The economic impact of COVID-19 learning deficits : a survey of the literature.
Date: 2024-01-29
Institutions: Directorate-General for Economic and Financial Affairs
Subjects: EU Member State, coronavirus disease, economic consequence, economic growth, education, education policy, education statistics, labour market, young person

ISSN 2443-8030 (online)

The Economic Impact 
of COVID-19 
Learning Deficits:             
A Survey of the Literature 

Joana Elisa Maldonado, 
Anneleen Vandeplas and Lukas Vogel 

ECONOMIC BRIEF 078 | JANUARY 2024

EUROPEAN ECONOMY

EUROPEAN 
ECONOMY

Economic and 
Financial Affairs

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European Commission  

Directorate-General for Economic and Financial Affairs  

The Economic Impact of COVID-19 Learning Deficits:             
A Survey of the Literature  
By Joana Elisa Maldonado, Anneleen Vandeplas and Lukas Vogel  

Abstract  

The COVID-19 pandemic has led to a temporary reduction in the quantity and quality of education, with 
school  closures  of  varying  degree  implemented  across  the  globe.  This  paper  reviews  the  literature  on 
learning deficits in compulsory education and their possible economic impact. Studies from different EU 
Member  States  show  significant  learning  deficits  in  primary  and  secondary  education,  equivalent  to 
about  two  months  of  learning  progress  during  a  regular  school  year  on  average.  The  impact  of  the 
pandemic on learning outcomes varies widely by country as well as by students’ age. As students with a 
lower  socioeconomic  status  or  weaker  previous  performance  experienced  larger  learning  deficits, 
inequality  in  educational  achievement  has  increased  between  students,  schools,  and  countries.  While 
labour market outcomes of the 2020 graduating cohort seem to be resilient at the current juncture of 
tight  labour  markets,  the  long-term  economic  impact  of  learning  deficits  is  likely  to  be  non-negligible. 
Existing studies project small productivity losses for the coming years but a significant impact in the long 
term,  peaking by  the  second  half  of  the 21st  century,  when  all  affected  cohorts  of  students  will  have 
entered the labour market. Estimates of the aggregate real GDP effects of 1-year learning deficits, given 
the number of affected cohorts of students (corresponding to around one third of the future labour force 
at  the  maximum)  and  assuming  that  no  remedial  action  is  taken  and  losses  are  not  recovered,  reach 
between –0.5% and –4.7% in 2050 in the contributions surveyed in this paper, compared to a baseline 
without any learning deficits. For an average learning deficit of circa one fifth of a school year this would 
translate into real GDP effects between -0.1% and -1% by 2050. 

JEL Classification: I21; I26; J24 

Keywords: learning losses, COVID-19, school closures, growth impact, human capital 

Acknowledgements:  The  paper  benefited  from  valuable  comments  and  contributions  by  Leonor 
Coutinho,  Aron  Kiss,  Géraldine  Mahieu,  Marco  Montanari,  Eric  Ruscher,  Anna  Thum-Thysen,  Alessandro 
Turrini, and Kristine Van Herck. 

joana-elisa.maldonado@ec.europa.eu,  European  Commission, 
Contact:  Joana  Elisa  Maldonado, 
Directorate-General  for  Economic  and  Financial  Affairs,  B2  Economics  of  Structural  Reforms  and 
Investment. 

EUROPEAN ECONOMY                                                                                         Economic Brief 078 

 
 
 
 
 
 
 
 
 
 
 
European Economy Economic Briefs                                                           Issue 078 | January 2024 

INTRODUCTION 

During the COVID-19 pandemic, EU education systems were strongly affected by containment measures 
aimed  at  reducing  the  spread  of  the  virus.  Between  March  2020  and  June  2021,  a  reduction  of  the 
quantity  and  quality  of  education  of  varying degree was  observed  across  the  EU.  In  most  EU  Member 
States,  schools  were  physically  closed  for  several  weeks  or  months,  and  classes at  school  were partly 
replaced  by  distance  learning  with  self-study  and  online  classes.1  After  the  first  lockdown  in  2020, 
partial  physical  school  closures  of  shorter  periods  and  reduced  hours  continued  to  be  implemented  in 
selected  grade  years  or  regions.  In  the  school  year  2021-2022,  regular  teaching  activities  resumed 
across  the  EU,  with  some  remote  teaching  practices  remaining  in  place  here  and  there,  particularly  at 
universities. 

Studies  from  different  EU  Member  States  show  negative  effects  of  the  physical  school  closures  and 
changes  in  schooling  on  the  level  and  equality  of  learning  outcomes.  A  combination  of  students 
forgetting  previously  learned  material  (‘learning  loss’)  and  new  learning  progressing  at  a  slower  pace 
than  before  (‘lost  progress’)  resulted  in  ‘learning  deficits’.  These  learning  deficits  were  systematically 
larger  for  students  from  a  disadvantaged  socioeconomic  background.  Given  these  negative  effects, 
which  simultaneously  affected  a  large  number  of  cohorts,  the  resulting  reduction  in  human  capital 
accumulation could negatively affect economic outcomes in the long run. 

Estimates  of  the  macroeconomic  implications  of  learning  deficits  vary  substantially  in  quantitative 
terms. The studies reviewed in this paper suggest real GDP (level) effects between –0.5% and -4.7% by 
2050  for  a  persisting  one  year  loss  of  schooling.  Model-based  studies  tend  to  indicate  smaller  losses 
than empirical estimates with looser theoretical structure. 

This paper summarises the existing evidence on learning deficits caused by the COVID-19 pandemic and 
provides an economic perspective on the possible long-term impact of the slowdown in learning. Section 
2 reviews the literature on the effects of the COVID-19 pandemic on educational outcomes. Section 3 
assesses  whether  effects  are  visible  in  short-term  labour  market  outcomes  and  describes  modelling 
results  and  estimates  of  the  possible  long-term  impact  of  the  learning  deficits  on  output.  Section  4 
concludes with a discussion of the policy implications. 

REVIEW OF EVIDENCE ON LEARNING DEFICITS IN THE EU 

This  section  provides  an  overview  of  the  evidence  on  the  impact  of  the  COVID-19  pandemic  on 
educational outcomes of school-aged children. It describes the changes in students’ learning outcomes 
and inequality in education observed in the EU after the pandemic. 

Effects on average learning outcomes in primary and secondary education 

To  date,  the  evidence  on  post-COVID-19  outcomes  from  standardised  international  tests,  which  are 
comparable across countries and years, remains very limited. Assessments are taking place only every 
few  years  and  have  in  many  cases  been  postponed  due  to  the  pandemic.  The  first  internationally 
comparative  evidence  on  the  post-COVID-19  reading  performance  of  4th-graders  from  57  countries 
comes from the Progress in International Reading Literacy Study (PIRLS) 2021, published in May 2023. 
23 Member States of the EU participated in the assessment, 16 of which have comparable trend data 

1 Complete school closures (without provision of distance teaching or blended learning) only took place for short periods in 
some  EU  Member  States,  mostly  in  the  form  of  extended  holidays.  In  this  paper,  the  term  ‘school  closure’  is  used  to 
describe the suspension of face-to-face schooling, while in most cases learning activities (partly) continued remotely. Data 
on school closures by country or region can be found in the report by European Commission/ EACEA/ Eurydice (2022) and 
in the Dashboards on the Global Monitoring of School Closures by UNESCO (2017). 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

available  from  previous  assessment  cycles  (Mullis  et  al.,  2023).2  12  Member  States  and  the  Flemish 
community  of  Belgium  experienced  a  significant  decrease  in  test  scores  compared  to  the  2016 
assessment (see Table A.1 in Annex 1).  

This is a noticeable negative result, likely at least in part attributable to the COVID-19 pandemic, as 10 
of these Member States had a positive or constant trend before 2016. Only three Member States and 
the  French  community  of  Belgium  recorded  no  significant  change  in  test  scores  from  2016  to  2021, 
while  no  Member  State  achieved  a  significant  positive  change  in  test  scores  in  this  period.  First 
correlations  with  national  data  on  school  closures  for  29  countries  globally  show  that  longer  school 
closures  were  negatively  associated  with  reading  scores,  with  a  one-year  school  closure  resulting  in  a 
learning deficit of half a school year (Kennedy & Strietholt, 2023). However, it has to be noted that the 
data quality of these results is limited due to the variations of school closures within countries. 

Reviews of studies covering different countries and age groups expose significant learning deficits after 
the  COVID-19  pandemic.  In  the  absence  of  comprehensive  internationally  comparable  student 
assessments  for  different  age  groups  and  subjects,  a  quickly  growing  body  of  literature  compares 
country-specific  studies  of  student  outcomes  in  compulsory  education  before  and  after  the  COVID-19 
school closures.  

One  year  after  the  first  school  closures,  early  reviews  of  these  studies  consistently  found  that  the 
COVID-19  pandemic  led,  on  average,  to  significant  learning  deficits  (e.g.,  Donnelly  &  Patrinos,  2021; 
Hammerstein,  2021;  Zierer,  2021).3  This  finding  was  confirmed  in  more  comprehensive  reviews  two 
years after the outbreak of the pandemic (e.g., Patrinos et al., 2022; Moscoviz & Evans, 2022 for high-
income countries).  

