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Federal Reserve Bank of Chicago

Women and the Phillips Curve: Do
Women’s and Men’s Labor Market
Outcomes Differentially Affect Real
Wage Growth and Inflation?
Katharine Anderson, Lisa Barrow and
Kristin F. Butcher

WP 2003-22

Comments Welcome

Women and the Phillips Curve:
Do Women’s and Men’s Labor Market Outcomes
Differentially Affect Real Wage Growth and Inflation?

November 2003

Katharine Anderson
Federal Reserve Bank of Chicago
Lisa Barrow
Federal Reserve Bank of Chicago
Kristin F. Butcher
Federal Reserve Bank of Chicago

Notes: We thank Jonas Fisher, Dan Sullivan, Rob Valleta, and participants at the Federal
Reserve System Micro Conference for helpful comments. The views expressed here are
the authors’ and do not necessarily reflect those of the Federal Reserve Bank of Chicago
or the Federal Reserve System. All errors are our own. Updated versions of the paper are
available by contacting Lisa Barrow, Economic Research, Federal Reserve Bank of
Chicago, 230 South LaSalle Street, Chicago, IL 60604 or lbarrow@frbchi.org.

Abstract
During the economic expansion of the 1990s, the United States enjoyed both low
inflation rates and low levels of unemployment. Juhn, Murphy, and Topel (2002) point
out that the low unemployment rates for men in the 1990s were accompanied by
historically high rates of non-employment suggesting that the 1990s economy was not as
strong as the unemployment rate might indicate. We include women in the analysis and
examine whether the Phillips curve relationships between real compensation growth,
changes in inflation, and labor market slackness are the same for men and women and
whether measures of “non-employment” better capture underlying economic activity, as
suggested by Juhn, Murphy, and Topel’s analysis. From 1965 to 2002 the increase in
women’s labor force participation more than offsets the decline for men, and low
unemployment rates in the 1990s were accompanied by historically low overall nonemployment rates. We find that women’s measures of labor market slackness do as well
as men’s in explaining real compensation growth and changes in inflation after 1983. We
also find some evidence that non-employment rates are more closely related to changes in
inflation than other measures of labor market slackness; however, we do not find the
same for real compensation growth.

1

I. Introduction
During the economic expansion of the 1990s, the United States enjoyed both low
inflation rates and low levels of unemployment. These facts have led some observers (for
example, Stiglitz 1997 and Staiger, Stock and Watson 2001) to question whether the
structure of the economy has changed, permanently lowering the natural rate of
unemployment or the Non-Accelerating Inflation Rate of Unemployment (NAIRU).
Against this backdrop, Juhn, Murphy, and Topel (2002) examine men’s
unemployment, labor force participation, and non-employment. In the 1990s, the nonemployment rate (the fraction of weeks per year spent not working) was at a historic high
compared to earlier periods of low unemployment. They conclude that although the
1990s saw very low unemployment rates for men, men shifting from unemployment to
non-participation could, in part, explain these low rates. Under these circumstances, they
suggested that one need not rethink the NAIRU. Instead, it may merely be that men were
re-labeling their activity from unemployment to non-participation. Thus, a given low
unemployment rate in the 1990s did not necessarily represent the same high level of
underlying economic activity as the same unemployment rate in earlier periods. As such,
declines in the unemployment rate in the 1990s did not put upward pressure on prices,
leading to an extended period of both low unemployment rates and low inflation.
The Juhn, Murphy, and Topel analysis explicitly focuses on men. In this paper,
we ask whether one would reach the same conclusion including women in the analysis,
i.e., that the low unemployment rate in the 1990s did not represent as much underlying
economic activity as similar unemployment rates in earlier time periods. Second, we
directly examine whether the Phillips curve relationships between real compensation

2

growth, changes in inflation, and labor market slackness are the same for men and
women. It may be the case that men’s unemployment rate, for example, puts a different
amount of pressure on wages than women’s unemployment rate. Additionally, we
examine Juhn, Murphy, and Topel’s suggestion that measures of “non-employment” may
better capture underlying economic activity, and thus may be more closely tied to real
compensation growth and changes in inflation.
Once women are included in the analysis, the 1990s again appear to be a period of
robust economic activity, even when considering changes in labor force participation
rates. Over the 1990s, women’s increased labor force participation more than offsets
men’s decreased labor force participation. Thus, low unemployment rates were
accompanied by historically low rates of non-employment. Although Phillips curve type
analyses over the entire 1965-2002 period suggest that women’s measures of labor
market slackness are less closely related to real wage growth and changes in inflation
than men’s measures, these results mask stark differences over time. Splitting the sample
in two, we find that women’s measures of labor market slackness do as well as men’s in
explaining real compensation growth and change in inflation after 1983.
In section II of the paper we follow Juhn, Murphy, and Topel (2002) in analyzing
labor force status, but construct the measures using both men and women. Next, we
present results from Phillips curve estimates using separate measure of labor market
slackness for men and women, as well as allowing the relationships to change over time.
In the final section we summarize and discuss our results.

II.

Labor Force Status of Men and Women

3

A. Data
Following Juhn, Murphy, and Topel (2002), we use the March Current Population
Surveys (CPS) from 1979 to 2001 to examine changes in weeks worked over time.1 In
particular, we use survey questions about labor market status that refer to the previous
year. We exclude people who may not have worked because of school or military service
and those living in group-quarters. Juhn, Murphy, and Topel (2002) also limit their
sample to men with 1 to 30 years of potential experience. Because we want to examine
outcomes for women as well, and since potential experience may be a noisier measure of
true labor market experience for women due to time out of the labor force for childrearing, we limit our sample based on reported age rather than potential experience.
When we limit the sample to younger individuals (aged 18-55), the results for men are
nearly identical to those for the Juhn, Murphy, and Topel (2002) sample of men with 1 to
30 years of potential experience. In other cases, we examine individuals aged 18 and
over.
Again following Juhn, Murphy, and Topel, we define the number of weeks of
non-employment as 52 minus the number of weeks the individual reports working in the
previous year. This measure combines weeks unemployed and weeks out of the labor
force. The number of weeks of nonparticipation is defined as 52 minus the number of
weeks worked last year and the number of weeks spent looking for work or on layoff.
Weeks unemployed is defined as the number of weeks looking for work or on layoff.2
We examine the percent of the year spent unemployed, out of the labor force, or
1

We use the CPS data available through Unecon.
In the CPS, weeks looking for work or on layoff applies to part-year workers. There is a separate
question about weeks looking for work for nonworkers. Weeks not in the labor force is defined as 52

2

4

nonemployed. Here, percent of the year is just 100 times the number of weeks spent in a
given state, divided by 52.

B. Results
In Figure 1a we present data on the percent of the year spent in unemployment,
out of the labor force (not participating), or non-employment for men aged 18-55 from
1978 to 2000. Despite the fact that we use age to define our sample and Juhn, Murphy
and Topel (2002) use potential experience, Figure 1a reproduces the basic results shown
in their Figure 3. As expected, unemployment and non-employment rise during cyclical
downturns and fall during booms. As Juhn, Murphy and Topel (2002) note, however,
nonparticipation among prime-aged men is rising over this period. As a result, nonemployment remains relatively high during the 1990s, even as unemployment falls to the
lowest levels in the period shown.
Juhn, Murphy and Topel make the point that the 1990s did not represent a
particularly robust labor market for men, especially for low-skilled men. They outline
several potential reasons for this. Changes in labor demand may have led to deterioration
of the labor market for low-skilled men, consistent with rising wage inequality over this
period. In addition, changes in eligibility rules for disability insurance in 1984 may have
allowed some men who were not previously eligible to withdraw from the labor market
and collect disability insurance. Autor and Duggan (2003) point out disability payments
are calculated in such a way that rising wage inequality increases the relative value of
disability payments for low skilled men. This, in turn, may have increased the take-up

minus weeks worked last year, minus the applicable variable for weeks spent looking for employment for
part-year and non-workers.

