Their evidence doesn’t assume recent macroeconomic trends – current data proves the unemployment-inflation trade-off

The Phillips curve captures the empirical inverse relationship between the level of inflation and unemployment. The reciprocal of its slope, sometimes referred to as the “sacrifice ratio,” represents the increase in the unemployment rate associated with a 1 percentage point reduction in the inflation rate. In this Chicago Fed Letter, we provide evidence that the Phillips curve has steepened in many industrialized countries since the start of the recovery from the Covid-19 pandemic. This suggests a lower sacrifice ratio now than before 2020.1 The gist of our main result is apparent in figure 1, which shows the Phillips curves of the United States, United Kingdom, and France during two time periods: the seven years (or 28 quarters) before the pandemic (2013:Q1–2019:Q4) and the six quarters of the recovery for which we have data (2021:Q1–2022:Q2).2 These three countries’ Phillips curves have steepened significantly during the recovery, in stark contrast with their flat Phillips curves of the pre-pandemic period. Before the pandemic, the U.S. Phillips curve had been flattening for over a decade and was considered “dead” by many economists.4 It appears this pattern has sharply reversed over the first six quarters following 2020.

In the rest of this article, we show that Phillips curves have steepened across a sample of 29 industrialized countries using data from the Organisation for Economic Co-operation and Development (OECD).5 This change in the relationship between unemployment and inflation is robust to the inclusion of crude oil prices, inflation expectations, and other measures of economic slack besides the unemployment gap.6 What drives our results is that declines in unemployment rates are associated with larger increases in inflation rates during the recovery from the pandemic than during the pre-pandemic period. Our results are generally the same when we examine countries individually (within-country variation) or when we study them pooled together (cross-country variation).