Europass CV Insights Report
Europass CV Insights Report
Overview
Europass is a European Union initiative for increasing transparency of qualification and mobility of citizens in Europe. It aims to make a person’s skills and qualifications clearly understood and consequently absorbed throughout Europe. Between February 2005 and November 2019, 150 million Europass curriculum vitae (CV) have been created online. Only in 2019, there were 25 million Europass CVs completed online - approximately 200 times more than in 2005.
Europass’ users survey is one of the largest survey of people who build their curiculum vitae worldwide. We run the data collection from 15th of June till the end of September. This year marks the first year we’ve run the survey and we collected a large amount of information regarding the supply of work and characteristics of people building their CV throughout Europe’s largest portal. Survey resulted in nearly 400,000 users, who committed their CV for statistical analysis. Precisely, 392,812 unique CVs were gathered, among which 353,518 qualified for analytical purposes based on the degree of their completion.
Demographics
Every month, about 2 million people visit Europass to build their CV. Ca 24% of these people are currently in education or training. The majority of our survey respondents declared that they have Italian nationality, live in Italy, and used Italian language to complete their CV. About 44% of respondents declared to be employed, and ca 94% are under the age of 45.
24% currently studying.
44% employed.
94% under the age of 45.
Age, Gender and Country
The demographic profile of the respondents and the Europass CV creation time series are presented bellow.
Age
Sex
Country of residence
Time series
## `geom_smooth()` using formula 'y ~ x'Most CVs were completed during the first three days of the week.
Most CVs were completed in September 2019.
The fewest CVs were completed during the first half of August 2019.
The average age of respondents is 28, and 75% are under 32. More than half of the respondents declared a sex (male or female), while most of them live in an EU country. Only a minority of users completed their CVs during the weekend, while the first few days of each week were the days we received most of the CVs. The number of CVs completed increased gradually throughout the survey, from July to September 2019.
Top nationalities
Overall
Sex
Age group
The majority of respondents (83%) have the nationality of a country of the European Union. The top 5 nationalities are: are Italian, Portuguese, Romanian, Spanish and Greek. For the age group 50-64 the Italian nationality climbs up to ~48%. Concerning sex, no significant difference among countries was noticed, except a slightly higher propention of Romanians to declare their sex while the opposite holds for Spanish.
Top languages
Every day, citizens of many countries around the world visit Europass to take advantage of its service. We received CVs in all EU languages as well as outside of EU, reflecting the huge amount of data gathered for the survey.
Overall
Sex
Age group
The majority of languages used to create a CV, as with nationalities, belong to the 24 EU official languages. The most used language is Italian, followed by English, Portuguese, Spanish and Romanian. Inspecting the age group 50-64, there is a significant drop in the use of English as a CV language, and Italian climbs up to ~48%. Similar effect is observed for female users where Portuguese is the second most popular language used.
Synopsis
Approximately 24% who visit Europass to build their CV are students. A high majority of the survey respondents hold Italian nationality. The majority of the respondents are less than 45 years old. The top 5 observed nationalities are Italians, Portuguese, Romanians, Spanish and Greeks.
Occupations
This section provides descriptive statistics on the work experience of the respondents. Most of the work experience entries were given in free-text format. Through use of multilingual fuzzy-matching methods, each free-text entry has been mapped to a standard occupation defined in the ESCO classification.
Most common work experiences
Statistics on the most common occupations done by respondents are presented with respect to ISCO Level 3.
Work Experience
Descriptive statistics about the work experience of the respondents is provided bellow.
Work experience
- Around 12% of respondents have not reported any work experience in their CV.
Highly experienced
- Respondents with more than 5 occupation-related entries in their CV make up about 23% of the total pool.
Sex and age groups
- Older male individuals tend to report more jobs than their female counterparts.
- The average number of jobs reported is otherwise very similar between the two sexes.
Comparing age and work experience, we see that young respondents with less work experience more likely work as waiters, sports and fitness workers, etc. Moving across the line to the right, professions requiring maturity start making appearance, such as managers and senior officials. Occupations above the line are most likely skill-based and allow entrance to the job market at a younger age, like in the case of cooks, assemblers and sales maangers. On the contrary, those bellow the line are more likely knowledge-based, as in the case of school teachers and life science professionals.
