The following Chapter provides information about the data used for this thesis, and summarises key decisions made regarding the samples used in the analysis – namely what countries and years to include in the analysis.
4.1.1 Individual-level data
Due to the multilevel approach of this thesis, several datasets are used for the analysis. For the dependent variable – the gender gap in top positions, the fifth wave of the European Working Conditions Survey (EWCS) is applied. The EWCS is a survey carried out by Eurofound every five years, interviewing both employees and self-employed people on their working and employment conditions. This fifth wave includes data from 44,000 workers from 34 European countries. The interviews were collected in 2010 and besides EU27 countries also Norway, Croatia, the former Yugoslav Republic of Macedonia, Turkey, Albania, Montenegro and Kosovo are included. During this time, about 216 million people were employed in EU27 countries, which serves as the main reference area of the survey. Thus, figures from the EWCS are based on a representative sample of European workers, but not on the whole population and serve as estimates (Eurofound, 2012). Before explaining the reasons for selecting the EWCS, the data sources for the independent variables are to be explained first.
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Sample
As a key contribution, the dependent variable is not only limited to vertical segregation and individual-level data, but in this thesis, the sample is also limited to individuals with tertiary education only. The importance of class when examining the structure of gender inequalities in the labour market, but also when examining the association mainly between family policies and occupational segregation, has been overlooked in previous studies, as discussed in Chapter 3.2 and pointed out by Korpi et al. (2013). The authors argue that hile major negative family policy effects for women with tertiary education are difficult to find in countries with well-developed policies supporting o e s employment and work-family reconciliation, family policies clearly differ in the extent to which they improve opportunities for women without university edu atio (p.1). They state that we need to look at women without a university degree differently since the female employment rate is lower for them then for well-educated women. This is due to different family policies impacting higher-paid female professionals differently than lower- earning women.
From a methodological point of view, narrowing down the sample to only tertiary- educated individuals allows us to avoid potential dilution effects, by researching the possibility that a decline in top-positions for women is due to the overall increase of the female workforce – especially in the less lucrative jobs. By doing so, we exclude the possibility that o e s career perspectives have only decreased because the overall size of the female workforce has increased, and thus reduced the share of women in top position that have already been part of the workforce before. While the number of highly educated women in the labour market stayed constant, more women with lower levels of education have joined the labour market. However, highly educated women are still more likely to be working. Thus, both groups reacted differently to external factors and need to be examined separately. This also becomes clear when comparing the share of highly educated women among managers/ professionals with women in top positions without tertiary education as discussed in Chapter 3.
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4.1.2 Family Policy data
Data on family policies stems largely from the Multilinks project and its online database for social policy indicators. While for the purpose of this thesis only family policy indicators are of interest, the overall aim of Multilinks is to examine how social context affects social integration, well-being and intergenerational solidarity in Europe. Multilinks collects data on 30 European countries between the years of 2004 and 2009, and indicators range from childcare, education, family benefits, pensions, long-term care and legal obligations to support (Keck and Saraceno, 2012). Data for the family policy typology developed by Lohmann and Zagel (2015) also stems from the Multilinks database.
In addition to the Multilinks database, this thesis also uses data on family policies from Eurostat, more specifically from the EU-SILC project. EU-SILC was launched in 2003, and data first was only collected for 12 EU-15 member states: Estonia, Norway and Iceland. From 2005 onwards, all EU-25 member states including Norway and Iceland participated in the EU-SILC survey. In 2006, Bulgaria, Romania, Turkey and Switzerland joined and Croatia in 2010. EU- SILC is based on a sample of the population and minimum effective sample sizes are defined. While for EU15 member states, the sample size for cross-sectional data needs to cover 156,000 individuals living in 80,000 private households (3250 for Luxembourg and 8250 for Denmark). For the 10 countries that joined in 2004, the minimum effective sample size is 95,000 individuals living in 41,000 private household's. Data is collected via the telephone, face-to-face interviews, computer- or paper-assisted personal interviews and other methods (Eurostat, 2015). Data stems from 2009 in order to take a lag effect into account.
Lastly, family policy indicators also stem from the OECD Family Database. This particular database consists of data from national and international databases within the OECD and external organisations. It covers 70 indicators ranging from data on the structure of families, labour market position of families, public policies for families and children, and
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child outcomes. Data for 2009 is available for up to 35 countries from the EU and/or OECD including 22 European states.
4.1.3 Labour market institutions data
With regards to indicators for labour market institutions, data stems from four different comparative databases. One is the Database on Institutional Characteristics of Trade Unions- Wage Setting, State Intervention and Social Pacts ICTWSS. It was collected in 34 countries between 1960 and 2012 and covers all EU and OECD members (Visser, 2016). Data stems from various national sources and comparative studies such as Pochet and Fajertag (2000) and Pochet, Keune and Natali (2010). As well as the Industrial Relations in Europe reports of the European Commission (2000, 2002, 2004, 2006, 2008, 2010, and 2012) and publications of the European Social and Economic Committee, the European Foundation for the Improvement of Living and Working Conditions, and the European Industrial Relations Observatory (EIRO).
Data also stems from the Comparative Welfare Entitlements Dataset, which provides macro-level data on institutional features of social insurance programs in 33 countries. Another comparative database used is the UNESCO Institute for Statistics data. This database covers up to 157 countries and data stems from international sources such as organisations like the OECD and Eurostat, and national sources.
Lastly, the OECD database for employment indicators is used as a source, which like the Family Database includes data for 35 OECD member states including a large amount of European countries. Employment indicators stem from international and national organisations and countries, and also include advice from country experts.
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