Sharp increase in inequality in education in times of the COVID-19-pandemic

Summary

A research article by Carla Haelermans, Roxanne Korthals, Madelon Jacobs and colleagues, published February 2, 2022 in PLoS ONE 17(2), on the increase in inequality in education during the COVID-19-pandemic. It analyzes declines in learning gains in primary education in the Netherlands for reading, spelling and math, using standardized test scores and register data on student and parental background for almost 300,000 unique students. The authors report large inequalities in learning loss based on parental education and parental income, on top of already existing inequalities, and call for a national focus on interventions targeting vulnerable students. The article notes that schools worldwide were closed for an average of almost 95 school days between March 2020 and February 2021.

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Citation: Haelermans C, Korthals R, Jacobs M, de
Leeuw S, Vermeulen S, van Vugt L, et al. (2022)
Sharp increase in inequality in education in times of
the COVID-19-pandemic. PLoS ONE 17(2):
€0261114. https://doi.org/10.1371/journal.
pone.0261114

Editor: Jér6me Prado, French National Center for
Scientific Research (CNRS) & University of Lyon,
FRANCE

Received: July 19, 2021
Accepted: November 25, 2021
Published: February 2, 2022

Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
process; therefore, we enable the publication of
all of the content of peer review and author
responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0261114

Copyright: © 2022 Haelermans et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.

Data Availability Statement: Data cannot be
shared publicly because they are part of the data at
Statistics Netherlands and cannot be exported from

RESEARCH ARTICLE
Sharp increase in inequality in education in
times of the COVID-19-pandemic

Carla Haelermans'2*, Roxanne Korthals'**, Madelon Jacobs', Suzanne de Leeuw",
Stan Vermeulen, Lynn van Vugt(»', Bas Aarts", Tijana Prokic-Breuer'**, Rolf van der
Velden'4, Sanne van Wetten', Inge de Wolf'**

1 Research Centre for Education and the Labour Market (ROA), School of Business and Economics,
Maastricht University, Maastricht, The Netherlands, 2 Initiative for Education Research (NRO), Den Haag,
The Netherlands, 3 Inspectorate of Education, Utrecht, The Netherlands, 4 Education Lab, School of
Business and Economics, Maastricht University, Maastricht, The Netherlands

* carla.haelermans @ maastrichtuniversity.nl

Abstract

The COVID-19-pandemic forced many countries to close schools abruptly in the spring of
2020. These school closures and the subsequent period of distance learning has led to con-
cerns about increasing inequality in education, as children from lower-educated and poorer
families have less access to (additional) resources at home. This study analyzes differences
in declines in learning gains in primary education in the Netherlands for reading, spelling
and math, using rich data on standardized test scores and register data on student and
parental background for almost 300,000 unique students. The results show large inequali-
ties in the learning loss based on parental education and parental income, on top of already
existing inequalities. The results call for a national focus on interventions specifically target-
ing vulnerable students.

Introduction

The COVID-19-pandemic of early 2020 interrupted or even completely halted the learning of
children in many countries around the world. Globally, schools were closed for an average of
almost 95 school days between March 2020 and February 2021 [1], which is equivalent to
almost half a school year in countries where a school year is 40 weeks. In many western coun-
tries, schools continued to teach remotely. However, there were many challenges related to dis-
tance learning, such as access to digital learning devices and digital learning gaps [e.g., 2-4].
This prompted serious worries of social-emotional problems and learning loss. Despite the
lack of adequate data in many countries, some studies appeared on the use of online learning
tools by students [e.g., 5] and on the effect of distance schooling on performance and learning
gains of students in primary education. Although some studies did not find significant learn-
ing losses [e.g., no effects on reading in the USA [6], no learning deficits on schools with a
large share of students with advantaged backgrounds in Australia [7]], most studies report
negative consequences of the school closures for children’s educational development [Belgium
[2], UK [4, 8], Italy [9], Switzerland [10], Germany [11], USA [12-15], Norway [16]]. For

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Sharp increase in inequality in education in times of the COVID-19-pandemic

the secured virtual environment at Statistics
Netherlands. However, all data underlying the
results presented are available at Statistics
Netherlands and use can be requested by national
and international researchers via the Netherlands
Cohort Study on Education (NCO) and Statistics
Netherlands; data access requests may be sent to
info@nationaalcohortonderzoek.nl. An extensive
description of the data and the access procedure
can be found in Haelermans, C., T. Huijgen, M.
Jacobs, M. Levels, R. van der Velden, L. van Vugt
and S. van Wetten (2020). Using Data to Advance
Educational Research, Policy and Practice: Design,
Content and Research Potential of the Netherlands
Cohort Study on Education. European Sociological
Review, 36(4), 643-662, https://academic.oup.
com/esr/article/36/4/643/5871552?login=true.

Funding: The authors gratefully acknowledge
financial support from The Netherlands
Organisation for Health Research and Development
(ZonMw, https://www.zonmw.nl/nl/) (project
10430 03201 0014). The funders had no role in
study design, data collection and analysis, decision
to publish, or preparation of the manuscript. Grant
acquired by CH, RK, TP-B, RvdV, IdW.

Competing interests: The authors have declared
that no competing interests exist.

higher education, the results are less consistent: some find negative effects [17] while others
indicate that distance learning might have made students more efficient [18] or see little effects
[19].

