# Investigating Participation Mechanisms in EU Code Week

Christel Sirocchi<EMAIL_ADDRESS>0000-0002-5011-3068 DiSPeA,
University of UrbinoPiazza della Repubblica, 13UrbinoItaly61029 ,  Annika
Ostergren Pofantis  DG Connect, European CommissionBrusselsBelgium
<EMAIL_ADDRESS>and  Alessandro Bogliolo  DiSPeA, University of
UrbinoPiazza della Repubblica, 13UrbinoItaly61029
<EMAIL_ADDRESS>
(2018)

###### Abstract.

Digital competence (DC) is a broad set of skills, attitudes, and knowledge for
confident, critical and responsible use of digital technologies in every
aspect of life. DC is fundamental to all people in conducting a productive and
fulfilling life in an increasingly digital world. However, prejudices,
misconceptions, and lack of awareness reduce the diffusion of DC, hindering
digital transformation and preventing countries and people from realising
their full potential. Teaching Informatics in the curriculum is increasingly
supported by the institutions but faces serious challenges, such as teacher
upskilling and support, and will require several years to observe sizeable
outcomes. In response, grassroots movements promoting computing literacy in an
informal setting have grown, including EU Code Week, whose vision is to
develop computing skills while promoting diversity and raising awareness of
the importance of digital skills. Code Week participation is a form of public
engagement that could be affected by socio-economic and demographic factors,
as any other form of participation. The aim of the manuscript is twofold:
first, to offer a detailed and comprehensive statistical description of Code
Week’s participation in the EU Member States in terms of penetration,
retention, demographic composition, and spatial distribution in order to
inform more effective awareness-raising campaigns; second, to investigate the
impact of socio-economic factors on Code Week involvement. The study
identifies a strong negative correlation between participation and income at
different geographical scales. It also suggests underlying mechanisms driving
participation that are coherent with the ”psychosocial” and the ”resource”
views, i.e. the two most widely accepted explanations of the effect of income
on public engagement.

digital skills, participation, awareness, income, Code Week

††copyright: acmcopyright††journalyear: 2018††doi: XXXXXXX.XXXXXXX††journal:
JACM††journalvolume: 37††journalnumber: 4††article: 111††publicationmonth:
8††price: 15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Social and professional
topics Computational thinking††ccs: Social and professional topics Computing
literacy††ccs: Social and professional topics Informal education††ccs: Social
and professional topics Geographic characteristics

##  1\. Introduction

In a highly interconnected world, where social cohesion and sustainable
development heavily depend on the competence of all citizens, recognising and
promoting valuable skills in the population is of critical importance. In
recent years, there has been a growing understanding that basic literacy must
include digital competence, which goes beyond basic digital skills but wholly
contributes to a young person’s skill set and ability to conduct a productive
and fulfilling life in an increasingly digital world. Digital competences have
emerged as fundamental for all citizens, particularly in the light of the
COVID-19 pandemic (Blaskó et al., 2021).

Late, insufficient or inadequate introduction of Informatics in compulsory
education hinders the widespread diffusion of digital literacy (Bocconi et
al., 2022), prevents citizens from recognising that Computer Science is the
fundamental discipline underpinning the digital transformation (Caspersen et
al., 2018), and leads to misunderstanding of common phrases, such as Digital
Competence (DC) or Computational Thinking (CT). It is still a common belief
that computing skills are professional skills to be acquired in the pursuit of
a specific career path and of little use in everyday life. On the contrary, DC
is a set of essential skills, attitudes and knowledge, broadly defined as the
confident, critical and responsible use of digital technologies for working,
learning, leisure, self-development and participating in society (Commission,
2018) (Carretero et al., 2017). DC entails understanding the underlying logic
and mechanisms, the opportunities, risks and limitations of evolving digital
technologies, as well as the ethical and legal principles involved when
engaging with them. It empowers citizens to use modern technologies in
supporting collaboration, creativity and innovation toward personal, social,
and commercial goals while enabling active citizenship and social inclusion
(Esteve-Mon et al., 2020). The safe and responsible use of digital tools
requires a critical, reflective, open-minded and forward-looking attitude to
these technologies(Commission, 2018). Hence, digital transformation, other
than acquiring digital tools, entails a deeper awareness and a change of
attitude toward the role of technology in our society. CT is one of the most
critical areas making up this digital competence (Esteve-Mon et al., 2020) and
refers to the thinking required in formulating a problem and expressing its
solution so that an executor, be it human or machine, can carry out (Wing,
2017). CT entails decomposing problems, recognising patterns, extracting basic
principles, developing algorithms, implementing automated solutions, and
debugging.

Critical aspects to building advanced digital skills are providing lifelong
learning opportunities, starting as early as possible (primary education),
embedding digital skills within every subject, and regularly revisiting skills
and approaches depending on the ever-evolving technologies (Ala-Mutka et al.,
2008). Early exposure to computing concepts is also relevant in the context of
inclusion, diversity and gender, promoting self-efficacy and tackling
stereotypes before prevailing norms become entrenched. Schools, particularly
at the primary and lower-secondary levels, are at the forefront of this
digital transformation of societies and economies, being the first providers
of digital education for the future generation of citizens. Young students
begin to create with digital technology through innovative and encouraging
approaches and gain a solid understanding of the digital world while
developing creativity, problem-solving, communication, collaboration and
analytical skills (Manches and Plowman, 2017). The EU Member States and many
more countries worldwide have increasingly supported teaching computational
thinking and other computer science notions in a formal education setting
(Bocconi et al., 2022). Integrating these concepts into the primary and lower
secondary curriculum is relatively new and faces several challenges, the most
significant of which is teacher upskilling and support. Recommendations for
the effective integration of DC and CT skills into compulsory education focus
on providing teacher professional development, raising awareness about the
purpose and benefits of CT skills, and prioritising measures to address gender
balance, equity, and inclusion (Bocconi et al., 2022). Among effective support
measures for teachers are collaborative support among teachers, such as
sharing experiences and networking, and ample access to high-quality learning
and teaching materials.

Recognising the urgent need to provide the young population with accessible
opportunities to develop digital skills, many institutions and organisations
have promoted coding literacy in a non-formal context. Among them is EU Code
Week (Cod, 2022), launched in 2013 and supported by the European Commission as
part of the Digital Single Market Strategy. Code Week aims to help more and
more people learn the fundamentals of coding and computational thinking while
raising awareness about the importance of computing skills for all citizens,
promoting diversity and battling stereotypes. It is a further opportunity for
young students to explore digital creativity and coding and develop digital
skills and attitudes. It also provides schools and teachers with free
professional development and offers a platform where teachers can connect and
share experiences. Teachers voluntarily join the initiative by organising
coding activities in their institute and adding them to the codeweek.eu map.
The level of Code Week participation, in terms of events, teachers and
institutes involved, is the most suitable metric to assess the initiative’s
impact. Code Week is an awareness-raising campaign rather than a coding
education provider, so its success is better defined by how many and which
people it reached. Characterising Code Week participation patterns would help
assess the campaign’s effectiveness and help design better outreach
strategies. Like other forms of voluntary participation, teacher engagement is
expected to be affected by the contextual factors of the socio-economic and
educational setting (Filetti and Janmaat, 2018). Investigating factors both at
the society and individual level would shed light on the underlying mechanisms
driving Code Week participation.

The aim of this paper is twofold: to provide a detailed and comprehensive
statistical description of the participation in EU Code Week and to analyse
its relationship with socio-economic factors by integrating Code Week data
with multiple open data sets. Code Week participation is intended as that of
teachers and hosting institutions organising events within the initiative
rather than that of attendees taking part in the activities. While the first
form of engagement is primarily voluntary, the latter is not because most
events take place in schools and students are not given a choice to
participate. Four types of participation metrics, i.e. penetration,
demographic, spatial and retention metrics, were calculated for EU countries
to characterise engagement at different levels and distinguish between
endogenous factors, i.e. affected by the country’s internal dynamics, and
exogenous factors, i.e. physiological to the Code Week campaign format. The
event number distribution, the growth trends, and the location of new teachers
indicate a degree of spatial associativity: teachers are more likely to
participate if there are other participating teachers in their proximity. This
suggests the presence of an offline network fostering collaboration among
teachers in the territory and fostering Code Week participation.

The study also identified a strong negative linear correlation between Code
Week participation, quantified as event number normalised per population, and
the GDP per capita. This relationship was further investigated in Italy at the
regional, provincial and municipal levels. Other socio-economic metrics
related to the development level, wealth inequality, and infrastructure
quality were considered but did not have significant explanatory power when
controlling for GDP, suggesting that the country’s resources directly affect
participation. Previous results on the effect of income and income inequality
on social and civic engagement identified a positive correlation. The study
argues that the peculiar characteristics of Code Week are responsible for the
unusual trend observed and discuss how the case under consideration is in
agreement with both the ”psychosocial” and ”resource” explanations of the
effect of income on participation. Finally, it was observed that the most
participating less developed regions are also those with lower levels of
digital literacy and access to technology, and less established computer
education in their curriculum, meaning that Code Week is more prevalent where
it is most needed.

The remainder of the paper is structured as follows: Section 2 revises
relevant literature related to the topic, Section 3 describes the data sets
used in the investigation, Section 4 defines the participation metrics
calculated on the Code Week data, Sections 5 and 6 present and discuss the
analysis results, respectively, Section 7 draws conclusions and discusses
future work.

##  2\. Previous Work

###  2.1. Computing Education in Europe

Computational thinking (CT) is generally intended as the ability to formulate
a problem and express its solution(s) such that a human or machine executor
can effectively carry out (Wing, 2017). CT and other digital skills have been
recognised as fundamental in enabling citizens to understand and operate in
the digital society. The European Commission elected ”A Europe fit for the
digital age” as one of the six priorities for 2019 - 2024, intending to
empower the population with a new generation of technologies (Commission,
2019). The Digital Education Action Plan 2021-2027 includes ”Enhancing digital
skills and competences for the digital transformation” in its priority areas
(Commission, 2021). Ministries of Education of most EU Member States have
responded well to Europe’s call to action, revising their statutory curriculum
by introducing computing education in primary and secondary schools.

The Joint Research Centre (JRC) of the European Commission has produced a
technical report in 2016 (Bocconi et al., 2016) and an update in 2022 (Bocconi
et al., 2022) which present recent policies and grassroots movements in
promoting computer education for schoolchildren. The study reports that 18 EU
Member States (AT, BE, CY, EL, ES, FI, FR, HR, HU, IE, LT, LU, MT, PL, PT, RO,
SE, SK) and 5 other EU countries (CH, NO, RS, RU, UK-ENG) have introduced CT
and basic computing concepts in their statutory curriculum. In these
countries, except for 6 (BE, CY, ES, HR, IE, RO), basic computer science
concepts are compulsory in both primary and lower secondary schools. Other EU
Member states have a strategic plan to introduce CT skills (CZ, IT, SI), are
running school pilots (DK), or are finalising a draft curriculum (BE) (Bocconi
et al., 2022).

CT skills integration follows three main approaches (Bocconi et al., 2016):
(i) as a cross-curricular theme addressed in all subjects, with all teachers
sharing the responsibility for developing CT skills; (ii) as part of a
separate subject related to computing (e.g. Informatics); (iii) integrated
within some other curriculum subject (e.g., Maths, Technology). The cross-
curricular approach is the most common in primary education, often combined
with one of the other two. At this level, teachers cover several subjects and
often do not have specialised subject-area training and expertise. On the
other hand, in lower secondary education, CT skills are either addressed in a
separate subject or within other subjects. In this case, appointed teachers
generally have domain-specific expertise (Bocconi et al., 2022).

