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Although the low-wage employment sector has enlarged over the past 20 years in the context of pronounced flexibility in restructured labor markets, gender differences in low-wage employment have declined in Germany, Austria and Switzerland. In this article, the authors examine reasons for declining gender inequalities, and most notably concentrate on explanations for the closing gender gap in low-wage employment risks. In addition, they identify differences and similarities among the German-speaking countries. Based on regression techniques and decomposition analyses (1996-2016), the authors find significantly decreasing labor market risks for the female workforce. Detailed analysis reveals that (1) the concrete positioning in the labor market shows greater importance in explaining declining gender differences compared to personal characteristics. (2) The changed composition of the labor markets has prevented the low-wage sector from increasing even more in general and works in favor of the female workforce and their low-wage employment risks in particular.
Social theory has long predicted that social mobility, in particular downward social mobility, is detrimental to the well-being of individuals. Dissociative and “falling from grace” theories suggest that mobility is stressful due to the weakening of social ties, feelings of alienation, and loss of status. In light of these theories, it is a puzzle that the majority of quantitative studies in this area have shown null results. Our approach to resolve the puzzle is two-fold. First, we argue for a broader conception of the mobility process than is often used and thus focus on intragenerational occupational class mobility rather than restricting ourselves to the more commonly studied intergenerational mobility. Second, we argue that self-reported measures may be biased by habituation (or “entrenched deprivation”). Using nurse-collected health and biomarker data from the UK Household Longitudinal Study (2010–2012, N = 4,123), we derive a measure of allostatic load as an objective gauge of physiological “wear and tear” and compare patterns of mobility effects with self-reports of health using diagonal reference models. Our findings indicate a strong class gradient in both allostatic load and self-rated health, and that both first and current job matter for current well-being outcomes. However, in terms of the effects of mobility itself, we find that intragenerational social mobility is consequential for allostatic load, but not for self-rated health. Downward mobility is detrimental and upward mobility beneficial for well-being as assessed by allostatic load. Thus, these findings do not support the idea of generalized stress from dissociation, but they do support the “falling from grace” hypothesis of negative downward mobility effects. Our findings have a further implication, namely that the differences in mobility effects between the objective and subjective outcome infer the presence of entrenched deprivation. Null results in studies of self-rated outcomes may therefore be a methodological artifact, rather than an outright rejection of decades-old social theory.
This paper seeks to address the relationship between social capital and perceived social origin in contemporary Austria. While the concept of social capital has been widely adopted in social sciences, so far research on the (pre)structured shape of social capital by social origin is scarce. Our aim is to close this gap. Therefore, we use the network-as-capital approach by following the “position generator” and apply latent class analysis (LCA) and path modelling on the basis of the 2018 Austrian Social Survey. The dataset comprises a representative sample of the Austrian residential population aged 18 and older. Our findings show that the diversity of social capital, and access to networks of people in more highly ranked positions is strongly influenced by one’s social background. The higher respondents assess their social origin, the greater the probability of being in this type of network. Furthermore, education and occupation have effects on membership in a class-specific network.
The gendered division of occupations is a persistent characteristic of the Austrian labour market. Furthermore, we can observe more flexible employment biographies, where sequential employment episodes and occupational transitions become an important part. On this account, the article argues that both gender inequalities and labour market movements need to be examined simultaneously. The authors therefore analyse gender-(un)typed horizontal occupational transitions and their influence on the vertical positioning, based on the Austrian Micro Census (2008–2018). The results reveal that gender-typed occupational transitions are regaining relevance and that the gender effect is reversing in that women increasingly leave gender-untyped occupations. The findings also demonstrate that this gender-typed horizontal movement yields a significant decline in occupational status for women, which even increases when women become mothers. Based on their models the authors find no negative effects for fathers.
Although the low-wage employment sector has enlarged over the past 20 years in the context of pronounced flexibility in restructured labor markets, gender differences in low-wage employment have declined in Germany, Austria and Switzerland. In this article, the authors examine reasons for declining gender inequalities, and most notably concentrate on explanations for the closing gender gap in low-wage employment risks. In addition, they identify differences and similarities among the German-speaking countries. Based on regression techniques and decomposition analyses (1996-2016), the authors find significantly decreasing labor market risks for the female workforce. Detailed analysis reveals that (1) the concrete positioning in the labor market shows greater importance in explaining declining gender differences compared to personal characteristics. (2) The changed composition of the labor markets has prevented the low-wage sector from increasing even more in general and works in favor of the female workforce and their low-wage employment risks in particular.
Singles in the city
(2021)
More people than ever are living in cities, and in these cities, more and more people are living alone. Using the example of Vienna, this paper investigates the subjective well-being of single households in the city. Previous research has identified positive and negative aspects of living alone (e.g., increased freedom vs. missing social embeddedness). We compare single households with other household types using data from the Viennese Quality of Life Survey (1995–2018). In our analysis, we consider overall life satisfaction as well as selected dimensions of subjective wellbeing (i.e., housing, financial situation, main activity, family, social contacts, leisure time). Our findings show that the subjective well-being of single households in Vienna is high and quite stable over time. While single households are found to have lower life satisfaction than two-adult households, this result is mainly explained by singles reporting lower satisfaction with family life. Compared to households with children, singles are more satisfied with their financial situation, leisure time and housing, which helps to offset the negative consequences of missing family ties (in particular with regard to single parents).
This paper seeks to address the relationship between social capital and perceived social origin in contemporary Austria. While the concept of social capital has been widely adopted in social sciences, so far research on the (pre)structured shape of social capital by social origin is scarce. Our aim is to close this gap. Therefore, we use the network-as-capital approach by following the “position generator” and apply latent class analysis (LCA) and path modelling on the basis of the 2018 Austrian Social Survey. The dataset comprises a representative sample of the Austrian residential population aged 18 and older. Our findings show that the diversity of social capital, and access to networks of people in more highly ranked positions is strongly influenced by one’s social background. The higher respondents assess their social origin, the greater the probability of being in this type of network. Furthermore, education and occupation have effects on membership in a class-specific network.
“Broadcast your gender.”
(2022)
Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.
“Broadcast your gender.”
(2022)
Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.