The effects of vocational training on income 1)

Transcription

1 The effects of vocational training on income 1) Torsten Lisson Imm.-Nr.: Emanuel Gasteiger Imm.-Nr.: Note: This talk is a contribution to the course Econometric Methods for Panel Data [040494UK] hosted by Prof. Robert M. Kunst on May the 22 th 2007 at the Department of Economics of the University of Vienna. We base this talk on Jürges, H., Schneider, K., (2006): Dynamische Lohneffekte beruflicher Weiterbildung, In: Weiss, M. (eds.), Tagungsband des Bildungsökonomischen Ausschusses des Vereins für Socialpolitik 2004/2005, Duncker & Humblodt, Berlin.

2 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 2

3 Vocational training is on the agenda in Austria. Some facts about vocational training in Austria: The Union has today set itself a new strategic goal for the next decade: to become the most competitive and dynamic knowledge-based economy in the world,... [Presidency Conclusions of the European Council, Lisbon, March 2000] Bundesgesetz vom 21. März 1973 über die Förderung der Erwachsenenbildung und des Volksbüchereiwesens [BGBl. Nr. 171/1973] The Austrian state offers several subsidies for vocational training: for the employee covering (parts of) fees, materials and travels for institutions that offer vocational training programmes for employees and employers as tax deductible for employees and institutions as prices and awards etc. Exhibit 3

4 All major players of the labour market have an interest in vocational training. Spending on vocational training in Austria in 1999 [in Mio. EUR] 1)2) : 1) Schneeberger, A., Mayr, T. (2004): "Beruflich Weiterbildung in Österreich und im europäischen Vergleich, ibw - Institut für Bildungsforschung der Wirtschaft, ibw-schriftenreihe, Nr. 126.Nr ) Shares are provided by Dr. Marion Weber, marion.weber@fh-joanneum.at. Exhibit 4

5 One can justify spending on vocational training from both a macro and micro economic perspective. Selected ways of thinking about vocational training: MACRO Global competition Depreciation of human capital Demographic change Budget More frequent changes in work histories More frequent changes in work profiles e.g. due to technological progress MICRO Investment good Consumption good Longer work life Signal Screening mechanism Exhibit 5

6 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 6

7 The idea of investment in human capital is quiet old in our profession. Human capital investment in the literature: Adam Smith (1776) believed that economic process is driven by:...the acquired and useful abilities of all the inhabitants or members of the society. The acquisition of such talents, by the maintenance of the acquirer during his education, study, or apprenticeship, always costs a real expense, which is a capital fixed and realized, as it were, in his person. 1) 1) Smith, Adam (1776): On the Nature, Accumulation, and Employment of Stock in An Inquiry into the Nature and Causes of the Wealth of Nations, Book II,. Exhibit 7

8 Seminal work was provided by Nobel Prize Laureates Schultz (1961) and Becker (1962). Human capital investment in the literature: Theodore W. Schultz (1961) defines investment in human capital as: Human resources obviously have both quantitative and qualitative dimensions.... quality components as skill, knowledge, and similar attributes that affect particular human capabilities to do productive work. In so far as expenditures to enhance such capabilities also increase the value productivity of human effort (labor), they will yield a positive rate of return. 1) Gary S. Becker (1962) refers to investment in human capital as:...activities that influence future real income through the imbedding of resources in people. 2) 1) Schultz, T.W. (1961): Investment in Human Capital. The American Economic Review 1(2), ) Becker, G.S. (1962): Investment in Human Capital: A Theoretical Analysis. The Journal of Political Economy (70) (5), Exhibit 8

9 Becker s (1962) model provides intuitive empirical implications. Brief information on the theoretical approach of Becker (1962): He provides a general theory of human capital investment. Some empirical implications of Becker (1962): Vocational training affects current and future marginal product of labour, Persons may earn more, as they invest more in themselves, People with higher ability tend to invest more in themselves, Vocational training has exactly the same effect on earnings as education, Younger persons tend to receive more training. Exhibit 9

10 Next to human capital, the observed link between vocational training and wage could also be caused by ability. Brief information on the theoretical approach of Becker (1962): Vocational Training + Human Capital observable unobservable Ability + Wage Exhibit 10

