Recent theoretical work has suggested a number of potentially important factors in causing incomplete pass-through of exchange rates to prices, including markup adjustment, local costs and barriers to price adjustment. We empirically analyse the determinants of incomplete pass-through in the coffee industry. The observed pass-through in this industry replicates key features of pass-through documented in aggregate data: prices respond sluggishly and incompletely to changes in costs. We use microdata on sales and prices to uncover the role of markup adjustment, local costs and barriers to price adjustment in determining incomplete pass-through using a structural oligopoly model that nests all three potential factors. The implied pricing model explains the main dynamic features of short and long-run pass-through. Local costs reduce long-run pass-through (after six quarters) by 59% relative to a Constant Elasticity of Substitution benchmark. Markup adjustment reduces pass-through by an additional 33%, where the extent of markup adjustment depends on the estimated “super-elasticity” of demand. The estimated menu costs are small (0.23% of revenue) and have a negligible effect on long-run pass-through but are quantitatively successful in explaining the delayed response of prices to costs. We find that delayed pass-through in the coffee industry occurs almost entirely at the wholesale rather than the retail level.

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.

We use rich historical data on military procurement to estimate the effects of government spending. We exploit regional variation in military buildups to estimate an “open economy relative multiplier” of approximately 1.5. We develop a framework for interpreting this estimate and relating it to estimates of the standard closed economy aggregate multiplier. The latter is highly sensitive to how strongly aggregate monetary and tax policy “leans against the wind.” Our open economy relative multiplier “differences out” these effects because monetary and tax policies are uniform across the nation. Our evidence indicates that demand shocks can have large effects on output.

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.

Forecasts of professional forecasters are anomalous: they are biased, and forecast errors are autocorrelated and predictable by forecast revisions. We propose that these anomalies arise because professional forecasters do not know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the world can generate all the prominent aggregate anomalies emphasized in the literature. We show this for professional forecasts of nominal interest rates and Congressional Budget Office forecasts of gross domestic product growth. Our learning model for interest rates can explain observed deviations from the expectations hypothesis of the term structure without relying on time variation in risk premia.