In all assessed countries, teachers have a degree of autonomy in delivering
the curriculum and handling implementation. Teachers adopt age-appropriate
pedagogical approaches, often borrowed from other learning contexts. Primary
teachers report using playful, hands-on activities with block-based visual
programming, programmable robots and unplugged methods, where students are
encouraged to work in groups and learn from their mistakes. Lower-secondary
teachers focus on fostering problem-solving skills and promoting student
autonomy and self-efficacy by working on real-life problems and encouraging
students to create their programs, applications, video games, etc. A key
aspect spanning both levels is the iterative approach and debugging to
identify and correct errors while emphasising the value of making mistakes in
strengthening one’s expertise (Bocconi et al., 2022).

The scarcity of teaching tools and resources and the shortage of teachers with
suitable background and adequate capabilities are among the main challenges to
the successful integration of CT skills into the curriculum (Li, 2021):
Computer Science teachers are scarce and difficult to recruit (Li, 2021);
generalist teachers often hesitate to include CT in their practices due to the
lack of confidence in their programming capabilities and deep understanding of
CT concepts (Zhang et al., 2020). To overcome the lack of resources and
competences, sustained financial support should be provided to schools so that
teachers can attend professional development programs and adequate digital
infrastructure is built. Moreover, whereas CT is taught as a cross-curricular
theme, collaboration and division of tasks and responsibilities among teachers
should be carefully planned and coordinated (Bocconi et al., 2016).

###  2.2. Computing Education in informal settings

Many institutions and organisations have offered opportunities to develop CT
and digital skills in an informal environment to support and supplement
computing education in the curriculum. Local institutions frequently organise
workshops and summer camps to introduce students to programming and emphasise
possible careers in computer science. In the last decades, many international
initiatives have stemmed, most notably Code.org, CoderDojo and EU Code Week.
Code.org is a non-profit organisation that facilitates access to computing
education for students, particularly for minorities and other disadvantaged
groups. It is a leading provider of K-12 computer science curricula in the
United States and is behind the successful ”Hour of Code” campaign, which has
engaged over 15% of students worldwide (Code.org, 2022). The CoderDojo
movement is a global network of freely accessible programming clubs managed by
local volunteers. Young students begin learning programming language through
hands-on projects, e.g. creating an app or a game, building a website, and
exploring digital technologies (CoderDojo, 2022).

EU Code Week is a grassroots initiative promoted by the European Commission to
bring coding and digital literacy to everybody. It relies on a network of
volunteer ambassadors in participating countries, who promote the initiative,
help spread the vision of Code Week, and encourage the organisation of coding
events within their area of relevance. Leading teachers, forming a network of
over 450 teachers across Europe, connect educators, companies and communities
interested in organising activities. They also hold professional development
training and promote the initiative locally, acting as a reference point in
the community (Cod, 2022). EU Code Week also offers a large body of teaching
and learning materials in the form of MOOC, presentations and tool-kits to
assist teachers in organising events. Code Week events include not only in-
school activities but also introductory and specialised workshops,
motivational talks, and open door days (Moreno-León and Robles, 2015).

###  2.3. Effect of socio-economic factors on participation

Civic and social participation are the two prominent ways individuals engage
with their community. The former generally refers to membership and
involvement in voluntary associations, such as neighbourhood committees,
charitable organisations, political parties, peace or environmental movements.
The latter is often defined as the informal bonds between people, particularly
friends and family, and differs from the first one for the lack of formal and
institutionalised ties. Both forms of participation have crucial effects on
all aspects of society. Civic engagement in general, and political
participation in particular, are fundamental to democracy (Verba et al.,
1995). It is the mechanism by which citizens are able to communicate their
needs and mobilise pressure for a government response (Dubrow, 2014). One of
its cornerstones is that participation must be voluntary so that citizens can
express their humanitarian and civic concerns and pursue their self-interest.
Voluntarism helps shape the allocation of economic, social and cultural
benefits, contributing to achieving collective goals (Verba et al., 1995).
Hence, voluntarism is fundamental to democracy because it reflects the
individual’s autonomy within the society. Social participation contributes to
the so-called ”social capital”, defined as the set of social features
(networks, norms, trust) that enable participants to work more effectively
together and pursue shared objectives (Putnam, 1995). The social capital
theory attests that social networks have value and that individual virtue is
most valuable when embedded in a web of social relations. Social participation
affects many individual outcomes, such as health, employment and education,
through the beneficial effects of this shared social capital. It also has a
crucial impact on the economy through sharing knowledge and personal
experience, increasing society’s human capital and promoting social trust,
which is known to improve the functioning of markets (Lopez-de Silanes et al.,
1997).

Many research studies primarily focused on individual characteristics
affecting the choice to engage or not in social activities. However, several
empirical studies have shown how features of societies or communities as a
whole affect the degree of participation of their members. Foremost, levels of
inequality in society have been shown to affect social and civic engagement.
Oliver (Oliver, 1999) first suggested that the economic context influences
civic participation in two ways: wealthy cities have fewer social needs
promoting political action, while heterogeneous cities have more competition
for public goods prompting citizens’ interest and participation. Hence, civic
participation is lowest in homogeneous, affluent cities and highest in
diverse, middle-income cities. Solt (Solt, 2008) demonstrated that higher
levels of income inequality powerfully depress political interest,
particularly in terms of the frequency of political discussion and
participation in elections. Filetti et al. (Filetti and Janmaat, 2018)
confirmed these results and observed that economic contraction influence the
within-country distribution of active citizens but not the overall level of
participation. Waldman (Levin-Waldman, 2013) investigated the link between
income, personal autonomy and civic engagement, claiming that the lack of
economic resources limits the individual’s self-sufficiency, which in turn
hinders participation. Having confirmed that higher household income leads to
higher civic engagement, the author speculated that wage policies aimed at
reducing income inequality would result in greater democracy.

Alesina et al.(Alesina and La Ferrara, 2000) showed that participation in a
variety of social activities, e.g. religious groups, youth groups and sports
clubs, is significantly lower in unequal and more ethnically or racially
segmented societies. Ulsaner et al. (Uslaner and Brown, 2005) argued that the
effect of inequality on participation arises when disadvantaged people do not
participate, either because they do not dispose of the necessary resources or
because they have lost trust in society and believe participation would be
fruitless. The authors refer to income inequality as ”the strongest
determinant of trust” over time and geographical areas. Wilkinson et al.
(Wilkinson and Pickett, 2010) observed that higher levels of inequality relate
to lower levels of social trust, poor health, higher crime rates, and lower
levels of participation. They suggested that inequality affects these outcomes
through psychosocial processes of status differences and related distress
caused by their disadvantaged position. Rotolo et al. (Rotolo and Wilson,
2014) confirmed that income inequality correlates with all types of voluntary
activity, while the effect of racial heterogeneity and segregation vary with
volunteering type. Collins et al. (Collins and Guidry, 2018) found that lower
civic engagement is associated with feeling less safe but also pointed out
that social capital, rather than civic engagement, mediates the relationship
between inequality and the sense of safety. Studies on the effect of income
inequality on civic participation intended as charitable giving yielded
contradicting results. According to Payne et al. (Payne and Smith, 2015),
increases in inequality increase donations, although results are sensitive to
the geographical dispersion of high- and low-income households. Duquette
(Duquette, 2018), on the other hand, identified an inverse correlation between
donations by high-income households and inequality in the USA, confirming this
result in an experimental setting (Duquette and Hargaden, 2021), suggesting
that charitable giving strengthens inequality over time.

Although previous studies lead to different conclusions on the socio-economic
aspects that affect civic and social participation, depending on the context
and on the variables they focus on, most of them indicate that social
inequality and low income hinder participation. In particular, the proposed
explanations for inequality’s impact on participation fall under two main
categories. The ”psychosocial explanation” claims that inequality undermines
social cohesion, eroding social capital, trust, safety and security of
citizens (Elgar and Aitken, 2011) (Collins and Guidry, 2018). Inequality - in
terms of income, wealth or social class - inhibits volunteering, club
membership and charitable giving because it removes the conditions promoting
social interaction and collaboration, including opportunities for personal
acquaintance, equal status between citizens, and the ability to share common
goals within the society (Allport et al., 1954). Homophily, i.e. the tendency
of individuals to associate with others similar to them, also contributes to
lowering trust, solidarity, and as a result, participation (McPherson et al.,
2001). The ”resource explanation” argues instead that the availability of
resources affects participation because resources are needed to achieve
desirable outcomes. In this scenario, equal societies participate more because
they can provide collective goods more and more equally to all community
members. In contrast, in unequal societies, low income and systematic
underinvestment across a wide range of human, physical, health, and social
infrastructure limit resources held by individuals and increase the unmet need
for public goods (Lynch et al., 2000). The two explanations are not mutually
exclusive. Several studies have investigated the tenability of both
perspectives to determine whether inequality affects outcomes through
psychosocial processes or through individual or collective resources that are
made available to individuals, (Elgar and Aitken, 2011) (Lancee and Van de
Werfhorst, 2012). For instance, Lancee et al. (Lancee and Van de Werfhorst,
2012) investigated both perspectives and found that the main effects of income
inequality on civic and social participation manifest via resources held at
the individual and societal levels.

A recent study reviewed 70 studies addressing the effect of various forms of
inequality on civic engagement in terms of charitable giving, non-profit
membership and volunteering and found that higher inequality is most often
negatively correlated with civic engagement (Schröder and Neumayr, 2021).
However, the study also pointed out that the generalisability and empirical
understanding obtained from the considered studies are limited. For all forms
of participation, and volunteering in particular, several studies reported an
opposite trend, albeit often with a non-significant coefficient. Most civic
engagement research (60-80%) is from the USA, characterised by a peculiar
profile of social inequality, and is rarely coherent with results from other
countries(Schröder and Neumayr, 2021). Also, cross-country and within-country
data yield mixed results, suggesting that different mechanisms operate a
different geographical scales, even though most studies only address one
geographical scale, guided by data availability. Several confounders, namely
individual income and education, appear to moderate the effect of inequality
on engagement, but findings are mixed, hence inconclusive(Schröder and
Neumayr, 2021). Finally, the study remarks that the three chosen forms of
civic engagement are not an entirely homogeneous set of activities, so
different mechanisms and motivations can be at work, resulting in different
participation patterns. The effect of socio-economic factors on civic
engagement appears to largely depend on the type of activity considered, the
context, and the geographic scale, suggesting that previous studies conducted
in different settings offer little to no explanatory power to the case under
investigation.

##  3\. Data description

###  3.1. Code Week

The anonymised dataset of Code Week, accessed in January 2022, comprises
321,213 events created within the Code Week initiative starting from the 2014
edition. A description of the variables included in the dataset is reported in
Table 1. Only activities between 2014 and 2021 were considered, thus excluding
834 events programmed to take place in 2022. The geographic coordinates of the
events were used for remapping events on EU territory according to the
Nomenclature of territorial units for statistics (NUTS) (Eurostat, 2021c). The
current NUTS 2021 classification was applied, valid from 1st January 2021, and
dividing the economic territory of the EU and the UK into 92 macro areas at
NUTS 1, 242 regions at NUTS 2 and 1,166 provinces at NUTS 3 level. 21,419
events mapped outside of this territory were excluded from the analysis.
Geographic coordinates approximated to 3 decimal places ( $\approx$ 100
meters) identify a hosting location.

Code Week activities can be organised and added to the database all year
round. However, the vast majority of the events occur during the official Code
Week period (covering 2 weeks since 2017 edition) or the days preceding it, as
shown in Figure 1. In Austria, however, only 13% of events are scheduled
during the official Code Week period and 25% of all events in the country (607
out of 2442) were registered to take place on two dates in 2021, suggesting a
bulk upload mechanism that might facilitate creating and uploading events,
hence overestimating this country’s participation. Events with more than 1,000
participants (632 events) or with an average age below 3 (455) or above 60
(252) were regarded as potentially erroneous entries and excluded from the
analysis.

