Forecasting progress towards the EU-level targets of the European Education Area : future trends and qualitative scenarios to facilitate target resetting for the 2026-30 cycle of the strategic framework for European cooperation in education and training.
Date: 2023-11-22
Institutions: Joint Research Centre
Subjects: adult, cooperation in the field of education, dropout, foresight, higher education, impact study, labour market, level of education, recognition of diplomas, report, vocational training, young person

ISSN 1831-9424 

JRC SCIENCE FOR POLICY REPORT 

Forecasting progress towards the EU-level 
targets of the European Education Area 

Future trends and qualitative scenarios to facilitate target resetting for the 
2026-30 cycle of the Strategic Framework for European Cooperation in 
Education and Training 

Bertoletti, A. 

2023 

 EUR 31623 EN 

 
  
 
 
This publication is a  Science  for  Policy  report  by  the  Joint  Research Centre  (JRC),  the  European  Commission’s  science  and knowledge 
service. It aims to provide evidence-based scientific support to the European policymaking process. The contents of this publication do 
not necessarily reflect the position or opinion of the European Commission. Neither the European Commission nor any person acting on 
behalf of the Commission is responsible for the use that might be made of this publication. For information on the methodology and 
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any opinion whatsoever on the part  of the European Union concerning the legal status of any country, territory, city or area or of its 
authorities, or concerning the delimitation of its frontiers or boundaries. 

Contact information  

Name: Alice Bertoletti 

Address: C. Inca Garcilaso, 3, 41092 Sevilla 

Email: Alice.BERTOLETTI@ec.europa.eu 

EU Science Hub 
https://joint-research-centre.ec.europa.eu 

JRC134039 

EUR 31623 EN 

PDF  

ISBN 978-92-68-06665-2  

ISSN 1831-9424  

doi:10.2760/146529   KJ-NA-31-623-EN-N 

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How to cite this report: Bertoletti, A., Forecasting progress towards the EU-level targets of the European Education Area, Publications Office 
of the European Union, Luxembourg, 2023, doi:10.2760/146529, JRC134039. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Executive Summary .................................................................................................................................................3 

1 

Introduction .......................................................................................................................................................4 

2  Empirical Strategy and data...............................................................................................................................6 

2.1  Early leavers form education and training ................................................................................................7 

2.1.1  Model specification: ELET ..............................................................................................................7 

2.1.2 

Selection of indicators and data description: ELET ........................................................................7 

2.2  Tertiary education attainment ..................................................................................................................9 

2.2.1  Model Specification: TEA ...............................................................................................................9 

2.2.2 

Selection of indicators and data: TEA ..........................................................................................10 

3  Forecast results ...............................................................................................................................................11 

3.1  ELET results .............................................................................................................................................11 

3.2  TEA results...............................................................................................................................................13 

4  Discussion on future scenarios ........................................................................................................................16 

4.1  The potential effects of Covid-19 pandemic ...........................................................................................16 

4.2  The potential impact of Russia’s invasion of Ukraine .............................................................................18 

5  Conclusions ......................................................................................................................................................21 

References ............................................................................................................................................................22 

List of abbreviations and definitions .....................................................................................................................26 

List of figures .........................................................................................................................................................26 

List of tables ..........................................................................................................................................................26 

Annexes .................................................................................................................................................................27 

Annex 1. Trends in the employment and income prospects ...........................................................................27 

Annex 2. Descriptive statistics .........................................................................................................................28 

Annex 3. Country-level forecasts .....................................................................................................................29 

Annex 4. Robustness checks ............................................................................................................................31 

i 

 
 
 
 
 
 
 
Abstract  

This report examines future trajectories in early school leavers and tertiary education attainment in the EU up to 
2030, based on the empirical model proposed by Dragomirescu-Gaina and Weber (2013). The forecasts indicate 
that the EU is likely to achieve the 45% target for tertiary education attainment by a considerable margin, while 
the achievement of the 9% target for early school leavers is more uncertain given the high heterogeneity across 
Member States. Moreover, the recent disruption of Covid-19 and Russia's invasion of Ukraine pose challenges in 
predicting these indicators. The report qualitatively discusses how the disruption caused by Covid-19 may have 
mixed impacts on educational attainment, while highlighting that the invasion and the subsequent energy crisis 
may  lead  to  educational  inequalities.  The  finding  may  inform  policy  makers  on  possible  adjustments  of  the 
educational targets and draw attention to the importance of monitoring these indicators. 

1 

 
 
 
 
 
 
 
 
Acknowledgements 

I would like to thank Zbigniew Karpiński and colleagues from DG EAC for their helpful and constructive comments 
on earlier versions of this report. 

Authors 

Alice Bertoletti, European Commission - Joint Research Centre (JRC), Edificio EXPO, calle Inca Garcilaso, 3, 41092, 
Seville, Spain. 

2 

 
 
 
 
 
Executive Summary 

This report examines future trends in early leavers from education and training (ELET) and tertiary education 
attainment  (TEA)  in  the  European  Union  (EU-27).  These  indicators  are  crucial  for  monitoring  educational 
attainment and promoting successful transitions of young adults into the labour market. The estimates of the 
future trajectories of ELET and TEA up to 2030 are based on the empirical model proposed by Dragomirescu-
Gaina and Weber (2013). These estimates serve as a quantitative baseline to start a more qualitative discussion 
on the future scenarios of educational attainment, considering the potential impact of Covid-19 disruption and 
Russia’s invasion of Ukraine. 

The Council Resolution on a strategic framework for European cooperation in education and training towards 
the  European  Education  Area  and  beyond  (2021-2030),  adopted  in  February  2021,  lays  out  the  educational 
targets for the EU. According to these targets, the EU aims to achieve a share of early leavers below 9% and a 
tertiary educational attainment rate exceeding 45% for the entire region. 

According to the empirical estimates reported in this report, the EU-27 countries are likely to achieve an average 
value  of  early  leavers  from  education  and  training  lower  than  9%,  which  is  the  EU  target.  However,  there  is 
considerable  uncertainty  associated  with  this  forecast,  and  there  is  significant  heterogeneity  across  Member 
States. Some countries have already achieved the 9% target, while others reported high levels of ELET and are 
less likely to go below 9% in 2030. In contrast to ELET, the EU is expected to exceed the 45% target for tertiary 
education attainment in 2030 by a considerable margin. However, also in this case, some countries are at risk of 
not achieving the target value by 2030. 

The  estimates  suggest  that  the  impact  of  Covid-19  did  not  have  a  significant  short-term  effect  on  the  two 
indicators, ELET and TEA. However, the analysis of the current literature indicates that Covid-19 disruption is 
linked to factors that can potentially have both negative and positive impacts on educational attainment among 
the  population  of  EU  countries.  The  bidirectional  effect  of  Covid-19  on  educational  attainment  makes  it 
challenging  to  predict  its  influence  on  ELET  and  TEA  indicators  over  the  next  decade.  Nonetheless,  the  most 
significant consequence appears to be the disparities in the distribution of these effects, with potential increased 
educational inequality among students. 

Russia’s invasion of Ukraine may generate potential negative consequences on the values of early leavers from 
education and tertiary education attainment. Due to the migration crisis caused by the invasion, there has been 
a notable influx of Ukrainian students into EU educational systems, and these students may require additional 
support to address their unique learning, social, and psychological needs. Unfortunately,  Russian invasion may 
cause investments in education to be redirected to address the crisis, potentially leading to a reprioritisation of 
investments  in  the  education  field.  In  addition,  the  energy  crisis  and  inflation  resulting  from  the  Russia’s 
aggression  could  disproportionately  affect  families  with  lower  socioeconomic  backgrounds,  further  harming 
educational attainment in European countries. 

Overall,  this  report  provides  an  overview  of  projected  trends  in  educational  attainment  indicators  in  the  EU 
assuming no policy changes. Such projections suggest that 2030 target levels could be raised as part of the 2026-
30 cycle of the strategic framework for European cooperation in education and training, should Member States 
wish to do so. However, while current target levels appear achievable by 2030, it is important to closely monitor 
the situation, particularly given ongoing uncertainties introduced by the Covid-19 pandemic and Russia’s invasion 
of Ukraine.   

3 

 
 
 
1 

Introduction 

Participation and completion rates in upper secondary and tertiary education are two key features for monitoring 
educational systems. Increasing educational attainment is indeed associated with important benefits both for 
society and individuals (Brunello and De Paola, 2014). The level of educational participation is closely linked to 
the transition of young people to the labour market, with under-educated people being at risk of unemployment 
and job insecurity (see Gonzalez-Rodriguez et al., 2019). Indeed, individuals with higher levels of education tend 
to have better employment outcomes and higher earnings. Moreover, higher levels of educational attainment 
are also associated with better access to information and resources, which can enable individuals to make, for 
instance, more informed decisions about their health and well-being (see Eide et al., 2011). Besides the individual 
benefits,  education  is  also  linked  to  several  positive  social  outcomes.  Firstly,  high-qualified  human  capital  is 
widely  recognised  as  a  key  driver  of  economic  development  and  innovation  (see,  for  instance,  Romer,  1990; 
Mankiw et al., 1992; Diebolt and Hippe, 2019). Additionally, education is also found to increase civic engagement, 
and reduce crime rates – having, thus, a significant impact also on social well-being (Campbell, 2009; Lochner, 
2004). 

