Jin Huang


Jin Huang










Welcome to my website! I am a PhD candidate in Economics at CEMFI
.

I am an applied microeconomist with primary research interest in the field of Industrial Organization. I also have research interests in Market Microstructure and Behavioral Economics.

I will be available for interviews at the SAEe 2016 in Bilbao (December 15-17, 2016), and the 2017 AEA/ASSA Meeting in Chicago (January 6-8, 2017).

 


Working Papers

To Glance or to Peruse: Observational and Active Learning from Peer Consumers   [PDF]
Job Market Paper

This paper examines consumer social learning patterns in decision making. I propose a novel model that decomposes the learning process into two stages: observational learning, where a consumer quickly updates the belief about a product after observing its salient social-based characteristics (such as popularity), and time-consuming active learning through descriptive information content (such as consumer reviews). By demonstrating the interplay between the two stages, the model brings together previous literature that studies these separately. I characterize the optimal learning time, and provide comparative statics which show that an increase in the discount rate or in the product's economic value drives consumers to rely more on observational learning. I test this model using unique transaction-level data for air purifiers sold on a Chinese online platform from January to March 2014. Exploiting an unexpected air pollution crisis in late February 2014, I find that past sales have greater weight as a reference for comparison among products during the pollution crisis than in regular times. I also document that, after the episode, consumers rely less on observational learning compared to periods before the crisis, which is consistent with the model's predictions as sales made during the crisis convey less information.


Should Google Profit like a Taxi Driver? [PDF]

In recent years, numerous European countries have taken or have considered taking regulatory actions against Google News with the aim of improving news quality. This paper explains how news aggregators affect newspapers’ incentives in quality investment from two novel perspectives: (i) a positive market-expansion effect of news aggregators by eliminating information asymmetry between newspapers and news readers, and (ii) a negative business-stealing effect by displaying excerpts of newspaper articles (snippets) on news aggregators’ own sites, which are substitutes of original news. The model illustrates both effects and can be used to evaluate taxation policies on snippets. A tax proportional to how much information extracted from the original news, or a click-through subsidy paid by newspapers to aggregators can discourage news aggregators from showing free previews to appropriate traffic. Moreover, I extend the benchmark setting from one single newspaper to multiple newspapers, capturing an additional competition-in-traffic effect among newspapers. Finally, I also show that the model is robust to many other generalizations.


Work in Progress 

Right Time or Right Action: A Theory of Waiting for Information Leakage (with Julio A. Crego)  [Preliminary draft available upon request]

We analyze the behavior of opening prices in stock markets. Empirical evidence using data from the NYSE and the Madrid Stock Exchange shows that the current day's opening price tends to revert to the previous day's close when they are sufficiently far away. This evidence suggests that prices are predictable, thereby challenging the efficient markets hypothesis. To reconcile this fact with agents' rational expectations, we propose a model where informed traders are heterogeneous in the quality of their private signals about fundamental value. The model suggests that when the gap between the opening price and the closing price (which proxies for investors' prior beliefs about the market) is large, traders with low quality private information tend to delay actions and infer information from realized trades. It also provides some testable predictions that are supported by the data. In particular, we document serial dependence between trades and prices in the short run, and we also find that the wider the gap between the opening and closing prices, the slower it takes for the opening price to revert.


Learning from Observational Learning

In the consumer search literature, it is commonly modeled that an increase in the search cost reduces price competition. However, if we take observational learning into account, in which consumers can observe predecessors' purchase decisions and extract information from them before making their own choices, then a higher search cost leads to more late consumers rationally "herding"; hence, a firm has an additional incentive to reduce its price, for the purpose of attracting early consumers to form informational cascade towards his product. As a result, with observational learning, an increase in the search cost may intensify price competition. The final effect of an increase in the search cost on price is ambiguous.