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FEDERAL RESERVE BANK of ATLANTA

Asymmetric Labor Force Participation
Decisions over the Business Cycle:
Evidence from U.S. Microdata
Julie L. Hotchkiss and John C. Robertson
Working Paper 2006-8
July 2006

WORKING PAPER SERIES

FEDERAL RESERVE BANK o f ATLANTA

WORKING PAPER SERIES

Asymmetric Labor Force Participation
Decisions over the Business Cycle:
Evidence from U.S. Microdata
Julie L. Hotchkiss and John C. Robertson
Working Paper 2006-8
July 2006
Abstract: The purpose of this paper is to explore the microfoundations of the observed asymmetric
movement in aggregate unemployment rates. Using U.S. data, we find that individual labor force
participation responds asymmetrically to changes in local labor market conditions, consistent with the
pattern of movements in the aggregate unemployment rate. Differences in the asymmetry and sensitivity
of labor force participation decisions are found across gender, age, and education groups, and these
differences are used to anticipate changes in the aggregate movements as population characteristics
change over time.
JEL classification: J21, J22, E24, E32
Key words: asymmetric labor force participation decision, unemployment rate, business cycle, gender,
education, age

The authors thank M. Laurel Graefe and Menbere Shiferaw for valuable research assistance. The views expressed here are the
authors’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors
are the authors’ responsibility.
Please address questions regarding content to Julie L. Hotchkiss, Research Department, Federal Reserve Bank of Atlanta, 1000
Peachtree Street, N.E., Atlanta, GA 30039-4470, 404-498-8198, julie.l.hotchkiss@atl.frb.org, or John C. Robertson, Research
Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street, N.E., Atlanta, GA 30039-4470, 404-498-8782,
john.c.robertson@atl.frb.org.
Federal Reserve Bank of Atlanta working papers, including revised versions, are available on the Atlanta Fed’s Web site at
www.frbatlanta.org. Click “Publications” and then “Working Papers.” Use the WebScriber Service (at www.frbatlanta.org) to
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Asymmetric Labor Force Participation Decisions Over the Business Cycle:
Evidence from U.S. Micro-data

I. Introduction
The tendency for the proportion of labor market participants who are unemployed to
increase quickly during periods of economic weakness and to fall slowly during the subsequent
recovery is a well-documented phenomenon (for example, see Neftci 1984 and Koop and Potter
1999). Figure 1 illustrates the asymmetric movement of the unemployment rate in the U.S.,
Canada, Australia and the U.K. In each case, large upswings in the unemployment rate tend to
be much steeper than the ensuing declines.1 Various explanations of this phenomenon have been
postulated, including behavioral inertia (Gay and Washer 1989), structural changes in the labor
market (Groshen and Potter 2003), differential response of firms to shocks across the business
cycle (Burgess and Turon 2005), and economic shocks affecting different aspects of the labor
market depending on the direction of the shock (Bardsen et al. 2003). Several statistical
descriptions of the asymmetric time series pattern have also been proposed, including Hamilton
(2005), Moshiri and Brown (2004), Rothman (2000), van Dijk et al. (2000), and Montgomery et
al. (1998).
[Figure 1 here]
The purpose of this paper is to identify and to explore the behavioral foundations of the
asymmetric movement of the unemployment rate by modeling individual labor supply responses
to changing labor market conditions. If an individual’s labor supply decision is different when
their employment prospects weaken than when they improve, then that asymmetric response
1

Similar asymmetric movement can be seen in the unemployment rate of other countries, as well (for example see
Moshiri and Brown 2004). Darby et al. (2001) document an opposite asymmetric movement of the unemployment
rate across the business cycle in Japan.

-1-

will, in turn, contribute to the nonlinearity observed in the aggregate unemployment rate over
time. Identifying the micro-foundations for the observed aggregate time-series relationship will
allow policy analysts to explain, and perhaps anticipate, changes to that relationship as individual
characteristics of the population change. Of course, because demand factors play an important
role in determining the flow into and out of unemployment, changes in labor force participation
behavior across the business cycle will not fully explain movements in the unemployment rate.
However, this paper does identify the role that labor supply decisions could play in accounting
for the observed asymmetry in aggregate unemployment rates.
The next section of the paper outlines the link between labor force participation decisions
and the unemployment rate, and describes theoretical reasons why individual labor force
sensitivity to labor market conditions could be expected to be asymmetric. An empirical analysis
of whether individual labor force participation decisions respond asymmetrically to changes in
employment prospects is presented in section three, using micro-data on U.S. regional labor
markets. The fourth section summarizes the results and suggests various empirical implications
for the aggregate unemployment rate. The fifth section concludes.

II. Theoretical Background
The basis for identifying the role that individual behavior plays in determining the source
of asymmetric movements in the unemployment rate is the well-known inverse relationship
between labor market conditions and individual labor force participation decisions. In a weak
labor market, unemployed individuals become discouraged and drop out of the labor market,
reducing the labor force participation rate (LFPR). Non-participants are also less likely to enter
the labor market under these conditions. In tight labor markets, individuals enter the labor

-2-

market to take advantage of promising employment opportunities, increasing the LFPR. The socalled "discouraged worker" effect provides the theoretical foundation for the observed countercyclicality of labor force participation rates (Long 1958).2 The question remains, however, as to
whether the labor force participation response to the relative strength of the labor market is
different depending on whether the labor market is weakening or strengthening. Evidence of
asymmetric labor force responses to changing labor market conditions would be the basis for
linking asymmetric movement in the aggregate unemployment rate to individual labor supply
decisions.
The standard labor/leisure choice model suggests why individual labor force sensitivity to
labor market conditions might be expected to be asymmetric. Assume that a person maximizes
utility over two goods, income and leisure:

max U = U (Y , L) .
L

Utility is increasing in both expected income (Y) and leisure (L), but there is a tradeoff between
income and leisure summarized by the budget constraint:

