Both adverse selection and moral hazard models predict a positive relationship between risk and insurance; yet the most common finding in empirical studies of insurance is that of a negative correlation. In this paper, we investigate the relationship between ex ante risk and private health insurance using Australian data. The institutional features of the Australian system make the effects of asymmetric information more readily identifiable than in most other countries. We find a strong positive association between self-assessed health and private health cover. By applying the Lokshin and Ravallion (J. Econ. Behav. Organ 2005; 56:141-172) technique we identify the factors responsible for this result and recover the conventional negative relationship predicted by adverse selection when using more objective indicators of health. Our results also provide support for the hypothesis that self-assessed health captures individual traits not necessarily related to risk of health expenditures, in particular, attitudes towards risk. Specifically, we find that those persons who engage in risk-taking behaviours are simultaneously less likely to be in good health and less likely to buy insurance.

Demands for formal and informal child care are estimated using a bivariate Tobit model. Predicted costs of child care are incorporated in the households' budget constraint and a discrete choice labour supply model is estimated. Separate models are estimated for couples and lone parents. Increases in the prices and costs of child care lead to reductions in labour supply for lone parents and partnered mothers. Results suggest the average elasticities in Australia are closer to those found in the UK and are smaller than the estimates for Canada and the US. Effects are stronger for single parents and mothers facing low wages.

Job loss, Divorce, Marriage duration, J12, J60, J63,

No abstract

Despite concerns about reporting biases and interpretation, self-assessed health (SAH) remains the measure of health most used by researchers, in part reflecting its ease of collection and in part the observed correlation between SAH and objective measures of health. Using a unique Australian data set, which consists of survey data linked to administrative individual medical records, we present empirical evidence demonstrating that SAH indeed predicts future health, as measured by hospitalizations, out-of-hospital medical services and prescription drugs. Our large sample size allows very disaggregate analysis and we find that SAH predicts more serious, chronic illnesses better than less serious illnesses. Finally we compare the predictive power of SAH relative to administrative data and an extensive set of selfreported health measures, SAH does not add to the predictive power of future utilization when the administrative data is included and improves prediction only marginally when the extensive survey-based health measures are included. Clearly there is value in the more extensive survey and administrative health data as well as greater cost of collection. Running title: Does self-assessed health measure health?

The finding of strong duration dependence in explaining the length of unemployment spells has influenced the design of many labor market policy reforms. However very little work has been done on more complex effects of labor market experiences and in particular on the causal effects of past outcomes involving other labor force states. In this paper we use longitudinal data to investigate the extent of state dependence in labor market outcomes for young Australians. The econometric model estimates the effects of past outcomes in three labor force states, employment, unemployment and out of the labor force, on current transitions between any two states allowing for observed and unobserved heterogeneity. Our findings suggest strong state dependence in all three states and we use the estimates to simulate various policy experiments.Keywords: Transition data, event history analysis, state dependence, unobserved heterogeneity, labor force states, policy effectiveness.J.E.L. Classification Numbers: C33, C41, J64, J68. Acknowledgment: We wish to thank seminar participants at the University of Melbourne, the University of Copenhagen, and the University of Aarhus. Tue Gørgens gratefully acknowledges the financial support of the Department of Family and Community Services. The views expressed in this paper reflect those of the authors and do not represent the opinions of the Department. * Address: School of Economics, University of New South Wales, Sydney NSW 2052, Australia. E-mail: D.Doiron@unsw.edu.au.† Address: SPEAR Centre RSSS, Australian National University, Canberra ACT 0200, Australia. E-mail: Tue.Gorgens@anu.edu.au. 1 Executive summary• In this paper, we extend the literature on youth labor market outcomes (employment, unemployment and being out of the labor force) by modeling the effects of labor market experiences in a general and flexible manner. The model is estimated on longitudinal data for young Australians.• Our estimates suggest that the effects from one's past experiences on the current and future labor market outcomes are large. Consequently periods of employment (unemployment) have long-term effects on the future probability of employment (unemployment) and the length of future employment (unemployment) spells. These effects are over and above the impact of many other personal characteristics and the state of the labor market.• From the perspective of society, the benefits of early intervention may therefore be substantial. If bad labor market outcomes increase the probability of future bad outcomes, a government program which is successful in placing young people in employment early would benefit the affected individuals in the immediate future and would have persistent effects in the longer-term as well.• Our findings have implications for the policy evaluation. The usual methods of policy evaluation which compare labor market outcomes before and shortly after a policy intervention provide an incomplete and possibly misleading estimate of the impacts of programs since they do not include t...

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. We analyse stated preference data over nursing jobs collected from two different discrete choice experiments: a multi-profile case best-worst scaling experiment (BWS) prompting selection of the best and worst among alternative jobs, and a profile case BWS wherein the respondents choose the best and worst job attributes. The latter allows identification of additional utility parameters and is believed to be cognitively easier. Results suggest that respondents place greater value on pecuniary over non-pecuniary gains in the multi-profile case. There is little evidence that this discrepancy is induced by the extra cognitive burden of processing several profiles at once in the multi-profile case. We offer thoughts on other likely mechanisms.JEL classification: C23, C25, C81, J44 Key words: discrete choice experiment, preference elicitation, rank-ordered data, latent class logit, best-worst scaling, maximum-difference model Highlights:• We compare preferences on nursing jobs elicited by profile and multiprofile case DCEs.• The paper is the first to contrast the two types of DCEs using monetary and nonmonetary attributes.• Preferences are comparable across the DCEs but only for non-monetary attributes.• Respondents value salary gains relatively more in the multi-profile DCEs.• The evidence suggests that this discrepancy is not due to the variation in cognitive difficulty.

This article investigates the preferences of student and newly graduated nurses for pecuniary and nonpecuniary aspects of nursing jobs. It is the first study applying methods based on discrete choice experiments to a developed country nursing workforce. It is also the first to focus on the transition through university training and into work. This is particularly important as junior nurses have the lowest retention levels in the profession. We sample 526 individuals from nursing programmes in two Australian universities. Flexible and newly developed models combining heteroscedasticity with unobserved heterogeneity in scale and preference weights are estimated. Overall, salary remains the most important feature in increasing the probability that a job will be selected. 'Supportive management/staff' and 'quality of care' follow as the most important attributes from a list of 11 nonpecuniary characteristics. However, the subset of new graduates rank 'supportive management/staff' above salary increases, emphasizing the importance of a supportive workplace in the transition from university to the workplace. We find substantial preference heterogeneity and some attributes, such as the opportunity for clinical rotations, are found to be attractive to some nurses while seen as negative by others. Nursing retention could be improved by designing different employment packages to appeal to these different tastes.