5

rate of disability benefits. Thus, from the demand side, firms may have wanted to hire
fewer low-skilled men. From the supply side, disability insurance may have become
relatively more attractive to low-skilled men with health challenges leading some of them
to withdraw from the labor force. In either case, the low unemployment rates for men in
the 1990s would to some extent represent a re-labeling of activity from unemployment to
non-participation rather than a change in economic activity.
Katz and Krueger (1999) make a related point about increasing incarceration rates
over this period. The Current Population Surveys only canvas the non-institutionalized
population. If incarceration is increasing, particularly among people who would likely
have high unemployment rates, then it may not be surprising that measured
unemployment rates among the non-institutionalized population declined over this
period. Similarly, we show below that over the period we study, low-skilled men are
decreasing their labor force participation, while higher-skilled women are increasing
theirs. In all of these cases, the low-skilled, who may have higher frictional rates of
unemployment due to search costs or matching problems, are removed from the labor
force. If this trend characterizes the 1990s, then it is perhaps not surprising that the
economy could sustain low rates of unemployment without rising inflation.
One of the reasons we care about whether the low unemployment rate in the
1990s really represents a robust labor market is because of the importance of the
relationships between unemployment, wages, and inflation for policy decisions. But
typically, policy makers do not look at the unemployment rate for a just single group,
such as prime-aged men. Thus, it is worth asking what unemployment, nonparticipation,

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and non-employment looked like for women and for the adult population overall during
this period.
Figure 1b repeats the exercise above for women aged 18 to 55. The bottom line
shows the percent of the year spent in unemployment. As for men during this period,
unemployment is at its lowest level in the late 1990s. Unlike the measures for men,
however, the percent of the year spent out of the labor force and the percent of the year
spent in non-employment both decline throughout the period.
Figure 2 shows these measures for the entire adult population, aged 18 and over.
Again, we clearly see that the percent of the year spent in unemployment is at its lowest
levels during the late 1990s. Taking men and women of all ages together, the increase in
women’s labor force participation more than offsets the decline in men’s labor force
participation, resulting in an overall decline in nonparticipation over the period. As a
result, the overall non-employment measure reaches historically low levels in the 1990s.
This would again lead one to question why the 1990s saw such robust labor market
activity coupled with little upward pressure on prices.
One might hypothesize that women and older people put less pressure on wage
growth than prime-aged males because on average they tend to work fewer hours in a
given week of employment. Thus, a given number of weeks worked represents different
amounts of underlying labor market activity for different groups. In order to consider the
differences in usual hours worked per week, we create a full-time-equivalent (FTE)
measure of non-employment. Here, we weight the number of weeks worked by the
number of hours typically worked in a given week, divided by 40. The FTE percent of
year in non-employment tracks our original measure of percent of year in non-

7

employment quite closely. Toward the beginning of the period, FTE non-employment is
somewhat higher than regular non-employment. By the end of the period, their relative
positions are reversed. Using the FTE measure of percent of year in non-employment,
one would still conclude that there has been a decline in the percent of year in nonemployment over the 1990s.
These overall measures of labor market activity lead us to believe that the labor
market of the 1990s was quite robust. Thus, one might again ask why there was not more
upward pressure on prices. However, men and women’s labor market activity measures
may have fundamentally different relationships with wage and price changes if women
and men put different pressures on wages, either because they work in different
occupations, or because their wages are set by different processes. We examine this
question in the next section.

III.

Phillips Curve Type Analyses

A. Macroeconometric Model
Following Blanchard and Katz (1997), there are three simple equations, which
macroeconometric models of the U.S. economy use to summarize the joint behavior of
wage inflation, price inflation, and unemployment. Changes in prices are related to
changes in wages as in equation (1), where p and w are the natural logarithms of the price
index and nominal wages, respectively. Similarly, changes in the logarithm of wages are
related to the unemployment rate as in equation (2), where u is the unemployment rate.
Here, αp and αw are constants, and εpt and εwt are error terms.

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(1)

∆pt = α p + ∆wt + ε pt

(2)

∆wt = α w + ∆pt −1 + β u t + ε wt

Combining equations 1 and 2, we get the standard Phillips curve formulation.
(3)

∆pt = α + ∆pt −1 + β u t + ε t

In our estimation, we focus on versions of equations (2) and (3) and examine the
effects of changes in measures of labor market slackness on real compensation growth
and changes in inflation. Specifically, we estimate:
(2a) ∆wt − ∆pt −1 = α w + β u t + ε wt and
(3a) ∆pt − ∆pt −1 = α + β u t + ε t
additionally allowing for lags of the dependent and right-hand-side variables in the
estimation. The exact lag structure is explained in detail below.

B. Data Section
Following Aaronson and Sullivan (2000), we use both different measures of wage
growth and different measures of labor market slackness in estimating the relationship
between labor market status and compensation growth.
We use two measures of compensation3: Average Hourly Earnings from the
Bureau of Labor Statistics (BLS) Current Employment Statistics and Hourly
Compensation from the BLS Productivity and Costs Report. Average Hourly Earnings
and Hourly Compensation are both available from 1964 through 2002.4 Each of these
compensation measures has somewhat different coverage. Average Hourly Earnings is
3

Aaronson and Sullivan also consider compensation growth as measured by the Employer Cost Index
(ECI). We do not consider the ECI here because it is not available before 1982.

9

based on data from the Payroll Survey and is calculated for production and nonsupervisory employees on private non-farm payrolls (approximately 80% of total
employment on private, non-farm payrolls). Because Average Hourly Earnings is based
on a survey of employers, it does not cover the self-employed or unpaid family workers.
In addition, Average Hourly Earnings does not include benefits such as health insurance.
However, it does include overtime pay.
Hourly Compensation is also based on the Current Employment Statistics, so we
would expect a certain amount of similarity between the two series. However, the Hourly
Compensation measure also incorporates data from other sources, giving it wider
coverage than Average Hourly Earnings. Hourly Compensation is calculated for the nonfarm business sector, and includes estimates of earnings for the self-employed and unpaid
family labor. Similar to Average Hourly Earnings, Hourly Compensation includes
overtime compensation. However it also includes benefits. Additionally, whereas
Average Hourly Earnings covers only the private sector, Hourly Compensation includes
the public sector as well. Finally, Average Hourly Earnings are reported on a monthly
basis while Hourly Compensation is reported quarterly.
We also estimate Phillips curve relationships represented by equation (3a) using
change in inflation as measured by the Personal Consumption Expenditure Chain-Type
Price Index (PCE). Throughout, all variables are averaged over the year to create annual
data, and the compensation measures are inflation-adjusted using the lagged value of the
PCE.

4

Hourly Compensation is available from 1947 to the present, but we only use data from 1964 on to match
data availability for Average Hourly Earnings.

10

We consider three measures of labor market slackness: the civilian unemployment
rate for those 16 years and older, the civilian unemployment rate for those 25-55 years of
age, and the non-employment rate (one minus the civilian employment-to-population
ratio, multiplied by 100) for persons 16 years and older. For each measure of labor
market slackness we use the measure for the overall population, men, and women. For
each combination of compensation growth or change in inflation and labor market
slackness, we estimate Phillips curve relationships.
The measures of labor market slackness used in this section are based on survey
responses about activity in the previous week, unlike the measures in the previous
section, which were based on weeks spent in various labor market activities during the
previous year. Before turning to our Phillips curve type analyses, we verify that both
measures of labor market status give us similar information about aggregate changes in
men’s, women’s, and overall labor market activity. Figure 3a and 3b are comparable to
Figures 1a and 1b, but use persons aged 16 and over. As in the earlier figures, we see that
men’s non-participation is rising over the period, hitting a new high in the late 1990s. At
the same time, women’s non-participation is falling fairly steadily. Figure 4 displays the
statistics for men and women combined, including all years available for these measures.
Vertical lines mark the 1978-2000 period used in the analysis above. Again, for the labor
market as a whole, the unemployment rate is low, and non-employment and
nonparticipation reach historical lows by the late 1990s.

