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Can the Phillips Curve Explain Inflation over the Past Half-Century?

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Abstract

Standard Phillips curve models of price inflation suggest that the United States should have experienced an episode of deflation during the Great Recession and the subsequent sluggish recovery. Although inflation reached very low levels, prices continued to rise rather than fall. More recently, many observers have argued that inflation should have increased as the unemployment rate declined and labor markets tightened, but inflation has remained below the Federal Reserve’s policy target. This paper confirms that the slope of the Phillips curve has declined over the past 50 years and is very close to zero today. The Phillips curve was modified to allow its slope to vary over time consistent with theories of price-setting behavior by firms when prices are costly to adjust and when information is costly to obtain or process. Adapting the Phillips curve to allow for time-variation in its slope helps explain the pattern of inflation, not only during and after the Great Recession, but also over the previous four decades.

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Notes

  1. The unemployment gap can be viewed as a proxy for marginal cost. See Mazumder (2010, 2011) for Phillips curve models using direct measures of marginal cost.

  2. By contrast, the New Keynesian Phillips curve predicts that inflation is expected to decline when unemployment falls below its natural rate as Roberts (1995) illustrated, an implication rejected by historical data.

  3. Mankiw et al. (2004) found that survey measures of expected inflation were not consistent with either rational expectations or adaptive expectations of the type used here. The traditional approach to estimating Phillips curve models was followed in maintaining that expected inflation depends on lagged values of actual inflation. For analysis that uses survey measures, see Coibion and Gorodnichenko (2015).

  4. All estimates in this paper use data available as of July 2017. The focus is on inflation measured using the PCE price index rather than the CPI because the PCE price index encompasses a broader array of consumer purchases and is the measure that the Federal Reserve emphasizes in its policy discussions. PCE data are from the U.S. Bureau of Economic Analysis (2017a) and unemployment data are from the U.S. Bureau of Labor Statistics (2017).

  5. As shown in Table 1, the hypothesis of a unit root can be rejected in both overall and core inflation for all time periods. Accordingly, the level of inflation was used when estimating equation (2).

  6. Results use core PCE inflation here and throughout the remainder of the paper.

  7. See Ball et al. (1988) on the sticky-price model’s implications for the slope of the Phillips curve.

  8. See Mankiw and Reis (2007, 2010) for overviews of price-setting models in which information is imperfect.

  9. Reis (2006) showed how greater uncertainty about a firm’s market conditions reduced the time between information updates, thereby increasing the responsiveness of prices and inflation to aggregate demand.

  10. The advantage of this approach compared with advanced time-series methodologies is that it links the variation in the slope coefficient directly to movement in variables suggested by economic theory and avoids having to specify in advance a stochastic process for how the slope coefficient evolves through time.

  11. This specification of the Phillips curve permits the slope coefficient to vary over time with the inflation environment and uncertainty about regional economic conditions. The equation is not intended to provide a formal test of the sticky-information or the sticky-price theories, but only to motivate why the slope might vary over time.

  12. When the inflation variance term is entered alone (not shown), its coefficient is not statistically significant.

  13. When the inflation variance term with the regional dispersion term (not shown) is included and the mean inflation term omitted, its coefficient is never statistically significant, while the dispersion term is negative and significant.

  14. An F-statistic is computed for a Chow test of the null hypothesis of no structural break at each observation over the interior 70% of the sample and then the maximum value is chosen as the test statistic. Confidence levels for the QLR statistic shown in Figure 2 are from Stock and Watson (2010b), Table 14.6.

  15. QLR tests for sample periods ending earlier than 2016 (not shown) also find no evidence of a structural break when the slope varies with the regional dispersion term. But for versions of equation (3) where the slope varies with the mean or variance of inflation (not shown), QLR tests always show strong evidence of a structural break.

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Acknowledgements

An earlier version of this paper was presented at the Western Economic Association International 93rd Annual Conference, June 26-30, 2018, Vancouver, British Columbia. I thank the editor and referee for their comments and Giridaran Subramaniam for expert research assistance. This research was supported in part by a Boston College Research Incentive Grant.

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  1. Department of Economics, Boston College, Chestnut Hill, MA, 02467, USA

    Robert G. Murphy

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Murphy, R.G. Can the Phillips Curve Explain Inflation over the Past Half-Century?. Int Adv Econ Res 25, 137–149 (2019). https://doi.org/10.1007/s11294-019-09730-x

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