Cindy Chung

Cindy Chung
PhD Student, Economic Analysis & Policy
PhD Program Office Graduate School of Business Stanford University 655 Knight Way Stanford, CA 94305

Cindy Chung

I am a PhD Candidate in Economics at Stanford Graduate School of Business, with work in finance and macroeconomics.

My research interests are in corporate finance, banking, and liquidity management, with a focus on using structural macro-finance models to study individual firm behavior and banks.

I am on the academic job market in 2025-2026.

Research Interests

  • Finance
  • Macroeconomics

Job Market Paper

This paper studies how small firms respond to monetary policy and recession shocks. A large literature modeling firms with financing constraints examines how firms adjust their borrowing. However, recent empirical work has shown that what is unique to small firms is their prevalent use of credit cards. Using new micro data on small firm credit card use, I study credit cards' dual role as both a financing tool and a liquidity service. With this data, I document a set of facts on small firm financing in the U.S. from 2014 to 2021. First, I show that credit card balances, limits, and slack (unused card capacity) increase with firm size, with balances increasing less than limits and slack. I also show that slack co-moves with aggregate deposits in the years from 2014 to 2019 before diverging during the start of COVID. To interpret these facts, I develop a quantitative model of constrained firms with liquidity. The model shows that firms with less wealth use credit cards more for borrowing and have less card slack and deposits for liquidity, while wealthier firms focus more on liquidity management. Finally, I show that monetary policy affects small firms by raising the cost of liquidity. This mechanism amplifies output responses among larger firms relative to smaller ones.

Publications

Journal of Economic Literature 2024, 62(2), 458–484

Recent data technology innovations, such as artificial intelligence and machine learning, have transformed the production of knowledge and increased the importance of data. This review explores how data—digitized information—has been modeled within classic macroeconomic frameworks. It compares the economics of data to other concepts such as ideas, patents, and learning-by-doing. This paper also shows potential ways to model applications for data, including innovation, process optimization, and matching. Because this research area is nascent, much of the article is devoted to open questions and directions for future data economy research.

Working Papers

This paper studies the impact of pricing distortions by correspondent banks on trade, output, and welfare. By incorporating a correspondent banking sector into a Melitz-style model of trade, this paper interprets trade costs not only as transport resource costs but also as banking transfers. This framework is then used to analyze the welfare implications of introducing Central Bank Digital Currency (CBDC) as an additional option for international payments. The paper's findings show that the competitive structure of the banking sector leads to misallocation of labor resources: smaller firms produce less while larger firms produce more than what is produced in the efficient allocation. Introducing CBDC leads to two main welfare effects: smaller firms produce more, leading to increased good varieties available for consumption while larger firms contract and leave the market, resulting in a loss of consumption.