Report on Social Returns to Productivity Growth

Historically, economic growth has had huge social benefits, lifting billions out of poverty and improving health outcomes around the world. This leads some to argue that accelerating economic growth, or at least productivity growth,[1] should be a major philanthropic and social priority going forward.[2]

I’ve written a report in which I evaluate this view in order to inform Open Philanthropy’s Global Health and Wellbeing (GHW) grantmaking. Specifically, I use a relatively simple model to estimate the social returns to directly funding research and development (R&D). I focus on R&D spending because it seems like a particularly promising way to accelerate productivity growth, but I think broadly similar conclusions would apply to other innovative activities.

My estimate, which draws heavily on the methodology of Jones and Summers (2020), asks two primary questions:

  1. How much would a little bit of extra R&D today increase people’s incomes into the future, holding fixed the amount of R&D conducted at later times?[3]
  2. How much welfare is produced by this increase in income?

In brief, I find that:

  • The social returns to marginal R&D are high, but typically not as high as the returns in other areas we’re interested in. Measured in our units of impact (where “1x” is giving cash to someone earning $50k/year) I estimate that the cost-effectiveness of funding R&D is 45x. This is ~4% as impactful as the (roughly 1,000x) GHW bar for funding.
    • Put another way, I estimate that $20 billion to “average” R&D has the same welfare benefit as increasing the incomes of 180 million people by 10% each for one year.
    • That said, the best R&D projects might have much higher returns. So could projects aimed at increasing the amount of R&D (for example, improving science policy).
    • This estimate is very rough, and I could readily imagine it being off by a factor of 2-3 in either direction, even before accounting for the limitations below.
  • Returns to R&D were plausibly much higher in the past. This is because R&D was much more neglected, and because of feedback loops where R&D increased the amount of R&D occurring at later times.
  • My estimate has many important limitations. For example, it omits potential downsides to R&D (e.g. increasing global catastrophic risks), and it focuses on a specific scenario in which historical rates of return to R&D continue to apply even as population growth stagnates.
    • Alternative scenarios might change the bottom line. For instance, R&D today might speed up the development of some future technology that drastically accelerates R&D progress. This would significantly increase the returns to R&D, but in my view would also strengthen the case for Open Phil to focus on reducing risks from that technology rather than accelerating its development.

Overall, the model implies that the best R&D-related projects might be above our GHW bar, but it also leaves us relatively skeptical of arguments that accelerating innovation should be the primary social priority going forward. 

In the full report, I also discuss:

  • How alternative scenarios might affect social returns to R&D.
  • What these returns might have looked like in the year 1800.
  • How my estimates compare to those of economics papers that use statistical techniques to estimate returns to R&D growth.
  • The ways in which my current views differ from those of certain thinkers in the Progress Studies movement.
  1. [1] If environmental constraints require that we reduce our use of various natural resources, productivity growth can allow us to maintain our standards of living while using fewer of these scarce inputs.

  2. [2] For example: in Stubborn Attachments, Tyler Cowen argues that the best way to improve the long-run future is to maximize the rate of sustainable economic growth. A similar view is held by many of those involved in the Progress Studies community.

  3. [3] An example of an intervention causing a temporary boost in R&D activity would be to fund some researchers for a limited period of time. Another example would be to bring forward in time a policy change that permanently increases the number of researchers.