Correcting the Bias in the Concentration Index when Income is Grouped

The problem introduced by grouping income data when measuring socioeconomic inequalities in health (and health care) has been highlighted in a recent study. We reexamine this issue and show there is a tendency to underestimate the concentration index at an increasing rate when lowering the number of income categories. This bias results from a form of measurement error and we propose two correction methods. Firstly, the use of instrumental variables (IV) can reduce the error within income categories. Secondly, through a simple formula for correction that is based only on the number of groups. We compare the performance of these methods using data from 15 European countries and the United States. We find that the simple correction formula reduces the impact of grouping and always outperforms the IV approach. Use of this correction can substantially improve comparisons of the concentration index both across countries and across time.concentration index; errors-in-variables; instrumental variables; categorical data; first-order correction

Measuring socio-economic inequality in illhealth using permanent income

In Belgium, income-related inequality in ill-health seems to favour the rich, meaning that the rich are generally in better health than the poor are. Restricting the analysis to subsamples of the Belgian population, slightly modifies the conclusion, i.e. there is no income-related inequality in ill-health among the 65+. Since it is not clear whether the absence in inequality stems from the limited variation in the income of the 65+ (because of welfare benefits) or whether it truly reflects reality, I did the analysis over again using estimates of permanent income instead of income. It turned out that inequality among the 65+ remained very limited indeed, yet robustness checks pointed to the fragility of the results

Measuring horizontal inequity in health care using Belgian panel data

We estimate the determinants of utilisation of physician and hospital services in Belgium using four different regression techniques. We apply a one- and two-part panel count data model, and a one- and two part cross-section count data model. We conclude that the two-part panel count data model is most appropriate as it controls for unobserved heterogeneity and allows for a two-part decision making process. The estimates of the determinants of utilisation of health care are then used to calculate indices of horizontal inequity. We find that there is little difference between the indices based on cross-section and the indices based on panel estimators. We further present longitudinal evidence on equity in Belgian health care and find that all indices are stable across years. We also find that general practitioner and hospital care are pro-poor distributed, while horizontal equity applies to specialist care. We further show that the inequity might be different for (i) the probability to contact the health care sector, (ii) the conditional number of visits, and (iii) the unconditional number of visits

The Bias of the Gini Coefficient due to Grouping

We propose a first order bias correction term for the Gini index to reduce the bias due to grouping. The first order correction term is obtained from studying the estimator of the Gini index within a measurement error framework. In addition, it reveals an intuitive formula for the remaining second order bias which is useful in empirical analyses. We analyze the empirical performance of our first order correction term using income data for 15 European countries and the US, and show that it reduces a considerable share of the bias due to grouping

The Dutch influenza vaccination policy and medication use, outpatient visits, hospitalization, and mortality at age 65.

Background: Our objective was to obtain estimates of the impact of the Dutch vaccination programme on medication use, outpatient visits, hospitalization and mortality at age 65. Methods: We linked population-wide mortality, hospitalization and municipality registries to identify influenza-related deaths and hospitalizations, and used health interview surveys to identify medication use and outpatient visits during 1996–2008. We applied a regression discontinuity design to estimate the intention-to-treat effect of the personal invitation for a free influenza vaccination sent to every Dutch inhabitant at age 65 years on each of the outcomes, separately in influenza-epidemic and non-epidemic months. Results: Invitation receipt for free influenza vaccination at age 65 led to a 9.8 percentage points [95% confidence interval (CI) = 3.5 to16.1; P < 0.01] rise in influenza vaccination. During influenza-epidemic months, it was associated with 1.5 fewer influenza/pneumonia deaths per 100 000 individuals (95% CI = 3.1 to 0.0; P = 0.05), a 15 percentage point lower probability to use prescribed medicines (95% CI = 28 to 3; P = 0.02) and 0.13 fewer General Practitioner (GP) visits per month (95% CI = 0.28 to 0.02; P = 0.09), while the association with hospitalizations due to influenza/pneumonia was small and imprecisely estimated (seven more hospitalizations per 100 000 individuals, 95% CI = 20 to 33; P = 0.63). No associations were found with any outcomes during non-epidemic months. Conclusions: Personal invitations for a free influenza vaccination sent to every Dutch inhabitant at age 65 took pressure off primary health care but had small effects on hospitalizations and mortality

