Students’ Time Allocation and School Performance: A Comparison between Student Jobs, Sports and Music Participation

Christian Pfeifer; Katja Seidel

doi:10.1515/jbnst-2018-0039

Article

Students’ Time Allocation and School Performance: A Comparison between Student Jobs, Sports and Music Participation

Christian Pfeifer and Katja Seidel

Published/Copyright: November 16, 2019

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From the journal Jahrbücher für Nationalökonomie und Statistik Volume 240 Issue 5

Abstract

The authors analyze the correlations between students’ time allocation and school performance in terms of grades and satisfaction with their own performance in math, German, first foreign language, and overall. They address the heterogeneity between three important extracurricular activities (student jobs, sports and music participation) and the heterogeneity within each activity by accounting for different types and participation length of an activity. The used cross-sectional survey data of 3388 students, who are about 17 years old and enrolled in a German secondary school, indeed reveal substantial heterogeneity between and within the activities. The empirical analysis is accompanied by an extensive survey of the empirical literature about the association between student jobs, sports, and music participation and school performance.

Keywords: human capital; jobs; music; school performance; sports; time allocation

JEL Classification: I21; J13; J22; J24 (Z11Z28)

Acknowledgements

We thank the participants of the workshop on “Leisure Time Activities” in Tuebingen on July 22, 2016, and two reviewers of this journal for their comments.

References

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Appendix

Table 6:

Literature survey.