Y = Y0 + (T − L)W * ,
where Y0 is a person's non-labor income, T is the total amount of time a person has available to
work, and W * is the person's expected wage and reflects the cost of one hour of leisure. The
expected wage can be thought of as the product of the value of the person's human capital (the
actual market wage for that person, W) and the probability that the person can find a job offering
a certain number of hours of work ( π ):
2

While the potential of an opposing "added worker" effect (Woytinsky 1940) is acknowledged, quantitatively the
discouraged worker almost uniformly dominates any added worker influences, resulting in the counter-cyclicality of
labor force participation. The added worker effect contends that when a worker in a family loses his/her job during
a recession, other members will enter the labor market (additional workers) to make up for the lost income. See
Borrow (2004) for further empirical evidence of the inverse relationship between the LFPR and the unemployment
rate.

-3-

W * = πW .
For someone in the labor market but unemployed, a deterioration of employment
prospects signals a reduction in π and hence a lower expected wage.3 This reduction in the
price of leisure has both a substitution and income effect.4 Other things equal, the lower
expected wage means leisure is now less expensive, causing the person to seek fewer hours of
work (reducing T−L), and in the extreme, to exit the labor market (T=L). However, the lower
expected wage also reduces expected income, causing the person to seek more hours of work.
The desired number of hours of work will decline if the substitution effect dominates the income
effect, and the discouraged worker will be one for whom the substitution effect is so large that
the individual exits the labor market.
Conversely, a strengthening of the labor market signals an increase in π , which will raise
the expected wage. For someone out of the labor market, this higher expected wage will have
only a substitution effect, raising the price of leisure and acting to pull the person into the labor
market. The key is that in this situation, there is no countervailing income effect; the entire
pressure is to pull non-participants into the labor market.
Consequently, during periods when the labor market is weakening the resulting reduction
in expected income lessens the pressure for unemployed workers to exit the labor market, but
during periods of strengthening labor markets there is no such opposing force on individuals who
are currently out of the labor force. The implication is that the impact of changes in labor market
conditions on exits from the labor market will be more sluggish than the impact on entrances.
This is consistent with the finding of Gay and Wascher (1989), who find persistence in labor

3

There is also some evidence that weaker labor market conditions also directly, and negatively, affect the market
wage (see Macis 2006).
4
The income effect is clearly not literal since a change in the expected wage does not literally affect a person's
income, but, rather, the person's expected income.

-4-

supply (once in the market, it takes longer for a person to leave, relative to how quickly a person
enters), although they attribute the persistence to high entry costs and accumulated human
capital, which they argue makes workers more insulated from transitory changes in labor
demand.5
Of course, labor force decisions have a feedback effect to the unemployment rate itself.
The prediction of weaker exit and stronger entrance pressures suggests that the unemployment
rate will rise relatively quickly during periods of economic weakness as people linger in
unemployment, and fall more slowly when the economy recovers as people pour into the labor
market to take advantage of new opportunities. This scenario results in the asymmetric
movement of the unemployment rate depicted in Figure 1.

III. Empirical Method
The first goal of the empirical analysis is to establish whether individual labor force
participation decisions respond asymmetrically to changes in the probability of finding
employment ( π ). In other words, does a given decline in employment prospects decrease a
person's tendency to be a labor market participant differently than it would increase if
employment prospects improved by the same amount? If so, then we know that at least some of
the observed asymmetric movement of the unemployment rate across the business cycle has
foundation in individual behavioral decisions.

Gay and Wascher (1989) also find greater persistence among those demographic groups for whom we find greater
sensitivity to labor market conditions.
5

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A. Estimation Strategy
The baseline model is a standard probit model of individual labor force participation
decisions, modified to incorporate movements in local labor market conditions. The
unemployment rate is used as the measure of labor market conditions because it is arguably the
most direct measure of the difficulty labor force participants have in finding employment ( π ).
Other measures that might be considered are not nearly as desirable for this investigation. For
example, it would be difficult to argue that individuals can easily digest reports of GDP and what
its movement means for their employment prospects. In addition, the popular Employment-toPopulation ratio confounds the labor supply decision with worker demand. The unemployment
rate (which specifically measures among workers who want a job the percent that can't get one,
and is widely reported and easily understood by the general population) is the closest measure of

π as it was defined in the previous section.
In the baseline model an individual's labor force participation decision at a particular
point in time is specified as a function of a number of individual demographic and human capital
characteristics, as well as the observed local unemployment rate in the previous period:

LFPi = φ ( β + Β' X i + γ URi ) + ε i ,

(1)

where X i are individual demographic and human capital characteristics, URi is the lagged statelevel unemployment rate, ε i is the random error component that is assumed to be Bernoulli
distributed, φ ( ⋅) is the normal density function, and

⎧1 if U (Yi , Li < T ) > U (Yi , Li = T ) ⎫
LFPi = ⎨
⎬ . In other words, a person is in the labor force
⎩0 otherwise
⎭

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( LFPi = 1 ) if the utility of supplying a positive number of hours ( Li < T ) is greater than not
working at all ( Li = T ).
The model in equation (1) is then modified to allow for the possibility of individuals'
participation decisions responding to the unemployment rate differently depending on whether
the unemployment rate is sufficiently higher or lower than it was a year ago. The specification
allows the response to vary according to the relative size of the unemployment rate change since
it is possible that for some range of unemployment rate differences the response is not
asymmetric. The modified specification is:6

