Do men and women behave differently while adjusting labor supply over the business cycle? Using data for the United States we show that women are signifi�cantly more likely to adjust along the intensive margin (number of hours), while men adjust more often along the extensive margin (employment). Older, single, and divorced/widowed adjust predominantly along the extensive margin.
Our �findings have crucial implications for the design of policy reforms, especially as governments desire to increase female labor force participation while facing demographic challenges

Dennis, Wesselbaum

Munich Personal RePEc Archive

Gender-specic Di¤erences in Labor Market Adjustment
Patterns: Evidence from the United States
Dennis Wesselbaum
University of Hamburg, German Physical Society, and EABCN
October 29, 2012.
Abstract
Do men and women behave di¤erently while adjusting labor supply over the business
cycle? Using data for the United States we show that women are signicantly more
likely to adjust along the intensive margin (number of hours), while men adjust more
often along the extensive margin (employment). Older, single, and divorced/widowed
adjust predominantly along the extensive margin.
Our ndings have crucial implications for the design of policy reforms, especially
as governments desire to increase female labor force participation while facing demo-
graphic challenges.
Keywords: Extensive Margin, Intensive Margin, Male and Female Labor Supply.
JEL codes: E32, J10, J20.
University of Hamburg, Chair of Computational Economics. Von Melle Park 5, 20146 Hamburg. Email:
Dennis.Wesselbaum@wiso.uni-hamburg.de
1
1 Introduction
Labor supply in the United States experienced a substantial change over the past decades.
While this generally holds for hours worked as a function of sex and marital status, it is par-
ticularly true for female labor supply. The ndings from Jones et al. (2003) show that hours
worked supplied by married women increased by roughly 30 percent while supply by married
men decreased slightly. Further, hours worked by single women and single men remained
almost constant. These trends have important and persistent economic and social e¤ects.
The literature tried to explain these stylized facts and made progress in understanding labor
supply decisions, in particular for women. The progress in understanding the factors driving
(female or family) labor supply not just only requires to analyze the changes in the gender
wage gap, changes in the fertility rate, decrease in marriage and increase in divorce rates, or
changing social norms, it also requires to understand the business cycle labor market adjust-
ment patterns of men and women. The latter has, to the best of our knowledge, been almost
neglected so far. The only exception is Blundell et al. (2011). However, they decompose
the total change of total hours into individual contributions from men and women across
di¤erent age groups. This is di¤erent from our analysis, since we are interested in the role of
extensive vs. intensive margin for the adjustment within a given age group over the business
cycle. Further, a technical di¤erence is that they decompose the change in hours between
periods by assuming that this change is driven by a strucutral factor and the intensive and
extensive margin. Therefore, our analysis refrains from econometrical or causal assumptions
and, in contrast, solely uses the statistical properties of the time series.
Our aim is to assess the question whether men and women behave di¤erently within the
labor market adjustment process over the business cycle and to make a rst attempt to close
this gap in the literature.
For this purpose, we use labor market data for the United States and isolate the business
cycle component, i.e. we decompose the adjustment over the cycle in extensive (the number
of workers) and intensive margin (the number of hours worked). Therefore, we are able to
assess the preferred margin of adjustment for men and women across di¤erent age groups,
marital statuses, and occupations.
Three main ndings stand out. First, along the age dimension, we nd that while women
and men age 16-19 do have the same ratio of extensive vs. intensive margin, when they grow
older men tend to adjust more likely along the extensive margin, while womens adjustment
appears to occur more often along the intensive margin.
Second, our results indicate a strong state-dependence of adjustment on marital status.
Third, while the extensive margin for di¤erent occupations is quite stable for men, there is
a dramatically di¤erent, unstable pattern for women.
The policy dimension of our analysis is far-reaching. It has been documented that many
countries established policy programs to increase the participation rate of women, e.g. tax
benets, increased child-care, or extended paid maternity leave (also for husbands). Along
this line, it is important to consider the split of the extensive-intensive margin as income
taxes or benet reforms may have larger disincentive e¤ects when the intensive margin is
more important, which would increase the labor supply elasticity.
In an earlier paper, Merkl and Wesselbaum (2011) provide evidence for the relative
importance of the two margins for business cycle dynamics using a data set for the United
2
States and Germany. They show that the extensive margin in both countries is the main
margin of adjustment. However, this study uses highly aggregated data, not allowing for
gender di¤erences.
2 Data Manipulation and Decomposition
For our empirical analysis, we use monthly data for individual hours worked and employment
from 1977:M1 to 2011:M12 (420 observations) for the United States provided by the Current
Population Survey of the Bureau of Labor Statistics (BLS, for short). According to the NBER
recessions dates, this time span covers ve recessions and therefore gives us enough variation
in the time series to be exploited by our decomposition. In particular, we compare extensive
and intensive margin for women and men along ve di¤erent age groups (16 years and over,
16 to 19 years, 16 to 17 years, 18 to 19 years, and 20 to 24 years), three di¤erent marital
statuses (married, other - including divorced and widowed - and single/never married all for
16 years and over), and ve di¤erent occupations (management, service, sales, construction,
and production). For the analysis along the occupation dimension, we use data from 2000:M1
to 2011:M12 (144 observations) provided by the BLS.
The reader should notice that data for other age groups is either not available or does only
cover much shorter time periods. Further, it is our belief that the remainig ve age groups
cover the important working age groups to draw conclusions for the theories of male and
female labor supply discussed later on. For example, average marriage age and average birth
age lie in the interval [24,25]. Therefore, we expect to observe di¤erences when agents move
from single to married and, as discussed later, turn from a single utility function maximizer
to a joint utility function maximizer.
Using time series for individual hours, H, and employment, N , we construct the series of
aggregate (or total) hours, T = H N . All time series are then written in logarithmic scale
and are ltered using the Baxter-King (1999) Band-pass lter to identify the business cycle
component. To be precise, we set the lower cycle period to 18 month, the high cycle period
to 96 month with 36 leads/lags. Therefore, the e¤ective sample size is 348 observations (72
for occupation). Writing total hours in log-linearized form,
t^ = h^+ n^; (1)
we can write the variance of total hours as
V ar
 