Employers
Statistics on employers identified in free text analysis of free text are presented below.
Age group
Sex
- Employers offering entry-level jobs are the most common across the board.
- Companies employing older respondents are often technology-related businesses.
- Male respondents are more likely to work as freelancers and in technology-related companies.
- Female respondents are more likely to report companies offering store clerk positions, especially in clothing.
Trends over the past 10 years
A sample of the top keywords related to occupations identified in English free-text entries is presented bellow. The x-axis displays the employment year of a work experience declared by a respondent.
Upward trends
## `geom_smooth()` using formula 'y ~ x'- Many of the respondents appear to have recently started working in jobs relating to mobile and web platforms.
- Recent terminology such as “fullstack” developer, developing both client and server-side software, display a high increase in frequency.
- The high frequency of recent service jobs can be explained by the young age of Europass users.
Downward trends
## `geom_smooth()` using formula 'y ~ x'- Increased specialization may have lead to a decrease of generic job title terms such as “economist” or “biologist”.
- Work reported for certain sectors such as the army and the police force are generally more common to have started less recently. This could be explained by their special nature as public service jobs and the lack of mobility these sectors tend to offer.
Consistent
## `geom_smooth()` using formula 'y ~ x'- Occupations related to the health sector and education are consistently reported throughout the period.
- Event planning jobs have also maintained the same frequency now compaired to ten years ago.
With respect to when each user began each specific job reported, certain trends can be identified over the course of the last 10 years. Jobs related to technology, especially the web and mobile platforms, display a major increase in frequency. Service jobs also appear to grow in frequency in recent years. Meanwhile, respondents that started working a public service job are less frequent, and there is a noticeable decrease in frequency in numerous jobs related to journalism. Traditionally enduring jobs such as that of the doctor and the teacher maintain their frequency mostly unchanged.
Headline job (“job applied for”)
The most popular jobs applied for are displayed bellow both across the board and broken down between distinct demographic groups. In the latter three figures we compare headline jobs between distinct demographic groups. Namely, sex, age group (15-24, 25-49) and country of residence (in EU, outside the EU). The top 10 favours one group, while the bottom 10 favours the other.
Overall
Sex
Age group
Country
Male respondents are more inclined to apply for jobs relating to technology, engineering and fields involving manual work. Female respondents tend to apply more for work related to children care and secretary positions. Younger respondents apply for jobs requiring less specialization. Respondents outside the EU are generally more inclined to apply for jobs in more specialised fields.
Synopsis
The results of our study indicate that the most popular occupations are “Waiters and bartenders”, “Shop salespersons” and “Administrative and specialised secretaries”. It is noteworthy that “Waiters and bartenders” is the most popular occupation in the age group 15-24 with 24%, but a decline sets in as the age group rises, with only 8% of the sample to be included in the age group 50-64.
Skills, Knowledge and Competences
Skills/competences were mapped to ESCO, the European Commission’ multilingual classification of Skills, Competences, Qualifications and Occupations. In ESCO, skills are split in different categories, 1) Languages (for example, English) and 2) Competences/Skills (for example JAVA, computer programming).
When filling in their Europass CV, users describe their skills grouped in standardised categories: Mother tongue, Foreign languages, Driving license, Communication skills, Organisational / managerial skills, Job-related skills and digital skills. Out of these 7 categories, only Communication skills, Organisational/managerial skills, Job-related skills and digital skills visualised, because they are the categories the respondents picked when they filled in their skills as free text.
Broad skill categories
Overall
Sex
Age group
- Overall, respondents filled out more Job-related and Communication skills than Organisational and Computer ones.
- Male respondents tend to include more Job-related and Computer skills than the female counterparts, while the reverse is true for Communication and Organisational skills.
- Younger users tend to include more Communication skills and less Job-related ones.
Linguistic skills
We consider a respondent to have a particular language skill if it has been declared as a foreign language or as mother tongue. Percentage of respondents speaking a particular language are displayed bellow.
Overall
Sex
Age group
English, filled in by more than 85% of the EPAS survey respondents, is the most popular language across the board.
