STEPHANIE XU/THE VARSITY

Imagine that university professors are graded on a bell curve based on their teaching qualities, not unlike how students are sometimes graded. This method of evaluation assumes that their quality must be ranked according to the bell curve and that they cannot deviate from it. This would result in many capable professors unfairly receiving poor evaluations, which are used as a basis for restricting promotions, or worse, letting them go entirely.

This is the type of error that UTM Assistant Professor Jiaying Gu hopes to avoid.

Gu works in the Department of Economics at UTM and specializes in applied econometrics — a branch of economics that uses statistical methods to analyze economic data.

“I enjoy the fact that econometrics is a collection of tools that allows data to tell a story,” wrote Gu in an email to The Varsity. “[It] is a collection of methods that allows one to learn something useful from the data.” She currently develops these very tools and is pleased that her methods are being used in empirical economic work.

Her development of new statistical methods to better understand economic data that can’t be easily measured — like innate ability or personal preference — won her the 2018 Polanyi Prize in Economic Science. The Polanyi Prize, named after U of T chemist and Nobel Prize recipient John C. Polanyi, is awarded each year to up to five young researchers conducting postdoctoral research at Ontario universities.

Gu was honoured alongside U of T faculty members Husam Abdel-Qadir and Jason Hunt, who won the Polanyi Prizes in Physiology/Medicine and Physics, respectively.

Gu is researching teacher evaluations, which typically consists of learning about an individual teacher’s quality from the test results of their students.

Despite teacher quality being a leading question in education economics, the method currently used to answer it was formed in the 1960s and relies on linear shrinkage with the assumption that the distribution of quality follows a bell curve. This method is problematic, according to Gu, as its data is too diverse to be analyzed one way and because “policy makers have been using these evaluation results (explicitly or implicitly) to make personnel decisions.”

Gu wants to relax this “normality assumption” to develop a method that more accurately evaluates teacher quality, with a particular focus on the impact of professors’ individual teaching styles, which can be difficult to observe.

“I believe the new method benefits not only academic researchers towards a better understanding of how teacher’s quality influence students’ test outcome and long run economic outcomes, but also provides a more reliable framework to educators and policy makers in designing better policy to our education system.

Gu received her undergraduate degree in economics from Fudan University in Shanghai, before pursuing a masters degree in the subject at the National University of Singapore. Gu has been at UTM since obtaining her doctorate in, of course, economics, from the University of Illinois. Despite her extensive education in the subject, however, Gu wasn’t always drawn to economics.

“[My] choice was neither a well-informed nor a well-thought decision,” she admitted. She added that in China, where she comes from, university admissions are very competitive, especially when it comes to economics. “For some reason, everybody thinks that doing economics in College leads you to a high paying job.

So, I thought, it must be a good thing if I can get myself into it,” wrote Gu. “It [was] during my Master’s study in Singapore that I start to appreciate the beauty of economics. I [realized] I may have a thing for Econometrics.”

There are no universally good methods when it comes to data analysis. Some methods may work extremely well under one setting but fail miserably under another.”

Gu encourages students to challenge themselves with more math-heavy courses in the early stages of their undergraduate careers, even if they prove difficult.

As for her own research, she is still deciding on what kind of problems she will examine next and what methods she will make for them. “This is the serendipity in doing research,” wrote Gu. “There [are] always new things to learn and new things to investigate.

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