There are worries that some groups of students experienced lower learning gains due to the
school closures and the COVID-19-pandemic than others. Our hypothesis is that the school
closures and the pandemic resulted in increased inequality in skill development for students
from specific backgrounds (socio-economic status, income and migration background). There
is reason to believe that inequalities have indeed increased due to the school closures. For
instance, in the Netherlands especially lower-educated parents felt less capable in helping their
children with their schoolwork [20, 21]. In the United Kingdom, we see that middle class
parents spent more time on home schooling than parents from the working class [22, 23]. If
this is the case, and these learning losses persist, they can be detrimental for development of
skills in the long run, and in turn lead to an increase of the existing inequalities in opportuni-
ties in education and on the labor market [24].

Previous studies on inequalities based on socioeconomic background variables in students’
learning gains during the school closures, were hampered by data limitations. Some were lim-
ited by their data on educational performance: they used relatively small samples, focused on a
specific region rather than a national representative sample, or were limited to only one grade
level or subject [6, 8, 9, 12, 13, 16]. Others had limited information on students’ background
characteristics. They used school level indicators [2, 7, 11] or relatively uninformative catego-
ries. For example, a recent study based on Dutch data was only able to distinguish between
families in which at least one parent had a lower secondary degree (92%) and families in which
both parents had less than a lower secondary degree (8%) [3]. Our study improves upon these
studies for several reasons: 1) as a result of the widespread use of standardized testing in the
Netherlands, we have a large sample of students who were tested shortly before and after the
first lockdown, 2) we have rich student background information at the individual level, includ-
ing multiple student background variables that indicate whether a student is disadvantaged or
not, based on meaningful and informative categories and 3) we focus on effects for separate
grade levels, and three different subjects (reading, spelling and mathematics) showing large
variation, instead of only looking at overall effects or one subject.

Therefore, in the study at hand, we are able to look in greater detail at background differ-
ences between students and present results showing that the learning loss due to the school
closures are unequally distributed: students from disadvantaged backgrounds have suffered
much more than their fellow students. To show this, we use standardized test score data from
the Netherlands and link this to register data on student and parental background for primary
school students.

COVID-19 educational policy changes in the Netherlands

Although compulsory education starts at age 5, Dutch children generally enter primary school
at age 4. They remain in primary school up to age 12, after which they enter secondary school
and are tracked according to their ability. Almost all schools in the Netherlands are public
schools (99%) funded by the Ministry of Education, Culture and Science [25].

February 27, 2020 the first COVID-19 patient was reported in the Netherlands. Primary
schools closed at March 16, 2020 and reopened May 11, 2020. Vulnerable children, and chil-
dren of parents with essential occupations who could not work from home, were allowed to
come to school during the school closure. However, these children usually followed the same
program as the children who had to stay at home and comprised only around 5% of all chil-
dren in this first period of school closure. Up to June 7, 2020 children only went to school half

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of the time. In this way, groups were smaller and it was easier to keep distance. From June 8
onwards schools went back their usual schedule. Children and teachers were still urged to stay
at home when they showed any symptoms associated with COVID-19.

The Netherlands was relatively well equipped for online education, as a total of 96% of the
Dutch households have internet access at home [26]. Additionally, the Dutch government
made 2.5 million euros available in March to support online learning. This money was used to
buy laptops and/or to provide internet access for 7,000 students. This money was supple-
mented with another 3.8 million euros in May 2020. In total, over 16,000 laptops and tablets
were financed in this way [27]. Nevertheless, the school closure happened relatively sudden
with no time to prepare. Teachers had to improvise, students suddenly had to structure their
own school day, and parents had to act as teachers for their children. Although we do not
know exactly how much education children received while schools were closed, there are
strong indications that children spent less time on their education than usual. Studies in Ger-
many and Switzerland report considerable reduction in studying time during school closings
[28, 29]. Moreover, a survey among Dutch parents revealed that parents, especially in disad-
vantaged families, did often not feel equipped to support their children during the school clos-
ing [20].

Children in countries with longer school closings and less internet access might have expe-
rienced larger learning losses and larger inequalities because they experienced prolonged peri-
ods of limited and unequal excess to education. In line with this hypothesis, a recent study in
Italy [9] reports larger learning losses (0.19 SD) than previous studies in the Netherlands (0.08
SD) [3]. In Italy schools were closed for 15 weeks (one of the first and longest school closings
in Europe). Moreover Italy has one of the lowest share of households with a broadband con-
nection [30] and 12% of the students between 6 and 17 years old did not have access to a com-
puter or digital tools at home in 2018/2019 [31]. However, contradicting the idea that longer
school closings result in larger learning deficits, a study in Belgium—where schools closed for
8.5 weeks—reports a reduction in mathematics scores of 0.19 SD [2] which is similar in size to
the effects found in Italy (15 weeks). Altogether, more research based on country comparisons
is needed to be able to state that longer lockdowns result in larger learning deficits and an
increase in educational inequalities.

Materials

In the Netherlands, students take standardized tests throughout grades 1 to grade 6 in primary
education. These standardized tests come from different suppliers, with the largest supplier
being CITO, with which we collaborated for this paper. Furthermore, schools use administra-
tion systems to store the information about the standardized test scores. Three administration
systems exported the data on standardized test scores from school year 2013/2014 onwards as
part of the Netherlands Cohort Study on Education (NCO) project, a national project initiated
by the Dutch Research Council (for a description of this project, see [32]). With permission of
the schools, the administration system exports the data on the standardized test scores to Sta-
tistics Netherlands, who pseudonominize the student-id and school-id. Before any data was
exported, parents were informed about the project and data export by the school, and were
given the opportunity (during 4 to 6 weeks) to object against export of their child(ren)’s data
(by informing the school written or orally). The school registered any objections in their
administration system, and data was not exported from those students whose parents
objected.

