Category Archives: Diffusion

“Immigration and the Diffusion of Technology: The Huguenot Diaspora in Prussia,” E. Hornung (2014)

Is immigration good for natives of the recipient country? This is a tough question to answer, particularly once we think about the short versus long run. Large-scale immigration might have bad short-run effects simply because more L plus fixed K means lower average incomes in essentially any economic specification, but even given that fact, immigrants bring with them tacit knowledge of techniques, ideas, and plans which might be relatively uncommon in the recipient country. Indeed, world history is filled with wise leaders who imported foreigners, occasionally by force, in order to access their knowledge. As that knowledge spreads among the domestic population, productivity increases and immigrants are in the long-run a net positive for native incomes.

How substantial can those long-run benefits be? History provides a nice experiment, described by Erik Hornung in a just-published paper. The Huguenots, French protestants, were largely expelled from France after the Edict of Nantes was revoked by the Sun King, Louis XIV. The Huguenots were generally in the skilled trades, and their expulsion to the UK, the Netherlands and modern Germany (primarily) led to a great deal of tacit technology transfer. And, no surprise, in the late 17th century, there was very little knowledge transfer aside from face-to-face contact.

In particular, Frederick William, Grand Elector of Brandenburg, offered his estates as refuge for the fleeing Huguenots. Much of his land had been depopulated in the plagues that followed the Thirty Years’ War. The centralized textile production facilities sponsored by nobles and run by Huguenots soon after the Huguenots arrived tended to fail quickly – there simply wasn’t enough demand in a place as poor as Prussia. Nonetheless, a contemporary mentions 46 professions brought to Prussia by the Huguenots, as well as new techniques in silk production, dyeing fabrics and cotton printing. When the initial factories failed, knowledge among the apprentices hired and purchased capital remained. Technology transfer to natives became more common as later generations integrated more tightly with natives, moving out of Huguenot settlements and intermarrying.

What’s particularly interesting with this history is that the quantitative importance of such technology transfer can be measured. In 1802, incredibly, the Prussians had a census of manufactories, or factories producing stock for a wide region, including capital and worker input data. Also, all immigrants were required to register yearly, and include their profession, in 18th century censuses. Further, Huguenots did not simply move to places with existing textile industries where their skills were most needed; indeed, they tended to be placed by the Prussians in areas which had suffered large population losses following the Thirty Years’ War. These population losses were highly localized (and don’t worry, before using population loss as an IV, Hornung makes sure that population loss from plague is not simply tracing out existing transportation highways). Using input data to estimate a Cobb-Douglas textile production function, an additional percentage point of the population with Huguenot origins in 1700 is associated with a 1.5 percentage point increase in textile productivity in 1800. This result is robust in the IV regression using wartime population loss to proxy for the percentage of Huguenot immigrants, as well as many other robustness checks. 1.5% is huge given the slow rate of growth in this era.

An interesting historical case. It is not obvious to me how relevant this estimation to modern immigration debates; clearly it must depend on the extent to which knowledge can be written down or communicated at distance. I would posit that the strong complementarity of factors of production (including VC funding, etc.) are much more important that tacit knowledge spread in modern agglomeration economies of scale, but that is surely a very difficult claim to investigate empirically using modern data.

2011 Working Paper (IDEAS version). Final paper published in the January 2014 AER.

“The ‘Industrial Revolution’ in the Home: Household Technology and Social Change in the 20th Century,” R. S. Cowan (1976)

The really fascinating thing about the “Second Industrial Revolution” (roughly 1870 until World War I) is how much of its effect is seen first for consumers and only later for production. Electricity is the famous example here; most energy-heavy industries were purposefully located near low-cost energy sources like fast-flowing water. Energy produced via transmitted electricity simply wasn’t that competitive until well into the 20th century in these industries.

Ruth Cowan, a historian, investigated how household production was affected by the introduction of electricity, which in the non-rural US roughly means between 1918 and the Great Depression; electrification rose from 25 percent to 80 percent during this period. Huge amounts of drudgery, once left to housewives and domestic workers, was reduced. Consider the task of ironing. Before electricity (barring gas irons, which were not widespread), ironing involved heating a heavy flatiron on a stove, carrying it to the ironing board and quickly knocking out wrinkles before the heat dissipated, bringing in back to stove, and so on. The replacement of the coal stove by central heating similarly limited tedious work, including constant cleaning of coal dust. Cowan traces diffusion of these technologies in part by examining advertisements in magazines like the Ladies’ Home Journal.

