Are Ukrainian values closer to Russia or to Europe?

Russian President Vladimir Putin has justified his decision to invade Ukraine by claiming that Ukrainian culture is closer to Russia than to Europe, and that the two countries are separated only by an artificial border. Tim Reeskens assesses this claim using data from the European Values Study

National identity is an ineffective tool for building public support for wealth redistribution among diverse populations

Providing the resources of the welfare state to increasingly diverse populations has been a controversial issue in several European countries, notably in the UK where the government has advocated restricting EU immigrants’ access to the benefits system. Matthew Wright and Tim Reeskens analyse the effectiveness of national identity as a tool to promote social cohesion in Europe. They argue that welfare chauvinism is never tempered and can actually be exacerbated by national identity. Promoting nationalist sentiment is therefore a poor way of securing broader public support for wealth redistribution, and other methods should be sought

Are we in this together?:Changes in anti-immigrant sentiments during the COVID-19 pandemic

The COVID-19 pandemic is posing a threat to people all across the globe. According to traditional literature, threat perceptions induce anti-immigrant sentiments, as ingroup identity and self-interest are strengthened at the expense of the outgroup. In this study, we investigate whether the COVID-19 pandemic indeed increases anti-immigrant sentiments, or that this type of threat elicits other or no group related responses. We also look at whether such responses are expressed more strongly among specific subgroups in Dutch society. To do so, we use unique longitudinal panel data based on the European Values Study 2017, with a repeated measure in May 2020, during the national 'intelligent lockdown' in the Netherlands. Based on structural equation modeling, we demonstrate that anti-immigrant sentiments have not increased due to (perceived threat of) the COVID-19 pandemic. In fact, negative opinions towards immigrants decreased between 2017 and 2020 in the Netherlands, for which we provide alternative explanations. Although some subgroups do experience more threat than others due to the coronavirus, such as women, first generation immigrants, and the elderly, this does not lead to more negative feelings towards outgroups. Whether this is due to the fact that individuals feel threatened by everyone, regardless of group membership, should be explored in future research

Being white is a full time job?:Explaining skin tone gradients in income in Mexico

While scholarship on wage discrimination has confirmed that ‘racism’ is persistent, recent insights indicate that ‘colorism’ – the idea that lighter skin tones are rewarded more compared to darker ones, all else equal – is often more relevant in some societies where race or ethnicity are less salient markers. In this article, the following underlying theoretical mechanisms are discussed and are subjected to an empirical test: differential investment in human capital, i.e. education; variation in occupational status, i.e. being employed in indoor white-collar vs outdoor blue-collar jobs; and concentration in richer vs poorer regions. Mexico, known as a country where race and ethnicity generally are less salient categories for social stratification while skin tone is more important, is used as a case. Based on regression analyses on the most recent 2017 wave of the National Survey on Discrimination in Mexico, we show that there is an effect of skin tone on income that is explained by differences in education, occupational status and, to a lesser extent, regional concentration. Triangulating the findings with data from 2010 indicate that if colorism is at work, it are the lightest tones that are privileged, not the darkest ones that are penalised

Welfare chauvinism in the face of ethnic diversity:A vignette experiment across diverse and homogenous neighbourhoods on the perceived deservingness of native and foreign-born welfare claimants

The tenuous relationship between ethnic diversity and welfare solidarity has become a central focus in sociological and political inquiry. Yet, the question whether ethnic composition of the residential environment affects welfare chauvinism (favouring an encompassing welfare state that is preserved for ingroup members) has remained fundamentally unanswered. This article integrates extensive experimental data on welfare solidarity with hypothetical, unemployed persons from domestic and foreign origin among 23,015 native participants (to isolate welfare chauvinism), and detailed registry data (on the residential neighbourhood of these participants) from the Netherlands. This combination of contextual and experimental data allows us to test rivalling theoretical arguments on the relationship between ethnic diversity and welfare chauvinism, namely conflict, contact, and constrict theory. The outcomes of this enriched vignette survey experiment show that ethnic diversity has a specific and sizeable effect on welfare chauvinism under a range of model specifications. Diverse neighbourhoods drive down natives’ support for welfare distribution with migrants but not with natives. Ethnic diversity thereby stimulates the deservingness gap between natives and migrants, i.e., welfare chauvinism. We discuss the implications of these findings for conflict, contact, and constrict theory

You can look (but you better not touch):Who justifies casual sex before and during the Covid-19 pandemic?

The COVID-19 pandemic and particularly lockdowns that were imposed to curtail the spread of the novel coronavirus have had a profound impact on our sociability, restricting our sex lives as a result. Less is known, however, about the extent to which people have justified casual sex less during the pandemic. Scholarship argues that such moral values are socialised at a young age, remain stable across the life course, and are therefore largely resistant against adverse experiences. The pandemic offers a unique opportunity to test this claim. In this chapter, we analyse data of the European Values Study for 1999, 2008and 2017, representative of the Netherlands, supplemented with additional data collections in May 2020 and October 2020, allowing for an evaluation of the specific nature of justifying casual sex. The analysis show that the increase in justifying casual sex came to a halt during the ‘intelligent lockdown’, which was imposed by the Dutch government to curtail the spread of the coronavirus. During the crisis, strong opposition to casual sex was expressed by Dutch respondents who were concerned about the virus. When lockdown measures were eased, justification of casual sex increased again. Although we findevidence for experiential explanations for justifying casual sex, the results of our study further suggest that these justifications are embedded in modernisation theor

Solving the "many variables" problem in MICE with principal component regression

Multiple Imputation (MI) is one of the most popular approaches to addressing missing values in questionnaires and surveys. MI with multivariate imputation by chained equations (MICE) allows flexible imputation of many types of data. In MICE, for each variable under imputation, the imputer needs to specify which variables should act as predictors in the imputation model. The selection of these predictors is a difficult, but fundamental, step in the MI procedure, especially when there are many variables in a data set. In this project, we explore the use of principal component regression (PCR) as a univariate imputation method in the MICE algorithm to automatically address the "many variables" problem that arises when imputing large social science data. We compare different implementations of PCR-based MICE with a correlation-thresholding strategy by means of a Monte Carlo simulation study and a case study. We find the use of PCR on a variable-by-variable basis to perform best and that it can perform closely to expertly designed imputation procedures

High-dimensional Imputation for the Social Sciences: a Comparison of State-of-the-art Methods

Including a large number of predictors in the imputation model underlying a multiple imputation (MI) procedure is one of the most challenging tasks imputers face. A variety of high-dimensional MI techniques can help, but there has been limited research on their relative performance. In this study, we investigated a wide range of extant high-dimensional MI techniques that can handle a large number of predictors in the imputation models and general missing data patterns. We assessed the relative performance of seven high-dimensional MI methods with a Monte Carlo simulation study and a resampling study based on real survey data. The performance of the methods was defined by the degree to which they facilitate unbiased and confidencevalid estimates of the parameters of complete data analysis models. We found that using lasso penalty or forward selection to select the predictors used in the MI model and using principal component analysis to reduce the dimensionality of auxiliary data produce the best results