The data was collected over a period of three months with two exports from the administra-
tion systems, the first export took place on the 30th of November 2020, the second on the 18th

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Table 1. Collection of data on standardized tests per school year and grade.

School year 2013/2014
School year 2014/2015
School year 2015/2016
School year 2016/2017
School year 2017/2018
School year 2018/2019
School year 2019/2020

Grade 1 Grade 2 Grade 3 Grade 4 Grade 5

of January 2021. In this export, information is collected from school years 2013/2014 to 2019/
2020 and gradually consists of more and more students in more and more grades. For more
information, see Table 1. In total, 1,319 schools and unique information of 291,635 students
was gathered on standardized test scores. After cleaning the data, the total sample used for
analyses of this paper comes down to 201,819 students in 1,178 schools.

National standardized test scores

From grade 1 to grade 6, students take standardized tests twice a year, a midterm test, most
often administered to students in the months January and February of the school year, and an
end-of-term test, mostly administered in the months June and July, right before the summer
holidays. For most schools, these are digital tests. Some schools opt for the pen-and-paper ver-
sion. Due to the school closure in the spring of 2020, for the school year 2019/2020 the end-of-
term test could be postponed until after the summer holidays, which many schools did: about
a quarter of schools decided to test their students after the summer holidays in August, Sep-
tember or even October. Test supplier CITO made a recalculation for the test scores in August,
September and October to account for the extra time until the test, and make them comparable
to the test scores of students who made the test before summer. Note that the tests written dur-
ing the pandemic were exactly the same type and format as before the pandemic, and there is
no within school variation between the type and format of the tests before and during the
pandemic.

We use test scores in the domains reading, spelling and math. Table 2 shows the number of
test records and unique students per domain. The test in math contains both abstract problems
and contextual problems that describe a concrete task. The reading test assesses the student’s
ability to understand written texts, including both factual and literary content. Lastly, the test
in spelling asks students to write down a series of words (no verbs), demonstrating that they
have learned the spelling rules. For reading, there is no mid-term test in the first grade, there-
fore the learning gains between the midterm test and end-of-term test cannot be calculated for
grade 1.

The learning gains are defined based on the standardized test scores and are calculated by
subtracting the score on the midterm test from the end-of-term test of each domain within a

Table 2. Number of test records per domain per grade.

Grade 1
Reading n/a
Spelling 54,424
Math 61,144

https://doi.org/10.1371/journal.pone.0261114.t002

Grade 2 Grade 3 Grade 4 Grade 5 Total
42,120 56,746 58,872 44,081 201,819
52,166 59,558 60,762 42,864 269,774
55,801 62,578 68,709 63,207 311,439

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school year, with the condition that the student must have taken a midterm and end-of-term
test within the same school year at the same school. To remove the influence of outliers, the
top and bottom 1% of the absolute learning gains scores are not included in the analyses.

Student background variables

In the secured virtual environment of Statistics Netherlands, standardized test scores can be
matched to background information of the students and their parents. Note that the data in
the environment of Statistics Netherlands are pseudonymized such that data are fully anony-
mous to the researchers that use these data. The data on background information that we use
are the highest education level and highest income of parents, student migration background
and student gender. Parental education is defined as low when the highest obtained degree of
(one) the parents is in pre-vocational secondary education (vmbo b/k), or a degree in upper
secondary vocational education (mbo 1), or grades 7 to 9 in pre-vocational secondary educa-
tion (vmbo gl/tl) or senior general secondary education or university preparatory education
(1), middle when a degree in upper secondary vocational education level 2, 3 or 4, or when
completed senior general secondary education or university preparatory education (2), and
high when a degree at a university of applied sciences is attained or higher (3). This division of
parental education over three categories is also being used in the Netherlands Cohort Study on
Education and leads to a division in categories that is not only relevant at the content level, but
also provides us with large enough groups to have statistical power. Highest parental income is
defined as low when the highest income of one of the parents is below the minimum income
level (1), middle when the income is higher than minimal level but below twice the minimum
income level (2) and high when the income of one of the parents is higher than twice the mini-
mum income. Students’ migration background is defined as either having a Dutch background
or a western background, or a non-western background. Students with a Dutch or western
background are combined into one category because the data contains only very few students
with a western background, and the results of these two groups are very comparable. In terms
of parental education and household income, students in our sample with a non-western
migration background are more likely to come from households with relatively low educated
parents (26% compared to 6% for the native Dutch and western migrant student sample) and
a relatively low income (45% compared to 16%). Lastly, the gender of the student is defined as
male or female.