A review of 42 studies from 15 countries, globally, finds a substantial average learning deficit of 35% of 
a regular school year’s learning progress (Betthäuser et al., 2023).4 For the EU Member States covered in 
the selected studies (see Table A.2 and Table A.3 in Annex 1), an average loss of 20% of a school year’s 
learning progress was recorded.5  

Assuming a duration of a regular school year of 8-9 months, this would be equivalent to the loss of the 
learning progress of almost two months during a regular school year. This learning deficit is equivalent 
to an 8 score-point decrease on the OECD’s PISA test (or 8% of a standard deviation), which is a large 
setback, given that only nine EU Member States have been able to improve performance in reading in 
PISA  from  2015  to  2018,  all  of  which  by  less  than  8  score  points  (OECD,  2019).  This  result  is  very 
similar  to  the  decrease  in  PIRLS  reading  scores  recorded  in  the  2021  assessment,  which  would  be 

2 In 2021, the following EU Member States participated in PIRLS: BE (Flemish and French communities), BG, CZ, DK, DE, IE, 
ES, FR, HR, IT, CY, LV, LT, HU, MT, NL, AT, PL, PT, SI, SK, FI, SE. Two of these countries (HR, CY) did not participate in the 
previous  2016  edition  and  other  countries  do  not  have  comparable  time  trend  data  due  to  other  reasons,  such  as 
structural breaks. 

3 Average learning deficits found in 2021 reviews amounted to 0.10-0.15 standard deviations (SD): 0.10 SD for reading 
and  mathematics  in  Hammerstein  et  al  (2021),  0.13  SD  for  mathematics  and  language  in  Donnelly  &  Patrinos  (2021), 
0.14  SD  for  mathematics  as  well  as  a  combination  of  different  subjects  in  Zierer  (2021),  and  0.15  SD  for  combined 
mathematics  and  reading  outcomes  in  Storey  &  Zhang  (2021),  respectively,  only  including  those  studies  which  provide 
results measured in standard deviations. 

4  The  authors  of  the  study,  published  in  Nature  Human  Behaviour,  conducted  a  systematic  review  of  the  literature  and 
found a learning deficit of 0.14 standard deviations, which can be translated to a loss of 35% of a regular school year 
(see Box 1). 

5 This estimate, equal to 0.08 standard deviations, is obtained from the authors’ own calculations based on the dataset 
and  code  provided  by  Betthäuser  et  al.  (2023),  restricting  the  sample  to  the  17  studies  from  seven  EU  Member  States 
included in their sample (Belgium, Denmark, Germany, Italy, Netherlands, Spain, Sweden). It has to be noted that, due to 
limited data availability, this estimate is not an accurate estimate for the EU, as a large number of Member States is not 
represented  (e.g.,  Baltic,  Central  and  Eastern  European  countries),  while  there  are  several  studies  from  other  Member 
States (e.g., Netherlands and Germany). Annex 1 lists all estimates included in the calculation. The reported number is an 
unweighted average of all estimates. Weighting estimates by the number of reported estimates in each study could yield 
a higher average. 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

approximately equivalent to a learning deficit of 25% of a regular school year’s learning for 4th graders 
in participating EU Member States.6  

BOX 1: MEASURES OF LEARNING PROGRESS  

In educational research, changes in learning outcomes are commonly measured in standard deviations (SD). 
This  statistical  measure  allows  comparing  the  effect  sizes  of  outcomes  with  different  scales  and  from 
different samples. It assumes  a normal distribution of the test scores, clustered in a bell curve around the 
mean. Raw test scores are standardised by rescaling to a mean of 0 and a standard deviation of 1. 

The standardised score (z-score) indicates how far an observation is lying above or below the mean. A score 
which  is  1  standard  deviation  above  (below)  the  mean  is  approximately  equivalent  to  the  84th  (16th) 
percentile,  that  is  34  percentile  points  above  (below)  the  mean.  Accordingly,  a  learning  deficit  of  0.1  (0.2) 
standard deviations shifts the distribution to the left, moving the student, who was at the median before the 
pandemic, down to the 46th (42nd) percentile. 

Measured  learning  outcomes  can  be  compared  to  benchmarks  for  the  learning  progress  observed  during  a 
regular  school  year,  as  established  in  the  educational  literature  (e.g.,  Bloom  et  al.,  2008;  Hill  et  al.,  2008). 
Learning deficits can then be expressed in terms of lost progress as a share of a regular school year. While 
school  productivity  varies  in  different  education  systems,  grade  levels  and  by  other  factors,  an  average 
learning  gain  benchmark  of  0.2-0.5  standard  deviations  in  one  school  year  is  commonly  assumed  (e.g., 
Azevedo et al., 2020). In this paper, we use an average learning gain benchmark of 0.4 standard deviations 
for a regular school year based on Hill et al. (2008).  

On  the  scale  of  the  OECD’s  PISA,  which  is  normalised  to  have  a  mean  of  500  score  points  and  standard 
deviation of 100 score points, a change in learning outcomes by 10% of a standard deviation equals a 10-
point difference (OECD, 2019). 

A  scientific  report  commissioned  by  the  European  Commission  (DG  EAC)  finds  an  even  larger  average 
learning deficit of 30% of a regular school year’s learning progress, equivalent to 12 PISA score points, 
in EU Member States (De Witte & François, 2023).7 The difference is possibly due to the strict selection 
based  on  a  risk-of-bias  assessment  by  Betthäuser  et al.  (2023), which excludes studies with  a  critical 
risk of bias, e.g. due to confounding, sample selection, or missing data.  

Similar  results  are  found  in  a  comprehensive  meta-analysis  by  the  European  Commission’s  Joint 
Research Centre covering 21 OECD countries (Di Pietro, 2023a), which estimates the pandemic resulted 
in an average learning deficit of 30-40% of a regular year’s learning progress, with a smaller learning 
deficit in OECD EU countries than in OECD non-EU countries. 

The observed decline in learning outcomes following the outbreak of the COVID-19 pandemic represents 
a combination of various effects. It includes the impact of a loss in instruction time, but also the lower 
effectiveness of instruction methods used in distance teaching, with varying support of parents and in 
the absence of (positive and negative) peer effects, as well as other effects, such as the psychological 
impact, caused by the exceptional situation of the pandemic. Yet, comparisons of schooling modes show 
that  the  effect  is  likely  driven  largely  by  the  lower  effectiveness  of  remote  learning  compared  to  in-

6 Table A.1 in Annex 1 shows that the average decrease in test scores from the 2016 to the 2021 PIRLS assessment in 
participating  EU  Member  States  with  comparable  data  available  equals  10  PIRLS  points.  Assuming  an  international 
standard deviation of 100 points (as defined in the first PIRLS assessment in 2001), this would equal 10% of a standard 
deviation. 

7  The  report  finds,  on  average,  a  learning  deficit  of  0.11  standard  deviations  for  European  countries,  including  the  UK. 
Taking  the  subset  of  included  studies  from  EU  Member  States,  excluding  the  UK,  gives  an  average  of  0.12  standard 
deviations. 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

person teaching (e.g., Goldhaber et al, 2023; Jack et al., 2023). The observed impact comprises both the 
reduction in learning progress and the loss of knowledge gained before the start of the pandemic.8 

The  learning  deficits  vary  widely  across  countries  and  subjects.  Students  in  middle-income  countries 
experienced  larger  learning  deficits  than  students  in  high-income  countries,  though  high-income 
countries  tend  to  be  overrepresented  in  research,  and  studies  from  low-income  countries  are  missing 
(Betthäuser et al., 2023). Di Pietro (2023a) argues that the smaller effects found for OECD EU countries 
than OECD non-EU countries are likely due to differences in the length (or intensity) of school closures. In 
the  EU,  no  impact  of  the  COVID-19  crisis  on  learning  outcomes  was  observed  in  Nordic  countries 
(Denmark, Finland, Sweden), while large negative effects were found in Greece and Poland (De Witte & 
François, 2023). Across subjects, most studies find significantly larger learning deficits in mathematics 
than in languages (Betthäuser et al., 2023) or no significant differences (Di Pietro, 2023a).  

Questions remain as to how long the learning deficits will persist. While it is possible that students catch 
up after the return to regular schooling, it is also possible that learning deficits accumulate over time, 
for  instance  if  the  curriculum  and  the  instruction  are  not  adequately  adjusted  to  students’  learning 
deficits upon their return to school (Kaffenberger, 2021). In the latter case, the accumulation of losses 
would amplify the long-term effect in the absence of mitigating policies.  

Many  EU  Member  States  have  already  increased  spending  on  education  and  have  taken  remedial 
measures  to  dampen  and  reverse  the  negative  effects  of  the  COVID-19  pandemic  (De  Witte  &  Smet, 
2021;  Di  Pietro,  2023a).  First  assessments  of  the  medium-term  impact  of  the  COVID-19  pandemic 
provide  a  mixed  picture  of  post-pandemic  trends  in  learning  outcomes.  The  systematic  review  of 
Betthäuser  et  al.  (2023)  finds  that  learning  deficits  appeared  early  in  the  pandemic  and  have  neither 
been significantly reduced, nor widened until spring 2022. While this indicates that school closures which 
took place after the initial lockdown have not increased learning deficits, it also means that efforts to 
compensate losses had not succeeded in reversing the negative trend by spring 2022. Di Pietro (2023a) 
even  finds  learning  deficits  to  increase  over  time  in  OECD  countries,  with  larger  cumulative  learning 
deficits in assessments made in 2021 or later compared to assessments from 2020. 

