Methods for Categorical Longitudinal Survey Data: Understanding Employment Status of Australian Women

Many variables that are of interest in social science research are nominal variables with two or more categories, such as employment status, occupation, political preference, or self-reported health status. With longitudinal survey data it is possible to analyse the transitions of individuals between different employment states or occupations (for example). In the statistical literature, models for analysing categorical dependent variables with repeated observations belong to the family of models known as generalized linear mixed models (GLMMs). The specific GLMM for a dependent variable with three or more categories is the multinomial logit random effects model. For these models, the marginal distribution of the response does not have a closed form solution and hence numerical integration must be used to obtain maximum likelihood estimates for the model parameters. Techniques for implementing the numerical integration are available but are computationally intensive requiring a large amount of computer processing time that increases with the number of clusters (or individuals) in the data and are not always readily accessible to the practitioner in standard software. For the purposes of analysing categorical response data from a longitudinal social survey, there is clearly a need to evaluate the existing procedures for estimating multinomial logit random effects model in terms of accuracy, efficiency and computing time. The computational time will have significant implications as to the preferred approach by researchers. In this paper we evaluate statistical software procedures that utilise adaptive Gaussian quadrature and MCMC methods, with specific application to modeling employment status of women using a GLMM, over three waves of the HILDA survey.

The effect of non-standard employment on work-family balance

Across the last four decades, the structure of the Australian labour market has changed profoundly as non-standard forms of employment have become more prevalent. According to many researchers, the growth of non-standard work has been driven by employee preferences, particularly among married women, for greater flexibility to balance paid work with domestic responsibilities and other non-work related pursuits. In contrast, other researchers argue that the increasing prevalence of non-standard employment reflects employer demands for greater staffing flexibility. From this perspective, non-standard forms of employment are considered to have a negative effect on work-family balance. This paper explores whether non-standard employment is associated with improved or poorer work-to-family conflict and tests whether experiences vary by gender. It concentrates on three common forms of non-standard employment: part-time employment, casual and fixed-term work contracts and flexible scheduling practices (such as evening work, weekend work and irregular rostering). Analysis is based on 2299 employed parents from the first wave of the Household, Income and Labour Dynamics on Australia (HILDA) project. Results show that few scheduling measures are significant determinants of work-family balance. However, part-time employment is associated with reduced work-to-family strain for both men and women, even after controlling for various other employment and household related characteristics. Casual employment, in contrast, incurs the cost of poorer work-family balance for men. Surprisingly, HILDA data show that overall men experience greater work-to-family strain than women.