Representativeness

The data on standardized test scores are only available for schools who gave permission to
export the test scores from their administrative system to Statistics Netherlands. As a result, we
do not have full population data and consequently selectivity of the sample might play a role.
In the schoolyear 2019/2020, we had a total number of 6,174 primary schools in the Nether-
lands. The 1,178 schools in our sample therefore comprise a proportion of 19% of the total
number of schools. Two main sources of selectivity into the sample can be identified. First, the
schools that decided to participate in the data collection might not be random. In exchange for
sharing the standardized test score, schools received a report on the performance of their
school relative to other schools with a comparable student population. We can expect that
active schools, which are keen to monitor their progress, are especially interested in the reports
and more likely to participate in the data collection project. Second, not every student is tested.
Schools tend to exclude students who are absent (e.g., due to illness) or have a very large learn-
ing loss. For these students, schools feel a test is not possible or useful. Usually, the number of
students per school which are excluded from the standardized tests is relatively small. However

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in 2020, after the school closed for several weeks, more schools decided to skip the standard-
ized tests for a larger share of the student population. It is reasonable to assume that students
with larger learning losses are less often tested. Therefore, it is likely that our data is not repre-
sentative for the whole population, and additional tests on our sample in comparison to the
full population confirm this. Table 3 shows the representativeness of our sample in comparison
to the full population (based on the National Cohort Study on Education; [in Dutch

Table 3. Representativeness of sample compared to full population on student and school background variables.

Full population Sample
Variables Percentage Percentage
Gender
Female 49.32 49.75
Migration background
Dutch & western migration background 82.22 75.95
Non-western migration background 17.51 24.04
Missing 0.27 0.01
Parental income
Low income 21.20 24.06
Medium income 53.38 50.54
High income 24.02 24.53
Missing 1.41 0.87
Parental education
Low educated 10.06 11.50
Medium educated 29.87 29.03
High educated 47.59 48.95
Missing 12.48 10.52
School size
Less than 141 students 36.71 28.95
Between 141-220 students 30.27 31.26
More than 220 students 33.01 39.79
School level pct of low educated parents
Below 5,5% 33.12 32.22
Between 5,5% and 12% 33.47 32.78
Above 12% 33.41 35.00
Urbanisation level
Low (< 500 adresses/km2) 7.86 5.63
Limited (500-1000 adresses/km2) 21.87 14.41
Medium (1000-1500 adresses/km2 17.58 12.50
Strong (1500-2500 adresses/km2) 30.93 32.66
Very strong (> = 2500 adresses/km2) 21.77 34.81
Denomination
Public school 29.79 30.55
Schools based on philosophies 6.10 3.77
Schools based on religious beliefs 64.02 65.69
Observations
Total number of students 2,458,376 263,553
Total number of schools in 2019/2020 6174 1178

Note: N of students is based on the number of unique students in the years 2017/2018, 2018/2019 and 2019/2020.

https://doi.org/10.1371/journal.pone.0261114.t003

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abbreviated as NCO] [32] on student and school background characteristics. Overall, we see
that our sample is over-represented in students with a non-western migration background,
and students with low parental income. Furthermore, schools in our sample tend to be larger
schools located in more urbanized areas.

To limit the impact of selectivity and over-representation of certain students and schools,
we use inverse probability weights. In calculating the weights, we use population data on all
students enrolled in Dutch primary education and calculate the probability to be in our test
score dataset separately per academic year, grade, and test subject domain as a function of stu-
dents’ observable characteristics. These characteristics are parental education, income, migra-
tion background, gender, percentage of students with low educated parents at the school,
number of students at the school, urbanisation level (based on location of the school), province
(based on location of the school) and school denomination.

Descriptive statistics

Table 4 shows the unstandardized learning growth for the three domains for the 2 years before
the pandemic and the year of the pandemic separately. It also shows the learning growth split
by group of parental education. Table 4 is used to calculate the normal average learning growth
per week in the 20 weeks between midterm and end-of-term test, and the deviation from this
in the COVID-19-year. For example, if the normal learning growth for reading is 7 (in 20
weeks time), and during the pandemic it’s only 5, the decline in learning growth in weeks is
(20-((20/7)*5) = 5.7.

Methods

In order to estimate the effect of the COVID-19 related school closure on students’ learning
gain, we compare the learning gain between the midterm and the end-of-term test of the
COVID-19-exposed cohort (2019/2020) to the learning gain of students from the two previous
cohorts using OLS regressions. To account for potential differences in observable characteris-
tics between students of different cohorts, we add controls for student gender, student house-
hold income, migration background, and parental educational background. Further, since for
some students of the 2019/2020 cohort the end-of-term test was postponed until the start of
the next academic year, we add a dummy indicating whether the test was taken at the end of
the 2019/2020 academic year or at the beginning of 2020/2021, resulting in the following
regression equation, resembling a difference-in-differences design:

Where Ay, stands for the difference in achievement between the end-of-term test and the
midterm for student i in grade j. T;; is an indicator for the COVID-19 exposed 2019/2020

Table 4. Descriptive statistics unstandardized learning gains per domain.

Overall

Low-parental education

Middle-parental education

High-parental education

https://doi.org/10.1371/journal.pone.0261114.t004

Reading Spelling Math
Learning growth 2017&2018 6.99 25.59 15.52
Learning growth 2019 5.17 22.08 13.50
Learning growth 2017&2018 6.05 25.44 15.56
Learning growth 2019 4.27 20.28 12.31
Learning growth 2017&2018 6.53 25.97 15.75
Learning growth 2019 4.21 21.42 13.14
Learning growth 2017&2018 7.53 25.85 15.52
Learning growth 2019 5.94 22.99 14.00

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Sharp increase in inequality in education in times of the COVID-19-pandemic

cohort, X;, is a vector consisting of the aforementioned control variables, and Ejjs is the school-
level clustered error term. f is our coefficient of interest, which captures the difference in aver-
age learning gain between the COVID-19 exposed 2019/2020 cohort and the average learning
gain of the (pooled) preceding two cohorts (2017/2018 and 2018/2019).

