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My view

The not-so-dismal science

Teasing logic from the glorious mess of human interactions and transactions
Mitchell Gu

It’s a Wednesday morning, and my labor economics class is talking about whether increases in the minimum wage reduce employment. Some equations in our notes suggest yes—but if we change the assumptions of the model, the results change. A study from the 1990s supports one hypothesis—but on second glance, we find that the methods are outdated.

So, you might ask, what’s the answer? Stick around a little longer and you will find, perhaps to your chagrin, that the answer is almost always “It depends.” On the surface, this may not appear to be a valuable intellectual pursuit. In labor economics, as in many of its sister economic subfields, the questions are thorny, the data noisy, and the conclusions often in flux.

And yet here I am, on the cusp of beginning a PhD in economics, looking forward to joining the legions of researchers who try to tease some logic out of the glorious mess of human interactions and transactions and behavior. The hope is that powerful statistical techniques and insightful theory will be developed in service of this pursuit, that the answers we are able to attain will bring us closer to understanding how policy changes will affect the real world, and how that world—with all its inequality and inefficiencies—came to be the way it is.

For MIT undergraduates, the economics department is something of a hidden gem; many spend years at the Institute before learning that it’s one of the most highly ranked in the country. Some take economics classes to dodge the long essays often required by other HASS courses—and emerge pleasantly surprised at how much they’ve enjoyed themselves. Some stay in the department for just one class or one UROP—and others, like me, find it so compelling that we rethink our majors and maybe even our career plans.

I made my first tentative forays into economics when I found, after spending my freshman year studying computer science, that I missed my high school history classes. And during one of those forays, in my sophomore fall, it occurred to me for the first time that I might want to be an economist. I can hardly say I understood then what economics was, but I did know that it would allow me to couple the tools I learned in math class with observations from fields like history, psychology, and political science.

By my junior year, when I began working as a research assistant in the department, I was sold. I worked on a study exploring whether the ideologies of two politicians who sat next to each other in parliament could become more similar as a result. That project applied text analysis techniques I’d seen only in computer science to political data. And the search for an answer seemed increasingly pertinent as I watched the country split into stark red and blue during the 2016 election season.

If you ask my engineer friends about their research, they will inevitably describe their labs—the walls within which innovation happens. Economists may not use any fancy equipment, but we employ a sophisticated arsenal of statistical tools, mathematical models, and often many lines of code. Economic innovation turns on the ability to ask the right questions—ones that are not too big, not too small, and suitable for the data sets and statistical and experimental tools at hand.

I spent the summer before my senior year helping answer one such question, about the effects of sleep on cognitive function among poor people in Indian cities. Sometimes, as in this project, the policy implications are clear: a positive result would indicate that interventions targeting behaviors such as sleep could be just as important as those targeting more traditional health outcomes. Other projects, like the study of political ideologies in parliaments, seek primarily to shed light on pressing social phenomena: we know polarization is increasing, but what can be done to combat it? Other examples of intriguing projects include investigations into the underlying causes of gender-based gaps in pay and achievement, studies of discrimination by police officers, and an examination of matching in the markets for adoptive children. What seem like millions of questions beckon to be answered, and every time I visit a professor’s office, I learn about something new I’d like to work on. So I’ve decided to dive in for good, and I hope to make my small contribution toward understanding our world. 

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