“Seeking the Roots of Entrepreneurship: Insights from Behavioral Economics,” T. Astebro, H. Herz, R. Nanda & R. Weber (2014)

Entrepreneurship is a strange thing. Entrepreneurs work longer hours, make less money in expectation, and have higher variance earnings than those working for firms; if anyone knows of solid evidence to the contrary, I would love to see the reference. The social value of entrepreneurship through greater product market competition, new goods, etc., is very high, so as a society the strange choice of entrepreneurs may be a net benefit. We even encourage it here at UT! Given these facts, why does anyone start a company anyway?

Astebro and coauthors, as part of a new JEP symposium on entrepreneurship, look at evidence from behavioral economics. The evidence isn’t totally conclusive, but it appears entrepreneurs are not any more risk-loving or ambiguity-loving than the average person. Though they are overoptimistic, you still see entrepreneurs in high-risk, low-performance firms even ten years after they are founded, at which point surely any overoptimism must have long since been beaten out of them.

It is, however, true that entrepreneurship is much more common among the well-off. If risk aversion can’t explain things, then perhaps entrepreneurship is in some sense consumption: the founders value independence and control. Experimental evidence provides fairly strong evidence for this hypothesis. For many entrepreneurs, it is more about not having a boss than about the small chance of becoming very rich.

This leads to a couple questions: why so many immigrant entrepreneurs, and what are we make of the declining rate of firm formation in the US? Pardon me if I speculate a bit here. The immigrant story may just be selection; almost by definition, those who move across borders, especially those who move for graduate school, tend to be quite independent! The declining rate of firm formation may be tied with inequality changes; to the extent that entrepreneurship involves consumption of a luxury good (control over one’s working life) in addition to standard risk-adjusted cost-benefit analysis, then changes in the income distribution will change that consumption pattern. More work is needed on these questions.

Summer 2014 JEP (RePEc IDEAS). As always, a big thumbs up to the JEP for being free to read! It is also worth checking out the companion articles by Bill Kerr and coauthors on experimentation, with some amazing stats using internal VC project evaluation data for which ex-ante projections were basically identical for ex-post failures and ex-post huge successes, and one by Haltiwanger and coauthors documenting the important role played by startups in job creation, the collapse in startup formation and job churn which began well before 2008, and the utter mystery about what is causing this collapse (which we can see across regions and across industries).

“Dynamic Commercialization Strategies for Disruptive Technologies: Evidence from the Speech Recognition Industry,” M. Marx, J. Gans & D. Hsu (2014)

Disruption. You can’t read a book about the tech industry without Clayton Christensen’s Innovator’s Dilemma coming up. Jobs loved it. Bezos loved it. Economists – well, they were a bit more confused. Here’s the story at its most elemental: in many industries, radical technologies are introduced. They perform very poorly initially, and so are ignored by the incumbent. These technologies rapidly improve, however, and the previously ignored entrants go on to dominate the industry. The lesson many tech industry folks take from this is that you ought to “disrupt yourself”. If there is a technology that can harm your most profitable business, then you should be the one to develop it; take Amazon’s “Lab126″ Kindle skunkworks as an example.

There are a couple problems with this strategy, however (well, many problems actually, but I’ll save the rest for Jill Lepore’s harsh but lucid takedown of the disruption concept which recently made waves in the New Yorker). First, it simply isn’t true that all innovative industries are swept by “gales of creative destruction” – consider automobiles or pharma or oil, where the major players are essentially all quite old. Gans, Hsu and Scott Stern pointed out in a RAND article many years ago that if the market for ideas worked well, you would expect entrants with good ideas to just sell to incumbents, since the total surplus would be higher (less duplication of sales assets and the like) and since rents captured by the incumbent would be higher (less product market competition). That is, there’s no particular reason that highly innovative industries require constant churn of industry leaders.

The second problem concerns disrupting oneself or waiting to see which technologies will last. Imagine it is costly to investigate potentially disruptive technologies for the incumbent. For instance, selling mp3s in 2002 would have cannibalized existing CD sales at a retailer with a large existing CD business. Early on, the potentially disruptive technology isn’t “that good”, hence it is not in and of itself that profitable. Eventually, some of these potentially disruptive technologies will reveal themselves to actually be great improvements on the status quo. If that is the case, then, why not just let the entrant make these improvements/drive down costs/learn about market demand, and then buy them once they reveal that the potentially disruptive product is actually great? Presumably the incumbent even by this time still retains its initial advantage in logistics, sales, brand, etc. By waiting and buying instead of disrupting yourself, you can still earn those high profits on the CD business in 2002 even if mp3s had turned out to be a flash in the pan.