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C. Results
Table 1 presents summary statistics for changes in inflation, real wage growth,
and our measures of labor market slackness. We graph compensation growth and
inflation in Figure 5, overall measures of labor market slackness in Figure 6a,
unemployment measures in Figure 6b, and non-employment measures in Figures 7a and
7b. Many researchers speculate that there have been changes in these relationships over
time, thus in we present summary statistics for two time periods: 1965-1983 and 19842002. Below we describe the reasons for splitting the sample in this way in more detail.
Figures 5, 6a, and 6b confirm well-known facts about the U.S. economy.
Inflation and unemployment have both been lower in more recent years. Real
compensation growth has also been somewhat lower in the second half of the data,
although growth rates were generally rising over the 1990s.5 Unemployment is lower
among prime-age individuals than among all individuals. Looking at Figure 6b, women
are more likely to be unemployed than men; however, since 1983 the levels of
unemployment for men and women and prime-aged men and women are much closer
together. Finally, in Figure 7a we can see that women are more likely to be nonemployed than men, but over the entire period, women’s non-employment fell
dramatically, while men’s rose slightly. Because of the strong secular trend in men and
women’s labor force participation, when we turn to estimating real compensation growth
and changes in inflation, we detrend the measures of non-employment. See Figure 7b.
5

Note that Hourly Compensation has grown faster in both periods than has Average Hourly Earnings. This
may be because Hourly Compensation includes benefits, and based on information from the Employer Cost
Index the benefits component of compensation has been growing more quickly than the wage component in

12

Each non-employment series is detrended by subtracting off its Hodrick-Prescott filter
trend component.6
Table 2 presents the results of our Phillips curve type analyses over the entire
1965-2002 period. As mentioned above and discussed in more detail below, there are
many reasons to believe that these relationships have changed over time; however, we
present results from the overall period for completeness and turn to estimates by separate
time periods in the next section.
Columns 1 and 2 report results for our two measures of real compensation
growth; column 3 presents results for the change in inflation. In each case, we include the
optimal lag structure indicated by appropriate information criteria tests. 7 Hourly
Compensation growth estimates include one lag of the dependent variable and the
contemporaneous measure for labor market slackness. The Average Hourly Earnings
growth estimates include one lag of the dependent variable, the contemporaneous
measure of labor market slackness, and, depending on the given measure, between one
and four additional lags of the measure of labor market slackness.8 The change-ininflation estimates include two lags of the dependent variable, and the contemporaneous
measure of labor market slackness. In all cases, we cannot reject the null hypothesis of

most years since the ECI began. However, the measures are also different in their coverage, so it is difficult
to pin down the precise source of their difference.
6
Following Ravn and Uhlig (2002) we set the smoothing parameter equal to 6 because we are using annual
data.
7
First we used Schwartz’s Bayesian Information Criterion to choose the optimal number of lags of the
dependent variable to include in each specification. Using the optimal dependent variable lag length as
determined above, we then estimated a series of regressions allowing for current and 0 to 4 lags of each
measure of labor market slackness and calculate the Bayesian Information Criterion (T*ln(RSS) + k*ln(T)
where T is the number of observations, k is the number of parameters estimated, and RSS is the residual
sum of squares) to choose the optimal lags of the measures of labor market slackness.
8
Specifically, the BIC selects one lag of overall unemployment, 3 lags of men’s and women’s
unemployment, 4 lags of prime age unemployment, 1 lag of men’s prime age unemployment, 3 lags of
women’s prime age unemployment, and 1 lag of overall, men’s, and women’s detrended non-employment.

13

no autocorrelation. That said, we use Newey-West standard error estimates allowing for
up to fourth-order autocorrelation.
Each group of results in the table summarizes one set of estimates. We report the
adjusted R-squared, which allows us to assess which measure of labor market slackness
explains the most variation in a given dependent variable. The implied “zero growth”
rate is analogous to the “non-accelerating inflation rate of unemployment” or NAIRU. In
the first two columns, it is the rate of labor market slackness consistent with zero real
wage growth. In the third column, it is the rate of labor market slackness consistent with
no change in the rate of inflation. 9 For detrended non-employment we have added back
the average of the HP-filter trend component in order to consider the levels of nonemployment associated with zero compensation growth and no acceleration in inflation.
Next, we report the p-value for the F-statistic on the joint significance of the measures of
labor market slackness followed by the p-value for the chi-squared statistic for the null
hypothesis of no first-order autocorrelation.10
Consider the results for the overall unemployment rate in table 2. The first row
reveals a pattern that is consistent throughout the table. The fraction of the variation in
the dependent variable that is explained by the model is highest for real Average Hourly
Earnings growth, next highest for real Hourly Compensation growth, and lowest for
changes in inflation. Despite the differences in their definitions and coverage, the two
measures of real compensation growth yield similar estimates for the rate of
unemployment that is consistent with no growth in real compensation. The implied zero

9

This is calculated from the regression results as the negative of the constant divided by the sum of the
coefficients on the relevant measures of labor market slackness (Staiger, Stock, and Watson 1997).
10
This is calculated using “Durbin’s alternative test” because most estimates include a lagged dependent
variable.

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growth rate of unemployment is 7.7 percent for real Hourly Compensation growth and
6.7 percent for Average Hourly Earnings growth.
The implication of the Phillips curve econometric model is that lower
unemployment increases inflation through wage growth, because in response to wage
pressures, producers increase prices. Thus, one would expect that accelerating inflation
would require that the unemployment rate be low enough to create real wage growth. The
implied levels of labor market slackness associated with zero growth in inflation are
consistent with this idea. In all cases, the estimate is below the implied level associated
with zero growth in real compensation, meaning that unemployment has to be low
enough to affect real compensation growth before it triggers inflation acceleration.11
The main point of this analysis is to compare the explanatory power of different
measures of labor market slackness for both the overall population and men and women
separately. First compare explanatory power across different overall measures of labor
market slackness for real Hourly Compensation growth. Looking down column 1 at the
first set of entries for each measure, we see that the prime-age unemployment rate
explains the most variation in real Hourly Compensation growth (58 percent), with the
overall unemployment rate explaining the second largest percentage (52 percent), and
non-employment explaining the least (38 percent). This is also the case for the Average
Hourly Earnings growth. These results suggest, perhaps not surprisingly, that what
matters for compensation growth is the labor market tightness for those whom we think
are most attached to the labor market. For estimates of the change in inflation, the results

11

Alternatively, one could calculate the level of unemployment consistent with compensation growth equal
to labor productivity growth. Over the analysis period, productivity growth averages 1.96 percent per year.
This raises the level of unemployment further above the level associated with no acceleration in inflation.
The same is true for prime-age unemployment rates.

15

are different in that the overall non-employment rate explains the most variation (48
percent). Prime age unemployment explains about 40 percent of the variation, and
overall unemployment explains about 35 percent.
These results provide no evidence that overall non-employment explains real
compensation growth better than measures of overall unemployment. However, this is
not entirely consistent with what we might expect given the secular trends in labor force
participation. If, over time, people are more or less inclined to label themselves as out of
the labor force rather than unemployed, the non-employment rate should be a more
consistent measure of labor market activity. Thus, we would expect that non-employment
might be more closely associated with changes in compensation growth than measures of
unemployment.12

Below, we evaluate whether this might be true for men more than

women and whether these relationships are changing over time.
Next, consider differences in men’s and women’s measures of labor market
slackness in explaining changes in wages and prices. Recall that our analysis above
shows that once women’s labor market participation is taken into consideration, the
1990s again appear to be a period of remarkably robust labor market activity combined
with low inflation. Juhn, Murphy, and Topel’s interpretation that one need not rethink
the 1990s implications for the natural rate of unemployment was based solely on the
labor market outcomes for men. Since women’s increased labor market participation
more than offsets men’s decreased labor market participation, the question of why the
United States was able to maintain such low unemployment and inflation during the

12

Additionally, we find the somewhat inconsistent result that the levels of non-employment associated with
zero Hourly Compensation growth are below those associated with no acceleration in inflation. That said,
levels of non-employment associated with compensation growth rates equal to average growth in labor
productivity exceed those associated with no inflation acceleration.

