Dataset Overview
Europass Survey
June - September 2019
Description
- Name: occupations_indicator
- Number of observations: 870,149
- Number of features: 16
- Description: This dataset brings insights on work experiences measured in collected CVs from the Europass CV online editor. More than 1.2 million work experiences were observed among 350K respondents. The analysis provides information on the most reported occupations across respondents based on the established international classifications of ISCO for occupations and EQF for educational level.
- Sources: Europass Survey: June - September 2019
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| Feature | Name | Description | Form1 | Stat2 | Text3 | |
|---|---|---|---|---|---|---|
| job_isco1 | Job ISCO 1 | ISCO level 1 classification for job. | ● | ● |
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| job_isco2 | Job ISCO 2 | ISCO level 2 classification for job. | ● | ● |
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| job_isco3 | Job ISCO 3 | ISCO level 3 classification for job. | ● | ● |
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| recruitment_year | Recruitment Year | Year of recruitment. | ● |
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| termination_year | Termination Year | Year of termination. | ● |
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| work_years | Years on Job | Years staying on a specific job. | ● |
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| work_experience | Work Experience | Cumulative work experience at the time of a particular job. | ● |
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| eqf_level | EQF Level | Highest education level mapped to EQF at the time of a particular job. | ● | ● |
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| locale | CV Language | Language used to write CV. | ● |
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| country | Country | Country of residence. | ● |
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| age_group1 | Broad Age Group | Broad age groups with three categories. | ● |
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| age_group2 | Narrow Age Group | Narrow age groups with five categories. | ● |
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| gender | Gender | Female, male or missing value. | ● |
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| nationality | Nationality | Nationality. | ● |
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| mother_tongue | Mother Tongue | Native language. | ● |
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| responses | Responses | Number of responses with a particular combination of values for the above variables. | ● |
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| 1 Variable derives directly from a Europass CV; | ||||||
| 2 Variable is a statistical transformation of one or more Europass CV variables; | ||||||
| 3 Variable is a result of information retrieval using text mining |
Summary Statistics
job_isco1
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 10 |
|
* Missing features required for estimation, or unidentifi ed from classification algorithm. |
job_isco2
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 42 |
|
* Missing features required for estimation, or unidentifi ed from classification algorithm. |
job_isco3
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 125 |
|
* Missing features required for estimation, or unidentifi ed from classification algorithm. |
recruitment_year
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 14257 ( 1.64% ) |
| Number of unique values | 61 |
| * WorkExperience.Period.From.Year missing. |
termination_year
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 148674 ( 17.09% ) |
| Number of unique values | 58 |
| * Education.Period.To.Year missing. |
work_years
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 150248 ( 17.27% ) |
| Number of unique values | 6 |
|
* WorkExperience.Period.From.Year missing, or value less than 0 years, or value more than 50 years. |
work_experience
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 150248 ( 17.27% ) |
| Number of unique values | 6 |
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* No other work experience entries filled, or WorkExperie nce.Period.From.Year and WorkExperience.Period.To.Year missing. |
eqf_level
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 171818 ( 19.75% ) |
| Number of unique values | 6 |
|
* Missing features required for estimation, or unidentifi ed from classification algorithm, or either of WorkExpe rience.Period.From.Year or Education.Period.To.Year mis sing. |
locale
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 29 |
| * SkillsPassport.Locale missing. |
country
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 44742 ( 5.14% ) |
| Number of unique values | 184 |
| * SkillsPassport…Country.Code missing. |
age_group1
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 310735 ( 35.71% ) |
| Number of unique values | 4 |
|
* SkillsPassport…Birthdate.Year missing, or over 65 yea rs old. |
age_group2
| Feature | Result |
|---|---|
| Variable type | ordered (ordinal) |
| Number of missing values* | 309604 ( 35.58% ) |
| Number of unique values | 6 |
| * SkillsPassport…Birthdate.Year missing. |
gender
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 387289 ( 44.51% ) |
| Number of unique values | 3 |
| * SkillsPassport…Gender.Code missing. |
nationality
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 415755 ( 47.78% ) |
| Number of unique values | 162 |
| * SkillsPassport…Nationality.Code missing. |
mother_tongue
| Feature | Result |
|---|---|
| Variable type | factor (nominal) |
| Number of missing values* | 162836 ( 18.71% ) |
| Number of unique values | 63 |
| * Skills.Linguistic.MotherTongue.Description missing. |
responses
| Feature | Result |
|---|---|
| Variable type | integer |
| Number of missing values* | 0 ( 0.00% ) |
| Number of unique values | 115 |
| Min. | 1 |
| 1st Qu. | 1 |
| Median | 1 |
| Mean | 1.29 |
| 3rd Qu. | 1 |
| Max. | 271 |
| * No missing values permitted. |
