Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/227365
Authors: 
Arenas, Andreu
Calsamiglia, Caterina
Loviglio, Annalisa
Year of Publication: 
2020
Series/Report no.: 
IZA Discussion Papers No. 13838
Abstract: 
The outbreak of COVID-19 in 2020 inhibited face-to-face education and constrained exam taking. In many countries worldwide, high-stakes exams happening at the end of the school year determine college admissions. This paper investigates the impact of using historical data of school and high-stakes exams results to train a model to predict high-stakes exams given the available data in the Spring. The most transparent and accurate model turns out to be a linear regression model with high school GPA as the main predictor. Further analysis of the predictions reflect how high-stakes exams relate to GPA in high school for different subgroups in the population. Predicted scores slightly advantage females and low SES individuals, who perform relatively worse in high-stakes exams than in high school. Our preferred model accounts for about 50% of the out-of- sample variation in the high-stakes exam. On average, the student rank using predicted scores differs from the actual rank by almost 17 percentiles. This suggests that either high-stakes exams capture individual skills that are not measured by high school grades or that high-stakes exams are a noisy measure of the same skill.
Subjects: 
performance prediction
high-stakes exams
college allocation
COVID-19
JEL: 
I23
I24
I28
Document Type: 
Working Paper

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