Immigrants’ participation in non-formal job-related training

ABSTRACT

Participation in job-related training among immigrants is becoming more important in Europe in view of large immigration inflows. This paper considers differences in training participation rates by immigrant background including by whether they are from Western or non-Western origins. Also considered is whether differences by immigrant background depend on whether the training was supported by employers. The analyses are based on individual-level data from the PIAAC database. Four countries are included in the analysis: Denmark, Finland, the Netherlands and Norway, countries that feature a highly educated labour force and social policies designed to support and incentivise skill development over the life span including disadvantaged groups. The analyses reveal that the differences in participation rate according to immigrant backgrounds are relatively small, in all countries except Finland. Still, the immigrants receive less employer-sponsored training than non-immigrants. The results indicate a high demand for training among immigrants.

KEYWORDS:

• Non-formal training
• job-related training
• immigrant backgrounds
• employer supported training

Acknowledgments

This work was funded by a grant from the Norwegian Research Council – Barriers and drivers regarding adult education, skills acquisition and innovative activity (BRAIN project, number 228258). The discussions among BRAIN project members at the project’s workshops are gratefully acknowledged, particularly the valuable comments to this paper by BRAIN-partner Professor Richard Desjardins, Department of Education, University of California.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The definition is widely used and in accordance with terms used by Adult Education Surveys (Eurostat, 2018) and by the predecessors to PIAAC, namely ALL and IALS (OECD, 2018).

2. In addition to the reasons referring to policy incentives for lifelong learning etc., other reasons for selecting the four countries are that relevant information on key variables such as first language of immigrants exists for the particular country in the database, and that we find that the number of countries that are compared needs to be restricted in this kind of analysis.

3. One example of new measures in Finland is that a National Core Curriculum for Integration Training for Adult immigrants was adopted in 2012 referring to the Act on the Promotion of Integration, adopted in 2010 (Finnish National Board of Education, 2012).

4. The weighting procedure is based on the full sample (final) weight which is in the PIAAC database. We have also used 80 replicate weights in the PIAAC database since participating countries have used different replication schemes. The weighting procedure ensures representative data. Data are weighted by using the ‘repest’ command in Stata. We have, in addition, used a country correction term in tables where the four countries are looped to ensure that all countries have the same influence on the average values for the pooled four countries (Table A.1, column 1, 6, 7 and 8). The average values in Table A.2, column 1, form the basis for the calculations in Figures 1 and 2. The country correction term is equal to the ratio of the number of persons in the actual sample in each country and the weighted number of observations in each country.

5. For immigrants living in Norway and Denmark who reported that one of the Scandinavian languages (Danish, Swedish and Norwegian) was their first language, these cases are coded here as having the language of the immigrant country as their first language because of the broad similarities between these Scandinavian languages.

6. We are aware that some scholars warn against comparing logit values between samples (Mood, 2010). Mood suggests that this can be done if using linear regression models. We have run extra regressions using linear models for each of the four countries. The results concerning possible significant differences between the countries regarding the coefficients of the independent variables when using the linear model coincide with the results when using the logistic model. We should also point out that Mood’s (2010) warning is discussed by other scholars, among them Buis (2017), who concludes differently.

7. When it comes to the effects of education and skills levels, the overall picture is that there are no significant country differences, with only two exceptions. These are that the effect of having a low education level differs between Finland and the Netherlands, and that the effects of numeracy skills level differ between Norway and Denmark. However, there are many significant country differences considering the effects of the dummy variables for industrial sector, and even more so when using a linear model than when using the logit model. There are also significant country differences in the effects of gender and age, both when the linear model is used and when the logit model is used.

8. Because of the low number base for immigrants in the individual country, there is little meaning to further examine the relationship between obligatory and employer-sponsored training by immigrant group.

9. For Finland, the difference in the employment rate refers to immigrants with unknown country of origin, which here comprises a larger group than non-Western immigrants, see Table A.2.

Additional information

Funding

This work was funded by a grant from the Norwegian Research Council – Barriers and drivers regarding adult education, skills acquisition and innovative activity [BRAIN project, 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 from education 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.