Associate Professor Mingliang Li is an applied microeconomist with a focus in econometrics, education, and labor economics. He received his PhD from the University of California – Irvine. You can find him teaching Economics of Education (ECO 440/544), Economic Fluctuations and Forecasting (ECO 461/561), Introduction to Econometrics (ECO 613), and Econometric Applications and Methods (ECO 614)!
Why did you choose economics? Why did you choose your subfield?
ML: Part of the reason that I was attracted to economics is that economics uses fascinating models to analyze complicated human behavior. It is encouraging to see that logical analysis can help to explain daily decisions made by human beings and interactions among individuals. Economics is both theoretical and empirical. Economics has a rich set of models that illustrate vividly the social and economic phenomena. At the same time, empirical data sets are widely available and theoretical models have to be consistent with daily observations. To corroborate economic theory with empirical finding is challenging but also rewarding. This is partly the reason that my subfields include econometrics and applied econometrics.
What research are you working on right now?
ML: Recently I have been working on analyzing the finite-sample behavior of some widely used empirical estimators and its implications for applied econometrics.
What was your favorite paper to write, and why?
ML: My favorite paper I worked on is ``Bayesian inference in a correlated random coefficients model: Modeling causal effect heterogeneity with an application to heterogeneous returns to schooling’’ (joint with Justin Tobias). In this paper, we combine data sets from High School and Beyond (HSB) and the 1980 Census. Our results strongly suggest that the quantity of schooling attained is determined, at least in part, by the individual’s own return to education. Specifically, a one percentage point increase in the return to schooling parameter is associated with the receipt of (approximately) 0.14 more years of schooling. Furthermore, when we control for variation in returns to education across individuals, we find no difference in predicted schooling levels for men and women. However, women are predicted to attain approximately a quarter of a year more schooling than men on average as a result of higher rates of return to investments in education.
What is your favorite class to teach, and why?
ML: My favorite classes to teach include econometric courses. It is always exciting to see that economic theories can be tested using rigorous statistical methods and carefully collected data sets. Data analysis is an important component of economic studying and research, and data analytical skills are highly rewarded in the job market.
What was your favorite class as an undergrad?
ML: My favorite classes as an undergraduate student include microeconomics and econometrics. Microeconomics is theoretical and econometrics is empirical. It is intriguing to see how theory and evidence can be combined to prove the power of economic analysis.
What is your top piece of advice for your students?
ML: Data analytical skills are highly rewarded in the job market. Economics is both a deductive science and an inductive science. While economic analysis starts with elegant models, it is always motivated by empirical findings and the mission of economic analysis and empirical research is that economic models eventually have to make sense.