Council Resolution on a strategic framework for European cooperation in education and training towards the 
European Education Area and beyond (2021-2030), adopted in February 2021, outlines seven targets at the EU 
level, with the first five aiming to be achieved by 2030 (Council Resolution on a strategic framework for European 
cooperation in education and training towards the European Education Area and beyond, 2021). The main intent 
of monitoring this indicators is to improve the overall quality of education and training systems in the EU and to 
promote  lifelong  learning  opportunities  for  individuals.  The  targets  aim  to  ensure  that  young  people  are 
equipped with the necessary skills and qualifications to succeed in today's job market and to increase the number 
of individuals with higher education degrees. 

Among these targets, the report specifically focuses on: early leavers from education  and training (ELET) and 
tertiary education attainment (TEA). Early leavers are defined as individuals between the ages of 18-24 who have 
completed no more than lower secondary education and were not in further education or training during the 
four weeks prior to the labour force survey (LFS). It is crucial to track this group's progress since those who leave 
education  and  training  early  may  face  difficulties  in  establishing  themselves  in  the  job  market.  Tertiary 
educational attainment  refers, instead, to the proportion of the population aged  25-34 who have completed 
tertiary  education  (ISCED  levels  5-8).  The  monitoring  of  this  indicator  is  essential  because  of  the  expected 
demand for highly skilled individuals with relevant education to fill flexible and complex job roles in the coming 
years. 

The EU set common targets for these two indicators by 2030: the share of early leavers should be below 9%, and 
the share of tertiary educational attainment should exceed 45% in the EU as a whole. In 2021, the EU recorded 
an average of 9.7% of early leavers from education and training, while the population with tertiary educational 
attainment had an average of 41.2%. However, there were variations among Member States, with some already 
meeting the EU's 2030 targets and others still far from the recommended values (European Commission 2022). 

This report aims at exploring the future trends of early leavers from education and training (ELET) and tertiary 
education attainment (TEA). Based on the model proposed by Dragomirescu-Gaina and Weber (2013), the study 
proposes scenarios for the evolution of the values of the two indicators up to 2030. The remaining five EU-level 
targets  are  not  investigated  in  the  report.  While  the  forecasts  of  ELET  and  TEA  can  rely  on  sound  forecast 
empirical models that have been previously employed in the literature, the remaining targets would be difficult 
to forecast with robust and credible estimates1.  

1 The EU-level targets monitoring the share of low achieving 15- and 8-year-old students in reading, math, science and digital skills rely on 
international large-scale assessment data (PISA and ICILS), and the infrequency of the data makes it challenging to make future forecasts. 
The EU-level target monitoring the participation in early childhood education and care of children aged 3+ relies on annual UOE data only 
available  for  the  years 2013-21  and,  in addition,  the  strong  influence  of  parental  choices  limits  the  ability  to  forecast  this indicator.  The 
remaining two EU-level targets, concerning work-based learning in vocational education and training and adult participation in learning, are 
based on brand-new indicators only available as of the 2021 and 2022 EU-LFS data respectively, with EU-level targets set for 2025 rather 
than 2030. 

4 

 
 
 
 
The publication by Dragomirescu-Gaina and Weber (2013) presented forecasts for the ELET and TEA indicators 
up to 2020. This report seeks to extend these projections until 2030. Continuous monitoring of these indicators 
and their projections is crucial for understanding the progress of EU countries towards their objectives. It also 
allows for identifying critical situations and potential target adjustments. Moreover, this report aims to place less 
emphasis on the methodological model already developed in Dragomirescu-Gaina and Weber (2013). Instead, it 
intends to provide a more qualitative discussion of the trends related to these two EU education indicators. 

The ELET and TEA forecasts are built under the hypothesis of “no policy change” – estimating the trends of these 
indicators in the case of no future changes in the countries’ policy actions. It is also worth noticing that the model 
cannot predict the potential impact over this decade of recent economic shocks, such as the ones recently trigged 
by the Covid-19 pandemic or Russia’s invasion of Ukraine. For this reason, the scenarios described in the first 
part of the report do not consider the effects generated by these two major events in the global economy and 
society.  The  quantitative  trajectories  provide  a  quantitative  baseline  for  the  second  part  of  the  report  that 
discusses in a more qualitatively way how the Covid-19 pandemic, Russia’s invasion of Ukraine are likely to affect 
ELET and TEA over the next decade, relying on information from the literature and past research.  

The  report  is  structured  as  follows.  Section  2  describes  the  empirical  strategy  and  the  model  specifications 
together with the variables employed. Section 3 presents the quantitative scenarios of ELET and TEA; while the 
qualitative  discussion,  given  the  potential  impact  of  the  Covid-19  pandemic  and  the  invasion  of  Ukraine,  is 
reported in Section 4. Specifically, Section 4.1. will deal with the potential impact of Covid-19 school closure, 
while  Section  4.2  will  focus  on  the  educational  consequences  of  Russia's  invasion  of  Ukraine,  in  terms  of 
migration  in  EU  countries,  and  the  impact  on  energy  prices  and  inflation.  Finally,  Section  5  contains  the 
conclusions of the report.  

5 

 
 
 
2  Empirical Strategy and data  

To build our quantitative scenarios, the forecasts of the future trends of early leavers from education and training 
and  higher  education  attainment  are  based  on  the  models  formerly  developed  by  Dragomirescu-Gaina  and 
Weber (2013) and Dragomirescu-Gaina et al. (2015). These forecast models were originally developed to predict 
the values of ELET and TEA and the EU countries between 2010 and 2020.  In this report,  the same empirical 
strategy proposed by Dragomirescu-Gaina and Weber (2013) is applied to updated data, in order to provide new 
estimates2 of the trajectories up to 2030. The term “forecast” is used in a purely technical, non-deterministic 
sense, i.e. as the result from an econometric model applied to project the possible evolution of a certain indicator 
under a “no-policy-change” hypothesis. 

These forecast models are built to describe the future trends of ELET and TEA in the context of socioeconomic 
and policy stability. For this reason, the empirical estimates are based on data up to 2019. More recent data, 
even  if  available,  have  been  excluded  to  obtain  results  that  are  not  affected  by  the  potential  economic  and 
educational shocks associated with the Covid-19 pandemic and  Russia’s invasion of  Ukraine. Accordingly, the 
forecasts refer to a time span between 2020 (included) and 2030. Thus, the forecasts for 2020, 2021 and 2022 
are not the observed values, but represent estimates of the ELET and TEA values if these two recent shocks had 
never occurred. This empirical strategy also allows to assess the short-term impact of Covid-19 on ELET and TEA 
by comparing the 2020, 2021 and 2022 forecasts (based on data up to 2019) with the observed values in these 
years.  

The empirical models used for forecasting are designed to prioritise simplicity, with the goal of creating reliable 
models based on a limited number of independent variables for which data are available across several years 
and EU countries (Dragomirescu-Gaina and Weber, 2013). This approach can produce models that are useful for 
supporting  policy-making  decisions.  However,  the  main  drawback  of  this  approach  is  the  omitted  variables 
problem. To address this issue, robustness checks are employed to demonstrate that the results remain reliable 
even when different specifications are considered. Additionally, it is important to note that the purpose of this 
work is not to construct a complete structural model of educational attainment, but rather to provide accurate 
approximations of the future development in the ELET and TEA indicators. 

The  forecast  results  presented  in  the  subsequent  sections  are  based  on  data  for  18  EU  countries:  Austria, 
Belgium,  Czechia,  Denmark,  Estonia,  Finland,  France,  Germany,  Greece,  Hungary,  Ireland,  Italy,  Netherlands, 
Poland, Portugal, Slovakia, Spain, and Sweden. The remaining of EU-27 countries (i.e., Bulgaria, Croatia, Cyprus, 
Latvia,  Lithuania,  Luxemburg,  Malta,  Romania,  Slovenia)  have  been  excluded  from  the  model  estimates  for 
reasons  related  to  missing  data  in  the  variables  or  being  identified  as  outliers3  in  the  regression  estimations 
(following the empirical approach of Dragomirescu-Gaina and Weber, 2013). It is worth noting that the in-sample 
countries cover over 91% of the EU population, ensuring a high level of representativeness for the model.  

As  a  primary  result,  the  report  provides  the  average  forecast  values  of  ELET  and  TEA  for  the  EU-27.  These 
forecasts are then directly compared with the EU targets, which are set for the entire European Area. However, 
being aware that the EU average values are the result of country-specific trends, the study also reports the ELET 
and TEA estimates for each EU-27 country (see Annex 3). More precisely, country-level estimates refer to two 
distinct groups: (i) the in-sample countries, which refer to the 18 countries that provide the data for estimating 
the  models;  (ii)  the  out-sample  countries,  which  represent  the  9  EU-27  countries  excluded  by  the  model 
estimations for problems of data availability. Accordingly, the forecasts concerning the second group are based 
on coefficients estimated on a set of countries different from the one for which the  forecast is computed. For 
this reason, these estimates should be interpreted with particular caution.   

Moreover,  the  EU-27  forecast  is  computed  using  coefficients  obtained  from  the  models  described  in  the 
subsequent sections, which utilise data from the in-sample countries. These coefficients are then multiplied with 
the EU-average data of the independent variables, directly retrieved from Eurostat, to obtain the forecast values. 

2 The terms “estimate” and “estimation” are used in general sense to indicate e process of employing data to estimate the values of unknown 

parameters or distributions adopting a statistical or econometric framework. 