Figure 1. Distribution of EU events from 2014 and 2022. Code Week official
week(s) are delimited by vertical lines

Table 1. Code Week events dataset Variable Name |  Variable Description |  Availability
---|---|---
geoposition |  geographical coordinates of the event location in reference system ”EPSG:4326” |  All
country_iso |  country where the event is taking place, as declared by the event creator |  All
start_date |  starting time and date of the event |  All
end_date |  ending time and date of the event |  All
creator_id |  ID number uniquely identifying an event creator |  All
participants_count |  number of participants at the event |  Report
average_participant_age |  average age of participants to the event |  Report
percentage_of_females |  percentage of female participants |  Report
reported_at |  time and date at which the report was submitted by the event creator |  Report
codeweek_for_all |  ID code characterising event within the CodeWeek4all initiative |  CodeWeek4All
organizer_type |  type of organising institute, selecting 1 from: school, non profit, private business, other, library, club |  Report
activity_type |  type of organised event, selecting 1 from: open-online, invite-in-person, open-in-person, invite-online, open-online & open-in-person |  All
language |  language in which the event is conducted |
tags |  1 or more tag words chosen by the event creator |  All
themes |  the main theme of the event, selecting at least 1 from from: Basic programming concepts, Playful coding activities, Motivation and awareness raising, Visual/Block programming, Software development, Promoting diversity, Robotics, Mobile app development, Art and creativity, Unplugged activities, Web development, Other, Hardware, Augmented reality, Data manipulation and visualisation, Internet of things and wearable computing, Game design, 3D printing, Artificial intelligence |  All
audiences |  the target audience for the event, selecting at least 1 from: pre-school children, elementary school students, high school students, graduate students, employed adults, unemployed adults |  All

###  3.2. Socio-economic and demographic statistics

Population data relating to NUTS 0, NUTS 2 and NUTS 3 statistical territories,
i.e. European countries, regions and provinces, respectively, were retrieved
from the Regional demographic statistics page of the Rural Development Data
Section published by Eurostat (Eurostat, 2021d). The dataset was last updated
on 03-06-2021 and includes census data from 2016 until 2020, where each value
refers to the population size on 1st January of the considered year. The study
uses 2016 census data to include the United Kingdom in the analysis, as more
recent data is unavailable for this country due to its departure from the
European Union. Gross Domestic Product (GDP) per capita for EU countries was
obtained from the ”Decent work and economic growth” section of the Sustainable
development indicators (Eurostat, 2021a) in Tables of EU policy published by
Eurostat. GDP reflects the total value of goods and services produced by an
economy within the year minus the value of those used in production. GDP per
capita is calculated as the GDP divided by the average population of a given
year, allowing to compare economies significantly different in absolute size.
As a measure of economic activity, it is often used to proxy a country’s
material living standards. However, it is not a comprehensive measure of
economic welfare because, for instance, it does not account for unpaid
household work or the harmful effects of economic activity (e.g. environmental
damage caused by industrialisation). The dataset contains statistics from 2000
to 2021 and was last updated on 25-03-2022. The study utilises 2014 and 2021
statistics, corresponding to the first and last Code Week editions considered
in the study.

Adjusted gross disposable income per capita was retrieved from the ”Reduced
Inequality” section of the Sustainable development indicators (Eurostat,
2021a). The index is calculated as the adjusted gross disposable income of
households and non-profit institutions divided by the Purchasing Power
Parities (PPP) of the individual consumption of households and by the total
resident population. It reflects households’ purchasing power and ability to
invest in goods and services or save for the future and consider taxes and
social contributions. The dataset records disposable income statistics for EU
countries from 2000 to 2019 and was last updated on 17-03-2022. Human
Development Index (HDI) was obtained from the Human Development Reports of the
United Nations Development Programme (Nations, 2021). HDI is a summary
indicator of achievements in three critical dimensions of human development: a
decent standard of living, access to knowledge and long and healthy life. HDI
is computed as the geometric mean of normalised indices for these three
dimensions. The education component is the average of the normalised Expected
years of schooling index and the Mean years of schooling index. The health and
standard of living index correspond to the normalised Life expectancy and GNI
per capita, respectively. The dataset contains HDI measures for all countries
worldwide from 1990 to 2019. GINI coefficient of equivalised disposable income
reported by the EU-SILC survey was obtained from the Social inclusion and
social policy indicators section in Tables of EU policy published by Eurostat
(survey, 2021). GINI index is an indicator of statistical dispersion used to
represent income and wealth inequality within a nation. The index is defined
as the relationship of cumulative shares of the population arranged according
to the level of equivalised disposable income to the cumulative share of the
equivalised total disposable income received by them. The dataset contains
GINI values for European countries from 2009 to 2020. Digital Economy and
Society Index (DESI) data published by the European Commission (Agenda, 2021)
are available from 2016 to 2021, each based on the latest available data
mainly from Eurostat. DESI is a composite indicator with a three-level
structure based on data mainly collected from the relevant authorities, aimed
at monitoring EU Member States’ progress across critical digital areas. The
index dimensions are four main interconnected areas that contribute to the
country’s digital development: Human capital, Integration of digital
technology, Connectivity, and Digital public services. Regional GDP per capita
data used in this investigation was produced by the European system of
national and regional accounts (ESA 2010) and published by Eurostat (Eurostat,
2021b). GDP per capita is expressed in PPS (purchasing power standards) to
eliminate differences in price levels between countries. Notably, this
indicator is the critical variable for determining the eligibility of NUTS 2
regions in the framework of the European Union’s structural policy. The
dataset was last updated on 29-03-2022 and reports statistics from 2009 to
2020. It does not include data for NUT 2 regions of the United Kingdom (UK)
and Serbia (RS), which won’t be considered in the regional part of the
analysis.

Boundaries of Italian administrative units (cities, provinces, regions and
macro-areas) were obtained from the Italian National Institute of Statistics
(ISTAT) (Ist, 2022a). Updated boundaries are published every year to reflect
administrative changes. The most updated shapefile, released on 1st January
2022, was used in this investigation. South Sardinia has experienced the most
changes in the last twenty years: it only accounted for the province of
Cagliari until 2005, was then split into three provinces (Cagliari, Medio
Campidano and Carbonia-Iglesias), and restructured into two (Cagliari and Sud
Sardegna) in 2016. GDP per capita estimates are not available for this
province due to the frequent changes in administrative units and the
incompatibility of different datasets. Population and urbanisation level data
was retrieved from the Open Data section of ISTAT and included census data for
each Italian city from 2016 to 2021 (Ist, 2022b). The degree of urbanisation
defined by Eurostat for the Local Administrative Units level 2 (LAU2)
classifies them into thinly, intermediate and densely populated areas. These
levels are determined using geographical contiguity and a minimum population
threshold based on population grid square cells of 1 km2. GDP per capita at
the municipal and provincial level was based on income declarations data
retrieved from the Department of Finance and Internal Affairs’ Open Data
section. The study utilises data published in 2015, which refer to the 2014
fiscal year. The income per capita was calculated as the total declared income
in a given territory divided by its population (Fin, 2022). The average
property value was chosen to proxy the socio-economic status at the
neighbourhood level. Italian property values were obtained from the Italian
Inland Revenue (Agenzia delle entrate) Open Data section (Ita, 2016). Each
municipality is divided into territorial areas (OMI area) reflecting
homogenous local real estate market sectors, hence characterised by
substantial uniformity in economic and social conditions. The municipal
territory is generally divided into zones, which aggregate contiguous and
homogenous OMI areas: Central, Semi-central, Suburban 1 (or Peripheric),
Suburban 2, Extraurban Zone. For each OMI area of each municipality, the
datasets define a min-max price range per unit of surface in euro per square
meter, accounting for the state of conservation and the type of property. The
study estimates the income level by calculating the mean of the maximum and
minimum price for residential buildings.

##  4\. Participation metrics

This study proposes and calculates participation metrics aimed at providing a
comprehensive spatial and temporal statistical description of participation in
Code Week. The metrics are defined in Table 2, grouped in four main
categories: penetration, demographic, retention and spatial metrics.
Penetration metrics estimate the number of activities, events creators and
hosting institutes in relation to the country’s population and the proportion
of sub-territories participating in the initiative to quantify the intensity
and distribution of Code Week activity. Demographic metrics describe the
characteristics of the hosting institute, the organiser and the participants
in terms of age, schooling level and gender. Retention metrics assess the
continuous participation of creators and locations over the years, estimated
as the number of editions per creator (or location) and the proportion of
creators (locations) who have joined Code Week for a given number of editions.
Spatial metrics estimate the geographical distribution of new events by
calculating the number of neighbours a given participant (creator or location)
has in a certain area. It also assesses whether new participants occur in a
location that has previously hosted events or is close to one. Finally, the
participation trend is calculated for all (2014-2021) and pre-pandemic
editions (2014-2019) to estimate the impact of the pandemic on the growth
rate.

Table 2. Participation Metrics Type |  Name |  Definition
---|---|---
Penetration |  Events Incidence |  number of events every 100000 people
|  Creators Incidence |  number of event creators every 100000 people
|  Locations Incidence |  number of locations hosting events every 100000 people
|  Creators Relevance |  average number of events per creator
|  Locations Relevance |  average number of events per location
|  Coverage |  proportion of provinces hosting at least 1 event
|  Total Coverage |  coverage calculated in the entire time period
|  Average Coverage |  average of the coverage calculated in each year
Demographic |  Event Size |  average of the reported participants number
|  Participants Age |  weighted average of the reported average participants age for each event, where the weights are the reported number of participants.
|  Female Participation |  weighted average of the percentage of female participants for each event, where the weights are the reported number of participants. This metric was calculated on all events, and events divided per age group: younger students (age 4-14), older students (14-24), adults (24+)
|  Audience Prevalence |  percentage of events containing the selected target audience among their target values. This metric was calculated on each target audience and three summative age groups: younger students (pre-school and elementary school children), older students (high school and graduate students), adults (employed and unemployed adults).
|  Organiser Prevalence |  percentage of events associated to the given type of organiser.
Retention |  Creator Recruiting |  percentage of new creators in a Code Week editions which have not participated in any previous edition
|  Location Recruiting |  percentage of new locations in a Code Week editions which have not participated in any previous edition
|  Creator Frequency |  average number of editions to which a creator has participated
|  Location Frequency |  average number of editions to which a location has participated
|  Creators per #editions |  percentage of creators which have participated in n editions
|  Locations per #editions |  percentage of location which have participated in n editions
Spatial |  Neighbours per Creator |  average number of creators found in a given area. The area considered is 5 km x 5 km and is identified by a geohash of precision 5
|  Neighbours per Location |  average number of creators found in a given area
|  (new) Creators in previous areas |  percentage of (new) creators hosting events in areas which have hosted events in the previous editions
|  (new) Creators in near areas |  percentage of (new) creators hosting events in areas neighbouring areas which have hosted events in previous editions

##  5\. Results

###  5.1. Statistical description of Code Week participation in European
countries

####  5.1.1. Penetration metrics

Penetrations statistics calculated for EU countries are listed in Table 3. The
selected statistics calculated for each year are shown in Figure 2. Entries
are sorted by decreasing Event Incidence, and the same order is preserved in
all Figures and Tables. In all countries, except Malta (MT), Italy (IT) and
Turkey (TR), the number of creators is comparable to or lower than the number
of locations. This suggests a one-to-one or one-to-many correspondence between
creators and locations: most creators are the sole participants in their
institute, and few organise events in multiple locations. Hence, Code Week’s
growing participation is not primarily driven by more teachers in
participating institutes becoming involved in the initiative but rather by
teachers in institutes joining the initiative. On the other hand, in those
three countries, the average number of creators per location is above 2,
suggesting that teachers also get to know and join the initiative through
participating colleagues. A one-to-one creators-location mapping could be
explained by the fact that one of the teachers is appointed to create and
upload all events for the organisation. However, it is unlikely to be
systematic because the average number of events per creator is relatively low
(3.8) and similar across countries. Only Luxembourg records more than 10
events per creator, with only 37 creators responsible for all events created
in the country and a 3-to-1 ratio between creators and locations. This
anomalous trend and the country’s low population bring Luxembourg high in the
event incidence ranking, but the country’s participation is likely
overestimated. For these reasons, this country was excluded from the
regression analyses in section 5.2.