16

1990s is germane. One possible explanation is that women’s labor market slackness puts
a different amount of downward pressure on wages and prices than does men’s. For
example, a one percentage point decrease in men’s unemployment might have a different
impact on wage growth than a one percentage point decrease in women’s unemployment.
There are many different models of the labor market that could generate such differences.
Consider, for example, a segmented labor market where men and women do not compete
for the same jobs. Alternatively, a model where men and women have very different
reservation wages, perhaps because of differences in the value of their home production
activities, could yield differences in the effect of men’s and women’s measures of labor
market slackness on real compensation growth and changes in inflation. Also,
provocative new evidence suggests that women are less likely to bargain for higher
wages than are men, perhaps suggesting that their labor market activities put less upward
pressure on wages, and thus prices (See Babcock and Leschever (2003)). An exhaustive
assessment of the validity of these hypotheses is beyond the scope of this paper, but it is
worth keeping in mind that there are many potential reasons that measures of labor
market slackness for men and women might have different macroeconomic implications.
It is an empirical question whether or not this is the case.
We now turn to an assessment of whether men’s and women’s measures of labor
market slackness differentially explain measures of real compensation growth and
changes in inflation. Looking down the R-squared statistics in the first column, men’s
measures of labor market slackness explain a greater share of the variation in real Hourly
Compensation growth than women’s measures of labor market slackness. The
specification using men’s overall unemployment rate explains about 58 percent of the

17

variation in real Hourly Compensation growth while the specification using women’s
overall unemployment rate only explains about 39 percent of the variation. The
comparisons between men’s and women’s measures are similar for the specifications
using prime-age unemployment (although the adjusted R-squared increases more for
women’s prime age unemployment than for men’s).
Estimates for Average Hourly Earnings growth are somewhat different. In the
case of overall unemployment, the women’s measure explains only slightly less of the
variation in the left-hand-side variable than the men’s. For prime-age unemployment, the
women’s measure does a bit better. In fact, overall men’s unemployment explains more
of the variation in real Average Hourly Earnings growth than men’s prime-age
unemployment rate. Differences in coverage may account for some of the difference in
the results for men and women between Average Hourly Earnings and Hourly
Compensation. For example, since women are less likely to be supervisors than men and
Average Hourly Earnings covers only non-supervisory workers, changes in Average
Hourly Earnings growth rates may more closely reflect changes in women’s
compensation than the Hourly Compensation measure.
Next, consider the models that include the detrended non-employment rate as the
measure of labor market slackness. Again, for both real Hourly Compensation growth
and real Average Hourly Earnings growth, the men’s measure of labor market slackness
has greater explanatory power than the women’s measure of labor market slackness. The
men’s detrended non-employment rate explains 40 percent of the variation in the growth
of real Hourly Compensation and 74 percent of the variation in the growth of real
Average Hourly Earnings. Women’s detrended non-employment explains 34 percent of

18

the variation in real Hourly Compensation growth and 64 percent of the variation in real
Average Hourly Earnings growth.
The results for changes in the inflation rate are similar to the previous results in
showing that men’s measures of labor market slackness explain a larger share of the
variation in the dependent variable than women’s measures of labor market slackness.
What is particularly striking, however, is that the specification using men’s nonemployment rate explains more of the variation in changes in inflation (49 percent) than
either the overall men’s unemployment rate or the men’s prime-age unemployment rate.
In contrast, for the cases of real Hourly Compensation growth and real Average Hourly
Earnings growth, corresponding measures of unemployment and prime-age
unemployment explain more of the variation in the dependent variable than the nonemployment rates.
In sum, there is fairly consistent evidence that men’s measures of labor market
slackness have more explanatory power in real compensation growth and changes in
inflation than do women’s measures of labor market slackness. Of all the measures,
men’s prime age unemployment explains the highest share of the variation in Hourly
Compensation growth and men’s detrended non-employment explains the highest share
of changes in inflation. These results have several implications. First, if women’s labor
force activities are less relevant for changes in compensation and prices than are men’s,
Juhn, Murphy, and Topel’s conclusions, which are based solely on men’s labor force
activities, may be right. In other words, the low unemployment and inflation that
characterized the U.S. economy in the 1990s labor market need not lead one to question
whether the natural rate has fallen. Second, there is some support for Juhn, Murphy, and

19

Topel’s implication that non-employment is a more relevant labor market statistic to
consider than (self-defined) measures of unemployment -- at least when considering
changes in inflation. That said, it is somewhat surprising that non-employment does not
also explain the largest share of the variation in real compensation growth given the
channel through which measures of labor market slackness are expected to affect
inflation.

Changes Over Time
The results above suggest that, in general, men’s measures of labor market
slackness are more closely related to growth in real compensation and changes in
inflation than women’s measures of labor market slackness. However, we know that
women’s labor market participation changed markedly over the years in our sample.
Therefore, we now investigate whether the relationships between changes in inflation and
real compensation growth and measures of men’s and women’s labor market slackness
have changed as women’s labor market participation increased. In other words, as
women’s labor market behavior was becoming more similar to men’s, did their measures
of labor market slackness begin to look the same in macroeconometric models?
Before examining Phillips curve estimates over different time periods, we first
consider changes in men and women’s labor force participation since 1964, in order to
understand when one might expect to see a change in the effect of women’s measures of
labor market slackness. Figure 8a shows labor force participation rates by 5 education
groups for men aged 25 years and over. We focus here on those 25 years and older to
capture participation decision among individuals who are more likely to have completed

20

their formal education. Figures 8b and 9 show analogous numbers for women and the
overall population, respectively.
Labor force participation declined for all men between 1964 and 2002, but there
were large differences by education group.13 Declines were steepest for those with less
than a high school diploma. However, in the late 1990s, the decline in the participation
rate among those men with only elementary education began to reverse, and by 2002,
their participation rates were comparable to those of similarly educated men in 1979. For
women, we see a very different picture. Participation rates were fairly flat for women
with less than a high school diploma. For women with a high school diploma or more
education, participation rates rose over this period. Combining women and men, we see
that labor force participation declined among the lowest education groups, was relatively
flat for those with a high school diploma, and increased among those with some college
or a college degree. Over the period, low-skilled men were replaced in the labor force by
relatively high-skilled women. As mentioned above, if higher skilled workers have lower
frictional unemployment because they are easier to match to jobs or have lower search
costs, this change could be another reason that the “natural” rate of unemployment
appeared to be lower later in the period.
If in the later time period, women’s labor force behavior is more similar to men’s,
then their labor market slackness measures maybe more closely tied to measures of real
compensation growth and changes in inflation. Additionally, as women’s labor force
participation increased, the civilian labor force became increasing female. In 1965, 35

13

The decline in men’s labor force participation does not simply represent changes in retirement behavior.
For men 25 to 54 years of age, the labor force participation rate declined from an average of 97 percent in
1964 to 91 percent in 2002. Over the same period, women’s labor force participation increased from 45 to
76 percent. Authors’ calculations based on monthly participation rates by age and sex from the BLS.

21

percent of the civilian labor force was comprised of women while by 2002, 47 percent of
the labor force was female.14 Mechanically, this means that women will also make up a
larger share of the data on compensation. Thus, it might not be surprising to find closer
relationships between women’s measures of labor market slackness and real
compensation growth in later years.
In figure 8b it appears that there is a change in women’s labor force participation
trends in the mid- to late-1980s. More highly educated women are increasing their labor
force participation at a fairly steep rate until roughly the middle of the analysis period. In
the latter part of the period, women’s labor force participation rates flatten out.
Additionally, as shown in figure 6b, men’s and women’s unemployment rates are more
similar since the mid-1980s. From 1964 to 1983 women’s rates of unemployment were
nearly 1.5 percentage points higher than men’s unemployment rates. Over the remainder
of the period, women’s unemployment rates averaged 0.1 percentage points below and
0.1 percentage points above men’s rates for overall and prime-age unemployment,
respectively.
In order to more formally establish a dividing year for our analysis, we tested for
a structural break in women’s labor force participation, using a sup Wald test (Cooper,
Braga, Kennedy, and Piehl 2003).15 We find evidence of a structural break in 1984, and
thus we separate our sample in to two periods, 1965-1983, and 1984-2002.
14

Based on authors’ calculations from aggregate CPS data available from the BLS.
First, we tested and rejected that women’s labor force participation follows a random walk, using the
Dickey-Fuller test. Next, we formed the sup Wald test by running separate regressions allowing the “post”
period to be defined as starting with a different year in each regression (with the restriction that there are
always at least 5 percent of the observations in either the pre or post periods). The largest F-statistic for the
“post” dummy, measured against the appropriate critical values, indicates where the structural break
occurs. Note that Monte Carlo simulations examining where this test would pick a structural break if
women’s labor force participation followed a straight time trend, place the break earlier in the period. See
15

22

Serendipitously, 1984 is also when eligibility rules for disability insurance were changed,
which many observers associate with the decline in low-skilled men’s labor force
participation.16
Table 3 presents the results for the Phillips curve estimation with the sample split
into two periods. Period one covers the 18 years from 1965 to 1983. Period two covers
the 19 years from 1984 to 2002. For the estimates shown, we allow each specification to
have a different lag structure, based on information criteria as described for the table 2
analysis. At the top of each column, we report which lags of the dependent variable are
included in each specification. At the top of each group of reported results, we report the
lags of the labor market slackness measure included in that estimation. In all cases, the
contemporaneous measure of labor market slackness is also included.