3 For example, due to the small dimensions of Luxembourg and Malta, these countries have been excluded by the model. Indeed, it is unlikely 

that they would adequately represent the trends of other EU countries. 

6 

 
 
 
The following subsections present the  empirical specifications and describe the data employed in the models 
separately for ELET and TEA. 

2.1  Early leavers form education and training 

2.1.1  Model specification: ELET 

Based  on  Dragomirescu-Gaina  and  Weber  (2013),  ELET  is  modelled  as  a  function  of  parents’  education  and 
employment prospects. The parental educational level is a good proxy for family background since it is strictly 
related  to  income,  job  role,  and  other  relevant  socioeconomic  characteristics  that  could  affect  educational 
decisions (see, for instance, Aakvik et al., 2005). Moreover, education is not subject to transitory shocks since 
educational  attainment  tends  to  remain  constant  after  a  certain  age  (see  Black  and  Devereux,  2011).  Also, 
compared to current family income, parents’ education provides a better proxy for measuring permanent family 
earnings and capturing the financial constraints that may affect children’s educational attainment (see Cameron 
and  Heckman,  1998).  Employment  prospects  are  included  for  modelling  the  educational  decisions  of  young 
individuals. Previous evidence in the literature highlights that early school leavers tend to be myopic in evaluating 
their  employment  prospects.  In  other  words,  this  group  of  young  people  considers starting  an  early  working 
career more important  than continuing studying to have better job conditions and income in the future (see 
Petrongolo and Segundo, 2002; Harrison, 2019).  

More specifically, the forecast model for ELET is represented by Equation 1: 

(1)                          ∆log (𝑒𝑎𝑟𝑙𝑦 𝑠𝑐ℎ𝑜𝑜𝑙 𝑙𝑒𝑎𝑣𝑒𝑟𝑠)𝑡,𝑐

= 𝛽0+ 𝛽1∆ log(parents′education)𝑡,𝑐 +   𝛽2∆ log(𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡 𝑝𝑟𝑜𝑠𝑝𝑒𝑐𝑡𝑠)𝑡−𝑘,𝑐 + γ𝑐 +   𝜀𝑡𝑐  

where  𝛽i are the model coefficients, γ𝑐 is the vector of country fixed effect, and  𝜀𝑡𝑐 is the error term for the 
country c and in the year t. As common for these types of analyses, the indicators are expressed in logarithmic 
terms, while first-difference specification is introduced to attenuate the potential criticalities related to residual 
autocorrelation  and  series  stationarity.  In  addition,  country  fixed  effects  are  included  in  the  specification  to 
account for differences in cultural or institutional features across the countries in the sample. This approach is 
consistent with the model presented in Dragomirescu-Gaina et al. (2015) and enables us to tailor the forecast to 
the specific trend of each country.  

As reported in Equation 1, the indicator for employment prospects is included in the model with a time lag equal 
to k years. Following Dragomirescu-Gaina and Weber (2013), the time lag (k) is not imposed a-priori but identified 
through an empirical process. More specifically, the lag has been selected based on several statistical indicators: 
R-squared, Akaike (AIC) and Bayesian (BIC) information criteria, and  out-of-sample RMSE. In the case of ELET, 
comparing these indicators across models with different time-lag specifications, the preferable time lag is 7 years 
(further details are provided in Section 3). Table A3 in Annex 4 reports the estimated results of the model with 
alternative  time  lags  (i.e.,  6  and  8  years).  The  statistical  indicators  show  that  the  models  based  on  these 
specifications have lower performance, even if the forecasted values are still relatively close to the ones reported 
in the main text.  

2.1.2  Selection of indicators and data description: ELET  

Table 1 reports the definitions of the variables included in Equation 1, together with the data sources and the 
years  in  which  the  variables  are  collected  and  used  to  build  the  forecast.  The  descriptive  statistics  of  these 
indicators are then reported in Annex 2 (see Table A1). 

Early leavers from education and training represent the dependent variables and are defined as the population 
aged between 18 and 24 years who have completed at most lower secondary education and are not in further 
education or training. The data refers to the period between 2002 and 2019 and are provided by Eurostat.  

The selection of the independent variables reflects the specifications used in Dragomirescu-Gaina and Weber 
(2013). The share of population in specific age brackets with low education is employed to measure parental 
education. Using macro-level data, it is not possible to observe the actual educational level of parents, thus, the 
model considers young adults belonging to a synthetic family composed of people with an age matching the most 

7 

 
 
common  parental  relationship  (i.e.,  the  reference  age  cohorts  for  potential  parents  of  students  enrolled  in 
secondary education). The educational level is modelled separately across female and male potential parents. 
For mother education, the share of low-educated female population aged 35-44 is employed, whereas father 
education refers to the age interval from 45 to 54 years old. The age brackets used in this study are based on the 
same specification as Dragomirescu-Gaina and Weber (2013). Nevertheless, to ensure robustness of the results, 
different age brackets were tested and the findings are presented in Annex 4. In particular, as the average age 
of women giving birth to their first child in the EU has increased to 29.4 years in 2019 (Eurostat, 2023)4, an older 
age  interval  in  considered.  The  robustness  check  indicates  that  the  results  obtained  with  the  specification 
described in Section 2.1.1 are consistent even when different assumptions on age brackets are employed (see 
Figure A5 in Annex 4). 

In line with the previous evidence, it is expected to find a positive relationship between low-educated parents 
and the share ELET. In fact, students with a low socioeconomic status are usually less motivated to proceed in 
their student career (see Chevalier et al., 2013), while disadvantaged socio-economic conditions could encourage 
youngers to undertake an early entrance into the job market (Pissarides, 1981; Petrongolo and Segundo, 2002; 
Harrison, 2019).  

As described in Equation 1, parental education is included in the model by assuming a simultaneous relationship 
with the dependent variable (without a time lag). For this reason, it is necessary to project parental education 
values  up  to  2030,  in  order  to  obtain  a  forecast  of  ELET  during  the  next  decade.  The  projections  have  been 
calculated by replicating an ageing process to cohorts of populations 10 years younger than the reference ones5.  

The  variable  on  employment  prospects  is  measured  by  the  total  unemployment  rate.  Data  come  from  the 
AMECO database6, which provides information on historical data and projections up to 2023. As explained in the 
previous sections, the coefficients of the forecast model are estimated using pre-pandemic data to avoid biases 
due to recent economic and educational shocks. However, for employment prospects, the empirical analysis also 
includes more recent data (i.e., observed values in 2020, and 2021 and projections for 2022 and 2023). This is 
indeed necessary to provide an estimate of the indicator until 2030 using the best time lag (i.e., 7 years). On the 
other hand, this choice should not affect the forecasts since the trend of unemployment does not show a short-
term shock in the data due to Covid-19 disruption (see Figure A1 in Annex 1). 

4 However, it should be noted that the increase in the age of mothers giving birth to  their first child is limited. For instance, the highest 

increase between 2015 and 2019 was 1 year in the case of Estonia (Eurostat, 2023). 

5 The values have been adjusted for the effects of migration flows and mortality rates (for more details on the extrapolation method, see 

Dragomirescu-Gaina et al., 2015). 

6 AMECO is the annual macro-economic database of the European Commission's Directorate General for Economic and Financial Affairs. 

AMECO database is available online at the following link: 
https://dashboard.tech.ec.europa.eu/qs_digit_dashboard_mt/public/sense/app/667e9fba-eea7-4d17-abf0-
ef20f6994336/sheet/f38b3b42-402c-44a8-9264-9d422233add2/state/analysis/  

8 

 
 
 
 
 
Table 1. Variables’ definitions: ELET forecast model 

Variable 
Early leavers from 
education and 
training 

Share of women 
with low education, 
age 35-44 

Definition 

Years 

Source 

Share of population aged 18-24 years who have completed at most a 
lower  secondary  education  and  were  not  in  further  education  or 
training. 

2002-2019  Eurostat 

Share of female population aged 35-44 years who have completed at 
most  lower  secondary  education  (ISCED  levels  0-2).  Projections  of 
years  between  2020  and  2030  have  been  calculated  based  on  an 
extrapolation method that replicates the ageing process to cohorts of 
populations 10 years younger than the reference ones. 

2002-2030  Eurostat 

Share of men with 
low education, age 
45-54 

Share of male population aged 45-54 years who have completed at 
most  lower  secondary  education  (ISCED  levels  0-2).  Projections  of 
years  between  2020  and  2030  have  been  calculated  based  on  an 
extrapolation method that replicates the ageing process to cohorts of 
populations 10 years younger than the reference ones. 

2002-2030  Eurostat 

Total 
unemployment 
rate 

Unemployment  rate  is  calculated  as  the  percentage  of  the  labour 
force.  Labour  force  refers  to  the  employed  and  not  employed 
population aged at least 15. An  unemployed person is  defined as a 
person who is without work during a reference week, who is available 
to start work within the next two weeks and who is actively seeking 
work. Projections of the indicator up to 2023 are provided by AMECO 
database. 

1995-2023  AMECO 

2.2  Tertiary education attainment 

2.2.1  Model Specification: TEA 

Based on Dragomirescu-Gaina  and Weber (2013) and Dragomirescu-Gaina  et  al.  (2015), TEA is modelled as a 
function of parental education and income prospects. Similarly to what was already discussed for ELET, parental 
education and, more generally, family context can have a great influence on the tertiary attainment of young 
individuals (see Impicciatore and Tosi, 2019; Albert, 2000) 

In the case of TEA, income prospects can be more suitable in modelling this behaviour compared to employment 
prospects (see Dragomirescu-Gaina and Weber, 2013). Young individuals who decide to attend universities are 
probably attracted by the possibility of having a better income with a tertiary education degree compared to 
their salary prospects with a lower educational level.  