Events, creators and locations incidence vary considerably among countries and
have a right-skewed distribution, where most countries have medium-low
participation while only a handful record very high levels of engagement. The
distribution of incidence metrics and the best fit normal and log-normal
distributions are shown in Figure 3 (a). The latter fits the data well,
suggesting that a logarithmic transformation can be applied to normalise the
data so that statistical techniques that assume normality and additive effects
can be employed. The rank-size plot in Figure 3 (b) depicts the distribution
of event incidence by the country’s rank in decreasing order of participation.
This approach is generally used to study data that vary significantly in scale
and often distribute according to a power law or other skewed distribution.
The rank-size distribution is segmented into two ranges, corresponding to
high- and low-participating countries, characterised by distinct behaviour.
While the former exhibits a moderate decrease in participation, the latter
sees a steep reduction. Subsequent analyses also suggest that European
countries have a dual attitude toward Code Week.

The total coverage achieved in the last eight editions of Code Week is either
complete or close to it for almost all countries. High participating countries
generally achieve full coverage immediately or within the first four editions
since first joining. Also, when provinces enter the initiative, they continue
to do so in the following editions. Low participating countries, however, even
when they achieve an overall good coverage (above 70%), record a low average
coverage (below 40%), meaning that only few provinces are active in each
edition and different ones participate every year. In these territories,
participation is not only lower in terms of the number of events but also
discontinuous. Figure 4 exemplifies event distribution and coverage of three
representative countries with comparable population size and province number,
characterised by immediate complete coverage, increasing to complete coverage
and low coverage, respectively.

Figure 2. Penetration Metrics

Figure 3. Event Incidence statistics

(a) Distribution of events incidence, creators incidence and location
incidence with best-fit normal and log-normal distributions. (b) Event
incidence - Rank plot for EU countries. (c) Participation growth trend in
2014-2021

Figure 4. Coverage and total event incidence in three reference countries

####  5.1.2. Demographic metrics

Demographic statistics calculated at the country level are in Table 4, and a
more detailed representation of selected statistics is found in Figure 5.
Demographic data on the participants and the hosting institution are made
available through the report, filled by the organiser after the event. Around
half of all activities have the report, and the percentage of completed
documents appears to be proportional to the degree of participation of the
country, with top-participating countries reporting around 70% of the time,
while low-participating countries about 20%. Completing an expected but not
compulsory task is expected to correlate positively with the level of
engagement in the initiative. Austria and Luxembourg are exceptions to this
rule, recording 4.8% and 9% filled reports but medium-high events incidence,
suggesting once again that a bulk upload mechanism is in place.

The average number of participants per event in different countries varies
between 20 and 80, except for the UK, which reports 98. This is likely due to
the different organiser composition in the UK with respect to other countries.
In top-participating countries, the vast majority of events ( ¿ 80 %) are
organised by teachers and held in a school setting, often during class time.
The remaining countries have a more varied composition, with a conspicuous
number of events organised by private businesses, then non-profits and
libraries (Figure 5 (c)). The UK records 82% of events from companies,
significantly more than any other country, and only 11% in schools. Profiles
of participation and organiser composition suggest that schools are the
driving force of Code Week dissemination. When they are heavily involved,
events incidence is high, and the initiative reaches many people. Schools are
pervasive and have access to a large group of people and the preferred target
audience for the initiative. Companies, however, have limited access to
potentially interested people and generally organise specialised events for a
dedicated group.

The majority of events are directed to primary and lower-secondary students (
$\approx$ 60 %), followed by high school and graduate students ( 28 %) and
adults ( 12 %), which reflect Code Week’s primary goal to bring computing
education to children as early as possible. Low-participating countries
(especially DE, CH, FR and UK) have a more significant fraction of events
dedicated to unemployed and employed adults and graduate students, mainly due
to the larger proportion of events organised by private businesses. Austria
(AT) reports the lowest ratio of activities for younger students (23 %) and
the highest for adults (31 %). Female participation is quite balanced in
events dedicated to students, but it grows inversely with the education level.
Low female attendance at the university level (age 19-24) observed in most
countries reflects the fact that male students disproportionally choose
computer science-related degrees. At the same time, events for adults (24+)
are predominantly attended by women, likely because these events are
workshops, training and professional development activities dedicated to
teachers, and most educators, especially at the primary level, are female
(fem, 2019).

Figure 5. Demographic Metrics

####  5.1.3. Retention metrics

Retention statistics were calculated for the considered countries and are
listed in Table 5, and yearly metrics are shown in Figure 6. Each year about
%72 of the event creators and %78 of locations have not participated before.
These values are consistent across European countries, although they tend to
be higher (about 80%) in low participating countries. The higher turnover of
locations is consistent with the one-to-many mapping between creators and
locations identified in the previous section, meaning that the same creator
organises events in new areas in the following editions. In high participating
countries, the percentage of new creators per edition is highest in the first
five editions (up to 90%) but decreases and stabilises at around 50% in the
last three editions. This result suggests a certain degree of saturation,
meaning that most people that could have been interested in participating have
already been reached, and it is unlikely that subsequent edition will recruit
a large proportion of new creators. In other countries, the ratio of new
creators is high ( over 90%) and constant throughout the years, suggesting a
fast but stable turnover (Figure 6 (a)). The same metrics calculated on
locations show that the 2019 edition saw an unusually high ratio of new
participating areas in most countries. Conversely, the 2021 edition recorded
the lowest percentage of new location across Europe (Figure 6 (b)).

The vast majority of creators (77 %) and location (81 %) has only taken part
in one edition, while only about 8% of events creators and 5% of hosting
institutes have joined the Code Week campaign for more than two years.
Teachers might have registered on the platform multiple times, recording
events with different IDs over the years. However, metrics calculated on
locations are reasonably similar, suggesting that this behaviour, while
possible, is not systematic. In low active countries, around 90% of creators
and locations only join once, coherent with the dispersive and inconsistent
participation pattern already observed. In these countries, almost none
participate more than 3 or 4 times, indicating low dedication to the cause
(Figure 6 (c) and (d)).

Figure 6. Retention Metrics

####  5.1.4. Spatial metrics

Spatial statistics for European countries are listed in Table 6 and relative
graphs are in Figure 7. Neighbours metrics indicate that areas of 5km x 5km
hosting events contain, on average, about four creators and three locations,
suggesting that participating creators and institutions are not randomly
scattered across the country but tend to form clusters. In Malta (MT), these
metrics rise to 25 and 14, respectively, indicating that participation is
concentrated in a small area, likely dictated by the demographic and
geographic characteristics of the country combined with its elevated
participation.

The ratio of creators in previous and near locations assesses the percentage
of creators found in areas or near areas that have previously hosted events.
On average, 67% of creators organise events in already participating areas,
while 13% in nearby areas. Only 30% of the creators are responsible for
bringing Code Week to new unexplored places. The same metrics calculated
solely considering new creators show that more than most new creators occur in
previous areas (% 58) or near them (16%). This result suggests strong spatial
associativity, meaning new creators are more likely to appear when other
creators are in the area. The 2021 edition records a higher proportion of
creators in previous locations than in earlier years, suggesting a degree of
saturation: new creators necessarily occur in areas that have previously
participated because most areas have already hosted events, and few are left
to reach. Saturation is evident in Figure 7 (c), which depicts areas active
each year in the city of Naples, discriminating among previous, near and new
hosting areas. Moreover, Figure 7 (d) shows a positive correlation between the
number of creators in a given area and the number of new creators in that same
area, suggesting that the probability of a creator appearing in the area is
proportional to the number of creators in that area.

Figure 7. Spatial Metrics

####  5.1.5. Trends

The overall participation trend over the considered period was estimated as
the coefficient of the regression line computed on event incidence of a given
country over the years. A coefficient between -0.5 and 0.5 indicates the lack
of an upward or downward trend but a fluctuation around a specific event
incidence value. Trend estimates for EU countries are shown in Figure 3 (c).
High participating countries exhibit an increasing trend, with a slope
coefficient proportional to the event incidence, meaning that the most active
countries are also those with the fastest participation growth. On the other
hand, less active countries record a stable trend, indicating that they not
only participate less but also show no tendency to participate more in the
future. Ireland (IE) and Iceland (IS) are the only two countries experiencing
a significant decrease in participation in the past decade. The participation
growth rate proportional to the level of engagement is responsible for the
increasing participation gap between more and less active countries and the
skewed distribution of event incidence. Combined with the spatial metrics
described in the previous section, these results suggest a mechanism of
spatial associativity: people are more likely to participate in the initiative
if others have already joined in their proximity, and the more people
participate, the more other people will be willing to join too. Simply put,
participation fosters more participation within the community, highlighting
the role of offline, personal relationships in promoting Code Week. For this
reason, Code Week should be assessed in both its civic and social dimensions.
A pre-pandemic trend was computed considering only editions from 2014 to 2019,
and a difference between the two trends was calculated to estimate a possible
”pandemic effect”. Pre-pandemic trends are higher for all countries other than
Austria (AT) and Norway (NO), suggesting that adopting distance and blended
learning and other pandemic measures might have hindered both the motivation
and the ability to join Code Week.

####  5.1.6. Themes

Themes associated with events provide further information on the tools
utilised (e.g. Visual/Block Programming, Unplugged Activities), the skills
acquired (e.g. Basic Programming Concepts, Software Development), and the aims
achieved, namely Motivation and Awareness, and Promoting Diversity, through
the Code Week activity. These last two themes are the 6th and 8th most chosen
themes and are evenly popular across countries and target audiences,
confirming Code Week value as an awareness-raising campaign other than a
coding education provider. The distribution of themes tends to be similar
across countries, as shown in Figure 8, with more popular general themes
(Playful Coding, Basic Programming Skills, Unplugged Activities) and less
frequent specialised themes (Artificial Intelligence, 3D Printing, Internet of
Things). As previously observed, in less active countries, more events are
organised by private businesses for an adult target audience, which is
reflected in fewer Unplugged Activities events in favour of more Software
Development ones. Events dedicated to a younger audience (i.e. primary and
lower-secondary education classes) favour Playful Coding, Unplugged
Activities, and Art and Creativity, reflecting Code Week’s goal to bring
coding education to children in an accessible and enjoyable way. Events
intended for adults focus on developing both general and specialised skills,
such as Robotics and Mobile App Development.

Figure 8. Themes

###  5.2. Code Week participation and Income

####  5.2.1. EU countries

This section investigates the relationship between Code Week participation and
the socio-economic status in European territories. The event incidence is
chosen to proxy the participation level of a given area. It has the highest
variability among all metrics; thus, it can better highlight differences in
participation among countries. A logarithmic transformation was applied to
this indicator before regression due to its skewed nature, as shown in Figure
3 (a). The normality test confirmed that the transformed index could not be
distinguished from the corresponding best fit normal distribution (p-value ¿
0.05).

