Dataset supporting the thesis: Pupil competence during the COVID-19-induced school closures: An analysis of the effect of distance learning and remediation policies using international assessment data in 30 countries

This dataset accompanies the PhD thesis Pupil Competence During the COVID-19-induced School Closures: An Analysis of the Effect of Distance Learning and Remediation Policies Using International Assessment Data in 30 Countries. The dataset compiles country-level data derived from large-scale international student assessments, specifically PISA and PIRLS, covering the period 2000–2022. It was created by harmonising publicly available microdata from the OECD (for PISA) and IEA (for PIRLS), aggregated to the national level. The data were collected and processed using StataNow 18.5. The dataset can be opened in StataNow 18.5 software. Stata .do files are also provided to allow full reproducibility of the data preparation and analysis. The dataset is specifically structured to support advanced statistical modelling, including Latent Growth Curve Modelling (LGCM), Synthetic Control (SC), and Synthetic Difference-in-Differences (SDID), to examine the effects of COVID-19 policies on pupil competence across diverse national contexts.

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