Identification of the COVID-19 effect hinges on the assumption that the learning gain of
the different cohorts would have followed a similar trend in the absence of the pandemic.
While this assumption is fundamentally untestable, we can provide supporting evidence for it
by looking at the variability of learning gains for all grades over time. If these trends are stable,
we can be reasonably sure that the difference between the 2019/2020 cohort and the previous
two cohorts was caused by the impact of the pandemic. The results of these analyses can be
found in Figs 5-10.

In order to estimate the heterogeneous impact of the COVID-19-pandemic along student
background characteristics, we add an interaction between the treatment-dummy and the stu-
dent characteristic of interest to the regression. This results in the following equation:

Where Cj stands for one of the aforementioned student characteristics: gender, parental
education, household income, and migration background. The vector of control variables Xj
still includes all other student characteristics. As a robustness check, we also present results of
analyses where apart from the interaction we do not include any of the other control variables,
with similar results (see Tables 5-10). Finally, a concern could be raised that some of the stu-
dent characteristics we observe are capturing similar things. For example, parental education
and household income are likely to be strongly correlated. In order to isolate the additional
impact of COVID-19 along (for example) household income, we therefore run analyses where
we control for the interaction between parental education and the treatment-dummy in addi-
tion to the interaction with household income. In addition to household income, we do this
for student gender and migration background as well, resulting in the following equation:

Ay, = %+ TB) + T,C,B, + +T,E;B, + Xj7 + €;, (3)

With Ej standing for the highest level of obtained parental education. As mentioned before,
in our main specification we use inverse probability weighting to obtain results representative
for the whole Dutch primary school population. As a robustness check, we run the same analy-
ses without employing weights as well as using entropy-balancing weights ensuring covariate
balance between the COVID-19 exposed cohort and the control cohorts (similar to the method
used by [3]), and obtain similar results (see Tables 5-10).

Results

In this section, we show the consequences for inequality during the COVID-19-pandemic by
comparing the learning gains of students in pre-COVID-19 times (school years 2017/2018 and
2018/2019) to the learning gains since the COVID-19-pandemic (school year 2019/2020) by
estimating Eqs (1) through (3). The results are presented in Figs 1-4. Fig 1 presents the results
of Eq (1), estimating the overall impact of COVID-19 on student learning gains. Fig 2 shows
the result of Eq (2), estimating the disparate impact along students’ parental education. Figs 3
and 4 shows the results from Eq (3), estimating the disparate impact along students’ household
income and migration background, in addition to the differences along parental education.
Tables 11-15 show all the underlying regression results.

Fig 1 shows that in all domains, students have a lower learning gains during the COVID-
19-pandemic compared to the growth of students in previous cohorts. On average there is 0.14

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Standardized coefficients learning growth in COVID-year as
compared to previous years

e spelling Average grade 1-5
oe Math
—@— Spelling Grade 1
* Math
® Readin
> Spelling Grade 2
Math
-—@— Reading
—@— Spelling Grade 3
® Math
—_@—_ meeting Grade 4
e pelling
Math
* Reading d
— Spelling Grade 5S
. Math

-0.40 0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10

Fig 1. Standardized coefficients learning gains in COVID-19-year as compared to previous years (zero line).

https://doi.org/10.1371/journal.pone.0261114.g001

standard deviations (SD) learning loss in reading, 0.15 SD in spelling and 0.21 SD in math.
Looking at the different grades, we see a gradual increase in the learning loss from grade 1 to
grade 5 onwards across all domains, with some outliers, like for instance spelling in grade 4.
For reading, we see students experience about 0.06 to 0.20 SD learning loss compared to stu-
dents from previous cohorts. Looking at spelling shows a similar result, where students experi-
ence about 0.13 to 0.18 SD learning loss. Math shows the largest deficits in learning with on
average 0.13 (grade 1) to 0.33 (grade 5) SD learning loss.

Although most students learned less in 2019/2020 than their peers in previous cohorts,
some students show larger learning loss than others, leading to (increasing) inequality between
students. We look at four dimensions of inequality: (1) by parental education, (2) by family
income, (3) by migration background and (4) by gender.

Fig 2 shows that children with low-educated parents learned less between the midterm and
end-of-year test than their peers with high-educated parents, and that the differences are larg-
est in grades 1, 2 and 3, and for spelling and math(note that alternative specifications in which
we use four categories of parental education, or in which we use three categories which are not
based on parental education, but on the indication (used for funding purposes) whether a
child is a regular child, has a disadvantaged background or a very disadvantaged background,
yield very similar results and the same conclusions.) The results show, for example, that chil-
dren of high-educated parents experience about 0.1 SD more learning gains during the year
2019/2020 compared to children of low-educated parents. In other words, the learning loss
due to school closings is larger for students of low-educated parents, and inequalities have
grown because of this. The differences between students of high- and low-educated parents are

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Sharp increase in inequality in education in times of the COVID-19-pandemic

Standardized coefficients learning growth in COVID-year 2019/2020
of low educated versus middle and high educated

2 Reading Average grade 1-5

|i ++ Spelling

= e Math

x @ Reading

e Spelling Grade 2
- ® Math

@ Reading
+ @—_ Spelling Grade 3

e Spelling

Math

Grade 4

<—e e Reading

7 a Spelling

: Math
0.05

0.10 0.15 0.20 0.25 0.30

Grade 5

0.20 0.15 -0.10 -0.05 0.00

Middle educated @ High educated

Fig 2. Standardized coefficients of learning gains in COVID-19-year 2019/2020 of low-educated (zero line) versus
middle- and high-educated.

https://doi.org/10.1371/journal.pone.0261 114.9002

statistically significant for spelling and math but not for reading, implying that the role of
parental background on educational development during the first school closure due to the
COVID-19-pandemic is largest for math and spelling, and less pronounced for reading com-
prehension. Altogether, these findings show that the existing differences in learning gains
based on parental education prior to the COVID-19-pandemic have increased during the
spring of 2020 when learning was disrupted. These increased differences based on parental
education are not surprising, since students were more dependent on the help their parents
could provide with schoolwork during the school closure. This finding is also confirmed by
other studies: parents in the Netherlands with lower educational attainment felt less capable to
help their children with schoolwork [20, 21].