Author (year)	Country; Years	Activity variables; School performance outcomes	Identification strategy	Results
Student jobs
Buscha et al. (2012)	USA 1988/1992	Hours worked part-time while in high school (grade 12): binary part-time in general, stratified by intensity (measured in hours) and occupation; Composite scores of math and reading tests	Semi-parametric propensity score matching combined with difference-in-differences/difference-in-differences-in differences	Negligibly small effects on reading and math performance when working part-time during 12th grade of high school
Dustmann and van Soest (2007)	England, Wales 1974–1975 (cohort 1958)	Index of hours worked part-time at the age of 16; Educational performance at the age of 16 (number of O’levels/CSE Grade Ones), economic activity at the age of 16 (staying in school, enrollment in training schemes, enter labor market full time)	Three equation model estimated separately and jointly: (1) hours worked (grouped regression model), (2) number of O’levels (censored regression model), (3) educational involvement (ordered probit model)	No negative effects for female students on educational performance or engagement; results indicate minor negative effect for male students on school outcomes and involvement; parents’ interest in the child’s educational achievements is most important for exam success and school leaving decision
Eckstein and Wolpin (1999)	USA 1979–1991	Hours worked during school, hourly wage rate; Course grades, dropout probabilities in high school	Sequential decision model	Worse school performance in high school for white male part-time worker; prohibiting work while in school legally, would reduce graduation rates only slightly and has almost no effect on grades
Marsh (1991)	USA 1980, 1982, 1984	Hours worked in sophomore year, junior year and senior year of high school; Outcome variables collected in sophomore and senior year of high school (standardized achievement tests, GPA, courses selected, self-concept, locus of control, self-esteem, educational and occupational aspiration), postsecondary outcomes (educational attainment, educational and occupational aspiration)	Multiple regression	Working during sophomore year seems to increase the probability to drop out of high school, is negatively related to almost all postsecondary outcomes (e. g. educational and occupational aspirations, unemployment) and to senior year outcomes (e. g. grades, homework, educational and occupational aspirations); working in order to save money for college has positive effects on school performance and aspirations in senior year and on postsecondary outcomes; less commitment to school (motivation/investment) causes negative effects on school outcomes, not the hours spend on working
Montmarquette et al. (2007)	Canada 1991	Working while in school conditional on individual’s preference for schooling or labor market entry (binary); Hours worked while in school, grades, probability of dropping out of high school	Joint maximum likelihood estimation (incl. utility of school performance, utility of working, utility of dropping out of school) conditional on the type of student	Female students, students from private schools, and students whose parents obtain a postsecondary degree prefer school over work; working a moderate number of hours per week during full time education has no negative effects on school performance and attainment
Schneider and Wagner (2003)	Germany 2000–2002	Binary part-time work, binary music, binary sports, binary reading, binary friends or binary volunteer work; School performance in math, German and first foreign language (descriptive statistics)	Descriptive statistics	No crowding out of good leisure activities such as sport, music, reading, friends or volunteer work; part-time workers seem to be more active; overall negligibly worse school performance for part-time worker while in school; starting to work part-time before turning 15 worsened school performance on average
Tully (2004)	Germany 2002	Part-time worker while in school; School performance measured in grades	Descriptive statistics	Correlations between part-time work and school performance close to zero
Sports participation
Anderson (1998)	USA 1980–1992, 1988–1994,	Binary sport in general, binary team sports, binary individual sports, binary football, binary baseball, binary basketball, binary other team sports (in and outside of school during the school year); Binary high school dropout, binary enrollment in a four-year college, years of completed education	OLS, IV	IV estimations reveal that differences between athletes and non-athletes seem to be driven by unobserved characteristics; lower high school dropout probabilities, higher college enrollment probabilities and more years in completed education for white female and male athletes; sports are less beneficial for minorities; female minorities have a lower dropout probability if they do team sports; no harmful effects for male minorities on educational success
Eiden and Ronan (2001)	USA 1980–1992	Binary sports participation in the sophomore year/participation in varsity sports in the senior year; Binary high school dropout in 1982, binary 4-year college or university enrollment between 1982–1984, binary college graduation until 1992	OLS, IV	Sport participation increases white female’s college attendance and graduation probabilities; results indicate negative effects for male athletes with respect to educational attainment; higher college attendance rates for black males; neither harmful nor beneficial effects on educational attainment for Hispanic male and female athletes
Felfe et al. (2016)	Germany 2003–2006	Binary sports regularly in a sports club (at least once a week) among children aged 3 to 10; Average school grade (math + German), health outcomes (subjective health, BMI, skinfold, pulse), behavioral outcomes (emotions, hyperactivity, peer relationships, antisocial behavior, total difficulties score)	Matching, IV	Results reveal positive effects of sports during early childhood (3 to 10) on health, school performance and behavior; in particular peer relationship problems are reduced and the subjective health is increased; estimations suggest crowding out of watching TV by sports participation in clubs; less time spend on unstructured activities and more contact to instructors and older peers might increase the development of personal skills; sport participation stimulates physical activity
Gorry (2016)	USA 1994–1997, 2001–2002, 2008 −2009	Binary sports, binary team sports and binary individual sports; Reported and transcript GPA, binary high school diploma	Fixed effects, quantile regression, IV	Positive effect of sport in general, team and individual sport on GPA and high school graduation rates; team sport participants seem to benefit the most; interacting with team members might help to develop further skills; low achieving participants benefit the most, being in contact with high achieving peers or incentives to continue doing sports leads to better grades and higher graduation probabilities
Long and Caudill (1991)	USA 1971, 1980	Binary varsity letter was earned in a college sport; College graduation	Logit regressions	Results suggest higher graduation probabilities for male and female college varsity athletes compared to non-athletes; being an athlete while in college might enhance discipline, competitiveness, motivation or other personnel traits that influence educational success positively
Maloney and McCormick (1993)	USA 1985–1988	Individual GPA over all courses taken in a term; Binary NCAA intercollegiate sport, binary revenue sports, binary non-revenue sports	ML censored-sample estimation	Athletes perform worse, but the overall effect is small; the effects differ across sports; only revenue sports such as football and men’s basketball show significantly negatively effects (one-tenth of a grade point worse); for revenue sports grades are worse in season than out of season, which indicates exploitation of this athletes; non-revenue athletes perform like non-athletes
Pfeifer and Cornelissen (2010)	Germany 2000–2005	Binary sports participation outside of school and binary participation in competitions during childhood and adolescence; Secondary school degrees, professional degrees	Generalized ordered probit regression, IV, linear treatment regression	Positive effects of sport during childhood and adolescence on educational achievements for men and women; outperformance might be a results of choosing leisure activities outside of school, which foster the educational productivity more; participation in competitions has only significant effect for women (increasing probability of an intermediate school degree and a lower probability to obtain the lowest school degree, higher probability of attaining vocational training); the larger effects for women who participate in competition might be due to an increased competitive orientation compared to men
Rees and Sabia (2010)	USA 1995, 1996	Binary sports participation during high school (not at all, 1 or 2 times, 3 or 4 times, 5 or more times the past week); Grades in math and English, comprehensive grade, difficulties paying attention in class at least once a week, difficulties in completing homework on time at least once a weak, college aspiration	OLS, Fixed effects, IV	OLS reports positive effects on grades and college aspirations; fixed effects and IV estimations reveal only small or no human capital spillover effects of sports on student grades or college aspirations; OLS estimations are driven by unobserved heterogeneity
Music participation