There is no significant difference among men and women regarding language skills. However, that English is more popular among younger people. The frequency of Italian language increases in the 50-64 age group.
Personal skills
To map the multilingual, free-text skills provided by users in the survey to the ESCO classification, a stricter classification process compared to other free-text matching was required. Specifically, it involved cross-referencing them with skills inferred by work experience. A more detailed outline of the process is developed in the Methodology section. The most popular skills detected are displayed bellow.
Most popular
Overall
Sex
Employment status
Female respondents tend to describe more skills related to teamwork, organisation or assistance more frequently, while male respondents focus more on technical skills, for instance programming languages.
There is not a big difference when we look at the top 15 skills based on employment, but we will see in the next section that there is indeed a difference when looking at the skills based on deviation.
Major skill differences between groups
Sex
Employment status
Computer programming skills are most popular among male users, while assistance/help of children is most popular among female users.
Employment and computer literacy skills are highly correlated: people possessing skills in programming language, or more generally, those who describe their digital skills, are most likely in employment.
Skills, occupations and work experience relationship
Skills over age and work experience
## `geom_smooth()` using formula 'y ~ x'Area A: Skills mostly related to customer service. They are being filled in by people with more work experience than the average, with age less than 30 years, most likely people that started working at a young age.
Area B: Skills filled in by people with more work experience than the average and more than 28 years of age. These skills are related to leading or managing other people, indicating that they belong to people with high work experience, most likely experts in what they do.
Area C: Skills filled in by people with less work experience than the average and more than 30 years of age, most likely people that have graduated from university, might have a PhD and specialize in what they do.
Area D: Skills mostly related to helping/assisting children and computer literacy. They are being filled in by people with less work experience than the average and less than 33 years of age.
Driving Licenses
Based on the vehicle type, European driving licenses are split into 4 broad categories and 15 specific classes valid in all EEA member states:
- Mopeds and motorcycles: AM, A1, A2, A
- Motor vehicles: B, BE, B1
- Large goods vehicles: C1, C1E, C, CE
- Buses: D1, D1E, D, DE
Overall
Sex
Age group
The most popular driving license category is “B - Motor vehicles (B1, B, BE)”, “Mopeds and motorcycles” (AM, A1, A2, A being the second most popular. More specifically, the “B” class counts for 95% among the 15 driving licence classes.
Except for the “B” driving licence class where there is a equal sex ratio, male respondents fill in other classes of driving licenses more often.
There is an overall balance in the “B” class. The age group 15-24 fills in “AM” class licenses (two-wheels up to 50 cc) more often compared to the other 2 age groups, while the age group 50-64 fills in “A” class licenses (motorcycles) more often comparatively.
ICT certificates
All Respondents
Sex
Age group
Country
Of all respondents, only about 7% have included ICT certificates. Almost 90% of female respondents who included ICT certificates, included only one. Male respondents are slightly more likely to include more certificates. Number of certificates rise with age. Respondents outside the European Union report a higher number of certificates on average.
Trends across age
Keywords are extracted from English free text and are grouped by age. Using the “slope” of the distribution along the age-axis (x-axis) we can distinguished skills popular among younger and older age groups. In particular, skills with negative slope are popular among younger people while skills with positive slope among older people.
Younger respondents
## `geom_smooth()` using formula 'y ~ x'- Younger respondents tend to report personal qualities and partitipation in events and programmes.
- Common technical skills that become more common among younger ages include video and photo editing software.
Older respondents
## `geom_smooth()` using formula 'y ~ x'- Skills related to legislation and policies are reported by respondents in older ages.
- Technical skills related to infrastructure, security and virtualization are also less common among younger users.
Younger respondents generally report on skills acquired through extra-curricular activities and participation in programmes such as Erasmus. Many of them are related to personal qualities such as “friendliness” and “communication”. The more technical ones often have to do with software capabilities, particularly in photo and video editing, but also popular programming languages. Older respondents meanwhile report on skills linked to legislation and policy, such as auditing. Technical skills related to Information Technology infrastructure are also observed in this category.
Free text analysis
Skills free text and broad categories
Organisational
Communication
Job Related
Computer
- Here, the free text of the 4 broad skill categories is being visualized. Each visualization is a graph of words that was picked based on best interest and word frequency. The size of the points shows how high or low the frequency of a word is, whereas the alpha of the edges shows how high or low a correlation between the connected words is.
