We present estimates of monetary non-neutrality based on evidence from high-frequency responses of real interest rates, expected inflation, and expected output growth. Our identifying assumption is that unexpected changes in interest rates in a 30-minute window surrounding scheduled Federal Reserve announcements arise from news about monetary policy. In response to an interest rate hike, nominal and real interest rates increase roughly one-for-one, several years out into the term structure, while the response of expected inflation is small. At the same time, forecasts about output growth also increase-the opposite of what standard models imply about a monetary tightening. To explain these facts, we build a model in which Fed announcements affect beliefs not only about monetary policy but also about other economic fundamentals. Our model implies that these information effects play an important role in the overall causal effect of monetary policy shocks on output.

Empirical evidence suggests that as much as one-third of the U.S. business cycle is due to nominal shocks. We calibrate a multisector menu cost model using new evidence on the cross-sectional distribution of the frequency and size of price changes in the U.S. economy. We augment the model to incorporate intermediate inputs. We show that the introduction of heterogeneity in the frequency of price change triples the degree of monetary non-neutrality generated by the model. We furthermore show that the introduction of intermediate inputs raises the degree of monetary non-neutrality by another factor of three, without adversely affecting the model's ability to match the large average size of price changes. A single-sector model with a frequency of price change equal to the median, rather than the mean, generates monetary non-neutrality similar to that in our multisector model. Our multisector model with intermediate inputs generates variation in real output in response to calibrated aggregate nominal shocks that can account for roughly 23% of the U.S. business cycle.

We establish five facts about prices in the U.S. economy: (1) For consumer prices, the median frequency of nonsale price change is roughly half of what it is including sales (9-12% per month versus 19-20% per month for identical items; 11-13% per month versus 21-22% per month including product substitutions). The median frequency of price change for finished-goods producer prices is comparable to that of consumer prices excluding sales. (2) One-third of nonsale price changes are price decreases. (3) The frequency of price increases covaries strongly with inflation, whereas the frequency of price decreases and the size of price increases and price decreases do not. (4) The frequency of price change is highly seasonal: it is highest in the first quarter and then declines. (5) We find no evidence of upward-sloping hazard functions of price changes for individual products. We show that the first, second, and third facts are consistent with a benchmark menu-cost model, whereas the fourth and fifth facts are not. (c) 2008 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology..

We study the consequences of “regime-induced” exchange rate depreciations by comparing outcomes for peggers versus floaters to the U.S. dollar in response to a dollar depreciation. Pegger currencies depreciate relative to floater currencies and these depreciations are strongly expansionary. The boom is associated with a fall in net exports, and (if anything) an increase in interest rates in the pegger countries. This suggests that expenditure switching and domestic monetary policy are not the main drivers of the boom. We show that a large class of existing models cannot match our estimated responses and develop a model with imperfect financial openness that can. Following a depreciation, uncovered interest parity deviations lower the costs of borrowing from abroad and stimulate the economy, as in the data. The model is consistent with (unconditional) exchange rate disconnect and the Mussa fact, even though exchange rates have large effects on the economy.

We estimate productivity growth in England from 1250 to 1870. Real wages over this period were heavily influenced by plague-induced swings in the population. Our estimates account for these Malthusian dynamics. We find that productivity growth was zero before 1600. Productivity growth began in 1600—almost a century before the Glorious Revolution. Thus, the onset of productivity growth preceded the bourgeois institutional reforms of seventeenth-century England. We estimate productivity growth of 2% per decade between 1600 and 1800, increasing to 5% per decade between 1810 and 1860. Much of the increase in output growth during the Industrial Revolution is explained by structural change—the falling importance of land in production—rather than faster productivity growth. Stagnant real wages in the eighteenth and early nineteenth centuries—Engels’ Pause—is explained by rapid population growth putting downward pressure on real wages. Yet feedback from population growth to real wages is sufficiently weak to permit sustained deviations from the “iron law of wages” prior to the Industrial Revolution.

We estimate the slope of the Phillips curve in the cross section of U.S. states using newly constructed state-level price indices for nontradeable goods back to 1978. Our estimates indicate that the slope of the Phillips curve is small and was small even during the early 1980s. We estimate only a modest decline in the slope of the Phillips curve since the 1980s. We use a multiregion model to infer the slope of the aggregate Phillips curve from our regional estimates. Applying our estimates to recent unemployment dynamics yields essentially no missing disinflation or missing reinflation over the past few business cycles. Our results imply that the sharp drop in core inflation in the early 1980s was mostly due to shifting expectations about long-run monetary policy as opposed to a steep Phillips curve, and the greater stability of inflation between 1990 and 2020 is mostly due to long-run inflation expectations becoming more firmly anchored.

A key policy question is: how high an inflation rate should central banks target? This depends crucially on the costs of inflation. An important concern is that high inflation will lead to inefficient price dispersion. Workhorse New Keynesian models imply that this cost of inflation is very large. An increase in steady-state inflation from 0% to 10% yields a welfare loss that is an order of magnitude greater than the welfare loss from business cycle fluctuations in output in these models. We assess this prediction empirically using a new data set on price behavior during the Great Inflation of the late 1970s and early 1980s in the United States. If price dispersion increases rapidly with inflation, we should see the absolute size of price changes increasing with inflation: price changes should become larger as prices drift further from their optimal level at higher inflation rates. We find no evidence that the absolute size of price changes rose during the Great Inflation. This suggests that the standard New Keynesian analysis of the welfare costs of inflation is wrong and its implications for the optimal inflation rate need to be reassessed. We also find that (nonsale) prices have not become more flexible over the past 40 years.