Regression on Gross National Income per capita for 2014 and 2021,
corresponding to the first and last Code Week editions considered in this
investigation, shows an inverse linear correlation with participation.
Correlation is strong and significant in 2014, with a Pearson correlation
coefficient = -0.712 and a p-value ¡ 0.001 (Figure 9 (a)). Dispersion is
higher, and correlation is lower in 2021 (Figure 9 (b)), although still
considerable and significant, mostly due to the change in Gross National
Income per capita occurred in Ireland, which increased by 78% from 2014
(40010) to 2021 (70920), whereas all other European countries experienced an
increase between 4% and 30%. The previous section showed how the participation
trend in the first editions strongly affects future participation, i.e.
countries heavily or mildly involved in the first four years continue to do so
in the subsequent four years. Ireland constitutes the only exception, having a
diffused and intense participation at the beginning and a significant decline
toward the end. Consequently, participation should be analysed in relation to
the country’s socio-economic status at the beginning of the considered period
(2014-2015).

Other development indicators were included to narrow down the socio-economical
factors affecting events incidence and begin to unveil the underlying
mechanisms influencing participation. Disposable income is more suitable than
GDP per capita to estimate the resources available to the citizen, as it
directly relates to the households’ purchasing power and ability to invest and
save for the future. Disposable income in 2014 shows a stronger correlation
than the GDP per capita in the same year (Figure 9 (c)), with a Pearson
correlation coefficient of -0.735 and p-value ¡ 0.001, indicating that
participation is higher when the average citizen has fewer resources available
to them. Italy is the only country that significantly deviates from this
trend, exhibiting higher participation than expected. The heterogeneous
development level that characterises this country, described in detail in the
next section, might explain this anomaly.

A country’s GDP strongly determines the quality of its education system. It
can be argued that the GDP affects participation through the infrastructure
and services that the government provides to its citizens. For instance,
citizens of regions with lower GDP might participate more in Code Week because
they do not benefit from high-quality education and are looking for further
opportunities elsewhere. The Human Development Index (HDI) is a more suitable
measure of a country’s ability to offer its citizens a good standard of
living. This indicator combines an economic dimension with a health and
education dimension to emphasise that development should consider people and
their capabilities, other than economic growth. It allows questioning
government national policies and priorities when two countries have comparable
GDP per capita but very different HDI. HDI also displays a negative
correlation with event incidence with a Pearson correlation coefficient of
-0.634 and p-value ¡ 0.001 (Figure 9 (d)). The four most participating
countries, i.e. Malta, Estonia, Poland and Italy, deviate from this trend,
having the highest event incidence and relatively high HDI. However, when
controlling for income, the effect of HDI on participation is not significant
(p-value = 0.976), indicating that the development degree of a country’s
population does not mediate the relationship between income and participation.
On the contrary, GDP per capita is likely to confound both variables.

A country’s GDP strongly correlates with the ability of its citizen to access
and use technology (e.g. computers, smartphones, internet broadband).
Consequently, the effect of GDP on participation in digital literacy campaigns
could be mediated by the country’s digital infrastructure and the population’s
digital capacity. Under this hypothesis, citizens of less developed regions
would be more prone to participate because they have had less opportunity to
use and learn about technology. DESI is a composite indicator that captures
the overall digital performance and competitiveness. Its correlation with
events incidence is significant (p-value = 0.050), with a Pearson correlation
coefficient of -0.395 (Figure 9 (e)). Notably, Malta and Estonia have a much
higher degree of digitisation with respect to their GDP, mostly because of
targeted government policies, but are also heavily involved in Code Week.
Similarly to HDI, the digital economy and society index does not significantly
affect participation when controlling for GDP per capita (p-value = 0.681),
disproving the digital infrastructure’s mediating role on participation.

Most studies on the impact of socio-economic status on civic engagement
identify social and economic heterogeneity as a determining factor. Income
inequality is known to negatively correlate with the average national income,
meaning that more developed countries also tend to distribute their wealth
more equally (Kim, 2016). The GINI coefficient of equivalised disposable
income is the most common indicator to quantify wealth inequality. It
correlates positively with events incidence, as one would expect, with a
moderate correlation coefficient of 0.461 and p-value = 0.007 (Figure 9 (f)).
However, the relationship is not significant when accounting for GDP per
capita (p-value = 0.690).

Many socio-economic statistics are available for European countries through
Eurostat, and many more correlations can be easily explored. However, spurious
correlations are likely to appear because of the strong correlation of GDP
with participation and most socio-economic metrics, where GDP acts as a
confounding variable.

Figure 9. Correlations of events incidence with socio-economic metrics

####  5.2.2. EU regions

The relationship between participation and GDP was then investigated at the
regional level to evaluate the participation distribution within countries and
verify whether the correlation identified in the previous section persists at
a smaller scale. GDP per capita was plotted against events incidence, both in
the logarithmic domain due to their highly skewed nature, as shown in Figure 3
(a). Linear regression confirmed the negative linear relationship found at the
country level with Pearson’s correlation coefficient of 0.485 and p-value ¡
0.001 (Figure 10 (a)). The missing GDP per capita data for the UK and Serbia
does not allow for direct comparison with the country-level data but confirms
the trend.

Less developed countries are characterised by high participation levels in all
regions. Participation remains close to the country average, even when the GDP
is vastly different. For instance, in Poland, all territories except one have
a GDP per capita in the 13000 - 20000 (PPS) range and participation between
120 and 240 events per 100000 people. The Warsaw region, which hosts the
country’s capital, claims more than double the GDP per capita (39700) but
retains a high events incidence of 206. A similar trend is observed in
developed countries, where the GDP per capita is consistently high and
participation is low across all regions. Less developed areas in otherwise
wealthy countries are too characterised by inconsistent and sporadic
participation.

The graph includes two fundamental GDP thresholds, 75% and 90% of the average
European Union GDP, which determine the eligibility of regions in the
framework of the European Union’s structural policy. Regions with a GDP per
capita of less than 75% of the EU average are designated as less developed and
assigned the most significant regional policy funding through the Regional
competitiveness and employment objective. Regions whose GDP per capita falls
between 75 and 90% are instead considered transition regions and have access
to less funding than the less developed regions but more than developed
regions. Notably, almost all regions classified as less developed, i.e.
falling behind the 75% mark, record very high event incidence.

EU countries are relatively homogeneous with respect to this classification.
Most EU’s ”old” Member States (AT, BE, DK, FI, FR, GR, IE, LU, NL, ES, and SE)
are characterised by all developed regions or a minority of transition
regions. In contrast, the new EU Member States (BG, HR, CZ, EE, HU, LT, LU,
PL, RO, SK, SI), together with the old members Portugal (PT) and Greece (EL),
consist of a majority of transition and less developed regions, and only a
minority of developed regions, generally corresponding to the capital area.
Italy (IT) is unique in this regard, as it appears to be split into three
parts. Its 2014-2020 EU regional policy classification has five less developed
regions, which account for $\approx$ 30% of the population, three transition
and 12 developed regions. Thus, the Italian situation requires individual
consideration.

Figure 10 (b) shows the same relationship between GDP and events, but with
regions classified based on the development level of the afferent country. In
high and low development countries, participation almost exclusively falls
within the 0-20 and 20-400 event incidence, respectively. Within a given
country, participation is comparable across regions, even in the case of
variations in GDP, suggesting that the average development level of the
country rather than the GDP of the particular area affects participation.
Italy constitutes an exception, as it comprises two territories with vastly
different socio-economic profiles. Participation is high overall in the entire
country, but more developed areas have significantly lower events incidence
than transition and less developed ones.

Figure 10 (c) confirms that variability in participation within a country is
relatively low in high participating countries, except for Italy, due to its
dual nature. OLS regression model with GDP per capita as the independent
variable had very little explanatory power (R-squared = 0.28). A significantly
better model (R-squared = 0.64) was obtained when adopting a discrete
development level variable (1: High, 2: Medium, 3: Low) as the independent
variable. Adding the GDP attribute did not improve the model’s accuracy, for
which only the development variable had a significant p-value. Figure 11 (a)
shows GDP per capita and events incidence to highlight the complementarity of
the two variables.

Figure 10. Relationship between Regional GDP per capita and events incidence
with regression line on (a) all regions (b) regions divided by development
level of the country. (c) Mean and standard deviation of regional GDP in EU
countries.

####  5.2.3. Italy

Given its heterogeneous nature and atypical participation pattern, the Italian
situation deserves further consideration. Regression between regional events
incidence and GDP per capita highlights a negative linear correlation, similar
to that observed at the European level, with a strong Pearson’s coefficient
(0.74) and a significant p-value (Figure 11 (b)). All transition and low-
development regions are heavily involved in Code Week, with over 50 events
every 100000 people, while participation is more varied in high development
regions. This pattern is similar to that observed at the country level, where
less developed areas consistently recorded high events incidence. In contrast,
higher developed areas had lower events incidence but higher variance due to a
minority of wealthy but participating regions.

The same analysis was repeated at the province level to study the
participation distribution within a region and yielded similar results (Figure
11 (c)). The negative linear correlation has a moderate Pearson’s coefficient
(0.56) and a significant p-value. Once again, participation tends to be
homogeneous in provinces within a region. All provinces in transition and less
developed areas have consistently high events incidence, while more developed
areas vary into a broader range of participation. Events distribution was then
analysed with respect to the urbanisation level of the hosting municipality.
Thinly, intermediate and densely populated areas hosted 15%, 50%, and 35% of
the events, respectively, suggesting that Code Week was not confined to large
cities but was significantly present at each urbanisation level and was most
popular in medium-sized settlements.

This section highlights a robust negative correlation between event incidence
and GDP at the country, region and province levels, suggesting that lower-
income individuals are more prone to participate. However, it could be
possible, albeit unlikely, that participation is concentrated in the wealthier
parts of the cities, driven by high-income individuals living in an overall
low-income setting. Participation was further investigated at the
neighbourhood level to exclude this possibility and draw conclusions on
participation trends at the individual level. The property value of the
hosting institution was used to proxy the income level of organisers and
participants

Events distribution was further analysed in two reference cities, Naples and
Milan, respectively, representing the country’s less developed south and the
more developed north. The events distribution in Naples in Figure 12 (a) (ii)
shows that almost every neighbourhood has hosted events and that the most
active areas correspond to those with the lowest property value. Notably, the
Scampia neighbourhood, highlighted in Figure 12 (a), records the lowest
average property value at X and the highest number of events at Y. According
to the OMI zone classification, shown in Figure 12 (a) (iii), over 62% of all
events were mapped in suburban neighbourhoods, while only 19% occurred in the
central areas, as shown in Figure 12 (a) (iv). The themes of events in these
two zones, characterised by opposite socio-economic profiles, are relatively
similar, except for a more significant proportion of events focused on
”Motivation and awareness-raising” in the suburbs (Figure 12 (a)(v)). In
Milan, all considered neighbourhoods have participated at least once, but
activities seem to be concentrated in the city’s centre, having the highest
property value(Figure 12 (b) (i-ii)). The central zone hosts 43% of the
events, followed by the peripheral (35%), semi-central (17%), and suburban(5%)
(Figure 12 (b) (iv)). Themes are fairly similar in the two most participating
zones, central and peripheral, with a preference for ”Promoting diversity” and
”Software development” in the centre (Figure 12 (b) (v)).

