Academic journal article NBER Reporter

What Inventions Are We Missing?

Academic journal article NBER Reporter

What Inventions Are We Missing?

Article excerpt

Academics and policymakers have long recognized that competitive markets may under-incentivize innovation. This concern has motivated the design of public policies such as the patent system, which aims to encourage research investments into new technologies by allowing inventors to capture a higher share of the social returns to their research investments.

A well-developed theoretical literature has analyzed optimal patent policy, with a focus on the trade-off between providing incentives for the development of new technologies and tolerating higher prices during the life of the patent. Although such theoretical models--and, importantly, public policies--typically assume that stronger (e.g. longer or broader) patents will induce additional research investments, there is remarkably little empirical evidence on how patents affect research investments in practice.

This question has been difficult to tackle empirically for at least two reasons. First, measuring research investments can be quite challenging. Second, finding variation in patent protection that can be leveraged in an empirical study is difficult. On paper, the U.S. patent system is uniform, providing a 20-year period of protection for all inventions. While historically some cross-country variation in patent laws has existed, because innovations are generally developed for a global market, country-specific patent law changes may often induce only a relatively small change in global research incentives.

My research agenda attempts to overcome both of these challenges in order to develop empirical estimates of the key parameters needed to inform optimal patent policy. By combining detailed new measures of research investments with novel sources of variation in the effective patent terms provided to otherwise similar inventions, my work aims to construct frameworks within which we can infer the volume, type, and value of "missing" research investments that would have occurred under counterfactual patent policies. In this piece, I summarize some of the main findings that have emerged from my research in this area.

Measuring Innovation

Traditionally, economists who study innovation have relied on patent counts (or citation-weighted patent counts) as a measure of innovation, often leveraging the data constructed by Bronwyn Hall, Adam Jaffe, and Manuel Trajtenberg. (1) Although this approach has been useful in many settings, it encounters two major limitations. First, in many cases it is difficult or impossible to match patents with the specific products they protect, or to identify specific groups of consumers that might benefit from those products. For example, the text in a patent protecting a delivery method for a breast cancer drug may have no information suggesting the patent is relevant to breast cancer patients. Hence, it can be very difficult to use patents to measure research investments in a way that can be linked to product-market or consumer-level outcomes. Second, by construction, patent data can only measure patented inventions. Because many technologies are not patented, changes in patent counts may in some settings reflect changing levels of inventors' willingness to file for patents on their research investments, rather than changes in the underlying research investments themselves.

A major focus of my research agenda has been to attempt to overcome these two challenges by compiling "real," non-patent measures of innovation. For example, Eric Budish, Ben Roin, and I aimed to develop measures of research investments in cancer drugs. (2) The core of our data construction was to take advantage of a National Cancer Institute (NCI) clinical trial registry that includes an explicit listing of the patient groups eligible to enroll in each clinical trial. Cancer treatment tends to be specific to an organ of origin, such as prostate, and stage of disease, for example, metastatic. As such, the organ-stage classification tends to be used both to label clinical trial-eligible groups (as in the NCI data, where such classifications are used to describe which patients can enroll in any given clinical trial) and to label patients in standard clinical datasets (e. …

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