In multilevel regression, centering the model variables produces effects that are different and sometimes unexpected compared with those in traditional regression analysis. In this article, the main contributions in terms of meaning, assumptions, and effects underlying a multilevel centering solution are reviewed, emphasizing advantages and critiques of this approach. In addition, in the spirit of Manski, contextual and correlated effects in a multilevel framework are defined to detect group effects. It is shown that the decision of centering in a multilevel analysis depends on the way the variables are centered, on whether the model has been specified with or without cross-level terms and group means, and on the purposes of the specific analysis.

In this paper we investigate how age affects the self-reported level of life satisfaction among the elderly in Europe. By using a vignette approach, we find evidence that age influences life satisfaction through two counterbalancing channels. On the one hand, controlling for the effects of all other variables, the own perceived level of life satisfaction increases with age. On the other hand, given the same true level of life satisfaction, older respondents are more likely to rank themselves as “dissatisfied” with their life than younger individuals. Detrimental health conditions and physical limitations play a crucial role in explaining scale biases in the reporting style of older individuals.

In a multilevel framework several researches have investigated the behavior of estimates in finite samples, particularly for continuous dependent variables. Some findings show poor precise estimates for the variance components. On the other hand, discrete response multilevel models have been investigated less widely. In this paper we analyze the influence of different factors on the accuracy of estimates and standard errors of estimates in a binary response 2-level model, through a Monte Carlo simulation study. We investigate the hypothesis of: (a) small sample sizes; (b) different intraclass correlation coefficients; (c) different numbers of quadrature points in the estimation procedure. Standard errors of estimates are studied through a noncoverage indicator. In all instances we have considered, the point estimates are unbiased (even with very small sample sizes), while the variance components are underestimated. The accuracy of the standard errors of variance estimates needs a very large number of groups.

Self-reported life satisfaction is highly heterogeneous across similar countries. We show that this phenomenon can by largely explained by the fact that individuals adopt different scales and benchmarks in evaluating themselves. Using a cross sectional dataset on individuals aged 50 and over in ten European countries, we compare estimates from an Ordered Probit in which life satisfaction scales are invariant across respondents with those from a Hopit model in which vignettes are used to correct for individual-specific scale biases. We find that variations in response scales explain a large part of the differences found in raw data. Moreover, the cross countries ranking in life satisfaction dramatically depends on scale biases.JEL classification: C42, D12, I31, J14.

Using data from SHARE (Survey of Health, Ageing and Retirement in Europe), we investigate the determinants of voluntary private health insurance (VPHI) among the over fifties in eleven European countries, and their effects on health care spending. Firstly, we find that the main determinants of VPHI are different in each country, reflecting differences in the underlying health care systems, but in most countries education levels and cognitive abilities have a strong positive effect on holding a VPHI policy. We also analyse the effect of holding a voluntary additional health insurance policy on out-of-pocket (OOP) health care spending. We adopt a simultaneous-equations approach to control for self-selection into VPHI policy holding and find that only in the Netherlands VPHI policyholders have lower OOP spending than the rest of the population while in some countries (Italy, Spain, Denmark and Austria) they spend significantly more. This could be due to increased utilisation but also to cost-sharing measures adopted by the insurers in order both to counter the effects of moral hazard and to keep adverse selection under control.