This dissertation considers organizational problems of market entry. The research follows the experimental path. Game theoretic models are combined with laboratory experiments to produce a set of empirical findings. Two market entry problems are studied. The first considers the chain store paradox developed by Selten (1978). This game considers an established chain store with locations in numerous towns. In each of these towns a different competitor decides whether to enter and compete with the chain store. When entry occurs, the chain store can respond cooperatively or aggressively. The game proceeds sequentially, the players are not symmetric, and the critical solution concept is the subgame perfect equilibrium. Three experiments are conducted for a total of 550 trials of the game. Experiments differ in the size of payoffs, the number of entrants, the anonymity of the chain store, and whether subjects play in both the role of the chain store and an entrant or in just one role. There is qualified support for the game theoretic prediction that a chain store cannot deter the sequential entry of competitors. Entry occurred on 459 of 550 trials; while some chain stores pursue deterrence, it largely is not effective in these specific experimental environments. It is suggested that deterrence might be effective if the number of entrants or payoffs are increased. The results have implications for discussions on predatory pricing, reputation, and the value of backwards induction as a solution concept. The second market entry problem is based on a simultaneous market entry game developed by Rapoport (1994). In this game symmetric players decide simultaneously whether to enter a market with a specified capacity. The game theoretic prediction for the number of entrants is based on a Nash equilibrium (in pure or mixed strategies). Again, experimental results support game theoretic predictions. Across three experiments the correlation between the number of entrants and the size of the market capacity is consistently above 0.90. Taken together, these experiments on market entry problems provide strong support for the conceptual use of game theory and the methodological use of controlled laboratory experiments in the field of strategic management.

The thesis includes two essays on asset pricing. In the first essay, "Asset Pricing in a Monetary Economy with Heterogeneous Beliefs", we shed new light on the role of monetary policy in asset pricing by focusing on the case where investors have heterogeneous expectations about future monetary policy. Under heterogeneity in beliefs, investors place bets against each other on the evolution of money supply, and as a result, the sharing of wealth in the economy evolves stochastically over time, making money non-neutral. Employing a continuous-time, general equilibrium model, we establish these fluctuations to be rich in implications, in that they majorly affect the equilibrium prices of all assets, as well as inflation. In particular, we find that the stock market volatility may be significantly increased by the heterogeneity in beliefs, a conclusion supported by our empirical analysis. The second essay is titled with " Asset Pricing and Welfare Analysis with Bounded Rational Investors". Motivated by the fact that investors have limited ability and insufficient knowledge to process information, I model investors' bounded-rational behavior in processing information and study its implications on asset pricing. Bounded rational investors perceive "correlated" information (which consists of news that is correlated with fundamentals, but provides no information on them) as "fundamental" information. This generates "bounded rational risk". Asset prices and volatilities of asset returns are derived. Specially, the equity premium and the stock volatility are raised under some conditions. I also analyze the welfare impact of bounded rationality.

This dissertation studies information economics in strategic and decision settings. In Chapter 1, I introduce flexible endogenous monitoring into dynamic moral hazard. A principal can commit to not only an employment plan but also the monitoring technology to incentivize dynamic effort from an agent. Optimal monitoring follows a Poisson process that produces rare informative signals, and the optimal employment plan features decreasing turnover. To incentivize persistent effort, the Poisson monitoring takes the form of "bad news'' that leads to immediate termination. Monitoring is non-stationary: the bad news becomes more precise and less frequent. In Chapter 2, which is joint work with Qingmin Liu, we analyze a model of strategic exploration in which competing players independently explore a set of alternatives. The model features a multiple-player multiple-armed bandit problem and captures a strategic trade-off between preemption---covert exploration of alternatives that the opponent will explore in the future---and prioritization---exploration of the most promising alternatives. Our results explain how the strategic trade-off shapes equilibrium behaviors and outcomes, e.g., in technology races between superpowers and R&D competitions between firms. We show that players compete on the same set of alternatives, leading to duplicated exploration from start to finish, and they explore alternatives that are a priori less promising before more promising ones are exhausted. In Chapter 3, I study how a forward-looking decision maker experiments on unknown alternatives of spatially correlated utilities, modeled by a Brownian motion so that similar alternatives yield similar utilities. For example, a firm experiments on its size that yields unknown, spatially correlated profitability. Experimentation trades off the opportunity cost of exploitation for the indirect inference from the explored alternatives to unknown ones. The optimal strategy is to explore unknown alternatives and then exploit the best known alternative when the explored becomes sufficiently worse than the best. The decision maker explores more quickly as the explored alternative worsens. My model predicts the conditional Gibrat's law and linear relation between firm size and profitability.