We have also run

the regressions restricting the lag structure to be the same within each column.17 The
results are broadly similar.
Using a full set of interactions, we test, and reject, that the coefficients are equal
in the two periods for all cases but do not report the results in this table.18 Instead, we
report the p-value for the F-test of whether the coefficients on the labor market slackness
variables are jointly equal across the time periods. For real Hourly Compensation
growth, we reject that the coefficients are equal on each of the overall measures of labor

Cooper, et al. (2003) for more details. For men, we could not reject a unit root in labor force participation,
which invalidates the sup Wald test for them.
16
Other researchers have also noted important changes in the economy before and after 1984. Atkeson and
Ohanian (2001) state that the business cycle, monetary policy, and inflation all are less volatile since 1984
than in the previous 15 years.
17
In this case, we set the dependent variable lags equal to the maximum lag length selected by the BIC for
that dependent variable in that particular time period. Similarly, we set the number of lags of the measure
of labor market slackness equal to the maximum number of lags chosen for that labor market slackness type
(unemployment, prime-age unemployment, or non-employment) within period. The results are available
from the authors on request.
18
These results are available from the authors on request.

23

market slackness. We can also reject joint equality for men’s overall and prime-age
unemployment rates as well as the detrended non-employment rate of women. Rejecting
equality across the time periods for men’s measures of unemployment and failing to
reject equality across the periods for men’s non-employment is concordant with the idea
that non-employment is a consistent measure of men’s labor market activity while
unemployment rates are subject to changes in whether men define themselves as
unemployed or out of the labor force. However, this pattern fails to hold up when
looking at real Average Hourly Earnings growth or changes in inflation. In contrast, we
reject equality of coefficients across periods on all labor market slackness measures when
we look at real Average Hourly Earnings growth. For changes in the rate of inflation, we
only reject the null hypothesis of equality of coefficients for women’s overall and primeage unemployment rates, failing to reject coefficient equality over time for all male
measures of labor market slackness.
The additional results reported for each specification are defined as in the
previous table. First consider estimates using Hourly Compensation growth. In period 1,
the specifications using men’s measures of labor market slackness explain more of the
variation in the left-hand-side measure than do specifications that use women’s measures.
In period 2, this has reversed for both overall unemployment and for non-employment
rates. For prime age unemployment the adjusted R-squareds are very close (0.536 for
men’s prime age unemployment and 0.523 for women’s). For Average Hourly Earnings
growth, men’s and women’s unemployment rates explain about the same fraction of the
variation in both period 1 and 2. However, in the specifications that include detrended
non-employment, the women’s measure does worse than the men’s in period 1 and the

24

about the same as the men’s in period 2. The estimates using changes in inflation as the
dependent variables show a similar pattern. Men’s measures of unemployment explain
somewhat more of the variation in changes in inflation than do women’s in the earlier
period. In the second period, each of the women’s measures explains more variation than
the men’s.
Table 3 also has interesting implications for whether men’s non-employment is a
more consistent measure of labor market activity than unemployment measures. If this is
the case, we would expect the men’s rate of non-employment to explain more of the
variation in real compensation growth and changes in inflation than men’s measures of
unemployment, particularly in the most recent period. While in general this is not the
case, we do see this pattern in the inflation specifications. Indeed, in both periods, and
for the overall, men’s, and women’s versions of the measures, detrended nonemployment explains more of the variation in changes in inflation than do other measures
of labor market slackness. Again, this result calls into question the precise mechanism
through which real wage growth gets translated into changes in inflation. However, it
also suggests that Juhn, Murphy, and Topel’s insight that we should focus on nonemployment rather than simply unemployment rates is important for changes in inflation.
In sum, the results in this section suggest that in the earlier period, women’s labor
market activities were less closely related to real compensation growth and changes in
inflation than were men’s. However, in the second period, when women constitute a
larger fraction of the labor force, their labor market activities are more closely tied to
growth in real compensation and changes in inflation as measured by adjusted R-squared
statistics and, in fact, often fit the data better than the men’s measures. Thus, when trying

25

to explain real compensation growth and changes in inflation, we conclude that one
should use measures of labor market slackness that include both men and women.
Once again, then, the 1990s pose something of a puzzle. The first section of the
paper demonstrated that when we include women in the analysis, labor market slackness,
whether measured by unemployment or by non-employment, was low in the 1990s, and
yet, they were accompanied by low and stable rates of inflation. This again leads one to
question whether the “natural rate” of labor market slackness changed over time. We
examine our data’s implications for the “natural rate of labor market slackness” in the
next section.

Implied Zero Growth Rates
Our analyses above allow us to calculate the rate of labor market slackness
associated with no wage growth or no increase in inflationary pressure. We can use these
calculations to examine whether this “natural rate” has changed between the two time
periods. Here we focus on the measures of labor market slackness that include both men
and women, and ask how the implied zero growth rates have changed over time.
In table 4 we present estimates of the levels of labor market slackness consistent
with zero compensation growth and a constant inflation rate. One might alternatively
calculate the levels of labor market slackness consistent with compensation growth
somewhat above zero because increases in labor productivity can allow for positive
compensation growth without leading to increases inflation. Thus, we also present
estimates of the level of labor market slackness associated with compensation growth
equal to average labor productivity growth in the period. For the detrended non-

26

employment rate specifications, we again add back the (within period) average of the HPfilter trend component to the calculations in order to report a non-employment rate level
associated with the compensation growth assumptions and constant inflation. Standard
errors for these estimates are calculated using the delta method with the Newey-West
standard error estimates allowing for up to fourth-order autocorrelation.
In all but one case (prime age unemployment, Average Hourly Earnings growth,
and average labor productivity growth), the level of labor market slackness in period 2
that is associated with either zero compensation growth or compensation growth equal to
average labor productivity growth is lower than the corresponding level in period 1.
However, the change in the prime-age unemployment rate associated with real Average
Hourly Earnings growth either equal to zero or equal to labor productivity growth is not
statistically different from zero.19 Likewise, the fall in the non-employment rate
associated with real Hourly Compensation growth set equal to either assumption is not
different from zero. For no change in the rate of inflation, the level of labor market
slackness fell in all cases and all changes are statistically different from zero. In sum, the
results largely show that significantly lower rates of labor market slackness are consistent
with no wage or inflation pressure in the latter period.
The table 4 results are consistent with the U.S. economy undergoing a change
such that it can now sustain lower rates of labor market slackness without sparking wage
and inflation pressure. Whether this constitutes a “structural change” depends on one’s
exact definition. The change could come from changes on the demand side—increased
productivity, changes in production practices, etc.—that allow the economy to absorb a

19

Testing the difference with the reported statistics and standard errors is not strictly correct given that the
samples have some overlap in data; however, the importance of this distinction is likely to be small.

27

greater amount of economic activity more easily. However, these results are equally
consistent with changes in the labor market, discussed above, such that in recent years
low-skilled individuals have withdrawn from the labor force, and higher skilled
individuals, who are thought to have lower frictional rates of unemployment, have taken
their place. In some sense, which of these two explanations is correct makes little
difference for some policy decisions. A policymaker asking whether currently measured
rates of overall unemployment and non-employment are consistent with stable wages and
prices could be told “yes” under either scenario. However, whether policies that tried to
absorb those who are currently out of the labor force into employment would be equally
consistent with stable wages and prices depends critically on whether the new lower
“natural rate” is due to demand side or supply side changes.
D. Summary and Conclusions
Despite very low levels of unemployment in the 1990s, men’s non-employment
remained high compared to earlier periods. This finding led Juhn, Murphy, and Topel
(2002) to conclude that the 1990s did not represent a historically robust labor market for
men, particularly for low-skilled men. Thus, questions about whether the 1990s
represented a new, lower “natural” rate of unemployment may have been misplaced.
Here we show that once women are added to the analysis, this question appears to be
relevant. Women’s labor force participation increased dramatically during the period that
men’s was falling. Indeed, women’s labor force participation increased enough to
compensate for men’s declining participation. Thus, overall non-employment levels are
near historic lows in the 1990s.