TEA is, thus, modelled as in Equation 2:  

(2)                          ∆log (tertiary education attainment)𝑡,𝑐

= 𝛽0+ 𝛽1∆ log(parents′education)𝑡,𝑐 +   𝛽2∆ log(𝑖𝑛𝑐𝑜𝑚𝑒 𝑝𝑟𝑜𝑠𝑝𝑒𝑐𝑡𝑠)𝑡−𝑘,𝑐 +   𝜀𝑡,𝑐  

where 𝛽i are the model coefficients, γ𝑐 is the vector of country fixed effect, and 𝜀𝑡𝑐 is the error term country c 
and in the year t. All the indicators are considered as a first difference and in log terms (see Section 2.1.1). As for 
ELET, income prospects are included in the model with a time lag, which has been defined empirically based on 
statistical indicators (i.e., R-squared, AIC, BIC, and out-of-sample RMSE). Comparing the results of models with 
different time-lag specifications, the best time lag has been set at 7 years (further details are provided in Section 
3). In Annex 4, Table A4 presents the estimated results of the model using alternative time lags (i.e., 6 and 8 
years).  However,  the  statistical  indicators  demonstrate  that  these  specifications  have  lower  performance, 
although the forecasted values remain relatively similar to those presented in the main text. 

9 

 
 
 
 
2.2.2  Selection of indicators and data: TEA  

Following Dragomirescu-Gaina and Weber (2013) and Dragomirescu-Gaina et al. (2015), the share of tertiary-
educated adults aged 55-64 is employed for measuring parental education in Equation 2. People aged 55-64 are 
the reference cohort of the population most likely to have children at universities. The hypothesis is that parents 
having  tertiary  educational  attainment  influence  their  children  to  attend  and  obtain  a  degree  at  university 
(Albert, 2000). Thus, the independent variable is expected to positively affect TEA. As for ELET, the projections 
of parental education up to 2030 are based on an extrapolation method that replicates the aging process of the 
population.  Contrary to the model used for ELET, in this case,  the model does  not  differentiate between the 
mother’s and father’s educational attainment. This specification allows using a simplified model that, however, 
has higher explanatory power compared with specifications separated by gender of population, as shown by 
Dragomirescu-Gaina  et  al.  (2015).  Parental  education  is,  indeed,  included  as  a  general  proxy  of  family 
background, and it is not expected that the role of mother and father have different influences on the educational 
choice  of  their  kids  (see  Dragomirescu-Gaina  et  al.,  2015).  Nevertheless,  Table  A4  in  Annex  4  presents  the 
robustness  check  analyses  that  were  conducted  using  various  assumptions  for  defining  parental  education, 
including  distinguishing  between  men's  and  women's  education.  The  estimates  indicate  that  alternative 
specifications of the primary model have minimal impact on the TEA forecast. 

Income  prospects  are  measured  through  an  indicator  of  labour  productivity  (see  Dragomirescu-Gaina  and 
Weber, 2013; Dragomirescu-Gaina et al., 2015). More specifically, the model includes the GDP per hour worked 
at current prices (HVGDH). The choice of this indicator is also due to its robustness to the Covid-19 disruption, 
which seems to have not produced a short-term shock in the values (see Figure A2 in Annex 1). Therefore, as for 
employment prospects in the ELET model, it is robust to include values of labour productivity up to 2023. The 
projections of the values in 2022 and 2023 are directly provided by the European Commission’s AMECO database. 
The validity of this indicator is further demonstrated by the results of the robustness checks presented Table A4 
(see Annex 4), which  includes an alternative definition of labour productivity based on average  annual hours 
worked  per  person,  as  proposed  by  Vera-Toscano  (2015).  The  estimates  from  this  alternative  model  show, 
indeed, a significantly lower R-squared compared to the main model. Described in Equation 2.  

Table 2 reports the detailed definitions of the variables included in Equation 2, together with the data sources 
and the years in which the variables are collected and used to build the forecast. The descriptive statistics of 
these indicators are reported in Annex 2 (see Table A2). 

Table 2. Variables’ definitions: TEA forecast model 

Variable 

Definition 

Years 

Source 

Share of population with 
tertiary education, 25-34 

Share  of  population  aged  25-34  years  who  have  completed 
tertiary education (ISCED levels 5-8). 

2002-
2019 

Eurostat 

Share of adults with 
tertiary education, age 55-
64 

Share  of  population  aged  55-64  years  who  have  completed 
tertiary  education  (ISCED  levels  5-8).  Projections  of  years 
between  2020  and  2030  have  been  calculated  based  on  an 
extrapolation  method  that  replicates  the  aging  process  to 
cohorts  of  populations  10  years  younger  than  the  reference 
ones. 

2002-
2030 

Eurostat 

HVGDH 

Gross  domestic  product  at  current  market  prices  per  hour 
worked (HVGDH). 

1995-
2023 

AMECO 

10 

 
 
 
 
 
3  Forecast results  

3.1  ELET results 

The  forecast  model  for  ELET  is  estimated  following  the  specification  described  in  the  previous  sections.  The 
results are reported in Table 3, together with the statistical indicators used to choose the time-lag specification. 
The estimated coefficients are in line with the previous findings for 2020 (see Dragomirescu-Gaina and Weber, 
2013; Vera-Toscano, 2015). As expected, the total unemployment rate is associated with a negative effect on the 
early  leavers  from  education  and  training,  while  having  parents  with  a  low  level  of  education  is  positively 
correlated to the share of ELET. The size of the coefficients is particularly large for father education and for the 
unemployment  rate.  Concerning  the  former,  the  estimates  suggest  that  a  1%  increase  in  the  share  of  low-
educated men is associated with a growth in ELET by an amount of 0.17%. A rise of 1% in the unemployment rate 
causes instead a decrease in the share of early leavers of 0.26%. Overall, the results shed light on the relevance 
of intergeneration mobility and economic conditions in affecting the value of ELET in the EU.  

Table 3. ELET estimations 

VARIABLES 
Δ log(share of women with low education, age 35-44) 

Δ log(share of men with low education, age 45-54) 

Δ log(total unemployment rate), lag 7th 

Constant 

Country fixed effects 
Year dummy 
Observations 
N. Countries 
R-squared 
RMSE out of sample (5 years ahead) 
BIC 
AIC 

Δ log(ELET) 
0.077* 
(0.052) 
0.172*** 
(0.055) 
-0.265*** 
(0.085) 
-0.020*** 
(0.005) 
yes 
no 
264 
18 
0.136 
0.107 
-620.21 
-634.51 

Notes: OLS estimates. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Author’s elaboration using Stata 15. 

The estimated coefficients in Table 3 are used to obtain the average ELET forecast for the EU-27 between 2020 
and 20307. The result is shown in Figure 1, which represents the trend of ELET over time, differentiating between 
observed data (2001-2019, in black colour) and forecast values (2020-2030, in blue colour). In 2020, 2021 and 
2022, both the forecast (blue line) and the observed data (black line) are displayed. The former represents an 
estimate of the early leavers from education and training in case the pandemic had not happened, while the 
latter reports the actual data of the indicator after Covid-19. As shown in Figure 1, the difference between the 
two lines is almost null, meaning that the Covid-19 forecast seems to produce no changes in the trend of the 
ELET in the short period (i.e., 2 years)8. In addition, the negligible distance between observed data and forecasted 
values in 2020-21 proves the quality of the estimation.  

The punctual estimate suggests that the EU-27 would be able to reach a value of early leavers from education 
and training lower than 9% before 2030 (i.e., the EU target represented with a red line in Figure 1). However, a 
lot of uncertainty is associated with this  forecast and, given that distance from the target is very short, there 
remains  a  possibility that the target  would not be achieved, as shown by the confidence interval in Figure 1. 

7  The estimated coefficients have been multiplied for the values of the dependent variables for the EU-27 average. EU average data are 

provided by Eurostat. 

8 A more comprehensive discussion on the effect of Covid-19 disruption on ELET trend is presented in Section 4.1. 

11 

 
 
 
 
 
 
 
 
Besides, the heterogeneity across countries is high. While some countries had already achieved the 9% EU-level 
target in 2019 (e.g., Greece, Ireland, Poland and Sweden), others reported high levels of ELET and are less likely 
to go below 9% in 2030 (e.g., Spain, Italy and Hungary). Additional details on the country-level estimates are 
reported in Annex 3.  

Nevertheless, for all the EU countries, the forecasts describe a remarkable decrease in the share of early leavers 
over the next decade. This is shown by the map in Figure 2, which compares the values of ELET in 2019 and the 
forecast for 2030 in each of the EU-27 countries.  

Figure 1. ELET forecast – average EU 27 

Source: Author’s elaboration using Stata 15. The figure reports the 95% confidence interval of forecast data9. 

9  Based on the model's specification, the degree of uncertainty associated with the forecasts is constant, as the time lags separating the 
independent variables from the dependent variables are fixed. Although greater temporal horizons generate increased uncertainty 
related to potential exogenous changes, such variables are not taken into consideration within the context of a "no policy changes" 
scenario adopted within the report. 

