We solve for the equilibrium dynamics of Information sharing in a large population Each agent is endowed with signals regarding the likely outcome of a random variable of common concern Individuals choose the effort with which they search for others from whom they can gather additional information. When two agents meet, they share their information The Information gathered is further shared at subsequent meetings, and so on. Equilibria exist in which agents search maximally until they acquire sufficient information precision and then search minimally A tax whose proceeds are used to Subsidize the costs of search improves information sharing and can, in some cases, increase welfare on the other hand, endowing agents with public signals reduces Information sharing and can, in some cases, decrease welfare

Schumpeter (1939) claims that recessions are periods of “creative destruction,” concentrating innovation that is useful for the long-term growth of the economy. However previous research finds that standard measures of firms’ innovation, such as R&D expenditures or raw patent counts, concentrate in booms. We argue that these measures do not capture shifts in firms’ innovative search strategies. We contemplate firms’ choice between exploration versus exploitation over the business cycle and find evidence with more nuanced measures of patent characteristics that firms shift toward exploration during contractions and exploitation during expansions, with a stronger effect for firms in more cyclical industries.

Unlike labor income, human capital is inseparable from individuals and does not completely accrue to creditors. Therefore, human capital investment is more resilient to “debt overhang” than labor supply. We develop a dynamic model displaying this difference. We find that while both labor supply and human capital investment are hump-shaped in household indebtedness, human capital investment declines less aggressively as indebtedness builds up. Importantly, because human capital is only valuable when households expect to supply labor, the greater reduction in labor supply due to debt overhang back-propagates into ex-ante human capital investment. We provide empirical support for the model.

We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale estimates are associated with substantially higher price‐to‐estimate ratios and lower buy‐in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.