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Thesis: Fox News's Effect on Social and Moral Preferences. Winner of the D.K. Smith Prize in Economics for best thesis.
Abstract: This paper examines how Fox News influences social and moral preferences: two crucial inputs in people's decision-making process. I conduct a survey among Americans aged 45 or older and use the variation in the channel positions of Fox News and MSNBC across different towns and cable providers as instruments. After confirming that these channel positions do not predict voting patterns before Fox started broadcasting, I find evidence that Fox shifted moral values to be more communal, some suggestive evidence that it decreased altruism and trust, and that Fox does not appear to affect negative reciprocity. In addition, these treatment effects are concentrated among those who did not vote for Bill Clinton or Bob Dole in the 1996 election.
Empirical: The Link Between Survey Response Rates and Nonresponse Bias: Theory, Simulations, and Empirical Evidence From the Household Pulse Survey
This was inspired by the work I had done at Brookings, where we found out about how the Household Pulse Survey is a really terrible survey. I was thinking about what the empirical relationship I see in the data between response rates and depression would be if there is nonresponse bias and realized that the answer is not immediately obvious. So I put on my theorist hat, drew upon my Ec 1011a knowledge, and wrote down a model for response rates and nonresponse bias, which eventually developed into this paper. Essentially, if one group has a higher response rate than the other group, and group membership changes, then the response rate will change in a way that reveals the sign of the nonresponse bias. For example, if unemployed individuals are more likely to respond to a survey and unemployment increases, then holding all else equal response rates will increase as well.
I took the paper offline because there was a mistake in the proof of lemma one. Essentially, I thought that my estimator was unbiased when in reality is it only consistent. That means that I have to rewrite a bunch of things, and I haven't gotten to that...
Theory: Axioms and Theorems in Voting Theory with a Brief Biography of Kenneth May.
I wrote this for the final paper of ECON 1080: Great Theorems of Economics taught by Professor Jerry Green. The class is about 2/3 microeconomic theory topics and 1/3 history of microeconomic theory. I also did a presentation on the subject and the slides can be found here.
Low Wage Gig Sector Increase Wages in Indivisible Labor Monopsony Labor Market (Rip I might never finish this paper)
Dobson, Emily, Carol Graham, Tim Hua, and Sergio Pinto. 2022. “Despair and Resilience in the US: Did the COVID Pandemic Worsen Mental Health Outcomes?” Working Paper 171. Brookings Global Working Paper Series. Brookings Institution. Brookings WP link
Hua, Tian., Kim, Chris Chankyo, Zhang, Zihan., & Lyford, Alex. 2021. "COVID-19 Tweets of Governors and Health Experts: Deaths, Masks, and the Economy"
Journal of Student Research 10 (1). https://doi.org/10.47611/jsr.v10i1.1171
PDF Dataset
Twitter Thread
Powerpoint Slide for spring Symposium
Preliminary update: I wrote this paper before I had taken econometrics, probability, or statistics. I wrote it before I really learned R. (Wild eh?) So the data analysis we conducted was somewhat limited. This is not necessarily a huge deal: we had a census of all tweets from the time period, so none of the findings are "wrong." However, in retrospect, there was a lot that I would have done differently. The biggest one among them being that I would have treated each individual user as a unit of observation, as opposed to pooling everything together by user category.
For now though, I present the following two randomization inference graphs on the rate at which Republican/Democrat governors mention death or masks in their COVID-19 related tweets. The graph on the left looks at the proportion of COVID-19 related tweets that contained words relating to death; the one on the right looks at the proportion of COVID-19 related tweets that contained the word "mask." We randomize the assignment of party labels. The p-values are 0.001667 and 0.013 respectively, suggesting that the difference between Democrat and Republican governors are, in one sense, statistically significant (i.e., Democrats mention deaths and masks more). For reference, a similar test with words relating to the economy yields a p-value of 0.883.