12 

 
 
 
 
 
 
 
 
 
 
 
Figure 2. ELET by EU country: 2019 (observed values) – 2030 (forecast) 

3.2  TEA results 

Source: Author’s elaboration using Tableau. 

The  model  described  in  Equation  2  has  been  used  to  perform  the  estimates  for  forecasting  the  TEA  of  EU 
countries. The results are reported in Table 4, together with the statistical indicators used to choose the time-
lag specification.  

The estimated coefficients confirm the main hypothesis adopted by the model and are coherent with the findings 
of Dragomirescu-Gaina and Weber (2013). Specifically, labour productivity is associated with a positive effect on 
tertiary  education  attainment.  The  extent  of  this  effect  is  particularly  large:  a  1%  rise  in  the  share  of  labour 
productivity is associated with an increase in tertiary education attainment by 0.32%. Also, having parents with 
tertiary  education  attainment  increases  the  probability  of  obtaining  a  tertiary  education  degree.  Again,  the 
findings highlight the central role of intergeneration mobility and economic conditions in determining the share 
of people with tertiary education across EU countries.  

Table 4. TEA estimations 

VARIABLES 
Δ log(share of adults with tertiary education, age 55-64) 

Δ log(labour productivity), lag 7th 

Constant 

Country dummies 
Year dummy 
Observations 
N. Countries 
R-squared 
RMSE out of sample (5 years ahead) 
BIC 
AIC 

Δ log(tertiary education attainment) 
0.134*** 
(0.039) 
0.321*** 
(0.064) 
0.014*** 
(0.004) 
yes 
no 
300 
18 
0.125 
0.034 
-1099.073 
-1087.962 

Notes: OLS estimates. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Source: Author’s elaboration using Stata 15. 

13 

 
 
 
 
 
 
The TEA forecast (2020-2030) is obtained by employing the coefficients reported in Table 4 over the mean values 
of  parents'  education  and  labour  productivity  for  EU-27  countries10.  Thus,  the  TEA  forecast  values  are 
represented with a blue line in Figure 3, while the observed data (2010-2022) are reported as a black line in the 
figure. In the time span when it is possible to compare the forecasted values and the observed data (i.e., 2020-
2022), the two series are overlapping. As said for ELET, this confirms the quality of the  forecast and highlights 
how the Covid-19 disruption seems not to have produced short-term changes in the trend of TEA11. The EU target 
of 45% of the population with tertiary education is represented  by a red line in Figure 3. Contrary to ELET, on 
average, the EU is expected to exceed the target for tertiary education attainment in 2030 by a wide margin. 
Nevertheless, some countries such as Italy, Hungary, Germany and Czechia seem at risk of not achieving the EU-
level target value by 2030 (see Figure A4 in Annex 3).  

As depicted in Figure 3, the average values of TEA show a linear increase until 2030. However, caution should be 
exercised when interpreting these results. While the model may capture the general trend, it may not be able to 
identify a likely steady state of the trend that could be achieved beyond a certain level of TEA. 

Finally, the comparison between the share of population with tertiary education in 2019 (observed values) and 
its forecast in 2030 sheds a light on how EU countries are expected to significantly increase this indicator over 
the next decade (see Figure 4).   

Figure 3. TEA forecast – average EU 27 

Source: Author’s elaboration using Stata 15. The figure reports the 95% confidence interval of forecast data12. 

10 EU average values are provided by Eurostat. 
11 A more comprehensive discussion on the effect of Covid-19 disruption on TEA trend is presented in Section 4.1. 
12 See footnote 9. 

14 

 
 
 
 
 
 
Figure 4. TEA by EU country: 2019 (observed values) – 2030 (forecast) 

Source: Author’s elaboration using Tableau. 

15 

 
 
 
 
 
 
 
 
4  Discussion on future scenarios  

4.1  The potential effects of Covid-19 pandemic 

As reported in the previous section, the Covid-19 disruption does not seem to have produced significant short-
term changes in the aggregate indicators of ELET and TEA. Indeed, Figures 1 and 3 show that the forecasts of the 
indicators (based on data before 2020) overlap the observed values in 2020, 2021 and 2022. On the other hand, 
it should be noted that the retrospective nature of these indicators (especially ELET) poses challenges in capturing 
the immediate impact of the Covid-19 pandemic. Specifically, the decision to leave education may have been 
made well before the reference year, and potentially prior to the pandemic. As a result, it is possible that the full 
effects of Covid-19 on educational attainment of the population are yet to be observed. The pandemic and the 
school closures all over the EU countries have generated a disruptive change in the education systems. While the 
immediate  effects  are  difficult  to  measure,  this  disruption  could  potentially  impact  access  to  education  and 
educational attainment in both positive and negative ways in the coming years.  

Studies on the direct effect of the pandemic on school dropouts and education participation are still limited in 
the  literature.  Farcnik  et  al.  (2022)  conducted  a  survey  among  university  students  in Slovenia,  revealing  that 
almost 50% of the students intended to adjust their educational plans due to the Covid-19 disruption. However, 
less than 7% considered dropping out and entering the labour market, while most of the students preferred to 
prolong their studies. By contrast, a survey at the University of La Laguna (Spain) revealed that a significant share 
of students considered abandoning their university education (López-Aguilar, 2022), especially the ones in social 
science courses where the intention of leaving university was nearly 40%. More significant positive correlations 
between  school  dropouts  and  the  Covid-19  pandemic  are,  instead,  provided  for  non-European  countries.  In 
Mexico, Jacobo-Galicia et  al.  (2021)  found a  positive correlation between the  fear of  Covid-19 and university 
dropout rates. Moreover, Moscoviz and Evans (2022) provided evidence of a growth in student dropouts due to 
Covid-19 disruption in low-income countries, particularly among adolescents. 

On the other hand, the literature provides ample evidence of the impact  of Covid-19 disruption on students' 
learning. For instance, recent meta-analyses highlight notable learning loss resulting from the disruptions caused 
by the pandemic. Betthäuser et al. (2023) found an average reduction of approximately -0.14 standard deviation. 
Similarly,  Patrinos  et  al.  (2022)  estimated  a    learning  loss  of  -0.17  standard  deviation,  while  Di  Pietro  (2023) 
observed a -0.19 standard deviation reduction. Also, it is crucial to note the considerable variability observed 
across countries, subjects, grades and student characteristics (see for instance Betthäuser et al., 2023; Bertoletti 
et al., 2023). However, while this loss may be linked to education participation and school dropping out, it is 
challenging to determine the precise effect of learning loss on ELET and TEA indicators.  

A learning loss may lead to a decline in motivation and educational expectations, increasing the likelihood of 
dropping out. Educational expectations and perception of their own educational experiences among students 
are critical factors that influence their decision to drop out or continue with their studies (Fan and Wolters, 2014; 
Athreya  and  Eberly,  2021).  Students  who  receive  both  extrinsic  and  intrinsic  rewards  tend  to  value  their 
education  experience  more,  prioritise  their  education,  anticipate  achieving  advanced  academic  goals,  and 
demonstrate higher persistence in university (Tinto, 2003). Furthermore, students who decided to leave school 
during the pandemic may not have the opportunity to recover missed content in subsequent years of education 
– increasing, therefore, the criticalities associated with the school dropout.  

On the other hand, dropping out and educational attainment tend to be more closely linked to students' grades 
and final evaluations rather than standardised test scores, which were typically used to estimate learning loss 
during  the  pandemic.  It  is  worth  noticing  that,  during  this  period,  there  was  a  weaker  correlation  between 
objective  measures  of  learning  (provided  by  the  standardised  test  scores)  and  grades,  as  an  effect  of  grade 
inflation  due to  the emergency situation.  Indeed, there is evidence  suggesting that teachers graded students 
more generously to account  for the challenges they  were facing during the pandemic  (Karadag, 2021), while 
some  national  governments  discouraged  the  possibility  to  fail  students  during  the  school  year  2019/20  (for 
example, in Italy - see Ordinanza Ministeriale of 16/05/2020). 

The literature also highlights that the pandemic has reduced access to education for students from disadvantaged 
backgrounds - particularly those from low-income families (Parolin et al., 2021; Betthäuser et al., 2023) as well 

16 

 
 
as students with special needs or disabilities (Di Pietro et al., 2020; De Witte and François, 2023). These students 
may face a number of barriers to remote learning, including lack of access to technology, lack of support from 
educators, and difficulty in maintaining engagement and motivation.  

Additionally, the pandemic has resulted in economic instability and uncertainty, which can create  barriers for 
planning and investing in education, particularly for low-income households, as reported by Luppi et al. (2021). 
The impact of this financial instability or its perception, however, varies across different countries. Eurofound 
(2020)  found  that  a  higher  proportion  of  individuals  in  Spain,  Italy,  and  Greece  expect  a  worsening  of  their 
financial situation in the future compared to other Western European countries. 