28

We then examine the relationship between women’s and men’s measures of labor
market slackness and real compensation growth and changes in inflation. Measures of
unemployment for men and women tend to explain a higher percentage of the variation in
real compensation growth than do measures of non-employment. Thus, non-employment
may be an important concept for summarizing individual’s activities, but in terms of
predicting real compensation changes, people’s self-definition of their activities seems to
matter. This makes sense if it is disproportionately those who are actively searching for
jobs who put downward pressure on wages. However, in models of changes in inflation,
(detrended) non-employment measures, particularly for men, tend to explain as much or
more of the variation than do measures of unemployment. This calls into question the
exact mechanism through which real wage growth translates into changes in price
inflation, and suggests that those interested in changes in inflation should consider nonemployment in addition to more traditional measures of labor market slackness.
Although in the full period from 1965 to 2002, men’s measures of labor market
slackness tend to explain more of the variation in compensation growth and changes in
inflation than do women’s this result masks a stark change over the period. After 1984,
women’s measures of labor market slackness, or measures of labor market slackness that
include women, tend to explain more of the variation in real compensation growth and
changes in inflation than do measures based on men alone. This suggests that around the
period when women’s individual labor force participation begins to look much more like
men’s, women’s measures of labor market slackness begin to behave very much like
men’s in econometric models of the macro economy.

29

Overall in the latter period, the U.S. economy appears able to sustain lower levels
of unemployment and non-employment without upward pressure on wages and prices.
Whether this is due to structural, demand side changes in the nature of the labor market
and macro economy, or simply due to compositional shifts in the labor force toward
higher skilled individuals who tend to have lower rates of unemployment and nonemployment, is an open question.

30

References

Aaronson, Daniel and Daniel S. Sullivan, “Unemployment and Wage Growth: Recent
Cross State Evidence,” Economic Perspectives, Federal Reserve Bank of Chicago,
(24:2), 2000, p. 54-71.
Atkeson, Andrew and Lee E. Ohanian, “Are Phillips Curves Useful for Forecasting
Inflation,” Federal Reserve Bank of Minneapolis Quarterly Review, vol. 25, no. 1,
Winter 2001, pp. 2-11.
Babcock, Linda and Sara Leschever, Women Don’t Ask: Negotiation and the Gender
Divide. 2003 Princeton, NJ: Princeton University Press.
Blanchard, Olivier and Lawrence F. Katz, “What We Know and Do Not Know About the
Natural Rate of Unemployment,” Journal of Economic Perspectives, 11(1),
Winter 1997, pp. 51-72.
Cooper, Suzanne J., Anthony Braga, David M. Kennedy, Anne Morrison, Piehl,
“Testing for Structural Breaks in the Evaluation of Programs, Review of
Economics and Statistics, 2003, forthcoming.

Juhn, Chinhui, Kevin M. Murphy, and Robert H. Topel, “Current Unemployment,
Historically Contemplated,” Brookings Papers on Economic Activity, vol I, 2002,
p.79-116.
Katz, Lawrence, and Alan Krueger, “The High-Pressure U.S. Labor Market of the
1990s,” Brookings Papers on Economic Activity, 1999, v. 0, iss. 1, pp. 1-65.
Ravn, Morten O. and Harald Uhlig, “On Adjusting the Hodrick-Prescott Filter for the
Frequency of Observations,” The Review of Economics and Statistics. 84(2),
2002, pp. 371-380.
Staiger, Douglas, James H. Stock, and Mark W. Watson, “Prices, Wages, and the U.S.
NAIRU in the 1990s,” in The Roaring Nineties: Can Full Employment be
Sustained, 2001, pp.3-60, New York: Russell Sage Foundation, Century
Foundation Press.
Stiglitz, Joseph, “Reflections on the Natural Rate Hypothesis,” Journal of Economic
Perspectives, vol. 11, no 1, 1997, pps 3-10.

31

Figure 1a: Percent of Weeks Per Year in Unemployment, Nonparticipation,
and Nonemployment, Men Aged 18 to 55
50
45
40

30
% of Year Unemployed
% of Year Not Employed
% of Year Not Participating

25
20
15
10
5
0
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00

Percent of Year

35

Note: Authors' calculations based on March Current Population Surveys. See text for details.

32

Figure 1b: Percent of Weeks Per Year in Unemployment, Nonparticipation, and
Nonemployment, Women Aged 18-55
50
45
40

30
% of Year Unemployed
% of Year Not Employed
% of Year Not Participating

25
20
15
10
5
0
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00

Percent of Year

35

Note: Authors' calculations based on March Current Population Surveys. See text for details.

33

Figure 2: Percent of Weeks Per Year in Unemployment, Nonparticipation, and
Nonemployment, Men & Women Aged 18 and over
50
45
40

Percent of Year

35
30
25

% of Year Unemployed
% of Year Not Participating
% of Year Not Employed
% of Year Not Employed, FTE

20
15
10
5

19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00

0

Note: Authors' calculations based on March Current Population Surveys. Full time equivalent (FTE) nonemployment adjusts for usual hours
worked per week. See text for details.

34

Figure 3a: Unemployment, Nonemployment and Nonparticipation Rates,
Men 16 and Over
55
50
45

35
30
Nonparticipation
25

Unemployment
Nonemployment

20
15
10
5
0
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00

Percent of Population

40

Notes: Data shown are annual averages of monthly aggregate CPS data available from the BLS.

35

Figure 3b: Unemployment, Nonemployment and Nonparticipation Rates,
Women 16 and Over
55
50
45

35
30
Nonparticipation
Unemployment

25

Nonemployment
20
15
10
5
0
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00

Percent of Population

40

Notes: Data shown are annual averages of monthly aggregate CPS data available from the BLS.

36

Figure 4: Unemployment, Nonemployment and Nonparticipation Rates,
Men and Women 16 and Over
55
50
45

Percent of Population

40
35
30
Nonparticipation

25

Unemployment
Nonemployment

20
15
10
5

19
50
19
52
19
54
19
56
19
58
19
60
19
62
19
64
19
66
19
68
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
20
02

0

Notes: Data shown are annual averages of monthly aggregate CPS data available from the BLS.

37

Figure 5: Real Compensation Growth and Inflation 1965 to 2002
12

10

8

4

2

0

-2

Hourly Compensation Growth

Average Hourly Wage Growth

20
01

19
99

19
97

19
95

19
93

19
91

19
89

19
87

19
85

19
83

19
81

19
79

19
77

19
75

19
73

19
71

19
69

19
67

-4
19
65

Percent

6

Inflation

38

Unemployment Rate

Prime-Age Unemployment Rate

Nonemployment Rate

20
01

19
99

19
97

19
95

19
93

34
19
91

0
19
89

36

19
87

2

19
85

38

19
83

4

19
81

40

19
79

6

19
77

42

19
75

8

19
73

44

19
71

10

19
69

46

19
67

12

19
65

Unemployment Rate

Figure 6a: Overall Measures of Labor Market Slackness 1965 to 2002

Nonemployment Rate

39

Figure 6b: Men's and Women's Unemployment Rates 1965 to 2002
11

9

Percent

7

5

3

UR--Male

UR--Female

Prime-Age UR Male

20
01

19
99

19
97

19
95

19
93

19
91

19
89

19
87

19
85

19
83

19
81

19
79

19
77

19
75

19
73

19
71

19
69

19
67

19
65

1

Prime-Age UR Female

40

Men
Women
20
01

19
99

19
97

19
95

19
93

19
91

19
89

19
87

19
85

19
83

19
81

19
79

19
77

19
75

19
73

19
71

19
69

19
67

19
65

Percent

Figure 7a: Nonemployment Rates

70

65

60

55

50

45

40

35

30

25

20

Overall

41

Overall
Men
20
01

19
99

19
97

19
95

19
93

19
91

19
89

19
87

19
85

19
83

19
81

19
79

19
77

19
75

19
73

19
71

19
69

19
67

19
65

Percent

Figure 7b: Detrended Nonemployment Rates

2

1.5

1

0.5

0

-0.5

-1

-1.5

-2

Year

Women

42

Figure 8a: Labor Force Participation Rates by Completed Education Levels:
Men Aged 25 and Over
1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

Elementary

Some HS

HS Graduate

Some College

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

1979

1978

1977

1976

1975

1974

1973

1972

1971

1970

1969

1968

1967

1966

1965

0.2

College Grad

Notes: Based on authors' caculations from Current Population Survey data.