LFPi = φ ( β + Β' X i + γ 0URi0 + γ 1URi+ + γ 2URi− ) + ε i

(2)

where
⎧UR if URt −1 − δ ≤ URt < URt −1 + δ ⎫
URi0 = ⎨ t
⎬
⎩0 otherwise
⎭
⎧UR if URt ≥ URt −1 + δ ⎫
URi+ = ⎨ t
⎬
⎩0 otherwise
⎭

(3)

⎧UR if URt < URt −1 − δ ⎫
URi− = ⎨ t
⎬
⎩0 otherwise
⎭
and δ > 0. This modified specification measures the extent to which individuals respond
asymmetrically to changes in the unemployment rate. The three parameters on the
unemployment rate regressors in equation (2) measure how the individual's probability of labor
force participation responds to the local unemployment rate when it is within δ units of what it

6

This specification is similar to that applied by Mocan and Bali (2005) to investigate the effect of movements in the
unemployment rate on criminal activity. Another application of a similar specification can be found in Koop and
Potter (1999).

-7-

was a year ago ( γ 0 ), when it is δ units higher than it was last year ( γ 1 ), and when it is δ units
lower than it was last year ( γ 2 ).7
The symmetry threshold parameter, δ , is the amount by which the current
unemployment rate has to differ from the unemployment rate last year before individuals
respond differently according to the direction of the change in the unemployment rate. Equation
(2) is estimated for different values of δ , incremented by 0.1 percentage points. The model
parameter estimates are those which result at the value of δ which yields the largest likelihood
function value. Separate models are estimated for different gender and education groups.
The model specification in equation (2) allows a direct statistical test of asymmetric
behavior. The null hypothesis is that labor force participation decisions respond the same to
changes in the unemployment rate regardless of whether it is higher or lower than it was a year
ago:
H0 : γ 0 = γ1 = γ 2

(4)

In addition to the testable hypothesis depicted in equation (4), the estimate of δ will
provide an indication of the practical significance of any statistically significant asymmetric
behavior. For example, even if the null hypothesis of equation (4) is rejected, the value of δ
may be so large as to preclude any useful expectation of differential behavior when the
unemployment rate rises or falls.
For the most part, previous studies of the inverse relationship between the unemployment
rate and labor force participation decisions have made use of aggregate data and have assumed

7

Various model specifications were estimates with no appreciable difference in any of the conclusions presented
here. Specifically, a model with a double-symmetry threshold parameter, a model with a continuous unemployment
rate difference regressor, and a model with a simple unemployment rate movement (up or down) indicator variable
were all explored. These models were determined to be less complete or to offer no additional insight to the results
obtained from estimating the model described by equation (2).

-8-

that the relationship is symmetrical (for example, see Darby et al. 2001, Gay and Wascher 1989,
and Lenten 2000). Exceptions include Blau (1978), who allows for a differential impact of the
unemployment rate on entry and exit decisions of married women, and Blank and Shierholz
(2005) who investigate how individual labor force decisions respond to labor market conditions
across gender and education levels. The analysis in this paper improves upon the investigation
by Blau by using multiple years of data and by allowing a threshold effect across gender and
education groups. By allowing for asymmetric labor force responses, the present analysis will
also expand upon the work by Blank and Shierholz.

B. Data
Data on the outgoing rotation groups from the March Current Population Survey (CPS) is
used to evaluate determinants of labor force participation behavior among men and women in the
U.S. between the ages of 25 and 54.8 The historical aggregate unemployment rate for this group
is presented in Figure 2, and displays similar characteristics to the unemployment rate for all
individuals over 16 shown in Figure 1.
[Figure 2 here]
The data used in the regression analysis cover the years 1994 through 2005; a major
survey re-design in 1994 makes comparisons of labor force participation before and after 1994
(especially for women) problematic (see Polivka 1996). The CPS is used for two primary
reasons. First, these are the data from which the Bureau of Labor Statistics estimates and reports

8

The CPS is a monthly survey of about 50,000 households conducted by the United States Bureau of the Census for
the Bureau of Labor Statistics. The CPS is the primary source of information on the labor force characteristics of the
U.S. population. The sample is selected to represent the civilian noninstitutional population, and respondents are
interviewed to obtain information about the employment status of each member of the household 15 years of age
and older.

-9-

the labor force participation rate. Second, it provides a consistent, long-running, and large
sample on which to investigate systematic determinants of observed behavior. All analyses are
performed using the March supplement weight, since this is the only weight that is valid since
2002 and since some of the regressors come from the supplemental part of the survey.
The usual demographic and human capital regressors are included in the specification.
These include age; age squared; number of children under six years; number of children between
six and 18 years; non-labor income; and amount of disability income, if any; and dummy
variables for education (of course these are omitted when the sample is stratified by education);
race; marital status; geographic region; and year in the sample. All dollar variables are in 2004
values. The unemployment rate regressors are defined as in equation (3), where URt
corresponds to the respondent's state-level unemployment rate for February in year t and URt −1 is
the respondent's state-level unemployment rate for February in the previous year.9 While the
sample time period includes only one national recession, within each year there is considerable
variation across state-level unemployment rates with some respondents facing weaker and some
facing stronger labor market conditions than in the previous year.
Sample means for the subgroups on which the model is estimated are presented in Table
1. The differences in the averages across the samples are typical. For example, women are less
likely to be in the labor force than men, women receive less disability income than men, and
women are less likely to have less than a high school education. In addition, college educated
women are slightly younger and are more likely to have young (rather than older) children, than

Ideally, π would be tailored specifically to reflect each individual's employment prospects. This would require a
measure of the unemployment rate specific to each person's demographics (such as race to account for the potential
impact of discrimination on employment opportunities), each person's human capital (such as education, industry,
and occupation), and each person's geographic location. In the absence of a more precise measure, we opt to use the
state-level unemployment rate. Geographic factors are likely to be important to an individual's specific employment
prospects, and race and gender unemployment rates are available only at the national level.

9

- 10 -

women with less than a college degree. Both college-educated men and women are also more
likely to married than their less-educated counterparts.
[Table 1 here]

IV. Results
There are several dimensions to the results of the analysis described above. First,
conditional on the estimate of δ , a statistical test is performed to determine if individual
responses are significantly different when the unemployment rate is higher, the same, or lower
than last year. The second dimension of results is how behavior differs across gender and
educational groups. Do the labor force participation decisions of men and women and those with
different education levels respond differently to a given change in the unemployment rate?
Lastly, there is an issue of practical significance of any identified asymmetrical behavior. Even
if the specification in equation (3) is preferred statistically to the specification in equation (1), the
estimated value of δ may imply that behavior is asymmetric for only extremely large changes in
the unemployment rate.
Table 2 summarizes the results across all three dimensions. The value of δ that
maximizes the likelihood function for each subgroup is reported in column 1. Column (1) is the
range of unemployment rate changes for which the response is symmetric. Columns (2)-(4) give
the marginal sensitivity of the labor force participation rate of group j when the unemployment
rate is, respectively, more than δ j higher, more than δ j lower, or within δ j of the rate in the
preceding year. The Wald test statistic corresponding to the null hypothesis in equation (4) is
reported in column 5. The ML parameter estimates at each sub-group's optimal symmetry
threshold ( δ ) are reported in Appendix A.

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[Table 2 here]

A. The Estimate of the Symmetry Threshold Parameter
The estimate of δ j indicates the range of unemployment rate values within which labor
force responses are symmetric for group j. This range is narrower for women and, within
gender, more narrow among the less educated. Specifically, the labor force participation of
women with less than a college education responds asymmetrically when the local area
unemployment rate is more than 0.6 percentage points different than the unemployment rate of
the previous year. The least asymmetric behavior is exhibited by college-educated men, whose
labor force response differs only when the unemployment rate is more than 2.1 percentage points
higher or lower than the previous year.
The smaller symmetry thresholds for women and the less-educated means that it takes
smaller movements in the unemployment rate to elicit asymmetric behavior from these
demographic groups. This implies that the aggregate unemployment rate should exhibit greater
asymmetry among women and the less-educated over time.
Some casual evidence of this prediction can be found by comparing the length of time it
takes the aggregate unemployment rate to go from peak to trough and from trough to peak
around recessionary episodes. Table 3 lists these average lengths of times (in quarters) for men,
women, those with less than a high school degree, and those with a college degree or more. As
expected, the average number of quarters it takes the unemployment rate to go from peak to
trough is longer for each group than the average number of quarters it takes to go from trough to
peak (this is the asymmetry seen in Figure 2). The larger difference between average peak-to-

- 12 -

trough quarters and trough-to-peak quarters for women and the less-educated provides some
corroborating evidence of the greater asymmetry in the labor supply response of these groups.
[Table 3 here]

B. Marginal Impact of the Unemployment Rate on Labor Force Participation
The results in columns 2-4 in Table 2 yield several predictable results. First of all, labor
force participation decisions of women and the less educated are more responsive to changes in
the unemployment rate than those of men and the more educated, respectively. This can be seen
by the larger negative marginal effects. For a 0.5 percentage point increase in the unemployment
rate (a change smaller than any group's symmetry threshold), the expected labor force
participation of a college-educated woman decreases by 0.18 percentage points and for a lesseducated woman by 0.87 percentage points. By contrast, for the same increase in the
unemployment rate, the expected labor force participation of a college-educated man will
decrease by 0.09 percentage points and of a less-educated man by 0.48 percentage points. Men
are half as sensitive to changes in labor market conditions as their educationally equivalent
female counterparts.
These results are to be expected given the weaker labor force attachment of women and
the less educated to the labor market.10 Weaker attachment means that the value of time out of
the labor market (the person's reservation wage) is higher. As a result, for any given decrease in
chances of finding a job ( π ), the expected wage will be more likely to fall below the reservation
wage for women and the less educated than it is for men or the more educated. In addition, when
prospects improve ( π increases), women and the less educated are more likely to be out of the

10

Evidence of weaker labor market attachment of women and the less-educated can be found in Erosa et al. (2005)
and Antecol and Bedard (2004).

- 13 -

labor market to begin with and be feeling the stronger pressures of the positive substitution effect
pulling them into the labor market.
The implication of this greater sensitivity to changing labor market conditions is that for
whatever asymmetric behavior exists, and for a given change in labor demand, women and the
less-educated enter and exit the labor force more quickly in response to changing labor market
conditions and, thus, the amplitude of the unemployment rate cycles, ceteris paribus, will be
smaller for women and the less educated. In other words, when more workers exit the labor
force as jobs are lost, the unemployment rate doesn't rise as high, and when more workers enter
the labor force when jobs are created, the unemployment rate doesn't fall as far. Since labor
demand is quite different for workers with different education levels, it would be difficult to
confirm the smaller amplitude implied by changes in labor supply for less-educated workers by
simply comparing unemployment rates cycles across education. However, Figure 3 suggests that
the implication of smaller amplitude of aggregate unemployment rate cycles for women is borne
out when comparing aggregate unemployment rate cycles of men and women, at least since the
1980s.
[Figure 3 here]
The second result of note from columns 2-4 in Table 2 is the uniformly larger impact of a
change in the unemployment rate on labor force participation decisions when the unemployment
rate has declined. In other words, the signal that local labor market conditions are improving
increases the probability of participating in the labor market by a larger amount across both
gender and education groups than the probability declines when the unemployment rate has
risen. For example, when the unemployment rate is more than 0.6 percentage points higher than
a year earlier, a one percentage point rise in the unemployment rate lowers the expected

