Additional information on data and methods The source of the data is the ESS, a semi-annual, cross-country survey administered in almost all European countries from 2002 to 2024. Respondents are asked a host of questions, including political and social attitudes, confidence in institutions, and a battery of demographic background questions. The analysis relies on two ESS sources: 1. a survey battery with the same questions going back to 2002 to elucidate trends over time, whereby we draw on several variables from the “politics” theme of the ESS time series; and 2. as the 2024 ESS survey wave contains a battery of questions specifically on gender attitudes, such as workplace gender equality, perceptions of equal treatment of men and women by state institutions, and preferences of gender equality across society, we draw on the 2024 cross section of survey data for a more in-depth analysis of recent years. Our sample relies on all available respondents from all 27 EU countries plus Norway, Switzerland, Iceland and the UK. In the 2024 survey wave, a total of 41,242 respondents were available from 24 countries. For the over-time data, we have a sample of over 460,000 respondents, bi-annually from 2002 to 2023, from all 27 EU countries plus Norway, Switzerland, Iceland and the UK. In addition to the full European sample models, we report individual country models for the five selected case study countries within the EQUALIZE project: Germany; Greece; Poland; Spain; and Sweden. Our main explanatory variables are respondent age and gender. In the case of age, we rely on a four­category ordered variable, whereby we are most interested in highlighting the results for the‘younger’ cohort – that is, Gen Z, which we code as 15-29 years old. These are compared with the age groups of 30-49, 50-64 and 65+ year olds. For respondent gender, we use a binary coding for men and women. In addition, we include a control variable for the respondent’s mother’s level of education(tertiary or higher=“1”, and“0” if otherwise), which is an exogenous variable often used as a proxy for SES. In all cross-country models, we account for country fixed effects and for those modelled over time. We include survey-year dummy variables to account for unobserved factors that contribute to overall trends in our variables of interest. Finally, we follow the advice of the ESS survey investigators and include analysis weights, which adjust for discrepancies between the sample and population(age, gender, education and region) to render the sample more of a reflection of the actual population. Our outcome variables of interest are derived from the literature review chapter. Firstly, we capture left­right ideology via a respondent’s self-placement on the left-right scale, whereby“0” is far left and“10” is far right. The question reads as follows: In politics, people sometimes talk of“left” and“right”. Using this card, where would you place yourself on this scale, where 0 means the left and 10 means the right? Secondly, we look at the degree to which other relevant political attitudes diverge by gender and age, such as attitudes on immigration, same­sex marriage, redistribution and satisfaction with democracy. Thirdly, we look at the following indicators of gender­equality attitudes from the ESS 2024 survey: • agreement: women are treated unfairly in hiring/ pay/promotions(“0”= no,“1”= yes); 86 EqualiZe