Skills free text and ISCO 3 occupations
Administrative and specialised secretaries
Manufacturing, mining, construction, and distribution managers
Other teaching professionals
University and higher education teachers
Waiters and bartenders
- Here, we take a look at the correlation between terms in the respondents’ input of skills and 5 of the most popular occupations.
Synopsis
English language proved to be the most popular, filled in by more than 85% of the survey respondents, and it is spoken the most by younger people. Furthermore, our study shows a correlation between female respondents and skills related to teamwork, organising or assistance while male respondents are linked to programming languages.
Qualifications
The European Qualifications Framework (EQF) has been introduced as a common reference framework for communicating the equivalence of the different countries’ qualifications across Europe. It defines eight levels of education, from primary (at Level 1) to doctoral (at Level 8). Users have been mapped to the highest level of education on which they have received at least some education. The ISCED Fields of Education and Training (FoET 2013) have been selected for the purpose of classifying individual qualifications.
EQF level distribution
Qualifications distribution of the respondents was identified by analysing free text and a portion of dropdown selection. Note that, unless stated explicitly, ongoing studies are counted towards the qualification level of a respondent.
Overall
Country
- Users from countries outside the European Union tend to have a higher educational level.
- The difference is less pronounced for users with level 7 on the EQF, however.
Sex
- Distribution of EQF level is almost identical between the two sexes.
- Male users declaring Level 5 qualifications are slightly more represented.
In the pool of survey respondents, about two thirds declared a tertiary education level. Around 32% has been evaluated to Level 6, which means a Bachelor’s degree or equivalent. Users with education equivalent to a Master’s degree, Level 7 on the EQF, make about 15% of the user base. Those with a Ph.D. or other doctorates represent just 4%. Users from countries outside the EU are more likely to have an education equivalent to Level 6 or 7 on the EQF than their EU counterparts. The distribution of education level is similar between sexes across the board.
Field of Education and Training
As with other free-text fields, the field of study is retrieved through text mining, fuzzy matching and machine learning. The distribution and the breakdown by category is given in the following subsections.
Sex
Level 5-6
Level 7-8
The distributions of the fields detected for undergraduate (EQF levels 5 and 6) and postgraduate (EQF levels 7 and 8) qualifications are very similar. Qualifications related to management and administration and teaching are the most popular. The highest degree of deviation is observed in fields relating to technology for male respondents, and fields relating to humanities and social sciences for female respondents.
Relationship Between educational level And age
Educational level vs. age
## `geom_smooth()` using formula 'y ~ x'- About 50% of respondents between 20 and 40+, declare a EQF level 5 and above.
Breakdown in Age Groups
- Over two thirds of respondents in the 25-49 age group have completed some tetriary education.
- A decrease in educational level is displayed for age group 50-64 compared to the former.
Over half of respondents reporting ages between 15 and 24 have completed secondary education at most, while most of the rest have completed an initial degree. Acquisition of a second degree peaks for the age group 25-49, particularly for respondents in their mid-30s. Respondents between their late 40’s and mid 50’s are more likely to have only completed secondary education at most than their immediately younger and older counterparts.
Most popular institutions
Italy
Portugal
Spain
Romania
Greece
The popularity of each country’s tertiary education institutions seems to be roughly in accordance to the institutions’ size. The Top 10 institutions of the five countries with most responses are being presented along with the equivalent EQF level of the education received in them by users.
Ongoing studies
The statistical distribution of respondents with ongoing studies is illustrated in the following plots.
Age group
Level progression
- Most respondents currently in Level 6 previously completed high school.
- Almost all respondents that completed a Level 5 qualification and continued studying progressed to Level 6.
- ~30% of PhD students (Level 8) started their doctorate straight from a Bachelor’s degree (Level 6), meaning they did not go declare a Master’s-level qualification (Level 7).
- Only a small percentage of students are enrolled in a programme equivalent to EQF level 5.
