We show that two-stage least squares (2SLS) estimates of interactions can be misleading in settings with essential heterogeneity (e.g., selection into gains) and where complier status to the instrument depends on the interaction variable. The 2SLS estimator cannot disentangle interaction effects from shifts in complier groups. Estimating marginal treatment effects addresses this problem by fixing the underlying population and unobserved heterogeneity. We illustrate this using the example of gene-environment studies, where the central parameter is the interaction effect between an endogenous, instrumented measure of environment or behavior and a predetermined measure of genetic endowment. Our application examines the effect of education on cognitive performance in old age. The results show complementarities between education and genetic predisposition in determining cognitive abilities. The marginal treatment effect estimates reveal a substantially larger gene-environment interaction, exceeding the 2SLS estimate by a factor of at least 2.5.

This article investigates the short- and long-term impacts of retirement on loneliness using panel data from the Survey of Health, Aging, and Retirement in Europe. To identify causal effects, we exploit differences in retirement rules across and within countries and use retirement thresholds in an instrumental variable setting. On average, we find that entering retirement leads to a reduction in loneliness in the long run and no clear effect in the short run. The reduction is driven by individuals being less likely to feel socially isolated and lacking companionship. Our results suggest that individuals adapt to retirement by increasing their activity levels and reap the benefits in terms of reduced loneliness and social isolation. The heterogeneity analysis shows that this is particularly true among the higher educated. The heterogeneity analysis also reveals that retirement increases feelings of loneliness for women in the short term and that the effect seems to be driven by women lacking companionship when their partner is not yet retired.

In 2019, the German government established the Hospital Future Fund, allocating 4.3 billion Euros, to support investments in the digital infrastructure of hospitals. The DigitalRadar consortium was commissioned by the German Ministry of Health in 2020 to develop a holistic digital maturity model and evaluate the current state of digitalization and the impact of the funding program. To date, the nationwide digitalization of German hospitals has remained a relatively understudied phenomenon. This study aims to address this gap in knowledge by examining the influence of various factors identified by the DigitalRadar maturity model on the digital maturity of hospitals in Germany. In doing so, it seeks to elucidate the implications these findings have for the development of a digital, patient-centred, safe, and high-quality hospital landscape in the country. The model was developed through a scoping review of digital maturity models, requirements set forth in the Hospital Future Act, analysis of components from existing models and feedback from a sounding board. Ultimately, the model includes 234 questions (items) categorized into 7 dimensions of digitalization. It was piloted in 12 hospitals and revised accordingly. 1,624 hospitals (91% of all German hospitals) participated in this self-assessment, as participation was mandatory to receive funding. The average DigitalRadar score on a 100-point scale is 33. Maturity is comparatively high in the structures and systems dimension, but low in the clinical processes, exchange of information, telemedicine and patient participation dimensions, suggesting that data exchange is hampered by a lack of interoperability. Drivers of digital maturity are teaching status, size, connectivity, and level of emergency services. The transparency gained allows hospitals and regulators to identify areas for improvement and develop digital strategies. Additionally, it enables researchers to analyse, for example, the correlation between digitalization and the quality of care, as well as the mechanisms of action of large-scale funding programs for hospital digitization.

German hospitals largely rely on public investments for digitization. As these have been insufficient, hospitals had to use own profits to foster digital transformation. Thus, we assess if profitability affects digital maturity, and what other factors might be influential. We use digital maturity data from the DigitalRadar (DR) project (2021) and financial statement data from the Hospital Rating Report from 2017 to 2019 (n = 860). We run linear regressions with the DR-score (continuous variable from 0 to 100) as dependent and three-year average EBITDA margin as independent variable. Besides, we conduct subgroup analyses stratifying by chain size. A one percentage point EBITDA margin increase is associated with a 0.359 points DR-score increase. This relationship holds in significance and holds or increases in magnitude for all specifications except when adding chain beds (0.212 point DR-score increase). Besides, chain membership and chain size are positively and significantly associated with hospitals’ DR-score. EBITDA margins of the subgroups “large chains” and “Big 3″, i.e., the three largest chains, were strongly associated with the DR-score (2.685 and 3.197 points DR-score increase respectively). Conclusions Higher profitability is associated with higher digital maturity. Larger chains are digitally more mature, because (1) they might follow a chain-wide IT-strategy, (2) can standardize IT-architecture, and policies and (3) might cross-finance investments.