- 14 -

participation rate of a college-educated woman by 0.37 percentage points. But when the
unemployment rate is more than 0.6 percentage points lower than a year earlier, a one percentage
point decline in the unemployment rate raises the participation probability by 0.60 percentage
points (almost twice the impact of an unemployment rate increase of the same absolute
magnitude).
The finding of a larger labor force participation response to improvements in local labor
market conditions relative to declines is consistent with the behavioral explanation offered for
observing asymmetric movement of the unemployment rate across the business cycle. The
conflicting income and substitution effect dampens the exiting of the labor market as the
economy weakens, but the entrance of non-participants as the economy improves is spurred by
the absence of an income effect.

C. Statistical and Practical Significance of Asymmetric Behavior
Column 5 of Table 2 provides a test of how significant the statistical difference is
between assuming symmetric labor force response to changes in the economy and allowing that
behavior to respond asymmetrically. The null hypothesis in equation (4) is rejected for all subgroups except college-educated men. Even for those groups for whom the null hypothesis is
rejected, there is also an issue of practical significance that must be addressed. For men (of both
education levels) the estimates of δ are especially large. In fact, for men with less than a
college education the response to the level of the unemployment rate is symmetric for 94.2
percent of the sample, and symmetric for 99.0 percent of the sample of college educated men
(see Table 4). So for almost all unemployment comparisons, the asymmetry does not influence
male participation decisions, although the level of the unemployment rate does. The asymmetry

- 15 -

is more relevant for females, and especially for less-educated women. For women with less than
a college education the response to the level of the unemployment rate is symmetric for 62.4
percent of the sample, and symmetric for 78.4 percent of the sample of college educated women.
[Table 4 here]

D. Implications of an Aging Population
A practical application of the analysis presented in this paper is to explore behavioral
labor supply responses to changes in labor market conditions across a variety of demographic
characteristics. The reason for doing this would be to anticipate the impact of changes in those
characteristics on movement in the aggregate unemployment rate. One important change that
will be taking place in the U.S. over the next twenty years is the aging of the population.
According to U.S. Census population projections, the fastest growing age group in the U.S.
between 2010 and 2030 are those between 65 and 85 years of age.11 Between 2000 and 2010,
the fastest growing age group will be those 45 to 64. A natural question is what sort of
implication this demographic shift might have on the behavior of the unemployment rate. In this
regard, the analysis described above is repeated for different age groups of men and women. For
three age groups, Table 5 contains the estimates of the symmetry threshold parameter, marginal
effects of a one percentage point change in the unemployment rate, and the Wald test statistic for
the null hypothesis of symmetric behavior. Of course, extrapolating these results into the future
requires strong assumptions about the constancy of preferences and worker demand.
[Table 5 here]

11

U.S. Census Bureau, "U.S. Interim Projections by Age, Sex, Race, and Hispanic Origin,"
http://www.census.gov/ipc/www/usinterimproj/ (accessed 12 May 2006).

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The attractiveness of alternative activities for the younger (e.g., schooling) and the older
(e.g., retirement) groups, means that they will naturally be more marginally attached to the labor
market, making their responses to changes in local labor market conditions stronger. For
example, for a one percentage point increase in the unemployment rate, the expected labor force
participation for women progresses from -1.64 percentage points for 18-24 year olds, to -1.12
percentage points for 25-54 year olds, to -1.27 for 55-74 year olds. For men, the responses are 1.35 for 18-24 year olds, -0.73 for 25-54 year olds, and -1.56 for 55-74 year olds.
The pattern seen earlier of greater labor market sensitivity of women aged 25-54, relative
to men, is also seen among the younger age group. A one percentage point increase in the
unemployment rate reduces women's expected labor force participation by 1.64 percentage
points but only reduces men's by 1.35 percentage points. The difference across gender is even
more dramatic when local labor market conditions are improving, because at any given time
women are more likely to be out of the labor market, feeling the pull of the substitution effect as
conditions improve. The relative sensitivity across gender is much more similar for the older age
group. For a 0.7 percentage point or less increase in the local unemployment rate, the expected
labor force participation among men declines by 1.20 percentage points and among women by
1.27 percentage points. In addition, the labor supply response is much more symmetric for older
women than it is at earlier ages or than it is for men. It takes an unemployment rate change of
1.8 percentage points or more for older women to respond differently to weakening or
strengthening labor market conditions.
As a whole, however, the labor supply decisions of older workers are generally more
responsive to changes in local labor market conditions than mid-aged workers, suggesting that
the aggregate unemployment rate (all else equal) will exhibit less variation over time as the

- 17 -

population ages. In addition, while the symmetry threshold declines with age among men, it
increases for women, and given that women are still (and will probably continue to be) the
marginal worker between the two, overall asymmetry should decrease, again, ceteris paribus.
Interestingly, the direction of the asymmetry reverses for older men, compared with all
other age and gender groups. The results in Table 5 suggest that the labor supply decisions of
older men respond more dramatically to rising unemployment rates than declining
unemployment rates. This is not consistent with the theoretical predictions presented earlier.
However, since men have typically accumulated greater wealth by retirement age, because of
their more extensive labor market experience, labor market exits may be more feasible at age 55
and beyond for men than for women. It may also be the case that older men have deteriorating
skills or physical abilities that make continued labor market attachment less attractive at older
ages. This may be less true for women as they age, given that skill or physical atrophy are
typically not as critical in traditional female occupations.

