Is it Worth to Study Two Majors? The Case of Poland

Dominik ButtlerEducation and Work: (Un-) equal TransitionsSofia, 24-25 September 2015

Schedule

• Context of the analysis and research question • Literature• Empirical strategy • Dataset • Results• Conclusions and further questions

Context of the analysis

Enrollment Ratio, tertiary level, Poland

1990/91 1995/96 2000/01 2005/06 2010/11 2011/12 2012/13 2013/14

gross 12,9 22,3 40,7 48,9 53,8 53,1 51,8 49,2

nett 9,8 17,2 30,6 38,0 40,8 40,6 40,2 38,6

Source: Central Statistical Office of Poland

Context of the analysis

Enrollment Ratio, tertiary level, Poland

1990/91 1995/96 2000/01 2005/06 2010/11 2011/12 2012/13 2013/14

gross 12,9 22,3 40,7 48,9 53,8 53,1 51,8 49,2

nett 9,8 17,2 30,6 38,0 40,8 40,6 40,2 38,6

90' 91' 92' 93' 94' 95' 96' 97' 98' 99' 00' 01' 02' 03' 04' 05' 06' 07' 08' 09' 10' 11' 12' 13'0

50100150200250300350

Higher education institutions in Poland, 1990-2014

private public

Source: Central Statistical Office of Poland

Context of the analysis

Share of students by field of HE in selected countries, 2013

country education humanities socialbusiness, law sum

Poland 12,0 7,0 35,7 54,7Bulgaria 6,8 5,0 40,2 52,0Switzerland 9,5 6,6 34,9 51,0France 2,5 8,8 38,3 49,6Germany 9,1 10,7 28,9 48,7Romania 2,0 6,9 39,6 48,5Czech Republic 9,3 7,0 31,6 47,9Sweden 12,4 8,8 26,7 47,9UK 8,0 8,8 26,1 42,9

Source: eurostat

Context of the analysisSa

lary

HE

degr

ee/s

alar

y ot

hers

Shar

e of

wor

kers

with

HE

degr

ee

Source: Gajderowicz, Grotkowska, Wincenciak, 2012

Salary and employment of workers with HE degree

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?

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)

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

Empirical strategyRegression analysis

Y – net monthly wageD – whether someone graduated from two majors (binary variable)X – set of independent variables:

Empirical strategyRegression analysis

Y – net monthly wageD – whether someone graduated from two majors (binary variable)X – set of independent variables:

job characteristics variables• professional experience• tenure• employment type• managerial position

Empirical strategyRegression analysis

Y – net monthly wageD – whether someone graduated from two majors (binary variable)X – set of independent variables:

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

Empirical strategyRegression analysis

Y – net monthly wageD – whether someone graduated from two majors (binary variable)X – set of independent variables:

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

Empirical strategyEstimation of the average treatment effect on the treated

Empirical strategyEstimation of the average treatment effect on the treated

Empirical strategyEstimation of the average treatment effect on the treated

Y – net monthly wageD – 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

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

ResultsTwo majors and field of study

Share of students who graduated from more than one major (in %) by filed of studyartistic 11,76social sciences 7,59humanities 7,52natural sciences 5,66education 5,48law 5,11economics 4,13biology 4,05formal sciences 4,04engineering 3,20medicine 2,75agriculture 1,53total 3,20N=15205

ResultsTwo majors and field of study

Most popular combinations of study fields (as a % of two majors graduates)education & education 10,27

humanities & humanities 8,83

humanities & education 6,37

education & social sciences 6,16

economics & social sciences 5,75

economics & economics 4,11

N=487

Share of students who graduated from more than one major (in %) by filed of studyartistic 11,76social sciences 7,59humanities 7,52natural sciences 5,66education 5,48law 5,11economics 4,13biology 4,05formal sciences 4,04engineering 3,20medicine 2,75agriculture 1,53total 3,20N=15205

Regression analysisDescriptive statistics

variable mean sd

lnwage 7,859 0,473

phd 0,016

master 0,761

two majors 0,043

postgraduate 0,107

tenure 9,788 8,613

experience 13,467 9,913

self employed 0,125

married 0,692

N=1547

Regression analysisDescriptive statistics

variable mean sd variable mean

lnwage 7,859 0,473 female 0,612

phd 0,016 village 0,261

master 0,761 city less 100 0,359

two majors 0,043 city more 100 0,346

postgraduate 0,107 warsaw 0,034

tenure 9,788 8,613 parents high 0,184

experience 13,467 9,913 vocational 0,319

self employed 0,125 job at school 0,293

married 0,692

N=1547

Determinants of monthly earnings (ln)OLS regression coefficients

model 1phd 0.601***master 0.087***two majors 0.117**postgraduate 0.149***tenuretenure2experienceexperience2self employedparents_hivocationaljob_schoolr2_a 0.145N 1547

variables not reported: married, female, female*married, city size, cons*** p<0.01; ** p<0,05; * p<0,1

