Prices are Sticky After All

Recent studies say prices change every four months. Economists have interpreted this high frequency as evidence against the importance of sticky prices for the monetary transmission mechanism. Theory implies that if most price changes are regular, that is, not temporary, then this interpretation is correct, but not if most price changes are temporary, as they are in the data. Temporary changes have two striking features: after a change, the nominal price returns exactly to its pre-existing level, and temporary changes are clustered in time. Our model, which replicates these features, implies that temporary changes cannot offset monetary shocks well, whereas regular changes can. Since regular prices are much stickier than temporary ones, our model, in which prices change as frequently as they do in the micro data, predicts that the aggregate price level is as sticky as in a standard model in which micro level prices change once every 12 months. In this sense, prices are sticky after all. ∗This paper is a greatly revised version of earlier drafts titled ”Sales and the Real Effects of Monetary Policy” and ”Temporary Price Changes and the Real Effects of Monetary Policy”. We thank Kathy Rolfe and Joan Gieseke for excellent editorial assistance. Kehoe thanks the National Science Foundation for financial support. The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System. A widely held view in macroeconomics is that monetary policy can be effective primarily because aggregate prices are sticky; when monetary policy changes, the aggregate price level cannot respond quickly enough to offset the intended real effects. This price stickiness is clearly at the heart of the widely used New Keynesian analysis. In standard New Keynesian models, the degree of aggregate price stickiness is determined by the frequency of price changes at the micro level: if individual good prices change rarely, then the aggregate price level is highly sticky and cannot offset monetary shock effects, whereas if good prices change often, then the aggregate price level is not sticky and can. In this model, in other words, the frequency of micro price changes has a one-to-one relationship with the degree of aggregate price stickiness. Until recently, micro level prices have been assumed to be quite sticky—changing relatively infrequently, only about once a year and, hence, aggregate prices have been assumed to be highly sticky. Recently, however, researchers have examined large micro price data series and determined that individual good prices change much more frequently than previously thought, about once every 4.5 months (Bils and Klenow 2004). According to these studies, that is, prices are quite flexible at the micro level. Interpreted through the lens of the standard New Keynesian model, this evidence implies that aggregate prices are quite flexible too, so monetary policy cannot have large real effects. We dispute this interpretation. Although it is true that this interpretation follows logically from a standard New Keynesian model, that model is grossly inconsistent with the pattern of price changes in the micro data. We build a simple extension of the standard model that is consistent with the micro data. We show that in this extension of the model, aggregate prices are, in fact, quite sticky and monetary policy has large real effects. The major shortcoming of standard New Keynesian models is their inability to simultaneously account for the pattern of high-frequency price flexibility and low-frequency price stickiness that we document using two sets of micro data. The pattern shows up in a simple example displayed in Figure 1, where we plot a fairly typical micro price series, decomposed into two parts: a trend and temporary deviations from trend. In this series, prices often temporarily move away from a slow-moving trend line, which tends to change rarely. We call the trend line the regular price and the deviations the temporary price. Notice that most price changes in this series are temporary and have two distinctive features: after a temporary change, the nominal price returns exactly to its pre-existing level, and temporary changes are clustered in time—for example, a change in one direction is usually followed quickly by another change in the opposite direction. These two distinctive features imply that for individual price series, even though there is a great deal of high-frequency price flexibility (in that actual prices change frequently), there is also a great deal of low-frequency price stickiness (in that the trend price changes infrequently). Standard New Keynesian models have only one type of price change and thus have no hope of generating the pattern of price stickiness observed in the micro data. We extend the standard model to allow firms to temporarily deviate from a sticky pre-existing price. We quantify our model and show that it reproduces the empirical micro pattern of regular and temporary price changes. We then study our model’s implication for the degree of aggregate price stickiness in response to monetary policy shocks. It is quite different from that of a New Keynesian model. Unlike that model, our model, which is consistent with the micro data pattern, still implies large real effects from monetary shocks. The key prediction of our model is that temporary micro price changes cannot offset monetary shocks well, whereas regular changes can—but they do so only infrequently because regular prices are much stickier than temporary prices. To measure how sticky our model says aggregate prices are, we translate its results into those of a standard model. Our model, in which micro prices change as frequently as they do in the data, predicts that the aggregate price level is as sticky as it is in a standard model in which micro prices change once every 12 months. Aggregate prices, the model says, are sticky after all. Our study is based on the salient features of the micro price data. To document these features, we study two sets of data: monthly price data from the U.S. Bureau of Labor Statistics (BLS) and weekly data from a chain of grocery stores, Dominick’s Finer Food retail chain. Our primary focus is on the statistics from the larger, more comprehensive BLS data set, but we use the Dominick’s data to demonstrate the robustness of that analysis. We find that the Dominick’s data set is also informative, despite its limited coverage, because these data are collected weekly and thus can better measure high-frequency price movements