Field
Most users who are currently students are studying either for a bachelor’s or a master’s degree. A measurable portion also appear to be on Ph.D. or other doctorate programmes. The majority of students aged between 15 and 24 are working towards a degree that puts them on Level 6 on the EQF. For ages between 25 and 49, about half are working for Bachelor’s while about a third of them are working for Master’s-equivalent degrees. Meanwhile, about 14% is working on a doctorate programme.
Mobility and studying abroad
European Union
- Nationalities displaying the highest rate of migration originate from Central and Southeastern Europe.
- Universities in Italy, the UK, Spain, Germany and the Netherlands are popular for many nationalities.
Candidate Countries
- Historical circumstances heavily affect countries chosen by respondents in EU candidate countries. For example, students with nationalities from the former Yugoslav republics are more likely to study in another country in the region.
- In many cases however, respondents have elected to complete part of their studies in EU institutions, with Germany being a popular choice for Turkish students, for example.
Effect on EQF level
The highest the EQF Level the more likely a user has studied abroad. Relationships between specific countries and nationalities emerge, based on linguistic, historical and various other circumstances. The percentage of respondents that studied abroad and hold a Master’s degree or a doctorate is twice that of respondents who studied exclusively in their country.
Trends over the past 20 years
A sample of the most popular keywords found in English free text qualifications is presented bellow. The frequency of each keyword has been calculated with respect to the enrollment year of the respective qualification from which it was extracted.
Upward trends
## `geom_smooth()` using formula 'y ~ x'- Education keywords related to technology is trending upwards.
- Youthpass, IELTS (International English Language Testing System) and GWO (Global Wind Organisation), as well as other certificates, tend to be mentioned a lot.
Downward trends
## `geom_smooth()` using formula 'y ~ x'- The frequency of subjects/topics related to general and vocational secondary education is trending downwards.
- Education titles such as that of engineers and lawyers tend to be less reported than in the past.
Consistent
## `geom_smooth()` using formula 'y ~ x'- Education on evergreen fields such as finance and tourism tend to be reported consistently.
- Keywords such as “hairdressing” and “biotechnology” display fluctuations, but they lack a noticeable trend within the defined window.
Changes in the education and academic qualifications reported to have been acquired by Europass users in the 20-year period between 1999 and 2019 can be noticed. Education related to computing is trending upwards, and it appears more recent students tend to be more involved in workshops and online courses. Meanwhile, secondary and technical education is generally reported less on a year-by-year basis based on the downwards trend of the related keywords. Certain keywords related to language acquisition, certificates and evergreen disciplines are being reported consistently.
Occupations and EQF level
Level 1-4
Level 5-6
Level 7-8
Distribution of occupations differs for respondents of different educational levels. Jobs in service and sales dominate in the lower levels of education. As level increases, more administrative occupations and professions requiring higher specialization emerge. Respondents with multiple tetriary degrees are commonly occupied with teaching, engineering and software-related professions. As level 5-6 generally includes a younger demographic with a larger proportion of ongoing studies, entry-level occupations such as Waiters and bartenders are also common among them.
Synopsis
Most users who are currently students are studying either for a bachelor’s or a master’s degree. A measurable portion also appear to be on Ph.D. or other doctorate programmes. Most respondents between 15 and 24 and currently in education or training prepare degree of EQF Level 6 on the EQF. Half of respondents between 25 and 49, prepare a Bachelor’s while about a third of them prepare a Master’s-equivalent degree. Meanwhile, over 10% is engaged in a doctorate programme.
Most respondents appear to have at least one higher education qualification, which is higher the higher the age. Current students are mostly working towards bachelor’s and master’s. Mobility can be noticed based on specific patterns, and there’s a high correlation between studying abroad and education level. Education on technology is trending upwards. Respondents of lower education tend to work in service and sales professions, while those with higher education are occupied with administrative, software and engineering professions.
Methodology
Approach
We followed a reproducible approach based on literate programming to support the data flow architecture (Xie 2015; Boettiger 2015). The statistical analysis of the received CVs specifies a data flow of strict order in which data are transformed from a raw dataset to statistical information and graphics. Each step contains multiple methods and processes of data cleansing, data wrangling and information retrieval (see the following info-graphic). The tidy data standard has been used to facilitate exploration and analysis of the data (Wickham 2014) and the visualizations pipeline is based on Wilkinson’s (2010) grammar of graphics. Parts of the analysis are inspired by Cedefop’s research project on Online job vacancies and skills analysis (Cedefop 2019).