V. Conclusions
Individual decisions about labor force participation (which are sensitive to the strength of
the labor market) are found to respond differently to local labor market conditions, depending on
whether the labor market is getting stronger, increasing employment opportunities, or whether
the labor market is getting weaker, decreasing employment opportunities. This asymmetric
individual labor supply response to changes in local labor market conditions is probably
responsible for at least some of the asymmetry observed in the aggregate movements of the U.S.
unemployment rate, although it doesn't rule out other potential contributors, such as fixed costs
to labor market entry or sticky wages.

- 18 -

It was determined that for those between the ages of 25 and 54, behavior among women
and the less-educated exhibits the greatest degree of asymmetry, whereas college-educated men
respond the same to changes in labor market conditions regardless of whether conditions are
improving or weakening. It was also found that the labor force participation decisions of women
and the less-educated are more sensitive overall, which, ceteris paribus, likely contributes to
flatter unemployment rate series for these groups. Given the results presented here, one might
expect that as women continue to become more attached to the labor market, their behavior will
more closely emulate that of men, with the result being greater symmetry and greater variation in
the U.S. aggregate unemployment rate series. In addition, the same tendencies will occur as the
percent of the population with a college degree continues to increase.
In consideration of the rapidly aging population in the U.S., the analysis was repeated for
younger (18-24 year olds) and older (55-74 year olds) age groups. The results suggest that as the
population ages, other things equal, the aggregate unemployment rate will likely becoming less
variable and more symmetric.
This type of analysis could easily be repeated to determine how other demographic
changes (e.g., race, marital status) might affect the nature of aggregate data series. Also, while
the quantitative results are applicable only to the U.S. experience, the conceptual and empirical
framework could be applied to individual labor supply responses in other countries as well.

- 19 -

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-J

Source: Australian Bureau of Statistics/Haver Analytics

14

12

6

4

4

2

2

0

-1-

United Kingdom

Source: Office for National Statistics\Haver

Source: Bureau of Labor Statistics/Haver

20

20

20

19

19

19

19

05

03

01

99

97

95

93

91

89

87

85

83

-J

-J

-J

-J

-J

-J

-J

-J

-J

-J

-J

-J

-J

an

an

an

an

an

an

an

an

an

an

an

an

an

an

Australia

19

19

19

19

81

0

19

0

19

2

an

an

4

2

-J

-J

-J

4

79

77

75

6

19

19

8

77 an
19 - Ja
79 n
19 - Ja
81 n
19 - Ja
83 n
19 - Ja
85 n
19 - Ja
87 n
19 - Ja
89 n
19 - Ja
91 n
19 - Ja
93 n
19 - Ja
95 n
19 - Ja
97 n
19 - Ja
99 n
20 - Ja
01 n
20 - Ja
03 n
20 - Ja
05 n
-J
an

75

19

19 Jan
77
19 Jan
79
19 Jan
81
19 Jan
83
19 Jan
85
19 Jan
87
19 Jan
89
19 Jan
91
19 Jan
93
19 Jan
95
19 Jan
97
19 Jan
99
-J
20
a
01 n
-J
20
a
03 n
20 Jan
05
-J
an

19
75
10

19

19

19 Ja
77 n
19 Ja
79 n
19 Ja
81 n
19 Ja
83 n
19 Ja
85 n
-J
19
a
87 n
19 Ja
89 n
19 Ja
91 n
-J
19
a
93 n
19 Ja
95 n
19 Ja
97 n
19 Ja
99 n
20 Ja
01 n
20 Ja
03 n
-J
20
a
05 n
-J
an

19
75

Figure 1. The Unemployment Rate 1975-2005: Australia, Canada, United Kingdom, and United States
14
Canada

12
12

10
8

6

Source: Statistics Canada\Haver Analytics

United States

12

10

10

8

8

6

0

19
75
19 - Q1
76
19 - Q2
77
19 - Q3
78
19 - Q4
80
19 - Q1
81
19 - Q2
82
19 - Q3
83
19 - Q4
85
19 - Q1
86
19 - Q2
87
19 - Q3
88
19 - Q4
90
19 - Q1
91
19 - Q2
92
19 - Q3
93
19 - Q4
95
19 - Q1
96
19 - Q2
97
19 - Q3
98
20 - Q4
00
20 - Q1
01
20 - Q2
02
20 - Q3
03
20 - Q4
05
-Q
1

Figure 2. The U.S. Unemployment Rate 1975-2005, ages 25-54.
Percent
10

9

8

7

6

5

4

3

2

1

0

Source: Bureau of Labor Statistics, Current Population Survey (http://www.cps.gov).

-1-

19
75
19 - Q1
76
19 - Q2
77
19 - Q3
78
19 - Q4
80
19 - Q1
81
19 - Q2
82
19 - Q3
83
19 - Q4
85
19 - Q1
86
19 - Q2
87
19 - Q3
88
19 - Q4
90
19 - Q1
91
19 - Q2
92
19 - Q3
93
19 - Q4
95
19 - Q1
96
19 - Q2
97
19 - Q3
98
20 - Q4
00
20 - Q1
01
20 - Q2
02
20 - Q3
03
20 - Q4
05
-Q
1

Figure 3. The U.S. Unemployment Rate 1975-2005, men and women separately, ages 25-54.
Percent
10

9
Women
Men

8

7

6

5

4

3

2

Source: Bureau of Labor Statistics, Current Population Survey (http://www.cps.gov).

-2-

Table 1. Sample means.

