Prospects For Inflation In A High Pressure Economy: Is The Philips Curve Dead Or Is It Just Hibernating?
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The Phillips Curve appears to function at the MSA level but not at the national level, with MSA data showing significant negative slopes & nonlinearities, suggesting it’s still relevant locally.
Peter Hooper, Frederic S. Mishkin, and Amir Sufi, "Prospects For Inflation In A High Pressure Economy: Is The Philips Curve Dead Or Is It Just Hibernating?" National Bureau of Economic Research, May 2019, https://www.nber.org/papers/w25792
"....National data going back to the 1950s and 60s yield strong evidence of negative slopes and significant nonlinearity in those slopes, with slopes much steeper in tight labor markets than in easy labor markets. This evidence of both slope and nonlinearity weakens dramatically based on macro data since the 1980s for the price Phillips curve, but not the wage Phillips curve. However, the endogeneity of monetary policy and the lack of variation of the unemployment gap, which has few episodes of being substantially below zero in tis sample period, makes the price Phillips curve estimates from this period less reliable. At the same time, state level and MSA level data since the 1980s yield significant evidence of both negative slope and nonlinearity in the Phillips curve. The difference between national and city/state results in recent decades can be explained by the success that monetary policy has had in quelling inflation and anchoring inflation expectations since the 1980s.We also review the experience of the 1960s, the last time inflation expectations became unanchored, and observe both parallels and differences with today. Our analysis suggests that reports of the death of the Phillips curve may be greatly exaggerated....Figure 3.2 shows a scatterplot and regression line of nominal wage inflation against the unemployment rate for the state-year level panel. The sample covers the 50 states and the District of Columbia from 1981 to 2017. To ensure that the scatterplot matches the regression specifications below, both the unemployment rate and nominal wage inflation are first regressed on state and year fixed effects before being plotted. As a result, the scatterplot contains deviations from the state and year mean for each state-year level observation. As the table below shows, the estimated coefficient on the unemployment rate is -0.41. Figure 3.2 shows a steep slope in the state-year level wage-Phillips curve. A state with a negative deviation from its normal unemployment rate sees a larger than average increase in nominal wage inflation. Column 1 of Table 3.1 shows the point estimate of the regression line, which is -0.41. Column 2 of Table 3.1 includes a control variable for lagged core price inflation at the regional level...The inclusion of this control variable is meant to capture inflation expectations or any other factor that may be determined by lagged inflation in that region. The point estimate on the unemployment rate is almost identical. The specification reported in Column 2 includes year fixed effects, which implies that the negative slope coefficient is not driven by national inflation dynamics. The state-year wage-Phillips curve shows a significant and steep negative slope.. the coefficient estimates for the wage-Phillips curve are similar for the national- and state-level specifications, both in terms of the level and the evidence for nonlinearities.... The state and MSA data, which has many more observations with very tight labor markets, suggests that the Phillips curve with nonlinearities might be alive and well. Thus the reports of the death of the Phillips curve might be greatly exaggerated. We should not discount the possibility that substantial nonlinearities are present in today’s Phillips curve and so a high-pressure economy could lead to inflationary pressures. Second, our evidence, both in the macro, time-series data and the state and MSA data in Sections 4 and 5, suggests that the wage-Phillips curve is alive and well.This is consistent with the observation that wage inflation has marched steadily, if slowly, upward in recent years as the labor market has tightened, as shown in Figure 6.1. However, Chair Powell (2018b) has noted, we did see something like this in the late 1990s-early 2000s without an inflation lift (Figure 6.2). But that was also a period during with strong dollar helped keep a tight lid on inflation, and the tight labor market was cut short by the recession of 2001, Could we see this play out again? Wage inflation did rise significantly in the late 90s (both in nominal terms and in excess of productivity trends), as it had in the mid 1960s. While the wage Phillips curve may have flattened some over time, it remains much steeper and evidence of nonlinearities much more robust than for the price Phillips curve in the post-1988 sample period. It is possible that the link between wage and price inflation has become weak, but this is a puzzle that requires further research..."
Overall findings:
Additionally they speculate that, “…. another possible reason for the steeper slope of the Phillips curve using MSA-level data is that this slope is not affected by the endogeneity of monetary policy. Monetary policy is the same for all MSAs and so is necessarily exogenous in this data. Hence the MSA-level data would not be subject to the bias that flattens the estimated slope of the Phillips curve and so could display steeper Phillips curve estimates. While the regional level data may help better identify the slope of the Phillips curve given more variation in the employment environment and the exogeneity of monetary policy, this comes at a theoretical cost. Such state and MSA level specifications are less well grounded in theory….”
They also note that the steeper estimates for MSA (Metropolitan Statistical Areas) are likely driven by sample size,"…One potential reason for the steeper estimate in MSA-level data is more statistical power for identification...If the price-Phillips curve is non-linear and especially steep at unemployment rates below 4% (as suggested by Table 3.2), the national level specification after 1988 will have a difficult time identifying this effect. In fact, from 1988 to 2017, there is only one year (2000) in which the unemployment rate reached 4% or lower. The post 1987 national sample Phillips curve estimate is produced with almost no data from the unemployment regime in which the MSA-level data say the slope is steepest. In contrast, there are a large number of observations (115) in the MSA-year level data with unemployment rates below 4%...."
Reviewed paper Sufi paper mention in WSJ that found Phillips curve working on local level. You asked, “ ….how do they reconcile local price increases drive by tight utilization to tight national utilization without increases?...”
Overall they finds that the difference btw national and city/state results can be explained by several factors (though they don’t attempt to estimate each factors contribution)“…. The difference between national and city/state results in recent decades can be explained by the success that monetary policy has had in quelling inflation and anchoring inflation expectations since the 1980s…”
Ed Commented:“Plz read and summarize the study below. How do they reconcile local price increases drive by tight utilization to tight national utilization without increases? You would think it would be the opposite: if utilization were low in some regions (rural) then the rest (urban) would be tight (if people weren't moving there) and wages would be rising despite low national utilization. If inflation were reflected in high real estate prices but wages were not rising as much then after-rent wages would be falling. That would seem to make Borges argument if immigrants were moving to faster growing cities with higher real estate costs and accepting lower after rent wages. Perhaps real estate prices are low in immigrant neighborhoods because higher-wage whites won't move there. That would hide the true cost of real estate to whites”