Department of Economics

Using a large-scale survey of U.S. households during the COVID-19 pandemic, we study how new information about fiscal and monetary policy responses to the crisis affects households’ expectations. We provide random subsets of participants in the Nielsen Homescan panel with different combinations of information about the severity of the pandemic, recent actions by the Federal Reserve, stimulus measures, as well as recommendations from health officials. This experiment allows us to assess to what extent these policy announcements alter the beliefs and spending plans of households. In short, they do not, contrary to the powerful effects they have in standard macroeconomic models.

Using a survey of French manufacturing firms, we study how firms’ expectations and actions are affected by both aggregate and industry-specific conditions. In response to industry-level shocks that have no aggregate effects, firms’ aggregate expectations respond persistently. This is consistent with “island” models in which firms use the local prices they observe to make inferences about broader aggregate conditions. These patterns are related to observable characteristics of firms and the industries in which they reside. Finally, we extend the analysis to firms’ expectations over their own future price changes and document how these respond to both industry and aggregate variation.

While the social sciences have made impressive progress in adopting transparent research practices that facilitate verification, replication, and reuse of materials, the problem of publication bias persists. Bias on the part of peer reviewers and journal editors, as well as the use of outdated research practices by authors, continues to skew literature toward statistically significant effects, many of which may be false positives. To mitigate this bias, we propose a framework to enable authors to report all results efficiently (RARE), with an initial focus on experimental and other prospective empirical social science research that utilizes public study registries. This framework depicts an integrated system that leverages the capacities of existing infrastructure in the form of public registries, institutional review boards, journals, and granting agencies, as well as investigators themselves, to efficiently incentivize full reporting and thereby, improve confidence in social science findings. In addition to increasing access to the results of scientific endeavors, a well-coordinated research ecosystem can prevent scholars from wasting time investigating the same questions in ways that have not worked in the past and reduce wasted funds on the part of granting agencies.

We construct an endogenous growth model that includes a cultural variable along the dimension of individualism-collectivism. The model predicts that more individualism leads to more innovation because of the social rewards associated with innovation in an individualist culture. This cultural effect may offset the negative effects of bad institutions on growth. Collectivism leads to efficiency gains relative to individualism, but these gains are static, unlike the dynamic effect of individualism on growth through innovation. Using genetic data as instruments for culture we provide strong evidence of a causal effect of individualism on income per worker and total factor productivity as well as on innovation. The baseline genetic markers we use are interpreted as proxies for cultural transmission but others have a direct effect on individualism and collectivism, in line with recent advances in biology and neuro-science. The effect of culture on long-run growth remains very robust even after controlling for the effect of institutions and other factors. We also provide evidence of a two-way causal effect between culture and institutions.

We evaluate the effects of inequality, fiscal policy, and COVID19 restrictions in a model of economic slack with potentially rigid capital operating costs. Rich households satiate their demand for goods/services (and consume an endowment on the margin), whereas poor households’ spending on goods/services is limited by their income (which in turn depends on spending by the rich and on fiscal transfers). The model implies that inequality has large negative effects on output, while also diminishing the effects of demand-side fiscal stimulus. COVID restrictions can reduce current-period GDP by more than is directly associated with the restrictions themselves when rigid capital costs induce firm exit. Higher inequality is associated with larger restriction multipliers. The effectiveness of fiscal policies depends on inequality and the joint distribution of capital operating costs and firm revenues. Furthermore, COVID19 restrictions can cause future inflation, as households tilt their expenditure toward the future.

We implement a new survey of firms, focusing on their higher-order macroeconomic expectations. The survey provides a novel set of stylized facts regarding the relationship between first-order and higher-order expectations of economic agents, including how they adjust their beliefs in response to a variety of information treatments. We show how these facts can be used to calibrate key parameters of noisy-information models with infinite regress as well as to test predictions made by this class of models. We also consider a range of extensions to the basic noisy-information model that can potentially better reconcile theory and empirics. Although some extensions like level-k thinking are unsuccessful, incorporating heterogeneous long-run priors can address the empirical shortcomings of the basic noisy-information model.