Graph  1  summarises  the  findings  on  average  learning  deficits  in  EU  Member  States  for  the  studies 
included  in  Betthäuser  et  al.  (2023).  Across  countries,  no  clear  pattern  of  improvement  over  time 
becomes visible. In Germany and Belgium, the average learning deficits recorded in 2021 even exceeded 
those measured in 2020. This widening of the learning deficits could be due to containment measures in 
schools having continued over this period or result from an accumulation of missed learning progress. 

Studies  are  difficult  to  compare,  as  they  vary  in  many  influencing  factors,  such  as  the  geographical 
context,  length  of  school  closure,  type  of  distance  teaching,  test  instruments,  student  samples,  and 
methodologies. Yet, three factors likely affect the size of the learning deficits: First, a longer duration of 
school closures is correlated with higher learning deficits (Di Pietro, 2023a; De Witte & François, 2023; 
Patrinos, 2022). Second, a high level of digitalisation of education before the pandemic was associated 
with lower learning deficits (De Witte & François, 2023). Finally, while most studies cover primary school 
students,  some  reviews  observe  a  correlation  with  the  age  of  students,  with  younger  students  more 
negatively affected than older students (De Witte & François, 2023). However, this correlation could be 
driven by differences in the length of school closures, which often differed by grade year, and is found to 
be statistically not significant in other reviews (Betthäuser et al., 2023; Di Pietro, 2023a).  

8 The use of the term ‘learning deficit’ emphasises that the estimates of studies measure a broad effect, which comprises 
different elements, rather than only ‘learning loss’. 

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Graph 1: Average learning deficits in EU Member States with robust data available 

Note:  This  graph  is  based  on  computations  by  the  authors,  using  the  dataset  provided  by  Betthäuser  et  al. 
(2023). It covers the subset of 17 studies from seven EU Member States (BE, DK, DE, ES, IT, NL, SE). Estimates 
are  averaged  across  grades  and  subjects.  The  average  learning  deficit  is  computed  as  average  across  all 
available EU estimates (separate by study, year, age, and subject). Learning deficits are expressed in negative 
numbers (lost share of a school year), with the largest learning deficits on the left side of the horizontal axis. 
The colour of the dots indicates the year of measurement of student outcomes (2020 in orange, 2021 in red). 
Values for the respective countries in 2020 and 2021 are generally based on  different samples of  studies, 
implying imperfect comparability. 

Graph  2  shows  that,  in  studies  from  EU  Member  States,  the  impact  of  age  is  not  clear.  Most  average 
learning deficits by age fall in the range of 10-30% of a regular school year, with exceptions at age 7 
(no negative effects found in a study from Sweden) and age 12 (exceptionally large learning deficits of 
80% of a school year found in the Netherlands). Educational theory suggests linearly decreasing effects 
by age, or a U-shaped relationship, with the early and the final years being the most crucial time of the 
school  career.  Concerning  gender,  the  evidence  remains  limited  and  mixed,  with  both  boys  and  girls 
affected negatively, but differences vary by study (De Witte & François, 2023; Patrinos et al., 2022). 

It  is  likely  that  the  learning  deficits  caused  by  the  COVID-19  pandemic  are  exacerbating  previous 
downward trends in learning outcomes. The methodological limitations of most studies make it difficult 
to disentangle the effects of COVID-19 from long-term trends, with most studies not controlling for the 
general  time  trend  when  using  pre-pandemic  results  of  previous  cohorts  as  reference  (De  Witte  & 
François, 2023). The causal effects can be more clearly identified in natural experiments with the same 
cohort  tested  before  and  after  the  pandemic.  For  example,  Engzell  et  al.  (2021)  record  significant 
learning  deficits  of  the  same  magnitude  as  the  EU  average  reported  above,  based  on  such  a  natural 
experiment. The fact that studies using different statistical methodologies yield comparable results (De 
Witte & François, 2023) could suggest that the learning deficits uncovered do not mainly reflect previous 
downward trends in learning outcomes.  

Next  to  the  effect  on  school-aged  children,  the  COVID-19  pandemic  likely  also  impacted  learning 
outcomes  in  early  childhood,  higher  education,  and  adult  learning.  Educational  activities  were  reduced, 
and  the  learning  environment  was  limited  due  to  containment  measures  for  learners  of  all  ages. 
Evidence  on  early  childhood,  before  children  enter  primary  education,  is  largely  limited  to  qualitative 
evaluations  and  studies  from  outside  the  EU  (Uğraş  et  al,  2023).  In  higher  education,  a  large  shift  to 
digital  instruction  and  a  temporary  reduction  of  international  mobility  occurred  (OECD,  2021).  The  few 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

studies  that  evaluate  effects  in  higher  education  and  are  broadly  comparable,  show  similar  learning 
deficits at the tertiary level, as at the primary and secondary level (Di Pietro, 2023b). For example, at an 
Italian university, students’ progress and performance was reduced as a result of online teaching during 
the first lockdown of the pandemic (De Paola et al., 2023) by a magnitude comparable to the average 
learning deficits observed at the primary and secondary level in the EU. 

Graph  2:  Estimates  of  learning  deficits  by  age,  observations  from  seven  EU  Member  States  with 
robust data available 

Note: This graph is based on computations by the authors, using the dataset by Betthäuser et al. (2023The 
grey  dots  represent  the  68  estimates  of  learning  deficits  (across  the  range  of  country,  year,  and  subject 
parameters) from a subset of 17 studies with distinct samples from seven EU Member States (BE, DK, DE, ES, 
IT, NL, SE). The age means represented by red diamonds are the means of the grey dots for each age at the 
time  of  assessment  (2020  or  2021).  The  average  learning  deficit  is  computed  as  the  average  across  all 
available EU estimates, i.e. all countries and all age groups. Learning deficits are expressed as a lost share of 
a school year, using age- and subject-specific benchmarks from Hill et al. (2008). Negative numbers represent 
a learning deficit; positive numbers indicate that students have learned more than previous cohorts who were 
not affected by COVID-19 school closures. The cut-off at 13 years is due to the limited availability of data for 
students in older age groups. 

Increased education inequality between students 

Analyses  of  different  dimensions  show  that  not  all  students  were  equally  affected  by  the  COVID-19 
pandemic and that inequality increased in the months and years following the start of the pandemic. 

First, increasing inequality is observed through a widening spread in the distribution of test scores, with 
increasing  differences  between  the  best  and  worst  performing  students.  For  example,  in  Belgium 
(Flanders)  inequality  had  increased  both  within  and  across  schools  one  year  after  the  pandemic 
(Maldonado  &  De  Witte,  2022).9  These  increases  in  inequality  were  found  to  be  slowing  down  but 
remaining present three years later (Gambi & De Witte, 2023). 

Second, differences in test scores by background characteristics of students or schools have increased. 
The  learning  deficits  strongly  depend  on  students’  socioeconomic  status  (Betthäuser  et  al.,  2023;  Di 

9 The changes in the distribution of test scores were measured by inequality indicators, such as the Gini coefficient and the 
90/10 ratio. 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

Pietro, 2023a; Patrinos et al., 2022) and previous performance level (De Witte & François, 2023; Patrinos 
et al., 2022). These differences are found in both mathematics and reading, in primary and secondary 
education and at each stage of the pandemic, no matter which indicator of socioeconomic status is used 
(Betthäuser  et  al.,  2023).  This  means  that  the  COVID-19  pandemic  has  increased  the  relevance  of 
students’ background and home environment in determining educational achievement and reduced the 
role of the school as ‘the great equaliser’.  

Third,  the  pandemic  has  likely  increased  inequalities  in  learning  outcomes  both  within  and  between 
countries  in  the  EU.  An  earlier  JRC  study  identified  the  risk  of  learning  deficits  and  rising  inequalities 
between  European  countries  based  on  pre-pandemic  standardised  test  data  from  2019  (Blaskó  et  al., 
2022). It found that those countries with the worst average student outcomes in the EU also tended to 
be the ones with the largest gap in student achievements within their national boundaries. With COVID-
19  learning  deficits  affecting  weakly  performing  students  more  strongly,  inequalities  in  learning 
outcomes could increase in the EU. 

Table  1  provides  an  overview  of  the  review  of  COVID-19  learning  deficits  in  primary  and  secondary 
education in the EU. 