Bilhartz et al. (1999)	USA 1997–1998	Binary music at different compliance and income levels; Stanford-Binet (SB) Intelligence Score of 4–6 years old children (composite score and subtests score (vocabulary, memory for sentences, bead memory, pattern analysis, quantitative reasoning)), Young Child Music Skills Assessment (MSA) score of 4–6 years old children (composite score and subtest score (steady beat, rhythmic pattern, vocal pitch, aural discrimination)),	ANOVA (Bonferroni corrective method), four-order partial correlations analysis	MSA: only the aural discrimination tests shows no significant improvement for the treated group (music involvement); in particular high income and high compliance children benefit the most; SB: even under minimal treating the bead memory score improves more compared to the control group; children who were treated fully improve their bead memory the most, developing kinesthetic, visualization, and aural skills by music training seem to improve visual imaginary and sequencing strategies (bead memory), no improvement in verbal reasoning abilities
Elpus (2013)	USA 2002, 2004	Binary music enrollment in high school, number of credits earned in music, binary music subareas; SAT scores	Fixed effects	Music participants do not perform better than non-music participants; better performing students and students with a higher status are more likely to select into music
Fitzpatrick (2006)	USA 2003–2004	Instrumental music students receiving free or reduced lunch, instrumental music students paying full price for lunch, non-instrumental students receiving free or reduced lunch, non-instrumental students paying full price for lunch (students in grades 9–12 during school year 2003–2004); Four scaled scores (Ohio Proficiency Test: citizenship, math, reading, science) at 4th, 6th and 9th grade	Two-tailed t-test statistic	Indication for self-selection into instrumental music courses; higher performing students sort into music courses; students who participated in instrumental music courses during school year 2003–2004 outperformed non-instrumental students in citizenship, math, reading and science in grade 4,6 and 9 (before they started playing instrumental music)
Hille and Schupp (2015)	Germany 2001–2012	Binary playing music at least for 9 years (8–17) and outside of school, binary sports participation for at least 9 years and regularly participating in competitions, binary playing theatre or dancing at least weekly; Cognitive skills (analogies, figures, and mathematics operators measured in std. deviations), school grades normalized within each secondary school type (math, German, first foreign language, average grade measured in std. deviations), personality traits (conscientiousness, extraversion, agreeableness, openness, neuroticism measured in std. deviations), time use (watching TV, reading books measured in percent), ambitions (school degree, university measured in percent)	Matching	Being musically active for at least 9 years during childhood improves cognitive skills, school grades, educational ambitions and increases the level of conscientiousness and openness; playing music lowers the probability of watching TV and increases the probability of reading books; compared to alternative activities, music has the strongest effects; music affects almost all outcome variables positive; dancing and/or playing theatre foster personality traits and academic ambitions; sport activities, on the contrary, affect academic ambitions positively
Southgate and Roscigno (2009)	USA 1988–2000	Binary music participation in school, binary music participation outside of school, binary parents attend concerts, amount of music coursework from 8th to 10th grade (measured in years); Standardized reading IRT scores, standardized mathematics IRT scores	Logit and OLS regressions	Music participation in school increases reading and math performance of children; music involvement in and outside of school increases math performance of adolescents; the intensive music is played (amount of music coursework from 8th to 10th grade), the better the performance of adolescents in math and reading; cultural capital is more able to explain school achievements; results indicate that music involvement is more a mediator variable than a predictor variable (statistically significant, but low explanation power)
Yang (2015)	Germany 2001–2009	Binary active in music, binary intensity (often, seldom), binary peers (alone, with), binary paid music lessons, binary started playing music at a age of (0–5, 6–10,>10); Track recommendation after elementary school and track at the age of 17 (lower, intermediate, higher track)	OLS and probit regressions, Fixed effects	Higher track recommendations and track at 17 for all music indicators; in particular starting early during childhood and practicing often have the largest effects; the effect of music is strongly affected by ability and educational background
Combined
Balsmeier and Peters (2009)	Germany 2000–2007	Binary part-time working, binary leisure time sport/club sport, binary school sport, binary paid music lessons, binary no extracurricular activities, binary TV, binary reading, binary voluntary activities; Binary high school graduation	Survival analysis (Cox proportional hazard rate model)	Significant higher likelihood of graduation if students work part-time during school; selection of more highly skilled adolescents into part-time working; female students benefit the most from working; while doing school sports increases likelihood of graduation and no extracurricular activity decreases the likelihood of graduation for female students, no significant effect for male students is observed
Barron et al. (2000)	USA 1972–1985 1979–1992	Binary participation in high school athletics, binary participation in high school athletics as a leader/most active, binary participation in other extracurricular activities (school-sponsored hobby or subject-matter clubs); only for men; Educational attainment	IV	Athletic involvement enhances productivity; higher educational attainment for high school athletes
Cabane et al. (2016)	Germany 2001–2012	Binary music (general, paid, monthly basis) and binary sports (general, competitive, non-competitive) participation (at least 3 years active); Grades, cognitive and non-cognitive skills, Big 5, educational engagement, health (subjective/current situation), other leisure activities (TV, playing computer, reading)	Matching, IV	Music improves school performance and increases academic ambitions more than sports; music participants read more books, watch less TV and play less computer compared to sport participants; sports improves health more than music; doing both activities vs. doing one activity improves educational performance
Covay and Carbonaro (2010)	USA 2002	Binary music, binary dance, binary sports, binary performing art activities, binary art in the last year; Approaches to learning 1–4 scale (math and reading)	Logit regressions, OLS	Being active during childhood improves non-cognitive skills and academic benefits; sports participations improve non-academic skills the most compared to other activities; through the interaction with authorities and privileged peer students who participate in extracurricular activities have access to non-cognitive skills and improve their school performance
Del Boca et al. (2017)	USA 1997, 2002, 2003, 2007	Weekly time investment decision of children (aged 6–10) and adolescents (aged 11–15) on leisure activities (aggregated leisure activities measured in hours are homework, doing arts and craft, sport, playing, attending performances and museums, religious activities); Standardized measure of cognitive ability, learning and reading abilities, comprehension and vocabulary skills, mathematical skills	OLS, Fixed effects	Time investment decisions of children during adolescence improve test scores more than time investment decisions by mothers; time investment decisions during childhood are more beneficial if rather made by mother than by children
Lipscomb (2007)	USA 1988, 1990, 1992	Binary participation in school-supported sports, binary participation in school-supported clubs, binary clubs conditional on highest math score among members; Scores in math and science at different school grades, educational expectations at different school grades (earning at least an B. A. or equivalent)	Fixed effects	Short-run learning effect of sports and club participation on student learning; long-run effect on educational attainment; sport participants perform better in math and science and have higher degree attainment expectations; club participants have higher math scores and higher degree attainment expectations; women benefit more from sports participation than men; participating in clubs with generally low scoring members do not help students learning; students who participate in clubs with high achieving members benefit more (higher degree attainment expectations)
Schellenberg (2004)	Canada	Binary music lessons for 36 weeks (keyboard or voice training), binary drama lessons 36 weeks, binary no music lessons; IQ score, fours index score (verbal comprehension, perceptual organization, freedom from distractibility, processing speed) and 12 subgroups (e. g. picture arrangement, coding, information, arithmetic), maladaptive and adaptive behaviors	Experimental design, 144 6-year-olds were offered a free weekly arts lesson for 36 weeks, randomly grouped into keyboard lessons, voice lessons, drama lessons or no lessons, descriptive statistics and analysis of variance	Small increases for music lessons on IQ; drama lessons on the contrary have positive effects on social behavior; multiple experience in music lessons might improve a range of abilities

Table 7:

Descriptive statistics for all variables.

	Mean	Std. Dev.	Min	Max
Grade Math	2.923	1.054	1	6
Grade German	2.828	0.848	1	6
Grade foreign language	2.890	0.915	1	6
Satisfaction with own performance in Math	6.293	2.564	0	10
Satisfaction with own performance in German	6.526	2.148	0	10
Satisfaction with own performance in foreign language	6.525	2.286	0	10
Satisfaction with own overall performance	6.547	1.965	0	10
Job (dummy)	0.443	0.497	0	1
Job participation length (dummies)
No job	0.557	0.497	0	1
≤ 1 year	0.088	0.283	0	1
2–3 years	0.280	0.449	0	1
≥ 4 years	0.076	0.264	0	1
Job type (dummies)
No job	0.557	0.497	0	1
Out of interest	0.071	0.257	0	1
For money	0.337	0.473	0	1
Other reason/no information	0.035	0.184	0	1
Sport (dummy)	0.750	0.433	0	1
Sport competition (dummies)
No sport	0.250	0.433	0	1
Sport without competition	0.394	0.489	0	1
Sport with competition	0.356	0.479	0	1
Sport participation length (dummies)
No sport	0.250	0.433	0	1
≤ 1 year	0.060	0.238	0	1
2–3 years	0.147	0.354	0	1
≥ 4 years	0.543	0.498	0	1
Sport type (dummies)
No sport	0.250	0.433	0	1
Extracurricular in school	0.077	0.266	0	1
Sports club etc.	0.427	0.495	0	1
Non-organized with others	0.109	0.311	0	1
Non-organized alone	0.077	0.266	0	1
No information	0.060	0.238	0	1
Music (dummy)	0.297	0.457	0	1
Music participation length (dummies)
No music	0.703	0.457	0	1
≤ 1 year	0.014	0.119	0	1
2–3 years	0.036	0.186	0	1
≥ 4 years	0.247	0.431	0	1
Music type (dummies)
No music	0.703	0.457	0	1
Alone/with teacher	0.135	0.342	0	1
With group	0.135	0.342	0	1
Other/no information	0.027	0.162	0	1
School type (dummies)
Secondary general school	0.102	0.302	0	1
Intermediate school	0.289	0.453	0	1
Comprehensive school/other	0.101	0.301	0	1
Upper secondary school	0.509	0.500	0	1
Students with migration background in class (dummies)
No students with migration background in class	0.222	0.415	0	1
Less than a quarter	0.478	0.500	0	1
About a quarter	0.143	0.350	0	1
About half	0.086	0.281	0	1
Most or all	0.071	0.258	0	1
Own migration background (dummy)	0.239	0.427	0	1
Female (dummy)	0.504	0.500	0	1
Students’ pocket money in Euros	42.671	42.005	0	600
Household income in Euros	3330.633	2013.484	0	35,000
Schooling of father (dummies)
Do not know	0.031	0.174	0	1
Second general school	0.254	0.435	0	1
Intermediate secondary school	0.282	0.450	0	1
Technical school degree	0.055	0.227	0	1
Upper secondary school	0.245	0.430	0	1
Other degree	0.108	0.310	0	1
No school degree	0.026	0.160	0	1
Schooling of mother (dummies)
Do not know	0.001	0.034	0	1
Second general school	0.193	0.395	0	1
Intermediate secondary school	0.415	0.493	0	1
Technical school degree	0.042	0.201	0	1
Upper secondary school	0.214	0.410	0	1
Other degree	0.113	0.317	0	1
No school degree	0.022	0.145	0	1
Federal state (dummies)
Schleswig-Holstein	0.036	0.187	0	1
Hamburg	0.013	0.114	0	1
Lower Saxony	0.102	0.303	0	1
Bremen	0.008	0.087	0	1
North Rhine-Westphalia	0.223	0.416	0	1
Hesse	0.068	0.253	0	1
Rhineland-Palatinate	0.045	0.207	0	1
Baden-Wuerttemberg	0.126	0.332	0	1
Bavaria	0.133	0.340	0	1
Saarland	0.008	0.089	0	1
Berlin	0.033	0.179	0	1
Brandenburg	0.043	0.202	0	1
Mecklenburg-West Pomerania	0.023	0.149	0	1
Saxony	0.060	0.237	0	1
Saxony-Anhalt	0.041	0.199	0	1
Thuringia	0.037	0.189	0	1
Survey year (dummies)
2001	0.077	0.267	0	1
2002	0.061	0.240	0	1
2003	0.061	0.240	0	1
2004	0.064	0.245	0	1
2005	0.064	0.244	0	1
2006	0.055	0.228	0	1
2007	0.066	0.247	0	1
2008	0.040	0.195	0	1
2009	0.043	0.204	0	1
2010	0.071	0.257	0	1
2011	0.088	0.283	0	1
2012	0.093	0.291	0	1
2013	0.116	0.321	0	1
2014	0.100	0.301	0	1

Notes: Number of yearly observations is N = 3,388.
Data source: SOEP 2001–2014 (youth), version 31, SOEP, 2015, doi:10.5684/soep.v31.

Table 8:

Complete OLS results for specifications with binary indicators for job, sports, and music.

