Essays in Behavioral Macroeconomics and Mechanism Design

Abstract

This thesis is in two parts. The first part of the thesis, "Essays in Behavioral Macroeconomics," is motivated by the simple observation that the macroeconomy is complicated; many households and firms interact across myriad markets in ways that change over time. This part of the thesis studies, empirically and theoretically, the microeconomic foundations and macroeconomic implications of hypotheses inspired by these complications: that people adopt simplified and misspecified narratives to understand the world; and that people will only pay attention to the macroeconomy when it is important to them.

In the first chapter, "The Macroeconomics of Narratives" (coauthored with Karthik A. Sastry), we study the macroeconomic implications of narratives, or beliefs about the economy that affect decisions and spread contagiously. Empirically, we use natural-language-processing methods to measure textual proxies for narratives in US public firms' end-of-year reports (Forms 10-K). We find that: (i) firms' hiring decisions respond strongly to narratives, (ii) narratives spread contagiously among firms, and (iii) this spread is responsive to macroeconomic conditions. To understand the macroeconomic implications of these forces, we embed a contagious optimistic narrative in a business-cycle model. We characterize, in terms of the decision-relevance and contagiousness of narratives, when the unique equilibrium features: (i) non-fundamental business cycles, (ii) non-linear belief dynamics (narratives "going viral") that generate multiple stable steady states (hysteresis), and (iii) the coexistence of hump-shaped responses to small shocks with regime-shifting behavior in response to large shocks. Our empirical estimates discipline both the static, general equilibrium effect of narratives on output and their dynamics. In the calibrated model, we find that contagious optimism explains 32% and 18% of the output reductions over the early 2000s recession and Great Recession, respectively, as well as 19% of the unconditional variance in output. We find that overall optimism is not sufficiently contagious to generate hysteresis, but other, more granular narratives are.

In the second chapter, "Attention Cycles" (coauthored with Karthik A. Sastry), we document that, in aggregate downturns, US public firms’ attention to macroeconomic conditions rises and the size of their input-choice mistakes falls. We explain these phenomena with a business-cycle model in which firms face a cognitive cost of making precise decisions. Because firms are owned by risk-averse households, there are greater incentives to deliver profits by making smaller input-choice mistakes when aggregate consumption is low. In the data, consistent with our model, financial markets punish mistakes more in downturns and macroeconomically attentive firms make smaller mistakes. Quantitatively, attention cycles generate asymmetric, state-dependent shock propagation and stochastic volatility of output growth.

In the third chapter, "Strategic Mistakes" (coauthored with Karthik A. Sastry), to study the equilibrium implications of decision frictions, we introduce a new class of control costs in continuum-player, continuum-action games in which agents interact via an aggregate of the actions of others. The costs that we study accommodate a rich class of decision frictions, including ex post misoptimization, imperfect ex ante planning, cognitive constraints that depend endogenously on the behavior of others, and consideration sets. We provide primitive conditions such that equilibria exist, are unique, are efficient, and feature monotone comparative statics for action distributions, aggregates, and the size of agents' mistakes. We apply the model to make robust equilibrium predictions in a monetary business-cycle model of price-setting with planning frictions and a model of consumption and savings during a liquidity trap when endogenous stress worsens decisions.

The second part of this thesis, "Essays in Mechanism Design," studies two contentious issues in the allocation of resources in the modern economy: How should we account for diversity when we allocate resources in two-sided matching markets? How should digital goods and information be priced and regulated?

In the fourth chapter, "Priority Design in Centralized Matching Markets" (coauthored with Oguzhan Celebi), we observe that in many centralized matching markets, agents' property rights over objects are derived from a coarse transformation of an underlying score. Prominent examples include the distance-based system employed by Boston Public Schools, where students who lived within a certain radius of each school were prioritized over all others, and the income-based system used in New York public housing allocation, where eligibility is determined by a sharp income cutoff. Motivated by this, we study how to optimally coarsen an underlying score. Our main result is that, for any continuous objective function and under stable matching mechanisms, the optimal design can be attained by splitting agents into at most three indifference classes for each object. We provide insights into this design problem in three applications: distance-based scores in Boston Public Schools, test-based scores for Chicago exam schools, and income-based scores in New York public housing allocation.

In the fifth chapter, "Adaptive Priority Mechanisms" (coauthored with Oguzhan Celebi), we ask how authorities that care about match quality and diversity should allocate resources when they are uncertain of the market they face? Such a question appears in many contexts, including the allocation of school seats to students from various socioeconomic groups with differing exam scores. We propose a new class of adaptive priority mechanisms (APM) that prioritize agents as a function of both scores that reflect match quality and the number of assigned agents with the same socioeconomic characteristics. When there is a single authority and preferences over scores and diversity are separable, we derive an APM that is optimal, generates a unique outcome, and can be specified solely in terms of the preferences of the authority. By contrast, the ubiquitous priority and quota mechanisms are optimal if and only if the authority is risk-neutral or extremely risk-averse over diversity, respectively. When there are many authorities, it is dominant for each of them to use the optimal APM, and each so doing implements the unique stable matching. However, this is generally inefficient for the authorities. A centralized allocation mechanism that first uses an aggregate APM and then implements authority-specific quotas restores efficiency. Using data from Chicago Public Schools, we estimate that the gains from adopting APM are considerable.

In the sixth and final chapter, "Nonlinear Pricing with Under-Utilization: A Theory of Multi-Part Tariffs" (coauthored with Roberto Corrao and Karthik A. Sastry), we study the nonlinear pricing of goods whose usage generates revenue for the seller and of which buyers can freely dispose. The optimal price schedule is a multi-part tariff, featuring tiers within which buyers pay a marginal price of zero. We apply our model to digital goods, for which advertising, data generation, and network effects make usage valuable, but monitoring legitimate usage is infeasible. Our results rationalize common pricing schemes including free products, free trials, and unlimited subscriptions. The possibility of free disposal harms producer and consumer welfare and makes both less sensitive to changes in usage-based revenue and demand.