1. Is it Worth to Study Two Majors? The Case of Poland Dominik Buttler Education and Work: (Un-) equal Transitions Sofia, 24-25 September 2015

2. Schedule Context of the analysis and research question Literature Empirical strategy Dataset Results Conclusions and further questions

3. Context of the analysis Enrollment Ratio, tertiary level, Poland 1990/911995/962000/012005/062010/112011/122012/132013/14 gross12,922,340,748,953,853,151,849,2 nett9,817,230,638,040,840,640,238,6 Source: Central Statistical Office of Poland

4. Context of the analysis Enrollment Ratio, tertiary level, Poland 1990/911995/962000/012005/062010/112011/122012/132013/14 gross12,922,340,748,953,853,151,849,2 nett9,817,230,638,040,840,640,238,6 Source: Central Statistical Office of Poland

5. Context of the analysis Share of students by field of HE in selected countries, 2013 countryeducationhumanities social business, law sum Poland12,07,035,754,7 Bulgaria6,85,040,252,0 Switzerland9,56,634,951,0 France2,58,838,349,6 Germany9,110,728,948,7 Romania2,06,939,648,5 Czech Republic9,37,031,647,9 Sweden12,48,826,747,9 UK8,08,826,142,9 Source: eurostat

6. Context of the analysis Salary HE degree/salary others Share of workers with HE degree Source: Gajderowicz, Grotkowska, Wincenciak, 2012 Salary and employment of workers with HE degree

7. Introduction of fees for studying a second major Second major (two majors) vs. double major justification referring both to economic efficiency and social justice full time students at public universities affected – 10,1 % students declared studying more than one major (8,5 % at public universities) possible negative impact on the financial stability of unpopular departments relatively strong protests of students new law declared to be unconstitutional Is it worth to study two majors, after all?

9. Literature human capital and/or signaling theory Hemelt (2010): a wage premium of 3,2% (slightly higher for women) among graduates in US (National Survey of College Graduates) Del Rossi and Hersch (2008): a wage premium up to 2,3% among graduates in US (National Survey of College Graduates), high premium of arts, humanities or social science students taking business as a second major Zafar (2012): Double Major: One for me, One for the Parents (sample of Northwestern University sophomores)

10. Empirical strategy The empirical analysis with the use of the unique dataset from the survey Study of Human Capital in Poland Descriptive statistics on what majors are combined Regression analysis, wage determinants in the sample of tertiary education graduates Propensity score techniques, estimation of the average treatment effect on the treated – Nearest neighbor matching – Kernel matching

11. Empirical strategy Regression analysis

12. job characteristics variables professional experience tenure employment type managerial position

13. Empirical strategy Regression analysis job characteristics variables professional experience tenure employment type managerial position two major decision variables education of parents whether someone had to work after high school whether someone graduated from vocational school

14. Empirical strategy Regression analysis job characteristics variables professional experience tenure employment type managerial position two major decision variables education of parents whether someone had to work after high school whether someone graduated from vocational school socio-demographic status educational level (PhD, master, postgraduate) gender marital status city size

15. Empirical strategy Estimation of the average treatment effect on the treated

17. Y – net monthly wage D – whether someone graduated from two majors (binary variable) PSM – based on (binary) variables which could possibly influence both the wage and the decision to study two majors gender whether at least one parent had HE degree whether someone had to work after high school whether someone graduated from vocational school at the secondary level

18. The Dataset Study of Human Capital in Poland Population/society survey (study of the population of working age) – people of working age (18-59 W, 18-64 M) – detailed information on employment status, job characteristics, curriculum, qualifications and competences, training needs – five waves (2010-2014), around 17 000 cases each (not a panel) only one wave (2013) used in the analysis due to the selection of variables – individuals with HE degree – full time employees or self-employed

19. Results Two majors and field of study Share of students who graduated from more than one major (in %) by filed of study artistic11,76 social sciences7,59 humanities7,52 natural sciences5,66 education5,48 law5,11 economics4,13 biology4,05 formal sciences4,04 engineering3,20 medicine2,75 agriculture1,53 total3,20 N=15205

20. Results Two majors and field of study Most popular combinations of study fields (as a % of two majors graduates) education & education10,27 humanities & humanities8,83 humanities & education6,37 education & social sciences6,16 economics & social sciences5,75 economics & economics4,11 N=487 Share of students who graduated from more than one major (in %) by filed of study artistic11,76 social sciences7,59 humanities7,52 natural sciences5,66 education5,48 law5,11 economics4,13 biology4,05 formal sciences4,04 engineering3,20 medicine2,75 agriculture1,53 total3,20 N=15205

