Research

Abstract: we argue that accounting for capital heterogeneity is crucial to understanding the sources and costs of misallocation. Allowing for different types of capital, such as structures and equipment, raises the measured costs of misallocation by 7 percentage points (19%) in the U.S. and 6 percentage points (24%) in India. Across countries, structures are consistently more misallocated than equipment. We then estimate a dynamic model to break down the sources of misallocation that account for the additional measured misallocation and the efficiency differences between equipment and structures. The results show that adjustment costs and imperfect information alone cannot fully explain the additional misallocation or why structures are more misallocated. Heterogeneous financial constraints and tax policies may contribute to the higher misallocation of structures, while differences in technology and measurement errors play only modest roles.

Abstract: the literature has well documented the causal effect of belief shocks on firms' behavior, but it is less clear through which expectations these belief shocks exert their influence. I develop a stylized firm investment model to demonstrate the mechanism by which a belief shock affects investment through various types of expectations. Using constructed belief shocks and the Duke CFO Survey, I empirically document the heterogeneous effects of belief shocks on different firm expectations. By linking firm expectations with fundamental datasets, I also measure the relative importance of these expectations in explaining firms' investments and efficiency. Finally, I construct an overarching expectation index that captures the main content of firms' different expectations using Principal Component Analysis. Estimating the causal effect of expectations on firms' investment can yield significantly different results when using the index compared to a single expectation.

Abstract: we study anchoring effects and loss aversion in the Non-Fungible Token market using comprehensive user-auction data from CryptoPunks. Using the last period's transaction price as the reference point, we separately measure anchoring effects and loss aversion for sellers and bidders. Our findings reveal that both sellers and bidders exhibit loss aversion, though the magnitude of loss aversion is significantly smaller for bidders. The analysis also confirms the presence of anchoring effects in both groups, with bidders displaying weaker effects than sellers, possibly due to signaling dynamics. Furthermore, we show that negotiations between sellers and bidders result in an intermediate level of loss aversion, falling between their individual measured levels. Within both groups, we observe variation in loss aversion across experience levels, suggesting that greater experience may mitigate sellers' reference dependence in the NFT market.

Abstract: we study regional and aggregate impact of belief shocks using state-level media sentiments from economic news. Using existing state-of-the-art text sentiment analysis tools, we construct a new data set of regional news sentiments, coverage and consensus. In our sample, we find that news sentiments, coverage and consensus have some commons with expectation data from survey. To understand the role of media sentiment shock for aggregate business cycle and spatial allocation, we develop and quantify model with investment under imperfect information, featuring capital allocation across states. When calibrated with actual state-level economic news, the model shows that: (1) media media generates sizable disturbances on the regional and aggregate capital allocations; (2) news media amplifies the aggregate C-K-Y-L co-movements in business cycles; (3) news media explains 3.5 % to 9 % spatial capital misallocation in the United States.