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This dissertation contains two independent but related papers which investigate theoretically and empirically qualitative aspects of firms' behavior in dynamic settings. CHAPTER 1. ENDOGENOUS ATTRITION OF FIRMS: An Investigation with COMPUSTAT Data Chapter One develops a dynamic programming model of firms' attrition and investigates econometric aspects of firms' exit decisions. Structural econometric analyses of exit decisions of firms involve rather naturally a number of qualitative dimensions. This chapter investigates the exit decision empirically by means of panel data from COMPUSTAT for U.S. manufacturing firms which are publicly traded. A number of different techniques are employed, which include Poisson models, structural form models and duration models. Our findings show that observable characteristics of the individual firms are important in understanding the dynamics of firm's attrition. Cyclical effects and macroeconomic variables have also a strong impact on bankruptcies, liquidations and reorganizations. Unobserved firm heterogeneity, modeled by means of random effects, is not significant in explaining exit decisions by firms. Firms' attrition is more likely to result from random events at the time of exit. CHAPTER 2 AN EMPIRICAL INVESTIGATION ON THE DYNAMICS OF QUALITATIVE DECISIONS OF FIRMS Chapter Two focuses on qualitative aspects of financing, investment and output decisions of firms. Such dimensions can be modeled econometrically by means of dynamic limited dependent variables models. We develop a partial equilibrium dynamic stochastic programming problem of investment, dividend and financing decisions for a typical firm and we use it to examine firms' behavior under exogenous borrowing constraints. We use panel data from COMPUSTAT for publicly traded U.S. manufacturing firms. We apply limited dependent variable models' techniques to study the discrete decisions of whether or not firms pay dividends, or whether they use borrowing or equity issue financing for investment. We study the pattern of transitions over time across various regimes that represent alternative modes of finance while controlling for individual heterogeneity with a general stochastic structure for unobservables. Structural form models show considerable success in explaining the dynamics of such decisions, with individual characteristics of the firms which include firm fundamentals, and lagged values of the decisions, showing a strong explanatory impact. The dynamics of the estimated models reveal high persistence in manufacturing firms to repeat their last period’s decision. Firm heterogeneity modeled by means of random effects, explains also a significant part of firms' qualitative decisions. / Ph. D.

This thesis focuses on learning from revealed preferences and their implications across operations management problems through an Inverse Problem perspective. For the first part of the thesis, we focus on decentralized platforms facilitating many-to-many matches between two sides of a marketplace. In the absence of direct matching, inefficiency in market outcomes can easily arise. For instance, popular supply agents may garner many units from the demand side, while other supply units may not receive any match. A central question for the platform is how to manage congestion and improve market outcomes. In Chapter One, we study the impact of a detail-free lever: the disclosure of information to agents on current competition levels. How large are the effects of this lever, and how do they affect overall market outcomes? We answer this question empirically. We partner with the largest service marketplace in Latin America, which sells non-exclusive labor market leads to workers. The key innovation in our approach is the proposal of a structural model that allows agents (workers) to respond to competitors through beliefs about competition at the lead level, which in turn implies an equilibrium at the platform level under the assumption of rational expectations. In this problem, we observe agents' best responses (actions), and from that, we need to infer their structural parameters. Identification follows from an exogenous intervention that changes agents' contextual information and the platform equilibrium. We then conduct counterfactual analyses to study the impact of signaling competition on workers' lead purchasing decisions, the platform's revenue, and the expected number of matches. We find that signaling competition is a powerful lever for the platform to reduce congestion, redirect demand, and ultimately improve the expected number of matches for the markets we analyze. For the second part of the thesis, we discuss both parametric and modelling approaches in Inverse Problems. In Chapter Two, we focus on Inverse Optimization Problems in a single-agent setting. Specifically, we study offline and online contextual optimization with feedback information, where instead of observing the loss, we observe, after-the-fact, the optimal action an oracle with full knowledge of the objective function would have taken. We aim to minimize regret, which is defined as the difference between our losses and the ones incurred by an all-knowing oracle. In the offline setting, the decision-maker has information available from past periods and needs to make one decision, while in the online setting, the decision-maker optimizes decisions dynamically over time based on a new set of feasible actions and contextual functions in each period. For the offline setting, we characterize the optimal minimax policy, establishing the performance that can be achieved as a function of the underlying geometry of the information induced by the data. In the online setting, we leverage this geometric characterization to optimize the cumulative regret. We develop an algorithm that yields the first regret bound for this problem, which is logarithmic in the time horizon. Furthermore, we show via simulation that our proposed algorithms outperform previous methods from the literature. Finally, in Chapter Three, we consider data-driven methods for general Inverse Problem formulations under a statistical framework (Statistical Inverse Problem-SIP) and demonstrate how Stochastic Gradient Descent (SGD) algorithms can be used to solve linear SIP. We provide consistency and finite sample bounds for the excess risk. We exemplify the algorithm in the Functional Linear Regression setting with an empirical application in predicting illegal activity from bitcoin wallets. We also discuss additional applications and extensions.