Labor Force Participant = 1
Age
No. of Children LT 6
No. of Children 6-18
Married, spouse present = 1
Less than high school degree
High school degree
College degree or more
Non-labor income
Black = 1
Disability income
Northeast = 1
Midwest = 1
South = 1
West = 1

Men
Less than
College or
College
More
0.890
0.959
38.920
39.874
0.262
0.316
0.582
0.587
0.588
0.686
0.182
0
0.818
0
0
1
22.044
31.214
0.138
0.069
114.824
69.561
0.181
0.221
0.239
0.232
0.364
0.327
0.215
0.219

Women
Less than
College or
College
More
0.738
0.844
39.358
39.012
0.288
0.324
0.783
0.585
0.618
0.671
0.154
0
0.846
0
0
1
36.229
58.808
0.158
0.093
63.324
62.525
0.186
0.227
0.236
0.231
0.370
0.338
0.208
0.203

No. of Observations

372,494

406,250

Variable

268,156

297,112

Note: All dollar values are 2004 values. Sample is from the outgoing rotation groups of the
March Current Population Survey and includes men and women 25-54 years of age.

-3-

Table 2. Estimation results, by gender and education, ages 25-54; the marginal impact on labor
force participation of a one percentage point change in the local unemployment rate.

(1)

∂P[ LFP = 1]
∂UR +
(2)

∂P[ LFP = 1]
∂UR 0
(3)

∂P[ LFP = 1]
∂UR −
(4)

Wald Test
H0 : γ 0 = γ1 = γ 2
(5)

1.8
2.1

-0.81
-0.01

-0.97
-0.19

-1.37
-0.51

21.98 (0.00)
3.50 (0.17)

0.6
0.9

-1.46
-0.37

-1.74
-0.37

-1.97
-0.60

45.85 (0.00)
8.36 (0.02)

δˆ
Men
LT College
College or more
Women
LT College
College or more

Notes: The marginal effects are calculated for each person, then averaged across the sample.
Parameter estimates from a maximum likelihood estimation of a probit model are contained in
Appendix A. Column 1 is the range of unemployment rate changes (since last year) for which
the response is symmetric. Columns 2-4 give the marginal sensitivity of the labor force
participation decision for group j when the unemployment rate is, respectively, more than δ j
higher, more than δ j lower, or within δ j of last year. Column 5 reports the Wald test statistic,
which is distributed as a chi-squared random variable with two degrees of freedom under the null
hypothesis that labor force participation decisions are made symmetrically with regard to
changes in the unemployment rate. The number in parentheses are the associated p-values.

-4-

Table 3. Average Number of Quarters from Peak to Trough and from Trough to Peak, U.S.
Quarterly Unemployment Rate 1978-2005.
Average Number of Quarters
Difference
Peak to Trough
Trough to Peak
(P→T)-(T→P)
Men
20.7
10.0
10.7
Women
20.7
9.3
11.4
College or more
32.0
8.0
23
Less than High School
33.0
9.0
25
Source: Bureau of Labor Statistics, Current Population Survey (www.bls.gov). The
unemployment rate movements for men and women correspond to those 25-54 years of age.
Movements in the unemployment rate by education level correspond to those 25 years and older.

-5-

Table 4. Distribution of Sample.

δˆ
(1)

N
(2)

URt < URt −1 − δˆ
(3)

URt −1 − δˆ ≤ URt < URt −1 + δˆ
(4)

URt ≥ URt −1 + δˆ
(5)

Men
Less than college degree
College degree or more

1.8
2.1

372,494
268,156

1.5%
0.4%

94.2%
99.0%

4.3%
0.6%

Women
Less than college degree
College degree or more

0.6
0.9

406,250
297,112

23.5%
10.6%

62.4%
78.4%

14.1%
11.0%

Notes: This table presents the percent of the sample that faces a state unemployment rate that is
more than d lower than last year's unemployment rate (column 3), that is within d of last year's
rate (column 4), and that is greater than d higher than last year's rate (column 5).

-6-

Table 5. Estimation results, by gender and age; the marginal impact on labor force participation
of a one percentage point change in the local unemployment rate.

Men
Ages 18-24
Ages 25-54
Ages 55-74
Women
Ages 18-24
Ages 25-54
Ages 55-74

δˆ
(1)

∂P[ LFP = 1]
∂UR +
(2)

∂P[ LFP = 1]
∂UR 0
(3)

∂P[ LFP = 1]
∂UR −
(4)

Wald Test
H0 : γ 0 = γ1 = γ 2
(5)

0.6
1.8
0.7

-1.35
-0.62
-1.56

-1.50
-0.73
-1.20

-1.74
-1.06
-1.30

9.39 (0.01)
22.94 (0.00)
6.79 (0.03)

0.2
0.6
1.8

-1.64
-1.12
-1.20

-1.49
-1.36
-1.27

-2.01
-1.56
-2.25

31.02 (0.00)
52.61 (0.00)
26.39 (0.00)

Notes: See notes to table 2. Parameter estimates from a maximum likelihood estimation of the
probit model are contained in Table A2.

-7-

Table A1. Maximum likelihood probit estimates of labor force participation (equation 2), by
gender and education, ages 25-54.