Parental income also plays a role: Fig 3 shows that children from medium and higher
income households increase their scores between the midterm and end-of-year test more
strongly in the COVID-19-year than their peers from a family with a lower household income,
with the largest effects in grades 2 and 3, and for spelling and math. For example, children
from medium and high household income experience about 0.05 SD more learning gains than
children in low-income households. Note that the relation between income and learning gains
is additive to the additional role of parental education during the pandemic. We explicitly take
into account the effect of parental education on learning gains and the additional role during
the pandemic, and we still see an effect of household income during the school closure. How-
ever, it is not surprising that we find an effect of parental income on top of the effect of paren-
tal education: parents with higher household income were more likely to afford additional

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Sharp increase in inequality in education in times of the COVID-19-pandemic

Standardized coefficients learning growth in COVID-year 2019/2020
of low income versus middle and high income

i—@ Spelling Average grade 1-5
® Spelli Grade 1
a Math
® =F Reading
= ; Spelling Grade 2
I eo + Reading
ti Spelling Grade 3
ad Math
é @ ae Grade 4
pelling
eo Reading Grade 5
_ Math
0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25

AMiddleincome @ High income

Fig 3. Standardized coefficients of learning gains in COVID-19-year 2019/2020 of low-income (zero line) versus
middle- and high-income.

https://doi.org/10.1371/journal.pone.0261 114.9003

help for their children during their time at home. One study suggested that they provide more
private access to additional online learning materials [33].

We also looked at the role of migration background, again on top of effects of parental edu-
cation. The results in Fig 4 indicate that, conditional on the effect of parental education, over-
all, students with a non-western migration background did not perform significantly worse
than other students (native and with a western migration background) during the COVID-
19-pandemic. We only find a small significant result for math for grade 2 and 3, and for all
grades taken together. Note that, if we do not condition on effects of parental education, we do
find significant differences for migration background. Hence, overall, we find that the
increased inequality during the pandemic is based on parental education and parental income
rather than on migration background.

Lastly, we find that there are no significant differences for gender, neither with nor without
controlling for the effect of parental education. Girls seem to perform slightly worse on reading
and math but the coefficients are small and almost only statistically significant when all grade
levels are taken together (see Table 14).

Robustness checks

Our preferred model, used throughout the main text, includes inverse probability weights to
obtain results that are representative for the entire Dutch primary school population, and we
compare the learning gains between the midterm and the end-of-term test in the COVID-
19-year to the gains in the two years prior. Furthermore, in the analyses mapping the disparate

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Sharp increase in inequality in education in times of the COVID-19-pandemic

Standardized coefficients learning growth in COVID-year 2019/2020
of no migration background versus migration background
6 Reading Average grade 1-5

——_@— Spelling
——_@— Math

Math

ad Spelling Grade 2

e Math

® 1 Reading
t @ 1 Spelling Grade 3
e Math

e Reading

Spelling
id Math

Grade 4

e ; Spelling
Math

0.10

un
=|
=)
a

-0.20 -0.1
@ Migration background

Fig 4. Standardized coefficients of learning gains in COVID-19-year 2019/2020 of no non-western migration
background (zero line) versus non-western migration background.

https://doi.org/10.1371/journal.pone.0261 114.9004

impact of COVID-19 along several student characteristics we control for all other background
characteristics. These choices could potentially influence our results and their interpretation.
Therefore, in this section we show the results of additional analyses where we change the speci-
fication of our main model.

To demonstrate how the choice of including inverse probability weights influences the
results, Tables 5 and 6 show the results when using entropy-balancing weights and unweighted
regressions, respectively. While there are some slight differences in terms of significance levels
of certain coefficients, the overall pattern of lower learning gains during the COVID-19-year
for students from low-income households and low educated parents, especially in spelling and
math, remains similar in magnitude.

In Table 7 we run our main specification without controlling for student gender and migra-
tion background, and the dummy accounting for whether the end-of-term test was taken at
the end of the school year of 2019/2020 or at the beginning of the 2020/2021 school year. It
could be that the interaction effects found on student household income and parental educa-
tional background only hold conditional on these other student characteristics. If so, this com-
plicates the interpretation of our results. Fortunately, the exclusion of these additional control
variables does not change the found associations.

Table 8 shows how the results change when we control for students’ learning gains that they
obtained in the previous year. Including prior performance helps in addressing potential dif-
ferences between cohorts in the trend of their cognitive development. However, the downside
of this specification is that we do not observe prior performance for students that are in the
first grade (or second grade for the reading domain), and they drop from the analyses as a

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Reading
Pre-Covid year
N 4
s
°
x
x
= a,
D
y =
Ny, a \ “n, %
T T T T T
2014/2015 2015/2016 2016/2017 2017/2018 2018/2019 2019/2020

Academic Year

mms Grade 2 Hiistiiisiiis $Grade 3

Fig 5. Trends in learning gain over time-reading.

https://doi.org/10.1371/journal.pone.0261114.g005

result. For the other grades, the results are similar to the main specification. Prior learning
gains are significantly positively related to later learning gains for all but one subgroup (grade
5 spelling), but its inclusion does not alter the size and significance of the main results.