t^

= V ar

h^

+ V ar (n^) + 2Cov

h^; n^

; (2)
= Cov
 
t^; n^

+ Cov

t^; h^

: (3)
The latter equality follows from the fact that the covariance term gives the variability ex-
plained by variations in the respective margin, both directly and through its correlations.
Following Fujita and Ramey (2009), the proportion of the intensive margin, #INT , of total
variation is given by
#INT =
Cov

t^; h^

V ar
 
t^
 ; (4)
3
Figure 1: Business Cycle Decomposition using Baxter-King Band-pass lter.
and the extensive margin, #EXT , is
#EXT =
Cov
 
t^; n^

V ar
 
t^
 : (5)
3 Results and Implications
We start our discussion by describing some descriptive statistic results. Figures 1 and 2
present our ltered time series for men, women respectively for di¤erent ages and marital
statuses, as dened above. The visual inspection indicates that employment follows total
hours very closely over the cycle. Further, we can deduce that the variance of individual
hours is smaller compared to the variance of employment and total hours, which holds for all
series at hand. The results for the extensive-intensive margin decomposition of labor market
adjustment are presented in Table 1.
We observe some interesting patterns. First, along the age dimension, we nd that while
women and men age 16-19 do have the same ratio of extensive vs. intensive margin, when
they grow older men tend to adjust more likely along the extensive margin, while womens
adjustment appears to occur more often along the intensive margin. For men, the share of
the extensive margin increases by three percentage points from 0.71 to 0.74. Simultaneously,
the extensive margin share of women decreases from 0.71 to 0.66, if we compare the 16-17
with the 20-24 group. The numbers also imply that women undergo a larger change in their
4
Figure 2: Business Cycle Decomposition using Baxter-King Band-pass lter.
adjustment behavior over time, being almost twice as large as the change observed for men.
Second, our results indicate a strong state-dependence of adjustment on marital status.
We nd that single men are much more likely (by seven percentage points) to adjust along the
extensive margin than married men. Similarly, but less strongly, single women adjust more
often along the extensive margin (compare 0.65 to 0.70). Changing states, from single to
married, will go in hand with an increase in the intensive margin for men (by seven percentage
points) and women (by ve percentage points). Even more dramatic is the increase in the
extensive margin when moving from married to other status. For men, this will result in an
increase of the extensive margin from 67 percent to 83 percent. For women, the change is
slightly smaller, increasing from 65 percent to 76 percent.
Third, the extensive margin for di¤erent occupations is quite stable at roughly 80 percent
for men, the only exception is service with almost 60 percent. However, we nd a dramatically
di¤erent pattern for women. We observe that the extensive margin varies between 50 percent
(service) and 99 percent (construction) for women. This implies that women in the service
state are equally likely to adjust along the employment and the hours worked dimension,
while women working in the construction state almost never adjust the number of hours
worked.
We can draw the conclusion, that women are more likely to adjust along the intensive
margin, while men adjust more often along the extensive margin. Older, single, and di-
5
Table 1: Extensive vs. Intensive Margin.
Extensive Intensive
Men Women Men Women
16 years & over 0.71 0.65 0.29 0.35
16 - 19 0.71 0.71 0.29 0.29
16 - 17 0.71 0.74 0.29 0.26
18 - 19 0.72 0.71 0.28 0.29
20 - 24 0.74 0.66 0.26 0.34
Married 0.67 0.65 0.33 0.35
Other Status 0.83 0.76 0.17 0.24
Single 0.74 0.70 0.26 0.30
Management / Professional 0.80 0.77 0.20 0.23
Service 0.59 0.50 0.41 0.50
Sales and O¢ ce 0.78 0.73 0.22 0.27
Construction / Maintenance 0.79 0.99 0.21 0.01
Production / Transportation 0.77 0.84 0.23 0.16
vorced/widowed are signicantly more likely to adjust along the extensive margin. The
extensive-intensive margin split for women does crucially depend on the occupation and
varies heavily. On the ipside, for men the extensive margin is fairly stable.
Finally, the observed patterns might be explained by demographic facts and household
decision making. The average women in the United States gives birth to 1.9 childs, the
rst at an average age of 25 years and marries with an average age of 24.8 according to the
CDC. Further, the unitary model of the household due to Samuelson (1956) implies that wife