Methodological Notes
This report is based on curriculum vitae submitted by 353.5K Europass users. We received a huge amount of information in multilingual form and we observed more than 171 unique nationalities. This is the number of responses we consider “qualified” for analytical purposes based on a completion index proportional to the ratio of completion of a CV and the inclusion of key fields like first name, email and address. Based on this index we discarded the bottom 10% of curriculum vitae from each demographic group based on locale, to increase the precision of the statistical analysis.
Respondents who participated where informed that all personal data will be removed 6 months after the completion of the survey.
The top responses came during the first three days of each week while the volume significantly increased during September.
The survey was fielded from June 15 to September 30.
Since respondents were recruited through our web application, people actively searching for a job are more likely to participate to our survey.
Respondents were mainly Europass users building their CV through our web application.
Our web application service is not equally accessible to all demographic groups. For example, people above 45 years of age and/or lower education level are underrepresented compared to the general population. This unanticipated limitation of our web service is also reflected in our survey and should be kept in mind when interpreting the results.
Our estimate of occupations comes from the ESCO classification for occupations. Users can either choose from a drop down an ESCO classification or enter free text. Most responses for occupations where in free text form and in more than 30 unique languages. Advanced methods of text mining, language identification and machine learning where used to match free text to the ESCO classification. The hierarchy of occupations as provided by the International Standard Classification of Occupations was used to increase the accuracy of the information retrieved.
Our estimate of skills and educational level comes from the ESCO classification as well. Users mostly provided relevant information in the form of multilingual free text. Similar to occupations, advanced methods of text mining, language identification and machine learning where used to match free text to the ESCO classification.
We collect data on user CVs to share (non-personal) information we think you might find interesting and questions we think you can answer. You can download a summary of the cleaned data.
In some cases, we converted numerical and categorical variables to simpler representations (like for example age is converted to age groups) for easier share of information.
Most of the respondents are younger than the average general population.
The provided CVs where mapped to numerical, nominal and ordinal variables. These variables are stored in four tidy data sets for demographics, occupations, competencies and qualifications of the respondents forming a suitable data model for statistical analysis and visualizations.
We used data from European skills, competences, qualifications and occupations to identify trends, patterns and measure properties and features of the workforce using the Europass web application.
We collect data on CVs to share (non-personal) information we think you might find interesting and questions we think you can answer. You can access the aggregated/anonymized data here.
What’s next?
This report can act as the starting point for further analysis towards more useful inferences about the job market in Europe and across the globe. Comparing the data derived from our model to other sources’ labour market datasets could reveal potential biases and drive to more precise conclusions. Additionally, more attentive handling of cases of multilingual text may disclose more patterns and help derive more trends in occupations, skills and qualifications.
Packages
xml2, parallel, rmdformats, text2vec, stringr, httr, cld2, cld3, DT, jsonlite, stopwords, magrittr, scales, ggplot2, colorspace, grid, grImport2, ISOcodes, data.table, fst, dplyr, tidyr, tidytext, ggalluvial, ggrepel, igraph, ggraph, shiny, shinyjs, shinyWidgets, shiny.router
References
Boettiger, Carl. 2015. “An Introduction to Docker for Reproducible Research.” SIGOPS Oper. Syst. Rev. 49 (1): 71–79. https://doi.org/10.1145/2723872.2723882.
Cedefop. 2019. “Online job vacancies and skills analysis.” https://www.cedefop.europa.eu/en/publications-and-resources/publications/4172.
Wickham, Hadley. 2014. “Tidy Data.” Journal of Statistical Software, Articles 59 (10): 1–23. https://doi.org/10.18637/jss.v059.i10.
Wilkinson, Leland. 2010. “The Grammar of Graphics.” WIREs Computational Statistics 2 (6): 673–77. https://doi.org/10.1002/wics.118.
Xie, Yihui. 2015. Dynamic Documents with R and Knitr, Second Edition (Chapman & Hall/Crc the R Series). Routledge. https://www.xarg.org/ref/a/1498716962/.