In the twenty-first century, the most valuable firms in the world are valued primarily for their data. This article describes a set of tools to measure and value data and highlights unanswered questions, where more research is needed.

Traditional asset pricing models predict that covariance between prices of different assets should be lower than what we observe in the data. This paper introduces markets for information that generate high price covariance within a rational expectations framework. When information is costly, rational investors only buy information about a subset of the assets. Because information production has high fixed costs, competitive producers charge more for low-demand information than for high-demand information. The low price of high-demand information makes investors want to purchase the same information that others are purchasing. When investors price assets using a common subset of information, news about one asset affects the other assets' prices; asset prices comove. The cross-sectional and time-series properties of comovement are consistent with this explanation.

Recent data technology innovations, such as artificial intelligence and machine learning, have transformed the production of knowledge and increased the importance of data. This review explores how data—digitized information—has been modeled within classic macroeconomic frameworks. It compares the economics of data to other concepts such as ideas, patents, and learning-by-doing. This paper also shows potential ways to model applications for data, including innovation, process optimization, and matching. Because this research area is nascent, much of the article is devoted to open questions and directions for future data economy research.

Emerging equity markets witness occasional surges in prices (frenzies) and cross-market price dispersion (herds), accompanied by abundant media coverage. An information market complementarity can explain these anomalies. Because information has high fixed costs, high volume makes it inexpensive. Low prices induce investors to buy information that others buy. Given two identical assets, investors learn about one; abundant information reduces its payoff risk and raises its price. Transitions between low-information/low-asset-price and high-information/high-asset-price equilibria resemble frenzies. Equity data and new panel data on news coverage support the model's predictions: Asset market movements generate news and news raises prices and price dispersion.

Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, where do differences in these patterns come from and how large are their effects? Using network analysis tools, we explore how different social network structures affect technology diffusion and thereby a country’s rate of growth. The correlation between high-diffusion networks and income is strongly positive. But when we use a model to isolate the effect of a change in social networks on growth, the effect can be positive, negative, or zero. The reason is that networks diffuse both ideas and disease. Low-diffusion networks have evolved in countries where disease is prevalent because limited connectivity protects residents from epidemics. But a low-diffusion network in a low-disease environment compromises the diffusion of good ideas. In general, social networks have evolved to fit their economic and epidemiological environment. Trying to change networks in one country to mimic those in a higher-income country may well be counterproductive.

We explore how optimal information choices change the predictions of strategic models. When a large number of agents play a game with strategic complementarity, information choices exhibit complementarity as well: if an agent wants to do what others do, they want to know what others know. This makes heterogeneous beliefs difficult to sustain and may generate multiple equilibria. In models with substitutability, agents prefer to differentiate their information choices. We use these theoretical results to examine the role of information choice in recent price-setting models and to propose modelling techniques that ensure equilibrium uniqueness.

One of the most dramatic economic transformations of the past century has been the entry of women into the labor force. While many theories explain why this change took place, we investigate the process of transition itself. We argue that local information transmission generates changes in participation that are geographically heterogeneous, locally correlated, and smooth in the aggregate, just like those observed in our data. In our model, women learn about the effects of maternal employment on children by observing nearby employed women. When few women participate in the labor force, data are scarce and participation rises slowly. As information accumulates in some regions, the effects of maternal employment become less uncertain and more women in that region participate. Learning accelerates, labor force participation rises faster, and regional participation rates diverge. Eventually, information diffuses throughout the economy, beliefs converge to the truth, participation flattens out, and regions become more similar again. To investigate the empirical relevance of our theory, we use a new county-level data set to compare our calibrated model to the time series and geographic patterns of participation.

If an investor wants to form a portfolio of risky assets and can exert effort to collect information on the future value of these assets before he invests, which assets should he learn about? The best assets to acquire information about are ones the investor expects to hold. But the assets the investor holds depend on the information he observes. We build a framework to solve jointly for investment and information choices, with general preferences and information cost functions. Although the optimal research strategies depend on preferences and costs, the main result is that the investor who can first collect information systematically deviates from holding a diversified portfolio. Information acquisition can rationalize investing in a diversified fund and a concentrated set of assets, an allocation often observed, but usually deemed anomalous.

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research.

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research.