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on Education |
| By: | Rosa Cheesman; Nicolai T. Borgen; Astrid M. J. Sandsor; Paul Hufe |
| Abstract: | We investigate whether better schools can compensate for the effects of children’s genetic differences. To this end, we combine data from the Norwegian Mother, Father, and Child Cohort Study (MoBa) with Norwegian register data to estimate the interaction between genetic endowments and school quality. We use MoBa’s genetic data to compute polygenic indices for educational attainment (PGIEA). Importantly, MoBa includes information on the genetic endowments of father-mother-child trios, allowing us to identify causal genetic effects using within-family variation. We calculate school value-added measures from Norwegian register data, allowing us to causally estimate school quality effects. Leveraging the advantages of both data sources, we provide the first causally identified study of geneenvironment interactions in the school context. We find evidence for substitutability of PGIEA and school quality in reading but not numeracy: a 1 SD increase of school quality decreases the impact of the PGIEA on reading test scores by 6%. The substitutability arises through gains of students at the lower end of the PGIEA distribution. This shows that investments in school quality may help students to overcome their draw in the genetic lottery |
| Date: | 2025–04–02 |
| URL: | https://d.repec.org/n?u=RePEc:bri:uobdis:25/811 |
| By: | Biroli, Pietro (University of Bologna); Di Girolamo, Amalia (University of Birmingham); Sorrenti, Giuseppe (University of Lausanne); Totarelli, Maddalena (Ifo Institute for Economic Research) |
| Abstract: | Educational disparities often limit students' access to relatable role models, constraining their aspirations and educational outcomes. We design and implement the Online Role Model Mentoring Program (ORME), a scalable, low-cost intervention connecting middle school students with successful role models from similar backgrounds. Using a randomized controlled trial with over 450 students in Campania, Italy, we find that ORME improves students' beliefs about the returns to effort, increases alignment between aspirations and expectations, and boosts school effort. Treated students also become more academically ambitious: they are more likely to enroll in academically oriented tracks and perform better on standardized language tests. These findings show that brief online mentoring sessions can have a meaningful impact on students’ attitudes and choices at a critical stage of schooling, highlighting a promising tool to support students in low-opportunity contexts. |
| Keywords: | role models, aspirations, mentoring, school interventions |
| JEL: | I21 I24 J24 D91 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18325 |
| By: | Shenglong Liu; Yuanyuan Wan; Shengxiang Xie; Xiaoming Zhang |
| Abstract: | Although education fever is widespread across East Asia, the role of public education investment in intensifying this fever remains underexamined. By leveraging the staggered rollout of county-level free senior high school education pilots in China, we find that this major expansion of public education increased the number of registrations at private tutoring centers by about 20% and doubled household spending on tutoring. Using administrative night-light data and elite university admission records, we show that the effect is driven by more intensive competition for scarce top-tier college placements rather than by declining public school quality. The response is strongest in regions with greater income inequality and lower elite university admission rates, but substantially weaker in areas with better outside options, such as higher local employment rates. Our findings suggest that expanding access to senior high school alone may exacerbate educational arms races, underscoring the need for complementary policies that reduce income disparities and broaden postsecondary opportunities. |
| Keywords: | Education Competition; Public Education Investment; Crowd-in Effect |
| JEL: | I22 I28 O15 H41 |
| Date: | 2025–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:tor:tecipa:tecipa-812 |
| By: | Mira Fischer (Federal Institute for Population Research, WZB Berlin, IZA - Institute of Labor Economics); Holger A. Rau (Georg-August-Universität Göttingen); Rainer Michael Rilke (WHU - Otto Beisheim School of Management) |
| Abstract: | We study how AI tutoring affects learning in higher education through a randomized experiment with 334 university students preparing for an incentivized exam. Students either received only textbook material, restricted access to an AI tutor requiring initial independent reading, or unrestricted access throughout the study period. AI tutor access raises test performance by 0.23 standard deviations relative to control. Surprisingly, unrestricted access significantly outperforms restricted access by 0.21 standard deviations, contradicting concerns about premature AI reliance. Behavioral analysis reveals that unrestricted access fosters gradual integration of AI support, while restricted access induces intensive bursts of prompting that disrupt learning flow. Benefits are heterogeneous: AI tutors prove most effective for students with lower baseline knowledge and stronger self-regulation skills, suggesting that seamless AI integration enhances learning when students can strategically combine independent study with targeted support. |
| Keywords: | ai tutors; large language models; self-regulated learning; higher education; |
| JEL: | C91 I21 D83 |
| Date: | 2025–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:557 |
| By: | Hertweck, Friederike (RWI – Leibniz Institute for Economic Research); Maris, Robbie (University College London); Tonin, Mirco (Free University of Bozen/Bolzano); Vlassopoulos, Michael (University of Southampton) |
| Abstract: | This paper examines university application patterns in the UK, focusing on the joint decision of selecting both an institution and a subject. Using administrative data from the Universities and Colleges Admissions Service (UCAS) covering almost all undergraduate applications between 2008 and 2021, we document three key facts: (i) students generally choose subject before university: they apply on average to around 1.6 subject areas across 4.6 institutions, and roughly half apply to a single field across multiple universities; (ii) there are significant gender gaps in application and offer rates that reflect field composition; (iii) high-socioeconomic status students submit more applications, apply less to local institutions, and obtain more offers, but these differences shrink sharply once we control for attainment and the selectivity of the programmes that students apply to. An expert survey suggests that several of these patterns run against conventional wisdom. |
| Keywords: | gender, UCAS data, application patterns, higher education, socioeconomic status |
| JEL: | I20 I23 M38 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18331 |