Mechanisms and Impacts of Innovation Policy.

• Author: Howell, Sabrina T.

The importance of innovation to job creation and economic growth--especially in young, high-growth firms--is widely accepted among economists as well as members of the business and policy communities. There is also a recognition that, at least at some times or in certain settings, the private sector underinvests in innovation, creating an opportunity for the public sector to step into the breach.

The longstanding problem is how. What tools are most effective?

There are myriad opportunities for government programs to fail. For example, if a program subsidizes only the "best projects," those that would likely have gone forward with private capital regardless of government involvement, this is likely to be a poor use of taxpayer dollars. Alternatively, if only poor-quality projects are supported, they might fail even with government support.

In my research, I seek to understand the effects of, and mechanisms behind, common policy tools that subsidize high-growth entrepreneurship and innovation in the United States. In doing so, I hope to inform policymaking and shed light on the constraints and trade-offs of the innovation process.

Three key themes emerge in my work. First, program design appears to be more important than the amount of funding. For example, it is important to enable innovators to pivot and to control the commercialization pathway of their ideas. Second, effectiveness depends on which firms decide to apply for support. Programs need to target firms with the potential to benefit, and succeed in getting them to apply for support. Finally, direct federal funding plays an important role in our innovation ecosystem and is not always substitutable with private or privately intermediated alternatives.

The Evaluation Challenge

Economists have long been interested in evaluating government innovation programs, but it has been hard to identify causal effects. Program administrators are typically loath to run experiments. My work has addressed this challenge by employing several empirical approaches.

The most important of these methods is a regression discontinuity design (RDD) in which I compare winning and losing applicants within a competition for a grant or contract. I control for the rank that the program assigns to each applicant. Importantly, the cutoff decision determining which ranks win is exogenous to the ranking process. The key insight is that near the cutoff for winning, winners and losers should be similar, creating a natural experiment.

In other work, I use staggered program rollout designs, while addressing potential bias from pretreatment observations being considered by the model as controls. A final method is to instrument for funding using plausibly exogenous shocks. All three of these methods can be applied in many policy evaluation settings, and if carefully executed can reveal causal effects.

Design and Selection: Evidence from the SBIR Program

The US Small Business Innovation Research (SBIR) program, which was established in 1982, is the main vehicle by which the federal government direcdy supports innovation at small firms and...