Before making a choice, we often have the opportunity to learn more about the options that are available. For example, we can check the characteristics of a product before buying it, or read different newspapers before a political election. Understanding what shapes the demand for information, and its role in the decision process, is important to study economic choices. This dissertation contains three essays in behavioral and information economics that utilize experimental data and modeling to analyze how people choose and use information to make decisions. The first chapter, "Coarse and Precise Information in Food Labeling," uses experimental data to determine whether precise food labels can be more effective and informative than coarse ones. In a preregistered online study conducted on a representative US sample, I manipulate front-of-package labels about foods' calorie content. I find that coarse-categorical labels generate a larger reduction in calories per serving compared to detailed-numerical labels despite providing less information. Choices violate the predictions of Bayesian decision theory, suggesting that consumers are less responsive to detailed information. Results also show that participants prefer coarse labels, suggesting a general preference for simple, easy-to-interpret information. The second chapter, "The Status Quo and Belief Polarization of Inattentive Agents," studies how differences across agents can drive information acquisition and generate polarization. In a rational inattention model, optimal information acquisition and subsequent belief formation depend crucially on the agent-specific status quo valuation. Beliefs can systematically update away from the realized truth and even agents with the same initial beliefs might become polarized. A laboratory experiment confirms the model's predictions about the information acquisition and its effect on beliefs. Differently from the model's predictions, participants display preferences for simple messages that can provide certainty. The third chapter, "Dynamic Information Choice with Biased Information Sources," uses experimental data to study how people decide what kind of information to acquire when they have multiple opportunities to learn. Standard theory predicts that decision makers should collect the stream of information that leads to the maximization of the expected reward from the final choice. An online experiment on sequential information acquisition shows that people systematically deviate from the predictions of the standard normative model. Participants display a certainty-seeking information acquisition behavior and under-respond to the new evidence collected, reviewing rarely their own information acquisition strategy.

In the first chapter, I investigate how external analyst forecasts influence managerial earnings decisions. Using shifts in analyst composition effected by brokerage mergers as a source of exogenous variation, I establish a one-to-one response of firm earnings to analyst forecasts. This response is driven by accounting accruals, consistent with short-termist earnings management. I find that the market perceives these accruals as costly to the firm. I present a model where this behavior emerges as a rational equilibrium, confirmed by a calibration that mirrors a one-to-one forecast-earnings relationship. Calibration outcomes align with real-world earnings and forecast patterns. In the second chapter (co-authored with Harrison Hong and Jeffrey Kubik), we estimate the cost to capital of climate policy. Many US states have set ambitious renewable portfolio standards (RPS) that require utilities to switch from fossil fuels toward renewables. RPS increases the renewables capacity, bond issuance, maturity, and yield spreads of investor-owned utilities compared to municipal producers that are exempted from this climate policy. Contrary to stranded-asset concerns, the hit to overall firm financial health is moderate. Falling cost of renewables and pass through of these costs to consumers mitigate the burden of RPS on firms. Using a Tobin’s 𝒒 model, we show that, absent these mitigating factors, the impact of RPS on firm valuations would have been severe. In the third chapter (co-authored with Lukas Fischer), we identify a source of peer group influence that is plausibly orthogonal to information provision, yet nonetheless affects economic decision-making: the shock to an equity analyst of their undergraduate college football team winning the NCAA Championship Game. We find that analysts’ forecasts respond positively to their undergraduate school’s football team winning the NCAA final. We then show that the shock of ‘winning’ spreads within an analyst’s brokerage, positively influencing the forecasts of their colleagues. Brokerages where the degree of this diffusion is greater have lower female representation in their analyst teams, as well as lower ESG scores.