and husband jointly maximize a common utility function. Then, the transformation from
single to married and married towards other status (divorced, widowed) would imply that
the economic agents face a common, individual utility function, respectively. This per se is
likely to give a di¤erent solution for the agents utility maximization problem and explain the
state-dependence of labor market adjustment patterns. Therefore, our results support the
view that married households will solve a joint maximization problem that creates di¤erent
incentives compared to a "single" optimization problem, as we observe a drop in the extensive
margin for the age group of 20-24 as well as the described marital status state-dependence.
Along this line, from a game-theoretic viewpoint, bargaining models of the marriage show
that women might have a lower bargaining power, as prospects outside the marriage are
poor since potential market wages are lower (gender-wage gap), as the time allocated to the
children will reduce experience and hence human capital. This implies that, on average,
women might be forced to take jobs in which adjustments along the intensive margin are
more likely and which pay lower wages. Observe that in our sample 38 (33) percent of women
(men) worked in the management state, 35 (17) percent worked in the sales state and 20 (13)
6
percent worked in the service state. Only six (18) percent worked in construction and roughly
one (18) percent worked in production. This result is in line with the above implication that
the solution to the households joint optimization problem implies that women might (self-)
select into jobs that allow for more exibility along the intensive margin, while we do not
control e.g. for gender-specic productivity.
Our results have several implications for the design of policy reforms. We observe that
the intensive margin is more important for women which implies that labor tax reforms or
benet reforms, just to mention two, will have a larger e¤ect on women than on men, as the
reaction of female labor supply elasticity will be larger. Hence, given the e¤ort to increase
the participation rate of women in the labor market, policy makers should take into account
the asymmetrically working e¤ects and choose instruments ensuring that multiple goals can
be achieved.
Moreover, because fertility is a choice variable that will depend on various factors in-
cluding perspectives on the labor market, policy actions that a¤ect female labor supply
inevitably will impact fertility. Hence, the relationship between fertility and labor supply
adds complexity to the policy problem from an intergenerational viewpoint, particularly in
an aging economy.
Policy reforms that aim at a specic occupation will have nearly identical e¤ects for
men, as the extensive margin is stable across occupations, with the exception being ser-
vice. For women, however, this does not hold and the extensive-intensive margin split does
largely depend on the given occupation. Therefore, policy reforms that aim at women in all
occupations will have di¤erent e¤ects within each occupation.
Lastly, as we have seen, economic agents in a marriage jointly solve a common maximiza-
tion problem which implies that isolated changes for one of the partners are likely to a¤ect
the other partner as well, through substitution and income e¤ects.
References
[1] Baxter, M. & King, R. G. (1999). Measuring Business Cycles: Approximate Band-Pass
Filters for Economic Time Series. The Review of Economics and Statistics, 81, 575-593.
[2] Blundell, R. & Bozio, A. & Laroque, G. (2011). Labor Supply and the Extensive Margin.
American Economic Review, 101(3), 482-486.
[3] Fujita, S., & Ramey, G. (2009). The Cyclicality of Separation and Job Finding Rates.
International Economic Review, 2, 415-430.
[4] Jones L. E. & Manuelli, R. E. & McGrattan, E.R. (2003). Why are married women
working so much? Sta¤ Report 317, Federal Reserve Bank of Minneapolis.
[5] Merkl, C. & Wesselbaum, D. (2011). Extensive vs. Intensive Margin in Germany and the
United States: Any Di¤erences? Applied Economics Letters, 18, 805-808.
[6] Samuelson, P. (1956). Social Indi¤erence Curves. Quarterly Journal of Economics, 70(1),
1-22.
7


Gender-speci�c Differences in Labor Market Adjustment Patterns: Evidence from the United States

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Gender-speci�c Differences in Labor Market Adjustment Patterns: Evidence from the United States

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