Published and Forthcoming Papers

A Structural Empirical Model of R&D, Firm Heterogeneity, and Industry Evolution, with Daniel Yi Xu [Feb. 2022] (Accepted at Journal of Industrial Economics)

Working Papers

Network Structure and Efficiency Gains from Mergers: Evidence from the U.S. Freight Railroads [Jan. 2023]

Abstract: In this paper, I study merger gains in a network-based industry and explore how network structure alters such efficiency gains. I build a model of oligopoly competition between transport firms where each firm chooses endogenously its own network infrastructure to maximize profits. In these networks, locations are arranged on a graph and goods can only be shipped through connected locations. The model endogenizes the firms’ pricing, routing, and maintenance allocation decisions. Using detailed waybill data on U.S. freight railroads, I document novel facts about merger gains. I then flexibly estimate the model. I use this setup to demonstrate (i) that cost efficiency gains from mergers and, most importantly, the strategic investment responses of non-merging firms are the main reasons behind the increase in markups after the merger wave and (ii) merger gains are correlated with standard measures of nework structure such as degree centrality and betweenness centrality. These mechanisms reveal a new role for network structure in understanding gains of horizontal mergers that was previously concealed by looking only at individual market-level changes.

Driving the Drivers: Algorithmic Wage-Setting in Ride-Hailing, with Yao Luo and Zhe Yuan [Dec.2022]

Abstract: Firms now use algorithms to regulate workers’ time and activities more stringently than ever before. Using rich transaction data from a ride-hailing company in Asia, we document algorithmic wage-setting and study its impact on worker behavior. The algorithm profiles drivers based on their working schedules. Our data show that drivers favored by the algorithm earn 8% more hourly than non-favored drivers. To quantify the welfare effects of such preferential algorithms, we construct and estimate a two-sided market model with time-varying demand and dynamic labor supply decisions. Results show that removing the preferential algorithm would, in the short term, reduce platform revenues by 12% and total surplus by 7%. In the long run, raising rider fares re-balances demand and supply, resulting in minimal welfare loss. Without the preferential algorithm, an additional 10% of drivers would switch to flexible schedules.

Collateral Damage: The Impact of Shale Gas on Mortgage Lending, with James Roberts, Christopher Timmins, and Ashley Vissing [Sep. 2021]

Work in Progress

“Platform of Platforms in Ride-Hailing”, with Yao Luo and Zhe Yuan

“Consumer Search with Picture Guidance”, with Lu Fang, Chiara Farronato, and Zhe Yuan

“Platform-Generated Quality Ratings: Theory, Empirics and Welfare Implications”, with Jie Bai, Daniel Yi Xu, and Zhe Yuan

“Long-Term Contracts and Secondary Markets: Theory and Evidence from Natural Gas Pipelines”, with Adam Wyonzek and Emmanuel Murray Leclair

Other Publications

Mortality Decline, Retirement Age, and Aggregate Savings (with Sau-Him Paul Lau), Apr 2016, Macroeconomic Dynamics 20, no. 3: 715-736.

Optimizing the Scale of Markets for Water Quality Trading (with Martin W. Doyle, Lauren A. Patterson, Kurt E. Schnier, and Andrew J. Yates), Sep 2014, Water Resources Research 50.9: 7231-7244

Economic Incentives to Target Species and Fish Size: Prices and Fine-Scale Product Attributes in Norwegian Fisheries (with Frank Asche and Martin D. Smith), Dec 2014, ICES Journal of Marine Science 72, no. 3: 733-740.