This is roughly the intuition in a new paper by Matt Marx – you may know his work on non-compete agreements – Gans and Hsu. Matt has also collected a great dataset from industry journals on every firm that ever operated in automated speech recognition. Using this data, the authors show that a policy by entrants of initial competition followed by licensing or acquisition is particularly common when the entrants come in with a “disruptive technology”. You should see these strategies, where the entrant proves the value of their technology and the incumbent waits to acquire, in industries where ideas are not terribly appropriable (why buy if you can steal?) and entry is not terribly expensive (in an area like biotech, clinical trials and the like are too expensive for very small firms). I would add that you also need complementary assets to be relatively hard to replicate; if they aren’t, the incumbent may well wind up being acquired rather than the entrant should the new technology prove successful!

Final July 2014 working paper (RePEc IDEAS). The paper is forthcoming in Management Science.

“The Rise and Fall of General Laws of Capitalism,” D. Acemoglu & J. Robinson (2014)

If there is one general economic law, it is that every economist worth their salt is obligated to put out twenty pages responding to Piketty’s Capital. An essay by Acemoglu and Robinson on this topic, though, is certainly worth reading. They present three particularly compelling arguments. First, in a series of appendices, they follow Debraj Ray, Krusell and Smith and others in trying to clarify exactly what Piketty is trying to say, theoretically. Second, they show that it is basically impossible to find any effect of the famed r-g on top inequality in statistical data. Third, they claim that institutional features are much more relevant to the impact of economic changes on societal outcomes, using South Africa and Sweden as examples. Let’s tackle these in turn.

First, the theory. It has been noted before that Piketty is, despite beginning his career as a very capable economist theorist (hired at MIT at age 22!), very disdainful of the prominence of theory. He, quite correctly, points out that we don’t even have any descriptive data on a huge number of topics of economic interest, inequality being principal among these. And indeed he is correct! But, shades of the Methodenstreit, he then goes on to ignore theory where it is most useful, in helping to understand, and extrapolate from, his wonderful data. It turns out that even in simple growth models, not only is it untrue that r>g necessarily holds, but the endogeneity of r and our standard estimates of the elasticity of substitution between labor and capital do not at all imply that capital-to-income ratios will continue to grow (see Matt Rognlie on this point). Further, Acemoglu and Robinson show that even relatively minor movement between classes is sufficient to keep the capital share from skyrocketing. Do not skip the appendices to A and R’s paper – these are what should have been included in the original Piketty book!

Second, the data. Acemoglu and Robinson point out, and it really is odd, that despite the claims of “fundamental laws of capitalism”, there is no formal statistical investigation of these laws in Piketty’s book. A and R look at data on growth rates, top inequality and the rate of return (either on government bonds, or on a computed economy-wide marginal return on capital), and find that, if anything, as r-g grows, top inequality shrinks. All of the data is post WW2, so there is no Great Depression or World War confounding things. How could this be?

The answer lies in the feedback between inequality and the economy. As inequality grows, political pressures change, the endogenous development and diffusion of technology changes, the relative use of capital and labor change, and so on. These effects, in the long run, dominate any “fundamental law” like r>g, even if such a law were theoretically supported. For instance, Sweden and South Africa have very similar patterns of top 1% inequality over the twentieth century: very high at the start, then falling in mid-century, and rising again recently. But the causes are totally different: in Sweden’s case, labor unrest led to a new political equilibrium with a high-growth welfare state. In South Africa’s case, the “poor white” supporters of Apartheid led to compressed wages at the top despite growing black-white inequality until 1994. So where are we left? The traditional explanations for inequality changes: technology and politics. And even without r>g, these issues are complex and interesting enough – what could be a more interesting economic problem for an American economist than diagnosing the stagnant incomes of Americans over the past 40 years?

August 2014 working paper (No IDEAS version yet). Incidentally, I have a little tracker on my web browser that lets me know when certain pages are updated. Having such a tracker follow Acemoglu’s working papers pages is, frankly, depressing – how does he write so many papers in such a short amount of time?

