The 2017 Clark Medal shows precarious state of economic history..

I wish I had written this post. Dave Donaldson recently won the Clark medal for his work on economic history of Indian railways.

A Fine Theorem blog points how difficult the journey has been. It highlights how difficult it is to publish economic history work in mainstream journals, given his railways paper has been forthcoming for 8 years!

Donaldson’s CV is a testament to how difficult this style of work is. He spent eight years at LSE before getting his PhD, and published only one paper in a peer reviewed journal in the 13 years following the start of his graduate work. “Railroads of the Raj” has been forthcoming at the AER for literally half a decade, despite the fact that this work is the core of what got Donaldson a junior position at MIT and a tenured position at Stanford. Is it any wonder that so few young economists want to pursue a style of research that is so challenging and so difficult to publish? Let us hope that Donaldson’s award encourages more of us to fully exploit both the incredible data we all now have access to, but also the beautiful body of theory that induces deep insights from that data.

Actually I read this railways paper long ago around 2010 and was really surprised to read it still remains forthcoming. One would have thought it would have been published given the  CLark Medal but it has bnot been published.

The Fine Theorm blog also discusses the paper in length and depth showing all the hard work that has gone into the research. There is original data collection from multiple sources and linking it all together to make a consistent story:

Let’s think about how many competent empirical microeconomists would go about investigating the effects of the British rail system. It would be a lot of grunt work, but many economists would spend the time collecting data from those dusty old colonial offices. They would then worry that railroads are endogenous to economic opportunity, so would hunt for reasonable instruments or placebos, such as railroads that were planned yet unbuilt, or railroad segments that skipped certain areas because of temporary random events. They would make some assumptions on how to map agricultural output into welfare, probably just restricting the dependent variable in their regressions to some aggregate measure of agricultural output normalized by price. All that would be left to do is run some regressions and claim that the arrival of the railroad on average raised agricultural income by X percent. And look, this wouldn’t be a bad paper. The setting is important, the data effort heroic, the causal factors plausibly exogenous: a paper of this form would have a good shot at a top journal.

When I say that Donaldson does “serious” work, what I mean is that he didn’t stop with those regressions. Not even close! Consider what we really want to know. It’s not “What is the average effect of a new railroad on incomes?” but rather, “How much did the railroad reduce shipping costs, in each region?”, “Why did railroads increase local incomes?”, “Are there alternative cheaper policies that could have generated the same income benefit?” and so on. That is, there are precise questions, often involving counterfactuals, which we would like to answer, and these questions and counterfactuals necessarily involve some sort of model mapping the observed data into hypotheticals.

Donaldson leverages both reduced-form, well-identified evidence, and that broader model we suggested was necessary, and does so with a paper which is beautifully organized. First, he writes down an Eaton-Kortum style model of trade (Happy 200th Birthday to the theory of comparative advantage!) where districts get productivity draws across goods then trade subject to shipping costs. Consider this intuition: if a new rail line connect Gujarat to Bihar, then the existence of this line will change Gujarat’s trade patterns with every other state, causing those other states to change their own trade patterns, causing a whole sequence of shifts in relative prices that depend on initial differences in trade patterns, the relative size of states, and so on.

What Donaldson notes is that if you care about welfare in Gujarat, all of those changes only affect Gujaratis if they affect what Gujaratis end up consuming, or equivalently if it affects the real income they earn from their production. Intuitively, if pre-railroad Gujarat’s local consumption was 90% locally produced, and after the railroad was 60% locally produced, then declining trade costs permitted the magic of comparative advantage to permit additional specialization and hence additional Ricardian rents. This is what is sometimes called a sufficient statistics approach: the model suggests that the entire effect of declining trade costs on welfare can be summarized by knowing agricultural productivity for each crop in each area, the local consumption share which is imported, and a few elasticity parameters. Note that the sufficient statistic is a result, not an assumption: the Eaton-Kortum model permits taste for variety, for instance, so we are not assuming away any of that. Now of course the model can be wrong, but that’s something we can actually investigate directly.

A pretty dense post which needs to be read carefully.

But then as I posted earlier, Donaldson showed lots of perseverance to stick to topic of Indian railways. Such a topic would not get any audience any India leave US. So one has to come with greater insights to first of all get some audience. To pull off a Clark is something unbelievable given the current status of economic history amidst economists..


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