Facets of Gender : Analyses of the Family and the Labour Market

This thesis contains four different studies on the dynamics of gender in households and workplaces. The relationship between family life and work life is in focus, particularly in the paper on labour market outcomes after divorce. In the introductory chapter, the Swedish context is briefly described. The description focuses on gender differences in the labour market and in the home. Theories concerning the division of work in the household are discussed, as are two theories on labour market discrimination, viz. taste discrimination and statistical discrimination. The theory part is concluded with a discussion of social closure processes and gendered organizational structures. The Reproduction of Gender. Housework and Attitudes Towards Gender Equality in the Home Among Swedish Boys and Girls. The housework boys and girls age 10 to 18 do, and their attitudes towards gender equality in the home are studied. One aim is to see whether the work children do is gendered and if so, whether they follow their parents’, often gendered, pattern in housework. A second aim is to see whether parents’ division of work is related to the children’s attitude towards gender equality in the home. The data used are taken from the Swedish Child Level of Living Survey (Child-LNU) 2000. Results indicate that girls and boys in two-parent families are more prone to engage in gender-atypical work the more their parent of the same sex engages in this kind of work. The fact that girls still do more housework than boys indicates that housework is gendered work also among children. No relation between parents’ division of work and the child’s attitude towards gender equality in the home was found. Dependence within Families and the Household Division of Labor – A Comparison between Sweden and the United States. This paper assesses the relative explanatory value of the resource-bargaining perspective and the doing-gender approach in analysing the division of housework in the United States and Sweden from the mid-1970s to 2000. Data from the Swedish Level of Living Survey (LNU) and the Panel Study of Income Dynamics (PSID) were used. Overall results indicate that housework is truly gendered work in both countries during the entire period. Even so, the results also indicate that gender deviance neutralization is more pronounced in the United States than in Sweden. Unlike Swedish women, American women seem to increase their time spent in housework when their husbands are to some extent economically dependent on them, as if to neutralize the presumed gender deviance. Divorce and Labour Market Outcomes. Do Women Suffer or Gain? In this paper, the interconnected nature of work and family is studied by looking at labour market outcomes after divorce. The data used are retrospective work and family histories collected in LNU 1991. A hazard regression model with competing risks reveals that women’s chances of improving their occupational prestige appear to be better after divorce compared to before. Increased working hours and perhaps also increased energy invested in the job may pay off in better occupational opportunities. Worth noting, however, is that the outcome among women with a less firm labour market attachment is more often to a job of lower prestige than one of higher prestige. Hence, the labour market outcome for women after divorce is to some extent conditioned by their labour market attachment at the time of divorce. Men, on the other hand, in most cases seem to suffer occupationally from divorce. For separated men the risk of negative changes in occupational prestige is greater than for cohabiting men. Formal On-the-job Training. A Gender-Typed Experience and Wage- Related Advantage? Formal on-the-job training (FOJT) can have a positive impact on wages and on promotion opportunities. According to theory and earlier research, a two-step model of gender inequality in FOJT is predicted: First, women are less likely than men to take part in FOJT and, second, once women do get the more remunerative training, they are not rewarded for their new skills to the same extent as men are. Pooled cross-sectional data from the Swedish Survey of Living Conditions (ULF) in the mid-nineties were used. Results show that women are significantly less likely than men to take part in FOJT. Among those who do receive training, women are more likely to take part in industry-specific training, whereas men are more likely to participate in general training and training that increases promotion opportunities. The two latter forms of training significantly raise a man’s annual earnings but not a woman’s. Hence, the theoretical model is supported and it is argued that this gender inequality is partly due to employers’ discriminatory practices.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.