The interesting aspect of this consumer revolution, however, was that it did not in fact reduce the amount of work done by housewives. By the end of the 1920s, urban women, most affected by these technological changes, were still doing more housework per week than rural women. It appears the standard story of how Industrial Revolution technologies affected industry – more specialization, more importance of managerial talent, disappearing emotional content of work – was not true of household production. Instead, upper middle class women no longer employed specialized domestic help (and the implied importance of managerial talent on the part of the housewife), and advertisements for new consumer goods frequently emphasized the emotional content of, e.g., the improved cleanliness of modern appliances with respect to children’s health. Indeed, technological progress tended to significantly increase the number of tasks women were expected to perform within the house. There’s not much reason in economic theory for TFP improvements to lead to reductions or increases in worker skill or autonomy, so perhaps it’s no surprise that the household sector saw a different pattern from certain industrial sectors.

Final version in Technology & Culture Jan 1976. If you’re not familiar with the term “Second Industrial Revolution”, Joel Mokyr has a nice summary of this period of frequent important macro/GPT inventions. Essentially, the big inventions of the late 19th century were much more reliant on scientific knowledge, and much more connected to network effects and increasing returns to scale, than those of the late 18th and early 19th century.

“Does Knowledge Accumulation Increase the Returns to Collaboration?,” A. Agrawal, A. Goldfarb & F. Teodoridis (2012)

The size of academic research “teams” has been increasing, inexorably, in essentially every field over the past few decades. This may be because of bad incentives for researchers (as Stan Liebowitz has argued), or because more expensive capital is required for research as in particle physics, or because communication technology has decreased the cost of collaboration. A much more worrying explanation is, simply, that reaching the research frontier is getting harder. This argument is most closely associated with my adviser Ben Jones, who has noticed that while team size has increased, the average age star researchers do their best work has increased, co-inventors on inventions has increased, and the number of researchers doing work across fields has decreased. If the knowledge frontier is becoming more expensive to reach, theory suggests a role for greater subsidization of early-career researchers and of potential development traps due to the complementary nature of specialized fields.

Agrawal et al use a clever device to investigate whether the frontier is indeed becoming more burdensome. Note that the fact that science advances does not mean, ipso facto, that reaching the frontier is harder: new capital like computers or Google Scholar may make it easier to investigate questions or get up to date in related fields, and certain developments completely subsume previous developments (think of, say, how a user of dynamic programming essentially does not need to bother learning the calculus of variations; the easier but more powerful technique makes the harder but less powerful technique unnecessary). Agrawal et al’s trick is to look at publication trends in mathematics. During the Soviet era, mathematics within the Soviet Union was highly advanced, particularly in certain areas of functional analysis, but Soviet researchers had little ability to interact with non-Soviets and they generally published only in Russian. After the fall of the Soviet Union, there was a “shock” to the knowledge frontier in mathematics as these top Soviet researchers began interacting with other mathematicians. A paper by Borjas and Doran in the QJE last year showed that Soviet mathematics were great in some areas and pretty limited in others. This allows for a diff-in-diff strategy: look at the change in team size following 1990 in fields where Soviets were particularly strong versus fields where the Soviets were weak.

Dropping papers with a Russian-named coauthor, classifying papers by fields using data from the AMS, the authors find that papers in Soviet-heavy fields had the number of coauthors increase from 1.34 to 1.78, whereas Soviet-weak fields teams grew only from 1.26 to 1.55. This difference appears quite robust, and is derived from hundreds of thousands of publications. To check that Soviet-rich fields actually had influence, they note that papers in Soviet-rich subfields cited Soviet-era publications at a greater rate after 1990 than Soviet-poor subfields, and that the increase in coauthoring tended to be driven by papers with a young coauthor. The story here is, roughly, that Soviet emigres would have tooled up young researchers in Soviet-rich fields, and then those young coauthors would have a lot of complementary skills which might drive collaboration with other researchers.

So it appears that the increasing burden of the knowledge frontier does drive some of the increase in team size. The relative importance of this factor, however, is something tough to tease out without some sort of structural model. Getting around the burden of knowledge by making it easier to reach the frontier is also worthy of investigation – a coauthor and I have a pretty cool new paper (still too early to make public) on exactly this topic, showing an intervention that has a social payoff an order of magnitude higher than funding new research.

Oct 2012 working paper (no IDEAS version). As a sidenote, the completely bizarre “copyright notice” on the first page is about the most ridiculous thing I have seen on a working paper recently: besides the fact that authors hold the copyright automatically without such a notice, the paper itself is literally about the social benefits of free knowledge flows! I can only hope that the copyright notice is the result of some misguided university policy.

