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Assistant Professor of Economics Mary Kaltenberg, PhD

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We talked with Professor Kaltenberg about the new computational economics program she co-created and her research interests.

You are co-director of the newly launched computational economics program, which is a collaboration with 91��Ƶ’s Dyson College of Arts and Sciences and Seidenberg School of Computer Science and Information Systems. How did this come about?

When I arrived at 91��Ƶ, I had been thinking about how economics has been changing and what tools students need to be competitive in the labor market, and I realized it’s the combination of skills that is learned in economics and computer science that provide a competitive edge. I brought this idea to Seidenberg’s administration which formed a fantastic team. We collaboratively designed a program, which launched fall 2024.

I believe it is the intersection of computer science’s quantitative methods of predictions and economic quantitative methods of causal thinking that provide a wholistic breadth of applied statistics. It is particularly in the context of using big data―which is in everything that we do. Every firm has data on shopping habits and trends of consumers, production details in manufacturing, logistical data on shipping, and so on–and how we can utilize this data as a competitive advantage. Further, students who employ economic thinking―how do people make decisions―in combination with coding in Python, R, Stata and SQL―will have a winning ticket to great jobs.

There are no other programs in the metropolitan region that are quite like this program, as it’s not a dual major in computer science and economics, but a tailored program of skills for anyone working with applied big data. There is a huge range of potential jobs in consulting, banking, finance, advertising, UX design, business analysis, and sales. If the job involves data, these students will have the tools to do that job successfully.

Your research interests include economics of innovation and labor economics. Tell us about how your students are benefitting from your research.

My research is primarily focused on the intersection of labor and innovation. As a graduate student, I was interested in how automation impacted the demand of skills within occupations and industries and how the diversity of certain skill combinations provided a wage premium. Prior work focused mostly on the returns to cognitive, physical, or social skills, but I was interested in how automation increased wage premiums for certain intersection of these skills (social and cognitive, for example) in knowledge-based industries such as finance and education. During my post-doc, I focused more on the skills of inventors―particularly how cognitive skills change as one ages and if that can be reflected in how disruptive an invention is over the life course. More recently, I’ve focused on policy-based interventions and their effect on labor market outcomes of parents, such as the effect of maternity leave on female inventors’ productivity and inventiveness, the impact of access to childcare and schooling during COVID-19, and its impact on a variety of labor market outcomes of parents.

Most of the classes that I teach are about how to do research. In the senior research course, Seminar in Economic Theory, students learn how to think of a good research question, find appropriate data, apply the appropriate methodology, and interpret the results in the current economic paradigm. This mostly means how we, as researchers, can contribute to a big economic question that we may have, such as what’s the effect of free childcare on labor markets, by using causal inference techniques. This is not the only way to do research in economics, but the current paradigm of how to approach research. These causal inference techniques are applied statistics, applied in economics, called econometrics. These statistical tools enable economists to measure the size of the effect of a policy such as, how much did steel tariffs increase prices for consumer goods?

Students come up with their own original research questions―many of them are quite creative, which is why we bring them to the Eastern Economic Association every year. Their projects have a broad range, such as, “Can you predict interest rate changes with speeches from the board of governors?” or “Does risk preference effect fertility decisions” or “Do NBA stars influence ticket sales?” or “Can a nudge encourage college students to register as organ donors?” It’s critical for students to learn how to do research because the process requires creative critical thinking; coming up with a novel research question and applying economic theory to formulate a hypothesis; and being resourceful, on how to find data or even web scrape to create a novel data set, apply quantitative methodologies to the appropriate research question and data, and write and present results that are convincing to a skeptical audience.

I believe these experiences have helped students get fantastic jobs that they love or continue their education by pursuing a PhD. Bringing students to these professional conferences provides them an opportunity to build upon the skills they learn in class, polish their work, and fine-tune their presentation skills. It also looks very good on their resume and in interviews to discuss their original research.

Our Economics department is unusual for an undergraduate economics degree in that we focus on applied research skills―the economic theory, the quantitative methods, and the application of research in practice.