11 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 11

12 The data set is derived from the GSOEP. 1) Facts on the data set: German Socio-Economic Panel states a representative longitudinal study of private households in the entire federal Republic of Germany. Some of the many topics include household composition, occupational biographies, employment, earnings, health and satisfaction indicators. The data are available to researchers in Germany and abroad in SPSS, SAS, Stata, and ASCII format for immediate use. Extensive documentation in English and German is available online. 1) Find detailed information on Exhibit 12

13 We deal with a total of observations in an unbalanced panel. Facts on the data set: Our data set comprises 18 waves between 1984 and The information is limited on employees within the age of 25 to 55 years. We do not care about Eastern Germany. There are observations for individuals, thus it is an unbalanced panel. Exhibit 13

14 Using an unbalanced panel changes parameter calculation. Facts on unbalanced panel: The full sample size is different: NT balanced vs. n " T i i=1 unbalanced Minor modifications are required for the computation of: Error term variance, Coefficients variances and Test statistics. Group means must be based on T i. Exhibit 14

15 We have a solid information base on-hand. Facts on the data set: Variable: Real wage Public sector employee Years of schooling Children with an age < 16 Size of employer Foreigner Marital status Vocational training (otj.) Cohorts Tenure Type of information: [in ln(w) ] [0/1] [in years] [# of children] [dummies for (20-199), ( ), ( ) employees] [0/1] [0/1] [Years of vocational training] [dummies for ( ), ( ), ( ), ( )] [in years] Exhibit 15

16 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 16

17 There are differences in income as well as schooling between people who received training and the ones who did not. Some descriptive information: Participants # 70? Non-Participants x.xxx Real wage ~ 31k EUR ~ 25k EUR Schooling ~ 12.4 a ~ 11.0 a Exhibit 17

18 All coefficients behave reasonable. A Logit estimation for descriptive purposes: P i = E(Voc.Tr.=1 X) = 1 1+ e "Z i Z i = " 0 + " 1 ln(w) +" 2 Schooling +" 3 Tenure +" 4 Public " 5 [20,199] + " 6 [200,1999] +" 7 [2000,+[ " 8 [1960,+]+ " 9 [1950,1959] + " 10 [1940,1949] +" 11 Year - +" 12 Marital status +" 13 Children(a < 16) +" 14 Foreigner +" 15 Female Significant at 5%-level Exhibit 18

19 There remain some issues that we can not address with our data. Critical remarks: The amount of vocational training tells us nothing about the quality. There is no information about the specificity of the training. Ability may have influence on the probability of taking part in a vocational training as well as on years of schooling which may be responsible for wage differences. Unfortunately ability can not be observed. Wage, if annual, then Christmas money etc. causes bias in effects, if it varies over time. Years of vocational training at time t for an individual i is the accumulated sum of training months divided by 12, i.e. VT it By construction VT it considers the fact that training affects current and future productivity. Exhibit 19

20 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 20

21 In the models we estimate, we can interpret the coefficient of vocational training directly as return on vocational training. Basic specification: ln(w it ) = µ i + " t + VT it # VT + X it # + u it, where µ i is an unobserved, time invariant individual component θ t an unobserved, time specific component X it is a vector of observable individual characteristics, and VT it is the number of years of vocational training, and the coefficient can be interpreted as average annual return on vocational training. Exhibit 21

22 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 22

23 The coefficients of pooled OLS are as expected. Results for pooled OLS: Coefficient of: All Female Male Vocational training * * * Schooling * * * Tenure * * * Tenure * * * Public sector employee * * * Size of employer [20,199] * * * [200,1999] * * * [2000,+ [ * * * Cohorts [1960,+] * [1950,1959] * [1940,1949] * * * Marital status * * Children with an age below * * * Foreigner * * * Constant * * * * Significant on the 5%-level. Exhibit 23

24 The pooled OLS regression suffers from several shortcomings. Remarks on pooled OLS: The coefficient of vocational training may be biased by ability. The coefficient of schooling may be biased by ability as well. Furthermore, these OLS estimators are not efficient. Exhibit 24

