Su, Dan2022-09-132022-09-132022-06https://hdl.handle.net/11299/241620University of Minnesota Ph.D. dissertation. 2022. Major: Business Administration. Advisor: Xiaoji Lin. 1 computer file (PDF); 197 pages.The primary goal of this dissertation is to understand how the business activities of companies impact the macroeconomy. More specifically, it contains three essays. In the first essay “Rise of Superstar Firms and Fall of the Price Mechanism”, I investigatethe misallocation implications of corporate internal financing. I introduce product market competition and corporate risk management into a standard continuous-time heterogeneous agent model with incomplete markets. I show that the economy’s ability to allocate resources across different agents through the price mechanism is bounded by corporate internal savings as there is no market to equalize the marginal value of internal resources across firms. In other words, corporate cash can help achieve dynamic efficiency across times at the firm level but not static efficiency across individuals at the macro level. More importantly, misallocation – defined as the static resource allocation efficiency across individuals – increases in the new economy where (superstar) firms rely more on internal financing due to the increased earnings risk. Finally, this model can quantitatively match the deteriorating capital allocation efficiency in the U.S. data. In the second essay “The Rise of (Mega-)Firms with Negative Net Earnings”, I document the prevalence of public companies with negative net earnings since the 1970s. The fraction of firms with negative net income has increased sharply from 18% in 1970 to 54% in 2019. Such an increase is mainly driven by the right shifts in the mean, i.e., the increasing popularity of sizable firms that are not profitable. Based on the existing literature on customer capital, I conjecture that the increasing returns-to-scale in the new economy is the main driver behind it. I provide three pieces of supporting evidence. First, earning losses mostly come from the growing customer capital expenses instead of production-related costs, capital investments, or R&D expenditures. Second, cross-sectionally, firms with higher markup tend to have lower net incomes. Third, industries with low marginal production costs, on average, have higher percentages of unprofitable companies. The last essay “The Macroeconomics of TechFin” is to investigate the business cycle implications of TechFin. Over the past few years, many large technology companies have started lending in the capital markets, i.e., “TechFin”. How should we modify our existing macro-finance theories to accommodate the rise of this new financial intermediary? In this paper, I introduce both a banking sector and a TechFin sector into a continuous-time general equilibrium model with heterogeneous entrepreneurs and incomplete markets. These two financial sectors are identical except for the types of borrowing constraints faced by entrepreneurs. Entrepreneurs borrowing from banks are subject to the standard collateral-based borrowing constraints. In contrast, technology advantages allow the big tech companies to resolve agency costs and perform cash flow-based lending. I use a deep learning neural network approach to obtain global solutions, and the main conclusions are twofold. First, this new TechFin credit system leads to a higher capital allocative efficiency in the steady state. Second, the existence of BigTech lending acts as a propagation mechanism and makes the economy sensitive to the second-moment uncertainty shocks: a small and transitory micro-uncertainty shock can lead to amplified and persistent changes in aggregate outputs. This new financialaccelerator mechanism, associated with the new TechFin sector, differs from the classic one (e.g. Bernanke and Gertler, 1989; Kiyotaki and Moore, 1997) in three aspects: micro uncertainty instead of aggregate productivity is the primitive shock; financial friction comes from earnings-based borrowing constraints instead of collateral-based ones; and the feedback loops happen between net worth inequality, instead of net worth level, and asset prices.enfinancefinancial acceleratorinternal financingmacroeconomysuperstar firmsThesis or Dissertation