Spencer Kwon - Academia.edu (original) (raw)

Papers by Spencer Kwon

We collect data on the size distribution of U.S. corporate businesses for nearly 100 years. We do... more We collect data on the size distribution of U.S. corporate businesses for nearly 100 years. We document that corporate concentration (e.g., asset share or sales share of the top 1%) in the U.S. economy has been increasing persistently over the past century. Across different industries, rising concentration was more pronounced in manufacturing, mining, and utilities before 1970s, and more pronounced in services, retail, and wholesale after 1970s. We find that the timing and the degree of rising concentration in an industry align closely with the investment intensity in research and development and information technology. In addition, industries with higher increases in concentration also exhibit higher output growth. The evidence suggests that the long-run trends of rising corporate concentration reflect increasingly stronger economies of scale. ∗We thank Nico Crouzet, Mike Cusick, Wenxin Du, Steve Kaplan, Anil Kashyap, Dmitry Kuvshinov, Chen Lian, Michael Minnis, Simon Oh, Lubos Pas...

Research Papers in Economics, Sep 1, 2021

People often estimate probabilities, such as the likelihood that an insurable risk will materiali... more People often estimate probabilities, such as the likelihood that an insurable risk will materialize or that an Irish person has red hair, by retrieving experiences from memory. We present a model of this process based on two established regularities of selective recall: similarity and interference. The model accounts for and reconciles a variety of conflicting empirical findings, such as overestimation of unlikely events when these are cued vs. neglect of non-cued ones, the availability heuristic, the representativeness heuristic, as well as over vs. underreaction to information in different situations. The model makes new predictions on how the content of a hypothesis (not just its objective probability) affects probability assessments by shaping the ease of recall. We experimentally evaluate these predictions and find strong experimental support.

The Quarterly Journal of Economics

In many economic decisions people estimate probabilities, such as the likelihood that a risk mate... more In many economic decisions people estimate probabilities, such as the likelihood that a risk materializes or that a job applicant will be a productive employee, by retrieving experiences from memory. We model this process based on two established regularities of selective recall: similarity and interference. We show that the similarity structure of a hypothesis and the way it is described (not just its objective probability) shape the recall of experiences and thus probability assessments. The model accounts for and reconciles a variety of empirical findings, such as overestimation of unlikely events when these are cued versus neglect of noncued ones, the availability heuristic, the representativeness heuristic, conjunction and disjunction fallacies, as well as over- versus underreaction to information in different situations. The model yields several new predictions, for which we find strong experimental support.

In the United States, large educational achievement gaps between groups (socioeconomic status, ra... more In the United States, large educational achievement gaps between groups (socioeconomic status, race, etc.) contribute to large gaps in poverty rates and other long-term economic outcomes. Simultaneously, there are also large gaps in education "inputs," with a strong correlation between study hours and academic performance. We examine the drivers of this variation by directly measuring students' beliefs and preferences about the education production function. Further, we investigate how these beliefs and preferences respond to a low-cost information intervention. We hope the evidence our study provides will help inform policy makers in their strive to reduce observed achievement gaps across socioeconomic statuses. This preregistration covers a follow-up survey using the same sample of students we initially surveyed in spring 2020.

Preregistration for February CLRN Survey

We introduce diagnostic expectations into a standard setting of price formation in which investor... more We introduce diagnostic expectations into a standard setting of price formation in which investors learn about the fundamental value of an asset and trade it. We study the interaction of diagnostic expectations with two well-known mechanisms: learning from prices and speculation (buying for resale). With diagnostic (but not with rational) expectations, these mechanisms lead to price paths exhibiting three phases: initial underreaction, followed by overshooting (the bubble), and finally a crash. With learning from prices, the model generates price extrapolation as a byproduct of fast moving beliefs about fundamentals, which lasts only as the bubble builds up. When investors speculate, even mild diagnostic distortions generate substantial bubbles. 1 Saïd Business School, University of Oxford, Bocconi University and IGIER, Harvard Business School and Harvard University. Gennaioli thanks the European Research Council (GA 647782) for financial support. We are grateful to Nicholas Barberi...

Using a comprehensive database of corporate news events, we show that stock prices tend to overre... more Using a comprehensive database of corporate news events, we show that stock prices tend to overreact to certain types of corporate news, such as leadership changes, mergers and acquisitions, and customer-related announcements, but underreact to others, such as earnings announcements. We propose a model in which investors use past significant observations – which we call exemplars – as reference points to evaluate new information. Consistent with our theory, we show that event-types with more extreme past return distributions have more overreaction and greater trading volume. We numerically calibrate the model to quantitatively fit the variation in investor overreaction and underreaction across different event-types.

SSRN Electronic Journal, 2020

SSRN Electronic Journal, 2021

We study how biases in expectations vary across different settings, through a large-scale randomi... more We study how biases in expectations vary across different settings, through a large-scale randomized experiment where participants forecast stable random processes. The experiment allows us to control the data generating process and the participants' relevant information sets, so we can cleanly measure forecast biases. We find that forecasts display significant overreaction to the most recent observation. Moreover, overreaction is especially pronounced for less persistent processes and longer forecast horizons. We also find that commonly-used expectations models do not easily account for the variation in overreaction across settings. We provide a theory of expectations formation with imperfect utilization of past information. Our model closely fits the empirical findings.