Constant
Age
Age Squared
No. of Children LT 6
No. of Children 6-18
Married, spouse present = 1
High school degree
Non-labor income
Black = 1
Disability income
Midwest = 1
South = 1
West = 1

URi+
URi0
URi−

Men
Less than
College or
College
More
1.350
-0.256
(0.107) *
(0.226)
0.010
0.110
^
(0.005)
(0.011) *
-0.000
-0.001
*
(0.000)
(0.000) *
0.026
0.091
*
(0.009)
(0.020) *
0.045
0.108
*
(0.005)
(0.013)*
0.578
0.470
*
(0.010)
(0.023) *
0.483
-*
(0.010)
-0.004
-0.002
(0.000) *
(0.000) *
-0.365
-0.253
(0.012) *
(0.031) *
-0.000
-0.000
(0.000) *
(0.000) *
0.070
0.093
(0.013) *
(0.026) *
0.012
-0.000
(0.012)
(0.024)
0.094
-0.019
(0.013) *
(0.026)
-0.048
-0.029
(0.006) *
(0.015) ^
-0.058
-0.022
(0.004) *
(0.009)+
-0.081
-0.062
(0.007) *
(0.024) *

Women
Less than
College or
College
More
-0.226
0.546
(0.078) *
(0.158) *
0.063
0.067
*
(0.004)
(0.008) *
-0.001
-0.001
*
(0.000)
(0.000) *
-0.346
-0.434
*
(0.005)
(0.009) *
-0.064
-0.136
*
(0.003)
(0.006) *
-0.057
-0.101
*
(0.007)
(0.015) *
0.669
-*
(0.008)
-0.002
-0.003
(0.000) *
(0.000) *
-0.014
0.171
(0.009)
(0.023) *
-0.000
-0.000
(0.000) *
(0.000) *
0.101
0.099
(0.009) *
(0.016) *
-0.029
-0.046
(0.008) *
(0.015) *
0.040
-0.067
(0.009) *
(0.017) *
-0.048
-0.017
(0.003) *
(0.007) +
-0.058
-0.017
(0.003) *
(0.006) *
-0.065
-0.028
(0.003) *
(0.007) *

No. of Observations
268,156
104,338
297,112
109,138
1.8
2.1
0.6
0.9
δˆ
Log-likelihood
-82,605.15
-16,414.43 -159,379.70 -42,432.96
Note: All dollar values are 2004 values. All estimations include a set of year fixed-effects. *
indicates significance at the one percent level, + indicates significance at the five percent level
and ^ indicates significance at the ten percent level.

-8-

Table A2. Maximum likelihood probit estimates of the labor force participation (equation 2) by gender and age.

Constant
Age
Age Squared
No. of Children LT 6
No. of Children 6-18
Married, spouse present = 1
High school degree
GE College
Non-labor income
Black=1
Disability income
Midwest = 1
South = 1
West = 1
URP
URM
UR0
No. of Observations
^

δ
Log-likelihood

Ages 18-24
-3.543
(0.799)*
0.309
(0.077)*
-0.004
(0.002)+
0.061
(0.019)*
-0.029
(0.008)*
0.522
(0.030)*
0.119
(0.015)*
0.175
(0.033)*
-0.003
(0.000)*
-0.385
(0.018)*
-0.000
(0.000)*
0.251
(0.018)*
0.128
(0.017)*
0.235
(0.019)*
-0.047
(0.007)*
-0.060
(0.007)*
-0.052
(0.006)*
76499
0.6

Men
Ages 25-54
0.875
(0.096)*
0.029
(0.005)*
-0.001
(0.000)*
0.046
(0.008)*
0.058
(0.005)*
0.556
(0.009)*
0.480
(0.010)*
0.865
(0.012)*
-0.003
(0.000)*
-0.352
(0.011)*
-0.000
(0.000)*
0.073
(0.011)*
0.011
(0.010)
0.075
(0.012)*
-0.043
(0.005)*
-0.074
(0.007)*
-0.051
(0.004)*
372494
1.8

Ages 55-74
14.580
(0.673)*
-0.351
(0.021)*
0.002
(0.000)*
0.060
(0.039)
0.125
(0.013)*
0.324
(0.012)*
0.299
(0.012)*
0.679
(0.014)*
-0.003
(0.000)*
-0.179
(0.016)*
-0.000
(0.000)*
-0.007
(0.014)
-0.065
(0.012)*
-0.026
(0.014)^
-0.050
(0.005)*
-0.039
(0.005)*
-0.042
(0.005)*
123080
0.7

Ages 18-24
-0.326
(0.722)
0.016
(0.069)
0.002
(0.002)
-0.245
(0.009)*
-0.011
(0.008)
-0.197
(0.016)*
0.394
(0.014)*
0.768
(0.028)*
-0.002
(0.000)*
-0.187
(0.016)*
-0.000
(0.000)^
0.215
(0.017)*
0.059
(0.016)*
0.125
(0.017)*
-0.049
(0.006)*
-0.061
(0.006)*
-0.045
(0.006)*
80551
0.2

Women
Ages 25-54 Ages 55-74
-0.133
7.873
(0.069)^
(0.625)*
0.056
-0.150
(0.003)*
(0.020)*
-0.001
0.000
(0.000)*
(0.000)+
-0.374
-0.104
(0.004)*
(0.035)*
-0.074
-0.054
(0.003)*
(0.016)*
-0.060
-0.237
(0.006)*
(0.009)*
0.675
0.470
(0.008)*
(0.011)*
1.035
0.743
(0.009)*
(0.015)*
-0.003
-0.001
(0.000)*
(0.000)*
0.009
-0.056
(0.008)
(0.014)*
-0.000
-0.000
(0.000)*
(0.000)*
0.097
0.059
(0.008)*
(0.012)*
-0.035
-0.074
(0.007)*
(0.011)*
0.020
-0.038
(0.008)+
(0.013)*
-0.040
-0.040
(0.003)*
(0.006)*
-0.056
-0.074
(0.003)*
(0.008)*
-0.049
-0.042
(0.003)*
(0.004)*
406250
141449
0.6
1.8

-39190.88

-99448.72

-67078.066

-46957.42

-202029.07

-75607.28

Note: All dollar values are 2004 values. All estimations include a set of year fixed-effects. * indicates significance at the one
percent level, + indicates significance at the five percent level and ^ indicates significance at the ten percent level.

-9-