“Path Dependence,” S. Page (2006)

When we talk about strategic equilibrium, we can talk in a very formal sense, as many refinements with their well-known epistemic conditions have been proposed, the nature of uncertainty in such equilibria has been completely described, the problems of sequential decisionmaking are properly handled, etc. So when we do analyze history, we have a useful tool to describe how changes in parameters altered the equilibrium incentives of various agents. Path dependence, the idea that past realizations of history matter (perhaps through small events, as in Brian Arthur’s work) is widespread. A typical explanation given is increasing returns. If I buy a car in 1900, I make you more likely to buy a car in 1901 by, at the margin, lowering the production cost due to increasing returns to scale or lowering the operating cost by increasing incentives for gas station operators to operate.

This is quite informal, though; worse, the explanation of increasing returns is neither necessary nor sufficient for history-dependence. How can this be? First, consider that “history-dependence” may mean (at least) six different things. History can effect either the path of history, or its long-run outcome. For example, any historical process satisfying the assumptions of the ergodic theorem can be history-dependent along a path, yet still converge to the same state (in the network diffusion paper discussed here last week, a simple property of the network structure tells me whether an epidemic will diffuse entirely in the long-run, but the exact path of that eventual diffusion clearly depends on something much more complicated). We may believe, for instance, that the early pattern of railroads affected the path of settlement of the West without believing that this pattern had much consequence for the 2010 distribution of population in California. Next, history-dependence in the long-run or short-run can depend either on a state variable (from a pre-defined set of states), the ordered set of past realizations, or the unordered set of past realizations (the latter called path and phat dependence, respectively, since phat dependence does not depend on order). History matters in elections due to incumbent bias, but that history-dependence can basically be summed up by a single variable denoting who is the current incumbent, omitting the rest of history’s outcomes. Phat dependence is likely in simple technology diffusion: I adopt a technology as a function of which of my contacts has adopted it, regardless of the order in which they adopted. Path dependence comes up, for example, in models of learning following Aumann and Geanakoplos/Polemarchakis, consensus among a group can be broken if agents do not observe the time at which messages were sent between third parties.

Now consider increasing returns. For which types of increasing returns is this necessary or sufficient? It turns out the answer is, for none of them! Take again the car example, but assume there are three types of cars in 1900, steam, electric and gasoline. For the same reasons that gas-powered cars had increasing returns, steam and electric cars do as well. But the relative strength of the network effect for gas-powered cars is stronger. Page thinks of this as a biased Polya process. I begin with five balls, 3 G, 1 S and 1 E, in an urn. I draw one at random. If I get an S or an E, I return it to the urn with another ball of the same type (thus making future draws of that type more common, hence increasing returns). If I draw a G, I return it to the urn along with 2t more G balls, where t is the time which increments by 1 after each draw. This process converges to having arbitrarily close to all balls of type G, even though S and E balls also exhibit increasing returns.

Why about the necessary condition? Surely, increasing returns are necessary for any type of history-dependence? Well, not really. All I need is some reason for past events to increase the likelihood of future actions of some type, in any convoluted way I choose. One simple mechanism is complementarities. If A and B are complements (adopting A makes B more valuable, and vice versa), while C and D are also complements, then we can have the following situation. An early adoption of A makes B more valuable, increasing the probability of adopting B the next period which itself makes future A more valuable, increasing the probability of adopting A the following period, and so on. Such reasoning is often implicit in the rhetoric linking market-based middle class to a democratic political process: some event causes a private sector to emerge, which increases pressure for democratic politics, which increases protection of capitalist firms, and so on. As another example, consider the famous QWERTY keyboard, the best-known example of path dependence we have. Increasing returns – that is, the fact that owning a QWERTY keyboard makes this keyboard more valuable for both myself and others due to standardization – is not sufficient for killing the Dvorak or other keyboards. This is simple to see: the fact that QWERTY has increasing returns doesn’t mean that the diffusion of something like DVD players is history-dependent. Rather, it is the combination of increasing returns for QWERTY and a negative externality on Dvorak that leads to history-dependence for Dvorak. If preferences among QWERTY and Dvorak are Leontief, and valuations for both have increasing returns, then I merely buy the keyboard I value highest – this means that purchases of QWERTY by others lead to QWERTY lock-in by lowering the demand curve for Dvorak, not merely by raising the demand curve for QWERTY. (And yes, if you are like me and were once told to never refer to effects mediated by the market as “externalities”, you should quibble with the vocabulary here, but the point remains the same.)

All in all interesting, and sufficient evidence that we need a better formal theory and taxonomy of history dependence than we are using now.

