Previous research on the determinants of age-training gaps

Lower training rates among older people are usually explained in terms of human capital theory (Becker Reference Becker1964). Theory predicts that the returns to human capital investments are lower for older workers because productivity and wage gains are experienced for a shorter time owing to the proximity of retirement. Therefore, older people will have less incentive to train and employers less incentive to invest in their training. As such, lower training rates among older people are consistent with predictions of human capital theory. One interpretation is that lower acceptance rates for training offers among older workers reflect entirely rational behaviour (O'Mahony and Peng Reference O'Mahony and Peng2008; McNair and Flynn Reference McNair and Flynn2005; Urwin Reference Urwin2006). However, Taylor and Urwin (Reference Taylor and Urwin2001) find that the lower incidence of training among older workers is mainly due to reduced offers from employers rather than rejections of offers by employees.

Nevertheless, the theoretical prediction that returns to training decline with age requires some qualification. For employers, the expected duration of employment, rather than age, should be the crucial factor in training decisions and rates of turnover and absenteeism are lower among older workers (Taylor and Urwin Reference Taylor and Urwin2001). Retirement age also varies with institutional arrangements and the generosity of pension plans by occupation and employer (Montizaan, Cörvers and De Grip Reference Montizaan, Cörvers and De Grip2010). There are also notable country differences in employment policies, pension arrangements and scope for lifelong learning (Blossfeld, Buchholz and Kurz Reference Blossfeld, Buchholz and Kurz2011). For example, the expectation might be that there are higher training rates for older people in Nordic social democracies where there are more opportunities for training over the lifecourse and strong active labour market policies (Hofäcker and Pollnerová Reference Hofäcker, Pollnerová, Blossfeld and Buchholz2006). In contrast, training rates among older people may be lower in conservative welfare states, such as Germany and the Netherlands, which have generous and extensive early retirement programmes (Blossfeld, Buchholz and Kurz Reference Blossfeld, Buchholz and Kurz2011: 17).

From the workers’ standpoint, wage returns to training will vary according to characteristics of the workplace. For example, final salary occupational pensions could raise the monetary returns to training but less so if age is a barrier to promotion. Wage returns to training may also be lower for those already on higher seniority-wages (Vignoles, Galindo-Rueda and Feinstein Reference Vignoles, Galindo-Rueda and Feinstein2004). Training costs will certainly impact on the decision to train and these vary with factors such as timing and duration. The inconvenience of time and location has been shown to be an important factor for older workers (Sussman Reference Sussman2002) and training during paid work-hours may therefore be a preferred option. Fees are a particular concern for older non-workers (Chapman, Crossley and Kim Reference Chapman, Crossley and Kim2003). Older people are also deterred by psychological costs of training linked to fear and lack of confidence in the ability to succeed (Cully et al. Reference Cully, Vanden-Heuvel, Wooden and Curtain2000).

From the employers’ perspective, it is reasonable to be selective about which employees to train. This selection appears to reflect preconceptions about the potential benefits of training to both the employee and the employer (Vignoles, Galindo-Rueda and Feinstein Reference Vignoles, Galindo-Rueda and Feinstein2004). As discussed, proximity to retirement is a factor but there is also evidence that employers believe older workers are less enthusiastic about training and less willing to adapt to new technology (McNair and Flynn Reference McNair and Flynn2005; Taylor and Walker Reference Taylor and Walker1994; van Dalen, Henkens and Schippers 2010). If employers also believe that ability to learn and retain information declines with age, they will expect the potential gains from training older workers to be lower. However, the evidence in support of this assumption is mixed (Waldman and Avolio Reference Waldman and Avolio1986; Wood, Wilkinson and Harcourt Reference Wood, Wilkinson and Harcourt2008; Wooden et al. Reference Wooden, Van den Heuvel, Cully and Curtain2001).

