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on Education |
| By: | Akos Valent (University of Pécs) |
| Abstract: | In our paper, we would like to compare the higher education admission systems of two neighboring Central European countries, those of Hungary and Slovakia. We found this comparison particularly interesting because, in Slovakia, most universities admit applicants without an entrance exam, based on their grades from high school (sometimes even without taking into account school leaving exam results). Universities define their admission re-quirements themselves and make decisions on which of the applicants they wish to give an opportunity to locally. There are no central quotas defined. A given student who applies to 3 universities might get 3 different results in the respective application processes and theoret-ically can parallelly get accepted by several institutions. In such a case, in Slovakia, the stu-dent concerned must make the decision where to study in the possession of the exact infor-mation about his choices.In contrast, Hungary has a centralized admission system under which high school students are admitted to the higher education system via a transparent points system. The number of points is calculated based on study points (a maximum of 200 points), school leaving exam points (a maximum of 200 points) and additional points (a maximum of 100 points). Under this system, students must define which institution they would prefer as early as the time of application; if they are accepted, they cannot change the priorities afterwards.We were seeking to find out what differences arise in the number of students accepted into a university as a result of the application of these two systems. To find out, we have assessed data from the given academic years about the number of students who finished high school and the number of those who were accepted into one of the higher education institutions. We used data from the Central Statistical Office (Hungary), the Ministry of Education (Slo-vakia) and Eurostat. |
| Keywords: | Higher Education, Admission, Hungary, Slovakia, Students number |
| JEL: | I23 I28 I29 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:sek:iacpro:9111525&r=all |
| By: | Clare Leaver; Renata Lemos; Daniela Scur |
| Abstract: | Why do some students learn more in some schools than others? One consideration receiving growing attention is school management. To study this, researchers need to be able to measure school management accurately and cheaply at scale, and also explain any observed relationship between school management and student learning. This paper introduces a new approach to measurement using existing public data, and applies it to build a management index covering 15,000 schools across 65 countries, and another index covering nearly all public schools in Brazil. Both indices show a strong, positive relationship between school management and student learning. The paper then develops a simple model that formalizes the intuition that strong management practices might be driving learning gains via incentive and selection effects among teachers, students and parents. The paper shows that the predictions of this model hold in public data for Latin America, and draws out implications for policy. |
| Keywords: | management, teacher selection, teacher incentives, cross-country |
| JEL: | M5 I2 J3 |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1656&r=all |
| By: | De Cao, Elisabetta; Barban, Nicola; Oreffice, Sonia; Quintana-Domeque, Climent |
| Abstract: | We investigate assortative mating on education using a sample of couples from the Health and Retirement Study. We estimate a reduced-form linear matching function, which links wife’s education to husband’s education and both wife’s and husband’s unobservable characteristics. Using OLS we find that an additional year in husband’s education is associated with an average increase in wife’s education of 0.4 years. To deal with omitted variable bias due to unobservable characteristics, we use a measure of genetic propensity (polygenic score) for husband’s education as an instrumental variable. Assuming that our instrument is valid, our 2SLS estimate suggests that an additional year in husband’s education increases wife’s education by about 0.5 years. Since greater genetic propensity for educational attainment has been linked to a range of personality and cognitive skills, we allow for the possibility that the exclusion restriction is violated using the plausible exogenous approach by Conley et al. (2012). ‘True’ assortativeness on education cannot be ruled out, as long as one standard deviation increase in husband’s genetic propensity for education directly increases wife’s education by less than 0.2 years. |
| JEL: | D10 J10 J12 |
| Date: | 2019–08 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:102271&r=all |