We develop a methodology to disentangle sources of capital “mis-allocation,” i.e., dispersion in value-added/capital. It measures the contributions of technological/informational frictions and a rich class of firm-specific factors. An application to Chinese manufacturing firms reveals that adjustment costs and uncertainty, while significant, explain only a modest fraction of the dispersion, which stems largely from other factors: a component correlated with productivity and a fixed effect. Adjustment costs are more salient for large US firms, though other factors still account for the bulk of the dispersion. Technological/markup heterogeneity explains a limited fraction in China, but a potentially large share in the United States.

We propose a theory linking imperfect information to resource misallocation and hence to aggregate productivity and output. In our setup, firms look to a variety of noisy information sources when making input decisions. We devise a novel empirical strategy that uses a combination of firm-level production and stock market data to pin down the information structure in the economy. Even when only capital is chosen under imperfect information, applying this methodology to data from the United States, China, and India reveals substantial losses in productivity and output due to the informational friction. Our estimates for these losses range from 7% to 10% for productivity and 10% to 14% for output in China and India, and are smaller, though still significant, in the United States. Losses are substantially higher when labor decisions are also made under imperfect information. We find that firms turn primarily to internal sources for information; learning from financial markets contributes little, even in the United States.

How should an investor value financial data? The answer is complicated because it depends on the characteristics of all investors. We develop a sufficient statistics approach that uses equilibrium asset return moments to summarize all relevant information about others’ characteristics. Our approach values public or private data, data about one or many assets, and data relevant for dividends or sentiment. While different data types, of course, have different valuations, heterogeneous investors also value the same data very differently. This finding suggests a low price elasticity for data demand. Heterogeneous investors’ data valuations are also affected very differentially by market illiquidity.

Since the finance industry is transforming into a data industry, measuring the quantity of data investors have about various assets is important. Informed by a structural model, we develop such a cross-sectional measure. We show how our measure differs from price informativeness and use it to document a new fact: data about large high-growth firms is becoming increasingly abundant, relative to data about other firms. Our structural model offers an explanation for this data divergence: large high-growth firms’ data became more valuable, as big firms got bigger and growth magnified the effect of these changes in size.

The Great Recession was a deep downturn with long-lasting effects on credit, employment, and output. While narratives about its causes abound, the persistence of gross domestic product below precrisis trends remains puzzling. We propose a simple persistence mechanism that can be quantified and combined with existing models. Our key premise is that agents do not know the true distribution of shocks but use data to estimate it nonparametrically. Then, transitory events, especially extreme ones, generate persistent changes in beliefs and macro outcomes. Embedding this mechanism in a neoclassical model, we find that it endogenously generates persistent drops in economic activity after tail events.

We study an economy’s response to an unexpected epidemic. The spread of the disease can be mitigated by reducing consumption and hours worked in the office. Working from home is subject to learning-by-doing. Private agents’ rational incentives are relatively weak and fatalistic. The planner recognizes infection and congestion externalities and implements front-loaded mitigation. Under our calibration, the planner reduces cumulative fatalities by 48% compared to 24% by private agents, although with a sharper drop in consumption. Our model can replicate key industry and/or occupational-level patterns and explain how large variations in outcomes across regions can stem from small initial differences.

We incorporate a search-theoretic model of imperfect competition into a standard model of asymmetric information with unrestricted contracts. We characterize the unique equilibrium and use our characterization to explore the interaction between adverse selection, screening, and imperfect competition. We show that the relationship between an agent’s type, the quantity he trades, and the price he pays is jointly determined by the severity of adverse selection and the concentration of market power. Therefore, quantifying the effects of adverse selection requires controlling for market structure. We also show that increasing competition and reducing informational asymmetries can decrease welfare.