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CINCH - Health Economics Research Center

New CINCH Working Paper

13.11.2020

A new working paper has been added to the CINCH working paper series: “Subjective Expectations and Demands for Contraception” by Grant Miller, Áureo de Paula, and Christine Valente.

Abstract: One quarter of married, fertile‐age women in Sub‐Saharan Africa report not wanting a pregnancy and yet do not use contraceptives. To study this issue, we collect detailed data on women’s subjective probabilistic beliefs and estimate a structural model of contraceptive choices. Our results indicate that costly interventions like eliminating supply constraints would only modestly increase contraceptive use. Alternatively, increasing partners’ approval of methods, aligning partners’ fertility preferences with women’s beliefs about pregnancy risk absent contraception have the potential to increase use considerably. Results from a before/after experiment testing this last finding are highly consistent with the structural estimates.

See all working papers.


Virtual Essen Health Economics Seminar

09.11.2020

On Monday, November 16 2020, 16:00 - 17:30, Ariel Stern (Harvard Business School) will present:

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.

Room: Due to the current situation regarding the COVID-19 pandemic, the talk will be held in a virtual seminar room. For more information click here.


Virtual Essen Health Economics Seminar

03.11.2020

On Monday, November 9 2020, 16:00 - 17:30, Martin Huber (Université de Fribourg) will present:

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.

Room: Due to the current situation regarding the COVID-19 pandemic, the talk will be held in a virtual seminar room. For more information click here.