Figure 11. Relationship between GDP per capita and events incidence in (a)
European regions, (b) Italian regions, and (c) Italian provinces

Figure 12. Income and participation statistics for two reference Italian
cities, (a) Naples and (b) Milan: (i) event count, (ii) property value, (iii)
municipality zones, (iv) event distribution per zones, (v) themes frequency

##  6\. Discussion

The participation metrics calculated in this study aimed at evaluating the
reach of the Code Week campaign across Europe in the past eight editions and
investigating the underlying participation mechanisms, discriminating between
traits common to all countries, hence endogenous to the Code Week program, and
features varying across countries, likely to be influenced by factors internal
to the respective country. The metrics aim to describe both incidence and
amount of participation, i.e. whether an individual chooses to participate (in
this case to organise and activity) and how often, while most empirical
studies in the literature only address one aspect (Schröder and Neumayr,
2021). Coverage statistics indicate that most European countries have hosted
events and that in over two-thirds of all considered countries (25 out of 36),
all provinces have participated at least once, and most take part in every
edition. Spatial metrics calculated in the last editions with respect to the
earlier ones indicate a degree of saturation, particularly in highly
participating countries, meaning most areas suitable to host events have
already joined the campaign. Code Week appears to have reached and become well
known in most European territories, confirming its success as an awareness-
raising campaign.

Events incidence, together with creator and location incidence, varies
considerably among countries, ranging between 1 and 250 events every 100000
people. The gap between high and low participating countries is large and
expected to widen since the former countries are also characterised by a fast
participation growth rate, while the latter fluctuate about a fixed value.
High participating countries have a continuous Code Week presence on the
territory. The number of active members within an area increases even though
the turnover is high, and members appear to form clusters, suggesting the
presence of offline, personal relationships promoting Code Week dissemination.
On the other hand, in less active countries, participation lacks both spatial
and temporal continuity, indicating the lack of an underpinning social
structure. Code Week recognised the importance of having active members on the
territory and established a net of ambassadors and leading teachers across
Europe. However, results suggest that relationships at the municipality or
neighbourhood level might be just as relevant in fostering participation.
Efforts should be directed toward building and maintaining a dense network of
close-range interaction, for instance, promoting group activities to be
completed by different classes of the same or neighbouring institutes.

All countries report relatively low creator and location relevance and low
retention levels, suggesting that creators generally join Code Week with few
events and only a few editions. Nevertheless, the number of Code Week events
is overall increasing in almost all countries, indicating that new creators
joining each year are the driving force to participation, rather than previous
creators remaining active in the program. These results suggest that Code Week
is more effective in recruiting new teachers to the program than retaining
them. Efforts should be made to maintain contact with previous participants
and inquire about possible dissatisfaction. Reasons leading teachers to
discontinue their participation should be further investigated, and retention
measures purposefully implemented. Demographic metrics and themes tend to be
homogeneous across countries, except for the type of event organiser and the
target audience. In low participating countries, a more significant portion of
events is organised by private businesses, generally for an adults audience,
and generally on a specialised IT topic, while in other countries, activities
are almost exclusively managed by schools for young students. Demographic data
found in reports is valuable in assessing the campaign’s effectiveness but is
only available when volunteered by the teachers and is rare in low
participating countries. Incentives could be offered to increase reporting and
obtain more comprehensive and representative demographic data.

The article also investigates the socio-economic determinants of participation
in the digital literacy campaign EU Code Week. The analysis identified linear
correlations at the country level with the socio-economic parameters relative
to 2014 and found GDP per capita and disposable income to be most relevant.
Investigating population development indices (HDI and DESI) while controlling
for GDP per capita demonstrated how income does not primarily affect
participation through the educational, technological, health, and social
infrastructure it provides to the population but rather directly through the
availability of resources. The strongest correlation is, in fact, observed
with disposable income, an indicator designed to reflect the average
households’ purchasing power and ability to invest in goods and services and
adequate to estimate the resources available to the citizen. Similarly,
studying the relationship with income inequality (GINI) while controlling for
GDP highlighted that the overall level of wealth, rather than its distribution
within the country, is a defining factor.

Code Week exhibits an opposite trend compared to that reported in most of the
previous work, where higher income has been associated with higher civic and
social participation levels. Explanations for the inhibited participation in
low-income and disadvantaged areas revolve around two main views: (a) the
conditions necessary for fruitful social interaction, namely opportunities for
personal acquaintance, equal status, and the ability to share common goals,
come to lack, progressively eroding the social capital and depressing
participation; (b) resources necessary for participation, either at the
individual or community level, are unobtainable, impeding participation
altogether. The counter-trend observed in this paper can be ascribed to the
different nature of the Code Week campaign, which provides teachers from all
over the world with the opportunity to join, connect and contribute to the
initiative, regardless of their socio-economic status. Teachers in less
developed areas who might feel isolated in their community, disappointed by
the inadequate infrastructure, and disillusioned by their institutions, can
find in Code Week a virtual community, transversal to all socio-economic
levels and untied from political or other formal affiliations. They have the
opportunity to form bonds and be part of a virtual network of like-minded
individuals, which might not have been possible in their original community.
Moreover, Code Week promotes the common goal of bringing coding and digital
literacy to as many people as possible in an enjoyable and innovative way. In
this shared vision, participating teachers are at the forefront of this
digital dissemination. By populating the event map, teachers become aware of
being part of a massive international undertaking. Hence, it can be argued
that Code Week fosters participation in less developed areas because it
restores its social capital by enabling those conditions that otherwise come
lacking: it offers opportunities to encounter like-minded people, guarantees
that all participants are treated equally, and encourages a shared vision for
its community.

Code Week is also free and accessible to all. It does not pose any
prerequisite or requirement for participation other than being able to connect
to the platform to upload the activity. Hence, a lack of resources in
disadvantaged areas does not deter participation because no resources are
required. On the contrary, Code Week provides resources to its participants in
terms of networking, mentoring, professional development and upskilling
opportunities, original and engaging teaching material, challenges and awards,
recognition, reputation and self-esteem from being part of an international
transformative initiative. In this scenario, it could be argued that
participation is not affected by the availability of resources necessary to
participate but by the perceived value assigned to the resources gained.
Assuming that the teacher is aware of the initiative, she/he will be likely to
engage in the program if the perceived benefits outweigh the effort required.
This generally consists of the time and energy to plan and conduct the
activity, obtain permission from the school administrator (if needed), and
upload the event details into the system. As the effort is generally limited,
the decisive factor becomes the perceived benefit, which is likely to be
strongly affected by the country’s socio-economic status. In most developed
countries, teachers and pupils have had full access to computers and broadband
internet for the past 20 years. Being more exposed to technology, they are
inherently more likely to become digitally competent. Computing education
built into the curriculum and professional development programs promoted by
the country’s ministry of education provide a formal framework for learning
and teaching computational thinking and digital skills. The country’s stable
social infrastructure can offer teachers plenty of networking and informal
professional development opportunities. In this setting, Code Week does not
always rise above the many other activities available to the teachers, who
might choose not to participate. On the contrary, less developed countries
have gained widespread access to modern technologies only in the last decade,
with many rural areas still without internet or computer access. Computing
education is either not contemplated or is only now being integrated into the
curriculum. However, systematic professional development efforts are not yet
in place, resulting in a severe skill gap. Teachers in less developed regions
understand the importance of including computational thinking activities in
their teaching and exposing students to these concepts from an early age but
face the lack of resources, technology and opportunities for networking and
upskilling in their community. They are aware of the importance of their role
in society, but they feel under-resourced and under-appreciated, so they look
for redemption opportunities. In this scenario, Code Week represents a unique
opportunity that compensates for institutional shortcomings. Overall, Code
Week appears to be more prevalent where it is most needed and can be most
beneficial.

While participation varies significantly among EU countries, it appears to be
relatively homogeneous within a country, especially in less developed ones,
where event incidence is consistently high regardless of income. A country-
wide participation trend is consistent with previous studies which identify
factors affecting social and civic engagement at the society level. In other
words, citizens of different areas of a country have a similar tendency to
join the Code Week because the main affecting factors exist at the country
level. Candidate socio-economic factors are the overall infrastructure level,
access to technology, the quality of government (and the faith citizens have
in their formal institutions), the human capital, and the degree of social
cohesion. Code Week-related aspects could be the ambassador’s effort to
promote the vision, the number and distribution of leading teachers, the
Ministry of Education’s support of the initiative, the media coverage, and the
competing or collaborative relationship with other coding dissemination
initiatives. Education-related factors to consider are the teachers’ level of
expertise, the availability of continuous professional development, and the
degree of integration of computing education in the compulsory curriculum. The
latter is likely to affect Code Week participation directly and would be
interesting to investigate. However, it is also difficult to quantify,
depending on a broad set of interacting variables: whether mandatory or
optional, since what age, how many hours a week, as a separate subject or
cross-curricular theme. The level of interest and action toward including CT
in the curriculum varies significantly across Europe (Bocconi et al., 2022):
countries not planning to teach CT, finalising an updated framework, running a
pilot program, integrating CT only recently, teaching CT for several years.
Moreover, the time necessary to successfully deliver the updated framework
after its approval is likely affected by the country’s infrastructure and
available resources.

The only significant within-country trend is observed in Italy, where
participation is more prominent in less developed regions. Unlike most
European countries, Italy is vastly heterogeneous from a political, economic
and social point of view. The southern regions (denoted ”Mezzogiorno”) report
an income per capita of less than two-thirds of that in the rest of the
country. Differences in social structures affect the country’s social
integrity, with horizontal structures more common in the North and
hierarchical forms more frequent in the South (Helliwell and Putnam, 1995).
Civic community and citizen involvement tend to be more prominent in the
North, where the government is also more efficient (Leonardi et al., 2001).
The overall development gap between the two halves is likely to affect Code
Week participation, but exact underlying mechanisms need to be further
investigated.

##  7\. Conclusion and Future Work

The study suggests that the counter-trend observed in Code Week compared to
most other forms of civic and social participation can find an explanation by
the peculiar features of the program: (a) it builds a social network of
collaborations among like-minded people, (b) it does not require but offers
resources to its participants, (c) the perceived added value of such resources
is inversely proportional to their availability. Other types of social and
civic engagements with comparable characteristics, e.g. Erasmus and eTwinning
programs, or opposite features (e.g. require an initial investment, do not
offer an immediate return, do not entail social interactions) should be
compared to Code Week participation to validate this view further.

There is also an evident correspondence between the development classification
of the EU Regional Fund and Code Week participation. Investigating a possible
causal relationship between the two is beyond the scope of the work but
deserves further consideration. The EU regional Fund allocates the most
funding to less developed regions. Local organisations apply for funding by
presenting a project in response to a regional call, and a significant portion
is financed. These regions could be more eager to join Code Week because (a)
they have an appreciation for the EU, having received funds to develop the
region, or (b) have acquired project-managing skills, having already developed
project proposals in a different context. Regression discontinuity on the
boundary of regions with different levels of development would help clarify
how the Regional Fund affects participation. If there exists a causal
relationship, a survey-based approach should be used to identify the main
underlying factor(s).

This study addressed the impact of Code Week in terms of its ability to reach
and engage people, particularly those who would most benefit from this
initiative. From this point of view, Code Week is undoubtedly a success story.
The campaign’s impact intended as computing skills acquired by participants
would be more challenging to measure, mainly due to the lack of participants’
feedback on the activities. However, that is a rather narrow and superficial
definition of effectiveness, which does not reflect the campaign’s vision, and
for which more suitable tools have been developed, such as SELFIE (Costa et
al., 2021). Code Week’s global vision entails raising awareness about the
importance of digital skills and the potential of modern technologies,
challenging stereotypes and defying prevailing norms, fostering creativity and
teamwork, and promoting coding skills for all ages. Participating in Code Week
means taking an active stand in achieving these goals, hence is a more
inclusive and appropriate measure of the effectiveness of this campaign.