Final version in the QJPS (No IDEAS version). The essay is written in a very qualitative/verbal manner, but more because of the audience than the author. Page graduated here at MEDS, initially teaching at Caltech, and his CV lists quite an all-star cast of theorist advisers: Myerson, Matt Jackson, Satterthwaite and Stanley Reiter!

“Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks,” B.A. Prakash et al (2011)

No need to separate economics from the rest of the social sciences, and no need to separate social science from the rest of science: we often learn quite a bit from our compatriot fields. Here’s a great example. Consider any epidemic diffusion, where a population (of nodes) is connected to each other (along, in this case, unweighted edges, equal to 1 if and only if there is a link between the nodes). Consider the case where nodes can become “infected” – in economics, we may think of nodes as people or cities adopting a new technology, or purchasing a new product. Does a given seeding on the network lead to an “infection” that spreads across the network, or is the network fairly impervious to infections?

This seems like it must be a tricky question, for nodes can be connected to other nodes in an arbitrary fashion. Let’s make it even more challenging for the analyst: allow there to be m “susceptible” states, an “exposed” state, an “infected” state, and N “vaccinated” states, who cannot be infected. Only exposed or infected agents can propagate an infection, and do so to each of their neighbors in any given period according to probabilities a and b, independently across neighbors. Parameters tell me the probability each agent transitions from susceptible or vaccinated states to other such states.

You may know the simple SIR model – susceptible, infected, recovered. In these models, all agents begin as susceptible pr infected. If my neighbor is infected and I am susceptible, he gives me the disease with probability a. If I am infected, I recover with probability c. This system spreads across the population if the first eigenvalue of the adjacency matrix (which equals 1 if two people are connected, and 0 otherwise) is greater than a/c. (Incredibly, I believe this proof dates back to Kermack and McKendrick in 1927). That is, the only way the network topology matters is in a single-valued summary statistic, the first eigenvalue. Pretty incredible.

The authors of the present paper show that this is a general property. For any epidemic model in which disease spreads over a network such that, first, transmissions are independent across neighbors, and second, one can only enter the exposed or infected state from an exposed or infected neighbor, the general property is the same: the disease spreads through the population if the first eigenvalue of the adjacency matrix is larger than a constant which depends only on model parameters and not on the topology of the network (and, in fact, these parameters are easy to characterize). It is a particularly nice proof. First we compute the probabilities of transitioning from each state to any other. This gives us a discrete-time nonlinear dynamic system. Such systems are asymptotically stable if all real eigenvalues of the nonlinear dynamic are less than one in absolute value. If there are no infections at all, the steady state is just the steady state of a Markov chain: only infected or exposed people can infect me, so the graph structure doesn’t matter if we assume no infections, and transition between the susceptible and vaccinated states are just Markov by assumption. We then note that the Jacobian has a nice block structure which limits the eigenvalues to being one of two types, show that the first type of eigenvalues are always less than one in absolute value, then show that the second types are less than one if and only if a property depending on model parameters only are satisfied; this property has nothing to do with the network topology.

The result tells you some interesting things as well. For example, say you wish to stop the spread of an epidemic. Should you immunize people with many friends? No – you should immunize the person who lowers the first eigenvalue of the adjacency matrix the most. This result is independent of the actual network topology or the properties of the disease (how long it incubates, how fast it transmits, how long people stay sick, how likely they are to develop natural immunity, etc.). Likewise, in the opposite problem, if you wish an innovation to diffuse through a society, how should you organize conferences or otherwise create a network? Create links between people or locations such that the first eigenvalue of the adjacency matrix increases by the highest amount. Again, this is independent of the current network topology or the properties of the particular invention you wish to diffuse. Nice.

Final conference paper from ICDM2011. (No IDEAS version).

“What Determines Productivity,” C. Syverson (2011)

Chad Syverson, along with Nick Bloom, John van Reenen, Pete Klenow and many others, has been at the forefront of a really interesting new strand of the economics literature: persistent differences in productivity. Syverson looked at productivity differences within 4-digit SIC industries in the US (quite narrow industries like “Greeting Cards” or “Industrial Sealants”) a number of years back, and found that in the average industry, the 90-10 ratio of total factor productivity plants was almost 2. That is, the top decile plant in the average industry produced twice as much output as the bottom decline plant, using exactly the same inputs! Hsieh and Klenow did a similar exercise in China and India and found even starker productivity differences, largely due a big left-tail of very low productivity firms. This basic result is robust to different measures of productivity, and to different techniques for identifying differences; you can make assumptions which let you recover a Solow residual directly, or run a regression (adjusting for differences in labor and capital quality, or not), or look at deviations like firms having higher marginal productivity of labor than the wage rate, etc. In the paper discussed in the post, Syverson summarizes the theoretical and empirical literature on persistent productivity differences.

