An influential body of work has identified a ‘welfare-state paradox’: work–family policies that bring women into the workforce also undermine women’s access to the top jobs. Missing from this literature is a consideration of how welfare-state interventions impact on women’s representation at the board-level specifically, rather than managerial and lucrative positions more generally. This database includes data that contribute to addressing this ‘gap’. It compiles existing secondary data from various sources into a single dataset. Both the raw and 'fuzzy' data used in a fuzzy-set Qualitative Comparative Analysis of 22 industrialised countries are available. Based on these data, analyses reveal how welfare-state interventions combine with gender boardroom quotas and targets in (not) bringing a ‘critical mass’ of women onto private-sector corporate boards. Overall, there is limited evidence in support of a welfare-state paradox; in fact, countries are unlikely to achieve a critical mass of women on boards in the absence of adequate childcare services. Furthermore, ‘hard’, mandatory gender boardroom quotas do not appear necessary for achieving more women on boards; ‘soft’, voluntary recommendations can also work under certain family policy constellations. The deposit additionally includes other data from the project that provide more context on work-family policy constellations, as they show how countries performance across multiple gendered employment outcomes spanning segregation and inequalities in employment participation, intensity and pay, with further differences by class.While policymakers in the UK and elsewhere have sought to increase women's employment rates by expanding childcare services and other work/family policies, research suggests these measures have the unintentional consequence of reinforcing the segregation of men and women into different 'types' of jobs and sectors (Mandel & Semyonov, 2006). Studies have shown that generous family policies lead employers to discriminate against women when it comes to hiring, training, and promotions, as employers assume that women are more likely to make use of statutory leaves and flexible working. Furthermore, state provision of health, education, and care draws women into stereotypically female service jobs in the public sector and away from (better-paid) jobs in the private sector. Accordingly, research suggests that the more 'women-friendly' a welfare state is, the harder it will be for women - especially if they are highly skilled - to break into male-dominated jobs and sectors, including the most lucrative managerial positions (Mandel, 2012). Yet, more recent evidence indicates that women's disadvantaged access to better jobs is not inevitable under generous welfare policies. For instance, women's share of senior management positions in Sweden, where women-friendly policies are most developed, now stands at 36%; this compares to a figure of 28% in the UK, where gender employment segregation has historically been lower (Eurostat, 2018). Thus, the aim of this project is to provide a clearer and fuller understanding of how welfare states impact on gender employment segregation by using innovative methods and approaches that have not been used to examine this research puzzle before. To this aim, the project is organised into three 'work packages' (WPs). WP1 examines how conditions at the country-level mediate the relationship between welfare states and gender segregation in employment across 21 advanced economies. This includes Central and Eastern European countries, which prior research has tended to overlook. The country-level conditions included are cultural norms, regulations regarding women's representation on corporate boards, and labour-market characteristics. Data will be compiled from the International Social Survey Programme, OECD, Eurostat, the Global Media Monitoring Project, the World Bank, and Deloitte's Women in the Boardroom project. WP2 then investigates how the impact of welfare-state policies on a woman's career progression varies according to her socioeconomic position and the specific economic and social context in which she lives, using regional and individual-level data from the European Social Survey. Subsequently, WP3 carries out systematic comparative case studies to explore in depth the underlying mechanisms that explain why certain welfare states and regions exhibit high levels of gender inequality but low levels of class inequality, while in other places, the opposite is true. Data are drawn from the same sources as for WP1 and WP2, as well as academic literature and other relevant sources (e.g. government websites). The project is important because its findings will inform policymakers about how their policies affect different groups of women and how to overcome the 'inclusion-inequality' dilemma (Pettit & Hook, 2009), i.e. bring more women into the workforce by providing adequate family policies and services, but without channelling women into stereotypically feminine occupations and undermining their career progression. Tackling such segregation matters because it is a leading cause of the gender pay gap (Mandel & Semyonov, 2014) and underpins the undervaluation of women's work (Grimshaw & Rubery, 2007). At the same time, bringing more women into positions of power can have positive 'trickle-down' benefits for lower-skilled working women, as gender-balanced top-management teams are associated with female-friendly workplace characteristics and practices that can benefit all women (Kowalewska, 2017b)

Secondary data that are freely available and have already been anonymised were collected from multiple sources. I accessed the various publicly available repositories - with all sources labelled in the deposit - and pooled them altogether. To transform raw data to 'fuzzy' data for the fuzzy-set Qualitative Comparative Analysis, I first established three qualitative ‘breakpoints’: 0 (lower breakpoint), which denotes a country as ‘fully out’ of the fuzzy set and as not displaying the variable of interest at all; 1 (upper breakpoint), which indicates a country is ‘fully in’ the fuzzy set and fully displays the variable of interest; and 0.5 (crossover point), which indicates a country is ‘neither in nor out’ of the fuzzy set. Countries receive a continuous score for each fuzzy set of between 0 and 1. Countries are ‘out’ of a fuzzy set when scoring 0.5. I used the Package ‘QCA’ for R, using the logistic transformation (S-function).