Such considerations suggest that the assumption of lower net benefits of training for older people may be based partly on stereotypical ageist attitudes. Research on the barriers to training faced by older workers has considered these concerns and finds that unavailability of training is an important issue (Maltby Reference Maltby, Loretto, Vickerstaff and White2007; McKay and Middleton Reference McKay and Middleton1998; Porcellato et al. Reference Porcellato, Carmichael, Hulme, Ingham and Prashar2010; Sussman Reference Sussman2002). This is not only because older workers are perceived to be more costly to train but also because of less specific, and potentially ageist attitudes of employers (Cully et al. Reference Cully, Vanden-Heuvel, Wooden and Curtain2000; Wooden et al. Reference Wooden, Van den Heuvel, Cully and Curtain2001). Similarly, Lundberg and Marshallsay (Reference Lundberg and Marshallsay2007) and Chapman et al. (Reference Chapman, Crossley and Kim2003) identify lack of employer support and negative attitudes toward training for older workers as a disincentive for training.

Previous research also suggests that a lack of motivation for training among older people is not simply due to their proximity to retirement but also because they perceive less need for training (Cully et al. Reference Cully, Vanden-Heuvel, Wooden and Curtain2000; Felstead Reference Felstead2010; Guthrie and Schwoerer Reference Guthrie and Schwoerer1996; Lundberg and Marshallsay Reference Lundberg and Marshallsay2007). This could be because older individuals believe they have accumulated sufficient skills and experience. Even where there is a perceived need, the poor quality of training may discourage participation. The importance of quality is confirmed by Lundberg and Marshallsay (Reference Lundberg and Marshallsay2007) who found that older employees valued training that enhanced their productivity. It is also a mistake to think of older workers as a homogenous group. As highlighted by Rix (Reference Rix2002), their training needs and expectations reflect their ‘diversity’ in terms of social, occupational and educational background.

To summarise, there are various reasons why older people may be less likely to participate in training. Proximity to retirement is clearly important but not the only determining factor. Recent research highlights the importance of workplace characteristics, institutional arrangements, the cost and quality of training and age-specific barriers such as the ageist attitudes of employers.

Data

The analysis uses individual-level data from the EU-LFS for the years 2004–07. The EU-LFS facilitates inter-country comparisons by including standardised EU variables that are sometimes withheld in the country-specific LFS datasets; 2004–07 represents a period where most of our selected variables are available in most years and across most of the EU-15.

Four years of data are used in order to identify consistent patterns that reflect more than annual fluctuations. Respondents between 20 and 64 years of age are included. The sample was restricted by the unavailability of some variables for some countries. In particular, some of the training variables were unavailable for some countries, therefore the sample sizes vary and these are reported in the regression tables. Taking these restrictions into account, the full data include 2,999,674 workers (employees only) and 1,856,550 non-workers (unemployed and inactive). The large sample size provides tight confidence bands on estimated parameters thus aiding statistical inference.

Our sample of workers is restricted to employees (EU-LFS variable STAPRO = 3) and excludes the self-employed and family workers because their work patterns are known to be very different to those of employees. Their inclusion might well have distorted the overall responses on training and working hours given their normal working hours are less precisely prescribed than those of employees. Our sample of non-workers includes both the unemployed and the economically inactive (WSTATOR = 3, 5). We account for the difference between these two categories by including unemployment status, as defined by the International Labour Organization (ILO), as an explanatory variable. We did not exclude the ‘inactive’ because the exact distinction between unemployed and inactive is problematic. Excluding the inactive could bias the analysis since some older people who wish to work may be technically classified as ‘early retired’. We were unable to restrict the sample with respect to disability status because the variable MAINSTAT, which identifies those who are disabled, is missing for Germany and the United Kingdom (UK).

Our main dependent variable TRAINED records participation in training outside the formal education system. We define three additional measures of training. TRAINED_WorkRel records whether the purpose of training was work related; TRAINED_InWrkHrs whether training occurred during normal working hours and TRAINED_Time the proportion of time spent training. All these variables are defined for the four weeks prior to the interview using the EU-LFS variables COURATT, COURPURP, COUTWOHR and COURLEN, respectively. These variables have only been consistently available in the EU-LFS since 2004 and no other training time-domains are available. Table 1 presents summary statistics and definitions for these dependent variables and the explanatory variables.