Why aren’t low productivity firms swept from the market? We know from theory that if entry is allowed, potentially infinite and instantaneous, then no firm can remain which is less productive than the entrants. This suggests that persistence of inefficient firms must result from either limits on entry, limits on expansion by efficient firms, or non-immediate efficiency because of learning-by-doing or similar (a famous study by Benkard of a Lockwood airplane showed that a plant could produce a plane with half the labor hours after producing 30, and half again after producing 100). Why don’t inefficient firms already in the market adopt best practices? This is related to the long literature on diffusion, which Syverson doesn’t cover in much detail, but essentially it is not obvious to a firm whether a “good” management practice at another firm is actually good or not. Everett Rogers, in his famous “Diffusion of Innovations” book, refers to a great example of this from Peru in the 1950s. A public health consultant was sent for two years to a small village, and tried to convince the locals to boil their water before drinking it. The water was terribly polluted and the health consequences of not boiling were incredible. After two years, only five percent of the town adopted the “innovation” of boiling. Some didn’t adopt because it was too hard, many didn’t adopt because of a local belief system that suggested only the already-sick ought drink boiled water, some didn’t adopt because they didn’t trust the experience of the advisor, et cetera. Diffusion is difficult.

Ok, so given that we have inefficient firms, what is the source of the inefficiency? It is difficult to decompose all of the effects. Learning-by-doing is absolutely relevant in many industries – we have plenty of evidence on this count. Nick Bloom and coauthors seem to suggest that management practices play a huge role. They have shown clear correlation between “best practice” management and high TFP across firms, and a recent randomized field experiment in India (discussed before on this site) showed massive impacts on productivity from management improvements. Regulation and labor/capital distortions also appear to play quite a big role. On this topic, James Schmitz wrote a very interesting paper, published in 2005 in the JPE, on iron ore producers. TFP in Great Lakes ore had been more or less constant for many decades, with very little entry or foreign competition until the 1980s. Once Brazil began exporting ore to the US, labor productivity doubled within a handful of years, and capital and total factor productivity also soared. A main driver of the change was more flexible workplace rules.

Final version in 2011 JEP (IDEAS version). Syverson was at Kellogg recently presenting a new paper of his, with an all-star cast of coauthors, on the medical market. It’s well worth reading. Medical productivity is similarly heterogeneous, and since the medical sector is coming up on 20% of GDP, the sources of inefficiency in medicine are particularly important!

“Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires,” J. Singh & A. Agrawal (2011)

Firms poach engineers and researchers from each other all the time. One important reason to do so is to gain access to the individual’s knowledge. A strain of theory going back to Becker, however, suggests that if, after the poaching, the knowledge remains embodied solely in the new employer, it will be difficult for the firm to profit: surely the new employee will have an enormous amount of bargaining power over wages if she actually possesses unique and valuable information. (As part of my own current research project, I learned recently that Charles Martin Hall, co-inventor of the Hall-Heroult process for aluminum smelting, was able to gather a fortune of around $300 million after he brought his idea to the company that would become Alcoa.)

In a resource-based view of the firm, then, you may hope to not only access a new employer’s knowledge, but also spread it to other employees at your firm. By doing this, you limit the wage bargaining power of the new hire, and hence can scrape off some rents. Singh and Agrawal break open the patent database to investigate this. First, use name and industry data to try to match patentees who have an individual patent with one firm at time t, and then another patent at a separate firm some time later; such an employee has “moved”. We can’t simply check whether the receiving firm cites this new employee’s old patents more often, as there is an obvious endogeneity problem. First, firms may recruit good scientists more aggressively. Second, they may recruit more aggressively in technology fields where they are already planning to do work in the future. This suggests that matching plus diff-in-diff may work. Match every patent to another patent held by an inventor who never switches firms, attempting to find a second patent with very similar citation behavior, inventor age, inventor experience, technology class, etc. Using our matched sample, check how much the propensity to cite the mover’s patent changes compares to the propensity to the cite the stayer’s patent. That is, let Joe move to General Electric. Joe had a patent while working at Intel. GE researchers were citing that Intel patent once per year before Joe moved. They were citing a “matched” patent 1 times per year. After the move, they cite the Intel patent 2 times per year, and the “matched” patent 1.1 times per year. The diff-in-diff then suggests that moving increases the propensity to cite the Intel patent at GE by (2-1)-(1.1-1)=.9 citations per year, where the first difference helps account for the first type of endogeneity we discussed above, and the second difference for the second type of endogeneity.

