Learning about Risk and Return: A Simple Model of Bubbles and Crashes

This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they need to make forecasts of the conditional variance of a stock’s return. Recursive updating of both the conditional variance and the expected return implies several mechanisms through which learning impacts stock prices. Extended periods of excess volatility, bubbles and crashes arise with a frequency that depends on the extent to which past data is discounted. A central role is played by changes over time in agents’ estimates of risk.

Expectations Traps and Coordination Failures with Discretionary Policymaking

Discretionary policymakers cannot manage private-sector expectations and cannot coordinate the actions of future policymakers. As a consequence, expectations traps and coordination failures can occur and multiple equilibria can arise. To utilize the explanatory power of models with multiple equilibria it is first necessary to understand how an economy arrives to a particular equilibrium. In this paper we employ notions of learnability and self-enforceability to motivate and identify equilibria of particular interest. Central among these criteria are whether the equilibrium is learnable by private agents and jointly learnable by private agents and the policymaker. We use two New Keynesian policy models to identify the strategic interactions that give rise to multiple equilibria and to illustrate our methods for identifying equilibria of interest. Importantly, unless the Pareto-preferred equilibrium is learnable by private agents, we find little reason to expect coordination on that equilibrium.

The Spillover Effects of Monitoring: A Field Experiment.

We provide field experimental evidence of the effects of monitoring in a context where productivity is multi-dimensional and only one dimension is monitored and incentivised. We hire students to do a job for us. The job consists of identifying euro coins. We study the effects of monitoring and penalising mistakes on work quality, and evaluate spillovers on non- incentivised dimensions of productivity (punctuality and theft). We .nd that monitoring improves work quality only if incentives are large, but reduces punctuality substantially irrespectively of the size of incentives. Monitoring does not affect theft, with ten per cent of participants stealing overall. Our setting also allows us to disentangle between possible theoretical mechanisms driving the adverse effects of monitoring. Our .ndings are supportive of a reciprocity mechanism, whereby workers retaliate for being distrusted.

The Impact of Information Provision on Agglomeration Bonus Performance: An Experimental Study on Local Networks

The Agglomeration Bonus (AB) is a mechanism to induce adjacent landowners to spatially coordinate their land use for the delivery of ecosystem services from farmland. This paper uses laboratory experiments to explore the performance of the AB in achieving the socially optimal land management configuration in a local network environment where the information available to subjects varies. The AB poses a coordination problem between two Nash equilibria: a Pareto dominant and a risk dominant equilibrium. The experiments indicate that if subjects are informed about both their direct and indirect neighbors’ actions, they are more likely to coordinate on the Pareto dominant equilibrium relative to the case where subjects have information about their direct neighbors’ action only. However, the extra information can only delay – and not prevent – the transition to the socially inferior risk dominant Nash equilibrium. In the long run, the AB mechanism may only be partially effective in enhancing delivery of ecosystem services on farming landscapes featuring local networks.