5th International INAP Conference - April, 23rd and 24th 2013

1. 5th International INAP Conference - April, 23rd and 24th 2013 Pre-training competencies and the productivity of apprentices Anika Jansen and Harald Pfeifer German Federal Institute forVocational Education and Training (BiBB)

2. Outline • Motivation „Why are we interested in that topic? Working hypothesis“ • Term explanation „How do we define pre-training competencies and productivity?“ • Data „Which data do we use? Selectivity issues?“ • Results „Descriptive results, multivariate analyses, robustness tests“ • Simulation „Effect on training costs?“ • Conclusion

3. Motivation • Decision to train depends on the expected (long term) cost benefit relation • Net costs = gross costs – productive contributions of apprentices • Many aspects of costs are exogenous, but a firm may have influence on productive contributions • Productive Contribution = Working time of apprentices x potential productivity of apprentices • Time allocations of apprentices • Selection of apprentices

4. Motivation • Here: Focus on selection of apprentices • Can a firm influence the cost benefit relation of training by choosing the right apprentices? • What exactly determines the productivity of apprentices at the firm? •  Human capital theory suggests that the work productivity is determined by previous education • Education = the competencies acquired at school • Our hypothesis: Productivity of apprentices depends, among others, on school developed key competencies

5. Definition • Four key school competencies • oral and written expression skills • basic mathematics skills • knowledge in information technology • problem solving skills • Competencies are measured by asking those who are responsible for human resources and training to evaluate their apprentice’s competencies at the beginning of the training period • On a scale from 1 (=very good) to 5 (=very bad)

6. Definition • Productivity = Wage of a skilled worker x performance level of an apprentice • How do we operationalize the apprentice‘s performance level? • The performance level of an apprentice is measured in percentage of the average performance level of a skilled worker in the same occupation • The firm states for example that the apprentice achieves 60% of the performance level of an average skilled worker in the firm • We use the absolute productivity to ensure comparability between the firms

7. Data • We use the data from the cost benefit survey 2007, collected in the II. quarter 2008; Initial data set: approximately 3000 firms • Questions about the costs and benefits of training with respect to a certain occupation • Problem: Question about school competencies can refer to various apprentices • Therefore, we reduce the dataset to firms that only have one apprentice in the chosen occupation! • 1163 firms and apprentices remain for the analyses

8. Selectivity? Comparison of samples – firm characteristics

9. Selectivity? Comparison of samples – apprentice characteristics

10. Descriptive Statistics Conditional Mean Values

11. Descriptive Statistics Bivariate OLS regression; dependent variable: logarithm of productivity; sample: firms with one apprentice Note: Standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1

12. Multivariate analysis • How can this relation be explained? • Different distribution of apprentices on firms

13. Distribution over occupations

14. Distribution over occupations - commercial

15. Distribution over occupations - industrial

16. Multivariate analysis Introduction of control variables: 1. Sorting: dummies for occupation, apprentice wage, retention strategy, firm size 2.As the apprentices are not only in the first year, the firm‘s training strategy might be a possible mediator variable • Time allocation: Unskilled and skilled tasks, and other learning activities • Training hours: How many hours is an apprentice supervised by a trainer?

17. Multivariate Analysis OLS regression; dependent variable: logarithm of productivity; sample: firms with one apprentice *Sorting: control for occupation, firm size, retention strategy, apprentice wage ** Training strategy: control for trainer hours, time allocation Standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1

18. Robustness check: sample with all firms OLS regression; dependent variable: logarithm of productivity; sample: all firms; apprentice specific variables are averages Control for: Occupation, firm size, retention strategy, apprentice wage, trainer hours, time allocation Standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1

19. Simulation • Example: Problem solving competencies  Coefficient suggests an increase of productivity of 7% • Benefit per years and apprentice of 11.648 Euro in the sample with one apprentice • That means by recruiting an apprentice with a better evaluation of one unit, firms save 815 Euro of net training costs per year and apprentice • For a three year training period this is equal to a saving of 2445 Euro • The benefit that is caused by retaining apprentices is not included in these numbers

20. Conclusion • Pre-training competencies of apprentices have an effect on the firm’s cost benefit relation of training • The analysis suggests that firms should especially pay attention to problem solving skills and oral and written expression skills in their recruitment decision • Basis mathematical skills, however, can also serve as a predictorfor productivity as they can be measured more easily • For the future: Observation within one firm and/or objective measures to verify the results

21. Thanks for your attention! jansen@bibb.de harald.pfeifer@bibb.de

22. Robustness check: occupational groups Overview of coefficients: Differentiations for occupational groups Control for: Occupation, Firm size, Retention strategy, apprentice wage, trainer hours, time allocation, independence, motivation Standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1