Training of various durations: Do we find the same social predictors as for training participation rates

ABSTRACT

Most studies on participation in training focus on participation versus non-participation. The individual’s participation varies, however, very much in terms of the duration of training, from until a few days to intensive participation. This study examines participation in non-formal training by the total amount of training during a year. In the analysis, we use PIAAC data for eight European countries, of which half represents a group of countries with high participation rates in non-formal training and the other half have lower participation rates. One purpose is to examine whether the duration of training varies between these groups of countries. We expected that countries which score high on training rates are characterised by high proportions participating in short courses. Another purpose is to examine the relationship between duration of training and educational levels and immigrant backgrounds. We expected that the relationship that is normally found between training rates and social background variables would be reversed when it comes to duration of training. In the analyses, controls are applied for several individual and workplace characteristics, including skills level, firm size, occupational level, and industrial sector. The estimation results indicate that overall, our expectations are not supported.

KEYWORDS:

• Non-formal training
• training intensity
• immigrant background
• educational level
• country-differences

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Non-formal training differs from informal learning, which can be organised but is not institutionalised (European Commission, 2006). Ferreira Sequeda, De Grip, and Van Der Velden (2015) study the difference in informal learning on the job between permanent and temporary workers based on PIAAC data.

2. See the OECD’s PIAAC website (http://www.oecd.org/skills/piaac/) for information about how the data have been collected and established, the sample questions and questionnaire, etc.

3. 53 per cent of the respondents responded in terms of days, 28 per cent in terms of hours and 19 per cent in terms of weeks. Among those who gave answers in terms of the number of days (the majority), it can theoretically vary between individuals what they put in the word “day”. We have no information about this and must use the information available. It seems reasonable to calculate a work week as five days, as this is common in Europe. When it comes to conversion from hours to days, it also seems reasonable to calculate a working day like 7 hours (lunch not included). If instead, one day was set to for example 8 hours, the distribution on the dependent variable would be practically identical.

4. EU/Western immigrants consist of persons born outside the reference group of countries (the four high-performing countries or the four low-performing countries) with an EU/Western origin. This category refers to EU/EEA countries plus North-America or Australia/New Zealand. Non-Western immigrants consist of persons born in the rest of the world, which implies that East-European countries outside EU are defined as non-Western. The number of cases in the category ‘immigrant with unknown country of birth’ is reduced by using information on the immigrants’ first language (‘What is the language that you first learned at home in childhood and still understand?’). We have coded the first language of immigrants with unknown country of birth as EU/Western and non-Western in the same way as country of birth, and then categorised them as EU/Western and non-Western immigrants instead of including them in the category ‘immigrant with ‘unknown country of birth’.

5. Similar control variables are also used in other relevant studies. Leuven and Oosterbeek (1999) and Leuven (2001) also include gender, age, education, occupation, immigrant background, working hours (full-time/ part-time) and industrial sector among the independent variables. Furthermore, Orrje (2000) includes gender, age, education, occupational level, working hours (part-time/full-time) and firm size among the independent variables.

6. We have chosen to use numeracy skills. The correlation between numeracy skills and literacy skills is very high (0.9).

7. The estimations for the multinomial regression (i.e. estimations displayed in the graphs and Table 6) are based on the following formulas of the response probabilities $\hspace{0.17em}{\mathrm{P}}_{\mathrm{j}}=\mathrm{e}\mathrm{x}\mathrm{p}({\mathrm{Z}}_{\mathrm{j}})/1+\sum { }_{\mathrm{h}}\mathrm{e}\mathrm{x}\mathrm{p}({\mathrm{Z}}_{\mathrm{h}}),$ where Z is the intercept plus the effects of the control variables (Z = B₀ + B₁X₁ + B₂X₂ + …), and j is an expression of the different outcomes on the dependent variable (the logit has j – 1 different sets of parameters), j = 1,2 (etc.). For an introduction to multinomial logit models, see Greene (2018) and Wooldridge (2010).

8. We have checked whether the correlations between occupation and educational levels are so high that there is a risk of multicollinearity, but they were found to be within acceptable limits.

Additional information

Funding

This work was supported by the Norges Forskningsråd [grant number 228258].

Notes on contributors

Liv Anne Støren

Liv Anne Støren is a sociologist and Research Professor at Nordic Institute of Studies in Innovation, Research and Education (NIFU) in Oslo. She is currently working with issues relating to adult learning in the workplace, as well as employer and graduate surveys. Her research revolves largely about what factors that affect the transition fromeducation to employment and educational careers, including studies of the situation among immigrants.

Pål Børing

Pål Børing is economist from the University of Oslo and senior researcher at NIFU. Børing has published scientific articles with focus on innovation, competence and training in firms, and articles in econometric journals. Børing is currently working as a WP leader in a research project funded by the Norwegian Research Council, called SILVER, which examines the relationship between lifelong learning and employability among older adults.