Table 9 adds school fixed effects to the regression. With this we control for the possibility
that time-invariant factors at the school level are driving our results. This could be the case, for
example, when students are strongly sorted into schools according to their background charac-
teristics. In this case the association between student characteristics and learning gains could
be driven by (unobserved) differences between schools that house different kinds of student
populations. The results from Table 9 however show that including school fixed effects does
not change the patterns of the found associations.

Finally, Table 10 shows the results of our main specification as well as the previously dis-
cussed robustness checks for the pooled sample over all grades. This table further demonstrates
that while there are some slight differences in terms of significance between specifications
when looking at grades separately, the overall picture of the disparate impact of COVID-19 on
student learning gains along household income and parental education levels remains strong
and is robust to various alternative model specifications.

Trends in learning gain over time

The interpretation of the differences in learning gains between the cohort affected by the
COVID-19 induced lockdown and previous cohorts as attributable to the impact of COVID-
19 hinges on the assumption that learning gains would have been similar in the absence of the
pandemic. While this assumption is untestable, we can provide supporting evidence for it by
looking at the variability of learning gains for all grades over time. If these trends are relatively

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Sharp increase in inequality in education in times of the COVID-19-pandemic

Spelling
Pre-Covid year
irs}
+
g =—
®
4
.
T T T T T
2014/2015 2015/2016 2016/2017 2017/2018 2018/2019 2019/2020

Academic Year

Fig 6. Trends in learning gain over time-spelling.

https://doi.org/10.1371/journal.pone.0261114.g006

stable, we can be reasonably sure that the difference between the 2019/2020 cohort and the
previous cohorts was caused by the impact of the pandemic. Figs 5-7 show the trends in learn-
ing gains per grade over time by plotting the (inverse probability population weighted) unstan-
dardized learning gains for all available cohorts in reading, spelling, and math respectively.
Because our data does not go back equally far for all grades, the lines are of different length. As
noted earlier, we do not have information on grade 1 learning gains for the reading domain, as
grade 1 students do not take a midterm test for this domain. For higher grades, we also have
fewer available cohorts due to the manner in which data was collected (see also Table 1).
Hence, for grade 2 we have data going back to academic year 2014/2015, for grade 3 from
2015/2016 onwards, for grade 4 starting in 2016/2017, and grade 5 starting 2017/2018.

The figures clearly show a marked decrease in learning gains between the COVID-19
affected cohort of the school year 2019/2020 relative to the prior cohorts in all domains and for
most grades. For the spelling and math domains, learning gains prior to the COVID-19
cohorts were remarkably stable over time across grades 1 through 4. For grade 5, the 2018/
2019 cohort had somewhat lower learning gains than the 2017/2018 cohort. However, since
these are the only 2 pre-COVID-19 cohorts for which grade 5 data is available, it is unclear
whether this represents a somewhat random fluctuation between cohorts, or whether it is part
of a longer trend in declining grade 5 learning gain. For reading, the results are less clear.
Grades 2 and 3 show a less stable trend over time than the other grades. For our main estima-
tion sample of the 2017 and 2018 cohorts comprising the control group however, the differ-
ences in learning gain between these two cohorts is relatively small for all grades.

A different way of showing whether the 2019/2020 COVID-19 affected cohort is somewhat
of an outlier in terms of their regular learning gain, is to plot the prior performance of this

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Sharp increase in inequality in education in times of the COVID-19-pandemic

Math
Pre-Covid year
=
=
Ss 4 ee
=
g “=<,
=
2
a
wo 4
T T T T T
2014/2015 2015/2016 2016/2017 2017/2018 2018/2019 2019/2020

Academic Year

Fig 7. Trends in learning gain over time-math.

cohort during the years in which there was no pandemic and compare it to the performance of
an earlier cohort. Figs 8-10 show the learning gain in all prior grades of the students who were
in grade 5 during the 2019/2020 school year and those who were in grade 5 during the 2018/
2019 school year for reading, spelling and math respectively. If the 2019/2020 cohort is rela-
tively similar to the prior cohort, we should expect to see similar levels (and trends) of learning
gain for grades 1 through 4. The pandemic occurred during grade 5 for the 2019/2020 cohort,
and we should therefore expect lower levels of learning gain in grade 5 for the 2019/2020
cohort compared to the students that were in grade 5 during the 2018/2019 school year.

Looking at the figures, this is indeed what we see for spelling and math. Both cohorts are on
remarkably similar learning gain trajectories from grade 1 through grade 4. In grade 5, the
2019/2020 cohort experiences a stronger decline in learning gain, especially in math, than the
students of the previous cohort that were unaffected by the pandemic. For reading, the results
are again less stable. The 2018/2019 cohort experienced a stronger decline in learning gain in
grade 3 relative to the other grades and the 2019/2020 cohort. The overall pattern of decreasing
learning gain from grade 2 to grade 3 and increasing learning gain from grade 3 to grade 4 is
visible for both cohorts, however, and the grade 5 learning gain of the pandemic-affected
2019/2020 cohort does decrease more strongly than the learning gain of the prior, unaffected
cohort.

