In instruments we trust

A central question in economics is almost always, “how does X affect Y?”. We look not merely for relationships between variables (as X increases, Y falls), but seek causal explanations (the fall in Y is a result of the increase in X). Unfortunately for this kind of econometric analysis, the world is complex. We seldom can control the settings we observe; many variables can be changing simultaneously. Proving causality in economics typically requires techniques that can isolate the effect of the specific relationship of interest.

An example to illustrate why this might be so: it is common knowledge that, on average, those who study at university earn higher incomes than those who left school at the first possible opportunity. So, education causes higher wages for those with education? Perhaps. But it is also possible — likely even — that if all universities closed tomorrow, and everyone left school at the same age, that (again, on average) those who would have gone to university might still have better career prospects and earn a higher income than those who would never have studied further if given the chance. That is, there is some other factor at play beyond education itself, which influences 1) the likelihood of someone pursuing education and 2) later-life employment outcomes.

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School’s out

Across much of the world, one of the initial responses to the covid-19 pandemic was to close schools. WIth the virus still circulating, many jurisdictions continue to grapple with the question of when and how to resume physical classes. Much of the political debate is focused on the health risks. Many are understandably concerned about how safe it is to send students and teachers back into classrooms. But history also points to the risks associated with maintaining shutdowns. Specifically, disrupting children’s schooling can have persistent effects on their overall level of schooling.

Can we evaluate the consequences of mandated school closures? Plainly, one can only guess at the long-term effects of a coronavirus-induced shutdown: in the absence of time travel, the lifetime outcomes of today’s kids is unknowable. But maybe past examples from other health crises can help to gauge the direction and magnitude of effects?

In new research, Meyers and Thomasson (2020) analyse one historical case involving widespread school closures: the 1916 polio epidemic in the US. Thanks to comprehensive immunisation, polio is today eradicated across the developed world. But no vaccine (or effective treatment) was available in the early 20th century. The 1916 epidemic was significant, with more than 23,000 reported cases distributed across just about every state in the US.

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Why economic history?

As a bachelor’s student, my exposure to economic history amounted to a few anecdotes. The European exchange rate mechanism (mark I). China’s Cultural Revolution and the Great Leap Forward. The story of bootleggers and baptists during the Prohibition era in the US. Interesting examples, but not a detailed look at the mechanisms that have helped to shape the modern economy.

My interest in economic history is only recently acquired, stemming from the observation that economic growth (at least in per capita terms) is a relatively new phenomenon. For much of human history, GDP per capita didn’t rise in any meaningful sense — there were ups and downs from year to year, but the long-term trend was largely flat. It was not until the advent of the industrial revolution that the underlying dynamics began to change.

This observation raises three questions, which form the basis of my interest in economic history:

  1. Why do we have economic growth?
  2. How do we measure the relative contribute of different factors to economic development?
  3. How can we even be sure that GDP per capita was flat centuries ago?
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