Previous studies have argued that entrepreneurs earn less and bear more risk than salaried workers with otherwise similar characteristics. In a simple model of entrepreneurship, I show that estimates of mean and variance of returns to entrepreneurship used by these previous studies are biased, as they fail to account for the option value of experimenting with new ideas. Using longitudinal data, I find patterns that are consistent with entrepreneurship as experimentation and returns to entrepreneurship that are more attractive than established by previous research.

Motivating innovation is important in many incentive problems. This paper shows that the optimal innovation‐motivating incentive scheme exhibits substantial tolerance (or even reward) for early failure and reward for long‐term success. Moreover, commitment to a long‐term compensation plan, job security, and timely feedback on performance are essential to motivate innovation. In the context of managerial compensation, the optimal innovation‐motivating incentive scheme can be implemented via a combination of stock options with long vesting periods, option repricing, golden parachutes, and managerial entrenchment.

– Since corporate debt tends to be riskier in recessions, transfers from equity holders to debt holders that accompany corporate decisions also tend to concentrate in recessions. Such systematic risk exposures of debt overhang have important implications for corporate investment and financing decisions, and for the ex ante costs of debt overhang. Using a calibrated dynamic capital structure model, we show that the costs of debt overhang become higher in the presence of macroeconomic risk. We also provide several new predictions on how the cyclicality of a firm’s assets in place and growth options affect its investment and capital structure decisions.

This article studies performance-sensitive debt (PSD), the class of debt obligations whose interest payments depend on some measure of the borrower’s performance. We demonstrate that the existence of PSD obligations cannot be explained by the trade-off theory of capital structure, as PSD leads to earlier default and lower equity value compared to fixed-rate debt of the same market value. We show that, consistent with the pecking-order theory, PSD can be used as an inexpensive screening device, and we find empirically that firms choosing PSD loans are more likely to improve their credit ratings than firms choosing fixed-interest loans. We also develop a method to value PSD obligations allowing for general payment profiles and obtain closed-form pricing formulas for step-up bonds and linear PSD.

Traditional theories argue that governance is strongest under a single large blockholder, as she has high incentives to undertake value-enhancing interventions. However, most firms are held by multiple small blockholders. This article shows that, while such a structure generates free-rider problems that hinder intervention, the same coordination difficulties strengthen a second governance mechanism: disciplining the manager through trading. Since multiple blockholders cannot coordinate to limit their orders and maximize combined trading profits, they trade competitively, impounding more information into prices. This strengthens the threat of disciplinary trading, inducing higher managerial effort. The optimal blockholder structure depends on the relative effectiveness of manager and blockholder effort, the complementarities in their outputs, information asymmetry, liquidity, monitoring costs, and the manager's contract.

We model the impact of public and private ownership structures on firms' incentives to invest in innovative projects. We show that it is optimal to go public when exploiting existing ideas and optimal to go private when exploring new ideas. This result derives from the fact that private firms are less transparent to outside investors than are public firms. In private firms, insiders can time the market by choosing an early exit strategy if they receive bad news. This option makes insiders more tolerant of failures and thus more inclined to invest in innovative projects. In contrast, the prices of publicly traded securities react quickly to good news, providing insiders with incentives to choose conventional projects and cash in early.

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.

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.