Research

Working Papers

Agree to Disagree: Measuring Hidden Dissent in FOMC Meetings
Available on SSRN, August 2023, Update: November 2024
[Download Paper]
Abstract Using FOMC votes and meeting transcripts from 1976–2018, we develop a deep learning model based on self-attention mechanism to quantify ``hidden dissent’’ among members. Although explicit dissent is rare, we find that members often have reservations with the policy decision, and hidden dissent is mostly driven by current or predicted macroeconomic data. Additionally, hidden dissent strongly correlates with data from the Summary of Economic Projections and a measure of monetary policy sub-optimality, suggesting it reflects both divergent preferences and differing economic outlooks among members. Finally, financial markets show an immediate response to the hidden dissent disclosed through meeting minutes.

ESG Rating Disagreement and Corporate Total Factor Productivity: Inference and Prediction
Available on SSRN, October 2024, Under Review
[Download Paper]
Abstract This paper examines how ESG rating disagreement (Dis) affects corporate total factor productivity (TFP) in China based on data of A-share listed companies from 2015 to 2022. We find that Dis reduces TFP, especially in state-owned, non-capital-intensive, and low-pollution firms. Mechanism analysis shows that green innovation strengthens the dampening effect of Dis on TFP, and that Dis lowers corporate TFP by increasing financing constraints. Furthermore, XGBoost regression demonstrates that Dis plays a significant role in predicting TFP, with SHAP showing that the dampening effect of ESG rating disagreement on TFP is still pronounced in firms with large Dis values.

Judicial Institution and Innovation: Evidence from China’s Intellectual Property Courts Reform
Available Upon Request, September 2024, Under Review

Abstract This paper examines the impact of intellectual property judicial institution on innovation, focusing on the intellectual property courts (IPCs) reform in China. We find that IPCs reform leads to a significant 14% increase in the number of invention patents at the city level, equating to an average rise of 172 annually. Notably, we rule out the possibility of inter-city spatial transfer of patents, indicating that the effect of the IPCs reform on innovation is not a zero-sum game among cities. Furthermore, we also witness improvements in patent quality. Mechanism analyses suggest that the IPCs reform enhances the judicial environment for intellectual property protection. This is primarily evidenced by increased social satisfaction with judicial protection of intellectual property, shorter case durations, and higher plaintiff winning rates in intellectual property cases.

The Impact of Stay-at-Home Orders on US Output: A Network Perspective
Available on SSRN, April 2020
[Download Paper]
Abstract Under the stay-at-home orders issued by states, economic activities are reduced or put on hold by some states across the U.S. to control the spread of COVID-19. By combining several sources of data, we estimate the output loss due to such restrictions using a network approach. Based on our most conservative estimates, the measures as of April 15, 2020 reduce 26% of total US output per period, and about 43% of which is due to the input-output connections in the production network. Using a SIR model with an inter-state infection network, we also calculate the cost of reducing each infection to be approximately 150,000 dollars during the period of March 19 to April 15, 2020. Simulation results of various hypothetical stay-at-home orders show that the unit cost of infection reduction of the existing order is about 13% higher than the local minimum.

Publications

Do Connections Pay Off in the Bitcoin Market?
Published in Journal of Empirical Finance, February 2022 (with Kwok Ping Tsang)
[Download Paper]
Abstract This paper identifies the bitcoin investor network and studies the relationship between connections and returns. Using transaction data recorded in the bitcoin blockchain from 2015 to 2020, we reach three conclusions. First, connectedness is not strongly correlated with higher returns in the first four years. However, the correlation becomes strong and significant in 2019 and 2020. Second, returns also differ among those connected addresses. By dividing the connected addresses into ten decile groups based on their centrality, we find that the top 20% most-connected addresses earn higher returns than their peers during most of our sample period. Third, eigenvector centrality is more related to higher returns than degree centrality for the top 20% most-connected addresses, implying that the quality of connections may matter more than quantity among those highly connected addresses.

The Market for Bitcoin Transactions
Published in Journal of Internantional Financial Market, Institution & Money, January 2021 (with Kwok Ping Tsang)
[Download Paper]
Abstract Transaction fees in the bitcoin system work differently from those in conventional payment systems due to the design of the bitcoin mining algorithm. In particular, transaction fees and transaction volume in the bitcoin system increase whenever the network is congested, and our VAR results confirm that is indeed the case. To account for the empirical findings, we build a model where users and miners together determine transaction fees and transaction volume. Even though the mechanism of fluctuating transaction fees in bitcoin introduces an extra cost of uncertainty to users, a back-of-envelope calculation shows that the cost of using the bitcoin network for transactions is still smaller than the cost of using the current conventional payment system with a fixed transaction fee rate. However, this calculation may underestimate the cost due to the crowding-out effect on small transactions during the congested period.

Price dispersion in bitcoin exchanges
Published in Economics Letters, September 2020 (with Kwok Ping Tsang)
[Download Paper]
Abstract Bitcoin is traded in a number of exchanges, and there is a large and time-varying price dispersion among them. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility, and we find that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion.