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The Role of Labour Market Expectations and Admission Probabilities in Students' Application Decisions on Higher Education: The Case of Hungary

2006; Taylor & Francis; Volume: 14; Issue: 3 Linguagem: Inglês

10.1080/09645290600777535

1469-5782

Júlia Varga,

Intergenerational and Educational Inequality Studies

Abstract This paper analyses students' application strategies to higher education, the effects of labour market expectations and admission probabilities. The starting hypothesis of this study is that students consider the expected utility of their choices, a function of expected net lifetime earnings and the probability of admission. Based on a survey carried out among Hungarian secondary school students, three aspects of application decisions are investigated: the number of applications; the selection between state‐funded and cost‐priced education; and the institutions/field specialization ranked first and last in students' choices. The results of this paper confirm that both expected wages and admission probabilities determine students' application strategies and that the seemingly irrational student preferences for institutions/orientations with less favourable labour market opportunities might be the result of a rational decision process. Key Words: Human capitalhigher educationfield of studyearnings expectations Acknowledgements This research was supported by a grant from the CERGE‐EI Foundation under a programme of the Global Development Network. Additional funds for grantees in the Balkan countries have been provided by the Austrian Government through WIIW, Vienna. All opinions expressed are those of the author(s) and have not been endorsed by CERGE‐EI, WIIW, or the GDN. Notes 1. In the Hungarian admission system, prospective students have to apply for a given orientation/institution (such as economics, education, medical studies, etc.) Students accepted to a degree programme in a certain field of study follow an established programme of courses and examinations. Mobility is low and it is difficult to change fields of study once accepted to a specific degree programme. Each year the Ministry of Education determines the number of students admitted to tuition‐free, state‐financed places by educational levels (university, college), fields of study and institutions. When determining state‐financed places the Ministry takes into account the excess demand for the different courses beside other considerations. The Ministry considers the total number of applications and the number of applications to the given institution and programme with first preference ranking. Prospective students may apply for as many programmes as they want but they have to rank their preferences. They also have to state if they apply for a state‐funded or a cost‐priced place, but it is acceptable if they submit two different applications to the same institution/field specialization (one for a state‐funded and another for a cost‐priced place). The minimum admission score is determined following the entrance examinations and changed from year to year depending on the number of applications, the average admission score of students applying to the given institution/programme and the number of places. The admission score of students is based partly (50%) on the points achieved by the applicant at the entrance examination, and partly on his/her secondary school achievements (final examination grades, grade point averages). This is his/her so‐called 'accumulated score'. The applicants are ranked based on the final score. On average, students apply for more than three programmes in addition to their first choice. 2. A detailed description of the survey can be found in Varga (Citation2001). 3. Data on average starting salaries of new graduates came from the Second Higher Education Survey on Young Graduates, which was a postal survey carried out in 2000. All students who graduated in 1999 received the questionnaire. The response rate was 22%. 4. The ZIP model contains two types of zero counts. A binary selection variable ci allows for a separate treatment of zero counts and strictly positive outcomes. Then we observe the underlying count data variable yi ∗ only if ci = 1: yi = yi ∗ if ci = 1, and yi = 0 if ci = 0. If the probability of ci = 1 is pi , then the probability function of yi can be written as: So the ZIP model contains two types of zero counts; one is obtained as ci = 0, and the other as ci = 1 and yi ∗ = 0. 5. The issue might be raised that the admission probability variable is endogenous in this model. But as different higher education institutions provide courses in the same field specializations, ranging from the most prestigious to third‐class institutions, the standard deviation of minimum admission scores is high in all field specializations. As the dependent variable in this model is the field specialization, there is no need to assume that the same unobserved characteristics affect the choice of field specialization and admission probability. 6. Data on minimum admission scores for institutions/programs come from the yearly data collection of the National Higher Education Admission Office, indicating the number of applicants and students admitted, and the scores of admission by institutions and programmes. 7. The Hungarian secondary school system is stratified, including vocational secondary schools with different orientations (technical, medical, economics, and other) and gymnasiums, which are general secondary schools where students can study either from fifth to 12th grades (gymnasium with eight grades), seventh to 12th grades (gymnasium with six grades) or ninth to 12th grades (gymnasium with four grades).