“Epistemic Game Theory,” E. Dekel & M. Siniscalchi (2014)

Here is a handbook chapter that is long overdue. The theory of epistemic games concerns a fairly novel justification for solution concepts under strategic uncertainty – that is, situations where what I want to do depends on other people do, and vice versa. We generally analyze these as games, and have a bunch of equilibrium (Nash, subgame perfection, etc.) and nonequilibrium (Nash bargain, rationalizability, etc.) solution concepts. So which should you use? I can think of four classes of justification for a game solution. First, the solution might be stable: if you told each player what to do, no one person (or sometimes group) would want to deviate. Maskin mentions this justification is particularly worthy when it comes to mechanism design. Second, the solution might be the outcome of a dynamic selection process, such as evolution or a particular learning rule. Third, the solution may be justified by certain axiomatic first principles; Shapley value is a good example in this class. The fourth class, however, is the one we most often teach students: a solution concept is good because it is justified by individual behavior assumptions. Nash, for example, is often thought to be justified by “rationality plus correct beliefs”. Backward induction is similarly justified by “common knowledge of rationality at all states.”

Those are informal arguments, however. The epistemic games (or sometimes, “interactive epistemology”) program seeks to formally analyze assumptions about the knowledge and rationality of players and what it implies for behavior. There remain many results we don’t know (for instance, I asked around and could only come up with one paper on the epistemics of coalitional games), but the results proven so far are actually fascinating. Let me give you three: rationality and common belief in rationality implies rationalizable strategies are played, the requirements for Nash are different depending on how players there are, and backward induction is surprisingly difficult to justify on epistemic grounds.

First, rationalizability. Take a game and remove any strictly dominated strategy for each player. Now in the reduced game, remove anything that is strictly dominated. Continue doing this until nothing is left to remove. The remaining strategies for each player are “rationalizable”. If players can hold any belief they want about what potential “types” opponents may be – where a given (Harsanyi) type specifies what an opponent will do – then as long as we are all rational, we all believe the opponents are rational, we all believe the opponents all believe that we all are rational, ad infinitum, the only possible outcomes to the game are the rationalizable ones. Proving this is actually quite complex: if we take as primitive the “hierarchy of beliefs” of each player (what do I believe my opponents will do, what do I believe they believe I will do, and so on), then we need to show that any hierarchy of beliefs can be written down in a type structure, then we need to be careful about how we define “rational” and “common belief” on a type structure, but all of this can be done. Note that many rationalizable strategies are not Nash equilibria.

So what further assumptions do we need to justify Nash? Recall the naive explanation: “rationality plus correct beliefs”. Nash takes us from rationalizability, where play is based on conjectures about opponent’s play, to an equilibrium, where play is based on correct conjectures. But which beliefs need to be correct? With two players and no uncertainty, the result is actually fairly straightforward: if our first order beliefs are (f,g), we mutually believe our first order beliefs are (f,g), and we mutually believe we are rational, then beliefs (f,g) represent a Nash equilibrium. You should notice three things here. First, we only need mutual belief (I know X, and you know I know X), not common belief, in rationality and in our first order beliefs. Second, the result is that our first-order beliefs are that a Nash equilibrium strategy will be played by all players; the result is about beliefs, not actual play. Third, with more than two players, we are clearly going to need assumptions about how my beliefs about our mutual opponent are related to your beliefs; that is, Nash will require more, epistemically, than “basic strategic reasoning”. Knowing these conditions can be quite useful. For instance, Terri Kneeland at UCL has investigated experimentally the extent to which each of the required epistemic conditions are satisfied, which helps us to understand situations in which Nash is harder to justify.

Finally, how about backward induction? Consider a centipede game. The backward induction rationale is that if we reached the final stage, the final player would defect, hence if we are in the second-to-last stage I should see that coming and defect before her, hence if we are in the third-to-last stage she will see that coming and defect before me, and so on. Imagine that, however, player 1 does not defect in the first stage. What am I to infer? Was this a mistake or am I perhaps facing an irrational opponent? Backward induction requires that I never make such an inference, and hence I defect in stage 2.

Here is a better justification for defection in the centipede game, though. If player 1 doesn’t defect in the first stage, then I “try my best” to retain a belief in his rationality. That is, if it is possible for him to have some belief about my actions in the second stage which rationally justified his first stage action, then I must believe that he holds those beliefs. For example, he may believe that I believe he will continue again in the third stage, hence that I will continue in the second stage, hence he will continue in the first stage then plan to defect in the third stage. Given his beliefs about me, his actions in the first stage were rational. But if that plan to defect in stage three were his justification, then I should defect in stage two. He realizes I will make these inferences, hence he will defect in stage 1. That is, the backward induction outcome is justified by forward induction. Now, it can be proven that rationality and common “strong belief in rationality” as loosely explained above, along with a suitably rich type structure for all players, generates a backward induction outcome. But the epistemic justification is completely based on the equivalence between forward and backward induction under those assumptions, not on any epistemic justification for backward induction reasoning per se. I think that’s a fantastic result.

