How does market concentration affect the potency of monetary policy? The ubiquitous monopolistic-competition framework is silent on this issue. To tackle this question we build a model with heterogeneous oligopolistic sectors. In each sector, a finite number of firms play a Bertrand dynamic game with staggered price rigidity. Following an extensive Industrial Organization literature, we focus on Markov equilibria within each sector. Aggregating up, we study monetary shocks and provide a closed form formula for the response of aggregate output, highlighting three measurable sufficient statistics: demand elasticities, market concentration, and markups. We calibrate our model to the empirical evidence on pass-through, and find that higher market concentration significantly amplifies the real effects of monetary policy. To separate the strategic effects of oligopoly from the effects this has on residual demand, we compare our model to one with monopolistic firms after modifying consumer preferences to ensure firms face comparable residual demands. Finally, the Phillips curve for our model displays inflation persistence and endogenous cost-push shocks.
I develop an analytical framework for monetary policy in a multi-sector economy with a general input-output network. I derive the Phillips curve and welfare as a function of the underlying production primitives. Building on these results, I characterize (i) the correct definition of aggregate inflation and (ii) how the optimal policy trades off inflation in different sectors, based on the production structure. I construct two novel inflation indicators. The first yields a well-specified Phillips curve. Consistent with the theory, this index provides a better fit in Phillips curve regressions than conventional specifications with consumer prices. The second is an optimal policy target, which captures the tradeoff between stabilizing aggregate output and relative output across sectors. Calibrating the model to the U.S. economy I find that targeting consumer inflation generates a welfare loss of 0.8% of per-period GDP relative to the optimal policy, while targeting the output gap is close to optimal.
We estimate the slope of the Phillips curve in the cross section of U.S. states using newly constructed state-level price indexes for non-tradeable goods back to 1978. Our estimates indicate that the Phillips curve is very flat and was very flat even during the early 1980s. We estimate only a modest decline in the slope of the Phillips curve since the 1980s. We use a multi-region 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 since the 1990s is mostly due to long-run inflationary expectations becoming more firmly anchored.
The negative relationship between inflation and unemployment (also known as the Phillips curve) has been repeatedly challenged in the last decades: missing inflation in 2013-2019, missing deflation in 2007-2010, missing inflation in the late 1990s, stagflation in the 1970s, contrasting with always strong regional Phillips curves. Using data from multiple sources, this paper helps to solve many empirical puzzles by distinguishing between fixed and flexible exchange rate regimes: in fixed exchange rate regimes, inflation is negatively correlated with unemployment but this relationship does not hold in flexible regimes. By contrast, there is a negative correlation between real exchange rate appreciation and unemployment, which remains consistent in both fixed and flexible regimes. These crucial observations have important implications for identifying the source of business cycle fluctuations, for normative analysis, and imply a significant departure from rational-expectation-based solutions to Phillips curve puzzles.