A. Potential Output Growth and the Output Gap

3. There is a wide variety of methodologies for estimating potential output, ranging from atheoretical approaches, involving various detrending techniques, to more structural methods, such as the production function approach. Since none of the commonly used methods is free from difficulties, four different methodologies were used to determine a range of reasonable estimates of potential growth. The results suggest that the growth rate in the recent period is roughly 2½ to 2¾ percent (Table 1).

Table 1.

Estimates of Potential Output Growth

Table 1.

Estimates of Potential Output Growth

Method/Source	Average Annual Growth Rate
Staff estimates:
Segmented trend	2.7 percent (1982–98)
Hodrick-Prescott	2.8 percent (1973–98)
Blanchard-Quah	2.5 percent (1990–98)
Production function	2.6–2.9 percent (1990–98)
Other estimates:
Congressional Budget Office	2.7 percent (1998–2009)
Office of Management and Budget	2.8 percent (1999–2002)
OECD	2.6 percent (1992–98)
	2.8 percent (1998)

View Table

Table 1.

Estimates of Potential Output Growth

Method/Source	Average Annual Growth Rate
Staff estimates:
Segmented trend	2.7 percent (1982–98)
Hodrick-Prescott	2.8 percent (1973–98)
Blanchard-Quah	2.5 percent (1990–98)
Production function	2.6–2.9 percent (1990–98)
Other estimates:
Congressional Budget Office	2.7 percent (1998–2009)
Office of Management and Budget	2.8 percent (1999–2002)
OECD	2.6 percent (1992–98)
	2.8 percent (1998)

4. Among the detrending techniques, the segmented trend approach attempts to identify points at which the trend rate of growth in GDP may have shifted. The potential rate of growth is assumed to be constant and roughly equivalent to the average growth rate over the interval between break points. Recursive residual tests were used to identify break points in the chain-linked real GDP series over the period 1959 to 1998. Two break points were found; one occurring in the first quarter of 1975, corresponding to just after the first oil price shock, and the other in the first quarter of 1982. Using these breakpoints, and based on log-linear regressions, potential output growth is estimated to have slowed from about 4 percent during the period 1960–75, to about 3¼ percent during the period 1975–82, and to 2¾ percent in the period thereafter.²

5. The Hodrick-Prescott (H-P) filter also was used to detrend GDP. This technique identifies a trend output which minimizes a weighted average of the gap between output and trend output and the rate of change in trend output.³ Although the H-P filter is less restrictive than the segmented-trend approach—in that the growth rate of potential can vary continuously—one disadvantage is that the end points of the H-P filtered trend output series tend to be quite sensitive to the last few observations in the series. To attempt to handle this problem, potential output was estimated to be about 2¾ percent per year over the period from the peak in output in the fourth quarter of 1973 to the end of 1998.

6. Another technique for estimating the growth rate of potential output is the Blanchard-Quah bivariate decomposition, in which output is divided into its cyclical and trend components. Rather than use only the information contained in the real output series—as in the case of the H-P filter—this technique also incorporates additional information from highly cyclical aggregate variables such as consumption and the unemployment rate.⁴ This approach allows for a stochastic trend of output without forcing the trend component to be modeled as a random walk. Thus, it is consistent with the widely held belief that the dynamics of the permanent component of output are driven partly by technological innovation. Based on the Blanchard-Quah decomposition, potential output growth was estimated to be about 2½ percent for the period 1990–98.

7. The main drawback to the detrending techniques is that they are mechanistic, in the sense that the productive limits of the economy are not estimated based on available factors of production. In contrast the production function approach explicitly models output in terms of underlying factors of production, expressing output as a function of capital, labor, and total factor productivity (TFP). This approach requires the assumption of a functional form for the aggregate production function and the construction of series for potential capital, potential labor, and TFP. Following established practice, a constant returns to scale Cobb-Douglas type production function was assumed, with constant shares over time for labor and capital ⁵

8. The series of potential inputs and TFP were estimated using three different methods. The first method assumed that factor inputs and total factor productivity were at their potential level in the years 1981 and 1990, which were cyclical peaks. The growth rate of TFP, as well as that of potential capital stock was assumed equal to the growth rate between those peak years. Potential labor was estimated to grow at the same rate as the historical population growth rate. The second method estimated the trend growth rate of TFP as in the previous method, but the series for potential inputs were obtained using the H-P filter, Finally, the third method extracted the potential series of factor inputs and trend TFP using the H-P filter. The growth rate of potential was estimated to be about 2.9 percent using the first two methods and 2.6 percent with the third method over the period 1990–98.

