[…] in the media, the study of intergenerational unemployment and cycles of disadvantage has received a lot of attention. Parental experiences such as being out of work may impact adversely on their children by limiting their opportunities from early on. Previous research has established a correlation between parental unemployment and that of their children using […]

[…] compared with the non-farm group. Regression results support the U-shaped life-cycle effect hypothesis. In terms of gender, for farm based females, the level of education and having an off-farm job has a positive impact on life satisfaction compared to males. For males, being in full time employment brings an increase in the life satisfaction overall.

[…] not just the ‘form’ that marital status takes, but also the quality of the relationship.  Being in an unhappy cohabiting relationship, for example, has a greater negative impact on health than being in a happy cohabiting relationship.  Sleep acts as a significant mediator of the link between marital status/marital quality and health – even […]

[…] (1) to identify reasons for the high rates of change in long-term illness/disability status, (2) to investigate whether the use of the initial filter question has significant impact on measured disability, and (3) to test question versions that would produce more stable measures. Initial results suggest that long-term health problems are not well defined. […]

[…] women’s probabilities of labour market entry and exit; and we explore how far these can be accounted for by a) human capital and demographic characteristics, b) the impact of relevant events (partnership and children), and c) differences in gender-role attitudes and religiosity. We find that, adjusting for all these factors, Indian and Caribbean women […]

[…] weight will partly reflect genuine growth/shrinkage or weight gain/loss, respectively. But given this will have been less pronounced for height than for weight, it seems likely that the eight biosocial characteristics associated with the difference (between self-reported and measured variables) in both height and weight are those with the greatest potential impact on reporting accuracy.