Research Database

In a stochastic equilibrium model some stochastic processes are usually exogenously given, while others are either chosen optimally by agents or emerge from market equilibrium conditions. When we simulate such a model, often we aim at studying the relations among variables in the model as we vary parameters of policy and of behavior of economic agents. We are no more certain (indeed often less certain) of what is reasonable or interesting behavior for the exogenous variables (some of which may be unobservable) than of the variables chosen by agents or fixed in markets. It turns out that if we are flexible about which variables’ behavior we take as given in the model solution computation, freeing ourselves from the convention that the variables exogenous to the model economy must be taken as given in the simulation computations, great computational savings may result.

This study uses John Rawls’ behind-the-veil of ignorance device as a fairness criterion to evaluate social policies and applies it to a contracting model in which the terms equality of opportunity and equality of result are well defined. The results suggest that fairness and inequality—even extreme inequality—are compatible. In a static world, when incentives must be provided, fairness implies equality of opportunity, but inequality of result. In a dynamic world of long-lived individuals, fairness implies not only inequality of result, but also, eventually, infinite inequality of result. If each period of the dynamic model is interpreted as a generation, then eventual infinite inequality holds for opportunity as well, as long as fairness is from the perspective of the first generation. If preferences of later generations are taken into account, then inequality of opportunity still occurs, although not at extreme levels.

This study focuses on the analysis of energy price shocks in the generation of business cycle phenomena. These shocks are transmitted through endogenous fluctuations in capital utilization. The production structure of the model gives rise to an empirical measure of ‘true’ technology growth that is exempt from recent criticisms levelled at the standard measure, i.e., Solow residual growth. The model is calibrated and evaluated for the U.S. economy using annual data over the 1960–1988 period. At business cycle frequencies, the model accounts for 74–91 percent of the volatility of U.S. output; closely matches the strong negative correlation between output and energy prices manifested in the U.S. data; and is generally consistent with other facts characterizing U.S. business cycles. Energy price shocks make a significant quantitative contribution to the model’s ability to explain the data.