This paper examines a model of dynamic price adjustment based on the assumption that information disseminates slowly throughout the population. Compared with the commonly used sticky-price model, this sticky-information model displays three related properties that are more consistent with accepted views about the effects of monetary policy. First, disinflations are always contractionary (although annoimced disinflations are less contractionary than surprise ones). Second, monetary policy shocks have their maximum impact on inflation with a substantial delay. Third, the change in inflation is positively correlated with the level of economic activity.

This paper proposes a theory of supply shocks, or shifts in the short-run Phillips curve, based on relative-price changes and frictions in nominal price adjustment. When price adjustment is costly, firms adjust to large shocks but not to small shocks, and so large shocks have disproportionate effects on the price level. Therefore, aggregate inflation depends on the distribution of relative-price changes: inflation rises when the distribution is skewed to the right, and falls when the distribution is skewed to the left. We show that this theoretical result explains a large fraction of movements in postwar U. S. inflation. Moreover, our model suggests measures of supply shocks that perform better than traditional measures, such as the relative prices of food and energy.

This paper examines whether the Solow growth model is consistent with the international variation in the standard of living. It shows that an augmented Solow model that includes accumulation of human as well as physical capital provides an excellent description of the cross-country data. The paper also examines the implications of the Solow model for convergence in standards of living, that is, for whether poor countries tend to grow faster than rich countries. The evidence indicates that, holding population growth and capital accumulation constant, countries converge at about the rate the augmented Solow model predicts.