When to Quit Under Uncertainty? A real options approach to smoking cessation

This paper models the decision to quit smoking like an investment decision where the quitter incurs a sunk withdrawal cost today and forgoes their consumer surplus from cigarettes (invests) and hopes to reap an uncertain reward of better health and therefore higher utility in the future (return). We show that a risk-averse mature smoker who expects to benefit from quitting may still rationally choose to delay quitting until they are more confident that quitting is the right decision for them. Such a decision by the smoker is due to the value associated with keeping their option of whether or not to quit open as they learn more about the damage that smoking will have on their future utility. Policies which reduce a smoker’s uncertainty about the damage that smoking with have on their future utility is likely to make them quit earlier.

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.

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.