Table 3. Penetration Statistics ISO | events | creators | locations | creator | location | events | creators | locations | total | average
---|---|---|---|---|---|---|---|---|---|---
| number | number | number | relevance | relevance | incidence | incidence | incidence | coverage | coverage
MT | 1294 | 420 | 273 | 3.1 | 4.7 | 262.2 | 93.2 | 60.6 | 1.00 | 0.94
EE | 2663 | 759 | 804 | 3.5 | 3.3 | 201.0 | 57.7 | 61.1 | 1.00 | 1.00
PL | 64654 | 13241 | 14314 | 4.9 | 4.5 | 170.3 | 34.9 | 37.7 | 1.00 | 0.98
IT | 94303 | 26275 | 17794 | 3.6 | 5.3 | 157.7 | 43.3 | 29.3 | 1.00 | 0.95
TR | 75041 | 30853 | 17578 | 2.4 | 4.3 | 91.5 | 39.2 | 22.3 | 1.00 | 0.75
HR | 3671 | 1132 | 1214 | 3.2 | 3.0 | 90.1 | 27.0 | 29.0 | 1.00 | 0.89
AL | 2240 | 479 | 460 | 4.7 | 4.9 | 78.3 | 16.7 | 16.0 | 1.00 | 0.57
LT | 2098 | 763 | 551 | 2.7 | 3.8 | 75.1 | 26.4 | 19.1 | 1.00 | 0.89
LU | 388 | 37 | 84 | 10.5 | 4.6 | 63.2 | 6.4 | 14.6 | 1.00 | 1.00
HU | 5891 | 877 | 1119 | 6.7 | 5.3 | 60.3 | 8.9 | 11.4 | 1.00 | 0.90
EL | 5555 | 2339 | 2427 | 2.4 | 2.3 | 51.8 | 21.7 | 22.5 | 1.00 | 0.82
MK | 1060 | 419 | 424 | 2.5 | 2.5 | 51.0 | 20.2 | 20.5 | 1.00 | 0.80
ME | 298 | 151 | 139 | 2.0 | 2.1 | 47.9 | 24.3 | 22.3 | 1.00 | 0.62
SI | 811 | 230 | 297 | 3.5 | 2.7 | 39.0 | 11.1 | 14.4 | 1.00 | 0.77
BG | 2544 | 792 | 927 | 3.2 | 2.7 | 36.3 | 11.1 | 13.0 | 1.00 | 0.83
RO | 6162 | 2375 | 2178 | 2.6 | 2.8 | 31.7 | 12.0 | 11.0 | 1.00 | 0.79
IE | 1529 | 487 | 816 | 3.1 | 1.9 | 31.2 | 10.3 | 17.3 | 1.00 | 1.00
RS | 2121 | 694 | 977 | 3.1 | 2.2 | 30.5 | 9.8 | 13.8 | 1.00 | 0.91
AT | 2442 | 595 | 635 | 4.1 | 3.8 | 27.6 | 6.8 | 7.3 | 1.00 | 0.42
CY | 241 | 86 | 112 | 2.8 | 2.2 | 27.5 | 10.1 | 13.2 | 1.00 | 1.00
LV | 527 | 222 | 235 | 2.4 | 2.2 | 27.4 | 11.3 | 11.9 | 1.00 | 0.77
PT | 2317 | 1021 | 1012 | 2.3 | 2.3 | 22.5 | 9.9 | 9.8 | 1.00 | 0.76
SK | 944 | 293 | 399 | 3.2 | 2.4 | 17.3 | 5.4 | 7.4 | 1.00 | 0.94
IS | 53 | 22 | 34 | 2.4 | 1.6 | 14.8 | 6.6 | 10.2 | 1.00 | 0.75
ES | 6270 | 2326 | 3000 | 2.7 | 2.1 | 13.4 | 5.0 | 6.5 | 1.00 | 0.74
SE | 1125 | 436 | 509 | 2.6 | 2.2 | 11.0 | 4.4 | 5.2 | 1.00 | 0.65
CZ | 950 | 304 | 391 | 3.1 | 2.4 | 8.9 | 2.9 | 3.7 | 1.00 | 0.84
BE | 1009 | 258 | 536 | 3.9 | 1.9 | 8.8 | 2.3 | 4.7 | 0.77 | 0.46
FI | 376 | 145 | 231 | 2.6 | 1.6 | 6.8 | 2.6 | 4.2 | 0.81 | 0.44
NL | 1019 | 158 | 307 | 6.4 | 3.3 | 5.9 | 0.9 | 1.8 | 0.85 | 0.38
DE | 4113 | 734 | 1262 | 5.6 | 3.3 | 5.0 | 0.9 | 1.5 | 0.55 | 0.18
CH | 415 | 113 | 118 | 3.7 | 3.5 | 4.9 | 1.4 | 1.4 | 0.70 | 0.28
FR | 2393 | 475 | 894 | 5.0 | 2.7 | 3.6 | 0.7 | 1.3 | 0.87 | 0.42
UK | 2245 | 240 | 448 | 9.4 | 5.0 | 3.4 | 0.4 | 0.7 | 0.66 | 0.23
DK | 108 | 28 | 80 | 3.9 | 1.4 | 1.9 | 0.5 | 1.4 | 0.82 | 0.44
NO | 78 | 40 | 51 | 2.0 | 1.5 | 1.5 | 0.8 | 1.0 | 0.77 | 0.31
mean | 8304 | 2495 | 2018 | 3.8 | 3.0 | 49.5 | 15.2 | 14.7 | 0.94 | 0.71
std err | 3515 | 1102 | 733 | 0.3 | 0.2 | 9.8 | 3.1 | 2.4 | 0.02 | 0.04
Table 4. Demographic Statistics ISO | %Filled | Event | Part. | Female | Audience Prevalence | Female Participation | Organiser Prevalence
---|---|---|---|---|---|---|---
| Reports | Size | Age | Particip. | 4-14 | 14-24 | 24+ | 4-14 | 14-24 | 24+ | School | Business
MT | 44.7 | 45 | 9.0 | 50.2 | 61.1 | 27.0 | 12.0 | 51.0 | 46.5 | 68.5 | 72.0 | 16.8
EE | 59.6 | 32 | 10.6 | 42.6 | 82.0 | 10.1 | 8.0 | 41.3 | 43.0 | 64.3 | 65.9 | 1.3
PL | 61.2 | 31 | 9.5 | 38.5 | 89.6 | 7.4 | 3.1 | 39.0 | 39.4 | 71.6 | 73.0 | 0.9
IT | 74.8 | 36 | 10.4 | 46.2 | 72.9 | 25.1 | 1.9 | 45.8 | 41.8 | 66.5 | 97.2 | 1.3
TR | 69.5 | 68 | 10.3 | 53.3 | 85.8 | 12.5 | 1.6 | 52.9 | 54.6 | 62.6 | 98.2 | 0.1
HR | 75.6 | 38 | 12.4 | 47.7 | 71.4 | 25.9 | 2.5 | 47.5 | 46.8 | 70.0 | 94.0 | 0.4
AL | 82.0 | 62 | 14.0 | 54.1 | 52.9 | 37.4 | 9.8 | 50.9 | 58.7 | 69.8 | 96.6 | 0.5
LT | 79.6 | 46 | 11.2 | 45.4 | 60.3 | 34.7 | 5.1 | 45.5 | 42.1 | 70.0 | 94.2 | 0.1
LU | 9.0 | 57 | 18.7 | 47.2 | 70.2 | 17.7 | 12.0 | 47.6 | 49.0 | 37.0 | 63.6 | 0.0
HU | 69.5 | 40 | 13.9 | 41.7 | 60.2 | 34.8 | 4.9 | 40.9 | 36.9 | 65.2 | 85.6 | 0.5
EL | 74.5 | 61 | 10.1 | 48.9 | 74.9 | 20.5 | 4.6 | 48.4 | 50.0 | 67.8 | 95.3 | 1.1
MK | 84.0 | 36 | 11.7 | 45.8 | 85.4 | 11.9 | 2.7 | 45.4 | 47.4 | 50.4 | 89.8 | 0.2
ME | 78.2 | 26 | 12.0 | 49.7 | 81.1 | 17.0 | 1.9 | 48.0 | 53.2 | 81.1 | 97.3 | 0.3
SI | 57.1 | 54 | 12.1 | 44.4 | 57.4 | 27.2 | 15.4 | 44.6 | 43.1 | 58.6 | 71.6 | 0.7
BG | 54.4 | 47 | 13.5 | 44.5 | 41.6 | 45.8 | 12.5 | 46.2 | 39.4 | 70.5 | 75.1 | 1.1
RO | 80.0 | 38 | 12.1 | 44.8 | 69.1 | 25.1 | 5.7 | 43.4 | 49.1 | 54.1 | 85.6 | 0.5
IE | 20.3 | 80 | 12.0 | 50.3 | 49.8 | 44.3 | 5.8 | 51.5 | 43.7 | 58.5 | 76.4 | 0.0
RS | 79.6 | 66 | 13.1 | 48.1 | 64.9 | 25.8 | 9.2 | 48.8 | 44.7 | 60.8 | 83.1 | 0.7
AT | 4.8 | 68 | 15.7 | 42.0 | 23.2 | 45.4 | 31.3 | 47.1 | 39.4 | 24.6 | 93.5 | 1.9
CY | 67.6 | 80 | 10.7 | 51.4 | 70.2 | 18.8 | 11.1 | 51.9 | 41.8 | 57.4 | 89.2 | 2.1
LV | 54.6 | 50 | 13.1 | 49.0 | 75.3 | 19.7 | 5.0 | 48.9 | 46.4 | 64.0 | 84.6 | 0.6
PT | 68.5 | 71 | 12.7 | 47.5 | 72.1 | 23.6 | 4.3 | 49.9 | 37.5 | 55.1 | 88.4 | 1.6
SK | 51.7 | 55 | 13.5 | 43.3 | 48.0 | 34.5 | 17.5 | 44.8 | 41.4 | 43.9 | 64.8 | 0.2
IS | 13.2 | 75 | 11.0 | 52.9 | 71.5 | 16.5 | 12.1 | 52.9 | 0.0 | 0.0 | 100.0 | 0.0
ES | 46.2 | 66 | 13.1 | 48.5 | 42.3 | 41.4 | 16.3 | 50.1 | 39.7 | 55.6 | 75.8 | 7.9
SE | 20.7 | 70 | 11.5 | 46.6 | 66.8 | 20.4 | 12.8 | 48.1 | 30.1 | 66.4 | 76.2 | 14.8
CZ | 56.7 | 50 | 13.8 | 46.8 | 56.6 | 22.1 | 21.3 | 44.5 | 49.4 | 65.0 | 56.9 | 6.2
BE | 30.3 | 79 | 17.3 | 53.0 | 39.9 | 41.2 | 18.8 | 48.9 | 59.6 | 58.6 | 42.1 | 7.9
FI | 32.2 | 51 | 14.7 | 46.5 | 54.5 | 23.1 | 22.3 | 46.6 | 41.6 | 49.3 | 71.5 | 0.8
NL | 51.8 | 32 | 11.5 | 47.4 | 56.9 | 29.6 | 13.5 | 48.9 | 45.2 | 46.9 | 65.0 | 17.9
DE | 23.8 | 35 | 14.8 | 42.4 | 29.8 | 48.4 | 21.9 | 40.3 | 44.6 | 43.0 | 20.7 | 21.0
CH | 19.5 | 33 | 11.1 | 47.5 | 31.3 | 39.5 | 29.2 | 46.7 | 48.9 | 73.7 | 26.6 | 56.2
FR | 16.6 | 44 | 13.4 | 44.6 | 32.6 | 39.7 | 27.7 | 45.3 | 42.7 | 40.7 | 27.1 | 54.0