	Grade – Math	Grade – German	Grade – Foreign	Satis – Math	Satis – German	Satis – Foreign	Satis – Overall
Job	0.035	0.004	0.048+	−0.098	−0.002	−0.132*	−0.057
	(0.038)	(0.028)	(0.031)	(0.092)	(0.075)	(0.080)	(0.069)
	[0.349]	[0.883]	[0.129]	[0.284]	[0.982]	[0.100]	[0.407]
Sport	−0.052	−0.012	0.002	0.213**	0.065	0.107	0.162*
	(0.043)	(0.033)	(0.038)	(0.106)	(0.089)	(0.096)	(0.084)
	[0.224]	[0.727]	[0.962]	[0.045]	[0.464]	[0.266]	[0.055]
Music	−0.119***	−0.143***	−0.097***	0.295***	0.266***	0.263***	0.200**
	(0.042)	(0.032)	(0.035)	(0.103)	(0.083)	(0.087)	(0.078)
	[0.005]	[0.000]	[0.006]	[0.004]	[0.001]	[0.003]	[0.011]
Intermediate school	−0.080	−0.060	0.042	−0.055	0.174+	0.237+	0.045
	(0.066)	(0.050)	(0.057)	(0.161)	(0.135)	(0.157)	(0.128)
	[0.225]	[0.234]	[0.468]	[0.734]	[0.196]	[0.129]	[0.728]
Comprehensive school/other	−0.043	−0.085+	−0.074	−0.459**	−0.160	0.138	0.013
	(0.081)	(0.061)	(0.072)	(0.200)	(0.167)	(0.190)	(0.153)
	[0.596]	[0.163]	[0.304]	[0.022]	[0.338]	[0.466]	[0.934]
Upper secondary school	−0.217***	−0.250***	−0.234***	−0.120	0.253*	0.472***	0.190+
	(0.069)	(0.053)	(0.059)	(0.168)	(0.138)	(0.160)	(0.129)
	[0.002]	[0.000]	[0.000]	[0.474]	[0.068]	[0.003]	[0.142]
Less than a quarter of students in class with migration background	0.007	0.010	0.039	0.147	0.021	0.045	−0.069
	(0.053)	(0.038)	(0.043)	(0.128)	(0.104)	(0.108)	(0.099)
	[0.902]	[0.785]	[0.366]	[0.251]	[0.842]	[0.675]	[0.489]
About a quarter with migration background	0.048	−0.034	−0.011	0.050	−0.046	−0.054	−0.120
	(0.069)	(0.051)	(0.057)	(0.170)	(0.142)	(0.150)	(0.130)
	[0.487]	[0.507]	[0.850]	[0.768]	[0.748]	[0.719]	[0.355]
About half with migration background	−0.003	0.026	0.124*	−0.011	−0.081	−0.345*	−0.247+
	(0.082)	(0.063)	(0.069)	(0.199)	(0.162)	(0.179)	(0.158)
	[0.974]	[0.684]	[0.073]	[0.957]	[0.618]	[0.055]	[0.118]
Most or all with migration background	0.055	−0.097+	0.028	−0.014	0.257+	0.066	−0.107
	(0.090)	(0.067)	(0.078)	(0.221)	(0.179)	(0.196)	(0.171)
	[0.539]	[0.143]	[0.717]	[0.949]	[0.152]	[0.736]	[0.534]
Own migration background	0.125**	0.046	−0.145***	−0.085	0.154+	0.571***	0.071
	(0.058)	(0.045)	(0.050)	(0.137)	(0.113)	(0.129)	(0.113)
	[0.032]	[0.304]	[0.004]	[0.537]	[0.175]	[0.000]	[0.530]
Female	0.045	−0.372***	−0.258***	−0.406***	0.721***	0.396***	0.292***
	(0.037)	(0.028)	(0.030)	(0.089)	(0.074)	(0.078)	(0.068)
	[0.222]	[0.000]	[0.000]	[0.000]	[0.000]	[0.000]	[0.000]
Students’ pocket money in Euros	0.001	−0.001+	−0.001**	−0.002+	0.001	0.002***	−0.000
	(0.000)	(0.000)	(0.000)	(0.001)	(0.001)	(0.001)	(0.001)
	[0.283]	[0.113]	[0.046]	[0.182]	[0.534]	[0.005]	[0.759]
Household income in Euros	−0.000	−0.000	−0.000	0.000	0.000	0.000+	0.000
	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
	[0.930]	[0.624]	[0.995]	[0.226]	[0.735]	[0.116]	[0.972]
Second general school of father	−0.083	0.049	0.020	0.564**	0.171	0.198	0.268
	(0.103)	(0.087)	(0.097)	(0.270)	(0.223)	(0.257)	(0.222)
	[0.425]	[0.575]	[0.834]	[0.037]	[0.444]	[0.440]	[0.226]
Intermediate secondary school of father	−0.099	0.036	0.008	0.590**	0.096	0.252	0.280
	(0.105)	(0.088)	(0.098)	(0.273)	(0.228)	(0.257)	(0.223)
	[0.348]	[0.680]	[0.935]	[0.031]	[0.675]	[0.325]	[0.210]
Technical school degree of father	−0.174+	0.138+	0.097	0.610*	0.012	−0.111	0.076
	(0.129)	(0.104)	(0.117)	(0.324)	(0.268)	(0.306)	(0.265)
	[0.178]	[0.186]	[0.408]	[0.060]	[0.966]	[0.716]	[0.776]
Upper secondary school of father	−0.270**	−0.051	−0.073	0.771***	0.208	0.355+	0.293