In many economic applications, a collective outcome experienced by a group of people is determined by individual decisions made by its constituents. Hence, understanding how individuals make decisions in group settings is important, but empirical and observational analyses are often complicated by confounding factors. This dissertation contains three essays that use controlled experiments designed to isolate, and measure the impact of, mechanisms predicted to affect behavior. Chapter 1 studies behavior under digital anonymity. A distinctive feature of the digital world is the ability to calibrate or withhold one's identifier: a person can be identified by a string of letters, an avatar, their real name, or even nothing at all. That digital identifiers allow a person to mask their physical identity also makes it difficult to attribute digital actions to a physical person, even when the actions are observed. I embed these features in an experiment where subjects play a finitely repeated, linear public goods game. Treated subjects are identified in one of three ways—by their photograph, by a random number, or by a self-designed cartoon avatar—and their individual choices are revealed and either attributed to, or decoupled from, their identifier. In line with the previous literature, identifying subjects and increasing the precision of attribution increases contributions relative to a baseline condition without identifiers or revealed individual choices. Remarkably, however, the largest impact on behavior comes from having an identifier in the first place: for a given level of attribution, the experimental data suggest that being identified by a number or by an avatar is as powerful as being identified by one's photograph. Chapter 2 studies whether and how individuals imbue digital avatars with self image and social image considerations. While digital avatars have become more commonplace and sophisticated, they need not resemble the physical appearance of the person using it. This inconsistency raises the question of how an avatar induces image considerations, relative to a person's physical appearance. I embed avatars into a dictator game and conduct two experiments, one addressing self image and the other social image. The direction of the treatment effect in the dictator game for both experiments suggests that individuals do attach image considerations to their avatars, though the effects are not statistically significant. Additionally, I find that subjects create significantly more positively perceived avatars when they know that their avatar will be shown to another subject who will decide how to allocate an endowment with them. Chapter 3, joint with Alessandra Casella and Michelle Jiang, studies the impact of an alternative voting system on the minority's turnout and resultant victories. We start from the observation that under majoritarian election systems, securing participation and representation of minorities remains an open problem, made salient in the US by its history of voter suppression. One remedy recommended by the courts is Cumulative Voting (CV): each voter has as many votes as open positions and can cumulate votes on as few candidates as desired. Theory predicts that CV encourages the minority to overcome obstacles to voting: although each voter is treated equally, CV increases minority's turnout relative to the majority, and the minority's share of seats won. A lab experiment based on a costly voting design strongly supports both predictions. Chapter 3 was published in Volume 141 of Games and Economic Behavior, pp. 133-155, September 2023, https://doi.org/10.1016/j.geb.2023.05.012.

In the first chapter of my thesis, titled “Interventions in Credit Markets and Effects on Economic Activity: Evidence from Brazil,” I investigate the impact of the Brazilian government policy implemented in March 2012, which aimed at increasing credit supply through public banks. Using bank branch level data, I find that the policy successfully increased overall credit supply, as increased lending of public banks did not significantly offset private lending. On the other hand, there is no evidence of significant client-switching between private and public banks. However, the effects of the policy on economic activity were limited and even negligible. I conduct a series of robustness checks to further explore this puzzling result. I find evidence suggesting that increased lending led to significant increases in deposits, indicating that borrowers leveraged easily accessible credit to take loans and save funds for future use. In the second chapter, titled “Television Introduction and Agricultural Production,” I investigate how improved information affected agricultural activity in the U.S. Specifically, I argue that the introduction of television brings more comprehensible weather forecast information to farmers, improving their decision making process. Using data about television entry and county level farming production in a difference-in-differences methodology, I estimate economically significant effect of television introduction on crop yields.