25 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 25

26 The one way FE model drops time constant information. Results for one way FE model: Coefficient of: All Female Male Vocational training * * * Schooling Tenure * * * Tenure Public sector employee Size of employer [20,199] * * * [200,1999] * * * [2000,+ [ * * * Cohorts [1960,+] [1950,1959] [1940,1949] Marital status * * Children with an age below * * Foreigner Constant * * * * Significant on the 5%-level. Exhibit 26

27 The poolability test suggest to consider individual effects. Poolability test: H 0 : µ i = 0,i=1,...,N H 1 : µ i 0, at least for one i vs. F(2.402, ) = 7.67 > 1.30 = Critical value Exhibit 27

28 The one way FE model cause new trouble. Remarks on one way FE model: Ability bias is solved. BUT: a lot of relevant information is excluded solely as it is constant over time (e.g. schooling): thus there is an increases in the error term variance, which affects efficiency, and if these variables are correlated with the time varying explaining variables, there is a new bias. Exhibit 28

29 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 29

30 The one way RE model retains the time invariant information. Results for one way RE model: Coefficient of: All Female Male Vocational training * * * Schooling * * * Tenure * * * Tenure * * Public sector employee * * Size of employer [20,199] * * * [200,1999] * * * [2000,+ [ * * * Cohorts [1960,+] * [1950,1959] * * [1940,1949] * * * Marital status * * Children with an age below * * Foreigner * Constant * * * * Significant on the 5%-level. Exhibit 30

31 The one way RE model is not an alternative. Remarks on one way RE model: Critical assumption of the RE model is, that the unobserved individual effects and the regressors are not correlated. This is not likely to be true if we consider ability and schooling. Retains information that is constant over time. Exhibit 31

32 The Hausman test suggests that the one way FE model is the appropriate model. The Hausman test: H 0 : RE model is true H 1 : FE model is true vs. Χ 2 (9) = < 113,59 = Hausman statistic (m) Exhibit 32

33 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 33

34 Within the two way FE model the coefficient of vocational training is not significant. Results for two way FE model: Coefficient of: All 1) Female 1) Male 1) Vocational training Schooling Tenure * * * Tenure * * * Public sector employee Size of employer [20,199] * * * [200,1999] * * * [2000,+ [ * * * Cohorts [1960,+] [1950,1959] [1940,1949] Marital status * * Children with an age below * * Foreigner Constant * * * 1) NOTE: Time dummies are not reported but behave as expected and are significant. * Significant on the 5%-level. Exhibit 34

35 Time effects are significant and thus a two way model seems to be more appropriate. Test for significance of time effects: H 0 : θ t = 0,t=1,...,T H 1 : θ t 0, at least for one t vs. F(16, ) = > 2.07 = Critical value Exhibit 35

36 One way and two way model suffer from the same shortcomings. Remarks on two way FE model: Ability bias is again solved. BUT: a lot of relevant information is excluded solely as it is constant over time (e.g. schooling). Exhibit 36

37 Agenda Motivation Notes on human capital investment Empirical analysis based on Jürges & Schneider (2006) The Data Descriptive information Basic specification Pooled OLS One way Fixed Effect model One way Random Effect model Two way Fixed Effect model Conclusion & outlook Exhibit 37

38 Jürges & Schneider follow a more sophisticated approach. The approach of Jürges & Schneider (2006): Jürges & Schneider (2006) deal with methods which can solve the shortcomings of the fixed effect models: Hausman-Taylor procedure, which solely improves efficiency Fixed-Growth model which considers a higher affinity for vocational training for people with higher wage growth, and Matching procedure, which compares a treatment and a control group. Jürges & Schneider (2006) conclude, that vocational training has no impact on income which is in line with our finding in the two way fixed effect model. Exhibit 38

39 There may be other reasons that justify vocational training. Alternative explanations of vocational training: Vocational training is perceived as a consumption good. People (unfortunately) interpret correlation as cause & effect relationship. Belief, that vocational training is protection against unemployment. Vocational training is way of rewarding employees. Obligatory vocational training (fire protection, first aid etc.). Exhibit 39

40 Thank you - comments welcome! Exhibit 40