Table 1. Summary statistics

^Notes:1. The sample of ‘workers’ includes employees only. Largest sample sizes are 1,856,550 for non-workers and 2,999,674 for workers. Sample sizes are slightly smaller for some estimations because of country-specific missing variables. ISCED: International Standard Classification of Education. ILO: International Labour Organization. NACE: Nomenclature statistique des activités économiques dans la Communauté Européenne.

Training incidence

In the EU-15 approximately 9.6 per cent of employees and 4.4 per cent of non-workers received some training in the four weeks prior to their survey interview (see Table 1). Mean training rates for older respondents are lower for both workers and non-workers (9.0 and 2.5%) in comparison with younger respondents (9.8 and 5.9%). The upper panel of Figure 1 shows that there is large variation across countries for both workers and non-workers and for both age groups (18–49 and 50–64). Training incidence is highest in the UK and Denmark but considerably lower in Greece, Portugal, Italy and Ireland. In Finland training rates are high among workers but low among non-workers.

Notes: GR: Greece. PT: Portugal. IT: Italy. IE: Ireland. DE: Germany. BE: Belgium. ES: Spain. FR: France. LU: Luxembourg. NL: The Netherlands. AT: Austria. SE: Sweden. FI: Finland. DK: Denmark. UK: United Kingdom.

Figure 1. Proportion who undertook training.

The lower panel in Figure 1 highlights that in most countries the proportion who trained is lower for the older age group. Italy is the only exception where the difference for workers is negligible. The largest age-gaps are for non-workers and can be found in the UK, Spain and France. The smallest gaps are found in Greece, Portugal and Italy where there is relatively little training to begin with but also in Finland and Denmark where training rates are high.

Work relatedness of training

EU-LFS respondents (excluding Sweden) were asked if the purpose of the training was mainly work-related or personal/social. From Table 1 we see that 5.3 per cent of workers undertook mainly work-related training. When compared to the 9.6 per cent that undertook any kind of training this indicates that just over half (55%) of training was work-related. Among the workless, only 1.3 per cent report undertaking work-related training, though exactly how non-workers interpret ‘mostly job related’ may vary depending on personal circumstances. Conditional on the aforementioned caveat, under one-third (29.5%) of non-workers’ training was work-related. These rates are lower still for older workers and older non-workers, 4.4 and 0.4 per cent, respectively, indicating that less than 50 per cent of the training by older workers and only 16 per cent of the training by older non-workers was work-related. The upper panel of Figure 2 confirms this; the older cohorts were less likely to participate in work-related training, with the exception of Italy. The lower panel shows that the age-training gap for non-workers is greatest in France and Denmark. The age-training gap for workers is greatest in France and the Netherlands.

Note: See Table 1 for country abbreviations.

Figure 2. Proportion who undertook work-related training.

Training during work-hours

Although we do not know from the data who paid for the training, we do have information on whether training was undertaken during work-hours. This variable is the one most likely to be correlated with an employer's commitment to training. This is because the opportunity costs of lost production are greater for those who trained during working hours than those who undertake training outside normal hours. Employers are more interested in investing in job-specific training, so this type of training is most likely to be undertaken during work-hours. Training that is less job-specific but increases workers’ utility by, for example, raising wages (possibly through a job change) is more likely to be undertaken outside normal work-hours. In line with this prediction, Bassanini et al. (2005: 56) found that 80 per cent of vocational courses in 16 Organisation for Economic Co-operation and Development (OECD) countries were paid for by employers.

