Yuge Hao (郝雨歌)

Welcome. I am currently a Business Analysis Manager at T-Mobile in Bellevue, WA. I graduated with PhD in Economics from The University of Arizona in Spring 2022. My research interests are in labor, health and applied microeconomics. Here is my resume.


E-mail: yuge.hao92@gmail.com

Research Papers

PrEP, Risky Sexual Behaviors and STIs (2021-2022 Job Market Paper)

(with Xiduo Chen)

Pre-exposure prophylaxis, or PrEP, is a daily medicine for people at high risk of contracting HIV to lower their chance of getting infected. Clinical studies show that PrEP can be highly effective, reducing the risk of contracting HIV through sex by more than 99%. Despite the great effectiveness of PrEP in preventing HIV infection, there is concern that PrEP may lead some people to more risky behaviors, such as reducing their use of condoms and increasing their number of sexual partners. Using public data from the Multicenter AIDS Cohort Study, this paper empirically examines such concerns. Exploiting the panel nature of the data, we use a difference-in-difference approach to study the effect of PrEP on risky sexual behaviors and other sexually transmitted diseases (STDs). Our analysis shows that PrEP makes individuals more likely to engage in unprotected sex, have more sexual partners, and more likely to contract syphilis.


Work in Progress

The Early Bird Gets the Worm: Strategic timing to live-stream on Huya.com

Using scraped data from an online video game live streaming platform, I study the streamers' strategic decisions of when to go online and the effects of these timing pattern on their success on the platform. Live streamers as the content creators face different amount of audience during different times of the day as the website traffic fluctuates over the course of 24 hours. They also face different levels of competition as the number of online streamers change in each hour. I use a simple model of streamers' behaviors as benchmark of market equilibrium and study how the real data deviate from the model predictions. The result show live streamers exhibit over competition and decreased gains in rush hour and in contrast they also present under competition and increased gains in the slow hour. Potential rationales are discussed to explain such phenomenon.