What do we find? It is true that, after a move, the average patent held by a mover is cited more often at the receiving firm, especially in the first couple years after a move. Unfortunately, about half of new patents which cite the new employee’s old patent after she moves are made by the new employee herself, and another fifteen percent or so are made by previous patent collaborators of the poached employee. What’s worse, if you examine these citations by year, even five years after the move, citations to the pre-move patent are still highly likely to come from the poached employee. That is, to the extent that the poached employee had some special knowledge, the firm appears to have simply bought that knowledge embodied in the new employee, rather than gained access to useful techniques that quickly spread through the firm.

Three quick comments. First, applied econometrician friends: is there any reason these days to do diff-in-diff linearly rather than using the nonparametric “changes-in-changes” of Athey and Imbens 2006, which allows recovery of the entire distribution of effects of treatment on the treated? Second, we learn from this paper that the mean poached research employee doesn’t see her knowledge spread through the new firm, which immediately suggests the question of whether there are certain circumstances in which such knowledge spreads. Third, this same exercise could be done using all patents held by the moving employee’s old firm – I may be buying access to general techniques owned by the employee’s old firm rather than the specific knowledge represented in that employee’s own pre-move patents. I wonder if there’s any difference.

Final Management Science version (IDEAS version). Big thumbs up to Jasjit Singh for putting final published versions of his papers up on his site.

“Diffusing New Technology Without Dissipating Rents: Some Historical Case Studies of Knowledge Sharing,” J. Bessen & A. Nuvolari (2012)

The most fundamental fact in the economic history of the world is that, from the dawn on mankind until the middle of the 19th century in a small corner of Europe, the material living standards of the average human varied within a very small range: perhaps the wealthiest places, ever, were five times richer than regions on the edge of subsistence. The end of this Malthusian world is generally credited to changes following the Industrial Revolution. The Industrial Revolution is sometimes credited to changes in the nature of invention in England and Holland in the 1700s. If you believe those claims, then understanding what spurred invention from that point to the present is of singular importance.

A traditional story, going back to North and others, is that property rights were very important here. England had patents. England had well-enforced contracts for labor and capital. But, at least as far as patents are concerned, recent evidence suggests they couldn’t have been too critical. Moser showed that only 10% or so of important inventions in the mid-1800s were ever patented in the UK. Bob Allen, who we’ve met before on this site, has inspired a large literature on collective invention, or periods of open-source style sharing of information among industry leaders during critical phases of tinkering with new techniques.

Why would you share, though? Doesn’t this simply dissipate your rents? If you publicize knowledge of a productive process for which you are earning some rent, imitators can just come in and replicate that technology, competing away your profit. And yet, and yet, this doesn’t appear to happen in many historical circumstances. Bessen (he of Bessen and Maskin 2009, one of my favorite recent theoretical papers on innovation) and Nuvolari examine three nineteenth century industries, American steel, Cornish steam engines and New England power weavers. They show that periods of open sharing on invention, free transfer of technology to rivals, industry newsletters detailing new techniques, etc. can predominate for periods a decade and longer. In all three cases, patents are unimportant in this initial stage, though (at least outside of Cornwall) quite frequently used later in the development of the industry. Further, many of the important cost reducing microinventions in these industries came precisely during the period of collective invention.

The paper has no model, but very simply, here is what is going on. Consider a fast growing industry where some factors important for entry are in fixed supply; for example, the engineer Alexander Holley personally helped design eight of the first nine American mills using Bessemer’s technology. Assume all inventions are cost reducing. Holding sales price and demand constant, cost reductions increase industry profit. Sharing your invention ensures that you will not be frozen out of sharing by others. Trying to rely only on your own inventions to gain a cost advantage is not as useful as in standard Bertrand, since the fixed factors for entry in a new industry mean you can’t expand fast enough to meet market demand even if you had the cost advantage. There is little worry about free riding since the inventions are natural by-products of day-to-day problem solving rather than the result of concentrated effort: early product improvement is often an engineering problem, not a scientific one. Why would I assume sales price is roughly constant? Imagine an industry where the new technology is replacing something already being produced by a competitive industry (link steel rail ties replaced iron ties). The early Bessemer-produced ties in America were exactly this story, initially being a tiny fraction of the rail tie market, so the market price for ties was being determined by the older vintage of technology.

Open source invention is nothing unusual, nor is it something new. It has long coexisted with the type of invention for which patents may (only may!) be more suitable vectors for development. Policies that gunk up these periods of collective invention can be really damaging. I will discuss some new research in coming weeks about a common policy that appears to provide exactly this sort of gunk: the strict enforcement of non-compete agreements in certain states.

