Pre-training competencies and the productivity of apprentices 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 for Vocational Education and Training (BiBB)

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

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

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

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)

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

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

Firms with one apprentice Selectivity? Comparison of samples – firm characteristics Firms with one apprentice All firms Observations 1163 2856 Wages Wage of a skilled worker 2198 2235 Apprentice wage 603 590 Retention strategy Always retain apprentice 0.45 0.47 Sometimes retain apprentice 0.28 0.30 Never retain apprentice 0.26 0.23 Firm size Until 9 employees 0.69 0.54 10 to 49 employees 0.25 0.33 50 to 499 employees 0.06 0.12 More than 500 employees 0.00 0.01 Trainer hours 20.47 16.75 Time allocation Unskilled work 0.35 0.36 Skilled work 0.43 Learning activities 0.20 0.21 Economic branch Trade 0.32 Commerce Services I 0.13 0.14 Services II 0.17 Administration/Education/Health 0.16 0.15

Selectivity? Comparison of samples – apprentice characteristics Firms with one apprentice All firms Mean S.D. Oral & written expression 2.31 .98 2.19 .96 Basic mathematics 2.46 1.07 1.06 Information technology 2.51 .95 2.45 .94 Problem solving .97 2.06 Mean of all four competencies 2.36 .80 2.24 .79 Productivity in Euro 1296.40 651.36 1291.02 645.62

Descriptive Statistics Conditional Mean Values

Descriptive Statistics Bivariate OLS regression; dependent variable: logarithm of productivity; sample: firms with one apprentice   ln(productivity) oral & written expression basic mathematics information technology problem solving Competencies 0.12*** 0.09*** 0.08*** (0.03) (0.02) Constant 6.76*** 6.81*** 6.83*** 6.77*** (0.07) Observations 1,160 1,081 R-squared 0.04 0.03 0.02 Note: Standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1

Multivariate analysis How can this relation be explained? Different distribution of apprentices on firms Mean value of school competencies Apprentice wage per month Firm size (average number of employees) Retention strategy (Share of firms saying they always retain apprentice) Frequency 520.24 € 9.75 25.00 % 8 1 598.32 € 28.10 49.21 % 126 2 600.82 € 36.79 42.78 % 395 3 624.00 € 54.07 51.85 % 513 4 654.19 € 60.41 60.33 % 121

Distribution over occupations

Distribution over occupations - commercial

Distribution over occupations - industrial

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?

Multivariate Analysis OLS regression; dependent variable: logarithm of productivity; sample: firms with one apprentice   ln(productivity) oral & written expression basic mathematics information technology problem solving Bivariate Model Coefficients 0.12*** 0.09*** 0.08*** 1.) Controlling for sorting* 0.06*** 0.05** 0.11*** (0.02) (0.03) R-squared 0.21 0.20 0.22 2.) Controlling for sorting and training strategy** 0.07*** 0.04 0.32 0.31 0.30 Observations 1,160 1,081 * 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

Robustness check: sample with all firms OLS regression; dependent variable: logarithm of productivity; sample: all firms; apprentice specific variables are averages   ln(productivity) oral & written expression basic mathematics information technology problem solving Competencies 0.06*** 0.03** 0.02 0.07*** (0.02) (0.01) Constant 3.91*** 3.96*** 3.84*** 3.88*** (0.37) (0.39) Observations 2,847 2,623 R-squared 0.22 0.21 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

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

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 predictor for productivity as they can be measured more easily For the future: Observation within one firm and/or objective measures to verify the results

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

Robustness check: occupational groups Overview of coefficients: Differentiations for occupational groups Competencies oral & written expression basic mathematics information technology problem solving Commercial (564 Observations) Full model 0.08** 0.04 0.05 0.16*** (0.04) (0.03) Bivariate model 0.12*** 0.09** 0.18*** Industrial – technical (596 Observations) 0.03 0.01 0.10*** 0.08*** 0.06** 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