One of the core goals of a universal health care system is to eliminate discrimination on the basis of socioeconomic status. We test for discrimination using patient waiting times for non-emergency treatment in public hospitals. Waiting time should reflect patients' clinical need with priority given to more urgent cases. Using data from Australia, we find evidence of prioritisation of the most socioeconomically advantaged patients at all quantiles of the waiting time distribution. These patients also benefit from variation in supply endowments. These results challenge the universal health system's core principle of equitable treatment.

This paper studies the dynamics of human mobility during the initial stage of the COVID-19 pandemic in countries around the world. The main goal of the analysis is to empirically separate voluntary reductions in mobility driven by the information about the location-specific pandemic trends from the effects of the government-imposed social distancing mandates. Google human mobility dataset is used to track the dynamics of mobility across a wide range of categories (e.g. workplace, retail and recreational activities, etc), while information on country-specific counts of COVID-19 cases and deaths is used as a proxy for the information about the spread of the pandemic available to the population. A detailed index of stringency of the government-imposed social distancing policies in around 100 countries is used as a measure of government response. We find that human mobility does respond in a significant way to the information about the spread of the pandemic. This channel can explain about 15 percentage points of the overall reduction in mobility across the affected countries. At the same time, our results imply that government-imposed policies account for the majority of the reduction in the mobility observed during this period.

The size of adverse selection and moral hazard effects in health insurance markets has important policy implications. For example, if adverse selection effects are small while moral hazard effects are large, conventional remedies for inefficiencies created by adverse selection (e.g., mandatory insurance enrolment) may lead to substantial increases in health care spending. Unfortunately, there is no consensus on the magnitudes of adverse selection vs. moral hazard. This paper sheds new light on this important topic by studying the US Medigap (supplemental) health insurance market. While both adverse selection and moral hazard effects of Medigap have been studied separately, this is the first paper to estimate both in an unified econometric framework.We develop an econometric model of insurance demand and health care expenditure, where adverse selection is measured by sensitivity of insurance demand to expected expenditure. The model allows for correlation between unobserved determinants of expenditure and insurance demand, and for heterogeneity in the size of moral hazard effects. Inference relies on an MCMC algorithm with data augmentation. Our results suggest there is adverse selection into Medigap, but the effect is small. A one standard deviation increase in expenditure risk raises the probability of insurance purchase by 0.037. In contrast, our estimate of the moral hazard effect is much larger. On average, Medigap coverage increases health care expenditure by 32%.

More than 45% of Australians buy health insurance for private treatment in hospital. This is despite having access to universal and free public hospital treatment. Anecdotal evidence suggests that avoidance of long waits for public treatment is one possible explanation for the high rate of insurance coverage. In this study, we investigate the effect of waiting on individual decisions to buy private health insurance. Individuals are assumed to form an expectation of their own waiting time as a function of their demographics and health status. We model waiting times using administrative data on the population hospitalised for elective procedures in public hospitals and use the parameter estimates to impute the expected waiting time and the probability of a long wait for a representative sample of the population. We find that expected waiting time does not increase the probability of buying insurance but a high probability of experiencing a long wait does. On average, waiting time has no significant impact on insurance. In addition, we find that favourable selection into private insurance, measured by self-assessed health, is no longer significant once waiting time variables are included. This result suggests that a source of favourable selection may be aversion to waiting among healthier people.

Households in many countries reach retirement with lump sums of financial wealth accumulated in defined contribution retirement plans. Retired households need to manage risks and generate income from their savings. We study the dynamics of retirement wealth and portfolio allocation using the three wealth waves of the Household, Income and Labour Dynamics in Australia panel survey. The average retired household maintained or accumulated wealth in 2002–2006 and decumulated in 2006–2010 consistent with trends in financial asset prices. At older ages, households prefer portfolios with less risk and more liquidity, while maintaining ownership of the family home. The probability of households exhausting financial assets increased over the sample, but households who depleted financial wealth did not liquidate their housing wealth at higher rates than other households. In contrast to the USA, the overall effect of health shocks on the wealth of retired Australian households is minimal, but financial shocks have large effects.