In the 11 EU countries for which data are available, Table 1 shows that 2.8 per cent of workers undertook their training mostly during normal work-hours. This equates to about 30 per cent of workers who undertook any kind of training. This suggests that a significant amount of training undertaken by workers is considered by employers to improve job-specific skills. However, the proportion of older workers undertaking training during work-hours is just 2.5 per cent, compared to 2.9 per cent for younger workers. The upper panel of Figure 3 shows that there is large variation across countries. Comparing Figures 1 and 3 we see that in Finland, Denmark and France the majority of training took place during normal work-hours. In other countries, such as the Netherlands, a very small proportion of the training took place during work-hours. The lower panel of Figure 3 illustrates that, with the exception of France and Luxembourg, in most countries there is only a small difference between the percentage of younger and older trainees undertaking training during work-hours.

Note: See Table 1 for country abbreviations.

Figure 3. Proportion of workers training during work-hours.

Time in training

Felstead (Reference Felstead2010: 1298) suggests that the time spent training is a revealing quality dimension as it indicates the ability level and knowledge demanded at work. We define the proportion of time spent training over a range of 0–160 hours for the four-week period. Table 1 shows that on average, workers in the EU-15 trained for about 2.3 per cent of their time (equivalent to about four hours). The time spent in training is much lower for the workless, 1.6 per cent of their time. Interestingly, the differences between younger workers and younger non-workers are small, 2.4 versus 2.5 per cent. The overall difference is driven by differences between older workers and older non-workers. Older workers spent on average 1.8 per cent of the previous four weeks in training but for older non-workers this is just 0.6 per cent.

The upper panel of Figure 4 shows considerable variation across countries with hours of training longest in the UK. In every country older respondents undertook fewer hours of training than younger respondents. The lower panel shows that most age-training gaps are very small because only a small proportion of time is spent training. The one exception is the UK where large age-training gaps are associated with a high proportion of time spent training.

Note: See Table 1 for country abbreviations.

Figure 4. Proportion of last four weeks spent in training.

Empirical specifications

We carry out separate analyses by employment status and gender. This isolates employment effects on training that might otherwise obscure the effect of age. For example, there are more opportunities for training among the older employed (Bassanini et al. Reference Bassanini, Booth, Brunello, de Paola and Leuven2005). The four measures of training participation are the dependent variables in the model estimations. The analysis is purely in terms of reduced-form equations as the EU-LFS does not include supply-side variables, such as offers of training, needed for structural estimates as in Taylor and Urwin (Reference Taylor and Urwin2001).

For the multivariate analysis of the binary dependent variables we report marginal effects based on the Probit estimator:

(1)$$\ln Lik. = \sum\limits_{t = 0} {\ln \Phi \left( {\displaystyle{{t_i - X_i \beta} \over \sigma}} \right)} + \sum\limits_{t = 1} {\ln \left( {1 - \Phi \left( {\displaystyle{{t_i - X_i \beta} \over \sigma}} \right)} \right)} $$

where t _i is the observed binary outcome of the latent continuous variable t_i. These record either participation in training (TRAINED=1), participation in work-related training (TRAINED_WorkRel=1) or participation in training during normal work-hours (TRAINED_InWrkHrs=1). The X _i are vectors of individual characteristics and the βs are vectors of corresponding parameters plus a constant. Φ(.)is the standard cumulative normal distribution and σ is the standard error of the regression. Maximisation of (1) yields estimates of the parameter vector $\^ \beta $ and the standard error $\^ \sigma $ associated with the highest likelihood of having observed the data outcomes given the estimated parameters.Footnote ¹

The proportion of time in training variable, TRAINED_Time, is continuous between 0 and 1 but is censored at 0 for those who undertook no training and at 1 for those who spent all their working time training in the previous four weeks. The double-censored Tobit of Nakamura and Nakamura (Reference Nakamura and Nakamura1983) is appropriate because it treats the uncensored observations as part of a linear model and the censored observations (TRAINED_Time=0, 1) as part of a non-linear Probit model thus making full use of the observed values on the dependent variable. Using Ordered Probit would not be appropriate in this setting because TRAINED_Time is measured on an interval scale. Tobit takes account of the scaling in the observations and does not apply arbitrary cut-off points to the continuous part of the dependent variable.

