009) Employee Demand for Skills: A Review of Evidence and Policy. UKCES Evidence Report 3. London: UK Commission for Employment and Skills.

26.

Manning

(2004) We can work it out: The impact of technological change on the demand for low-skill workers. Scottish Journal of Political Economy 51(5): 581–608.

27.

Munro

Rainbird

(2004) Opening doors as well as banging on tables: An assessment of UNISON/employer partnerships on learning in the UK public sector. Industrial Relations Journal 35(5): 419–433.

28.

Newton

Hurstfield

Miller

. (2005) Training Participation by Age amongst Unemployed and Inactive People. Department for Work and Pensions Research Report 291. London: Stationery Office.

29.

Oesch

Rodriguez Menes

(2011) Upgrading or polarization? Occupational change in Britain, Germany, Spain and Switzerland, 1990–2008. Socio-Economic Review 9(3): 503–531.

30.

Office for National Statistics (2009) Labour Force Survey (Social and Vital Statistics Division and Northern Ireland Statistics and Research Agency). Colchester: UK Data Archive.

31.

O’Leary

Oakley

(2008) The Skills Paradox. London: DEMOS.

32.

Oxenbridge

Brown

(2002) The two faces of partnership? An assessment of partnership and co-operative employer/trade union relationships. Employee Relations 24(3): 262–276.

33.

Sousounis

Bladen-Hovell

(2010) Persistence in the determination of work-related training participation: Evidence from the BHPS, 1991–1997. Economics of Education Review 29(6): 1005–1015.

34.

Spilsbury

(2003) Learning and Training at Work. Department for Education and Skills Research Report RR399. London: Department for Education and Skills.

35.

Stuart

Cook

Cutter

. (2010) Assessing the Impact of Union Learning Representatives and the Union Learning Fund: Union and Employer Perspectives. Leeds: University of Leeds.

36.

Taylor

Urwin

(2001) Age and participation in vocational education and training. Work, Employment and Society 15(4): 763–779.

37.

Tregaskis

Brewster

(1998) Training and development in the UK context: An emerging polarisation? Journal of European Industrial Training 22(4/5): 180–189.

38.

TUC (Trades Union Congress) (2005) Workplace Training: A Race for Opportunity. London: TUC.

39.

Whitfield

(2000) High-performance workplaces, training, and the distribution of skills. Industrial Relations 39(1): 1–25.

40.

Wilson

Hogarth

(2003) Tackling the Low Skills Equilibrium: A Review of Some Issues and Some New Evidence. London: DTI.