Final version, prepared for the new Handbook of Game Theory. I don’t see a version on RePEc IDEAS.

“The Tragedy of the Commons in a Violent World,” P. Sekeris (2014)

The prisoner’s dilemma is one of the great insights in the history of the social sciences. Why would people ever take actions that make everyone worse off? Because we all realize that if everyone took the socially optimal action, we would each be better off individually by cheating and doing something else. Even if we interact many times, that incentive to cheat will remain in our final interaction, hence cooperation will unravel all the way back to the present. In the absence of some ability to commit or contract, then, it is no surprise we see things like oligopolies who sell more than the quantity which maximizes industry profit, or countries who exhaust common fisheries faster than they would if the fishery were wholly within national waters, and so on.

But there is a wrinkle: the dreaded folk theorem. As is well known, if we play frequently enough, and the probability that any given game is the last is low enough, then any feasible outcome which is better than what players can guarantee themselves regardless of other player’s action can be sustained as an equilibrium; this, of course, includes the socially optimal outcome. And the punishment strategies necessary to get to that social optimum are often fairly straightforward. Consider oligopoly: if your firm produces more than half the monopoly output, then I produce the Cournot duopoly quantity in the next period. If you think I will produce Cournot, your best response is also to produce Cournot, and we will do so forever. Therefore, if we are setting prices frequently enough, the benefit to you of cheating today is not enough to overcome the lower profits you will earn in every future period, and hence we are able to collude at the monopoly level of output.

Folk theorems are really robust. What if we only observe some random public signal of what each of us did in the last period? The folk theorem holds. What if we only privately observe some random signal of what the other people did last period? No problem, the folk theorem holds. There are many more generalizations. Any applied theorist has surely run into the folk theorem problem – how do I let players use “reasonable” strategies in a repeated game but disallow crazy strategies which might permit tacit collusion?

This is Sekeris’ problem in the present paper. Consider two nations sharing a common pool of resources like fish. We know from Hotelling how to solve the optimal resource extraction problem if there is only one nation. With more than one nation, each party has an incentive to overfish today because they don’t take sufficient account of the fact that their fishing today lowers the amount of fish left for the opponent tomorrow, but the folk theorem tells us that we can still sustain cooperation if we interact frequently enough. Indeed, Ostrom won the Nobel a few years ago for showing how such punishments operate in many real world situations. But, but! – why then do we see fisheries and other common pool resources overdepleted so often?

There are a few ways to get around the folk theorem. First, it may just be that players do not interact forever, at least probabalistically; some firms may last longer than others, for instance. Second, it may be that firms cannot change their strategies frequently enough, so that you will not be punished so harshly if you deviate from the cooperative optimum. Third, Mallesh Pai and coauthors show in a recent paper that with a large number of players and sufficient differential obfuscation of signals, it becomes too difficult to “catch cheaters” and hence the stage game equilibrium is retained. Sekeris proposes an alternative to all of these: allow players to take actions which change the form of the stage game in the future. In particular, he allows players to fight for control of a bigger share of the common pool if they wish. Fighting requires expending resources from the pool building arms, and the fight itself also diminishes the size of the pool by destroying resources.

As the remaining resource pool gets smaller and smaller, then each player is willing to waste fewer resources arming themselves in a fight over that smaller pool. This means that if conflict does break out, fewer resources will be destroyed in the “low intensity” fight. Because fighting is less costly when the pool is small, as the pool is depleted through cooperative extraction, eventually the players will fight over what remains. Since players will have asymmetric access to the pool following the outcome of the fight, there are fewer ways for the “smaller” player to harm the bigger one after the fight, and hence less ability to use threats of such harm to maintain folk-theorem cooperation before the fight. Therefore, the cooperative equilibrium partially unravels and players do not fully cooperate even at the start of the game when the common pool is big.

That’s a nice methodological trick, but also somewhat reasonable in the context of common resource pool management. If you don’t overfish today, it must be because you fear I will punish you by overfishing myself tomorrow. If you know I will enact such punishment, then you will just invade me tomorrow (perhaps metaphorically via trade agreements or similar) before I can enact such punishment. This possibility limits the type of credible threats that can be made off the equilibrium path.

Final working paper (RePEc IDEAS. Paper published in Fall 2014 RAND.

