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.
This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors (belief distortions) embedded in survey responses. We find that distortions are large on average even for professional forecasters, with all respondent-types over-weighting their own beliefs relative to other information. Forecasts of inflation and GDP growth oscillate between optimism and pessimism by large margins, with over-optimism associated with an increase in aggregate economic activity. Biases in expectations evolve dynamically in response to cyclical shocks. Biases about economic growth display greater initial under-reaction while those about inflation display greater delayed over-reaction.