Aggregate Shocks or Aggregate Information? Costly Information and Business Cycle Comovement

Synchronized expansions and contractions across sectors define business cycles. Yet synchronization is puzzling because productivity across sectors exhibits weak correlation. While previous work examined production complementarity, our analysis explores complementarity in information acquisition. Because information about future productivity has a high fixed cost of production and a low marginal cost of replication, sectors can share the cost to forecast their sector-specific productivity. Sectors with common, aggregate information make highly correlated productions choices. By filtering out sector-specific shocks and transmitting aggregate ones, information markets amplify business-cycle comovement.

Nature or nurture? learning and female labor force dynamics

In the last century, the evolution of female labor force participation has been S-shaped: It rose slowly at first, then quickly, and has leveled off recently. Central to this dramatic rise has been the entry of women with young children. We argue that this S-shaped dynamic came from generations of women learning about the relative importance of nature (endowed ability) and nurture (time spent child-rearing) in determining children's outcomes. Each generation updates the beliefs of their parents, by observing others' outcomes. When few women participate in the labor force, most outcomes are uninformative about the effect of labor force participation and participation rises slowly. As information accumulates and the effects of labor force participation become less uncertain, more women participate, learning accelerates and labor force participation rises faster. As beliefs converge to the truth, participation flattens out. Learning offers a rational explanation for the differences in employment preferences that have been the focus of a large empirical literature. Survey data, wage data and participation data support our story and distinguish it from alternative explanations.Women - Employment

Aggregate shocks or aggregate information? costly information and business cycle comovement

When similar patterns of expansion and contraction are observed across sectors, we call this a business cycle. Yet explaining the similarity and synchronization of these cycles across industries remains a puzzle. Whereas output growth across industries is highly correlated, identifiable shocks, like shocks to productivity, are far less correlated. While previous work has examined complementarities in production, we propose that sectors make similar input decisions because of complementarities in information acquisition. Because information about driving forces has a high fixed cost of production and a low marginal cost of replication, it can be more efficient for firms to share the cost of discovering common shocks than to invest in uncovering detailed sectoral information. Firms basing their decisions on this common information make highly correlated production choices. This mechanism amplifies the effects of common shocks, relative to sectoral shocks.Business cycles

Information Immobility and the Home Bias Puzzle

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why doesn't global information access 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 out-performance and asset prices. Finally, we propose new avenues for empirical research.

Learning Asymmetries in Real Business Cycles

When an economic boom ends, the downturn is generally sharp and short. When growth resumes, the boom is more gradual. Our explanation for this pattern rests on learning about productivity. When agents believe productivity is high, they work, invest, and produce more. More production generates higher precision information. When the economy passes the peak of a productivity boom, precise estimates of the slowdown prompt quick, decisive reactions: Investment and labor fall sharply. At the end of a slump, low production yields noisy estimates of the recovery. The noise impedes learning, slows the recovery, and makes booms more gradual than crashes. A calibrated model generates asymmetry in growth rates similar to macroeconomic aggregates. Fluctuations in agents’ forecast precision match observed countercyclical dispersion in analysts’ macroeconomic forecasts. “There is, however, another characteristic of what we call the trade cycle that our explanation must cover; namely, the phenomenon of the crisis the fact that the substitution of a downward for an upward tendency often takes place suddenly and violently , whereas there is, as a rule, no such sharp turning point when an upward is substituted for a downward tendency.” J.M. Keynes (1936)

Information Markets and the Comovement of Asset Prices

Traditional asset pricing models predict that covariance between prices of different assets should be lower than what we observe in the data. This model generates this high covariance within a rational expectations framework by introducing markets for information about asset payoffs. When information is costly, rational investors will not buy information about all assets; they will learn about a subset. Because information production has high fixed costs, competitive producers charge more for low-demand information than for high-demand information. A price that declines in quantity makes investors want to purchase a common subset of information. If investors price many assets using a common subset of information, then a shock to one signal is passed on as a common shock to many asset prices. These common shocks to asset prices generate `excess covariance.' The cross-sectional and time-series properties of asset price covariance are consistent with this explanation