“Housing Market Spillovers: Evidence from the End of Rent Control in Cambridge, MA,” D. Autor, C. Palmer & P. Pathak (2014)

Why don’t people like renters? Looking for rental housing up here in Toronto (where under any reasonable set of parameters, there looks to be a serious housing bubble at the moment), it seems very rare for houses to be rented and also very rare for rental and owned homes to appear in the same neighborhood. Why might this be? Housing externalities is one answer: a single run-down house on the block greatly harms the value of surrounding houses. Social opprobrium among homeowners may be sufficient to induce them to internalize these externalities in a way that is not true of landlords. The very first “real” paper I helped with back at the Fed showed a huge impact of renovating run-down properties on neighborhood land values in Richmond, Virginia.

Given that housing externalities exist, we may worry about policies that distort the rent-buy decision. Rent control may not only limit incentives for landlords to upgrade the quality of their own property, but may also damage the value of neighboring properties. Autor, Palmer and Pathak investigate a quasiexperiment in Cambridge, MA (right next door to my birthplace of Boston, I used to hear Cambridge referred to as the PRC!). In 1994, Massachusetts held a referendum on banning rent control, which was enforced very strongly in Cambridge. It passed 51-49.

The units previously under rent control, no surprise, saw a big spurt of investment and a large increase in their value. If the rent controlled house was in a block with lots of other rent controlled houses, however, the price rose even more. That is, there was a substantial indirect impact where upgrades on neighboring houses increases the value of my previously rent-controlled house. Looking at houses that were never rent controlled, those close to previously rent-controlled units rose in price much faster than otherwise-similar houses in the same area which didn’t have rent-controlled units on the same block. Overall, Autor et al estimate that rent decontrol raised the value of Cambridge property by 2 billion, and that over 80 percent of this increase was due to indirect effects (aka housing externalities). No wonder people are so worried about a rental unit popping up in their neighborhood!

Final version in June 2014 JPE (IDEAS version).

“Upstream Innovation and Product Variety in the U.S. Home PC Market,” A. Eizenberg (2014)

Who benefits from innovation? The trivial answer would be that everyone weakly benefits, but since innovation can change the incentives of firms to offer different varieties of a product, heterogeneous tastes among buyers may imply that some types of innovation makes large groups of people worse off. Consider computers, a rapidly evolving technology. If Lenovo introduces a laptop with a faster processor, they may wish to discontinue production of a slower laptop, because offering both types flattens the demand curve for each, and hence lowers the profit-maximizing markup that can be charged for the better machine. This effect, combined with a fixed cost of maintaining a product line, may push firms to offer too little variety in equilibrium.

As an empirical matter, however, things may well go the other direction. Spence’s famous product selection paper suggests that firms may produce too much variety, because they don’t take into account that part of the profit they earn from a new product is just cannibalization of other firm’s existing product lines. Is it possible to separate things out from data? Note that this question has two features that essentially require a structural setup: the variable of interest is “welfare”, a completely theoretical concept, and lots of the relevant numbers like product line fixed costs are unobservable to the econometrician, hence they must be backed out from other data via theory.

There are some nice IO tricks to get this done. Using a near-universe of laptop sales in the early 2000s, Eizenberg estimates heterogeneous household demand using standard BLP-style methods. Supply is tougher. He assumed that firms get a fixed cost per product line shock, then pick their product mix each quarter, then observe consumer demand, then finally play Nash-Bertrand differentiated product pricing. The problem is that the pricing game often has multiple equilibria (e.g., with two symmetric firms, one may offer a high-end product and the other a low-end one, or vice versa). Since the pricing game equilibria are going to be used to back out fixed costs, we are in a bit of a bind. Rather than select equilibria using some ad hoc approach (how would you even do so in the symmetric case just mentioned?), Eizenberg cleverly just partially identifies fixed costs as backed out from any possible pricing game equilibrium, using bounds in the style of Pakes, Porter, Ho and Ishii. This means that welfare effects are also only partially identified.

Throwing this model at the PC data shows that the mean consumer in the early 2000s wasn’t willing to pay any extra for a laptop, but there was a ton of heterogeneity in willingness to pay both for laptops and for faster speed on those laptops. Every year, the willingness to pay for a given computer fell $257 – technology was rapidly evolving and lots of substitute computers were constantly coming onto the market.