9. Based on these four different techniques for estimating the growth rate of potential output, 2½ to 2¾ was chosen as a range of reasonable estimates on which to establish the level of potential output and, therefore, the output gap (Figure 2). The level of potential output was determined by applying the annualized growth rate of potential to the full employment level of output which occurred in the third quarter of 1990. The output gap derived from this potential output series was in the range of -¾ ot 1¼ percent in 1998.

United States: Potential Output and Output Gaps

Citation: IMF Staff Country Reports 1999, 101; 10.5089/9781451839579.002.A001

Source: Staff estimates.

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United States: Potential Output and Output Gaps

Citation: IMF Staff Country Reports 1999, 101; 10.5089/9781451839579.002.A001

Source: Staff estimates.

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United States: Potential Output and Output Gaps

Citation: IMF Staff Country Reports 1999, 101; 10.5089/9781451839579.002.A001

Source: Staff estimates.

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B. NAIRU and the Employment Gap

10. The NAIRU is the rate of unemployment that would keep the rate of inflation constant, and the employment gap is simply the difference between NAIRU and the actual unemployment rate. These concepts are derived from the Phillips curve, which captures the inverse relationship between inflation and unemployment. Empirically, the rate of inflation has tended to increase (decrease) when the rate of unemployment lies below (above) NAIRU. For the past several years, the U.S. unemployment rate has remained below most estimates of NAIRU, but inflation has remained quiescent. This puzzle has led economists to hypothesize that changes in the U.S. economy have reduced the NAIRU allowing the economy to reach a lower level of unemployment without sparking inflation. More recent estimates suggest that NAIRU has declined to 4½-5¾ percent, from a peak of about 7 percent in the early 1980s (Table 2). ⁶

Table 2.

Recent Estimates of NAIRU

^1/Estimate assumes that discrete jump in NAIRU occurred in 1993, and that the level of NAIRU remained unchanged thereafter.

^2/Range represents the tightest of the 95 percent confidence intervals estimates; point estimate is 5.8 percent.

^3/The lower estimate is based on an equation using the personal consumption deflator, whereas the higher estimates is based on the GDP deflator.

Table 2.

Recent Estimates of NAIRU

Source	NAIRU Estimate (Year)	Methodology
Staff estimates	5 percent (1998)	Structural approach
	4¼ to 5 percent (1998)	Okun’s Law
	4½ (1998)	Phillip’s Curve-ordinary least squares
Congressional Budget Office (1999)	5½ percent (1998)	Phillip’s Curve-ordinary least squares
Office of Management and Budget (1999)	5¼ percent (1999)	Unavailable
Murphy (1998)1/	5¾ percent (1993–97)	Time varying-discrete jump
Staiger, Stock and Watson (1997)2/	4¾ to 6½ percent (1994)	Time varying
Gordon (1998)3/	5 to 5¼ percent (1998)	Time varying

^1/Estimate assumes that discrete jump in NAIRU occurred in 1993, and that the level of NAIRU remained unchanged thereafter.

^2/Range represents the tightest of the 95 percent confidence intervals estimates; point estimate is 5.8 percent.

^3/The lower estimate is based on an equation using the personal consumption deflator, whereas the higher estimates is based on the GDP deflator.

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Table 2.

Recent Estimates of NAIRU

Source	NAIRU Estimate (Year)	Methodology
Staff estimates	5 percent (1998)	Structural approach
	4¼ to 5 percent (1998)	Okun’s Law
	4½ (1998)	Phillip’s Curve-ordinary least squares
Congressional Budget Office (1999)	5½ percent (1998)	Phillip’s Curve-ordinary least squares
Office of Management and Budget (1999)	5¼ percent (1999)	Unavailable
Murphy (1998)1/	5¾ percent (1993–97)	Time varying-discrete jump
Staiger, Stock and Watson (1997)2/	4¾ to 6½ percent (1994)	Time varying
Gordon (1998)3/	5 to 5¼ percent (1998)	Time varying

^1/Estimate assumes that discrete jump in NAIRU occurred in 1993, and that the level of NAIRU remained unchanged thereafter.

^2/Range represents the tightest of the 95 percent confidence intervals estimates; point estimate is 5.8 percent.

^3/The lower estimate is based on an equation using the personal consumption deflator, whereas the higher estimates is based on the GDP deflator.

11. Following Adams and Coe (1990), NAIRU was calculated using an approach in which cyclical and structural variables were used to explain the unemployment rate. NAIRU is derived from the estimated long-run values of the employment-population ratio (E/P) and the labor force participation rate (L/P), that is:

12. The long-run employment-population ratio was estimated as a function of two structural variables—the unionization rate, and the minimum wage, both of which are negatively correlated with the dependent variable—and two cyclical variables—the output gap and the wage gap (which is the change in real compensation per hour relative to output per hour in the nonfarm business sector), both of which are positively correlated with the dependent variable. The long-run participation rate was estimated as a function of a nonlinear time trend, the child-dependency ratio, which is negatively correlated with the dependent variable, and the same two cyclical variables. Based on this approach, NAIRU was estimated to be about 5 percent in 1998.