21. Regression analysis Descriptive statistics variablemeansd lnwage7,8590,473 phd0,016 master0,761 two majors0,043 postgraduate0,107 tenure9,7888,613 experience13,4679,913 self employed0,125 married0,692 N=1547

22. Regression analysis Descriptive statistics variablemeansdvariablemean lnwage7,8590,473female0,612 phd0,016village0,261 master0,761city less 1000,359 two majors0,043city more 1000,346 postgraduate0,107warsaw0,034 tenure9,7888,613parents high0,184 experience13,4679,913vocational0,319 self employed0,125job at school0,293 married0,692 N=1547

23. Determinants of monthly earnings (ln) OLS regression coefficients model 1 phd0.601*** master0.087*** two majors0.117** postgraduate0.149*** tenure tenure2 experience experience2 self employed parents_hi vocational job_school r2_a0.145 N1547 variables not reported: married, female, female*married, city size, cons *** p<0.01; ** p<0,05; * p<0,1

24. Determinants of monthly earnings (ln) OLS regression coefficients model 1model 2 phd0.601***0.515*** master0.087***0.065** two majors0.117**0.104** postgraduate0.149***0.098*** tenure0.018*** tenure2-0.000*** experience0.014*** experience2-0.000 self employed0.166*** parents_hi vocational job_school r2_a0.1450.226 N1547 variables not reported: married, female, female*married, city size, cons *** p<0.01; ** p<0,05; * p<0,1

25. Determinants of monthly earnings (ln) OLS regression coefficients model 1model 2model 3 phd0.601***0.515***0.491*** master0.087***0.065**0.048* two majors0.117**0.104**0.099* postgraduate0.149***0.098***0.109*** tenure0.018***0.017*** tenure2-0.000*** experience0.014***0.015*** experience2-0.000 self employed0.166***0.164*** parents_hi0.072*** vocational-0.044* job_school-0.089*** r2_a0.1450.2260.238 N1547 variables not reported: married, female, female*married, city size, cons *** p<0.01; ** p<0,05; * p<0,1

26. Determinants of monthly earnings (ln) OLS regression coefficients model 1model 2model 3model 4 phd0.601***0.515***0.491***0.488*** master0.087***0.065**0.048* two majors0.117**0.104**0.099*0.059 postgraduate0.149***0.098***0.109***0.107*** tenure0.018***0.017*** tenure2-0.000*** experience0.014***0.015*** experience2-0.000 self employed0.166***0.164***0.163*** parents_hi0.072***0.073*** vocational-0.044*-0.045* job_school-0.089***-0.087*** r2_a0.1450.2260.2380.237 N1547 variables not reported: married, female, female*married, city size, cons *** p<0.01; ** p<0,05; * p<0,1

27. Estimation of treatment effects determinants of studying two majors, logistic regression coefficients coeff. female0,148 parents_hi0,248* vocational-0,272** job_school0,204* cons-1,859*** ps_r20.0235 N1547 *** p<0.01; ** p<0,05; * p<0,1

28. Estimation of treatment effects average treatment effect on the treated, ATT MatchingtreatedcontrolsdifferenceATT S.E. (corr) T-stat (corr) treatment matched % N30921909,511183,040,619371,373,18100 Kernel30922808,19283,800,101206,651,37100

29. Estimation of treatment effects PSM, NN matching, balance test mean%reduct variabletreatedcontrolbiastp>t vocational schoolU0,1940,324-2,240,025 M0,194 10001

30. Estimation of treatment effects PSM, NN matching, balance test mean%reduct variabletreatedcontrolbiastp>t vocational schoolU0,1940,324-2,240,025 M0,194 10001 parents heU0,2830,1792,160,031 M0,283 10001 job at schoolU0,3730,2891,480,14 M0,373 10001 femaleU0,7010,6081,530,125 M0,701 10001

31. PSM, kaliper matching, balance test mean%reduct variabletreatedcontrolbiastp>t vocational schoolU0,1940,324-2,240,025 M0,1940,20591,2-0,160,869 parents heU0,2830,1792,160,031 M0,2830,26380,50,260,793 job at schoolU0,3730,2891,480,14 M0,3730,36489,60,100,918 femaleU0,7010,6081,530,125 M0,7010,74948,5-0,620,536

32. Conclusions and further research Some evidence of a positive return to graduating from two majors – results not very robust – lack of useful variables, especially in PSM procedure Other possible effects worth investigation, e.g. impact on competences, unemployment, job match, quality of life Determinants of studying two majors – What characteristics share students of two majors? – Studying two majors as a safe studying strategy?

33. Thank you for your attention!