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CINCH - Forschungszentrum für Gesundheitsökonomik

Virtual Essen Health Economics Seminar

Am Montag, den 16. November 2020, 16:00 - 17:30 Uhr, wird Ariel Stern (Harvard Business School) ihre Forschung vorstellen:

Product Recalls and New Product Development: Own Firm Distractions and Competitor Firm Opportunities

Product recalls create significant challenges for R&D intensive firms, but simultaneously generate potentially lucrative opportunities for competitors. Using the U.S. medical device industry as our setting, we develop predictions and provide evidence that own firm recalls slow new product development activities, while competitor firm recalls accelerate them. We also examine two firm-level moderators that influence the recall and new product development relationship: product scope and ownership structure. We find that own firm recalls slow new product development for all firm types: a single own firm recall slows new product development up to 43 days, equating to more than $10 million in revenue lost in this high-margin and highly competitive setting. We also find that competitor firm recalls are associated with accelerated development times, but only for broad (vs. narrow) product scope firms and public (vs. private) firms. A one standard deviation increase in competitor firm recalls for these firm types accelerates new product development by more than two weeks. Organizational resources and financial incentives are thus key determinants of whether firms can effectively capitalize on the potential market opportunities created by competitor recalls. In post-hoc analyses, we explore whether future product quality is predicted by post-recall submission times, but find no evidence for this relationship. This result suggests that new product development delays following own firm recalls are more likely driven by organizational distractions than by product quality learning, and that firms react strategically and rationally by speeding new products to market after competitor recalls.

Raum: Aufgrund der aktuellen Lage in Bezug auf die COVID-19-Pandemie findet der Vortrag in einem virtuellen Seminarraum statt. Für mehr Informationen klicken Sie bitte hier.


Virtual Essen Health Economics Seminar

Am Montag, den 9. November 2020, 16:00 - 17:30 Uhr, wird Martin Huber (Université de Fribourg) seine Forschung vorstellen:

Double machine learning for (weighted) dynamic treatment effects

We consider evaluating the causal effects of dynamic treatments, i.e. of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-observables assumption. To this end, we make use of so-called Neyman-orthogonal score functions, which imply the robustness of treatment effect estimation to moderate misspecifications of the dynamic outcome and treatment models. This robustness property permits approximating outcome and treatment models by double machine learning even under high dimensional covariates and is combined with data splitting to prevent overfitting. In addition to effect estimation for the total population, we consider weighted estimation that permits assessing dynamic treatment effects in specific subgroups, e.g. among those treated in the first treatment period. We demonstrate that the estimators are asymptotically normal and root-n consistent under specific regularity conditions and investigate their finite sample properties in a simulation study. Finally, we apply the methods to the Job Corps study in order to assess different sequences of training programs under a large set of covariates.

Raum: Aufgrund der aktuellen Lage in Bezug auf die COVID-19-Pandemie findet der Vortrag in einem virtuellen Seminarraum statt. Für mehr Informationen klicken Sie bitte hier.


Virtual Essen Health Economics Seminar

Am Montag, den 2. November 2020, 16:00 - 17:30 Uhr, wird Libertad González (Universitat Pompeu Fabra) ihre Forschung vorstellen:

Prenatal Transfers and Infant Health: Evidence from Spain

We estimate the impact of a cash transfer to women on their (future) children’s birth outcomes, exploiting the introduction of a universal child benefit in Spain. Using administrative data from birth records and a regression discontinuity approach, we find that low-income women who received the benefit were much less likely to give birth to low birth-weight children, several years down the road. A 2,500-euro transfer led to a 2.2 decline in low birth-weight status among women in poor households. Given that about 6% of children were low birth-weight, this represents a 36% reduction. We find that the effect is driven by both longer gestation and faster intrauterine growth. We provide some evidence of improved maternal health behaviors and outcomes. Recent research suggests that benefits targeting pregnant women may be more effective than later interventions, given the strong persistence of fetal health effects. Our results suggest that the impact may be even stronger if women are targeted even earlier, before conception.

Raum: Aufgrund der aktuellen Lage in Bezug auf die COVID-19-Pandemie findet der Vortrag in einem virtuellen Seminarraum statt. Für mehr Informationen klicken Sie bitte hier.