43

Figure 8b: Labor Force Participation Rates by Completed Education Levels:
Women Aged 25 and Over
0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Elementary

Some HS

HS Graduate

Some College

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

1979

1978

1977

1976

1975

1974

1973

1972

1971

1970

1969

1968

1967

1966

1965

0

College Grad

Notes: Based on authors' calculations from Current Population Survey data.

44

Figure 9: Labor Force Participation Rates by Completed Education Levels:
Men and Women Aged 25 and Over
0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

Elementary

Some HS

HS Graduate

Some College

20
02

20
00

19
98

19
96

19
94

19
92

19
90

19
88

19
86

19
84

19
82

19
80

19
78

19
76

19
74

19
72

19
70

19
68

19
66

19
64

0.1

College Grad

Note: Based on authors' calculations from Current Population Survey data.

45

Table 1: Summary Statistics
(Standard Errors)

Inflation
Change in Inflation
Real Hourly
Compensation Growth
Real Average
Hourly Earnings Growth
Unemployment Rate
Overall
Men
Women
Prime Age Unemployment
Overall
Men
Women
Nonemployment
Overall
Men
Women

Number of Obs.

Overall
1965-2002

Period 1
1965-1983

Period 2
1984-2002

4.207
(0.388)
-0.003
(0.224)
1.496
(0.289)
0.562
(0.280)

5.774
(0.560)
0.150
(0.437)
1.924
(0.460)
0.827
(0.495)

2.724
(0.233)
-0.148
(0.146)
1.091
(0.343)
0.311
(0.281)

5.993
(0.256)
5.750
(0.284)
6.379
(0.236)

6.164
(0.460)
5.637
(0.520)
6.984
(0.370)

5.832
(0.251)
5.857
(0.269)
5.805
(0.237)

4.619
(0.230)
4.312
(0.263)
5.108
(0.199)

4.506
(0.413)
3.947
(0.464)
5.428
(0.333)

4.726
(0.229)
4.657
(0.251)
4.805
(0.210)

39.827
(0.435)
27.488
(0.420)
50.928
(1.051)

42.213
(0.242)
25.947
(0.679)
56.709
(0.806)

37.567
(0.316)
28.948
(0.179)
45.450
(0.545)

37

18

19

Notes: Compensation growth is inflation adjusted using the Personal Consumption
Expenditure Chain-Type Prince Index.

46

Table 2: Estimated Relationships between Measures of Labor Market Slackness and Measures
of Real Compensation Growth and Change in Inflation
(Standard Errors)

Unemployment Rate
Overall
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of UR lags
P-value for AR(1)
Male
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of UR lags
P-value for AR(1)
Female
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of UR lags
P-value for AR(1)
Prime Age Unemployment Rate
Overall
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of UR lags
P-value for AR(1)
Male
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of UR lags
P-value for AR(1)
Female
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of UR lags
P-value for AR(1)

Real Hourly
Compensation
Growth

Real Average
Hourly
Earnings Growth

Change in
Inflation

0.5170
7.714
(0.337)
0.0000
0.5634

0.7472
6.712
(0.330)
0.0001
0.1778

0.3488
6.012
(0.457)
0.0000
0.8605

0.5754
7.597
(0.286)
0.0000
0.9057

0.8147
6.364
(0.145)
0.0000
0.2381

0.3925
5.770
(0.401)
0.0000
0.7029

0.3905
8.223
(0.601)
0.0009
0.4985

0.7966
6.886
(0.261)
0.0000
0.1219

0.2455
6.398
(0.666)
0.0109
0.3722

0.5772
6.117
(0.240)
0.0000
0.8224

0.8410
5.102
(0.090)
0.0000
0.7583

0.4028
4.636
(0.320)
0.0000
0.7788

0.5930
6.021
(0.238)
0.0000
0.8425

0.7732
4.997
(0.187)
0.0001
0.1677

0.4157
4.332
(0.309)
0.0000
0.4660

0.4622
6.497
(0.358)
0.0000
0.3946

0.8351
5.534
(0.163)
0.0000
0.1370

0.3187
5.122
(0.426)
0.0006
0.3944

47

Detrended Nonemployment
Rate
Overall
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of Nonemp.
lags
P-value for AR(1)
Male
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of Nonemp.
lags
P-value for AR(1)
Female
Adjusted R-square
Implied "zero growth" rate
P-value for joint sig. of Nonemp.
lags
P-value for AR(1)
Number of Observations

0.3833
40.500
(0.175)

0.7109
39.714
(0.163)

0.4751
39.829
(0.109)

0.0113
0.756

0.0000
0.3451

0.0000
0.5432

0.4025
28.309
(0.193)

0.7373
27.371
(0.190)

0.4879
27.489
(0.129)

0.0034
0.8886

0.0000
0.1994

0.0000
0.3155

0.3390
51.549
(0.216)

0.6383
50.798
(0.179)

0.4220
50.931
(0.103)

0.0465
0.7174

0.0000
0.7349

0.0000
0.9599

37

37

37

Notes: Columns 1 and 2 include one lag of the dependent variable and the contemporaneous
measure of the relevant labor market slackeness. In addition for column 2 estimates lags of the
relevant right-hand-side variables are included as follows: 1 lag of overall unemployment, 3 lags
of men’s and women’s unemployment, 4 lags of prime age unemployment, 1 lag of men’s prime
age unemployment, 3 lags of women’s prime age unemployment, and 1 lag of overall, men’s,
and women’s detrended non-employment. Column 3 estimates include 2 lags of the change in
inflation and the relevant contemporaneous measure of labor market slackenss. The implied
"zero growth" rates are calculated as the negative of the constant divided by the sum of the
coefficients on the relevant measure of labor market slackness. For non-employment we add
back in the average of the HP filter trend component in order to report the implied rate in levels.
Standard errors on the implied "zero growth" rates are calculated using the delta method with
Newey-West standard error estimates for the underlying coefficients, allowing for up to fourthorder autocorrelation.

48

Table 3: Estimated Relationships between Measures of Labor Market Slackness and
Measures of Real Compensation Growth by Period
Real Hourly Compensation
Growth
1965-1983
1984-2002
Period 1
Period 2
Lags of the Dependent Variable

1

Unemployment Rate
Overall
P-value for UR coefficients equal
Lags of the unemployment rate
Adjusted R-squared
P-value for joint sig. of UR lags
P-value for AR(1)

1-2
0.799
0.000
0.576

Male
P-value for UR coefficients equal
Lags of the unemployment rate
Adjusted R-squared
P-value for joint sig. of UR lags
P-value for AR(1)

1-2
0.828
0.000
0.947

Female
P-value for UR coefficients equal
Lags of the unemployment rate
Adjusted R-squared
P-value for joint sig. of UR lags
P-value for AR(1)

1-4
0.732
0.000
0.321

Real Average Hourly Earnings
Growth
1965-1983
1984-2002
Period 1
Period 2

1-3

1

0
0.514
0.003
0.778

1-4
0.856
0.000
0.392

1
0.194
0.014
0.643

1-4
0.840
0.000
0.683

0
0.517
0.005
0.847

1-4
0.844
0.000
0.216

0.050

Change in Inflation
1965-1983
1984-2002
Period 1
Period 2

1

1-4

1-2
0.861
0.000
0.336

0
0.571
0.013
0.642

1-2
0.863
0.000
0.415

0
0.593
0.006
0.495

1
0.842
0.000
0.636

1-4
0.572
0.013
0.034

0.001

0.024

0.702

0.007

0.120

0

1
0.199
0.004
0.639

0.712

0.002

0
0.186
0.001
0.691

0.043
1
0.224
0.003
0.525

49

Prime Age Unemployment Rate
Overall
P-value for UR coefficients equal
Lags of the prime-age UR
Adjusted R-squared
P-value for joint sig. of UR lags
P-value for AR(1)

