In David McKenzie’s most recent Weekly Links, he posted a link to an interview with Erik Hurst, who is an economist at the University of Chicago’s Booth School of Business. The interview spanned a wide range of topics including Erik’s work on income inequality, the decline of the US manufacturing industry, college attendance, and (what Erik calls) endogenous gentrification. The whole interview is fascinating to read, but I’d like to highlight one part of the interview that I find particularly interesting.
Question: Many people have an image of the typical entrepreneur in their head and it often includes a significant taste for risk and large long-term aspirations. What does your work on entrepreneurship suggest about that profile?
Erik Hurst: Most small businesses are plumbers and dry cleaners and local shopkeepers and house painters. These are great and important occupations, but empirically essentially none of them grow. They start small and stay small well into their life cycle. A plumber often starts out by himself and then hires just one or two people. And when you ask them if they want to be big over time, they say no. That’s not their ambition. This is important because a lot of our models assume businesses want to grow. Thinking most small businesses are like Google is not even close to being accurate. They are a tiny fraction.
My work with Ben Pugsley has been emphasizing the importance of nonpecuniary benefits to small-business formation. Because when you ask small-business people what their favorite part of their job is, it’s not making a lot of money. They do earn an income and they’re very happy with it, but they get even more satisfaction from being their own boss and having flexibility and all of those other nonpecuniary benefits that come with being the median entrepreneur in the United States.
In our culture we seem to want to subsidize small businesses because it’s the American dream. I think that could be fine, if you believe that there’s a friction out there preventing some small businesses from starting or growing. But you might want to target that friction directly as opposed to targeting all small businesses generically. Ben and I have a recent paper in which we show in a simple model that if you subsidize all small businesses, in a world with nonpecuniary benefits being the big driver of small-business entry, the policy is highly regressive. Why? High-wealth people are already small-business owners because they can afford the nonpecuniary benefits that come with owning a small business. So in that world, we’re basically just transferring money to high-wealth people when we subsidize small businesses overall, and that’s something we need to consider.
Excellent points, I think. I’ve been having discussions with some colleagues of mine about similar issues in developing countries. A lot of very popular international development policies aim to support entrepreneurship in developing countries (i.e. microcredit, business skills training, etc.). Unfortunately many of these programs often fail to pave a smooth and wide road out of poverty for the average household. One reason to explain this failure is that it seems many assume that everyone who owns a business is an entrepreneur who is risk-loving and aspires to grow and expand their business beyond it’s current level. This assumption breaks down in many developing countries (in similar ways to how Erik describes the breakdown in the US).
This has lead my colleagues and I to conclude that we many need a different word for, and a method for identifying, an “entrepreneur who doesn’t aspire to grow their business”. This sort of semantic and empirical innovation would have several interrelated benefits.
First, it would help policymakers better target certain policies and programs to individuals who would actually benefit from the program. Instead of rolling out a micro-loan product or a business skills training program to the population in general (which may lead to either diluted average effects or regressive benefits – as discussed above) programs could target specific individuals who actually aspire to grow and expand their business. Including everyone else will be wasteful because even if all the necessary external constraints are lifted (i.e. credit, skills, insurance, access to markets, etc.), these individuals may still not grow their business because they do not desire to do so.
Second, this innovation would also help empirical development economists who run experiments to estimate the impacts and cost-effectiveness of policies and programs. If it were possible to correctly diagnose development interventions that aim to support entrepreneurship and administer them only to those who are risk-loving and aspire to grow their business then, statistical tests would be much more accurate. As Bruce Wydick explains in a recent blog post on Diagnosis and Development Impact:
Suppose that a is the average treatment effect of an intervention on the properly diagnosed, e is the externality of the intervention to all others in the treatment group (with no externality to the control), and d is the percent of the treatment group that is diagnosed correctly. In this case, the ITT is just the weighted average of these effects between the properly diagnosed and others (the misdiagnosed) and is ad + e(1 – d). To estimate the ITT with 95% confidence and 80% power, the condition must hold that ad + e(1 – d) = 2.8SE , or that the percent correctly diagnosed must be equal to d = 2.8SE – e / a – e (where 2.8 is the sum of corresponding z-scores of 1.96 and 0.84 and SE is the standard error.) Assuming a > e, first differentiation shows this yields a negative relationship between d and e; analysis of the second derivative shows the relationship to be concave, as shown in the figure [in this link]. This yields a continuous set of statistical power contours that illustrate the trade-off in ITT estimations between correct diagnosis and the strength of externalities within the pool of subjects exposed to the intervention. Statistically speaking, the result is quite similar to the loss in statistical power one experiences with a treatment that is targeted at treatment group, but where very few people actually take up the treatment.
So, I’d say we need to think of a term for “entrepreneurs who don’t aspire to grow their business”. That is the first hurdle to jump. The second is to find ways to effectively diagnose risk aptitude and aspirations (or better yet hope for business growth). Some ideas come to mind – methods for estimating risk preferences are well established and I have a working paper currently out for review that makes some initial steps in validating a method for quantifying measures of hope and aspirations – but these methods need to be further investigated and tested.
HT: David McKenzie