The shift to remote learning due to the Covid-19 pandemic has resulted in a significant lack of social interaction 
among students. This deprivation of social interaction may have a detrimental impact on student wellbeing, as 
evidenced by studies conducted by Mushquash and Grassia (2021) and Copeland et al. (2021). Furthermore, the 
challenges posed by online instruction, including the absence of face-to-face interaction with instructors and the 
difficulty in maintaining focus and attention, may contribute to a decrease in motivation and engagement among 
students  (Tan,  2020).  This  decrease  in  motivation  and  engagement  may  subsequently  increase  the  risk  of 
dropout,  particularly  among  those  already  at  a  higher  risk  of  educational  disengagement  and  those  from 
disadvantaged  backgrounds  (Fan  and  Wolters,  2014).  Additionally,  the  physiological  distress  and  anxiety 
experienced  by  students  during  school  closures  have  been  linked  to  an  increased  likelihood  of  dropout.  It  is 
important  to  note  that  studies  have  shown  gender  differences  in  the  impact  of  the  pandemic  on  students' 
psychological well-being, with girls reporting higher levels of anxiety and psychological distress due to the effect 
of school closure, as observed by Bertoletti et al. (2023) and Prowse et al. (2021).  

The  factors  mentioned  earlier  may  heighten  the  risk  of  disadvantaged  students  dropping  out  of  education, 
leading to further educational inequality. Although the overall levels of early leaving education and training and 
tertiary educational attainment may not be significantly impacted by school closures due to Covid-19, specific 
groups of students such as those from  low-income families,  women, and students with disabilities could still 
experience  significant  changes.  In  other  words,  the  pandemic's  effects  on  education  may  disproportionately 
affect vulnerable student populations. 

On the other hand, the Covid-19 pandemic  can  also be associated with factors that lead to improvements in 
educational attainment. One of the main advantages of online education is its flexibility, which enables students 
to  learn  in  their  preferred  environment  (Sadeghi,  2019;  Chaney,  2010).  This  flexibility  could  be  especially 
important for certain groups of students, such as working students at the university level who need to balance 
their learning needs with job demands (Thomson, 2010). Indeed, before the pandemic, having a job (with several 
hours per week) was one of the main risk factors for university dropout (Lassibille and Navarro Gomez, 2011; 
Darolia, 2014). 

Since the start of the Covid-19 emergency, many universities in Europe offered courses online or in a blended 
format. For off-site students, this shift towards online education meant they could access university courses from 
their own homes, without the need to relocate to other cities or pay additional living expenses (Sadeghi, 2019). 
This was particularly advantageous for low-income families, who may have struggled to afford the cost of living 
in other cities to attend university. From this perspective, the shift towards online education had the potential 
to  improve  the  educational  attainment  of  disadvantaged  students  and  encourage  them  to  attend  higher 
educational levels.  

More generally, remote learning has the potential to make education more accessible to students who may have 
previously faced barriers to attending traditional schools or universities. For example, students living in remote 
or rural areas may find it challenging to attend a physical school or university due to the distance and travel time 
required.  In  such  cases,  online  education  can  provide  an  effective  alternative  that  allows  students  to  access 
education from their homes (Sadeghi, 2019). However, it is also true that rural areas may face challenges related 
to internet connectivity and limited access to digital devices, which are essential for conducting online learning 
during  school  closures  (Dube,  2020).  These  challenges  could  exacerbate  existing  educational  inequalities  and 
make it difficult for students from disadvantaged backgrounds to access education. Additionally, rural schools 
may lack the resources and infrastructure required to support online learning. As such, while online education 
presents opportunities to improve educational access, it is crucial to address the digital divide and ensure that 

17 

 
 
all students, regardless of their geographical location or socioeconomic background, have equal access to high-
quality education. 

In response to the Covid-19 pandemic, the European Union established the Recovery and Resilience Facility (RRF) 
to  provide  support  to  EU  Member  States  for  significant  investments  in  various  sectors,  including  education. 
Specifically,  approximately  14%  of  the  total  RRF  investments,  equivalent  to  €71  billion,  is  designated  for 
education and skills (Fack et al., 2022). These investments are likely to mainly address the challenges brought 
about by the pandemic, particularly in terms of improving the ICT facilities and digital skills of both teachers and 
students (De Witte and François, 2023). By increasing digitalisation in schools and providing new facilities, these 
investments  may  have  a  positive  impact  on  students'  academic  achievement  and  further  motivate  them  to 
pursue their education. 

As discussed in Section 2, ELET and TEA indicators are closely linked to the income and employment prospects of 
countries that have been impacted by the Covid-19 emergency. The economic disruption caused by the pandemic 
has the potential to limit job opportunities for young adults, especially those who are entering the workforce for 
the first time. This could lead to an increase in competition for available jobs, as well as a reduction in starting 
salaries and benefits, resulting in a great deal of uncertainty about their future occupational position and income. 
This, in turn, may impact students’ life choices (Bryson and Harvey, 2000). Consequently, many young adults 
could choose to remain in the educational system to improve their chances of securing employment in the future 
(Farcnik et al., 2022). This situation can motivate them to seek higher education or vocational training to increase 
their employability and competitiveness in the job market.  

This  mechanism  could  be  more  relevant  in  some  EU  countries  compared  to  others,  since  the  effects  of  the 
pandemic on the job market appear to vary across Member States. According to Eurofound's report (2020), the 
probability  of  losing  a  job  during  2020  was  notably  higher  in  Southern  European  countries,  particularly  for 
workers with temporary and precarious job positions, when compared to other Western European countries. As 
young individuals are more likely to be employed under temporary or casual contracts, this situation exposes 
them to a higher risk of unemployment, especially in Southern European countries, where the job market is more 
vulnerable (Barbieri and Scherer, 2009; Gebel and Giesecke, 2016).  

4.2  The potential impact of Russia’s invasion of Ukraine  

The ongoing Russian invasion of Ukraine has disrupted the education of more than five million Ukrainian children 
and young people, leading to potentially lifelong consequences on their future prospects and the future of an 
entire generation (UNICEF, 2023).  

However, the consequences of the invasion of Ukraine may extend beyond its borders and impact the present 
and future educational attainment  of EU countries.  The European Union's  Temporary Protection Scheme  has 
enabled  harmonised  rights and unrestricted movement  across EU countries for a  period of time. In terms of 
education, individuals under 18 years of age who have temporary protection status are being granted access to 
the education system of EU member states under equal conditions as their citizens and EU nationals.  

Accordingly, EU countries are hosting a number of Ukrainians in their schools, and it is estimated that one-third 
of Ukrainian students are currently enrolled in the educational systems of the hosting countries (UNICEF, 2023). 
The remaining students have been internally displaced to less affected areas within Ukraine, while others have 
received  online  or  blended  education  (Angrist  et  al.,  2022).  On  the  other  hand,  the  persistent  electricity 
interruptions and blackouts make the adoption of online learning challenging, and the ongoing conflict may result 
in an increase of Ukrainian people relocating to EU countries. 

As  a  result,  several  EU  countries  are  facing  the  challenge  of  accommodating  the  sudden  arrival  of  Ukrainian 
students into their educational systems. The countries closest to Ukraine, which historically had strong ties with 
the country and the highest number of Ukrainian citizens residing within their borders before the invasion, are 
the most affected. In December 2022, Poland was hosting the highest number of Ukrainian refugees, with around 
96,000 beneficiaries of temporary protection. Germany followed with approximately 94,000, and Czechia with 
43,000, as shown in Table 5. However, when considering the relative impact of Ukrainian refugees compared to 
the population of the host country, Czechia (4.1%) and Estonia (2.9%) had particularly high percentages, while 
Germany had only 1.1% of temporary protection beneficiaries. Table 5 also shows that a significant portion of 

18 

 
 
Ukrainian refugees are either under 18 years old (around 32%) or between 18 and 34 years old, the age range 
for potential university students (around 24%). This highlights the importance of monitoring the impact of this 
migration phenomenon on the local educational systems of Member States. However, it is also important to note 
that there is uncertainty about the rates of Ukrainian children residing in EU countries that have enrolled in local 
schools. While some Ukrainian students in EU countries are indeed enrolled in local schools, others follow online 
education from their Ukrainian schools.  

To accommodate Ukrainian students into the EU, it is necessary for the educational systems to adapt accordingly. 
First,  this  required  a  short-notice  boost  of  the school  capacity  in  terms  of  infrastructure  and  staff  (European 
Commission, 2022). It is also important to remove administrative and financial barriers to access education. For 
instance, making more flexible and lighter the procedure of enrolment or providing targeted financial support to 
cover  school  fees,  transports  and  meal  expenses  for  Ukrainian  students  (European  Commission,  2022). 
Moreover, an effective inclusion of these pupils requires developing flexible curricula in schools that are tailored 
to meet the unique needs of these students, and by offering them skills that will be useful for their future career 
paths upon their return to Ukraine (OECD, 2022). It is also important to provide language support to ensure that 
students  do  not  fall  behind  in  their  subject  content,  as  mastery  of  the  host  country's  language  is  crucial  for 
academic success (OECD, 2022). However, it is equally essential to take into account the social and emotional 
needs of the students, as these may differ from those of their peers (Cerna, 2019). For instance, some students 
may  require  access  to  counselling  services,  psychological  advice,  and  extracurricular  activities  to  help  them 
integrate  into  their  new  environment  and  form  connections  with  local  communities.  As  a  consequence,  the 
increase in Ukrainian students coming to the EU may lead to a rise in demand for certain professional roles, such 
as linguists, interpreters, psychologists, counsellors, and social workers (UNSECO, 2O22).  