2012 Working Paper (IDEAS version)

Learning and Liberty Ships, P. Thompson

(Note: This post refers to “How Much Did the Liberty Shipbuilders Learn? New Evidence for an Old Case Study” (2001) and “How Much Did the Liberty Shipbuilders Forget?” (2007), both by Peter Thompson.)

It’s taken for granted now that organizations “learn” as their workers gain knowledge while producing and “forget” when not actively involved in some project. Identifying the importance of such learning-by-doing and organizational forgetting is quite a challenging empirical task. We would need a case where an easily measurable final product was produced over and over by different groups using the same capital and technology, with data fully recorded. And a 1945 article by a man named Searle found just an example: the US Navy Liberty Ships. These standardized ships were produced by the thousand by a couple dozen shipyards during World War II. Searle showed clearly that organizations get better at making ships as they accumulate experience, and the productivity gain of such learning-by-doing is enormous. His data was used in a more rigorous manner by researchers in the decades afterward, generally confirming the learning-by-doing and also showing that shipyards which stopped producing Liberty ships for a month or two very quickly saw their productivity plummet.

But rarely is the real world so clean. Peter Thompson, in this pair of papers (as well as a third published in the AER but discussed here), throws cold water on both the claim that organizations learn rapidly and that they forget just as rapidly. The problem is two fold. First, capital at the shipyards was assumed to be roughly constant. In fact, it was not. Almost all of the Liberty shipyards took some time to gear up their equipment when they began construction. Peter dug up some basic information on capital at each yard from deep in the national archives. Indeed, the terminal capital stock at each yard was three times the initial capital on average. Including a measure of capital in the equation estimating learning-by-doing reduces the importance of learning-by-doing by half.

It gets worse. Fractures were found frequently, accounting for more than 60% of ships built at the most sloppy yard. Speed was encouraged by contract, and hence some of the “learning-by-doing” may simply have been learning how to get away with low quality welding and other tricks. Thompson adjusts the time it took to build each ship to account for an estimate of the repair time required on average for each yard at each point in time. Fixing this measurement error further reduces productivity growth due to learning-by-doing by six percent. The upshot? Organizational learning is real, but the magnitudes everyone knows from the Searle data are vastly overstated. This matters: Bob Lucas, in his well-known East Asian growth miracle paper, notes that worldwide innovation, human capital and physical capital are not enough to account for sustained 6-7% growth like we saw in places like Korea in the 70s and 80s. He suggests that learning-by-doing as firms move up the export-goods quality ladder might account for such rapid growth. But such a growth miracle requires quite rapid on the job productivity increases. (The Lucas paper is also great historical reading: he notes that rapid growth in Korea and other tigers – in 1991, as rich as Mexico and Yugoslavia, what a miracle! – will continue, except, perhaps, in the sad case of Hong Kong!)

Thompson also investigates organizational forgetting. Old estimates using Liberty ship data find worker productivity on Liberty ships falling a full 25% per month when the workers were not building Liberty ships. Perhaps this is because the shipyards’ “institutional memory” was insufficient to transmit the tricks that had been learned, or because labor turnover meant good workers left in the interim period. The mystery of organizational forgetting in Liberty yards turns out to have a simpler explanation: measurement error. Yards would work on Liberty ships, then break for a few months to work on a special product or custom ship of some kind, then return to the Liberty. But actual production was not so discontinuous: some capital and labor transitioned (in a way not noticed before) back to the Liberty ships with delay. This appears in the data as decreased productivity right after a return to Liberty production, with rapid “learning” to get back to the frontier. Any estimate of such a nonlinear quantity is bound to be vague, but Peter’s specifications give organizational forgetting in Liberty ship production of 3-5% per month, and finds little evidence that this is related to labor turnover. This estimate is similar to other recent production line productivity forgetting estimates, such as that found in Benkard’s 2000 AER on the aircraft industry.

How Much did the Liberty Shipbuilders Learn? (final published version (IDEAS page). Final version published in JPE 109.1 2001.

How Much did the Liberty Shipbuilders Forget? (2005 working paper) (IDEAS page). Final paper in Management Science 53.6, 2007.