13. Another approach to deriving NAIRU is to use Okun’s Law, which establishes an empirical relationship between the output gap and the employment gap.⁷ Based on the staffs estimate of an output gap of -¾ to 1¼ percent in 1998, an actual unemployment rate of 4½ percent, and an Okun coefficient of 2½, NAIRU is computed to be in the range 4¼ to 5 percent,

14. For purposes of comparison, recently published estimates of NAIRU based on different methodologies are included in Table 2. The Congressional Budget Office (CBO) estimate of NAIRU is based on a Phillips-curve equation, which relates the inflation rate to lagged inflation, lagged levels of the unemployment rate, productivity growth, and variables to control for changes in food and energy prices. To derive NAIRU, the estimated equation was solved for the rate of unemployment that would deliver constant inflation. This method yields an estimate of 5½ percent in 1998. From its own augmented Phillips curve, the staff derives an estimate for NAIRU of 4½ percent in 1998.⁸

15. Other Phillips-curve estimation approaches allow NAIRU to vary over time, rather than to produce a single point estimate, and are therefore designed to track changes in NARIU. For example, Murphy (1998) uses a discrete jump approach. Phillips curves are estimated for subperiods during 1960–97. For each subperiod, a level of unemployment that delivers constant inflation is derived. Murphy finds that NAIRU was about 5¼ percent for the period 1960–72, increased to 6½ percent during 1973–85, and to 7 percent in 1986–92, and then declined to about 5¾ percent for the subperiod 1993–97.

16. Staiger, Stock, and Watson (1997) and Gordon (1998) use a more flexible approach, which allows NAIRU to vary continuously over time, rather than at specific break points. The Phillips curve is estimated jointly with a second equation that allows NAIRU to vary over time. The Staiger, Stock, and Watson results confirm that NAIRU declined from about 7 percent in the mid-1980s, to about 5¾ percent in 1994. For this estimate, the confidence interval ranges from 4¾ to 6½ percent in the best case, and between 2¾ to 7¾ percent, in the worst case. Gordon’s results indicate that NAIRU declined from about 6¼ percent in the mid-1980s to between 5 to 5¼ percent in 1998.

17. These recent estimates provide compelling evidence that NAIRU has fallen during the 1990s. A number of factors have been identified as contributing to this decline.⁹ First, as the baby-boom generation has aged, the United States now has a more mature labor force and older workers tend to experience less frequent spells of unemployment. Second, the unexpected pickup in productivity growth over the past few years may have temporarily depressed NAIRU, as workers accept wages that are lower than what their higher rate of productivity would indicate. Third, product and labor markets have become more competitive since the early 1980s, as international trade has increased and unionization has declined.

C. Capacity Utilization

18. Another measure of resource utilization commonly used to assess potential inflationary pressures is the rate of capacity utilization published by the Federal Reserve Board. Capacity utilization is the ratio of the actual level of output to an estimated sustainable maximum level of output. The actual level of output is based on the monthly industrial production indexes. The capacity data are based on survey data collected at the plant level for the fourth quarter of each year and alternative sources of data on capacity change (such as, growth in an industry’s available capital input, or in the case of some industries, capacity measured in physical units). The annual capacity estimates are then interpolated to a monthly frequency.

19. As capacity utilization reaches a high level, inflation rises because the marginal cost of producing goods increases and leads to higher prices. Empirical evidence suggests that inflation begins to accelerate when capacity utilization exceeds a threshold near 82 percent, and this relationship has remained fairly consistent over the last 30 years.¹⁰ Despite the empirical robustness in explaining the acceleration in inflation, there are a number of shortcomings in using capacity utilization as an indicator for economy-wide inflationary pressures. Capacity utilization is based primarily on the goods-producing sector and ignores the rapidly growing service sector.¹¹ With the rapid adoption of new technology, gains in productivity may not be adequately captured by measured capacity. The capacity utilization rate also does not capture the effect that foreign-produced goods may have on inflation,

20. During the current economic expansion, the capacity utilization rate on an annual basis increased from a low of about 78 percent in 1991, to a peak in 1994 of nearly 83 percent, before falling off to 80½ percent in May 1999. The decline in the capacity utilization rate reflects, in part, the rapid pace of business investment in recent years. The growth of industrial capacity averaged 2¾ percent over the period 1967–90; yet since 1994 growth in capacity has sutstantially exceeded this average, peaking in 1997 at about 51/2 percent. The decline in capacity utilization since 1994 also reflects the weakness in activity in the goodsproducing sector owing to appreciation of the dollar and the Asian financial crisis.