	(0.108)	(0.091)	(0.101)	(0.279)	(0.235)	(0.264)	(0.230)
	[0.013]	[0.577]	[0.468]	[0.006]	[0.377]	[0.178]	[0.204]
Other degree of father	−0.217*	0.042	−0.017	0.518*	−0.065	0.055	0.024
	(0.119)	(0.097)	(0.108)	(0.312)	(0.257)	(0.288)	(0.252)
	[0.067]	[0.667]	[0.876]	[0.096]	[0.800]	[0.848]	[0.926]
No school degree of father	−0.125	0.166+	0.099	0.549+	−0.079	0.050	0.186
	(0.155)	(0.114)	(0.136)	(0.384)	(0.316)	(0.361)	(0.296)
	[0.420]	[0.147]	[0.464]	[0.153]	[0.803]	[0.890]	[0.530]
Second general school of mother	−0.438*	−0.169	−0.239	0.625+	1.774***	0.984***	1.063***
	(0.257)	(0.165)	(0.320)	(0.420)	(0.560)	(0.299)	(0.351)
	[0.088]	[0.305]	[0.456]	[0.137]	[0.002]	[0.001]	[0.002]
Intermediate secondary school of mother	−0.556**	−0.276*	−0.406	0.911**	1.814***	1.247***	1.164***
	(0.255)	(0.163)	(0.319)	(0.413)	(0.557)	(0.292)	(0.346)
	[0.030]	[0.091]	[0.204]	[0.028]	[0.001]	[0.000]	[0.001]
Technical school degree of mother	−0.595**	−0.296*	−0.348	1.025**	2.098***	1.102***	1.227***
	(0.268)	(0.177)	(0.326)	(0.457)	(0.580)	(0.338)	(0.377)
	[0.026]	[0.095]	[0.287]	[0.025]	[0.000]	[0.001]	[0.001]
Upper secondary school of mother	−0.635**	−0.377**	−0.512+	0.899**	1.967***	1.270***	1.439***
	(0.260)	(0.167)	(0.322)	(0.429)	(0.565)	(0.306)	(0.356)
	[0.015]	[0.024]	[0.112]	[0.036]	[0.001]	[0.000]	[0.000]
Other degree of mother	−0.660**	−0.221+	−0.347	1.068**	1.645***	1.248***	1.104***
	(0.265)	(0.171)	(0.324)	(0.448)	(0.577)	(0.331)	(0.371)
	[0.013]	[0.198]	[0.284]	[0.017]	[0.004]	[0.000]	[0.003]
No school degree of mother	−0.622**	−0.188	−0.248	1.094**	1.759***	0.863**	1.222***
	(0.290)	(0.186)	(0.339)	(0.549)	(0.627)	(0.429)	(0.432)
	[0.032]	[0.311]	[0.464]	[0.046]	[0.005]	[0.044]	[0.005]
Hamburg	−0.112	−0.083	0.049	0.069	−0.023	−0.678*	0.101
	(0.177)	(0.152)	(0.158)	(0.422)	(0.362)	(0.393)	(0.315)
	[0.527]	[0.585]	[0.755]	[0.870]	[0.949]	[0.084]	[0.748]
Lower Saxony	−0.131	−0.078	0.126+	0.192	0.068	−0.408*	0.025
	(0.103)	(0.092)	(0.093)	(0.246)	(0.217)	(0.227)	(0.200)
	[0.204]	[0.394]	[0.177]	[0.435]	[0.754]	[0.072]	[0.902]
Bremen	0.006	−0.102	0.037	0.183	0.795*	−0.210	0.637+
	(0.190)	(0.179)	(0.213)	(0.489)	(0.420)	(0.606)	(0.434)
	[0.975]	[0.569]	[0.862]	[0.709]	[0.058]	[0.729]	[0.143]
North Rhine-Westphalia	−0.103	−0.058	0.022	−0.016	−0.014	−0.328+	−0.019
	(0.097)	(0.087)	(0.090)	(0.231)	(0.203)	(0.215)	(0.188)
	[0.286]	[0.504]	[0.802]	[0.946]	[0.946]	[0.127]	[0.921]
Hesse	−0.167+	−0.080	0.044	0.201	0.183	−0.322	0.141
	(0.115)	(0.100)	(0.102)	(0.273)	(0.229)	(0.255)	(0.212)
	[0.147]	[0.426]	[0.668]	[0.461]	[0.425]	[0.208]	[0.506]
Rhineland-Palatinate	−0.239**	−0.154+	−0.014	0.089	−0.080	−0.271	0.021
	(0.119)	(0.106)	(0.109)	(0.284)	(0.242)	(0.260)	(0.219)
	[0.045]	[0.145]	[0.894]	[0.754]	[0.740]	[0.298]	[0.925]
Baden-Wuerttemberg	−0.226**	−0.282***	−0.179*	−0.257	0.060	−0.476**	0.068
	(0.104)	(0.089)	(0.092)	(0.250)	(0.214)	(0.225)	(0.199)
	[0.029]	[0.002]	[0.052]	[0.305]	[0.780]	[0.034]	[0.734]
Bavaria	−0.010	0.000	0.032	−0.414*	−0.298+	−0.497**	−0.046
	(0.103)	(0.088)	(0.092)	(0.243)	(0.212)	(0.222)	(0.193)
	[0.925]	[0.997]	[0.730]	[0.089]	[0.160]	[0.025]	[0.811]
Saarland	−0.594***	0.026	0.040	0.626	−0.153	−0.081	0.516+
	(0.165)	(0.186)	(0.193)	(0.510)	(0.497)	(0.455)	(0.387)
	[0.000]	[0.888]	[0.837]	[0.220]	[0.759]	[0.858]	[0.182]
Berlin	−0.105	−0.076	0.049	−0.334	−0.307	−0.406+	−0.286
	(0.137)	(0.111)	(0.122)	(0.338)	(0.290)	(0.303)	(0.273)
	[0.441]	[0.496]	[0.690]	[0.324]	[0.289]	[0.180]	[0.295]
