OR: Mike reads chapters 4&5 of Wealth of Nations
This is the 3rd in a series of posts wherein I attempt to apply economics principles to neuroscience. Econoneuroscience if you will. Previous posts covered transaction costs and specialization.
While currency (or money) seems simple and obvious - you trade goods for money, and then vice versa - it has great nuance. In the age of physical currency, money ranged from gigantic stones rings to cigarettes to gold. In our digital world, most money is not physical, but simply bits representing our bank account.
Since science is a (haphazardly planned) market, I wondered what our currency is, and perhaps if we could do better. Luckily for me, Adam Smith covered the basics of currency just after considering specialization, which I summarize here.
Adam Smith on currency
In the first three chapters of Wealth of Nations, Smith explored how labour specialization increases productivity, and in chapters four and five, he considered the implications of specialized labor on the market. Specialized labourers produce a "superfluity" of a single good, and must trade it for other goods. For some professions, like bakers, it is easy to trade the surplus: just trade bread for a nail. For others, it is awkward. How does a shepherd get a beer, when all he has are oxen?
Rather than barter we use money. Metals are especially useful for money because they are non-perishable, unlike bread or cows; and metals are easily divisible, so you can trade for the precise worth of goods. (Smith further describes how metals went via bars to become marked coins, and how those coins got debased, but that is irrelevant for us.)
Once a society starts using money, it next needs to find the price of each commodity. Smith argues that the economically "real" value of any commodity is how much labor it took to produce. Thus, if you buy a commodity with money, you are indirectly buying the producers' labor. Now, if you're measuring commodities' worth in labor, how do you then value the labor? Sometimes an hour's work is strenuous, and other times it is easy; and people invest in education and training to increase their productivity. The answer is money: if a commodity requires arduous work, or skill to produce, it will cost more. Thus money allows us to value and exchange labour, the real commodity. (Here, Smith goes on a long exploration of the real and nominal values of labor/money/commodities, which I briefly discuss at the end of this post.)
Putting these ideas together, you can create the classic definition of money: a store of value, medium of exchange, and unit of account. So what fits these properties for science?
Identifying the scientific currency
For simplicity, I would argue that there are two types of agents in the scientific market: labs, and funding agencies. Labs specialize in producing various forms of data, and seek to trade data for funding; the funding agencies "produce" funding, and seek to purchase the best data.
"Now, wait a second Mike, isn't money the currency of science?" This has obvious merit. Labs that produce more data generally get more funding, broadly fulfilling the unit of account aspect. But thinking of funding as a medium of exchange is strange, since funding agencies "produce" funding, rather than exchange something for funding. Indeed, most labs I'm aware of don't trade funding to other labs in exchange for data, which you would expect if funding were a medium of exchange. And funding is a terrible store of value since it runs out in 3-5 years, and labs are forced to spend their entire budgets while they can. While funding is an obvious currency, it does not fit well.
Instead, I would argue that in practice, papers are the currency of science. First, papers are a unit of account. From a lab's perspective, high-profile papers theoretically contain more, higher value, and labor intensive data than low-profile papers; and from funding agencies' perspective, more funding is given to labs with more and better papers.
This also emphasizes the second aspect of currency, namely that it acts as a medium of exchange. Labs trade data for papers, then trade papers for funding. Labs also sometimes collaborate together to produce data for papers. Funding agencies can't directly buy data, due to the circuitous route data production often takes (if only buying desired data was possible!). Instead, they must buy data after the fact, by giving funding to labs that produce papers.
Finally, papers act as a store of value. If I publish a paper in 2012, I will be able to "exchange" that paper for funding or positions, years down the line.
It may be counterintuitive to think of scientific papers as a currency, but they have all the requisite characteristics. There are, of course, many problems with this currency.
