Yunie Uyen Le
I am a second year Ph.D student in Economics at Claremont Graduate University (CGU) and a member of the Computational Justice Lab. Before coming to CGU, I obtained my B.A. in Economics with a minor in Mathematics from University of California, San Diego, then a M.S. in Statistics from San Jose State University and a M.A. in Applied Economics from University of Wisconsin - Madison.
Applied microeconomics with an emphasis on using causal inference to identify the impacts of laws and policies on issues related to education, children and women’s development, and health. I’m also interested in how the changes in laws and policies alter the behaviors of individuals in the decision-making process.
Unemployment Shocks, Gun Laws, and Suicide (with M. Garner and G. DeAngelo)
Unemployment is linked to over 45,000 annual suicides worldwide; accounting for about a 20% of all suicides. We use changes in international trade between the United States and other countries, and its impact on small contained economies within a commuting zone. We use the portion of employment per industry to weight the effect of the international trade. Since small contained economies have little effect of international trade decisions, the changes in trade come as an exogenous shock to employment at the local level. In this paper we use this employment shock as an instrument for unemployment to establishes the causal pathway between unemployment and suicides. We then determine whether or not stricter gun laws mitigate the effect of unemployment on suicide.
Cooperative Games: Fairness of Matching Games and Applications of Matching Theory
In recent years, among the field of cooperative game theory, matching theory is getting a lot of attentions due to its high practicability. Since the first introduction in 1962, matching theory has been developed from a beautiful, yet simple, theory to highly practical applications in market design. One of the concern about cooperative games is the fairness of the games. Is the matching fair to all players from both sides of the market? This paper discusses about the fairness of matching games and surveys the developments of matching theory and its applications in solving resource allocation problems in different fields.
Bayesian Blocks: Applications in Big Data and Segmentation (with R. Shiroma, S. Deo, and K. Lenk)
The idea of this project is to model arrival-time data with the ultimate goal being to model the shape of gamma ray burst radiation intensities. We assume that gamma ray bursts follow some underlying radiation intensity that varies over time. We might expect to see intensity to start off constant for some time(background radiation), then a spike of radiation intensity at the start of the burst, then a gradual reduction in intensity thereafter. This intensity comes in the form of individual photons hitting a sensor in a gamma ray telescope. The photon data can be collected and analyzed in a variety of ways, mostly involving some form of binning photons. We would therefore like to capture the shape of this intensity over time over multiple piecewise intensity functions. The current method, Bayesian blocks developed by Jeff Scargle, captures this gamma ray burst shape by representing it as a step function over time (piecewise constant blocks). Our method takes it one step further and generalizes Bayesian blocks to allow for non-constant blocks. If we can model differently shaped blocks rather than patterns of piecewise constant blocks, we can hopefully better identify gamma ray bursts in the data.