More generally, this entails a growth of the  resource and expenditures in education and several EU  Member 
States have implemented financial measures in response (UNESCO, 2O22). Italy, for instance, allocated an extra 
€1 million to support the integration of Ukrainian students, while Romania provided subsidies to educational 
institutions to accommodate more students. Slovenia decided to cover expenses related to the recognition and 
evaluation of education for persons granted international protection. Furthermore, many countries are offering 
financial support directly to students, with a focus on higher education. Austria waived tuition fees for Ukrainian 
students enrolled in Austrian universities, while Lithuania is sponsoring studies for Ukrainian citizens, and some 
institutions are waiving fees or offering significant discounts. Bulgaria is offering a reduction in tuition fees for 
over 1,000 Ukrainian students, and dormitory fee exemptions are under consideration. 

Considering the factors described above, the migration of Ukrainian boys and girls may have relevant effects on 
the  educational  systems  of  European  countries,  and  therefore  on  the  indicators  addressed  in  this  report. 
However, it is difficult to provide an estimate of the magnitude and duration of these potential effects. There is 
indeed a lot of uncertainty about the exact number of Ukrainian students enrolled in schools in EU countries, 
and it is even more uncertain how long these students will remain in the host country. 

On the other hand, a clearer consequence of Russia’s invasion of Ukraine has been the onset of energy crisis and 
generating  consequentially  an  increase  in  inflation  rates  in  all  the  EU  countries.  This  can  have  a  detrimental 
impact on the purchasing power of households and make it increasingly difficult for families to afford the costs 
associated with education, such as tuition and fees, school lunches and school trips. In addition, the impact of 
the price increase is likely not to be equally distributed across population (Gürer and Weichenrieder, 2020; He et 
al., 2021). In many EU countries (i.e. Belgium, Greece, Italy, Ireland, Latvia, Lithuania, the Netherlands and Spain) 
inflation has affected the poorest households, with inflation rates between 2.6 percentage points (in the case of 
Ireland) and 5.3 percentage points (in Italy) higher than those experienced by the wealthiest households (Villani 
and Vidal Lorda, 2022). As a result, the rise of inflation rates can lead to reduced access to education, particularly 
for low-income families and students.  

Moreover, inflation can cause the value of savings and investments to decrease, which can adversely affect the 
financial stability of households (Mosk and Welz, 2022). As a result, students may face difficulties in financing 
their education through such sources as student loans. This is compounded by the rise in the cost of living, which 
can place a significant strain on low-income students, forcing them to take on additional debt or work longer 
hours to cover their expenses. 

19 

 
 
From  this  perspective,  the  energy  crisis  and  the  rising  inflation  rates  are  likely  to  worsen  wealth  distribution 
issues, which may lead to an increase in educational inequalities in terms of educational attainment within the 
EU countries.  

Table 5. Refugees-hosting countries in European Union. 

Ukrainian 
Beneficiaries 
of temporary 
protection 

Ukrainian 
Beneficiaries of 
temporary protection 
% of the hosting 
country population 

956760 
936375 
431310 
158310 
146805 
145005 
102550 
99930 
94975 
86485 
70495 
65835 
65110 
61760 
45560 
44845 
44225 
38235 
35520 
31815 
28690 
21315 
18810 
12440 
7600 
4490 
1505 
385 

2.54% 
1.12% 
4.10% 
0.33% 
2.15% 
0.25% 
0.58% 
0.52% 
1.75% 
0.96% 
1.39% 
0.10% 
2.32% 
0.53% 
0.44% 
0.81% 
0.43% 
2.87% 
1.89% 
0.54% 
0.30% 
0.20% 
0.48% 
1.38% 
0.36% 
0.70% 
0.29% 
0.98% 

Ukrainian 
beneficiaries with 
less than 14 years, 
as % total 
Ukrainian 
beneficiaries 
36% 
26% 
24% 
27% 
29% 
30% 
23% 
29% 
26% 
26% 
n.a. 
0% 
28% 
24% 
25% 
24% 
26% 
27% 
24% 
28% 
n.a. 
24% 
27% 
21% 
26% 
26% 
25% 
19% 

Country 

Poland 
Germany 
Czechia 
Spain 
Bulgaria 
Italy 
Netherlands 
Romania 
Slovakia 
Austria 
Ireland 
France 
Lithuania 
Belgium 
Sweden 
Finland 
Portugal 
Estonia 
Latvia 
Denmark 
Hungary 
Greece 
Croatia 
Cyprus 
Slovenia 
Luxembourg 
Malta 
Liechtenstein 

Ukrainian 
beneficiaries with less 
than 18 years, as % 
total Ukrainian 
beneficiaries 

45% 
34% 
32% 
34% 
36% 
37% 
30% 
35% 
34% 
35% 
n.a. 
0% 
37% 
34% 
32% 
31% 
32% 
35% 
31% 
35% 
n.a. 
30% 
34% 
28% 
35% 
33% 
31% 
31% 

Ukrainian 
beneficiaries 
from 18 to 34 
years, as % total 
Ukrainian 
beneficiaries 
22% 
22% 
29% 
25% 
20% 
23% 
28% 
24% 
25% 
21% 
n.a. 
32% 
23% 
27% 
25% 
27% 
30% 
26% 
25% 
24% 
n.a. 
24% 
23% 
27% 
18% 
20% 
23% 
19% 

Source: Eurostat (Statistics | Eurostat (europa.eu). Note: All data on Ukrainian beneficiaries of temporary protection refers 
to  December  2022. Population  data  for  EU  countries  (used  in  the  third  column)  refers  to  2022  and  were  obtained  from 
Eurostat. The values are sorted by the number of refugees recorded in each country (second column). 

20 

 
 
 
 
5  Conclusions 

This report describes the projected trajectories of early leavers from education and training, as well as tertiary 
education attainment in the EU-27. The estimates indicate that the EU-27 is likely to achieve its targeted trends. 
However, the accuracy of the forecast for the percentage of early leavers is particularly uncertain, and there is 
significant variation across countries. Some countries, such as Greece, Ireland, Poland, and Sweden, have already 
achieved the 9% target in 2019, while others, such as Spain, Italy, and Hungary, have reported high levels of early 
leavers and are less likely to meet the 9% target by 2030. On the other hand, the EU is projected to surpass the 
45%  target  for  tertiary  education  attainment  by  a  significant  margin  in  2030.  Nonetheless,  certain  countries, 
including Italy, Hungary, Germany, and Czechia, are less likely to achieve the target value by 2030. Overall, the 
results suggest that EU countries will see a significant increase in this indicator over the next decade. 

The empirical analyses show that the impact of Covid-19 did not have a short-term effect on the two indicators, 
ELET and TEA. However, the analysis of the current literature suggests that Covid-19 disruption is linked to factors 
that can potentially have both negative and positive impacts on educational attainment among the population 
of EU countries. The bidirectional effect of Covid-19 on educational attainment makes it challenging to predict 
its influence on ELET and TEA indicators over the next decade.  Nonetheless, the most significant consequence 
appears to be the disparities in the distribution of these effects among different groups of the population, which 
raises  concerns  about  the  potential  increase  in  educational  inequality  within  society.  Therefore,  continuous 
monitoring of these indicators is crucial, which should devote particular attention to disparities across students 
with different socioeconomic backgrounds. 

In addition, the report provides insights into the potential consequences of Russia’s invasion of Ukraine on early 
leavers  from  education  and  higher  education  attainment.  The  migration  crisis  generated  by  the  Russian 
aggression has led to a significant influx of Ukrainian students into EU education systems. These young adults 
have particular learning, social and psychological needs and are potentially associated with a high risk of leaving 
education. Some EU member states, such as Poland and Czech Republic, are more exposed to this issue and may 
experience more relevant effects on the educational indicators. However, while the migration of Ukrainians may 
impact ELET and TEA indicators, it's challenging to provide a precise estimate due to the uncertain number of 
Ukrainian students enrolled in schools in EU countries and their length of stay. Nonetheless, the impact may be 
more noticeable considering the energy crisis and consequent inflation surge in all European countries triggered 
by  the  Russian  invasion.  Studies  available  in  the  literature  suggest  that  the  increase  in  inflation  may 
disproportionately  affect  disadvantaged  families  and  exacerbate  social  inequalities,  particularly  in  some 
European  countries.  Therefore,  it  is  central  to  focus  on  social  differences  in  educational  attainment  and 
implement targeted compensatory policies to support more disadvantaged students. 

In summary, this report presents an analysis of future developments in educational achievement indicators in 
the EU, under the assumption of no policy changes. Based on these projections, may be a possibility of increasing 
the 2030 target levels during the 2026-30 cycle of the strategic framework for European cooperation in education 
and training, subject to the preference of Member States. Nevertheless, it is important to monitor the situation 
closely, particularly given the strong elements of unpredictability induced by the Covid-19 pandemic and Russia's 
invasion of Ukraine. 

21 

 
 
 
 
 
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25 

 
 
 
 
 
List of abbreviations and definitions 

AIC             Akaike information criteria 

BIC             Bayesian information criteria 

ELET 

Early leavers from education and training 

HVGDH     Gross domestic product at current market prices per hour worked 

ISCED         International Standard Classification of Education  

RMSE         Root-mean-square error 

TEA 

Tertiary education attainment 

List of figures 

Figure 1. ELET forecast – average EU 27 ............................................................................................... 12 

Figure 2. ELET by EU country: 2019 (observed values) – 2030 (forecast) ............................................. 13 

Figure 3. TEA forecast – average EU 27 ................................................................................................. 14 

List of tables 

Table 1. Variables’ definitions: ELET forecast model .............................................................................. 9 

Table 2. Variables’ definitions: TEA forecast model .............................................................................. 10 

Table 3. ELET estimations ...................................................................................................................... 11 

Table 4. TEA estimations ....................................................................................................................... 13 

Table 5. Refugees-hosting countries in European Union. ..................................................................... 20 

26 

 
 
 
 
 
 
 
 
Annexes 

Annex 1. Trends in the employment and income prospects  

Figure A1. Trend in Total unemployment rate: ELET forecast model 

Source: Author’s elaboration based on AMECO data. 

