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On the productivity effects of training apprentices in Hungary: evidence from a unique matched employer–employee dataset

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Abstract

This paper investigates the effects of training apprentices on the productivity of Hungarian firms. In order to retrieve a causal estimate of the effects of apprenticeship training on firm performance, we apply a set of dynamic panel data estimation techniques. We create a unique administrative matched employer–employee panel dataset containing over 40,000 employers in Hungary over the period between 2003 and 2011 in the manufacturing, construction, wholesale and retail, and hotels and restaurants sector. Our results indicate that an increase in the share of apprentices (per full-time equivalent worker) decreases the productivity of Hungarian firms in all four sectors. Further, we observe that retention rates of apprentices are low and further slacken in the final years of observation.

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Fig. 1

Source: Authors own design based on CEDEFOP (2011)

Fig. 2

Source: Compiled by the authors

Fig. 3

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Fig. 4

Source: Compiled by the authors

Fig. 5

Source: Compiled by the authors

Fig. 6

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Notes

  1. Finland (71.3%), Croatia (70.4%) and Austria (69.5%) capture the largest shares of students enrolled in VET, while Malta (12.7%), Cyprus (15.6%) and Hungary (23.2%) the lowest shares.

  2. According to Mohrenweiser and Backes-Gellner (2010), in Germany, 18.5% of firms follow a substitution strategy, while 43.7% follow an investment strategy.

  3. Hungarian legislation stipulates that apprentices have to work with special government-regulated contracts.

  4. Retention rates of about 77% are mentioned for the German manufacturing sector. These rates can be compared with 72% for the trade and commercial sector and 61.5% for the crafts and construction sector (Mohrenweiser and Zwick 2009, p. 632, footnote 5).

  5. It should be noted that the compulsory education age in Hungary changed in 2012 to 3 years (entrance age) and 16 years. However, this change happened after our examined time period of 2003–2011.

  6. This figure excludes 64,984 students from post-secondary non-tertiary VET who are enrolled in SZKI.

  7. These shares were relatively stable during our examined period (see CEDEFOP 2011, Fig. 4, page 31.).

  8. In September 2010, ‘early VET programs’ (előrehozott szakiskolai képzés) were introduced, which offer 3 years of vocational training right after the completion of lower secondary education.

  9. In 2011, about 60% of the 11th- and 12th-grade VET students participated in on-the-job training programs, while 40% of VET students participated in school-based workshops (based on KIR-STAT 2011, Table a05t24). Apparently, on-the-job training is more popular among VET students than school-based training.

  10. For the former two options, firms cannot use the whole amount of their mandatory VET tax. They can use up to 70% of the total tax for offering direct support for secondary institutions and 35% for supporting tertiary institutions. Larger firms can use up to 33% and small and medium enterprises up to 60% of the total tax to train their own workers. Also the possibility of training own employees ended in 2012 because of a legislation change, but this does not affect our analysis.

  11. To estimate the skill level of the different workers, we used the highest skilled job between 2003 and 2011, based on occupational codes, as a proxy for skill level. We also have used the available information on educational attainment to improve the classification.

  12. The compulsory school age was 18 years at the time of our observed period, so there is only a very small group of workers, younger than 20 years, but working full time. This can be problematic for our estimation. Therefore, younger than 25 years old would be a more suitable control group. However, to check the robustness of our results we estimated our main tables using a cutoff at age 19 years. We conclude that the interpretation of our results remains the same.

  13. Conversion rate: HUF 100 is equal to EUR 0.314256628 (February 19, 2019).

  14. Small firms are the ones with at most ten employees, medium ones with 11–50 and large firms with more than 50 workers. Ownership mean majority of shares is owned by foreign or domestic owners.

  15. Since selection into the different types of practical training is highly decentralized (students can organize their own workplace-based training themselves), it might not be random (Horn 2016).

  16. For Table 2, we ran the regressions jointly for the four industries. However, as a robustness check we estimated the regressions separately as well. Our results remained basically the same.

  17. For application, we have used the user created command md_ar1 in Stata (Söderbom 2009).

  18. As a robustness check, we included a dummy for workers with more than 2 years of experience at the firm (apprentices usually stay at most 2 years), so the new reference group became the low-skilled regular employees with low tenure. Doing so, we loose the first 2 years from our data so that the results are not directly comparable with our original tables in the paper. The results show that the share of days worked by experienced workers has a significantly positive coefficient; however, our main coefficients basically remain unchanged. Therefore, we decided to keep our original approach for the results in the paper.

  19. Small firms—less than ten employees; medium-sized firms—10–50 employees; and large firms—more than 50 employees.

  20. Domestic majority ownership or foreign majority ownership.

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Funding

The authors received Project Funding from the Horizon 2020 Research and Innovation Programme of the European Union, Grant Agreement No. 691676.

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Authors and Affiliations

  1. Research Institute for Work and Society (HIVA), KU Leuven, Parkstraat 47, PO 5300, 3000, Leuven, Belgium

    Sofie Cabus

  2. Maastricht Graduate School of Governance, Maastricht University, 6200 MD, Maastricht, The Netherlands

    Sofie Cabus

  3. Centre for Economic and Regional Studies, Institute of Economics, Hungarian Academy of Sciences, Tóth Kálmán u. 4, Budapest, 1097, Hungary

    Eszter Nagy

  4. Central European University, Budapest, Hungary

    Eszter Nagy

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  1. Sofie Cabus
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Correspondence to Sofie Cabus.

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Appendix

Appendix

See Figs. 7 and 8 and Tables 4, 5, 6, 7, 8, 9, 10, 11 and 12.

Fig. 7
figure 7

Source: Compiled by the authors

Mean share of apprentices by size and ownership in firms with at least one apprentice.

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Fig. 8
figure 8

Source: Compiled by the authors

Mean retention rates by size and ownership in firms with at least one apprentice.

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Table 4 Data cleaning and sample size
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Table 5 OLS results
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Table 6 Summary of results using fixed effects, all coefficients
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Table 7 Summary results using system GMM, all coefficients
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Table 8 Results by level of experience of apprentices
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Table 9 Fixed effects results by firm size
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Table 10 System GMM results by firm size
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Table 11 Results by ownership
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Table 12 Descriptive statistics by ownership
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Cabus, S., Nagy, E. On the productivity effects of training apprentices in Hungary: evidence from a unique matched employer–employee dataset. Empir Econ 60, 1685–1718 (2021). https://doi.org/10.1007/s00181-019-01817-y

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