Welcome to Increasing Returns
An introduction to my goals and themes for this blog/newsletter
Welcome to Increasing Returns, my blog/newsletter where I plan to write (at least) weekly about the idea of increasing returns in economics and its wider implications for the history of economic thought, for understanding our world, and for navigating certain policy debates.
My focus on the idea of increasing returns comes from reflecting on the disparate strands of my own research projects and agendas over the years. I’ve written or find myself writing about copyright law, Wikipedia, crowdfunding, blockchain, economic history of intellectual property laws, freedom of speech, infant industries arguments, and the interplay of networks & institutions in innovation and economic growth. Perhaps the single unifying theme is the idea of increasing returns.
What do I mean by “increasing returns?” I’ll certainly need future posts to elaborate just about this term. Increasing returns to scale is a concept in modern microeconomic theory referring to a technology where outputs increase (scale) at a faster rate than proportionate increases in (all) inputs. Economists model this phenomenon using a production function that relates specified quantities of inputs to a quantity of output. Under a technology with increasing returns to scale, if a firm were to double all of its inputs, it would get more than double the output.
This idea is a relatively mundane technical tool in production theory that undergrads learn. It is easy for an undergrad to get lost in this part of an economics sequence amidst the many technical terms and modeling tools like isoquants, diminishing marginal returns, marginal rate of technical substitution, economies of scale, economies of scope, etc.
To my mind, increasing returns is far more than merely that. I see it as part of the problem that modern economic theory, with its focus on modeling techniques and constraining assumptions to make the math tractable, passes over the idea of increasing returns. Perhaps I am attacking a strawman in terms of what other practitioners of economic modeling do, but this is certainly what I see as an educator.
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It is far more than a neat feature of some technologies - understood in a certain light, it is the very reason that some societies are wealthy and others are poor. In 1776, Adam Smith famously described the role of the “division of labor” as a chief cause of “the wealth of nations”.
In my view, people often misinterpret Smith’s idea of the division of labor, especially with his example of a pin factory (how dividing up the process of making a pin into 18 unique tasks that workers specialize in, they can together produce 48,000 pins/day, rather than 20/day if each worker made full pins themselves). This sounds to the modern ear a lot like a factory system or an “assembly line” mode of production, a la Henry Ford. The takeaway then seems to be: build a factory and your business will be more productive. This might be true: a factory can potentially produce more output at a faster rate than not having a factory. I’ll expand on this in a later essay, but a factory that may be technologically feasible will not be economically feasible without a large market.
The main point of Smith’s insights, in my view, is the importance of specialization and exchange at the bigger-picture level of society, and how expanding market opportunities generates hitherto-unknown levels of prosperity. In fact, I prefer Smith’s example of the woolen coat to describe the division of labor. A thread of scholarly thought since Smith’s day preserves this important insight at the forefront of economic analysis, but the insight merely treads water in a sea dominated by economic models with more tractable assumptions.
To be fair to the last few hundred years of economic thought, these more tractable models (such as that of “perfect competition”) have generated a lot of insight and predictive value, and I’ve gained tremendous value from them myself. Formal economic theory over the last 100-150 years has largely built on an assumption of constant returns to scale (or sometimes decreasing returns to scale). Economists seem to have made a tradeoff favoring analytical tractability over intelligibility — but it is better to be precisely wrong than to be vaguely correct? I think something important is lost when we shift which assumptions and ideas are in the “foreground” of our analysis, and which are in the “background,” to paraphrase my dissertation adviser, Richard Wagner. Increasing returns seem to be considered a background concept by many - occasionally pulled out of the drawer to make a few brief and interesting points, or to explain something “unique”, but then displaced by more standard economic models.
Hence, this project of mine. I am hoping to create some increasing returns with this project by sharing some small shred of insights for others to read — making it a public good. My own interest in this is as a commitment device: to keep me writing and force me to think through some tough issues that I want to understand better, and where I am unclear about my stance. By “learning out loud” and grappling through issues in writing, I can make my own continuing education spill over and benefit others.
Due to my primary goal of wanting to better educating myself, the interests and focus areas will be somewhat eclectic, ranging between history of economic thought, research summaries, current policy debates, and frameworks or models used among academic economists. In terms of tools (that I am untrained in), I hope to learn much more about the economics of complexity, network theory, agent-based modeling, and other computational models that I think better capture and model increasing returns phenomenon. Note that I still intend to write on my personal blog at ryansafner.com on more data science and related tools. In terms of ideas and issues that I want to grapple with, all slightly out of my comfort zone, but continue to fascinate me:
What would an economics that focused on increasing returns look like? I think about this both in terms of models for research and policy, as well as university-level education. What would a course or a textbook on increasing returns, or even a principles of economics approach, that emphasized them?
What exactly happened to cause increasing returns to remain in the intellectual background? I will need to dive more into the history of thought, perhaps focusing on the marginal productivity debates.
Is there a case for industrial policy and restrictions on free trade (whether to promote increasing returns industries, national & geopolitical security, repatriating and protecting “supply chains”, etc)?
Should we own and be compensated for supplying our data over the internet? How would this affect the internet and online ecosystem?
What is the role of blockchain in bringing about a more efficient, decentralized, prosperous future?
How do crowdfunding, quadratic voting, quadratic funding, and other similar innovations affect our ability as a society to produce public goods?
How do all of these things affect policy and the role of the State in the economy?
When pressed, I have knee-jerk reactions to many of these ideas or debates based on my priors. But the educator and scholar in me realize that there are important gaps in my understanding and I want to consider all of the nuances before pontificating on them.
So welcome to my public journey. Whether this becomes a productive conversation or I end up just ranting into the void, I know that it will at least have been useful to me.
One of my favorite exam questions asks students to explain the difference between diminishing returns, decreasing returns to scale, and diseconomies of scale. They are very different things!
For example, the writings of Charles Babbage, Alwyn Young, W. Brian Arthur, James Buchanan, and Yong Yoon. I am certain I am neglecting to name many others, both writers I know of, and those I have yet to learn about.
Glen Weyl and RadicalXchange seems to have made increasing returns phenomena the fundamental focus of his research and advocacy. While I may not agree on everything, I commend this clarity and focus!
Thank you, Ryan! I appreciate you sharing your knowledge.