This paper presents new international comparative evidence on the factors driving inequalities in the use of GP and specialist services in 12 EU member states. The data are taken from the 1996 wave of the European Community Household Panel (ECHP). We examine two types of utilisation (the probability of a visit and the conditional number of positive visits) for two types of medical care: general practitioner and medical specialist visits using probit, truncated Negbin and generalised Negbin models. We find little or no evidence of income-related inequity in the probability of a GP visit in these countries. Conditional upon at least one visit, there is even evidence of a somewhat pro-poor distribution. By contrast, substantial pro-rich inequity emerges in virtually every country with respect to the probability of contacting a medical specialist. Despite their lower needs for such care, wealthier and higher educated individuals appear to be much more likely to see a specialist than the less well-off. This phenomenon is universal in Europe, but stronger in countries where either private insurance cover or private practice options are offered to purchase quicker and/or preferential access. Pro-rich inequity in subsequent visits adds to this access inequity but appears more related to regional disparities in utilisation than to other factors. Despite decades of universal and fairly comprehensive coverage in European countries, utilisation patterns suggest that rich and poor are not treated equally.

SUMMARYThis paper considers the dynamics of a categorical indicator of self-assessed health using eight waves (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998) of the British Household Panel Survey (BHPS). Our analysis has three focal points: the relative contributions of state dependence and heterogeneity in explaining the dynamics of health, the existence and consequences of health-related sample attrition, and the investigation of the effects of measures of socioeconomic status, with a particular focus on educational attainment and income. To investigate these issues we use dynamic panel ordered probit models. There is clear evidence of health-related attrition in the data but this does not distort the estimates of state dependence and of the socioeconomic gradient in health. The models show strong positive state dependence and heterogeneity accounts for around 30% of the unexplained variation in health.

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This paper uses the British Health and Lifestyle Survey (1984-1985) data and the longitudinal follow-up of May 2003 to investigate the determinants of premature mortality risk in Great Britain. A behavioral model, which relates premature mortality to a set of observable and unobservable factors, is considered. We focus on unobservable individual heterogeneity and endogeneity affecting the mortality equation. A MSL approach for a multivariate probit (MVP) is used to estimate a recursive system of equations for deaths and lifestyles. This model is then compared with the univariate probit models that include or exclude lifestyles. In order to detect inequality in the distribution of health within the population and to calculate the contribution of socioeconomic factors, we compare the range measure of health inequality to the Gini coefficient for overall health inequality. A Gini decomposition analysis for predicted premature mortality shows that endogenous lifestyles and unobservable heterogeneity strongly contribute to inequality in mortality, reducing the role of socio-economic status. JEL codes I1 C0

We use a set of biomarkers to measure inequality of opportunity (IOp) in the risk of major chronic conditions in the UK. Applying a direct ex ante IOp approach, we find that inequalities in biomarkers attributed to circumstances account for a non-trivial part of the total variation. For example, observed circumstances account for 20% of the total inequalities in our composite measure of multi-system health risk, allostatic load.We propose an extension to the decomposition of ex ante IOp to complement the meanbased approach, analysing the contribution of circumstances across the quantiles of the biomarker distributions. Shapley decompositions show that, for most of the biomarkers, the percentage contribution of socioeconomic circumstances (education and childhood socioeconomic status), relative to differences attributable to age and gender, increase towards the right tail of the biomarker distribution, where health risks are more pronounced.

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The annual 5% increase in tobacco taxes in real terms proposed in the recent White Paper on smoking has reaf®rmed the commitment of successive UK Governments to above-in¯ation increases in tobacco taxation to encourage people to stop smoking. This paper presents evidence on the determinants of starting and quitting smoking by using data from the British Health and Lifestyle Survey and is the ®rst to identify tax elasticities for starting and quitting smoking using British data. Self-reported individual smoking histories are coupled with a long time series for the tax rate on cigarettes to construct a longitudinal data set. Estimates are obtained for the effect of abovein¯ation tax rises on the age of starting smoking and the number of years of smoking. The estimates of the tax elasticity of the age of starting smoking are 0.16 for men and 0.08 for women. The estimates of the tax elasticity of quitting are À0.60 for men and À0.46 for women. These are robust to different speci®cations.

The paper considers health-related non-response in the first 11 waves of the British Household Panel Survey and the full eight waves of the European Community Household Panel and explores its consequences for dynamic models of the association between socioeconomic status and self-assessed health. We describe the pattern of health-related non-response that is revealed by the British Household Panel Survey and European Community Household Panel data. We both test and correct for non-response in empirical models of the effect of socioeconomic status on self-assessed health. Descriptive evidence shows that there is health-related non-response in the data, with those in very poor initial health more likely to drop out, and variable addition tests provide evidence of non-response bias in the panel data models of self-reported health. Nevertheless a comparison of estimates-based on the balanced sample, the unbalanced sample and corrected for non-response by using inverse probability weights-shows that, on the whole, there are not substantive differences in the average partial effects of the variables of interest. The main differences are between unweighted and one form of inverse-probability-weighted estimates for the average partial effects of income and education in those countries that have fewer than eight waves of data. Similar findings have been reported concerning the limited influence of non-response bias in models of various labour market outcomes; we discuss possible explanations for our results. Copyright 2006 Royal Statistical Society.