Ludovico Carrino

Eligibility and Inclusiveness of Long-Term Care Institutional Frameworks in Europe: A Cross-Country Comparison

Although economic literature has recently started to concentrate on the design, the scope and the regulations of main public programmes of Long-Term-Care in Europe, no analysis have, so far, compared different systems in terms of their degree of inclusiveness with respect to vulnerable elderly�s health status. Focusing on several European countries, this paper investigate how LTC regulations assess vulnerability, as well as how they define a minimum level of objective-dependency that would entitle individuals to receive public benefits (in-kind or in-cash) for home-based care. Our contribution is threefold. We provide detailed information on assessment and eligibility frameworks for eleven LTC programmes in Europe. We show that substantial heterogeneities exist both at the extensive margin (the health-outcomes that are included in the vulnerability-assessment) and at the intensive margin (the minimum vulnerability threshold that defines benefit eligibility) of the assessment strategies. Building on this information, we compare LTC programmes in terms of their degree of inclusiveness, i.e., we investigate the extent to which each programme is able to cover a standard population of elderly individuals facing functional and cognitive limitations. The comparison is performed following both a directly- and an indirectly- adjusted strategy using SHARE data.

The future of Long Term Care in Europe. An investigation using a dynamic microsimulation model

In this paper we investigate the evolution of public European LTC systems in the forthcoming decades, using the Europe Future Elderly Model (EuFEM), a dynamic microsimulation model which projects the health and socio-economic characteristics of the 50+ population of ten European countries, augmented with the explicit modelling of the eligibility rules of 5 countries. The use of SHARE data allows to have a better understanding of the trends in the demand for LTC differentiated by age groups, gender, and other demographic and social characteristics in order to better assess the distributional effects. We estimate the future potential coverage (or gap of coverage) of each national/regional public home-care system, and then disentangle the differences between countries in a population and a regulation effects. Our analysis offers new insights on how would the demand for LTC evolve over time, what would the distributional effects of different LTC policies be if no action is taken, and what could be potential impact of alternative care policies.

The Role of Normalisation in Building Composite Indicators. Rationale and Consequences of Different Strategies, Applied to Social Inclusion

In the context of the multidimensional measurement of complex phenomena, the major focus of the recent literature has been on the choice of the dimensions’ weights and the shape of the aggregation function, while few studies have concentrated on how normalisation influences the results. With the aim of building a measure of Social Inclusion for 63 European regions in 2012, we adopt a standard linear aggregation framework and compare three alternative normalisation approaches: a data-driven min-max function and a data-driven Z-score, whose parameters depend solely on the available data, and an expert-based function, whose parameters are elicited through a survey at the University of Venice Ca’ Foscari. Regardless of the adopted strategy, we show that normalisation plays a crucial part in defining variables’ weighting. The data-driven strategies allocate a large relative weight to the longevity dimension, whereas the survey-driven results in a rather equal distribution of weights. Data-driven approaches produce trade-offs that are hard to interpret in economic terms and debatable from a social desirability perspective, thus constituting a positive analysis of Social Inclusion. Conversely, the expert-based normalisation is heavily affected by elicitation techniques, and allows for a normative interpretation of the resulting index. Furthermore, the three strategies lead to substantially different conclusions in terms of levels (both between and within countries) and distribution of Inclusion: numerous rank-reversals occur when switching the normalisation methods.

31. Eligibility regulations and formal home-care utilisation among the vulnerable older people in SHARE Wave 5

▸ Eligibility matters and differs across countries ▸ Potential failures of Long-term Care (LTC) systems arise when objective vulnerable elders are left out of home-care programmes, or when formal care is provided to healthy individuals ▸ Education plays a crucial role in determining the access to formal home-care for eligible individuals ▸ Diabetes, cancer, Parkinson, fractures partially explain why non-vulnerable individuals receive home-care

The Weighting Role of Normalisation in a Multidimensional Analysis of Social Inclusion

In the context of the multidimensional measurement of complex phenomena, the major focus of the recent literature has been on the choice of the dimensions� weights and the shape of the aggregation function, while few studies have concentrated on how normalisation influences the results. With the aim of building a measure of Social Inclusion for European regions between 2004 and 2012, we adopt a CES aggregation framework and compare two alternative normalisation strategies: a data-driven min-max function, where the parameters depends solely on the available data, and an expert-based function where parameters are elicited through a survey at the University of Venice Ca� Foscari. Regardless of the adopted strategy, we show that normalisation plays a crucial part in defining variables� weighting and trade-offs. The data-driven strategy produces trade-offs that are hard to interpret in economic terms and debatable from a social desirability perspective, thus generating an aggregate measure with a �positive� interpretation. Moreover, it softens the aftermaths of the recent economic crisis on Social Inclusion, by putting a consistent weight on the longevity variable. Conversely, the expert-based normalisation has strikingly different parameters and allows for a normative interpretation of the resulting index. Furthermore, it emphasizes the worsening trends in long- term unemployment and the relevance of early school leaving in the Social Inclusion measure. As a result, numerous rank-reversals occur between regions when switching the normalisation methods.