The extent of the scientific market

Last year, I wrote about how the new age of genetic mouse models means the transaction costs for distributing material (viz. mice) between labs has gone up. I argued that this, in turn, creates strong incentives towards agglomeration, or grouping together labs with similar interests (or at least similar mouse models). Today I want to explore a related economic idea: if we reorganize scientists into larger groups, we will have an opportunity to redefine their roles, increasing specialization.

Adam Smith on the division of labor

On one of the many econ blogs I read, I stumbled on Adam Smith's classic idea, "the division of labour is limited by the extent of the market." (Is it more cliché to quote Adam Smith, or a dictionary?) I've long felt that neuroscience labs aren't nearly specialized enough, so I turned to the Wealth of Nations, to see what Smith wrote.


I'm no economist, but this is what I understood from the first three chapters. In the first chapter, Smith observed that specialized workmen have higher productivity than generalists, and hypothesized that productivity improves through the effects of the division of labor. He observed three reasons for this: 1.) increased skill of workers; 2.) reduced transaction costs in switching between tasks; and 3.) invention of machines that improve task-specific productivity. Of these, I think the first is most important for neuroscience, and my post today.


Then in chapters 2 and 3, Smith speculated on how and why labor was divided. First, labor was able to be divided because people could trade their goods. If I'm adept at shoemaking, I can make more shoes than other people, then trade the shoes for food. Micro-econ 101.


Second, Smith observed the quote that started me thinking: that the division of labour is limited by the extent of the market. His explanation was brief, but the basic idea is that if I'm a good shoemaker, and can make one shoe per day, I need to live in a place that can absorb 250 shoes per year; I can't work as a shoemaker in a hamlet of ten people. On the other hand, if I lived in a city of a million people, I could further specialize the shoemaking into its various components, and increase productivity even further.

So what does this have to do with neuroscience? The two key questions are: how can we divide the labor; and what is the extent of the market?

The division of labor

Neuroscience is the most integrative biological science, which makes dividing labor straightforward: by discipline.


To make this concrete, I'll draw on my experience in grad school. In the Yasuda lab, we studied the cellular mechanisms of synaptic plasticity. On a purely theoretical level, we studied cell signaling pathways (although surprisingly nothing about learning and memory). Then on a primary technical level, we performed experiments via imaging on a microscope. Once we had data, we had to analyze it. This could be quite complicated, involving software design, statistics, and simple modeling. On a secondary technical level, there were preparations before each experiment, which included doing dissections, or subcloning constructs. To verify our results, we often performed Westerns.

In total, you could theoretically divide the Yasuda Lab Labor Market into: literature research; imaging and microscopy; programming (software, statistics, and modeling); surgery; molecular biology; and biochemistry. Six (to eight) jobs in a lab of ten people. In practice, there was relatively little specialization. Everyone knew a little bit about microscopy, data analysis, and molecular biology. The only specialists per se were two post-docs who performed a lot of molecular biology and biochemistry (and of course, Ryohei, who knew everything).

The extent of the market

Which brings me to the second question, what is the extent of the market? On a large scale, one might think that the market includes all of neuroscience, 30,000+ people. But remember, the market is defined by trade, and most of these scientists don't "trade" with each other often, either in people or matériel.

So what group of people can practically trade time or resources? A lab. (Or, one might argue, a department, which I address below.) However, in a lab of ten people, each working on their own project, the market is quite small. This means that the benefits to specialization are limited.

Beyond lab size, the market is also limited by the duration of employment, typically 2-5 years. Specialties take time to master, and the most useful forms of specialization take the most time. Yet if people require years to learn a specialty, they will leave as soon as they master them. This is not necessarily a problem for the entire neuroscience community, but for individual groups looking to increase productivity.

In summary, I think neuroscience, as a multidisciplinary field, is ripe for specialization, but is held back by the organizational structure of research units that are too small, and short employment periods.

Counter-arguments

I can think of three counter-arguments against specialization. First, there is a big push now towards interdisciplinary science. Many people believe that there are new discoveries to be made, simply by synthesizing existing fields. However, I would say specialization and collaboration/interdisciplinary science go hand-in-hand: each of the disciplines have their own experts who need to communicate.

The second counter-argument is that there is much to be gained from generalization, and seeing the big picture. This is undoubtedly true. So I would restrict my argument in favor of specialization to saying that production should be specialized, if perspective is not. Getting a general view is what conferences and socializing is for!

Finally, increasing specialization implicitly requires improved coordination. I don't have much to say here, except to argue that we have such small working groups, and such limited specialization, that any increase in coordination costs should be easily offset by improved productivity.

Amelioration

So what can be done to increase specialization? Some larger groups, namely department-size entities, have made some progress. fMRI departments have a fairly clear split between the programmers and the cognitive scientists. Many departments have "core" facilities like 2-photon microscopes, or micro-array processing. Janelia and Max Planck institutes have professional cloners making constructs. Indeed, here in Geneva, we have a very good shop that can (with time) handle most equipment building needs. Most of these core facilities help with experimental setup or data acquisition.

We need to go farther. Working within the department paradigm, I would suggest creating department-centered positions for post-experiment processes like data analysis. This might be problematic in a department with diverse interests: molecular biologists might be unhappy about supporting (even indirectly) statisticians for systems neuroscience, and vice versa. Which only reemphasizes how important it is to focus neuroscience departments on overlapping interests. If you do it right, you just might make your market bigger, divide your labor, and conquer.

Addendum:

How does this apply to me now? The tasks I perform include: reading literature; surgery; recording; data analysis (light software development, and kludged stats); histology; and now behaviour design and running. Things that have been "specialized away" for me are mouse colony maintenance (by a tech), and equipment manufacture (by the shop). If I could specialize things further, I think surgery, data analysis, and behaviour are tricky enough that I'm still improving at them. As it is, I'm constantly torn between different tasks (Smith's #2 hypothesis), and it's hard to prioritize what to improve at.