Can Risk Aversion in Firms Reduce Unemployment Persistence?

This paper contributes to the growing literature that attempts to explain unemployment persistence. We show that when the economy is struck by a negative transitory (or permanent) demand or supply shock, firms can find their way back quicker to the pre-shock (or new) employment levels if they are risk-averse. The reason is that risk aversion in firms creates a self-adjusting mechanism whereby cautious firms adjust hiring and wage-setting decisions to try to regain the pre-shock employment levels and minimize fluctuations in profits. Therefore, perhaps surprisingly, risk aversion in firms is seen as a stabilizing macroeconomic force that reduces unemployment inertia.Unemployment, Persistence, Risk Aversion

The 24/7 Society and Multiple Habits

We examine a model where households develop external habits by following norms and therefore have multiple habits in both consumption and labour supply. In doing so, they contribute to habit formation and hence pose an externality effect on others. Our findings are: first, that consumption and work habit (‘work ethic’) drive us towards a 24/7 society; both forms of habit increase the labour supply of households. Second, the two externalities involved in external habit work in opposite directions. For consumption, external habit is a negative externality as it reduces the utility of others in the economy. By contrast work ethic reduces the disutility and is therefore a positive externality. Third, as a result of our second finding, multiple habits can involve both a consumption tax and subsidy to correct for these externalities. Fourth, with plausible parameter values, the welfare consequences of multiple habits are far greater where there are long-run inefficiencies compared with only transitional inefficiency.Catching-up with the Joneses, Work Ethic, Savings, Output Inefficiency and Taxation

Dynamic importance sampling for queueing networks

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network setting (e.g., a two-node tandem network). Exploiting connections between importance sampling, differential games, and classical subsolutions of the corresponding Isaacs equation, we show how to design and analyze simple and efficient dynamic importance sampling schemes for general classes of networks. The models used to illustrate the approach include $d$-node tandem Jackson networks and a two-node network with feedback, and the rare events studied are those of large queueing backlogs, including total population overflow and the overflow of individual buffers.Comment: Published in at http://dx.doi.org/10.1214/105051607000000122 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org