Cross-sectional data from the Survey of Health, Ageing and Retirement in Europe (SHARE) are a common source of information
in comparative studies of population health in Europe. In the largest part, these data are based on longitudinal samples,
which are subject to health-specific attrition. This implies that estimates of population health based on cross-sectional
SHARE datasets are biased as the data are selected on the outcome variable of interest. We examine whether cross-sectional
datasets are selected based on health status. We compare estimates of the prevalence of full health, healthy life years at
age 50 (HLY), and rankings of 18 European countries by HLY based on the observed, cross-sectional SHARE wave 7 datasets and
full samples. The full samples consist of SHARE observed and attrited respondents, whose health trajectories are imputed by
microsimulation. Health status is operationalised across the global index of limitations in activities of daily living (GALI).
HLY stands for life expectancy free of activity limitations. Cross-sectional datasets are selected based on health status,
as health limitations increase the odds of attrition from the panel in older age groups and reduce them in younger ones. In
older age groups, the prevalence of full health is higher in the observed cross-sectional data than in the full sample in
most countries. In most countries, HLY is overestimated based on the cross-sectional data, and in some countries, the opposite
effect is observed. While, due to the small sample sizes of national surveys, the confidence intervals are large, the direction
of the effect is persistent across countries. We also observe shifts in the ranking of countries according to HLYs of the
observed data versus the HLYs of the full sample. We conclude that estimates on population health based on cross-sectional
datasets from longitudinal, attrited SHARE samples are over-optimistic.
Projections show sharp increases in public spending on long-term care (LTC) services across Europe. However, a purely cost-based
focus on LTC services is economically misleading. Private and public expenditure on LTC services directly and indirectly generates
income in the form of salaries, taxes, and social security contributions. The aim of this paper is to quantify the economic
impact and multipliers of LTC services for Austria. Based on an econometric regional Input-Output model for Austria, we estimate
the direct, indirect, and induced effects of public and private expenditures on value added, employment, taxes, and social
security contributions. According to our results, each euro spent on LTC services is associated with domestic value added
of 1.70 €; 70 cents per euro spent flows into public budgets in the form of taxes and social security contributions. The economic
multipliers of the LTC services are comparatively high due to the high share of wages and salaries in direct expenditure and
the associated high direct value added. Public expenditure on professional care services should therefore not be regarded
merely as a cost factor in the public budget. Rather, this rapidly growing economic sector is also an increasingly important
economic factor in a time of ageing societies. While the model does not provide information on the causal economic effect
of the LTC sector, the findings are still highly important for planners of LTC reforms, as they provide information on the
total value added associated with LTC expenditures and on the total number of jobs that these expenditures sustain.
The choice of an appropriate e-commerce strategy for the listing in price comparison platforms (eBay, Amazon, and price search
engines) is crucial for the survival of online stores in B2C e-commerce business. We use a comprehensive dataset from the
Austrian price search engine geizhals.at to identify successful e-commerce strategies with regard to these listing decisions.
An e-commerce strategy is a set of choices including the listing decision, availability decision, and decisions on a price
path and shipping cost. We apply cluster analysis to identify the different strategies that have been used by online retailers.
Using various success measures such as revenue, clicks, market share, and the survival of firms, as dependent variables in
our regression analyses, we present causal evidence on the effectiveness of different e-commerce strategies.