This paper develops a method informed by data and models to recover information about investor beliefs. Our approach uses information embedded in forward-looking asset prices in conjunction with asset pricing models. We step back from presuming rational expectations and entertain potential belief distortions bounded by a statistical measure of discrepancy. Additionally, our method allows for the direct use of sparse survey evidence to make these bounds more informative. Within our framework, market-implied beliefs may differ from those implied by rational expectations due to behavioral/psychological biases of investors, ambiguity aversion, or omitted permanent components to valuation. Formally, we represent evidence about investor beliefs using a nonlinear expectation function deduced using model-implied moment conditions and bounds on statistical divergence. We illustrate our method with a prototypical example from macrofinance using asset market data to infer belief restrictions for macroeconomic growth rates.
The drift-diffusion model (DDM) is a model of sequential sampling with diffusion signals, where the decision maker accumulates evidence until the process hits either an upper or lower stopping boundary and then stops and chooses the alternative that corresponds to that boundary. In perceptual tasks, the drift of the process is related to which choice is objectively correct, whereas in consumption tasks, the drift is related to the relative appeal of the alternatives. The simplest version of the DDM assumes that the stopping boundaries are constant over time. More recently, a number of papers have used nonconstant boundaries to better fit the data. This paper provides a statistical test for DDMs with general, nonconstant boundaries. As a by-product, we show that the drift and the boundary are uniquely identified. We use our condition to nonparametrically estimate the drift and the boundary and construct a test statistic based on finite samples.
Poor early childhood development in low- and middle-income countries is a major public health problem. Efficacy trials have shown the potential of early childhood development interventions but scaling up is costly and challenging. Guidance on effective interventions’ delivery is needed. In an open-label cluster-randomized control trial, we compared the effectiveness of weekly home visits and weekly mother-child group sessions. Both included nutritional education, whose effectiveness was tested separately.
This comment points out mismeasurement of variables in the DSGE model in Smets and Wouters (2007) and in models that follow the Smets-Wouters measurement procedures. The mismeasurement errors appear to be large.
What are the effects of school closures during the Covid-19 pandemic on children’s education? Online education is an imperfect substitute for in-person learning, particularly for children from low-income families. Peer effects also change: schools allow children from different socio-economic backgrounds to mix together, and this effect is lost when schools are closed. Another factor is the response of parents, some of whom compensate for the changed environment through their own efforts, while others are unable to do so. We examine the interaction of these factors with the aid of a structural model of skill formation. We find that school closures have a large and persistent effect on educational outcomes that is highly unequal. High school students from poor neighborhoods suffer a learning loss of 0.4 standard deviations, whereas children from rich neighborhoods remain unscathed. The channels operating through schools, peers, and parents all contribute to growing educational inequality during the pandemic.
We propose a model of how multiple societies respond to a common crisis. A government faces a “damned-either-way” policy-making dilemma: aggressive intervention contains the crisis, but the resulting good outcome makes people skeptical of the costly response; light intervention worsens the crisis and causes the government to be faulted for not doing enough. This dilemma can be mitigated for the society that encounters the crisis first if another society faces the same crisis afterward. Our model predicts that the later society does not necessarily perform better despite having more information, while the earlier society might benefit from a dynamic counterfactual effect.
A principal faces an agent who is better informed but biased toward higher actions. She can verify the agent’s information and specify his permissible actions. We show that if the verification cost is small enough, a threshold with an escape clause (TEC) is optimal: the agent either chooses an action below a threshold or requests verification and the efficient action above the threshold. For higher costs, however, the principal may require verification only for intermediate actions, dividing the delegation set. TEC is always optimal if the principal cannot commit to inefficient allocations following the verification decision and result.
We propose a model of how multiple societies respond to a common crisis. A government faces a "damned-either-way" policymaking dilemma: aggressive intervention contains the crisis, but the resulting good outcome makes people skeptical about the costly response; light intervention worsens the crisis and causes the government to be faulted for not doing enough. When multiple societies encounter the crisis sequentially, due to this policymaking dilemma, late societies may underperform despite having more information, while early societies can benefit from a dynamic counterfactual effect.
This paper studies how an improved information environment affects consumer search and firm competition. We find conditions for information improvement to have unambiguous impacts on search duration, price and consumer welfare. In many cases consumers benefit from information improvement regardless of how it affects the market price, but there are also cases where information improvement raises price significantly so that consumers suffer from it. Our model provides a unified way to consider the market implications of various types of information improvement such as search advertising, personalized recommendations, filtering, and VR shopping technology.