by Tyler Cowen November 18, 2019 at 1:09 am in Data Source Economics History
That is the title of my new paper with Ben Southwood, here is one segment from the introduction:
Our task is simple: we will consider whether the rate of scientific progress has slowed down, and more generally what we know about the rate of scientific progress, based on these literatures and other metrics we have been investigating. This investigation will take the form of a conceptual survey of the available data. We will consider which measures are out there, what they show, and how we should best interpret them, to attempt to create the most comprehensive and wide-ranging survey of metrics for the progress of science. In particular, we integrate a number of strands in the productivity growth literature, the “science of science” literature, and various historical literatures on the nature of human progress. In our view, however, a mere reporting of different metrics does not suffice to answer the cluster of questions surrounding scientific progress. It is also necessary to ask some difficult questions about what science means, what progress means, and how the literatures on economic productivity and “science on its own terms” might connect with each other.
Mostly we think scientific progress is indeed slowing down, and this is supported by a wide variety of metrics, surveyed in the paper. The gleam of optimism comes from this:
And to the extent that progress in science has not been slowing down, which is indeed the case under some of our metrics, that may give us new insight into where the strengths of modern and contemporary science truly lie. For instance, our analysis stresses the distinction between per capita progress and progress in the aggregate. As we will see later, a wide variety of “per capita” measures do indeed suggest that various metrics for growth, progress and productivity are slowing down. On the other side of that coin, a no less strong variety of metrics show that measures of total, aggregate progress are usually doing quite well. So the final answer to the progress question likely depends on how we weight per capita rates of progress vs. measures of total progress in the aggregate.
What do the data on productivity not tell us about scientific progress? By how much is the contribution of the internet undervalued? What can we learn from data on crop yields, life expectancy, and Moore’s Law? Might the social sciences count as an example of progress in the sciences not slowing down? Is the Solow model distinction between “once and for all changes” and “ongoing increases in the rate of innovation” sound? And much more.
Your comments on this paper would be very much welcome, either on MR or through email. I will be blogging some particular ideas from the paper over the next week or two.