The Bias of the Gini Coefficient due to Grouping

We propose a first order bias correction term for the Gini index to reduce the bias due to grouping. The first order correction term is obtained from studying the estimator of the Gini index within a measurement error framework. In addition, it reveals an intuitive formula for the remaining second order bias which is useful in empirical analyses. We analyze the empirical performance of our first order correction term using income data for 15 European countries and the US, and show that it reduces a considerable share of the bias due to grouping

The Effect of Growth and Inequality in Incomes on Health Inequality: Theory and Empirical Evidence from the European Panel

Europe aims at combining income growth with improvements in social cohesion as measured by income and health inequalities. We show that, theoretically, both aims can be reconciled only under very specific conditions concerning the type of growth and the income responsiveness of health. We investigate whether these conditions held in Europe in the nineties using panel data from the European Community Household Panel surveys. We use pooled interval regressions and inequality decompositions to demonstrate that (i) in all countries except Austria, the income elasticity of health is positive and increases with income, and (ii) that income growth was not pro-rich in most EU countries, resulting in little or no reductions in income inequality and modest increases in income-related health inequality in the majority of countries.Keywords:income inequality, health inequality, income elasticity of health, Europe.Classification-JEL: D30, D31, I10, I12

Armoedemaatstaven, IGL-curven en de bootstrap: een empirische studie naar de evolutie van het armoedeniveau in België tussen 1985 en 1997

In toegepast onderzoek naar de evolutie van het armoedeniveau gebruikt men meestal de headcount, die het proportioneel aantal armen meet. A. Sen merkte reeds in de jaren zeventig op dat andere aspecten van armoede even belangrijk kunnen zijn, met name het gemiddeld inkomen van de armen en de inkomensongelijkheid onder de armen. In deze studie ga ik na in hoeverre armoedemeting aan de hand van deze additionele aspecten een ander beeld oplevert dan armoedemeting aan de hand van de headcount. Bovendien ga ik na of de veranderingen in het Belgische armoedeniveau tussen 1985 en 1997 statistisch significant zijn

Aversion to health inequality — Pure, income-related and income-caused

We design a novel experiment to identify aversion to pure (univariate) health inequality separately from aversion to income-related and income-caused health inequality. Participants allocate resources to determine health of individuals. Identification comes from random variation in resource productivity and information on income and its causal effect. We gather data (26,286 observations) from a sample of UK adults (n = 337) and estimate pooled and participant-specific social preferences while accounting for noise. The median person has strong aversion to pure health inequality, challenging the health maximisation objective of economic evaluation. Aversion to health inequality is even stronger when it is related to income. However, the median person prioritises health of poorer individuals less than is assumed in the standard measure of income-related health inequality. On average, aversion to that inequality does not become stronger when low income is known to cause ill-health. There is substantial heterogeneity in all three types of inequality aversion.</p

Reducing socioeconomic health inequalities?:A questionnaire study of majorization and invariance conditions

We study the appeal of basic preference conditions that underpin health inequality indices, including the widely used concentration index. We did a lab experiment in which 349 respondents had to choose repeatedly between two policies that generated a distribution of income and health among five groups in society. We found stronger support for preference conditions that focus on inequality in the marginal distribution of health (and income) than for preference conditions that favor reduced correlation between both dimensions. Respondents’ choices were more in line with the principle of income related health transfers when policies did not affect the ranking of groups in terms of health. Respondents also expressed more concern about the correlation between income and health when health was expressed as a shortfall rather than an attainment. Support for the preference conditions was unaffected when all groups in society experienced the same absolute or relative health change