Table 1: Review of COVID-19 learning deficits in the EU   

17 studies with 70 estimates from seven EU Member States (BE, DK, DE, ES, IT, NL, SE) 

Tests in two subjects:  
reading & 
mathematics 

Evaluated in 2020 & 2021:  
3-15 months after the  
start of the pandemic 

Primary & secondary level: 
6–18-year-old students  

Average learning deficit of 20% of a regular school year’s progress 
≈ 8 PISA score points (8% of a standard deviation) 

Similar learning deficit in 2020 & 2021: learning deficit not reversed, but not worsened  

No clear pattern by age or grade level 
No learning deficits in Nordic countries (DK & SE), largest deficits in BE, IT, NL 
Smaller deficits for shorter duration of school closure or higher level of digitalisation 
Larger deficits for students with low previous performance or low socioeconomic status 

Note: This table provides a summary of COVID-19 learning deficits in primary and secondary education in the EU 
and presents calculations by the authors based on Betthäuser et al. (2023)’s dataset. 

POTENTIAL ECONOMIC IMPACT 

This  section  sketches  the  possible  impact  of  the  observed  learning  deficits  on  economic  outcomes.  It 
describes the first evidence on labour market outcomes of cohorts that graduated during the pandemic 
and  provides  a  tentative  quantitative  assessment  of  the  effect  of  the  observed  learning  deficits  on 
potential output in the long-term.  

Short-term effects observed in the labour market 

Data  on  the  labour  market  outcomes  of  youth  immediately  following  the  COVID-19  pandemic  likely 
reflect the impact of the recession, rather than the disruption to learning.  

Literature suggests that even in the absence of learning disruptions, young people who graduate during 
a  recession  may  face  negative  consequences  in  terms  of  their  socioeconomic  outcomes  (including 
earnings)  for  up  to  10-15  years  after  graduation  (Oreopoulos  et  al.,  2012;  Schwandt  &  Von  Wachter, 
2019; Regan, 2020). This may be less of a concern in the current context, where the pandemic-related 
increase in youth unemployment was nowhere near the large increase observed in the aftermath of the 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

financial crisis, helped by substantial policy efforts to stabilise labour markets during the pandemic (such 
as short-time work schemes), reducing risks of scarring effects.  

Job  finding  rates  tend  to  be  driven  mainly  by  the  business  cycle  but  could  partly  reflect  changes  in 
students’ performance. An empirical study by the Institute for Fiscal Studies shows a negative effect of 
the pandemic in the short run, but resilient employment rates one year after graduation for youth who 
graduated  during  the  pandemic-related  school  closures  in  the  UK  (Ray-Chaudhuri  &  Xu,  2023).  In 
particular, students who graduated in 2020 were less likely to find a job 3-6 months after graduation 
and more likely to start in lower-paid occupations than previous cohorts, but they recovered to similar 
outcomes compared to previous cohorts 9-12 months after graduation. 

Graph 3: Unemployment rates by age group, EU27 

Source: EU-LFS [lfsa_urgaed]. 

Graph 4: Unemployment rates by age group and qualification level, EU27 

Source: EU-LFS [lfsa_urgaed]. 
Note: MS (medium level of schooling), HS (high level of schooling), GEN (general education), VOC (vocational 
education). 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

Data on EU youth unemployment suggest that youth unemployment rates reached an all-time low just 
before the pandemic and picked up moderately (more so than prime-age unemployment rates) in 2020 
and 2021 (Graph 3). These short-run impacts are more likely to reflect the direct impact of the recession 
on  labour  demand  than  the  disruptions  to  learning.  Youth  may  have  been  more  affected  by  the 
pandemic as they are more likely to work on temporary contracts, and in contact-intensive sectors such 
as  hospitality.  In  general  youth  unemployment  tends  to  be  more  sensitive  to  the  business  cycle  than 
prime-age unemployment.  

The  EU  labour  market  recovered  fast,  and  by  2022,  unemployment  rates  for  youth,  like  for  other  age 
groups, declined beyond their pre-pandemic level, bringing them to historically low levels. They remained 
slightly above their pre-pandemic level only for the 15-19 age group; while participation rates exceeded 
their  2019  levels  for  all  age  groups  considered,  but  even  more  so  for  young  age  than  for  prime  age 
cohorts.  

Demographic trends are likely to play an important role, as the EU27 working-age population is shrinking 
and young cohorts entering the labour market are significantly smaller than older cohorts retiring from 
the  labour  market.  In  all,  the  tight  labour  market  is likely  to  mask  or  counteract  the  possibly  negative 
impact of the pandemic associated with less human capital accumulation. Further research that relies on 
micro-level  data  would  be  required  to  assess  more  precisely  the  impact  of  the  pandemic  on  labour 
market outcomes through learning disruptions.  

Despite  improving  labour  market  outcomes  of  youth  over  the  post-COVID  recovery,  it  is  possible  that 
learning  gaps  will  have  an  impact  in  the  medium-  to  long-term.  Despite  good  employment  rates  of 
graduates, lower levels of hard and soft skills and reduced learning on the job can also affect long-term 
labour  market  outcomes  of  young  people.  For  example,  some  companies  in  the  UK  report  weaker 
performance  of  new  employees  who  graduated  during  the  pandemic  (O’Dwyer,  2023).  To  date, 
quantitative studies on the long-term economic impact of the learning deficits have drawn on simulation 
models, which are presented in the next section. 

Recently graduating cohorts, having completed most of their school years before the pandemic, are likely 
to  be  relatively  less  affected  by  school  closures  than  the  youngest  cohorts.  As  mentioned  before, 
economic  models  often  assume  either  linearly  decreasing  or  U-shaped  marginal  returns  to  education, 
with the latter suggesting the highest returns in primary and tertiary education (OECD, 2022). Students 
experiencing  interruptions  of  schooling  and learning  deficits  during  their first  years  at  school,  in which 
the  largest  learning  progress  is  commonly  recorded  (Bloom  et  al.,  2008;  Hill  et  al.,  2008),  could 
potentially  carry  the  resulting  learning  gaps  throughout  their  school  career  and  suffer  the  largest 
negative  impact in  the  long  term.  Nevertheless,  some catching  up  of  losses  and  compensation  effects 
from entire cohorts being affected by the learning loss remain a possibility.10 

Modelling long-term effects 

The negative impact of the COVID-19 pandemic on students’ learning is likely to affect macroeconomic 
outcomes  through  a  reduction  in  individual  lifetime  earnings  and  skilled  labour  supply.  It  is  well-
established that high-quality education leads to higher earnings, better health, longer working lives, and 
an  improved  quality  of  life.  In  addition,  a  skilled  labour  force  contributes  to  economic  growth  through 
increased productivity and innovation, although the benefits of investment in education usually only take 
effect with a long time lag (OECD, 2022).11  

Historical evidence demonstrates that school closures can have negative economic effects. Studies using 
natural  experiments,  such  as  teacher  strikes  and  natural  disasters,  find  lasting  economic  effects  for 

10  Some  channels  of  the  transmission  of  individual  learning  deficits  to  individual  income  have  been  muted  during  the 
pandemic  in  many  countries.  These  include  the  (partial)  catching-up  on  lost  parts  of  the  curriculum  as  well  as  the 
(endogenous) adjustment of exam scores in graduating cohorts that reduce the risk that school leavers would enter the 
labour market or tertiary education with worse formal qualifications than previous cohorts. 

11  Chapter  1  of  OECD  (2022)  gives  a  comprehensive  overview  of  the  role  of  human  capital  in  growth  models  and  the 
macroeconomic effects of investment in education. 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

affected individuals.12 Similarly, learning breaks during long summer holidays also have negative long-
term effects on individual economic outcomes (Kuhfeld et al., 2020). However, the COVID-19 pandemic 
situation  of  far-reaching  worldwide  interruptions  of  face-to-face  learning  with  possibilities  of  digital 
schooling is very different from previous situations of school closures. 

Research  draws  on  structural  models  and  projection  models  to  predict  the  economic  impact  of  the 
COVID-19 learning deficits. Structural models introduce a school closure shock in terms of a reduction of 
public  investment  in  education  in  calibrated  macroeconomic  frameworks.  Projection  models  use 
established correlations between educational and economic outcomes to simulate the effect of learning 
deficits  on  economic  growth.  All  estimates  presented  in  this  section  adhere  to  the  assumption  of  no 
policy change, i.e. they abstract from remedial measures, and they assume that learning deficits persist 
over  time.  Hence,  one  can  understand  these  results  as  conditional  (worst-case)  projections  in  the 
absence of policy support, which may deviate from the best guess about actual policy responses. 

Structural models 

Structural  models  predict  real  GDP  effects  of  a  1-year  learning  deficit  between  –0.5%  and  –3.4%  by 
2050, compared to a baseline without learning deficits. Structural models are a simplification of reality 
and  attempt  to  specify  (and  quantify)  the  main  transmission  channels  from  shocks  or  policies  to 
economic outcomes. The model parameters are estimated or calibrated to match empirical regularities 
of  interest.  Model  results  need  to  be  interpreted  against  the  background  of  underlying  theory  and 
assumptions and the parameter choices. Structural models allow to simulate counterfactuals (“what if”) 
that illustrate the dependence of transmission channels and net outcomes on structural features of the 
economy and policy responses. 