Smith noted that metals were commonly used as currency, since they are non-perishable, and easily divisible. In contrast, papers are neither. While a paper published in 2012 retains its value for a few years, that value constantly decreases; a paper published ten years ago will get you little funding or positions today. Indeed, this causes people to constantly trade papers for funding to generate more data; one might even call this inflation. I'm not sure any scientific currency can solve this problem since ten-year-old data is almost always less valuable than new data; the ten year old experiments have already been done (and hopefully replicated).
Papers are indivisible as well; in other words, they work as a poor unit of account. From the top-down perspective, it is difficult to compare the value of papers from different journals. Is a Nature paper worth the same funding as two Neuron papers, and four Journal of Neuroscience papers? Or perhaps we should rank the journals by impact factor, and the papers by citations? Whatever metric ones comes up with will be flawed.
From the bottom-up perspective, it is hard to identify how much a paper's constituent parts are worth. Smith claimed the value of money was how much labor it can command. How much data or labour goes into a paper? Nature papers have 4-6 main figures, but can have over a dozen supplemental figures. In contrast Neuron papers are 6-8 figures long, but have 5-8 supplemental figures. Which required more data? How does one compare different fields? Is a Western blot worth a two-photon image? And if someone uses better technology to get their data, should their paper include more figures or less? These are difficult questions, only made more so by filtering through the oxen of papers.
A new currency?
Biotech companies are lucky, in that they can use actual money as their currency: they produce data which is used to make products that get sold. What are we in academia to do?
Fundamentally, the problem with using papers as a currency is that they're bad units of account: they're too big, and only vaguely tied to value. It's as if we were trading oxen by only knowing their weight, and ignoring their parentage and health.
The size issue is relatively easy to solve: limit papers to just a few figures. Some people denigrate the idea of "salami science," but it's a much more precise accounting. The last paper I reviewed was published in Nature, and had six main figures, and fifteen supplemental figures. In comparison, another Nature paper last year had three main figures, and four supplemental (and much smaller figures to boot; note that both are fine papers, and am simply commenting on size). Wouldn't a more fair accounting system have split the first paper in three? They could even be published in different journals. It would also de-emphasize the pernicious idea of "storytelling," and simply let people publish nuggets of data that may not fit into a grand arc.
The issue of trying to assign value to data is a harder nut to crack. We could try to follow Smith, and measure the man-months taken to produce data. To account for time-saving innovations, we could assign a multiplier to innovative techniques. Yet, how would we account for effort, skill, or the simple dead time in the middle of running a Western? It would be easier to value data post-hoc, rather than summing the labour inputs.
Ultimately, I think the best appraisal of value is the one proposed many times before: let citations and the community weigh the value of data, rather than a few, arbitrarily chosen reviewers. Community rating may be subjective and have its biases - favouring established labs, or flashy results - but science is imprecise enough that I can't think of a better metric.
My core conclusion from thinking about scientific currency - that we need to ditch peer-reviewed papers, and replace them with smaller, post-publication-evaluated data bites (in some form) - is not new. Perhaps this idea is my panacea. Yet, the route was virgin. By looking at science as an exchange between labs producing data, and funding agencies providing money, you can see the fundamental question is how to value data. Regardless of other complaints about publishing - its delays, and arbitrariness - trying to connect data to funding via papers is like trying to run an economy by trading oxen for beer.
(Looking over my notes, Smith has some other interesting nuggets that did not fit into the main narrative of this post. He discriminates between value in use (what a good can do; water has this) vs value in exchange (what you can trade for; e.g. gold is expensive). In science, anatomical studies are often useful, but don't yield high-profile papers. In contrast, many flashy papers get published in Nature and Science, but are often simply wrong.
Smith also distinguishes between the real price (in units of labor) and nominal price (in money) of commodities. These often change with supply and demand, or due to technological innovation. For example, electron microscopy has probably had a stable real and nominal value over the last 20-30 years, and both the real and nominal value of Western blots has cratered due to improved technology. In contrast, the nominal value of imaging has gone up as fluorophores improved, even as the labor necessary to produce images has gone down. This further emphasizes the difficulty in trying to value papers by their inputs.)
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