Using data on annual individual labor income from three representative panel datasets (German SOEP, British BHPS, Australian HILDA) we investigate a) the selectivity of item non-response (INR) and b) the impact of imputation as a prominent post-survey means to cope with this type of measurement...

Trust is a concept that has attracted significant attention in economic theory and research within the last two decades: it has been applied in a number of contexts and has been investigated both as an explanatory and as a dependent variable. In this paper, we explore the questions of what...

After reviewing the literature on item non-response we focus on three issues: First, is there significant heterogeneity in item non-response across financial questions and in the association of covariates with item non-response across outcomes? Second, can the informational value of surveys be...

In this paper we look at the relationship between health and income as mediated by "lifestyle" choices; that is, a set of behaviours which are thought to influence health and are generally considered to invoke a substantial degree of free choice. The main underlying assumption is that...

Using several waves of the China Health and Nutrition Survey (CHNS), this study analyzes the effect of long work hours on health and lifestyles in a sample of 18- to 65-year-old Chinese workers. Although working long hours does significantly increase the probabilities of high blood pressure and...

In an influential study Case et al. (2002) documented a positive relationship between family income and child health in the US, with the slope of the gradient being larger for older than younger children. In this paper we explore the child health income gradient in England, which has a...

We measure unfair health inequality in the UK using a novel data-driven empirical approach. We explain health variability as the result of circumstances beyond individual control and health-related behaviours. We do this using model-based recursive partitioning, a supervised machine learning...