Figure A2. Trend in the income prospects (HVGDH): TEA forecast model 

Source: Author’s elaboration based on AMECO data. 

27 

 
 
 
 
 
 
 
Annex 2. Descriptive statistics  

Table A1. Descriptive statistics: ELET forecast model 

Variable 

Obs. 

Mean 

Std. Dev. 

Min 

Max 

Early leavers form education and training 

431 

14.058 

8.892 

Share of females’ with low education, age 35-
44 

502 

18.088 

12.776 

4.1 

2.4 

50 

68.8 

Share of males’ with low education, age 45-54 

509 

24.846 

17.747 

3.7 

82.1 

Total unemployment rate 

522 

8.313 

3.300 

2.2 

18.1 

Source: Author’s elaboration using Stata 

Table A2. Descriptive statistics: TEA forecast model  

Variable 

Obs. 

Mean 

Std. Dev.  Min 

Max 

Share of population with tertiary education, 25-34 

449 

30.660 

11.089 

7.0 

56.2 

Share of adults with tertiary education, age 55-64 

518 

22.558 

9.413 

6.0 

50.9 

HVGDH 

521 

0.311 

0.958 

0.0 

7.7 

Source: Author’s elaboration using Stata 

28 

 
 
 
 
 
 
Annex 3. Country-level forecasts 

Figure A3. ELET forecast: in-sample EU country  

Source: Author’s elaboration using Stata 

Figure A4. ELET forecast: out-sample EU country 

Source: Author’s elaboration using Stata 

29 

 
 
 
 
Figure A5. TEA forecast: in-sample EU country  

Source: Author’s elaboration using Stata 

Figure A6. TEA forecast: out-sample EU country  

Source: Author’s elaboration using Stata 

30 

 
 
 
 
 
Annex 4. Robustness checks 

This section presents an analysis conducted to assess the robustness of the models used to forecast ELET and 
TEA by exploring alternative specifications.  

Table A3 reports the estimates of the main ELET model (Equation 1) and the estimates of coefficients derived 
from the alternative specifications. Models (2) and (3) present the estimates associated with different time lags 
of the total unemployment rate13, while model (4) uses an older age bracket for women education (i.e., 45-54 
years old), and model (5) employs older age brackets for both men and women. Model (6) does not differentiate 
between men and women education and uses the share of adults with low education, age 45-54. 

The estimates associated with the six models are relatively similar, both in terms of coefficients and the statistical 
indices used to assess the goodness of the forecasting. Notably, models (5) and (6) show good performance, 
especially  in  terms  of  BIC  (which  is  lower  than  the  main  model).  However,  the  AIC  and  R-squared  statistics 
indicate that the preferable model is (1). 

Furthermore, the forecasts of ELET remain relatively stable across the six models, as displayed in Figure A5. This 
indicates that the ELET forecast presented in the main report is robust and reliable.  

Table A3. ELET estimations with alternative specifications 

VARIABLES 

Δ log(ELET) 

Δ log(ELET) 

Δ log(ELET) 

Δ log(ELET) 

Δ log(ELET) 

Δ log(ELET) 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

Δ log(share of women with low 
education, age 35-44) 

Δ log(share of women with low 
education, age 45-54) 

Δ log(share of men with low education, 
age 45-54) 

Δ log(share of men with low education, 
age 55-64) 

Δ log(share of adults with low 
education, age 45-54) 

Δ log(total unemployment rate), lag 7th 

Δ log(total unemployment rate), lag 6th 

Δ log(total unemployment rate), lag 8th 

Constant 

Country fixed effects 
Year dummy 
Observations 
N. Countries 
R-squared 
RMSE out of sample (5 years ahead) 
BIC 
AIC 

0.077* 

(0.052) 

0.018 

0.032 

(0.052) 

(0.052) 

0.247*** 

0.211*** 

(0.081) 

(0.081) 

0.172*** 

0.193*** 

0.209*** 

0.175*** 

(0.055) 

(0.062) 

(0.062) 

(0.061) 

0.268*** 

(0.091) 

-0.222*** 
(0.075) 

-0.200*** 
(0.073) 

0.431*** 

(0.092) 
-0.225*** 
(0.072) 

-0.010 
(0.007) 
yes 
no 
272 
18 
0.108 
0.108 
-574.25 
-559.82 

-0.008 
(0.007) 
yes 
no 
304 
18 
0.102 
0.112 
-630.71 
-615.85 

-0.010 
(0.006) 
yes 
no 
304 
18 
0.105 
0.109 
-633.68 
-622.53 

-0.265*** 
(0.085) 

-0.020*** 
(0.005) 
yes 
no 
264 
18 
0.136 
0.107 
-620.21 
-634.51 

-0.158** 
(0.075) 

-0.021*** 
(0.006) 
yes 
no 
264 
18 
0.057 
0.110 
-561.71 
-547.41 

-0.157** 
(0.080) 
-0.020*** 
(0.006) 
yes 
no 
264 
18 
0.055 
0.107 
-561.14 
-546.84 

Notes: OLS estimates. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The first column reports the estimates associated 
with the main model (see Table 3). Source: Author’s elaboration using Stata 15. 

13 It is worth noting that in order to determine the optimal time lag for the main model (lag 7), all possible time lags were tested. In Table A3, 

only lags 6 and 8 are reported.  

31 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A5. ELET forecast – Main model and alternative specifications 

Source: Author’s elaboration using Stata 15. 

Similarly, robustness analyses were conducted to test the TEA models. Table A4 presents the results of the main 
models (as described in Equation 2 - model (1)) and alternative specifications. Models (2) and (3) display the 
estimates with different time lags for labour productivity, while model (4) uses a younger age bracket for adult 
education (i.e. 45-54 years old). Model (5) differentiates between women and men for the variable of the share 
of people with higher education, similar to the approach used in ELET. Finally, model (6) shows the estimates of 
the model that uses an alternative definition of labour productivity based on average annual hours worked per 
person (as used in Vera-Toscano, 2015). 

The estimates do not differ substantially from the main model (model (1)). Looking at statistical indicators, they 
suggest that model (6) has a particularly low R-squared, justifying the use of HVGDH as an indicator of labour 
productivity in the main model. Models (4) and (5) have, instead, high R-squared values, but they exhibit a worse 
fit in terms of BIC and AIC. 

Finally, Figure A6 demonstrates that alternative specifications of the main model do not significantly affect the 
forecast of TEA. Hence, the results in the main section appear to be robust and reliable. 

32 

 
 
 
 
 
Table A4. TEA estimations with alternative specifications 

(1) 

(2) 

(3) 

(4) 

(5) 

(6) 

Δ 
log(tertiary 
education 
attainment) 

Δ 
log(tertiary 
education 
attainment) 

Δ 
log(tertiary 
education 
attainment) 

Δ 
log(tertiary 
education 
attainment) 

Δ 
log(tertiary 
education 
attainment) 

Δ 
log(tertiary 
education 
attainment) 

0.134*** 

0.133*** 

0.118*** 

(0.039) 

(0.039) 

(0.041) 

0.086* 

(0.047) 

VARIABLES 

Δ log(share of adults with high 
education, age 55-64) 

Δ log(share of adults with high 
education, age 45-54) 

Δ log(share of women with high 
education, age 55-64) 

Δ log(share of men with high 
education, age 55-64) 

Δ%(labour productivity), lag 7th 

Δ%(labour productivity), lag 6th 

0.321*** 
(0.064) 

Δ%(labour productivity), lag 8th 

0.353*** 
(0.070) 

Δ%(h worked), lag 11th 

Constant 

Country dummies 
Year dummy 
Observations 
N. Countries 
R-squared 
RMSE out of sample (5 years 
ahead) 
BIC 
AIC 

0.014*** 
(0.004) 
yes 
no 
300 
18 
0.125 

0.014*** 
(0.004) 
yes 
no 
300 
18 
0.124 

0.397*** 

(0.056) 

0.299*** 
(0.060) 

0.070 

(0.046) 

0.288*** 

(0.046) 
0.283*** 
(0.059) 

0.310*** 
(0.066) 

0.013*** 
(0.004) 
yes 
no 
283 
18 
0.104 

0.007* 
(0.004) 
yes 
no 
304 
18 
0.222 

0.011*** 
(0.004) 
yes 
no 
304 
18 
0.262 

-0.210 
(0.182) 
0.028*** 
(0.003) 
yes 
no 
225 
18 
0.023 

0.034 

0.032 

0.033 

0.037 

0.034 

0.033 

-1099.07 
-1087.96 

-1088.11 
-1099.23 

-1044.66 
-1033.72 

-1052.14 
-1040.99 

-1065.97 
-1051.10 

-2140.55 
-1064.74 

Notes: OLS estimates. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The first column reports the estimates associated 
with the main model (see Table 4). Source: Author’s elaboration using Stata 15. 

33 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure A6. TEA forecast – Main model and alternative specifications 

Source: Author’s elaboration using Stata 15. 

34 

 
 
 
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