Brandenburg	−0.249**	−0.358***	−0.003	−0.187	0.142	−0.797***	−0.208
	(0.125)	(0.102)	(0.111)	(0.298)	(0.251)	(0.274)	(0.235)
	[0.046]	[0.000]	[0.979]	[0.530]	[0.572]	[0.004]	[0.376]
Mecklenburg-West Pomerania	−0.295**	−0.251**	−0.034	0.230	0.165	−0.119	0.169
	(0.147)	(0.120)	(0.127)	(0.352)	(0.281)	(0.314)	(0.264)
	[0.044]	[0.037]	[0.791]	[0.513]	[0.559]	[0.704]	[0.521]
Saxony	−0.242**	−0.312***	−0.171+	−0.098	0.070	−0.562**	−0.127
	(0.117)	(0.097)	(0.105)	(0.283)	(0.247)	(0.261)	(0.231)
	[0.039]	[0.001]	[0.104]	[0.729]	[0.777]	[0.032]	[0.582]
Saxony-Anhalt	−0.352***	−0.390***	−0.132	−0.161	0.222	−0.339	−0.269
	(0.135)	(0.108)	(0.118)	(0.318)	(0.270)	(0.273)	(0.258)
	[0.009]	[0.000]	[0.265]	[0.613]	[0.411]	[0.214]	[0.297]
Thuringia	−0.276**	−0.391***	−0.237**	0.020	0.233	−0.438+	−0.093
	(0.129)	(0.107)	(0.116)	(0.310)	(0.266)	(0.283)	(0.246)
	[0.032]	[0.000]	[0.041]	[0.949]	[0.382]	[0.122]	[0.706]
Survey year 2002	−0.086	0.204***	0.149*	0.307	−0.320+	−0.362*	−0.096
	(0.096)	(0.074)	(0.083)	(0.250)	(0.214)	(0.213)	(0.188)
	[0.370]	[0.006]	[0.071]	[0.221]	[0.134]	[0.089]	[0.612]
Survey year 2003	−0.057	0.144*	0.206**	0.188	−0.164	−0.218	0.058
	(0.101)	(0.077)	(0.081)	(0.252)	(0.212)	(0.209)	(0.190)
	[0.573]	[0.063]	[0.011]	[0.456]	[0.439]	[0.296]	[0.760]
Survey year 2004	−0.005	0.091	0.165**	0.278	−0.089	−0.432**	−0.073
	(0.098)	(0.072)	(0.084)	(0.244)	(0.204)	(0.214)	(0.188)
	[0.958]	[0.209]	[0.049]	[0.255]	[0.662]	[0.044]	[0.697]
Survey year 2005	0.052	0.194***	0.163**	0.061	−0.148	−0.200	−0.029
	(0.097)	(0.075)	(0.081)	(0.247)	(0.205)	(0.214)	(0.191)
	[0.594]	[0.009]	[0.044]	[0.806]	[0.469]	[0.349]	[0.877]
Survey year 2006	−0.040	0.141*	0.041	0.135	−0.305+	−0.423*	−0.071
	(0.100)	(0.077)	(0.083)	(0.252)	(0.220)	(0.230)	(0.200)
	[0.687]	[0.067]	[0.619]	[0.593]	[0.165]	[0.066]	[0.721]
Survey year 2007	0.040	0.150**	0.173**	0.043	−0.148	−0.081	−0.067
	(0.095)	(0.072)	(0.078)	(0.243)	(0.206)	(0.207)	(0.192)
	[0.671]	[0.038]	[0.027]	[0.861]	[0.472]	[0.696]	[0.727]
Survey year 2008	0.047	0.197**	0.162*	0.268	−0.290	−0.087	−0.016
	(0.118)	(0.087)	(0.094)	(0.275)	(0.243)	(0.234)	(0.221)
	[0.691]	[0.023]	[0.083]	[0.329]	[0.234]	[0.710]	[0.944]
Survey year 2009	−0.031	−0.070	0.022	0.249	0.401*	−0.000	0.097
	(0.112)	(0.090)	(0.095)	(0.274)	(0.218)	(0.247)	(0.205)
	[0.782]	[0.435]	[0.820]	[0.364]	[0.066]	[0.999]	[0.635]
Survey year 2010	−0.105	0.061	0.121+	0.231	0.059	−0.057	0.122
	(0.098)	(0.072)	(0.078)	(0.250)	(0.201)	(0.214)	(0.186)
	[0.282]	[0.399]	[0.119]	[0.355]	[0.769]	[0.789]	[0.512]
Survey year 2011	−0.016	0.029	0.041	0.400*	0.239	0.346*	0.285*
	(0.095)	(0.071)	(0.076)	(0.226)	(0.194)	(0.187)	(0.171)
	[0.868]	[0.682]	[0.586]	[0.076]	[0.219]	[0.064]	[0.095]
Survey year 2012	−0.025	0.030	−0.001	0.063	0.039	−0.063	0.026
	(0.095)	(0.071)	(0.076)	(0.233)	(0.198)	(0.201)	(0.177)
	[0.793]	[0.674]	[0.989]	[0.788]	[0.846]	[0.755]	[0.882]
Survey year 2013	−0.139+	0.037	−0.007	0.341+	0.032	−0.096	0.150
	(0.087)	(0.067)	(0.073)	(0.217)	(0.185)	(0.188)	(0.163)
	[0.110]	[0.586]	[0.922]	[0.116]	[0.864]	[0.610]	[0.358]
Survey year 2014	−0.145+	−0.029	−0.051	0.235	0.249+	0.097	0.329*
	(0.091)	(0.069)	(0.075)	(0.226)	(0.187)	(0.191)	(0.172)
	[0.110]	[0.675]	[0.499]	[0.299]	[0.182]	[0.610]	[0.056]
Constant	3.951***	3.563***	3.516***	4.715***	3.831***	4.659***	4.717***
	(0.306)	(0.215)	(0.352)	(0.587)	(0.661)	(0.480)	(0.479)
	[0.000]	[0.000]	[0.000]	[0.000]	[0.000]	[0.000]	[0.000]
R²	0.053	0.143	0.101	0.035	0.060	0.058	0.034

