Learning loss due to school closures during the COVID-19 pandemic

In this study, we present evidence on the pandemic’s effect on student progress in The Netherlands, using a dataset covering 15% of Dutch primary schools throughout the years 2017 to 2020 (n ≈ 350,000). The data include biannual test scores in core subjects for students aged 8 to 11 y, as well as student demographics and school characteristics. Hypotheses and analysis protocols for this study were preregistered (SI Appendix, section 4.1). Our main interest is whether learning stalled during lockdown and whether students from less-educated homes were disproportionately affected. In addition, we examine differences by sex, school grade, subject, and prior performance.

The Dutch school system combines centralized and equitable school funding with a high degree of autonomy in school management. The country is close to the Organization for Economic Cooperation and Development (OECD) average in school spending and reading performance, but among its top performers in math. No other country has higher rates of broadband penetration, and efforts were made early in the pandemic to ensure access to home learning devices. School closures were short in comparative perspective (SI Appendix, section 1), and the first wave of the pandemic had less of an impact than in other European countries. For these reasons, The Netherlands presents a “best-case” scenario, providing a likely lower bound on learning loss elsewhere in Europe and the world. Despite favorable conditions, survey evidence from lockdown indicates high levels of dissatisfaction with remote learning and considerable disparities in help with schoolwork and learning resources.

Key to our study design is the fact that national assessments take place twice a year in The Netherlands: halfway into the school year in January to February and at the end of the school year in June. In 2020, these testing dates occurred just before and after the first nationwide school closures that lasted 8 wk starting March 16 (Fig. 1). Access to data from 3 y prior to the pandemic allows us to create a natural benchmark against which to assess learning loss. We do so using a difference-in-differences design (SI Appendix, section 4.2) and address loss to follow-up using various techniques: regression adjustment, rebalancing on propensity scores and maximum-entropy weights, and fixed-effects designs that compare students within the same schools and families.