nep-neu New Economics Papers
on Neuroeconomics
Issue of 2021‒01‒04
three papers chosen by

  1. From asking to observing. Behavioural measures of socio-emotional and motivational skills in large-scale assessments By Francesca Borgonovi; Alessandro Ferrara; Mario Piacentini
  2. High-Ability Influencers? The Heterogeneous Effects of Gifted Classmates By Simone Balestra; Aurélien Sallin; Stefan C. Wolter
  3. Non-Cognitive Skills in Training Curricula and Heterogeneous Wage Returns By Fabienne Kiener; Ann-Sophie Gnehm; Uschi Backes-Gellner

  1. By: Francesca Borgonovi (UCL Social Research Institute, United Kingdom); Alessandro Ferrara (Department of Political and Social Sciences, European University Institute, Italy); Mario Piacentini (Directorate for Education and Skills, France)
    Abstract: Socio-emotional and motivational skills are routinely measured using self-reports in large-scale educational assessments. Measures exploiting test-takers’ behaviour during the completion of questionnaires or cognitive tests are increasingly used as alternatives to self-reports in the economics of education literature. We use cross-sectional and longitudinal evidence to evaluate if behavioural measures can provide unbiased measures of socio-emotional and motivational skills to be used in empirical research using data from the Programme for International Student Assessment (PISA). We find that behavioural measures capture important aspects of students’ academic profiles: some are importantly associated with contemporaneous performance and educational attainment. However, these measures are only limitedly correlated among themselves and have low correlations with self-report measures of the same constructs. Moreover, behavioural measures have different levels of stability over time and sensitivity to design considerations. These results suggest that more research is needed before measures of students’ behaviour on a cognitive test can be used as valid indicators of socio-emotional and motivational skills.
    Keywords: Socio-emotional and motivational skills; cross-country; PISA; large-scale assessments; behavioural measures; self-reports; education.
    JEL: I20 I24 I26
    Date: 2020–12–01
  2. By: Simone Balestra; Aurélien Sallin; Stefan C. Wolter
    Abstract: This paper examines how exposure to students identified as gifted (IQ ≥ 130) affects achievement in secondary school, enrollment in post-compulsory education, and occupational choices. By using student-level administrative data on achievement combined with psychological examination records, we study the causal impact of gifted students on their classmates in unprecedented detail. We find a positive and significant effect of the exposure to gifted students on school achievement in both math and language. The impact of gifted students is, however, highly heterogeneous along three dimensions. First, we observe the strongest effects among male students and high achievers. Second, we show that male students benefit from the presence of gifted peers in all subjects regardless of their gender, whereas female students seem to benefit primarily from the presence of female gifted students. Third, we find that gifted students diagnosed with emotional or behavioral disorders have zero-to-negative effects on their classmates’ performance, a detrimental effect more pronounced for female students. Finally, exposure to gifted students in school has consequences that extend beyond the classroom: it increases the likelihood of choosing a selective academic track as well as occupations in STEM fields.
    Keywords: gifted students, peer quality, gender, math, peer effects
    JEL: I21 I24 I26 J24
    Date: 2020
  3. By: Fabienne Kiener; Ann-Sophie Gnehm; Uschi Backes-Gellner
    Abstract: For non-cognitive skills, economics research has focused primarily on social skills as one element. One important, largely unexplored element is self-competence, the ability to act responsibly for oneself. We therefore study returns to self-competence adding heterogeneous and complementary returns to the literature on non-cognitive skills. Using texts of training curricula as data source, we apply machine-learning methods to identify self-competence in occupations. Combining these measures with labor market data, we find heterogeneous returns to self-competence: A medium level of self-competence has the strongest wage returns compared to low or high levels, but with high cognitive requirements also high self-competence pays.
    Keywords: non-cognitive skills, human capital, text as data, curricula content analyses, vocational education and training
    JEL: I26 J24 M53
    Date: 2020–12

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