We investigate the impacts of economic incentives on the duration and outcome of temporary disability insurance (TDI) spells. The analysis is based on a large quasi-experiment in Norway, with a complete overhaul of the TDI benefit system. Our findings show that the labor supply of TDI claimants...

In the domain of Artificial Intelligence, deep learning is part of a broader family of machine learning methods based on deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks that have been applied to fields including computer vision, medical...

We investigate whether patents on human genes have affected follow-on scientific research and product development. Using administrative data on successful and unsuccessful patent applications submitted to the US Patent and Trademark Office, we link the exact gene sequences claimed in each...

The goal of this study is to show emerging applications of deep learning technology in cancer imaging. Deep learning technology is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior....

We present a simple graphical framework to illustrate the potential welfare gains from a “top-up” health insurance policy requiring patients to pay the incremental price for more expensive treatment options. We apply this framework to breast cancer treatments, where lumpectomy with radiation...

Patents award innovators a fixed period of market exclusivity, e.g., 20 years in the United States. Yet, since in many industries firms file patents at the time of discovery (“invention”) rather than first sale (“commercialization”), effective patent terms vary: inventions that...

We investigate whether private research investments are distorted away from long-term projects. Our theoretical model highlights two potential sources of this distortion: short-termism and the fixed patent term. Our empirical context is cancer research, where clinical trials – and hence,...

Debates over whether and when to recommend screening for a potential disease focus on the causal impact of screening for a typical individual covered by the recommendation, who may differ from the typical individual who responds to the recommendation. We explore this distinction in the context...