Eizenberg uses these estimates to investigate a particularly interesting counterfactual: what was the effect of the introduction of the lighter Pentium M mobile processor? As Pentium M was introduced, older Pentium III based laptops were, over time, no longer offered by the major notebook makers. The M raised predicted notebook sales by 5.8 to 23.8%, raised mean notebook price by $43 to $86, and lowered Pentium III share in the notebook market from 16-23% down to 7.7%. Here’s what’s especially interesting, though: total consumer surplus is higher with the M available, but all of the extra consumer surplus accrues to the 20% least price-sensitive buyers (as should be intuitive, since only those with high willingness-to-pay are buying cutting edge notebooks). What if a social planner had forced firms to keep offering the Pentium III models after the M was introduced? Net consumer plus producer surplus may have actually been positive, and the benefits would have especially accrued to those at the bottom end of the market!

Now, as a policy matter, we are (of course) not going to force firms to offer money-losing legacy products. But this result is worth keeping in mind anyway: because firms are concerned about pricing pressure, they may not be offering a socially optimal variety of products, and this may limit the “trickle-down” benefits of high tech products.

2011 working paper (No IDEAS version). Final version in ReStud 2014 (gated).

Laboratory Life, B. Latour & S. Woolgar (1979)

Let’s do one more post on the economics of science; if you haven’t heard of Latour and the book that made him famous, all I can say is that it is 30% completely crazy (the author is a French philosopher, after all!), 70% incredibly insightful, and overall a must read for anyone trying to understand how science proceeds or how scientists are motivated.

Latour is best known for two ideas: that facts are socially constructed (and hence science really isn’t that different from other human pursuits) and that objects/ideas/networks have agency. He rose to prominence with Laboratory Life, which followed two years observing a lab, that of future Nobel Winner Roger Guillemin at the Salk Institute at UCSD.

What he notes is that science is really strange if you observe it proceeding without any priors. Basically, a big group of people use a bunch of animals and chemicals and technical devices to produce beakers of fluids and points on curves and colored tabs. Somehow, after a great amount of informal discussion, all of these outputs are synthesized into a written article a few pages long. Perhaps, many years later, modalities about what had been written will be dropped; “X is a valid test for Y” rather than “W and Z (1967) claim that X is a valid test for Y” or even “It has been conjectured that X may be a valid test for Y”. Often, the printed literature will later change its mind; “X was once considered a valid test for Y, but that result is no longer considered convincing.”

Surely no one denies that the last paragraph accurately describes how science proceeds. But recall the schoolboy description, in which there are facts in the world, and then scientists do some work and run some tests, after which a fact has been “discovered”. Whoa! Look at all that is left out! How did we decide what to test, or what particulars constitute distinct things? How did we synthesize all of the experimental data into a few pages of formal writeup? Through what process did statements begin to be taken for granted, losing their modalities? If scientists actually discover facts, then how can a “fact” be overturned in the future? Latour argues, and gives tons of anecdotal evidence from his time at Salk, that providing answers to those questions basically constitutes the majority of what scientists actually do. That is, it is not that the fact is out there in nature waiting to be discovered, but that the fact is constructed by scientists over time.

That statement can be misconstrued, of course. That something is constructed does not mean that it isn’t real; the English language is both real and it is uncontroversial to point out that it is socially constructed. Latour and Woolgar: “To say that [a particular hormone] is constructed is not to deny its solidity as a fact. Rather, it is to emphasize how, where and why it was created.” Or later, “We do not wish to say that facts do not exist nor that there is no such thing as reality. In this simple sense we are not relativist. Our point is that ‘out-there-ness’ is the consequence of scientific work rather than its cause.” Putting their idea another way, the exact same object or evidence can at one point be considered up for debate or perhaps just a statistical artefact, yet later is considered a “settled fact” and yet later still will occasionally revert again. That is, the “realness” of the scientific evidence is not a property of the evidence itself, which does not change, but a property of the social process by which science reifies that evidence into an object of significance.

Latour and Woolgar also have an interesting discussion of why scientists care about credit. The story of credit as a reward, or credit-giving as some sort of gift exchange is hard to square with certain facts about why people do or do not cite. Rather, credit can be seen as a sort of capital. If you are credited with a certain breakthrough, you can use that capital to get a better position, more equipment and lab space, etc. Without further breakthroughs for which you are credited, you will eventually run out of such capital. This is an interesting way to think about why and when scientists care about who is credited with particular work.

Amazon link. This is a book without a nice summary article, I’m afraid, so you’ll have to stop by your library.

