PhD Candidate, Xinwei Ma, did not start his academic career with the intention of studying economics. Rather, after taking several courses, including micro-, metrics, and international economics as an undergraduate student studying biology and statistics (for which he earned his B.S. in 2010), Xinwei found himself drawn to questions economists raised about human behavior. How do people make decisions; how do strategic interactions affect overall efficiency; how are rigorous statistical methods developed and applied when conducting randomized experiments is not feasible?

Initially unsure whether he should apply to a Ph.D. program directly, Xinwei felt a master’s would be a better starting point to learn more about the field before committing to a research career and enrolled in a joint program between Peking University and the University of Hong Kong. While working on his master’s thesis, Xinwei encountered limitations in the applications of various statistics tools as they were not designed for finance/economics (or, more generally, social sciences), and quickly became interested in econometrics—a branch of economics focused on developing mathematical and statistical methods to quantitatively analyze economic phenomena and evaluate economic models. In the hopes of developing suitable tools tailored for data which cannot be tackled by classical means, Xinwei decided to pursue a doctorate in economics as well as a career in research after graduating with Master’s degrees in both Finance and Economics in 2013.

Although pursuing a graduate degree is not easy and requires quite the investment of time, Xinwei has found great support in his family and in his advisor, Matias Cattaneo, a U-M professor whose focus is econometric theory. Through the relationship with his advisor, Xinwei has learned much about being a scholar and how they work to solve problems. He has seen the frontier of the very technical field of econometrics, as well, and has been focused on contributing to pushing it further by completing several successful projects developing tools and methodologies for analyzing data for economic application.

Motivated by a desire to explore and conquer new territories, Xinwei finds it very challenging yet rewarding to develop methods to test theories about how people will react to policies and incentives. 

 

One such contribution is inspired by the empirical difficulty in accounting for the deviations between observed data and what theory predicts (as well as by the lack of a satisfactory solution available in economics and econometrics literature). As Xinwei explains, many economic policies involve discontinuities to which individuals react. For example, with a progressive tax system, tax payers in the US respond to tax rate discontinuities and bunch in these regions. Recently, many researchers try to utilize this “bunching” phenomena to recover key economic parameters (such as the elasticity of taxable income) which may have important implications in policy designs. However, this is quite challenging due to the lack of an appropriate toolkit. Xinwei’s work, “Estimation and Inference in Bunching Designs,” joint with Matias Cattaneo, Michael Jansson and Joel Slemrod, focuses on developing solid statistical techniques to analyze such bunching data.

Motivated by a desire to explore and conquer new territories—as individuals routinely make complicated decisions and interact with each other—Xinwei finds it very challenging yet rewarding to develop methods to test theories about how people will react to policies and incentives. As he prepares to enter the job market, Xinwei is excited by the prospect of becoming a professional Econometrician and looks forward to the new challenges and opportunities that lay before him.

To those considering an advanced degree in economics, in addition to learning as much math possible, Xinwei would suggest talking to current graduate students: discuss your interests, goals, and career plans thoroughly. Pursuing a PhD is an important decision and once you’ve decided to go for it, decide your field and start working on research as early as possible. If there is anything you need to learn by taking classes from other departments (such as programming, statistics, mathematics, etc.), finish as early as possible. It takes years to consolidate knowledge for any field and form a concrete research agenda.

In addition to his doctoral degree in economics, Xinwei is expected to graduate in 2019 with an M.A. in Statistics from the University of Michigan.