UK | 8.1 | 98 | 11.1 | 47.9 | 30.9 | 41.7 | 27.3 | 49.7 | 42.0 | 58.8 | 11.0 | 82.4
DK | 13.9 | 18 | 18.8 | 35.3 | 53.1 | 21.9 | 25.0 | 22.2 | 33.9 | 54.7 | 37.0 | 7.4
NO | 17.9 | 44 | 10.3 | 59.9 | 56.1 | 34.1 | 9.9 | 59.5 | 0.0 | 78.0 | 68.0 | 0.0
mean | 48.7 | 52 | 12.6 | 47.1 | 59.5 | 28.1 | 12.4 | 46.8 | 44.5 | 59.5 | 72.6 | 9.7
std err | 4.2 | 3 | 0.4 | 0.7 | 2.8 | 1.8 | 1.4 | 0.9 | 1.1 | 2.0 | 3.9 | 3.3
Table 5. Retention Statistics ISO | Creator | Location | Creators | Locations | Creators per #editions | Locations per #editions
---|---|---|---|---|---|---
| Recruiting | Recruiting | Frequency | Frequency | 1 | 2 | 2+ | 1 | 2 | 2+
MT | 78 | 73 | 1.27 | 1.39 | 81.0 | 13.6 | 5.5 | 74.4 | 16.5 | 9.2
EE | 74 | 73 | 1.42 | 1.39 | 73.3 | 17.9 | 8.9 | 73.9 | 17.3 | 8.7
PL | 72 | 76 | 1.46 | 1.36 | 71.0 | 18.3 | 10.6 | 75.8 | 16.3 | 7.8
IT | 73 | 72 | 1.43 | 1.43 | 74.3 | 15.6 | 10.1 | 72.3 | 17.7 | 10.0
TR | 82 | 84 | 1.29 | 1.31 | 78.9 | 15.1 | 5.9 | 76.3 | 17.1 | 6.6
HR | 61 | 65 | 1.66 | 1.50 | 64.2 | 20.5 | 15.2 | 71.4 | 16.3 | 12.3
AL | 77 | 85 | 1.34 | 1.27 | 77.5 | 15.2 | 7.3 | 81.3 | 12.8 | 5.8
LT | 65 | 68 | 1.60 | 1.42 | 64.4 | 21.0 | 14.7 | 72.2 | 19.4 | 8.4
LU | 58 | 73 | 1.46 | 1.21 | 78.4 | 8.1 | 13.5 | 88.1 | 4.8 | 7.2
HU | 67 | 70 | 1.57 | 1.42 | 68.8 | 16.1 | 15.2 | 75.5 | 13.9 | 10.6
EL | 61 | 75 | 1.46 | 1.28 | 72.6 | 17.2 | 10.2 | 79.9 | 14.0 | 6.1
MK | 66 | 80 | 1.51 | 1.25 | 72.1 | 15.0 | 12.9 | 80.7 | 14.2 | 5.2
ME | 73 | 79 | 1.15 | 1.12 | 86.1 | 12.6 | 1.3 | 88.5 | 10.8 | 0.7
SI | 74 | 73 | 1.34 | 1.37 | 77.4 | 13.9 | 8.6 | 74.7 | 16.5 | 8.7
BG | 68 | 76 | 1.46 | 1.29 | 71.5 | 17.2 | 11.3 | 80.0 | 13.8 | 6.1
RO | 77 | 82 | 1.42 | 1.27 | 72.6 | 17.3 | 10.3 | 78.7 | 16.4 | 4.9
IE | 75 | 84 | 1.27 | 1.12 | 81.7 | 11.9 | 6.3 | 90.4 | 7.8 | 1.7
RS | 59 | 71 | 1.61 | 1.33 | 68.0 | 17.9 | 14.1 | 77.4 | 15.8 | 6.8
AT | 77 | 78 | 1.22 | 1.23 | 80.2 | 18.3 | 1.6 | 79.7 | 18.4 | 1.9
CY | 61 | 77 | 1.57 | 1.21 | 68.6 | 17.4 | 14.0 | 85.7 | 10.7 | 3.6
LV | 74 | 83 | 1.36 | 1.21 | 72.5 | 20.7 | 6.9 | 84.7 | 10.6 | 4.7
PT | 74 | 82 | 1.34 | 1.22 | 77.4 | 15.4 | 7.3 | 83.0 | 13.4 | 3.6
SK | 55 | 66 | 1.50 | 1.34 | 73.7 | 13.7 | 12.6 | 80.2 | 11.8 | 8.1
IS | 76 | 94 | 1.18 | 1.06 | 86.4 | 9.1 | 4.5 | 94.1 | 5.9 | 0.0
ES | 70 | 74 | 1.41 | 1.33 | 75.3 | 15.0 | 9.7 | 78.0 | 14.6 | 7.6
SE | 76 | 77 | 1.20 | 1.18 | 83.9 | 13.1 | 3.0 | 86.6 | 10.4 | 3.0
CZ | 66 | 72 | 1.57 | 1.42 | 68.8 | 15.8 | 15.5 | 73.1 | 16.6 | 10.3
BE | 70 | 76 | 1.37 | 1.27 | 75.2 | 15.9 | 8.9 | 81.5 | 12.7 | 5.8
FI | 72 | 85 | 1.20 | 1.12 | 85.5 | 10.3 | 4.2 | 90.0 | 8.7 | 1.3
NL | 81 | 87 | 1.20 | 1.12 | 86.1 | 8.9 | 5.0 | 89.6 | 9.1 | 1.3
DE | 81 | 82 | 1.28 | 1.27 | 78.9 | 15.5 | 5.6 | 79.7 | 14.9 | 5.5
CH | 85 | 74 | 1.15 | 1.27 | 88.5 | 8.8 | 2.7 | 78.0 | 16.9 | 5.1
FR | 76 | 81 | 1.25 | 1.20 | 83.8 | 10.1 | 6.0 | 86.8 | 8.4 | 4.8
UK | 85 | 83 | 1.13 | 1.21 | 90.4 | 7.5 | 2.0 | 85.7 | 8.3 | 6.0
DK | 75 | 91 | 1.25 | 1.08 | 89.3 | 7.1 | 3.6 | 92.5 | 7.5 | 0.0
NO | 81 | 91 | 1.15 | 1.08 | 87.5 | 10.0 | 2.5 | 92.2 | 7.8 | 0.0
mean | 72.1 | 78.1 | 1.36 | 1.27 | 77.4 | 14.4 | 8.3 | 81.5 | 13.0 | 5.5
std err | 1.2 | 1.1 | 0.02 | 0.02 | 1.2 | 0.6 | 0.7 | 1.1 | 0.6 | 0.5
Table 6. Spatial and Trend Statistics ISO | Neighbours | Neighbours | Creators | New Creators | Trend
---|---|---|---|---|---
| per Creator | per Location | previous loc. | near loc. | previous loc. | near loc. | 2014-21 | 2014-19 | diff.
MT | 25.2 | 13.6 | 92.7 | 5.4 | 91.5 | 5.7 | 3.18 | 9.19 | -6.01
EE | 3.6 | 2.9 | 71.9 | 11.8 | 64.7 | 13.9 | 1.57 | 7.18 | -5.61
PL | 3.9 | 3.2 | 72.8 | 13.0 | 64.0 | 16.7 | 2.35 | 9.33 | -6.98
IT | 8.2 | 4.8 | 75.4 | 11.2 | 71.4 | 13.2 | 0.03 | 2.31 | -2.28
TR | 9.4 | 4.5 | 61.3 | 14.0 | 58.3 | 13.5 | 1.99 | 5.93 | -3.94
HR | 3.3 | 3.0 | 79.7 | 9.7 | 67.0 | 13.6 | 1.13 | 4.41 | -3.28
AL | 4.3 | 2.7 | 61.2 | 8.7 | 59.5 | 6.4 | 1.25 | 5.33 | -4.08
LT | 5.3 | 3.2 | 73.8 | 10.1 | 63.7 | 12.7 | 0.75 | 3.45 | -2.70
LU | 2.2 | 2.3 | 68.3 | 18.7 | 62.5 | 25.5 | 1.37 | 2.66 | -1.29
HU | 3.1 | 2.7 | 76.8 | 7.2 | 62.1 | 11.2 | 0.49 | 2.10 | -1.61
EL | 4.3 | 3.5 | 79.3 | 9.4 | 71.4 | 11.9 | 0.58 | 1.62 | -1.04
MK | 4.7 | 3.7 | 68.8 | 9.0 | 66.4 | 9.3 | 0.55 | 2.65 | -2.10
ME | 2.8 | 2.2 | 41.3 | 17.7 | 36.5 | 22.0 | 0.70 | 4.17 | -3.47
SI | 2.6 | 2.3 | 68.4 | 9.9 | 54.4 | 16.7 | 0.08 | 1.15 | -1.07
BG | 4.8 | 3.7 | 71.6 | 12.8 | 70.7 | 11.9 | 0.35 | 1.87 | -1.52
RO | 4.5 | 3.1 | 61.4 | 9.6 | 54.3 | 11.1 | 0.60 | 2.02 | -1.42
IE | 2.3 | 2.3 | 68.7 | 18.6 | 64.5 | 20.8 | -0.78 | -0.28 | -0.50
RS | 3.2 | 3.3 | 79.6 | 7.9 | 73.9 | 10.5 | -0.11 | 0.64 | -0.75
AT | 1.8 | 1.7 | 68.3 | 11.9 | 55.5 | 18.5 | 0.76 | 0.68 | 0.08
CY | 2.4 | 2.2 | 68.9 | 16.2 | 55.0 | 19.7 | 0.19 | 1.27 | -1.08
LV | 2.2 | 1.8 | 44.2 | 21.5 | 32.6 | 28.8 | 0.41 | 1.26 | -0.85
PT | 3.5 | 2.8 | 64.9 | 17.8 | 58.7 | 19.7 | 0.29 | 1.13 | -0.84
SK | 2.4 | 2.5 | 79.8 | 9.7 | 62.2 | 15.8 | -0.06 | 0.40 | -0.46
IS | 1.7 | 1.9 | 59.8 | 20.2 | 41.7 | 28.3 | -0.53 | -0.37 | -0.16
ES | 2.8 | 2.8 | 74.5 | 11.0 | 63.4 | 14.4 | 0.00 | 0.37 | -0.37
SE | 2.4 | 2.2 | 68.8 | 14.0 | 59.4 | 14.1 | -0.08 | 0.24 | -0.32
CZ | 2.2 | 2.0 | 63.0 | 12.4 | 49.7 | 16.3 | 0.07 | 0.35 | -0.28
BE | 2.5 | 2.3 | 70.2 | 14.0 | 71.9 | 15.2 | 0.00 | 0.25 | -0.25
FI | 1.8 | 1.8 | 55.9 | 15.8 | 42.1 | 22.6 | -0.05 | 0.11 | -0.16
NL | 2.0 | 1.9 | 64.4 | 16.3 | 51.3 | 28.9 | 0.10 | 0.16 | -0.06
DE | 2.4 | 2.3 | 67.7 | 14.0 | 55.7 | 16.2 | 0.06 | 0.23 | -0.17
CH | 2.7 | 2.1 | 75.1 | 12.5 | 66.3 | 16.3 | -0.02 | 0.08 | -0.10
FR | 2.1 | 2.3 | 69.6 | 12.8 | 52.2 | 12.5 | -0.03 | 0.06 | -0.09
UK | 1.7 | 1.6 | 53.7 | 18.2 | 35.3 | 21.0 | -0.01 | 0.11 | -0.12
DK | 1.5 | 1.5 | 51.7 | 6.3 | 68.8 | 14.1 | -0.02 | -0.02 | 0.00
NO | 1.4 | 1.5 | 38.8 | 17.1 | 21.8 | 12.6 | -0.04 | -0.06 | 0.02
mean | 3.8 | 2.9 | 67.0 | 13.0 | 58.3 | 16.2 | 0.50 | 2.00 | -1.50
sem | 0.7 | 0.3 | 1.8 | 0.7 | 2.2 | 0.9 | 0.10 | 0.40 | 0.30

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