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Little is known about perceptions of medical expenditure risks despite their presumed relevance to the demand for health insurance. This is the first study to examine households’ beliefs about their future spending on health care. The study made a unique elicitation of subjective probabilities of medical expenditures from rural Ethiopians participating in a panel survey and offered the opportunity to enrol in a health insurance programme. The vast majority of respondents give logically consistent responses to the subjective probability questions. The data indicate that the cross-sectional variance of realized expenditures, which is often used to proxy risk exposure, greatly overestimate the risk faced by any single household. Consistent with the serial correlation observed in realized expenditures, expectations are positively correlated with past expenses. They are revised upward in response to an increase in realized expenditure and, to some extent, they predict expenditure incurred in the year ahead. Despite containing information on future medical expenditures, there is no evidence that expectations influence the decision to take out health insurance, although plans to insure are positively related to the perceived volatility of expenses.

These results suggest that adverse selection may not threaten the viability of voluntary health insurance. A caveat is that measurement error in the reported probabilities may weaken the test for adverse selection. Notwithstanding this limitation, measurement of household-specific distributions of future medical expenses is feasible and avoids relying on the cross-sectional variance, which provides an upwardly biased estimate of medical expenditure risk.

Most politicians and ethical observers are not interested in pure health inequalities, as they want to distinguish between different causes of health differences. Measures of “unfair” inequality – direct unfairness and the fairness gap, but also the popular standardized concentration index (CI) – therefore neutralize the effects of what are considered to be “legitimate” causes of inequality. This neutralization is performed by putting a subset of the explanatory variables at reference values, for example, their means. We analyze how the inequality ranking of different policies depends on the specific choice of reference values. We show with mortality data from the Netherlands that the problem is empirically relevant and we suggest a statistical method for fixing the reference values.

There is widespread concern about low adherence to clinical practice guidelines (CPGs) and the low adoption of new medical technologies. To assist the regulatory response, we propose benchmarking clinical practice on the lower bound on the probability that a recommended treatment/new technology achieves a better outcome. This inequality–probability bound can be estimated from marginal outcome distributions. We illustrate the approach by comparing Swedish cardiologists' adoption of drug-eluting stents (DESs) with the inequality–probability bound on this technology improving outcomes. A substantial fraction of cardiologists are below the benchmark.

Traditional indices of bi-dimensional inequality and polarization were developed for cardinal variables and cannot be used to quantify dispersion in ordinal measures of socioeconomic status and health. This chapter develops two approaches to the measurement of inequality and bi-polarization using only ordinal information. An empirical illustration is given for 24 European Union countries in 2004–2006 and 2011. Results suggest that inequalities and bi-polarization in income and health are especially large in Estonia and Portugal, and that inequalities have significantly increased in recent years in Austria, Belgium, Finland, Germany, and the Netherlands, whereas bi-polarization significantly decreased in France, Portugal, and the United Kingdom.

In this chapter we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).

We compare two approaches to measuring inequity in the health distribution. The first is the concentration index. The second is the calculation of the inequality in an overall measure of individual well-being, capturing both the income and health dimensions. We introduce the concept of equivalent income as a measure of well-being that respects preferences with respect to the trade-off between income and health, but is not subjectively welfarist since it does not rely on the direct measurement of happiness. Using data from a representative survey in France, we show that equivalent incomes can be measured using a contingent valuation method. We present counterfactual simulations to illustrate the different perspectives of the approaches with respect to distributive justice.