This note provides new evidence on how small business owners have been impacted by COVID-19, and how these effects have evolved since the passage of the CARES Act. As part of a broader and ongoing project, we collected survey data from more than 8,000 small business owners in the U.S. from March 28th, one day after the CARES Act was passed, through April 20th. The data include information on firm size, layoffs, beliefs about the future prospects of their businesses, as well as awareness of existing government relief programs. We provide three main findings. First, by the time the CARES Act was passed, surveyed small business owners were already severely impacted by COVID-19-related disruptions: 60% had already laid off at least one worker. Second, business owners’ expectations about the future are negative and have deteriorated throughout our study period, with 37% of respondents in the first week reporting that they did not expect to recover within 2 years, growing to 46% by the last week. Third, the smallest businesses had the least awareness of government assistance programs, the slowest growth in awareness after the passage of the CARES Act, and never caught up with larger businesses. The last finding indicates that small businesses may have missed out on initial Paycheck Protection Program funds because of low baseline awareness and differential access to information relative to larger firms.
We analyze how to optimally engage in social distancing (SD) in order to minimize the spread of an infectious disease. We identify conditions under which the optimal policy is single-peaked, i.e., first engages in increasingly more social distancing and subsequently decreases its intensity. We show that the optimal policy might delay measures that decrease the transmission rate substantially to create “herd-immunity” and that engaging in social distancing sub-optimally early can increase the number of fatalities. Finally, we find that optimal social distancing can be an effective measure in substantially reducing the death rate of a disease.
As children reach adolescence, peer interactions become increasingly central to their development, whereas the direct influence of parents wanes. Nevertheless, parents may continue to exert leverage by shaping their children’s peer groups. We study interactions of parenting style and peer effects in a model where children’s skill accumulation depends on both parental inputs and peers, and where parents can affect the peer group by restricting who their children can interact with. We estimate the model and show that it can capture empirical patterns regarding the interaction of peer characteristics, parental behavior, and skill accumulation among US high school students. We use the estimated model for policy simulations. We find that interventions (e.g., busing) that move children to a more favorable neighborhood have large effects but lose impact when they are scaled up because parents’ equilibrium responses push against successful integration with the new peer group.
Forming beliefs or expectations about others’ behavior is fundamental to strategy, as it co-determines the outcomes of interactions in and across organizations. In the game theoretic conception of rationality, agents reason iteratively about each other to form expectations about behavior. According to prior scholarship, actual strategists fall short of this ideal, and attempts to understand the underlying cognitive processes of forming expectations about others are in their infancy. We propose that emotions help regulate iterative reasoning, that is, their tendency to not only reflect on what others think, but also on what others think about their thinking. Drawing on a controlled experiment, we find that a negative emotion (fear) deepens the tendency to engage in iterative reasoning, compared to a positive emotion (amusement). Moreover, neutral emotions yield even deeper levels of reasoning. We tentatively interpret these early findings and speculate about the broader link of emotions and expectations in the context of strategic management. Extending the view of emotional regulation as a capability, emotions may be building blocks of rational heuristics for strategic interaction and enable interactive decision-making when strategists have little experience with the environment.
Financial econometrics brings financial theory and econometric methods together with the power of data to advance understanding of the global financial universe upon which all modern economies depend. Financial Econometric Modeling is an introductory text that meets the learning challenge of integrating theory, measurement, data, and software to understand the modern world of finance. Empirical applications with financial data play a central position in this book’s exposition. Each chapter is a how-to guide that takes readers from ideas and theories through to the practical realities of modeling, interpreting, and forecasting financial data. The book reaches out to a wide audience of students, applied researchers, and industry practitioners, guiding readers of diverse backgrounds on the models, methods, and empirical practice of modern financial econometrics.
Financial Econometric Modeling delivers a self-contained first course in financial econometrics, providing foundational ideas from financial theory and relevant econometric technique. From this foundation, the book covers a vast arena of modern financial econometrics that opens up empirical applications with data of the many different types that are now generated in financial markets. Every chapter follows the same principle ensuring that all results reported in the book may be reproduced using standard econometric software packages such as Stata or EViews, with a full set of data and programs provided to ensure easy implementation.
We argue that the US tax system is biased against labor and in favor of capital and has become more so in recent years. As a consequence, it has promoted inefficiently high levels of automation. Moving from the US tax system in the 2010s to optimal taxation of capital and labor would raise employment by 4.02% and the labor share by 0.78 percentage points, and restore the optimal level of automation. If moving to optimal taxes is infeasible, more modest reforms can still increase employment by 1.14–1.96%, but in this case efficiency can be increased by imposing an additional automation tax to reduce the equilibrium level of automation. This is because marginal automated tasks do not bring much productivity gains but displace workers, reducing employment below its socially optimal level. We additionally show that reducing labor taxes or combining lower capital taxes with automation taxes can increase employment much more than the uniform reductions in capital taxes enacted between 2000 and 2018.
Consider a market with identical firms offering a homogeneous good. A consumer obtains price quotes from a subset of firms and buys from the firm offering the lowest price. The “price count” is the number of firms from which the consumer obtains a quote. For any given ex ante distribution of the price count, we obtain a tight upper bound (under first-order stochastic dominance) on the equilibrium distribution of sale prices. The bound holds across all models of firms’ common-prior higher-order beliefs about the price count, including the extreme cases of full information ( firms know the price count) and no information (firms only know the ex-ante distribution of the price count). A qualitative implication of our results is that a small ex ante probability that the price count is one can lead to a large increase in the expected price. The bound also applies in a wide class of models where the price count distribution is endogenized, including models of simultaneous and sequential consumer search.
Consider a market with many identical firms offering a homogeneous good. A consumer obtains price quotes from a subset of firms and buys from the firm offering the lowest price. The “price count” is the number of firms from which the consumer obtains a quote. For any given ex ante distribution of the price count, we obtain a tight upper bound (under first-order stochastic dominance) on the equilibrium distribution of sale prices. The bound holds across all models of firms’ common-prior higher-order beliefs about the price count, including the extreme cases of complete information ( firms know the price count exactly) and no information ( firms only know the ex ante distribution of the price count). A qualitative implication of our results is that even a small ex ante probability that the price count is one can lead to dramatic increases in the expected price. The bound also applies in a wide class of models where the price count distribution is endogenized, including models of simultaneous and sequential consumer search.