“Why Did Universities Start Patenting?: Institution Building and the Road to the Bayh-Dole Act,” E. P. Berman (2008)

It goes without saying that the Bayh-Dole Act had huge ramifications for science in the United States. Passed in 1980, Bayh-Dole permitted (indeed, encouraged) universities to patent the output of federally-funded science. I think the empirical evidence is still not complete on whether this increase in university patenting has been good (more, perhaps, incentive to develop products based on university research), bad (patents generate static deadweight loss, and exclusive patent licenses limit future developers) or “worse than the alternative” (if the main benefit of Bayh-Dole is encouraging universities to promote their research to the private sector, we can achieve that goal without the deadweight loss of patents).

As a matter of theory, however, it’s hard for me to see how university patenting could be beneficial. The usual static tradeoff with patents is deadweight loss after the product is developed in exchange for the quasirents that incentivize fixed costs of research to be paid by the initial developer. With university research, you don’t even get that benefit, since the research is being done anyway. This means you have to believe the “increased incentive for someone to commercialize” under patents is enough to outweight the static deadweight loss; it is not even clear that there is any increased incentive in the first place. Scientists seem to understand what is going on: witness the license manager of the enormously profitable Cohen-Boyer recombinant DNA patent, “[W]hether we licensed it or not, commercialisation of recombinant DNA was going forward. As I mentioned, a non-exclusive licensing program, at its heart, is really a tax … [b]ut it’s always nice to say technology transfer.” That is, it is clear why cash-strapped universities like Bayh-Dole regardless of the social benefit.

In today’s paper, Elizabeth Popp Berman, a sociologist, poses an interesting question. How did Bayh-Dole ever pass given the widespread antipathy toward “locking up the results of public research” in the decades before its passage? She makes two points of particular interest. First, it’s not obvious that there is any structural break in 1980 in university patenting, as university patents increased 250% in the 12 years before the Act and about 300% in the 12 years afterward. Second, this pattern holds because the development of institutions and interested groups necessary for the law to change was a fairly continuous process beginning perhaps as early as the creation of the Research Corporation in 1912. What this means for economists is that we should be much more careful about seeing changes in law as “exogenous” since law generally just formalized already changing practice, and that our understanding of economic events driven by rational agents acting under constraints ought sometimes focus more on the constraints and how they develop rather than the rational action.

Here’s the history. Following World War II, the federal government became a far more important source of funding for university and private-sector science in the United States. Individual funding agencies differed in their patent policy; for instance, the Atomic Energy Commission essentially did not allow university scientists to patent the output of federally-funded research, whereas the Department of Defense permitted patents from their contactors. Patents were particularly contentious since over 90% of federal R&D in this period went to corporations rather than universities. Through the 1960s, the NIH began to fund more and more university science, and they hired a patent attorney in 1963, Norman Latker, who was very much in favor of private patent rights.

Latker received support for his position from two white papers published in 1968 that suggested the HEW (the parent of the NIH) was letting medical research languish because they wouldn’t grant exclusive licenses to pharma firms, who in turn argued that without the exclusive license they wouldn’t develop the research into a product. The politics of this report allowed Latker enough bureaucratic power to freely develop agreements with individual universities allowing them to retain patents in some cases. The rise of these agreements led many universities to hire patent officers, who would later organize into a formal lobbying group pushing for more ability to patent federally-funded research. Note essentially what is going on: individual actors or small groups take actions in each period which change the payoffs to future games (partly by incurring sunk costs) or by introducing additional constraints (reports that limit the political space for patent opponents, for example). The eventual passage of Bayh-Dole, and its effects, necessarily depend on that sort of institution building which is often left unmodeled in economic or political analysis. Of course, the full paper has much more detail about how this program came to be, and is worth reading in full.

Final version in Social Studies of Science (gated). I’m afraid I could not find an ungated copy.

“How do Patents Affect Follow-On Innovation: Evidence from the Human Genome,” B. Sampat & H. Williams (2014)

This paper, by Heidi Williams (who surely you know already) and Bhaven Sampat (who is perhaps best known for his almost-sociological work on the Bayh-Dole Act with Mowery), made quite a stir at the NBER last week. Heidi’s job market paper a few years ago, on the effect of openness in the Human Genome Project as compared to Celera, is often cited as an “anti-patent” paper. Essentially, she found that portions of the human genome sequenced by the HGP, which placed their sequences in the public domain, were much more likely to be studied by scientists and used in tests than portions sequenced by Celera, who initially required fairly burdensome contractual steps to be followed. This result was very much in line with research done by Fiona Murray, Jeff Furman, Scott Stern and others which also found that minor differences in openness or accessibility can have substantial impacts on follow-on use (I have a paper with Yasin Ozcan showing a similar result). Since the cumulative nature of research is thought to be critical, and since patents are a common method of “restricting openness”, you might imagine that Heidi and the rest of these economists were arguing that patents were harmful for innovation.