For the multivariate analysis on the censored TRAINED_Time we report marginal effects conditional on being uncensored using the Tobit estimator:

(2)$$\ln Lik. = \sum\limits_{t = 0} {\ln \Phi \left( {\displaystyle{{t_i - X_i \beta} \over \sigma}} \right)} - {\textstyle{1 \over 2}}\sum\limits_{0 \lt t \lt 1} {\left( {\ln 2\pi \sigma ^2 + \left( {\displaystyle{{t_i - X_1 \beta} \over \sigma}} \right)^2} \right)} + \sum\limits_{t = 1} {\ln \left( {1 - \Phi \left( {\displaystyle{{t_i - X_i \beta} \over \sigma}} \right)} \right)} $$

where Equation (2) is similar to Equation (1) with the addition of an intermediate summation term that captures the uncensored observations on the observed dependent variable 0 < t < 1. The first summation term in Equation (2) captures all individuals who undertook no training. The second summation term captures the intermediate cases of individuals who spent an intermediate proportion of their time training. The third summation term captures all individuals who spent 160 hours or more in training during the four-week period. Maximisation of Equation (2) yields estimates of the parameter vector $\^ \beta $ and the standard error $\^ \sigma $.

Regression results

The marginal effects for the Probit estimates for the binary dependent variables (TRAINED, TRAINED_WorkRel, TRAINED_InWrkHrs) are reported in Tables 2–5. Table 6 reports marginal effects for the proportion of time spent training (TRAINED_Time) using the Tobit estimator. Marginal effects indicate changes in the probability of the dependent variable resulting from changes in the regressors. For binary regressors, marginal effects indicate changes in the probability of a positive outcome on the dependent variable. For continuous regressors, marginal effects provide the usual slope effects.

Table 2. Probit marginal effects on whether training was undertaken

^Notes:1. Employees only. See Table 1 for definitions of the variables.

Significance levels:* 0.05 and ** 0.01 level of significance (based on t-statistics or χ²-statistics).

Table 3. Country-specific Probits, only marginal effects on OLDER reported

^Notes:1. Employees only. See Table 1 for definitions of the variables.

Significance levels:* 0.05 and ** 0.01 level of significance (based on t-statistics or χ²-statistics).

Table 4. Probit marginal effects on undertaking work-related training

^Notes:1. Employees only. See Table 1 for definitions of the variables.

Significance levels:* 0.05 and ** 0.01 level of significance (based on t-statistics or χ²-statistics).

Table 5. Probit marginal effects on whether training was during work-hours

^Notes:1. Employees only. See Table 1 for definitions of the variables.

Significance levels:* 0.05 and ** 0.01 level of significance (based on t-statistics or χ²-statistics).

Table 6. Tobit marginal effects on proportion of last four weeks spent training

^Notes:1. Employees only. See Table 1 for definitions of the variables.

Significance levels:* 0.05 and ** 0.01 level of significance (based on t-statistics or χ²-statistics).

Summary and implications

The analysis in this paper indicates that overall training rates in the EU-15 are highest in the UK and Nordic countries (Denmark, Finland and Sweden). They are lowest in Ireland, Germany and most of Southern Europe (Greece, Italy and Portugal). Training rates in the other EU-15 economies lie between these two groupings. The ‘varieties of capitalism’ perspective (Esping-Anderson Reference Esping-Anderson1999; Hall and Soskice Reference Hall and Soskice2001) provides some interpretation for these findings. For example, higher training rates in the UK's liberal market economy are likely to be driven by low job security in a labour market where the low-skilled and least educated are exposed to high risks of unemployment. The higher Nordic training rates are attributable to the social democratic commitment to full employment coupled with strong training policies and support for lifelong learning. In contrast, there are fewer opportunities for lifelong learning in conservative-coordinated economies such as Germany where the education system is highly stratified and there is strong support for apprenticeships. The lower training rates in the Southern European mixed-market economies (Hall and Gingerich Reference Hall and Gingerich2009) are consistent with weaker social policies and restricted public support. It is more difficult to interpret the low Irish training rates as Ireland is usually categorised as a liberal market economy alongside the UK (Hall and Gingerich Reference Hall and Gingerich2009).