A  school  closure  shock  of  one  year  yields  average  losses  in  the  present  discounted  value  of  lifetime 
earnings  of  affected  children  of  2.1%  in  the  partial-equilibrium  life-cycle  model  with  overlapping 
generations  by  Fuchs-Schündeln  et  al.  (2022),  calibrated  to  US  data,  equivalent  to  welfare  losses  of 
about 1.2% of permanent consumption, and, when aggregated, to 3% of 2019 US GDP. In addition, this 
model  finds  large  differences  by  children’s  age  and  background,  with  younger  children  affected  more 
than  older  children,  and  children  from  the  most  disadvantaged  households  experiencing  four  times 
larger  welfare  losses  than  children  from  the  most  privileged  households.  This  approach  is  likely  to 
overestimate the impact of school closures by ignoring schooling through distance teaching, i.e., ignoring 
that  schooling  of  a  different  kind  continued  (to  various  degrees  depending  on  the  countries  and  age 
cohorts considered) during the physical closure of schools during the pandemic.  

A  similar  framework  (Jang  &  Yum,  2022),  calibrated  to  the  US  economy,  finds  negative  effects  for 
aggregate output for up to 15 decades, reaching a trough after 55 years, with an output decline at the 
trough of around 0.3%, 0.8% and 1.5% for full school closures of 0.5, 1 and 1.5 years respectively.13 In 
contrast  to  other  research,  this  model  suggests  larger  negative  effects  for  older  children,  whereas 
younger  children  are  (assumed  to  be)  able  to  make  up  for  pandemic-related  losses  over  the  longer 
remaining duration of their educational career.14 The model also produces a significant decrease in the 
intergenerational  mobility  of  educational  attainment,  as  children  become  more  dependent  on  parental 
input (and investment in private tutoring services) during school closures. Virtual schooling almost halves 
the aggregate impact of the learning deficits, but further increases inequality in their model. This result 

12  For  example,  students  affected  by  school  closures  in  Virginia  in  the  19th  century  had  a  lower  income  later  in  life 
compared  to  students  who  were  not  affected  by  school  closures  (Winfree,  2023)  and  teacher  strikes  in  Belgium  had 
lasting negative effects on educational outcomes of affected students (Belot & Webbink, 2010). 

13 The extreme persistence of the effect in Jang and Yum (2022), with output, labour and capital returning to the no-COVID 
baseline  only  after  150  years,  derives  from  the  importance  of  private  (parental)  investment  in  child  education.  This 
investment depends on parental human capital and income, which provides the basis for some intergenerational transfer 
of learning deficits in their model. 

14 The model-implied increase of individual losses with students’  age does not account for the theory of human capital 
accumulation,  which  supposes  self-productivity  in  human  capital  and  predicts  the  COVID-19  shock  to  affect  both  the 
current level of human capital and its future accumulation (Schady et al., 2023). 

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is in line with a structural model of skill formation, which suggests that the negative effects of school 
closures on human capital formation are highly unequal and persistent (Agostinelli et al., 2022). 

Simulations by Viana Costa et al. (2021) with a rich structural model (Penn Wharton Budget Model) on 
US data also suggest an impact of COVID-related learning deficits on labour productivity and output. In 
particular, the model simulation finds a negative impact on both variables, which is increasing over a 45-
year  horizon.  For  a  learning  deficit  of  one  year,  the  results  would  translate  to  a  2.9%  reduction  in 
productivity and a 3.4% drop in output in 2050 compared to a no-COVID baseline. This simulated output 
effect is significantly larger than the Jang and Yum (2022) result (0.5% output loss after 30 years for a 
1-year  learning  deficit).  The  difference  may  be  attributable  to  the  assumption  in  Viana  Costa  et  al. 
(2021)  of  separate  labour  productivity  effects  by  students’  socioeconomic  background.  Comparability 
with Fuchs-Schündeln et al. (2022) is limited by the fact that the latter do not report the dynamics of 
macro variables, but only present discounted aggregate losses. 

The  substantial differences  in  estimates  between  structural  models  are  due  to  the  strong  influence  of 
assumptions  and  modelling  choices  on  the  results.  In general,  the  simulations  consider  only  (different) 
subsets of potential transmission mechanisms and possible behavioural responses of students, parents, 
and teachers.  

Correlation-based projections 

Projection  models  suggest  real  GDP  effects  of  a  1-year  learning  deficit  of  up  to  4.7%  by  2050, 
compared to a baseline without learning deficits. This approach exploits regularities in the data, notably 
correlations  between  the  variable  of  interest  and  possible  determinants,  without  imposing  a  tight 
theoretical structure.  

Hanushek and Woessmann (2012) suggest that a reform bringing about an improvement in PISA scores 
by 25 points (equivalent to 25% of a standard deviation or 2/3 of the usual learning gain over a school 
year following Hill et al. (2008)) would bring about a 0.5 percentage point higher long-run growth rate in 
EU  Member  States,  or  a  cumulative  economic  gain  of  EUR  35  trillion  in  present  value  until  2090 
(corresponding  to  a  6.2%  increase  in  discounted  future  GDP).  The  authors  correlate  economic  growth 
with  measures  of  the  quantity  as  well  as  the  quality  of  education  in  cross-country  comparisons.  In 
particular,  they  regress  countries’  average  GDP  growth  on  student  test  scores  from  the  PISA  survey, 
years of schooling as well as initial GDP per capita. The estimated 'growth coefficient' of PISA test scores 
is  then  used  for  projections  of  future  growth,  in  the  spirit  of  endogenous  growth  models.15  While  the 
estimates  could  be  biased  by  endogeneity  or  reverse  causality,  the  authors  show  that  the  results  are 
robust  when  controlling  for  potentially  omitted  variables  (e.g.,  economic  institutions,  geographical 
location,  political  stability,  capital  stock,  and  population  growth).  Balart  et  al.  (2018)  find  that  the 
relationship  between  student  test  scores  and  economic  growth  is  smaller  but  remains  robust  when 
accounting for non-cognitive skills.  

By implication, and inverting signs, if the learning deficit in the EU equivalent to an 8-point decrease in 
PISA  scores  were  to  persist  and  would  apply  to  the  entire  population,  this  would  translate  into  a  0.2 
percentage point lower long-run growth rate. Given that the pandemic only implies a temporary negative 
shock  on  learning  outcomes,  which in  the long  run  would  affect  at  most  one  third  of  the working-age 
population16, the impact of the pandemic would be more contained, but still substantial.  

Hanushek and Woessmann (2020) simulate a temporary school closure of various lengths and find that 
a  one-year  school  closure  results  in  a  permanent  individual  income  loss  of  7.7%.  The  estimates  for 
lifetime income losses are the sum of lost individual returns to education. Hence, it is assumed that the 
income loss due to the learning deficits does not decrease if all students are affected simultaneously, 
which likely makes it an upper bound estimate.  

15 The authors also present an alternative projection model based on the neoclassical growth framework. The gains are 
somewhat smaller, but still substantial. In the neoclassical growth model, changes in test scores lead to higher steady-
state levels of income, but they do not permanently affect the growth rate. 

16  Assuming  that  the  12-16  age  cohorts  have  been  affected  by  schooling  under  COVID-19  conditions,  and  assuming  a 
working life of around 50 years.   

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

The same paper suggests aggregate losses of 4.3% of GDP on average for the remainder of the century, 
equivalent  to  a  cumulative  GDP  loss  of  the  magnitude  of  200%  of  current  GDP  by  2100,  drawing  on 
data  from  a  sample  of  50  lower-middle  to  high-income  economies.  By  2100,  the  reduction  in  annual 
GDP would amount to 7.5% compared to a baseline without learning deficits, assuming 80 years with a 
lower achieving labour force (corresponding to the average life expectancy of somebody born in 2020); 
by 2050, real GDP would be lower by around 4.7% compared to the no-loss benchmark.17  

To  arrive  at  those  estimates,  the  authors  assume  that  annual  economic  growth  increases  by  about  2 
percentage  points  per  standard  deviation  in  educational  achievement  of  the labour  force,  an  effect  of 
similar magnitude as the assumption used by Hanushek and Woessmann (2012). The estimates assume 
the  complete  loss  of  a  school  year,  neglecting  mitigating  effects  of  distance  learning.  Scaling  the 
numbers  to  a learning loss of 20%  of  a  school  year  would  imply  a  GDP  level 0.9% below  baseline  by 
2050.18 

A study that uses a similar approach for the US, as it correlates US-specific standardised test outcomes 
to  long-term  growth,  but  considers  in  addition  the  effects  of  school  drop-outs,  produces  smaller 
estimates, i.e. –1.1% to –1.8% GDP level reduction by 2040 for a one-year learning deficit (Dorn et al., 
2020).19 

Recent  work  by  the  OECD  finds  that  expected  productivity  losses  are  initially  small,  but  build  up  over 
time and peak after 45 years when affected cohorts are in the older part of the labour force, with an 
overall productivity (TFP) loss of 1.1% at the peak for a 1-year school closure (de la Maisonneuve et al., 
2022).20  These  negative  effects  diminish  when  affected  cohorts  gradually  retire  as  of  2068  and 
disappear when all affected cohorts have retired by 2083. The timing of the peak impact derives from 
the assumption that all cohorts are affected equally, with no possibility for younger students to catch up 
learning  deficits.  If  older  students were  affected  more  than younger  students, who  have  more  time  to 
recover from the shock, the trough would be at an earlier point in time, when most affected cohorts are 
at prime working age.  

