To isolate the causal impact of cash flows on development decisions, we exploit a second source of variation: remaining drug exclusivity (patent life plus additional exclusivity granted by the FDA). Even among firms with the same focus on the elderly, those with more time to enjoy monopoly rights on their products are likely to generate greater profits.
With these two dimensions of variation—elderly share and remaining exclusivity–we can better control for confounders arising from both individual dimensions. For example, firms with more existing drugs for the elderly may differentially see a greater increase in investment opportunities as a result of Part D, even absent any changes to cash flow.
Meanwhile, firms with longer remaining exclusivity periods on their products may have different development strategies than firms whose drugs face imminent competition, again, even absent changes to cash flows. Our strategy thus compares firms with the same share of drugs sold to the elderly and the same remaining exclusivity periods across their overall drug portfolio, but that differ in how their remaining patent exclusivity is distributed across drugs of varying elder shares. This strategy allows us to identify differences in expected cash flow among firms with similar investment opportunities, and at similar points in their overall product lifecycle.
We find that treated firms develop more new drug candidates. Importantly, this effect is driven by an increase in the number of chemically novel candidates, as opposed to “me-too” candidates. Further, these new candidates are aimed at a variety of conditions, not simply ones with a high share of elderly patients, implying that our identification strategy is at least partially successful in isolating a shock to cash flows, and not simply picking up an increase in investment opportunities for high elderly share drugs.
In addition, we find some evidence that firm managers have a preference for diversification. The marginal drug candidates that treated firms pursue often include drugs that focus on different diseases, or operate using a different mechanism (target), relative to the drugs that the firm has previously developed. These findings suggest that firms use marginal increases in cash to diversify their portfolios and undertake more exploratory development strategies, a fact consistent with models of investment with financial frictions (Froot et al., 1993), or poorly diversified managers (Smith and Stulz, 1985).
Finally, our point estimates imply sensible returns to R&D. A one standard deviation increase in Part D exposure leads to an 11 percent increase in subsequent drug development, relative to less exposed firms. For the subset of firms for which we are able to identify cash flow, this translates into an elasticity of the number of drug candidate with respect of R& expenditure of about 0.75.
We obtain a higher elasticity for the most novel drugs (1.01 to 1.59) and a lower elasticity for the most similar drugs (0.02 to 0.31). For comparison, estimates of the elasticity of output with respect to demand (or cash flow) shocks in the innovation literature range from 0.3 to 4 (Henderson and Cockburn, 1996; Acemoglu and Linn, 2004; Azoulay, Graff-Zivin, Li, and Sampat, 2016; Blume-Kohout and Sood, 2013; Dranove, Garthwaite, and Hermosilla, 2014).
Our results suggest that financial frictions likely play a role in limiting the development of novel drug candidates. The ability to observe the returns associated with individual projects is an important advantage of our setting that allows us to make a distinct contribution to the literature studying the impact of financial frictions on firm investment decisions. Existing studies typically observe the response of investment (or hiring) aggregated at the level of individual firms or geographic locations.
By contrast, our setting allows us to observe the risk and return of the marginal project being undertaken as a result of relaxing financial constraints, and hence allows us to infer the type of investments that may be more susceptible to financing frictions. We find that, relaxing financing constraints leads to more innovation, both at the extensive margin (i.e., more drug candidates) but also at the intensive margin (i.e., more novel drugs). Given that novel drugs are less likely to be approved by the FDA, the findings in our paper echo those in Metrick and Nicholson (2009), who document that firms that score higher in terms of a Kaplan-Zingles index of financial constraints are more likely to develop drugs that pass FDA approval.
By providing a new measure of novelty, our work contributes to the literature focusing on the measurement and determinants of innovation. Our novelty measure is based on the notion of chemical similarity (Johnson and Maggiora, 1990), which is widely used in the process of pharmaceutical discovery.
Chemists use molecular similarity calculations to help them search chemical space, build libraries for drug screening (Wawer, Li, Gustafsdottir, Ljosa, Bodycombe, Marton, Sokolnicki, Bray, Kemp, Winchester, Taylor, Grant, Hon, Duvall, Wilson, Bittker, Danˇc´ık, Narayan, Subramanian, Winckler, Golub, Carpenter, Shamji, Schreiber, and Clemons, 2014), quantify the “drug-like” properties of a compound (Bickerton, Paolini, Besnard, Muresan, and Hopkins, 2012), and expand medicinal chemistry techniques (Maggiora, Vogt, Stumpfe, and Bajorath, 2014). In parallel work, Pye, Bertin, Lokey, Gerwick, and Linington (2017) use chemical similarity measures to measure novelty and productivity in the discovery of natural products.
Our measure of innovation is based on ex-ante information—the similarity of a drug’s molecular structure to prior drugs—and therefore avoids some of the truncation issues associated with patent citations (Hall et al., 2005). Further, since our measure is based only on ex-ante data, it does not conflate the ex-ante novelty of an idea with measures of ex-post success or of market size. By contrast, existing work typically measures “major” innovations using metrics based on ex-post successful outcomes, which may also be related to market size.
Examples include whether a drug candidate gets FDA Priority Review status (Dranove et al., 2014), or whether a drug has highly-cited patents (Henderson and Cockburn, 1996). A potential concern with these types of measures is that a firm will be credited with pursing novel drug candidates only if these candidates succeed and not when—as is true in the vast majority of cases—they fail. Similarly, outcomes such as whether a drug is first in class or is an FDA orphan drug (Dranove et al., 2014; DiMasi and Faden, 2011; Lanthier, Miller, Nardinelli, and Woodcock, 2013; DiMasi and Paquette, 2004) may conflate market size with novelty and may fail to measure novelty of candidates within a particular class.
For example, it is easier to be the first candidate to treat a rare condition than a common condition because fewer firms have incentives to develop treatments for the former. Further, measuring novelty as first in class will label all subsequent treatments in an area as incremental, even if they are indeed novel.
Our paper also relates to work that examines how regulatory policies and market conditions distort the direction of drug development efforts (Budish, Roin, and Williams, 2015); and how changes in market demand affect innovation in the pharmaceutical sector (Acemoglu and Linn, 2004; Blume-Kohout and Sood, 2013; Dranove et al., 2014). Similar to us, Blume-Kohout and Sood (2013) and Dranove et al. (2014) exploit the passage of Medicare Part D, and find more innovation in markets that receive a greater demand shock (drugs targeted to the elderly).
Even though we use the same policy shock, our work additionally exploits differences in drug exclusivity for specific drugs to identify the effect of cash flow shocks separately from changes in product demand that may increase firm investment opportunities. Indeed, we find that treated firms invest in new drugs across different categories—as opposed to those that only target the elderly—strongly suggesting that our identification strategy effectively isolates cash flow shocks from improvements in investment opportunities.
Last, our measure of novelty can help shed light on several debates in the innovation literature. For instance, Jones (2010); Bloom, Jones, Reenen, and Webb (2017) argue for the presence of decreasing returns to innovation. Consistent with this view, we find that drug novelty has decreased over time. An important caveat is that our novelty measure cannot be computed for biologics, which represent a vibrant research area.
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