Conclusions

This study describes the additional inequality in learning gains of primary school students in
the Netherlands during 12 weeks of disrupted learning due to the COVID-19-pandemic for
three domains: reading, spelling and math. We show large inequalities in the learning loss

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Reading

10

Learning growth

Grade

wes Cohort 2018/2019 =e = Cohort 2019/2020

Fig 8. Learning gain: Grade 5 students of 2018/2019 compared to Grade 5 students of 2019/2020 -reading.

based on parental education and parental income, on top of already existing inequalities. The
additional inequality in learning gains is largest among grades 2 and 3.

These results are quite alarming and indicate an average delay in learning of about 5.5
weeks for reading, and around 3 weeks for spelling and math with larger deficits in the higher
grades. Relative to the period between the midterm- and end-of-term tests of around 20
weeks, this is rather a lot. It is to some extent reassuring that in general the decline in learning
gains do not take place in the lowest grades in which the foundation for math and language
skills are laid [34]. On the other hand, the decline in learning gains is larger for students from
a low socioeconomic background (lower parental education and household income) for spell-
ing an math and these inequalities are higher in the early grades. We see a delay of around 4
weeks for spelling and math for students with low-educated parents. This implies that during
the school closure period students with low-educated parents hardly learned anything. How-
ever, there are no statistically significant socioeconomic status differences in reading scores.
Previous research has shown that the home environment is important for the development of
literacy skills and reading motivation [35, 36]. In line with this finding some have also sug-
gested that center-based reading interventions might be less effective than mathematics inter-
ventions [37, 38]. Our finding that reading skills are less affected by the school closure support
the idea that the family environment plays an important role in the development of reading
skills, also when schools are open. In contrast, the limited increase in socioeconomic inequali-
ties in reading skills is not in line with our expectations. Normally, family environment is an
important source of inequality in reading skills [36] and we would expect inequalities to rise
when schools closed and the role of family environment increased. We attribute the additional
inequality in learning loss of students in math and spelling based on parental education and

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Spelling

30 40 50
|

Learning growth

20

10

1 2 3 4 5
Grade

wes Cohort 2018/2019 =e mm = Cohort 2019/2020

Fig 9. Learning gain: Grade 5 students of 2018/2019 compared to Grade 5 students of 2019/2020 -spelling.
https://doi.org/10.1371/journal.pone.0261 114.g009

Math
wo
N
o 4
N
$
=
x
~
T T T T T
1 2 3 4 5

Grade

wus = Cohort 2018/2019 =m = Cohort 2019/2020

Fig 10. Learning gain: Grade 5 students of 2018/2019 compared to Grade 5 students of 2019/2020 -math.

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income to better resources these students had: Students with higher-educated parents most
likely all possessed a laptop, had parents that were able and willing to help with schoolwork
and could even afford additional private tutoring if needed.

The results call for national focus on reducing the learning loss of students from lower-edu-
cated parents and lower household income. It is worrisome and unfortunately not unlikely
that the increased inequalities in learning loss due to the pandemic may lead to long lasting
inequalities, deepening the gap in adult outcomes between groups in the population. This very
much stresses the need for targeted interventions to reduce the current inequalities in learning
loss caused by the pandemic.

This article shows that schools matter, specifically for the most vulnerable groups. Distance
learning may prevent part of the damage but cannot compensate for classroom teaching. The
policy implications of these findings are therefore twofold: 1) Government budgets that are
made available to make up for learning loss should give schools with many students from low-
educated parents and low household income a large share of the pie, and 2) in an event of
another crisis, or the current COVID-19-pandemic continues, schools should be closed only
as a very last resort to avoid further inequalities.

Author Contributions

Conceptualization: Carla Haelermans, Roxanne Korthals, Stan Vermeulen, Tijana Prokic-
Breuer, Rolf van der Velden, Sanne van Wetten, Inge de Wolf.

Data curation: Carla Haelermans, Roxanne Korthals, Madelon Jacobs, Suzanne de Leeuw,
Stan Vermeulen, Lynn van Vugt.

Formal analysis: Carla Haelermans, Roxanne Korthals, Madelon Jacobs, Suzanne de Leeuw,
Stan Vermeulen, Lynn van Vugt.

Funding acquisition: Carla Haelermans, Roxanne Korthals, Tijana Prokic-Breuer, Rolf van
der Velden, Inge de Wolf.

Investigation: Carla Haelermans, Roxanne Korthals.

Methodology: Carla Haelermans, Roxanne Korthals, Madelon Jacobs, Suzanne de Leeuw,
Stan Vermeulen, Lynn van Vugt.

Project administration: Carla Haelermans, Roxanne Korthals, Tijana Prokic-Breuer, Rolf van
der Velden, Inge de Wolf.

Supervision: Tijana Prokic-Breuer, Rolf van der Velden, Inge de Wolf.
Validation: Carla Haelermans, Roxanne Korthals.
Visualization: Madelon Jacobs, Suzanne de Leeuw, Stan Vermeulen.

Writing - original draft: Carla Haelermans, Roxanne Korthals, Madelon Jacobs, Suzanne de
Leeuw, Stan Vermeulen, Lynn van Vugt.

Writing - review & editing: Carla Haelermans, Roxanne Korthals, Bas Aarts, Tijana Prokic-
Breuer, Rolf van der Velden, Sanne van Wetten, Inge de Wolf.

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