The  estimated  learning  deficits  for  the  EU  of  approximately  10%  of  a  standard  deviation,  or  1/5  of  a 
school year (see above) come closest to the lower bound impact of a 12-week school closure in de la 
Maisonneuve et al. (2022), which they translate to a 0.2% reduction in overall human capital by 2067, 
when all affected cohorts have entered the labour force. This reduction in human capital is predicted to 
cause  productivity  losses  until  the  retirement  of  the  last  affected  cohort  in  2083,  and  to  peak  at  a 
productivity loss (compared to a no-COVID-19 baseline) of 0.4% in 2067.  

Table 2 summarises the estimated effects of the COVID-19 learning deficits on economic output from 
both structural models and projection models. 

17 The value for 2050 is taken from the comparison in de la Maisonneuve et al. (2022). 

18 A simple back-of-the-envelope calculation provides somewhat smaller magnitudes. Taken the value from Jones (2002) 
of an additional year of schooling raising labour productivity by 7%, missing 1/5 of a year of learning progress implies a 
productivity  loss  by  1%  for  the  (future)  workers  concerned.  As  the  age  cohorts  concerned  will  account  at  maximum  for 
around one third of the labour force in the future, this would suggest aggregate income losses peaking at around 0.3%. 
The survey by Sianesi and van Reenen (2003) reports effects of a one-year increase of average education on per capita 
output  of  3-6%  in  a  neo-classical  growth  specification,  or  a  1  pp  increase  in  the  growth  rate  according  to  endogenous 
growth theories. 

19 See the comparison in de la Maisonneuve et al. (2022).  

20 The model uses a new measure of the human capital stock and multivariate productivity regressions (Égert et al., 2022). 
The new measure is composed of the cohort-weighted average of past student test scores and mean years of schooling, 
to reflect both the quality and quantity of education of the working-age population.  The authors compute the effect of 
the pandemic on human capital as the sum of population-weighted averages for each of the 16 cohorts of school-aged 
children. The effect on productivity is derived from regressions, which (controlling also for other factors) suggest that a 1 
percent decrease in human capital is associated with more than 2 percent fall in the long-term total factor productivity 
(TFP) level. 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

Table 2: Estimated effects of a 1-year learning deficit on economic output 

Approach and sample 

Dependent 
variables 

Main results 

­ Partial-equilibrium life-cycle model 

with overlapping generations 

Lifetime earnings 
of affected children 

­ Calibrated to US data 

Structural models 

Fuchs-
Schündeln et al. 
(2022) 

Jang & Yum 
(2022) 

Viana Costa et 
al. (2021) 

Projection models 

Hanushek & 
Woessmann 
(2020) 

Dorn et al. 
(2020) 

de la 
Maisonneuve et 
al. (2022) 

­ General equilibrium model with 
overlapping generations (OLG) 

­ Calibrated to US data 
­ Younger students are assumed to 

catch up over time 

­ OLG macro model with rich 

heterogeneity across households in 
which an individual’s labour 
productivity changes throughout 
lifetime and is affected by learning 
deficits 

­ Calibrated to US data 

­ Regression of countries’ average GDP 
growth on student test scores (PISA), 
years of schooling and initial GDP per 
capita; estimated 'growth coefficient' 
used in endogenous growth model 
(2% higher growth per standard 
deviation in educational achievement) 

­ Data from OECD countries and 

emerging economies 

­ Hanushek & Woessmann (2008) 

correlation of academic achievement 
to GDP growth, combined with impact 
of school drop-outs due to the 
pandemic 

­ Simulation for the US 
­ New measure of the human capital 
stock (cohort-weighted average of 
past student test scores and mean 
years of schooling of current cohorts) 
and multivariate productivity 
regressions (1-percent decrease in 
human capital associated with >2-
percent fall in long-term TFP) 

­ Assumes 16 cohorts to be affected 
equally, without catching up of 
younger students 

­ Sample of OECD countries 

Range of 
macroeconomic 
aggregates 

Range of 
macroeconomic 
aggregates 

Present discounted 
earnings loss of 2.1% for 
affected children, on 
aggregate equivalent to 
3% of 2019 US GDP 

Reduction of annual 
output during several 
decades with trough in 
2080 at -0.7% (-0.5% in 
2050) 

Reduction of annual 
output, worsening during 
several decades until 
forecast horizon in 2056 
(GDP effect -3.4% and 
labour productivity –2.9% 
in 2050) 

Lifetime income 
Output growth 

GDP –7.5% in 2100  
(-4.7% by 2050) 
compared to no-COVID-
19 baseline 

Output in 2040 

Productivity (TFP) 

Output reduction of 1.1-
1.8% of GDP in 2040 (no 
results reported for other 
years) 

Productivity losses until 
expected retirement of 
affected cohorts in 2083, 
peaking in 2067 at -1.1% 
TFP compared to no-
COVID-19 baseline 

Note:  The  presented  estimates  are  specific  for  the  COVID-19  pandemic,  as  they  assume  all  cohorts  that  are  in 
school during the learning shock to be affected. For comparison purposes, reported effects for different lengths of 
school  closure  are  proportionally  translated  into  a  learning  deficit  of  one  school  year.  Based  on  the  estimates  of 
learning deficits in the EU of, on average, 20% of a school year, the economic impact for the EU could be scaled to 
20% of the numbers presented in this table. 

14 

 
 
 
 
 
 
 
 
 
European Economy Economic Briefs                                                           Issue 078 | January 2024 

Studies  based  on  data  from  non-EU  OECD  countries  could  overestimate  the  potential  economic  impact  of 
learning losses for EU Member States. Even after considering that learning deficits were, on average, smaller 
in EU countries than non-EU OECD countries due to differences in the length of school closures, the level of 
digitalisation,  and  the  quantity  and  quality  of  distance  teaching,  countries  may  differ  in  the  channels  of 
transmission from lower human capital to economic outcomes.  

Countries also vary considerably with respect to the individual returns to skills, with significantly higher returns 
to  skills  found  in  the  United  States  compared  to  European  countries  (Hanushek  et  al.,  2015).  Contributing 
factors  could  be  higher  union  density,  stricter  employment  protection  legislation,  and  larger  public  sectors, 
which are related to lower wage inequality and thus lower individual returns to skills (Hanushek et al., 2015), 
inversely  implying  a  lower  economic  impact  of  decreasing  skills.  Therefore,  studies  based  on  US  data  could 
overestimate  the  economic  impact  of  learning  deficits  for  the  EU,  which  may  furthermore  differ  widely 
between EU Member States. Finally, differences in remedial policies to compensate learning deficits, which are 
not  accounted  for  by  any  of  the  estimates  presented,  could  diversify  the  actual  economic  impact  across 
countries in coming years. 

CONCLUSIONS 

The evidence on the impact of the COVID-19 pandemic on educational outcomes suggests significant learning 
deficits  for  school-aged  children  in  several  EU  Member  States,  which  equal  approximately  20%  of  a  school 
year’s learning progress on average. Importantly, large inequalities in the learning deficits, driven particularly 
by students’ socioeconomic status, could increase disparities in socio-economic outcomes.  

Labour market  outcomes  of  the  2020 graduating  age  cohort  seem  to be  resilient  at  the  current  juncture  of 
tight labour markets and simulations suggest small productivity losses for the coming years. A larger effect 
can be expected in the long term, peaking when all affected students will have entered the labour market, by 
the second half of the 21st century. The estimated long-term real GDP effects for an average learning deficit 
of approximately 1/5 of a school year in the EU range from -0.1% to -1% by 2050, compared to a baseline 
without any learning deficits and assuming no effective remedial action is taken. Realisations are likely to fall 
closer to the lower bound of this range in absolute value terms (-0.1%), since upper bound estimates rest on 
assumptions  of  a  very  strong  and  persistent  deterioration  of  the  quality  of  the  labour  force,  assuming  no 
policy change and no or little scope for compensating losses over time. Jack & Oster (2023) note that this is a 
very strong assumption, as the unprecedented school closures will likely trigger unprecedented compensation 
efforts, making it difficult to extrapolate short-term results on long-term consequences. 

The estimates of learning deficits provided in this paper are based on available studies for a small selection of 
EU Member States and have limitations. Currently available study results are possibly biased by the selection 
of non-representative samples, missing data and potential measurement errors (Betthäuser et al., 2023). To 
reduce bias, the review of the empirical literature discussed in this paper excluded studies with small sample 
sizes, convenience samples, and absence of statistical adjustment for confounding factors. 

At  the  same  time,  first  internationally  comparable  data  from  the  PIRLS  2021  reading  assessment  for  4th 
graders showed a decline of learning outcomes of a magnitude similar to the estimates put forward in this 
paper, reinforcing previously recorded negative time trends. Computing representative estimates for different 
subjects  and  age  groups  that  are  comparable  across  countries  will  only  be possible  after  the publication  of 
forthcoming comparative international studies, such as evaluations of the OECD’s PISA 2022 for mathematics, 
reading and science, IEA TIMMS 2023 for mathematics and science, and IEA ICILS 2023 for digital skills.21  

Monitoring the evolution of student achievement will be crucial to determine the persistence of learning losses 
over time. It will provide evidence on whether the affected age cohorts are able to catch up over the duration 

21 With the great advantage of providing comparable indicators, these international assessments of student achievement 
come with  the  disadvantage  of being  published  with  a  delay.  Another  drawback  of  these  tests is  the  small selection  of 
subjects and grade years covered. For example, OECD’s PISA, which tests 15-year-old students, includes varying optional 
components, resulting in a limited sample of countries. Also, some countries may choose not to participate. 

