Variable Definition and derivation Number of
available observations
Outcomes:
Employed Variable label: ilostat
Question in the survey: ILO working status of the respondent Coding of the variable: 1 - ‘Employed’; 0 – ‘Unemployed/Inactive/Military service’.
1,192,564
Income decile
(monthly pay) Variable label: incdecil Question in the survey: Monthly (take home) pay from main job (deciles) of the respondent
Coding of the variable: numbers in decile (from 1 to 10).
185,043
(available only for 2014)
Overqualification Variable label: overqual
Question in the survey: Is the respondent overqualified for the current job?
Coding of the variable: 1 – ‘Yes’; 0 – ‘No’.
312,162
(available only for 2014, conditional on being employed) Permanent
contract Variable label: temp Question in the survey: The respondent’s permanency of the job
Coding of the variable: 1 – ‘Person has a permanent job or work contract of unlimited duration’; 0 – ‘Person has temporary job/work contract of limited duration’.
744,533
(conditional on being employed)
Supervisory
tasks Variable label: supvisor Question in the survey: Supervisory responsibilities at the job of the respondent
Coding of the variable: 1 – ‘Yes’; 0 – ‘No’.
734,953
(conditional on being employed)
Part-time work Variable label: ftpt
Question in the survey: Full-time/Part-time distinction of the respondent
Coding of the variable: 1 – ‘Part-time’; 0 – ‘Full-time’.
883,646
(conditional on being employed)
Atypical work Variable labels: shiftwk, evenwk, nightwk, satwk, sunwk Question in the survey: Shift work, evening work, night work, Saturday work, Sunday work
Coding of the variables: each variable is coded as 1 – ‘Yes’; 0 – ‘No’. Then we derive a combined indicator ‘atypical work’ as an average of the five variables.
841,766
(conditional on being employed)
Hours worked Variable labels: hwusual
Question in the survey: Number of hours per week usually worked in the main job
Coding of the variable: actual number of hours worked (1- 100)
744,209
(conditional on being employed)
Explanatory variables:
Foreign national Variable label: national
Coding of the variable: ‘National’ – if a citizen of the reporting country; ‘Foreign, EU’ – if a citizen of another EU Member State, ‘Foreign, TCN’ – if a citizen of a country outside the EU. Origin region Variable label: national
Question in the survey: Nationality of the respondent
Coding of the variable: country of nationality in the EU LFS is not directly reported; countries are grouped as follows: 000 –National/Native of own Country, 001 – EU15, 002 – NMS10 (10 new Member States of 2004), 003 – NMS3 (3 new Member States of 2007 and 2013), 006 – EFTA, 007 – Other Europe, 009 – North Africa, 010 – Other Africa, 011 – Near and Middle East, 012 – East Asia, 013 – South and South East Asia, 016 – North America, 017 – Central America (and Caribbean), 018 – South America, 019 – Australia and Oceania
Age group Variable label: age
Question in the survey: Age of the respondent, calculated Coding of the variable: in five-year intervals (20-24, 25-29, 30- 34, 35-39, 40-44, 45-49, 50-55)
Gender Variable label: sex
Question in the survey: Gender of the respondent Coding of the variable: 1 – ‘Female’; 0 – ‘Male’. Education Variable label: hatlev
Question in the survey: Highest educational attainment level of the respondent
Coding of the variable: 1 – ‘Low’ – no schooling, primary or middle school (ISCED 0-2); 2 – ‘Middle’ – completed high school or vocational degree (ISCED 3-4); 3 – ‘High’ – tertiary degree (ISCED 5-6).
Reason for
migration Variable label: ahm2014_migreas, ahm2008_migreas Question in the survey: Reason for migration
Coding of the variable (categories corresponding to migration reasons): 1 – ‘Employment’, 2 – ‘Family reasons’, 3 – ‘Study’, 4 – ‘International protection or asylum’
2008: 44,668 2014: 30,713
Restriction Variable label: ahm2008_restracc
Question in the survey: Is current legal access to the labour market restricted?
Coding of the variable: The answer is coded as 1 if individuals report that their access is a) restricted to employment for specific employers/sectors/occupations, b) restricted to self- employment, c) not allowing self-employment, d) falls under any combination of a, b and c. Otherwise, the variable is set to zero. Available in 2008 only TCNs: 25,025 observations Main obstacle in the labour market
Variable label: ahm2014_jobobst1
Question in the survey: What is the main obstacle to getting a job corresponding to the person’s qualifications or to getting a job at all?
Coding of the variable (categories corresponding to the
Available in 2014 only
TCNs: 30,429 observations
reported obstacles): 1 – ‘Language’ (Lack of language skills in host-country language(s)), 2 – ‘Recognition of qualifications’ (Lack of recognition of qualifications obtained abroad), 3 – ‘Restricted rights’ (Restricted right to work because of citizenship or residence permission), 4 – ‘Background’ (Origin, religion or social background).
Source: Authors, 2018
European Social Survey, waves 2002-16
To measure life quality indicators – subjective health, subjective happiness and perceived discrimination – we use data from the European Social Survey (ESS). The biannual survey has been conducted since 2002 across Europe with newly selected cross-sectional samples. The survey aims to monitor social structures and social developments in Europe: respondents are asked about their life conditions, social behaviour, beliefs, attitudes, and judgements of key aspects in their societies. Along thematic variables, the survey also collects key socio-economic indicators, such as age, education, family structure, economic participation, country of origin, etc. These data are representative of the European population and are considered highly reliable. The data have been widely used by researchers in economics and social sciences.456
For this project we use all available waves of the survey conducted between 2002 and 2016. We restrict the sample to individuals aged 20 to 55 (to focus on working individuals) and residing in one of the following countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden and the United Kingdom. We decided to limit the sample to these countries because most of them are well represented across all rounds of the survey. Annex 5: Table 26 summarises the availability of data by participating country and survey wave.