That may in fact be the case, but note something strange: essentially none of the earlier papers on open science are specifically about patents; rather, they are about openness. Indeed, on the theory side, Suzanne Scotchmer has a pair of very well-known papers arguing that patents effectively incentivize cumulative innovation if there are no transaction costs to licensing, no spillovers from sequential research, and no incentive for early researchers to limit licenses in order to protect their existing business (consider the case of Farnsworth and the FM radio), and if potential follow-on innovators can be identified before they sink costs. That is a lot of conditions, but it’s not hard to imagine industries where inventions are clearly demarcated, where holders of basic patents are better off licensing than sitting on the patent (perhaps because potential licensors are not also competitors), and where patentholders are better off not bothering academics who technically infringe on their patent.

What industry might have such characteristics? Sampat and Williams look at gene patents. Incredibly, about 30 percent of human genes have sequences that are claimed under a patent in the United States. Are “patented genes” still used by scientists and developers of medical diagnostics after the patent grant, or is the patent enough of a burden to openness to restrict such use? What is interesting about this case is that the patentholder generally wants people to build on their patent. If academics find some interesting genotype-phenotype links based on their sequence, or if another firm develops a disease test based on the sequence, there are more rents for the patentholder to garner. In surveys, it seems that most academics simply ignore patents of this type, and most gene patentholders don’t interfere in research. Anecdotally, licenses between the sequence patentholder and follow-on innovators are frequent.

In general, it is really hard to know whether patents have any effect on anything, however; there is very little variation over time and space in patent strength. Sampat and Williams take advantage of two quasi-experiments, however. First, they compare applied-for-but-rejected gene patents to applied-for-but-granted patents. At least for gene patents, there is very little difference in terms of measurables before the patent office decision across the two classes. Clearly this is not true for patents as a whole – rejected patents are almost surely of worse quality – but gene patents tend to come from scientifically competent firms rather than backyard hobbyists, and tend to have fairly straightforward claims. Why are any rejected, then? The authors’ second trick is to look directly at patent examiner “leniency”. It turns out that some examiners have rejection rates much higher than others, despite roughly random assignment of patents within a technology class. Much of the difference in rejection probability is driven by the random assignment of examiners, which justifies the first rejected-vs-granted technique, and also suggested an instrumental variable to further investigate the data.

With either technique, patent status essentially generates no difference in the use of genes by scientific researchers and diagnostic test developers. Don’t interpret this result as turning over Heidi’s earlier genome paper, though! There is now a ton of evidence that minor impediments to openness are harmful to cumulative innovation. What Sampat and Williams tell us is that we need to be careful in how we think about “openness”. Patents can be open if the patentholder has no incentive to restrict further use, if downstream innovators are easy to locate, and if there is no uncertainty about the validity or scope of a patent. Indeed, in these cases the patentholder will want to make it as easy as possible for follow-on innovators to build on their patent. On the other hand, patentholders are legally allowed to put all sorts of anti-openness burdens on the use of their patented invention by anyone, including purely academic researchers. In many industries, such restrictions are in the interest of the patentholder, and hence patents serve to limit openness; this is especially true where private sector product development generates spillovers. Theory as in Scotchmer-Green has proven quite correct in this regard.

One final comment: all of these types of quasi-experimental methods are always a bit weak when it comes to the extensive margin. It may very well be that individual patents do not restrict follow-on work on that patent when licenses can be granted, but at the same time the IP system as a whole can limit work in an entire technological area. Think of something like sampling in music. Because all music labels have large teams of lawyers who want every sample to be “cleared”, hip-hop musicians stopped using sampled beats to the extent they did in the 1980s. If you investigated whether a particular sample was less likely to be used conditional on its copyright status, you very well might find no effect, as the legal burden of chatting with the lawyers and figuring out who owns what may be enough of a limit to openness that musicians give up samples altogether. Likewise, in the complete absence of gene patents, you might imagine that firms would change their behavior toward research based on sequenced genes since the entire area is more open; this is true even if the particular gene sequence they want to investigate was unpatented in the first place, since having to spend time investigating the legal status of a sequence is a burden in and of itself.

July 2014 Working Paper (No IDEAS version). Joshua Gans has also posted a very interesting interpretation of this paper in terms of Coasean contractability.

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