15 

 
 
 
 
 
 
European Economy Economic Briefs                                                           Issue 078 | January 2024 

of  their  remaining  educational  career,  or  whether,  to  the  contrary,  learning  deficits  are  accumulating  and 
increasing over time. For that matter, monitoring efforts at the national and regional levels remain important 
complements to comparative international studies, as local standardised tests often provide more timely and 
more  detailed  insights  than  large-scale  international  assessments.  In  addition  to  changes  in  student 
achievement,  a  wider  range  of  student  outcomes,  which  are  not  yet  measured  in  a  systematic  way  (e.g., 
mental health), could be considered when modelling the macroeconomic impact of the school closures in the 
future. 

Compensatory  policies,  such  as  summer  schools  or  tutoring  programmes,  have  been  shown  to  mitigate  the 
learning deficits caused by the COVID-19 pandemic (De Witte & François, 2023). Tutoring, that is instruction in 
small groups, is a well-researched education method, which has proven to deliver large learning effects (e.g., 
Nickow et al, 2020; Dietrichson et al., 2017). Following the comparison of effect sizes made by Goldhaber et 
al.  (2023),  on  average,  20%  of  students  would  need  to  be  offered  high-dosage  tutoring  (i.e.,  in  groups  of 
fewer than four students three to five times per week for at least 30 minutes) to compensate the estimated 
EU  learning  deficit.  De  Witte  &  François  (2023)  further  recommend  a  stronger  focus  of  the  curriculum  and 
investments on the digitalisation of education, including by strengthening internet connectivity, access to ICT 
tools, and the professional development of teachers in this area. 

Given  the  rise  in  inequality,  many  remedial  programmes  focus  on  disadvantaged  students.  While  this  is 
important to ensure equity, inclusion and fairness in education, teachers also note that the strongest students 
are often neglected when inequality in the classroom is high. Gambi & De Witte (2023) find that, in Flemish 
schools,  the  weak  performance  of  the  best-performing  students  is  contributing  to  an  increase  in  learning 
deficits over time. As both basic skills and top skills are important drivers of economic growth, it is important 
to design compensatory policies in a way that gives learning opportunities to all students. 

As all remedial actions require staff, addressing the teacher shortages currently observed in many EU Member 
States will be crucial to reverse the negative trend in learning outcomes. Teachers are playing an important 
role in reducing the COVID-19 learning deficits, since quality teaching is a key factor in improving educational 
outcomes. For the Flemish region of Belgium, Gambi & De Witte (2023) find that average learning deficits in 
2022  were  larger  in  schools  with  high  shares  of  teacher  shortages.  Recruitment  and  retention  of  quality 
teachers can be ensured through financial incentives, work-stress reduction, teacher training and opening of 
alternative routes to teacher qualifications (Fack et al., 2023; de Witte et al., 2023). 

On a positive note, the COVID-19 pandemic has been speeding up the digital transition in schools and given a 
stimulus  to  experiment  with  new  ways  of  teaching.  While  access  to  technology  alone  is  insufficient  to 
contribute to skill formation, computer-assisted learning (software designed to improve academic skills) and 
technology-enabled  behavioural  interventions  (e.g.  text  messages  to  guide  students  towards  desirable 
behaviours)  are  effective  in  improving  educational  outcomes,  when  implemented  by  qualified  teachers 
(Escueta et al., 2020).  

Going forward, the experiences gained during the pandemic and the progress in digitalisation can be used to 
improve  the  quality  of  education  in  the  EU.  In  the  context  of  the  Recovery  and  Resilience  Facility,  Member 
States  have  planned  measures  worth  EUR  51  billion  to  improve  ‘general  education’  and  ‘early  childhood 
education  and  care’,  including  investment  in  digital  education,  and,  for  some  Member  States,  targeted 
measures to mitigate learning deficits caused by the COVID-19 pandemic. The European Commission is also 
working with Member States through a recently created ‘Learning Lab on Investing in Quality Education and 
Training’22 to help them design policies and programmes which can make the EU educational systems more 
effective and equitable. 

22 https://education.ec.europa.eu/focus-topics/improving-quality/learning-lab. 

16 

 
 
 
 
 
 
 
 
European Economy Economic Briefs                                                           Issue 078 | January 2024 

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European Economy Economic Briefs                                                           Issue 078 | January 2024 

ANNEX 1 – STUDIES ON COVID-19 LEARNING DEFICITS FROM 
EU MEMBER STATES 

Table A.1 PIRLS 2021 results from EU Member States (Reading in Grade 4) 

Member State 

Change from 2016 to 2021 

Change from 2011 to 2016 

Austria 

-11 

Belgium (Flemish) 

-14 

Belgium (French) 

no significant change 

no data 

Bulgaria 

Czech Republic 

Denmark 

Finland 

France 

Germany 

Italy 

Netherlands 

Poland 

Portugal 

Slovakia 

Slovenia 

Spain 

Sweden 

Average 

no significant change 

no significant change 

no significant change 

no significant change 

no significant change 

no significant change 

no data 

no significant change 

no significant change 

-12 

-8 

-17 

-13 

-11 

-18 

-16 

-8 

-23 

-7 

-12 

-10 PIRLS points  
≈ 0.1 standard deviations  
≈ 25% of a regular school year 

Note: Results as reported in IEA's Progress in International Reading Literacy Study - PIRLS 2021. Changes are 
expressed  in  PIRLS  scale  points,  which,  at  the  first  assessment  in  2001,  was  scaled  to  have  500  points  as 
centre  point  and  a  standard  deviation  of  100.  Downward  (upward)  pointing  arrows  indicate  a  significant 
negative (positive) change in test scores compared to the previous edition of the assessment. For EU Member 
States not included in this table, which participated in 2021, no comparable data was available from previous 
assessments. Bold arrows in the left column indicate that a previously positive or constant trend (2011-2016) 
has been reversed to a downward trend (2016-2021). 

20 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
European Economy Economic Briefs                                                           Issue 078 | January 2024 

Table A.2 Learning deficits measured in 2020 in EU Member States 

Member 
State 
Belgium 

Belgium 

Germany 

Germany 

Germany 

Germany 

Germany 

Germany 

Italy 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Netherlands 

Age  Grade  Subject 
11 

Mathematics 

6 

Learning deficit 
(share of a 
regular school 
year) 
-0.40 

Source 
Maldonado & De Witte (2021) 

11 

9 

9 

10 

10 

10 

10 

8 

6 

7 

7 

8 

8 

8 

8 

9 

9 

9 

9 

10 

10 

10 

10 

10 

10 

11 

11 

11 

11 

12 

6 

4 

4 

5 

5 

5 

5 

3 

1 

2 

2 

3 

3 

4 

4 

4 

4 

5 

5 

5 

5 

5 

5 

6 

6 

6 

6 

7 

7 

7 

Reading 

-0.50 

Maldonado & De Witte (2021) 

Mathematics 

-0.08 

Depping et al. (2021) 

Reading 

0.00 

Depping et al. (2021) 

Mathematics 

-0.17 

Schult et al. (2022a) 

Mathematics 

-0.05 

Depping et al. (2021) 

Reading 

Reading 

-0.18 

0.13 

Schult et al. (2022a) 

Depping et al. (2021) 

Mathematics 

-0.47 

Contini et al. (2022) 

Mathematics 

-0.33 

Haelermans et al. (2022) 

Mathematics 

-0.32 

Haelermans et al. (2022) 

Reading 

-0.16 

Haelermans et al. (2022) 

Mathematics 

-0.42 

Haelermans et al. (2022) 

Reading 

-0.41 

Haelermans et al. (2022) 

Mathematics 

-0.13 

Engzell et al. (2021) 

Reading 

-0.12 

Engzell et al. (2021) 

Mathematics 

-0.66 

Haelermans et al. (2022) 

Reading 

-0.29 

Haelermans et al. (2022) 

Mathematics 

-0.18 

Engzell et al. (2021) 

Reading 

-0.21 

Engzell et al. (2021) 

Mathematics 

-1.53 

Schuurman et al. (2021) 

Mathematics 

-0.82 

Haelermans et al. (2022) 

Reading 

Reading 

-0.62 

-0.50 

Schuurman et al. (2021) 

Haelermans et al. (2022) 

Mathematics 

-0.24 

Engzell et al. (2021) 

Reading 

-0.20 

Engzell et al. (2021) 

Mathematics 

-0.64 

Schuurman et al. (2021) 

Reading 

0.56 

Schuurman et al. (2021) 

Mathematics 

-0.14 

Engzell et al. (2021) 

Reading 

-0.17 

Engzell et al. (2021) 

Mathematics 

-0.62 

Schuurman et al. (2021) 

21 

 
 
 
 
European Economy Economic Briefs                                                           Issue 078 | January 2024 

Netherlands 

12 

Sweden 

Sweden 

Sweden 

Sweden 

Sweden 

Sweden 

7 

7 

8 

8 

9 

9 

Average 2020 

7 

1 

1 

2 

2 

3 

3 

Reading 

Reading 

Reading 

Reading 

Reading 

Reading 

Reading 

-0.51 

0.12 

0.45 

0.05 

0.12 

0.18 

0.21 

-0.24 

Schuurman et al. (2021) 

Hallin et al. (2022) 

Hallin et al. (2022) 

Hallin et al. (2022) 

Hallin et al. (2022) 

Hallin et al. (2022) 

Hallin et al. (2022) 

Note: Estimates from EU Member States selected by Betthäuser et al. (2023), sorted by Member State, age, 
grade, and subject. An average learning progress of 0.4 standard deviations per year is assumed (Hill et al., 
2008). 

