Tuesday, December 5, 2017

Nature Versus Nurture in the Propensity to Innovate

A colleague pointed me to a paper today that I wanted to share. NBER researchers have managed to tie patent inventor data to tax returns to test scores in order to show who is likely to be an inventor on a patent. The study makes some intuitive and nonintuitive findings. The paper, by Alexander M. Bell (Harvard Econ), Raj Chetty (Stanford Econ), Xavier Jaravel (LSE), Neviana Petkova (U.S. Treasury), and John Van Reenen (MIT Econ), is here:
We characterize the factors that determine who becomes an inventor in America by using de-identified data on 1.2 million inventors from patent records linked to tax records. We establish three sets of results. First, children from high-income (top 1%) families are ten times as likely to become inventors as those from below-median income families. There are similarly large gaps by race and gender. Differences in innate ability, as measured by test scores in early childhood, explain relatively little of these gaps. Second, exposure to innovation during childhood has significant causal effects on children's propensities to become inventors. Growing up in a neighborhood or family with a high innovation rate in a specific technology class leads to a higher probability of patenting in exactly the same technology class. These exposure effects are gender-specific: girls are more likely to become inventors in a particular technology class if they grow up in an area with more female inventors in that technology class. Third, the financial returns to inventions are extremely skewed and highly correlated with their scientific impact, as measured by citations. Consistent with the importance of exposure effects and contrary to standard models of career selection, women and disadvantaged youth are as under-represented among high-impact inventors as they are among inventors as a whole. We develop a simple model of inventors' careers that matches these empirical results. The model implies that increasing exposure to innovation in childhood may have larger impacts on innovation than increasing the financial incentives to innovate, for instance by cutting tax rates. In particular, there are many “lost Einsteins” — individuals who would have had highly impactful inventions had they been exposed to innovation.
The finding that inventors are white males that come from higher income families is hardly surprising, and one might imagine a variety of reasons: access to education, access to capital, risk aversion, discrimination, etc. But the interesting part of this paper, as I discuss below, is that the authors believe they can causally show it is not really any of the usual suspects, at least not directly.

First, the authors consider innate ability, and find that test scores (at least those for inventors from the areas where they could get data) do not explain all of the differences. Third grade math scores only explain 1/3 of the differences by income, and even less for race and gender. Eighth grade math scores, on the other hand, explain half of the differences based on income. The authors take this to mean not only that the ability gap widens over time (a sad but consistent with the literature finding in itself), but also that the widening gap means that environment must be playing some large role. They are careful to note that neither math nor test scores can perfectly explain ability to invent.

So the authors then turn to other indicia of environment as the core causal story of their research. They start by examining neighborhood data, and find that those kids who grow up in certain high inventing areas are much more likely to invent. This alone, of course, proves nothing. It could be that these areas are more affluent, or more white, or what have you. So, they look at technology class, and find that the propensity of children to invent is both highly tied to the technology classes that parents are inventing, and highly tied to gender. That is, girls invent in the same classes as local women, and men invent in the same classes as local men. They test this down to 450+ technology classes (amplifiers v. antennas in their example).

More interesting, these trends stick even if the children move away from the technology zone. So kids from Silicon Valley who live in Boston are more likely to work in computers (not a great example, given computers in Boston), and kids from Minneapolis are more likely to invent medical devices, even if they live somewhere else as adults. The authors believe that these can only be explained by exposure to inventing, because the different areas are all affluent, and it is unlikely that the neighborhoods would be uniquely training people with "general" technology specific proclivities. Frankly, a lot (though not the entire analysis) is riding on the technology class analysis, and deep examination of the calculations will be warranted.

The authors then consider whether the propensity to invent really comes from youth, or might be determined through college or work (that is, access to capital). They find that students from the colleges producing the most inventors (why not all colleges?) invent at the same rates regardless of income. That is, by college, the income gap disappears, supporting the claim that access to capital does not explain the differences. Finally, they review post-college income, and find that it is highly skewed- those who create the most valuable (that is, most cited) patents are the highest paid (score one for citation analysis, which has come under fire lately), though such income often comes before the "big patent." This, too, is interesting information.

Also interesting is that those from underrepresented groups (gender, race, income) have similar citation and earnings patterns. However, they are just as underrepresented. This implies that childhood makes the difference (getting into inventing). It is an important finding for social policy. The authors refer to this as "lost Einsteins" - capable children who do not get into inventing.

But their solution is different than often floated. They do not believe that education is the key (though it surely can't hurt), but exposure. I am involved with a FIRST robotics league, whose goal is to get children exposed to STEM. The benefits seem pretty clear. They build a model that shows that reducing barriers to entry will not help without exposure (as can be seen in all the areas where there are no barriers to entry but also no exposure) and that financial incentives (in the form of tax breaks) will not have a positive effect.

The model is a weaker part of the paper. First, the view of financial incentives is relatively narrow. It is easy to see why a tax incentive might have little value, but there may be other incentives that might be more fruitful. Second, the model seems static. For example, a dynamic effect would be if the financial incentives cause parents to increase exposure prior to the point of career choice. In other words, in a static model, exposure may be best, but not everyone can move to a high exposure area, so other ways to increase exposure (including non-tax financial incentives) should be the goal.

And this leads to a final point - the empirical model, while interesting and highly useful, misses a lot of endogenous effects (a word that doesn't appear in the paper). Parent inventorship and neighborhood say a lot about exposure, but also about income and race (not so much gender). And while I'm willing to accept the "ruling out" of these other explanations as a causal basis for inventing through the authors' careful analysis, the policy prescription is not so clear. If we were to, say, just increase inventing in poor areas, would that help? It would be nice to see the authors find a poor area that invents a lot to test it. And are the parents inventors because the area has higher income? Or does the area have higher income because the parents are inventors? And can neighborhood alone create change (assuming parents are not inventors)? Are people supposed to move? What if there is exposure without any of these things? What would it look like? These are all tough questions that I look forward to seeing addressed in future research.

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