Publications

We analyze lifecycle saving strategies using a recursive utility model calibrated to match empirical estimates for the value of a statistical life. We show that, with a positive value of life, risk aversion reduces savings and annuity purchase. Risk averse agents are willing to make an early death a not-so-adverse outcome by enjoying greater consumption when young and bequeathing wealth in case of death. We also find that greater risk aversion lowers stock market participation. We show that this model can rationalize low annuity demand while also matching empirically documented levels of wealth and private investments in stocks. Our findings stand in contrast to studies that implicitly assume a negative value of life.

The coronavirus (COVID-19) pandemic halted economic activity worldwide, hurting firms and pushing many of them toward bankruptcy. This paper discusses four central issues that have emerged in the academic and policy debates related to firm financing during the downturn. First, the economic crisis triggered by the pandemic is radically different from past crises, with important consequences for optimal policy responses. Second, it is important to preserve firms’ relationships with key stakeholders (like workers, suppliers, customers, and creditors) to avoid inefficient bankruptcies and long-term detrimental economic effects. Third, firms can benefit from “hibernation,” incurring the minimum bare expenses necessary to withstand the pandemic, while using credit if needed to remain alive until the crisis subdues. Fourth, the existing legal and regulatory infrastructure is ill-equipped to deal with an exogenous systemic shock like a pandemic. Financial sector policies can help increase the provision of credit, while posing difficult choices and trade-offs.

We present an approach to analyze learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models, and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly specified learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either

We present an approach to analyze learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. Our main results provide general criteria to determine—without the need to explicitly analyze learning dynamics—when beliefs in a given environment converge to some long-run belief either locally or globally (i.e., from some or all initial beliefs). The key ingredient underlying these criteria is a novel “prediction accuracy” ordering over subjective models that refines existing comparisons based on Kullback-Leibler divergence. We show that these criteria can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to identify and analyze a natural class of environments, including costly information acquisition and sequential social learning, where unlike most settings the literature has focused on so far, long-run beliefs can fail to be robust to the details of the true data generating process or agents’ perception thereof. In particular, even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can lead to extreme failures of learning.

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 iterative 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.

We analyze how to optimally engage in social distancing in order to minimize the spread of an infectious disease. We identify conditions under which any optimal policy is single-peaked, i.e., first engages in increasingly more social distancing and subsequently decreases its intensity. We show that an 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 and can substantially reduce the death rate of a disease.