Table A.3 Learning deficits measured in 2021 in EU Member States 

Member 
State 
Belgium 

Age  Grade  Subject 
11 

Mathematics 

6 

Learning deficit 
(share of a regular 
school year) 
-0.35 

Belgium 

11 

Denmark 

Denmark 

Denmark 

Denmark 

Denmark 

Denmark 

Denmark 

Germany 

Germany 

Germany 

Italy 

Italy 

Italy 

Italy 

Italy 

Italy 

Italy 

Italy 

Italy 

7 

8 

9 

11 

11 

13 

13 

10 

10 

11 

10 

10 

10 

10 

13 

13 

13 

13 

18 

6 

2 

3 

4 

6 

6 

8 

8 

5 

5 

4 

5 

5 

5 

5 

8 

8 

8 

8 

Reading 

Reading 

-0.75 

0.20 

Mathematics  0.05 

Reading 

0.22 

Source 
Gambi & De Witte (2021) 

Gambi & De Witte (2021) 

Birkelund et al. (2021)  

Birkelund et al. (2021)  

Birkelund et al. (2021)  

Mathematics 

-0.03 

Birkelund et al. (2021)  

Reading 

0.13 

Birkelund et al. (2021)  

Mathematics 

-0.03 

Birkelund et al. (2021)  

Reading 

-0.11 

Birkelund et al. (2021)  

Mathematics 

-0.18 

Schult et al. (2022b) 

Reading 

Reading 

-0.04 

-0.35 

Schult et al. (2022b) 

Ludewig et al. (2022) 

Mathematics 

-0.36 

Bazoli et al. (2022) 

Mathematics  0.09 

Borgonovi & Ferrara (2022) 

Reading 

Reading 

0.08 

0.14 

Mathematics 

-0.73 

Borgonovi & Ferrara (2022) 

Bazoli et al. (2022) 

Bazoli et al. (2022) 

Mathematics 

-0.35 

Borgonovi & Ferrara (2022) 

Borgonovi & Ferrara (2022) 

Bazoli et al. (2022) 

Bazoli et al. (2022) 

Reading 

Reading 

-0.19 

0.00 

13 

Mathematics 

-0.68 

22 

 
 
 
 
 
European Economy Economic Briefs                                                           Issue 078 | January 2024 

Italy 

18 

13 

Reading 

-0.79 

Bazoli et al. (2022) 

Netherlands  8 

Netherlands  8 

Netherlands  9 

Netherlands  9 

Spain 

Spain 

Spain 

Sweden 

Sweden 

Sweden 

13 

13 

13 

7 

8 

9 

Average 2021 

1-5 

1-5 

2-5 

2-5 

8 

8 

8 

1 

2 

3 

Mathematics 

-0.31 

Haelermans et al. (2021) 

Mathematics 

-0.26 

Haelermans (2021) 

Reading 

Reading 

-0.43 

-0.17 

Haelermans et al. (2021) 

Haelermans (2021) 

Mathematics 

-0.20 

Arenas & Gortazar (2022) 

Reading 

Reading 

Reading 

Reading 

Reading 

-0.14 

0.00 

0.14 

0.12 

0.12 

-0.16 

Arenas & Gortazar (2022) 

Arenas & Gortazar (2022) 

Hallin et al. (2022) 

Hallin et al. (2022) 

Hallin et al. (2022) 

Note: Estimates from EU Member States selected by Betthäuser et al. (2023), sorted by Member State, age, 
grade, and subject. An average learning progress of 0.4 standard deviations per year is assumed (Hill et al., 
2008). 

REFERENCES OF STUDIES LISTED IN THE TABLES 

Arenas, A., & Gortazar, L. (2022), "Learning loss One Year After School Closures", Esade Working Paper. 

Bazoli, N., Marzadro, S., Schizzerotto, A. & Vergolini, L. (2022), "Learning Loss and Students’ Social Origins 
during the COVID-19 Pandemic in Italy", FBK-IRVAPP Working Papers 3. 

Birkelund, J. F. & Karlson, K. B. (2022), "No evidence of a major learning slide 14 months into the COVID-
19 pandemic in Denmark", European Societies https://doi.org/10.1080/14616696.2022.2129085. 

Borgonovi, F., & Ferrara, A. (2022), "The effects of COVID-19 on inequalities in educational achievement 
in Italy.", Preprint at SSRN https://doi.org/10.2139/ssrn.4171968.  

Contini,  D.,  Di  Tommaso, M.  L.,  Muratori,  C.,  Piazzalunga,  D.  &  Schiavon, L.  (2022),  "Who  lost  the  most? 
Mathematics  achievement  during  the  COVID-19  pandemic",  The  B.E.  Journal  of  Economic  Analysis  & 
Policy 22, 399–408.  

Depping,  D.,  Lücken,  M.,  Musekamp,  F.  &  Thonke,  F.  (2021),  "Neue  Ergebnisse  und  Überblick  über  ein 
dynamisches Forschungsfeld. In Schule während der Corona-Pandemie", (eds Fickermann, D. & Edelstein, 
B.) 51–79, Münster & New York: Waxmann. 

Engzell, P., Frey, A., & Verhagen, M.D. (2021), "Learning loss due to school closures during the COVID-19 
pandemic", Proceedings of the National Academy of Sciences, 118(17), e2022376118. 

Gambi,  L.,  &  De  Witte,  K.  (2021),  "The  resiliency  of  school  outcomes  after  the  COVID-19  pandemic. 
Standardised test scores and inequality one year after long term school closures", FEB Research Report 
Department of Economics. 

Haelermans,  C.  (2021),  "Learning  Growth  and  Inequality  in  Primary  Education:  Policy  Lessons  from  the 
COVID-19 Crisis", The European Liberal Forum (ELF)-FORES. 

23 

 
 
 
 
 
 
 
European Economy Economic Briefs                                                           Issue 078 | January 2024 

Haelermans,  C.  et  al.  (2021),  "A  Full  Year  COVID-19  Crisis  with  Interrupted  Learning  and  Two  School 
Closures: The Effects on Learning Growth and Inequality in Primary Education", Maastricht Univ., Research 
Centre for Education and the Labour Market (ROA). 

Haelermans,  C.  et  al.  (2022),  "Sharp  increase  in  inequality  in  education  in  times  of  the  COVID-19-
pandemic", PLoS ONE 17, e0261114. 

Hallin,  A.  E.,  Danielsson,  H.,  Nordström,  T.  &  Fälth,  L.  (2022),  "No  learning  loss  in  Sweden  during  the 
pandemic  evidence  from  primary  school  reading  assessments",  International  Journal  of  Educational 
Research 114, 102011.  

Ludewig,  U.  et  al.  (2022),  "Die  COVID-19  Pandemie  und  Lesekompetenz  von  Viertklässler*innen: 
Ergebnisse  der  IFS-Schulpanelstudie  2016–2021",  Institut  für  Schulentwicklungsforschung,  Univ. 
Dortmund. 

Maldonado,  J.E.,  &  De  Witte,  K.  (2022),  "The  effect  of  school  closures  on  standardised  student  test 
outcomes", British Educational Research Journal, 48(1), 49-94. 

Schuurman, T. M., Henrichs, L. F., Schuurman, N. K., Polderdijk, S. & Hornstra, L. (2021), "Learning loss in 
vulnerable student populations after the first COVID-19 school closure in the Netherlands", Scandinavian 
Journal of Educational Research. https://doi.org/10.1080/00313831.2021.2006307. 

Schult,  J.,  Mahler,  N.,  Fauth,  B.,  &  Lindner,  M.A.  (2022a),  "Did  students  learn  less  during  the  COVID-19 
pandemic?  Reading  and  mathematics  competencies  before  and  after  the  first  pandemic  wave",  School 
Effectiveness and School Improvement, 1-20. 

Schult,  J.,  Mahler,  N.,  Fauth,  B.,  &  Lindner,  M.A  (2022b),  "Long-Term  Consequences  of  Repeated  School 
Closures  During  the  COVID-19  Pandemic  for  Reading  and  Mathematics  Competencies",  Frontiers  in 
Education. 13:867316. https://doi.org/10.3389/feduc.2022.867316. 

24 

 
 
 
 
 
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