Student Spotlight: Agustín Indaco
Agustín Indaco is a PhD student in Economics at the City University of New York. His research interests lie at the intersection of applied microeconomics, health, and big data. He is a research fellow at the Software Studies Initiative as well as a research assistant for a National Science Foundation project investigating the economic impact of high-skilled immigration in the US. He is attending this week’s UChicago Price Theory Summer Camp to improve his skills in developing theoretical models that help economists understand the underlying effects that drive human decision-making.
Agustín, where are you from, and how did you become interested in economics?
I am from Buenos Aires, Argentina. I always had an interest in history, sociology, politics, and philosophy and had been thinking of getting my bachelor’s degree in one of those topics. But in 2001 – 2002, when I was in my final year of high school, Argentina went through a deep economic crisis: GDP fell by 12% in 2002, and unemployment rate rose to 25%. That’s when I decided I wanted to study economics; it felt like the most complete discipline to understand society, human interaction, and decision-making.
One of your working papers investigates social media inequality in New York City. What is the motivation behind this work, and what kinds of research questions are you trying to answer?
Coming to New York, I was amazed at how many people were always on the street, going somewhere, meeting up with friends, going to work etc. Through Lev Manovich, who is my coauthor on this paper and director of the Software Studies Initiative lab where I work, I started working with big data, particularly social media data. Social media data allows us to study questions that were previously hard to study because we did not have granular location data showing where people were at different points in time or days of the week. One of the questions we wanted to study is how Instagram images are distributed throughout Manhattan. Since we live in NYC, we had a vague idea that the distribution would be unequal and that some particular spots were more popular than others, but we wanted to quantify our suspicions. Quantifying these metrics is important for understanding how people move throughout a city, where they go, what they find interesting etc. We also divided our sample among users we believe are locals and those who are tourists, and compared the distribution of images between these two groups. Tourists’ Instagram posts are much more concentrated and share 50% of their images from areas that represent only 12% of Manhattan. These data open up a lot of questions: What do these popular places have in common? What is it that attracts tourists to these places? Should city officials be promoting other places in the city as well?
Among locals, we found that areas with a below average income see a significant decrease in the number of images during the daytime, relative to the nighttime. After further analysis, it seems that this observation is driven by commuting patterns: most jobs are concentrated in the more affluent neighborhoods, so people commute there to work during the day, and then go back home at night. We can see these patterns in Instagram posts. For example, Washington Heights has fewer images during the day than at night, while the complete opposite is true of the Financial District.
Our next step is to calculate and compare social media inequality in other cities in the world. We are unsure what we’ll find, but it will be interesting to capture the existing differences in some of the most important and vibrant cities in the world.
Can you briefly describe, for a general audience, what price theory is and how it relates to your work?
For this question, I will cite Glen Weyl : “analysis that reduces rich (e.g. high-dimensional heterogeneity, many individuals) and often incompletely specified models into ‘prices’ sufficient to characterize approximate solutions to simple (e.g. one-dimensional policy) allocative problems.”
I think economic issues are innate in almost every decision we make nowadays. Although every decision seems very complicated and different from one another, in the end you can almost always reduce them to some simple model. In every decision, there is some objective or desired outcome we wish to accomplish. We have a limited amount of resources to accomplish it, and there are opportunity costs to consider (i.e., alternatives we cannot carry out if we follow some other path). Whether buying tickets to see a game, deciding what plate to order at lunch, or getting married, you can almost always represent decisions in these terms.
Even though my work is in applied econometrics, thinking about the problems I am studying using this simple theoretical model helps me understand the underlying decision-making process that individuals are carrying out, and thus allows me to more clearly measure their effects in the data.
You are attending your first UChicago Price Theory Summer Camp this week. What are you expectations heading into the program, and are there any speakers, sessions, or topics that you are especially excited about hearing?
Since this is my first time attending one of these camps, I am obviously very excited about the opportunity. I am particularly interested in the work of Steven Levitt and John List: I really admire their intuitive approach to tackling problems.
My main goal is to improve my skills in developing theoretical models and gain a little more intuition in parsing out some of the underlying effects that drive our decision-making process. I also look forward to meeting PhD students from other institutions to see what they are working on and learn from them and their experiences.
Monday’s program is about halfway finished. What are your initial impressions?
Right from the start, the program has been really amazing. I had very high expectations coming in, but day one has already exceeded what I was hoping for. During the morning session, Kevin Murphy presented a lot of simple, Microeconomics I type of concepts, but his approach had my jaw on the floor. Though I have heard this material dozens of times, his explanations made me feel like I was hearing it for the first time. He elegantly presented the nuances of what it means to maximize a utility function, giving us all a deeper understanding of the broader implications of the math. I am already seeing ways to apply what I heard today to my work – by going deeper into these models, I can better understand what drives people’s behavior, and that is precisely what I wanted to take away from the camp.
Steve Levitt, award winning economist and author of Freakonomics, also gave a wonderful talk in which he presented three papers. One of them was his current work on Uber and surge pricing. Levitt also took the time to impart some advice. He told us that academia doesn’t have to be for everyone; there are a lot of interesting career choices where we can use the knowledge we learn in graduate school and make our mark on the field. It was so refreshing to have someone say those words and point out that there are a lot of equally fulfilling paths we can take – there is no shame, there is no winning or losing, there are just different career paths.
Overall, the two speakers so far nicely complimented one another – they were two brilliant people coming from opposite perspectives. Kevin Murphy dove into the theoretical details of the algebra, while Steve Levitt focused on intuitiveness and what lies behind the decisions people make. Day one is hardly over and I’m already blown away.
The Price Theory Summer Camp, offered under the auspices of the Becker Friedman Institute for Research in Economics, is one of a host of summer research activities highlighted in this series.