Karthik Srinivasan


I am an Assistant Professor at the University of Michigan's School of Information, joining in Fall 2024. 

I work on topics in behavioral economics, labor economics, and political economy. Currently, I'm interested in the economics of social media. 

Feel free to reach out to me at karthiks.625@gmail.com or take a look at my CV

Thanks for visiting my website! 

Working Papers

Paying Attention [pdf

Humans are social animals. Is the desire for attention from other people a quantitatively important non-monetary incentive? I consider this question in the context of social media, where platforms like Reddit and TikTok successfully attract a large volume of user-generated content without offering financial incentives to most users. Using data on two billion Reddit posts and a new sample of TikTok posts, I estimate the elasticity of content production with respect to attention, as measured by the number of likes and comments that a post receives. I isolate plausibly exogenous variation in attention by studying posts that go viral. After going viral, producers more than double their rate of content production for a month. I complement these reduced form estimates with a large-scale field experiment on Reddit. I randomly allocate attention by adding comments to posts. I use generative AI to produce responsive comments in real time, and distribute these comments via a network of bots. Adding comments increases production, though treatment efficacy depends on comment quality. Across empirical approaches, the attention labor supply curve is concave: producers value initial units of attention highly, but the marginal value of attention rapidly diminishes. Motivated by this fact, I propose a model of a social media platform which manages a two-sided market composed of content producers and consumers. The key trade-off is that consumers dislike low-quality content, but including low-quality content provides attention to producers, which boosts the supply of high-quality content in equilibrium. If the attention labor supply curve is sufficiently concave, then the platform includes some low-quality content, though a social planner would include even more.

Judicial Scarring [pdf] [ssrn]

Can making decisions in extreme cases bias subsequent decisions? I study this question in a high-stakes field setting: felony sentencing. I estimate the effect of sentencing a first-degree murder on the length of sentences issued to subsequent defendants. I use data on the universe of felony sentencing decisions in Cook County to estimate a difference-in-differences design comparing judges in the same courthouse who have and have not recently sentenced a first-degree murder. Judges issue sentences that are 13% longer in the 10 days after they sentence a first-degree murder. Effects are twice as large for defendants who share the same race as the murderer and defendants who face high-class felony charges. A back- of-the-envelope calculation suggests that this bias affects 6% of defendants on an ongoing basis, because judges regularly sentence first-degree murders.

Do Journalists Drive Media Slant? [pdf] [ssrn

I study the scope of a principal-agent problem in the field. I analyze news firms and journalists with possibly misaligned preferences over the partisan slant of content, and find that the firm's ability to exert control is limited. I construct a dataset that links 2,700 journalists to firms, news articles, and Twitter profiles. I measure article slant with a machine learning algorithm I train to identify partisan phrases. Using a movers design, I find that firm ideology does not change the slant of a journalist’s writing. In contrast, journalist ideology, estimated using the following decisions of Twitter users, is strongly correlated with article slant.

Work In Progress 

Free Speech, Echo Chambers, and Content Moderation with Scott Behmer and Rafael Jiménez-Durán

Status: Pilot completed. This project has been awarded three research grants totaling $25,000 in funding. 


Math Camp Instructor (2020-22) [Lecture Notes

Together with my friend Walter Zhang, I taught a math camp to incoming Booth PhD students covering linear algebra, real analysis, probability, statistical inference, optimization and dynamic programming.