1-2
0.826
0.000
0.957

Male
P-value for UR coefficients equal
Lags of the prime-age UR
Adjusted R-squared
P-value for joint sig. of UR lags
P-value for AR(1)

1-2
0.823
0.000
0.678

Female
P-value for UR coefficients equal
Lags of the prime-age UR
Adjusted R-squared
P-value for joint sig. of UR lags
P-value for AR(1)

1-2
0.754
0.000
0.630

Detrended Nonemployment Rate
Overall
P-value for NR coefficients equal
Lags of nonemployment
Adjusted R-squared
P-value for joint sig. of Nonemp.
lags
P-value for AR(1)

0.040

0.001
1
0.527
0.018
0.621

1-4
0.854
0.000
0.736

1
0.536
0.012
0.581

1-4
0.807
0.000
0.885

0
0.523
0.003
0.802

1-4
0.869
0.000
0.295

1-2
0.583

1
0.389

0.005
0.377

0.051
0.662

0.489
1-2
0.861
0.000
0.299

0
0.610
0.003
0.560

1-2
0.860
0.000
0.451

0
0.623
0.001
0.396

1-2
0.856
0.000
0.145

1-4
0.606
0.004
0.044

1
0.688

1
0.816

1-4
0.679

0
0.340

0.000
0.423

0.019
0.359

0.000
0.014

0.000
0.221

0.042

0.005

0.154

0.381

0.012

0.006

0
0.170
0.002
0.728

0
0.173
0.002
0.700

0.038

0.034

1
0.256
0.002
0.579

0.023

Male

50

P-value for NR coefficients equal
Lags of nonemployment
Adjusted R-squared
P-value for joint sig. of Nonemp.
lags
P-value for AR(1)
Female
P-value for NR coefficients equal
Lags of nonemployment
Adjusted R-squared
P-value for joint sig. of Nonemp.
lags
P-value for AR(1)
Number of Observations

0.473

0.042

0.596

1-2
0.590

0
0.358

1
0.726

1
0.809

1-4
0.692

0
0.284

0.003
0.313

0.228
0.794

0.000
0.220

0.047
0.413

0.000
0.025

0.000
0.370

0.002

0.035

0.034

1-2
0.546

1-3
0.535

1
0.582

1
0.819

1-4
0.609

0
0.379

0.008
0.508

0.003
0.220

0.000
0.824

0.0004
0.301

0.013
0.086

0.000
0.139

18

19

18

19

18

19

Notes: Each combination of dependent variable and measure of labor market slackness is estimated separately by time period. All estimates
include the contemporaneous measure of the relevant right-hand-side variable. Additionally, lags of the dependent variable and the relevant
labor market slackness measure are also included as reported in the table. Lag structure was chosen using information criteria as described
in the text. The P-value for the F-test that the labor market slackness variable coefficients are equal across the time periods comes from a
single regression including a full set of period interactions and the maximum number of lags included in the separate specifications.

51

Table 4: Estimates of Labor Market Slackness Levels Associated with
Various Compensation Growth Rates and No Acceleration in Inflation
Real Hourly
Compensation
Growth
196519841983
2002
Period 1
Period 2
Unemployment Rate
zero
average labor productivity
standard error
Prime-Age Unemployment
Rate
zero
average labor productivity
standard error
Nonemployment Rate
zero
average labor productivity
standard error

Real Average Hourly
Earnings Growth
196519841983
2002
Period 1
Period 2

9.251
10.965
(0.653)

7.159
9.040
(0.325)

6.870
8.584
(0.191)

6.150
8.031
(0.197)

7.205
8.918
(0.483)

6.048
7.929
(0.318)

5.198
6.912
(0.160)

5.042
6.923
(0.175)

40.796
42.510
(1.907)

38.084
39.965
(0.171)

42.021
43.734
(0.228)

37.328
39.209
(0.219)

Change in
Inflation
196519841983
2002
Period 1
Period 2

8.301

5.136

(1.168)

(0.528)

6.284

4.225

(0.717)

(0.489)

42.413

37.430

(0.195)

(0.059)

Notes: Calculations are based on the estimates shown in Table 3 for the "overall" measures of labor
market slackness. Standard errors, in parentheses, are calculated using the delta method. For the
non-employment rate calculations we add back the average of the HP filter trend component for each
period in order to show the level of non-employment associated with zero compensation growth and
no acceleration of inflation. Average growth in labor productivity is equal to 1.71 percent in period 1
and 1.88 percent in period 2.

52

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Trade Structure, Industrial Structure, and International Business Cycles
Marianne Baxter and Michael A. Kouparitsas

WP-02-30

Estimating the Returns to Community College Schooling for Displaced Workers
Louis Jacobson, Robert LaLonde and Daniel G. Sullivan

WP-02-31

A Proposal for Efficiently Resolving Out-of-the-Money Swap Positions
at Large Insolvent Banks
George G. Kaufman

WP-03-01

Depositor Liquidity and Loss-Sharing in Bank Failure Resolutions
George G. Kaufman

WP-03-02

Subordinated Debt and Prompt Corrective Regulatory Action
Douglas D. Evanoff and Larry D. Wall

WP-03-03

When is Inter-Transaction Time Informative?
Craig Furfine

WP-03-04

Tenure Choice with Location Selection: The Case of Hispanic Neighborhoods
in Chicago
Maude Toussaint-Comeau and Sherrie L.W. Rhine

WP-03-05

Distinguishing Limited Commitment from Moral Hazard in Models of
Growth with Inequality*
Anna L. Paulson and Robert Townsend

WP-03-06

Resolving Large Complex Financial Organizations
Robert R. Bliss

WP-03-07

6

Working Paper Series (continued)
The Case of the Missing Productivity Growth:
Or, Does information technology explain why productivity accelerated in the United States
but not the United Kingdom?
Susanto Basu, John G. Fernald, Nicholas Oulton and Sylaja Srinivasan

WP-03-08

Inside-Outside Money Competition
Ramon Marimon, Juan Pablo Nicolini and Pedro Teles

WP-03-09

The Importance of Check-Cashing Businesses to the Unbanked: Racial/Ethnic Differences
William H. Greene, Sherrie L.W. Rhine and Maude Toussaint-Comeau

WP-03-10

A Structural Empirical Model of Firm Growth, Learning, and Survival
Jaap H. Abbring and Jeffrey R. Campbell

WP-03-11

Market Size Matters
Jeffrey R. Campbell and Hugo A. Hopenhayn

WP-03-12

The Cost of Business Cycles under Endogenous Growth
Gadi Barlevy

WP-03-13

The Past, Present, and Probable Future for Community Banks
Robert DeYoung, William C. Hunter and Gregory F. Udell

WP-03-14

Measuring Productivity Growth in Asia: Do Market Imperfections Matter?
John Fernald and Brent Neiman

WP-03-15

Revised Estimates of Intergenerational Income Mobility in the United States
Bhashkar Mazumder

WP-03-16

Product Market Evidence on the Employment Effects of the Minimum Wage
Daniel Aaronson and Eric French

WP-03-17

Estimating Models of On-the-Job Search using Record Statistics
Gadi Barlevy

WP-03-18

Banking Market Conditions and Deposit Interest Rates
Richard J. Rosen

WP-03-19

Creating a National State Rainy Day Fund: A Modest Proposal to Improve Future
State Fiscal Performance
Richard Mattoon

WP-03-20

Managerial Incentive and Financial Contagion
Sujit Chakravorti, Anna Llyina and Subir Lall

WP-03-21

Women and the Phillips Curve: Do Women’s and Men’s Labor Market Outcomes
Differentially Affect Real Wage Growth and Inflation?
Katharine Anderson, Lisa Barrow and Kristin F. Butcher

WP-03-22

7