“The Future of Taypayer-Funded Research,” Committee for Economic Development (2012)

It’s one month after SOPA/PIPA. Congress is currently considering two bills. The Federal Research Public Access Act would require federal funders to insist on open-access publication of funded research papers after an embargo period. The NIH currently has such a policy, with a one year embargo. As of now, the FRPAA has essentially no chance of passing. On the other hand, the Fair Copyright in Research Works Act would reverse the current NIH policy and ban any other federal funders from setting similar access mandates. It has heavy Congressional support. How should you think of this as an economist? (A quick side note for economists: the world we live in, where working papers are universally available on author’s personal websites, is almost unheard of in other fields. Only about 20% of academic papers published last year were available online in ungated versions. This is about 100% in economics and high energy physics and a few other fields, and close to 0% otherwise.)

I did some consulting in the fall for a Kaufmann-funded CED report released yesterday called The Future of Taxpayer-Funded Research. There is a simple necessary condition that any government policy concerning new goods should not violate: call it The First Law of Zero Marginal Product Goods. The First Law says that if some policy increases consumption of something with zero marginal cost (an idea, an academic paper, a song, an e-book, etc.), a minimum, necessary condition to restrict that policy is that the variety of affected new goods must decrease. So if music piracy increases the number of songs consumed (and the number of songs illegally downloaded in any period of time is currently much higher than worldwide sales during that period), a minimum economic justification for a government crackdown on piracy is that the number of new songs created has decreased (in this case, they have not). Applying The First Law to open access mandates, a minimum economic justification for opposing such mandates is that either open access has no benefits, or that open access will make peer reviewed journals economically infeasible. To keep this post from becoming a mess of links, I leave out citations, but you can find all of the numbers below in the main report.

On the first point, open access has a ton of benefits even when most universities subscribe to nearly all the important journals. It “speeds up” the rate at which knowledge diffuses, which is important because science is cumulative. It helps solve access difficulties for private sector researchers and clinicians, who generally do not have subscriptions due to the cost; this website is proof that non-academics have interest in reading academic work, as I regularly receive email from private sector workers or the simply curious. Most importantly, even the minor access difficulties caused by the current gated system, such as having to go to a publisher website, having to click “Accept terms & conditions”, etc., versus just reading a pdf, matter. Look at the work by Fiona Murray and Scott Stern and Heidi Williams and others, much of which has been covered on this website: minor restrictions on ease can cause major deviations to efficiency in a world where results are cumulative. Such effects are only going to become more important as we move into a world where computer programs search and synthesize and translate research results.

The second point, whether open access makes peer review infeasible, is more important. The answer is that open access appears to have no such effects. Over time, we have seen many funders and universities, from MIT to the Wellcome Trust, impose open access mandates on their researchers. This has, to my knowledge, not led to the shutdown of even a single prominent journal. Not one. Profits in science publishing remain really, really high, as you’d expect in an industry with a lot of market power due to lock-in. Cross-sectionally, there is a ton of heterogeneity in norms: every high energy physicist and mathematician puts their work on arXiv, and every economist backs up their work online, yet none of this has led to the demise of peer reviewed journals and their dissemination function in those fields. Even within fields, radically different policies have proven sustainable. The New England Journal of Medicine makes all articles freely accessible after 6 months. The PLoS journals are totally open access, charging only a publication fee of $1350 upon acceptance. Other journals keep their entire archive gated. All are financially sustainable models, though of course they may differ in terms of how much profit the journal can extract.

One more point, and it’s an important one. Though the American Economics Association has not taken a position on these bills – as far as I know, the AEA does very little lobbying at all, keeping its membership fee low, for which I’m glad! – many other scholarly societies have taken a position. And I think many of their members would be surprised that their own associations oppose public access, something which I think can safely be said to be supported by nearly all of their members. Here is a full list of responses to the recent White House RFI on public access mandates. The American Anthropological Association opposes public access. The American Sociological Association and the American Psychological Association both strongly oppose public access. These groups all claim first that there is no access problem to begin with – simply untrue for the reasons above, all of which are expanded on in the CED paper – and that open access is incompatible with social science publishing, where articles are long and even rejected articles regularly receive many comments from peer review. But we know from the cross section that this isn’t true. Many learned societies publish open access journals, even in the social sciences, and many of them don’t charge any publication fee at all. The two main societies in economics, thankfully, both publish OA journals: the AEA’s Journal of Economic Perspectives, and the Econometric Society’s TE and QE. And even non-OA economics journals essentially face an open access mandate with a 0-month embargo, since everyone puts their working papers online. Econ is not unique in the social sciences: the Royal Society’s Philosophical Transactions, for instance, is open access. If you’re a member of the APA, ASA or AAA, you ought voice your displeasure!

http://www.ced.org/images/content/issues/innovation-technology/DCCReport_Final_2_9-12.pdf (Final published version of CED report – freely available online, of course!)

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