Notes: Number of yearly observations is N = 3,388. All regressions include the full set of control variables: four school types, five categories for the share of students with migration background in the class, a dummy variable for having a migration background, a dummy variable for being female, students’ pocket money in Euros, the household income in Euros, seven categories for the schooling of the father and of the mother, 16 federal states, and 14 survey years. The explanatory variables of interest are dummies (share in percent). School grades range from 1 (very good) to 6 (failed). Satisfaction with own school performance is measured on an 11-point-Likert scale (0: very unhappy, 10: very happy). Robust standard errors are in parentheses and p-values in brackets. Coefficients are significant at +p < 0.20, * p < 0.10, ** p < 0.05, *** p < 0.01.
Data source: SOEP 2001–2014 (youth), version 31, SOEP, 2015, doi:10.5684/soep.v31.

Table 9:

Ordered probit results for specifications with binary indicators for job, sports, and music.

	Grade – Math	Grade – German	Grade – Foreign	Satis – Math	Satis – German	Satis – Foreign	Satis – Overall
Job (44.33 %)	0.035	0.005	0.056+	−0.037	−0.006	−0.066*	−0.043
	(0.038)	(0.039)	(0.038)	(0.037)	(0.036)	(0.036)	(0.036)
	[0.354]	[0.892]	[0.140]	[0.310]	[0.877]	[0.069]	[0.238]
Sports (74.97 %)	−0.050	−0.014	0.006	0.081*	0.025	0.029	0.068+
	(0.043)	(0.045)	(0.046)	(0.042)	(0.043)	(0.044)	(0.044)
	[0.247]	[0.758]	[0.898]	[0.056]	[0.558]	[0.502]	[0.122]
Music (29.72 %)	−0.124***	−0.197***	−0.120***	0.133***	0.142***	0.124***	0.115***
	(0.043)	(0.043)	(0.043)	(0.042)	(0.041)	(0.040)	(0.041)
	[0.004]	[0.000]	[0.005]	[0.001]	[0.001]	[0.002]	[0.005]
Mean (SD) of outcome	2.92	2.83	2.89	6.29	6.53	6.53	6.55
Mean (SD) of outcome	(1.05)	(0.85)	(0.91)	(2.56)	(2.15)	(2.29)	(1.96)

Notes: Number of yearly observations is N = 3,388. All ordered probit regressions include the full set of control variables: four school types, five categories for the share of students with migration background in the class, a dummy variable for having a migration background, a dummy variable for being female, students’ pocket money in Euros, the household income in Euros, seven categories for the schooling of the father and of the mother, 16 federal states, and 14 survey years. The explanatory variables of interest are dummies (share in percent). School grades range from 1 (very good) to 6 (failed). Satisfaction with own school performance is measured on an 11-point-Likert scale (0: very unhappy, 10: very happy). Robust standard errors are in parentheses and p-values in brackets. Coefficients are significant at +p < 0.20, * p < 0.10, ** p < 0.05, *** p < 0.01.
Data source: SOEP 2001–2014 (youth), version 31, SOEP, 2015, doi:10.5684/soep.v31.

Table 10:

OLS results for specifications with binary indicators for job, sports, and music without controlling for students’ pocket money.

	Grade – Math	Grade – German	Grade – Foreign	Satis – Math	Satis – German	Satis – Foreign	Satis – Overall
Job (44.33 %)	0.033	0.006	0.050+	−0.093	−0.004	−0.140*	−0.057
	(0.038)	(0.028)	(0.031)	(0.092)	(0.075)	(0.080)	(0.069)
	[0.373]	[0.834]	[0.111]	[0.309]	[0.961]	[0.081]	[0.414]
Sports (74.97 %)	−0.051	−0.013	0.000	0.211**	0.066	0.112	0.161*
	(0.043)	(0.033)	(0.038)	(0.106)	(0.089)	(0.096)	(0.084)
	[0.233]	[0.704]	[0.991]	[0.048]	[0.457]	[0.247]	[0.055]
Music (29.72 %)	−0.121***	−0.140***	−0.093***	0.303***	0.263***	0.251***	0.201**
	(0.042)	(0.032)	(0.035)	(0.102)	(0.083)	(0.087)	(0.078)
	[0.004]	[0.000]	[0.008]	[0.003]	[0.002]	[0.004]	[0.010]
R²	0.052	0.142	0.100	0.034	0.060	0.056	0.034
Mean (SD) of outcome	2.92	2.83	2.89	6.29	6.53	6.53	6.55
Mean (SD) of outcome	(1.05)	(0.85)	(0.91)	(2.56)	(2.15)	(2.29)	(1.96)

Notes: Number of yearly observations is N = 3,388. All regressions include the full set of control variables without students’ pocket money in Euros: four school types, five categories for the share of students with migration background in the class, a dummy variable for having a migration background, a dummy variable for being female, the household income in Euros, seven categories for the schooling of the father and of the mother, 16 federal states, and 14 survey years. The explanatory variables of interest are dummies (share in percent). School grades range from 1 (very good) to 6 (failed). Satisfaction with own school performance is measured on an 11-point-Likert scale (0: very unhappy, 10: very happy). Robust standard errors are in parentheses and p-values in brackets. Coefficients are significant at +p < 0.20, * p < 0.10, ** p < 0.05, *** p < 0.01.
Data source: SOEP 2001–2014 (youth), version 31, SOEP, 2015, doi:10.5684/soep.v31.

Received: 2018-05-04

Revised: 2019-09-11

Accepted: 2019-09-11

Published Online: 2019-11-16

Published in Print: 2020-10-25

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Articles in the same Issue

https://doi.org/10.1515/jbnst-2018-0039

Keywords for this article

human capital; jobs; music; school performance; sports; time allocation