Description

Name: occupations_progression_aggregate
Number of observations: 742,400
Number of features: 13
Description: This dataset describes aggregate job progression statistics based on responses collected in the Europass Survey. The values of some of its features derive directly from the Europass CV creation form fields, while others are a result of data analysis upon the given responses. Certain features are simple statistics, while others are mappings of free text to standardized taxonomies made using fuzzy matching techniques.
Sources: Europass Survey: June - September 2019
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Feature	Name	Description	Form¹	Stat²	Text³
from_job_esco	From Job ESCO	ESCO classification for initial job.	●		●
from_job_isco	From Job ISCO 3	ISCO level 3 classification for initial job.	●		●
to_job_esco	To Job ESCO	ESCO classification for subsequent job.	●		●
to_job_isco	To Job ISCO 3	ISCO level 3 classification for subsequent job.	●		●
recruitment_year	Recruitment Year	Year of recruitment.	●
termination_year	Termination Year	Year of termination.	●
preceding_work_years	Preceding Work Years	Total work years until recruitment date of a particular job.		●
locale	CV Language	Language used to write CV.	●
country	Country	Country of residence.	●
birth_year	Birth Year	Year of birth.	●
gender	Gender	Female, male or missing value.	●
nationality	Nationality	Nationality.	●
responses	Responses	Number of responses with a particular combination of values for the above variables.		●
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

Feature	Result
Variable type	character (nominal)
Number of missing values*	0 ( 0.00% )
Number of unique values	2916
^* Missing features required for estimation, or unidentifi ed from classification algorithm.

Feature	Result
Variable type	character (nominal)
Number of missing values*	0 ( 0.00% )
Number of unique values	125
^* Missing features required for estimation, or unidentifi ed from classification algorithm.

Feature	Result
Variable type	character (nominal)
Number of missing values*	0 ( 0.00% )
Number of unique values	2912
^* Missing features required for estimation, or unidentifi ed from classification algorithm.

Feature	Result
Variable type	character (nominal)
Number of missing values*	0 ( 0.00% )
Number of unique values	125
^* Missing features required for estimation, or unidentifi ed from classification algorithm.

Feature	Result
Variable type	numeric
Number of missing values*	0 ( 0.00% )
Number of unique values	57
Min.	1960
1st Qu.	2009
Median	2014
Mean	2011.96
3rd Qu.	2017
Max.	2019
^* WorkExperience.Period.From.Year missing.

Feature	Result
Variable type	numeric
Number of missing values*	71330 ( 9.61% )
Number of unique values	54
Min.	1967
1st Qu.	2011
Median	2015
Mean	2013.36
3rd Qu.	2017
Max.	2020
NA’s	71330
^* Education.Period.To.Year missing.

Feature	Result
Variable type	numeric
Number of missing values*	0 ( 0.00% )
Number of unique values	200
Min.	0
1st Qu.	0
Median	3
Mean	6.08
3rd Qu.	8
Max.	290
^* No other work experience entries filled, or both WorkEx perience.Period.From.Year and WorkExperience.Period.To. Year missing from prior jobs.

Feature	Result
Variable type	character (nominal)
Number of missing values*	0 ( 0.00% )
Number of unique values	29
^* SkillsPassport.Locale missing.

Feature	Result
Variable type	character (nominal)
Number of missing values*	32164 ( 4.33% )
Number of unique values	182
^* SkillsPassport…Country.Code missing.

Feature	Result
Variable type	character (nominal)
Number of missing values*	287247 ( 38.69% )
Number of unique values	77
^* SkillsPassport…Birthdate.Year missing.

Feature	Result
Variable type	character (nominal)
Number of missing values*	347271 ( 46.78% )
Number of unique values	3
^* SkillsPassport…Gender.Code missing.

Feature	Result
Variable type	character (nominal)
Number of missing values*	362951 ( 48.89% )
Number of unique values	164
^* SkillsPassport…Nationality.Code missing.