The results in Tables 2–6 confirm that a common feature within the EU-15 is that older people aged 50–64 are less likely to participate in training even when confounding factors are controlled for. In addition, the results suggest that older people are less likely to participate in the kinds of training that are work-related, take place during work-hours and last for a longer period of time. These age-training gaps are wider among non-workers. There are also notable cross-country variations in the level and significance of age-training gaps (Figures 1–4;Table 3). Conditional age-gaps in general training (Table 3) are largest in countries with high overall training rates such as Sweden and the UK. They are also high for Danish men and for non-working men in Spain. Conversely, these conditional age-gaps are smallest in countries with low general training rates such as Italy and Greece.

Although the age-training gaps in UK and Nordic countries are larger, the training rates of older people are actually higher in these countries, particularly in Denmark, reflecting the fact that training rates are high for both younger and older people. As highlighted in Blossfeld, Buchholz and Kurz (Reference Blossfeld, Buchholz and Kurz2011), extensive re-training measures are offered in Denmark and Sweden. van Dalen, Henkens and Schippers (Reference van Dalen, Henkens and Schippers2009) also find that employers are more willing to implement training programmes for older workers in the UK than in other EU countries. Nevertheless, conditional training rates in the UK are still significantly higher for younger cohorts. The Netherlands provides one exception where conditional age-training gaps (Table 3) are small despite the fact that Dutch training rates are above the EU-15 average. This is consistent with Montizaan, Cörvers and De Grip (Reference Montizaan, Cörvers and De Grip2010) who have suggested that increased training rates among older Dutch public-sector employees are linked to reductions in pension entitlements.

There are some notable gender differences in the relationship between age and training. In particular, while there is a systematic age-related decline in training for men (working and non-working), this pattern is not systematic for working women. In ‘general training’ (Table 2) and ‘time spent training’ (Table 6) working women aged 40–49 experience a slight resurgence in training compared to their 30–39-year-old counterparts. This pattern is more pronounced for work-related training (Table 4) and training during work-hours (Table 5) where working women aged 40–49 actually experience an increase in training compared to their youngest counterparts before experiencing a downturn in their fifties. Table 3 highlights country-level differences in the training participation of men versus women. For example, in Denmark and Ireland, older non-working women experience higher rates of training than the youngest women. In Finland, this is true for working and non-working older women.

One interpretation of the results is that older people are simply less likely to train because of their close proximity to retirement. Increases in statutory retirement ages in various EU-15 countries may therefore mitigate this effect. However, the statistical significance of the age-training differences is also consistent with alternative explanations including: higher costs of training, ageist attitudes, differences in learning ability, lack of perceived need and institutional factors. Some of the country-level differences, in particular, will reflect national differences in these factors. Macro-economic changes are also important. The results from this study refer to 2004–07, the final years of a macro-economic boom where unemployment rates were low and the pension problem had not yet become a pension crisis. During economic downturns older workers face higher risks of labour market exit (Blossfeld, Buchholz and Kurz Reference Blossfeld, Buchholz and Kurz2011) and the current economic crisis, triggered in 2008, highlights the need for lifelong training.

These results have implications for policy interventions that aim to increase the employability of older people by encouraging them to undertake more training. First, they need to be offered more opportunities for training and this may require changes in attitudes towards age. Policies such as raising or abolishing mandated retirement ages may encourage older workers to take up training but will do little to help unemployed older people if they have limited scope for re-entering employment. Second, training for its own sake is not the objective, the nature of the training matters. To ensure that the training extends working lives by enhancing the labour market opportunities of older workers, policies should be carefully designed to specifically promote the work-relevance of the training. These policies need to be credible; the value of this training needs to be tangible and training needs to be an ongoing process spanning entire working lives.