Determinants of monthly earnings (ln)OLS regression coefficients

model 1 model 2phd 0.601*** 0.515***master 0.087*** 0.065**two majors 0.117** 0.104**postgraduate 0.149*** 0.098***tenure 0.018***tenure2 -0.000***experience 0.014***experience2 -0.000self employed 0.166***parents_hivocationaljob_schoolr2_a 0.145 0.226N 1547 1547

variables not reported: married, female, female*married, city size, cons*** p<0.01; ** p<0,05; * p<0,1

Determinants of monthly earnings (ln)OLS regression coefficients

model 1 model 2 model 3phd 0.601*** 0.515*** 0.491***master 0.087*** 0.065** 0.048*two majors 0.117** 0.104** 0.099*postgraduate 0.149*** 0.098*** 0.109***tenure 0.018*** 0.017***tenure2 -0.000*** -0.000***experience 0.014*** 0.015***experience2 -0.000 -0.000self employed 0.166*** 0.164***parents_hi 0.072***vocational -0.044*job_school -0.089***r2_a 0.145 0.226 0.238N 1547 1547 1547

variables not reported: married, female, female*married, city size, cons*** p<0.01; ** p<0,05; * p<0,1

Determinants of monthly earnings (ln)OLS regression coefficients

model 1 model 2 model 3 model 4phd 0.601*** 0.515*** 0.491*** 0.488***master 0.087*** 0.065** 0.048* 0.048* two majors 0.117** 0.104** 0.099* 0.059 postgraduate 0.149*** 0.098*** 0.109*** 0.107***tenure 0.018*** 0.017*** 0.017***tenure2 -0.000*** -0.000*** -0.000***experience 0.014*** 0.015*** 0.015***experience2 -0.000 -0.000 -0.000 self employed 0.166*** 0.164*** 0.163***parents_hi 0.072*** 0.073***vocational -0.044* -0.045* job_school -0.089*** -0.087***r2_a 0.145 0.226 0.238 0.237 N 1547 1547 1547 1547

variables not reported: married, female, female*married, city size, cons*** p<0.01; ** p<0,05; * p<0,1

Estimation of treatment effectsdeterminants of studying two majors, logistic regression coefficients

coeff.female 0,148parents_hi 0,248*vocational -0,272**job_school 0,204*cons -1,859***ps_r2 0.0235N 1547

*** p<0.01; ** p<0,05; * p<0,1

Estimation of treatment effectsaverage treatment effect on the treated, ATT

Matching treated controls difference ATTS.E. (corr) T-stat (corr)

treatment matched %

NN 3092 1909,51 1183,04 0,619 371,37 3,18 100

Kernel 3092 2808,19 283,80 0,101 206,65 1,37 100

Estimation of treatment effectsPSM, NN matching , balance test

mean %reductvariable treated control bias t p>t

vocational school U 0,194 0,324 -2,24 0,025

M 0,194 0,194 100 0 1

Estimation of treatment effectsPSM, NN matching , balance test

mean %reductvariable treated control bias t p>t

vocational school U 0,194 0,324 -2,24 0,025

M 0,194 0,194 100 0 1

parents he U 0,283 0,179 2,16 0,031

M 0,283 0,283 100 0 1

job at school U 0,373 0,289 1,48 0,14

M 0,373 0,373 100 0 1

female U 0,701 0,608 1,53 0,125

M 0,701 0,701 100 0 1

PSM, kaliper matching , balance test

mean %reductvariable treated control bias t p>t

vocational school U 0,194 0,324 -2,24 0,025

M 0,194 0,205 91,2 -0,16 0,869

parents he U 0,283 0,179 2,16 0,031

M 0,283 0,263 80,5 0,26 0,793

job at school U 0,373 0,289 1,48 0,14

M 0,373 0,364 89,6 0,10 0,918

female U 0,701 0,608 1,53 0,125

M 0,701 0,749 48,5 -0,62 0,536

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?

Thank you for your attention!