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Federal Reserve Ban
of
Fourth Quarter 1999
Economic
perspectives
2
Is the EMU a viable common currency area?
AVAR analysis of regional business cycles
Regional employment growth and the business cycle
40
Call for Papers
42
Child care costs and the retum-to-work decisions
of new mothers
56
Will a common European monetary policy have
asymmetric effects?
76
Index for 1999
11
perspectives
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ISSN 0164-0682
Contents
Fourth Quarter 1999, Volume XXIII, Issue 4
Is the EMU a viable common currency area?
A VAR analysis of regional business cycles
Michael A. Kouparitsas
Many commentators are skeptical about the long-run viability of the European Monetary
Union (EMU). This article compares the EMU with a well-functioning currency union, the
U.S., and finds that they are similar based on key criteria. On the basis of this analysis,
the EMU may be as viable as the U.S. monetary union.
21
Regional employment growth and the business cycle
Ellen R. Rissman
Employment growth is highly correlated across regions. The author uses joint movements in
regional employment growth to define and estimate a common factor, analogous to the
business cycle. Regions differ substantially in the relative importance of cyclical shocks and
idiosyncratic shocks in explaining the steady state variance in regional employment growth.
For example, cyclical shocks account for almost 90 percent of the steady state variance in
employment growth in the East South Central region and about 40 percent in the West South
Central region.
Call for Papers
Child care costs and the retum-to-work decisions of new mothers
Lisa Barrow
Women’s labor force participation has nearly doubled in the past 50 years. The increase has
been even more dramatic for women with young children, and recent reforms to welfare
programs are likely to push the participation rate for this segment even higher. This article
examines the economic determinants of a woman’s decision to return to work quickly
following childbirth, looking in particular at sensitivity to child care costs.
Will a common European monetary policy have
asymmetric effects?
Luigi Guiso, Anil K Kashyap, Fabio Panetta, and Daniele Terlizzese
This article reviews the evidence on differences in the transmission of monetary policy across
European countries. The authors argue that the existing evidence, based almost exclusively on
macroeconomic data, does not allow one to decide whether a common monetary policy will
have asymmetric effects. A first peek at microeconomic data suggests this may be a promising
route for further work.
Index for 1999
Is the EMU a viable common currency area?
A VAR analysis of regional business cycles
Michael A. Kouparitsas
Introduction and summary
In January 1999, 11 European countries bravely
launched into a common currency area known as the
European Monetary Union (EMU). By joining the
common currency area, member countries have agreed
to keep the value of their national currency fixed in
terms of the currencies of the other EMU countries
for an indefinite period. Consumers and businesses
in these countries will, however, find that very little
has changed. The most noticeable change will not
occur until 2002 when national currencies are replaced
by a common currency known as the euro. In the intervening period, prices will be denominated in terms
of existing national currencies and euros. Consumers
using cash will pay the national currency price, while
consumers using credit cards (including U.S. visitors
to the euro zone) will notice that their transactions are
carried out in euros.
Although they might disagree about the exact
size of the gains, most economists would agree that
the EMU will yield significant microeconomic benefits through lower transactions and hedging costs.
According to the European Commission, the gains
from carrying out transactions in a single currency
could be as high as 0.5 percent of European Union
gross domestic product (GDP) per year. However,
many economists are skeptical about the long-run
viability of the EMU. Euro-zone members have given
up the right to set their own interest rates and the option of moving their exchange rates against each other.
The widespread view is that this loss of flexibility
may involve significant costs (in the form of persistent
high unemployment and low output growth) if their
economies do not behave as one or cannot easily
adjust in other ways. The ultimate concern is that
for some countries, these macroeconomic costs will
eventually outweigh the microeconomic benefits and
lead them to abandon the EMU.
2
How well the EMU performs along the macro
dimension will depend on how closely it fits the notion
of an optimal currency area (OCA). Beginning
with Mundell (1961), economists have long agreed
that the following four criteria must be met for a
region to be an optimal currency area: 1) countries
should be exposed to similar sources of disturbances
(common shocks); 2) the relative importance of these
common shocks should be similar (symmetric shocks);
3) countries should have similar responses to common
shocks (symmetric responses); and 4) if countries are
affected by country-specific sources of disturbance
(idiosyncratic shocks), they need to be able to adjust
quickly. The basic idea is that countries satisfying
these criteria would have similar business cycles, so
a common monetary policy response would be optimal.
How far the euro zone is from an OCA is an open
question for research, as is the more important question of whether the apparent deviation from an OCA
is sufficient to question the long-run viability of the
EMU. On the surface, the data seem to support the
skeptics view that the EMU is not an OCA. First,
euro-zone countries have experienced frequent and
often large idiosyncratic shocks over recent years. A
well-known example is German reunification, which
many argue led to the breakdown of the precursor to
the EMU known as the European Monetary System
(EMS) in 1992.1 Second, persistently high unemployment rates throughout Europe suggest that EMU
economies (especially their labor markets) are slow
to adjust to all economic disturbances.
Michael A. Kouparitsas is an economist at the Federal
Reserve Bank of Chicago. The author would like to thank
David Marshall and Jonas Fisher for useful comments
on an earlier draft.
Economic Perspectives
The purpose of this article is to formally assess
the long-run viability of the EMU. I do this by comparing the sources and responses to economic shocks
to the EMU with those from a well-functioning currency union, the U.S. My working hypothesis is that
if the EMU is as close to an OCA as the U.S. is,
based on the criteria outlined above, it may well be
a viable currency union in the long run. If, on the
other hand, the EMU is less like an OCA than the
U.S. is, one might question the long-run viability of
this monetary union.
Despite all the effort that has gone into the EMU
debate, there is little in the way of empirical research
on the sources and responses to economic shocks to
this region. I use a statistical technique known as a
structural vector autoregression (VAR) to extract these
components from the data. My analysis suggests that
U.S. regions are highly symmetric. U.S. regions face
common sources of disturbance, to which they respond
in a similar way. In contrast, the EMU countries can
be grouped into a symmetric center and a clearly
asymmetric periphery. Center countries are Austria,
Belgium-Luxembourg (treated as one country for
data purposes), France, Germany, Italy, the Netherlands,
Portugal, and Spain, while the periphery countries are
Finland and Ireland. Center countries display many of
the characteristics of U.S. regions when compared on
OCA criteria. Periphery countries appear to have quite
different sources of disturbance from the center. In
addition, they seem to respond to common shocks in
a different way from the center countries. I conclude
on the basis of this statistical analysis that the EMU
will be a viable currency union for the center countries,
but question the viability of a union with countries in
the periphery.
Previous empirical analysis of the EMU
The EMU has spawned a number of empirical
papers aimed at understanding the nature of regional
business cycles and the regional impact of fiscal and
monetary polices. The approaches vary considerably.
For example, Carlino and DeFina (1998b) examine
the regional effects of monetary policy within the
EMU. Their approach is indirect. In earlier work,
Carlino and DeFina (1998a) estimated the effects
of U.S. monetary policy on the 48 contiguous U.S.
states (and eight Bureau of Economic Analysis [BEA]
regions). They build on this analysis in the later paper
by estimating the cross-sectional relationship between
the long-run regional output response to monetary
policy and industry structure. Their findings suggest
that monetary policy has a larger impact on more
industrial-oriented U.S. regions, such as the Great
Lakes. They use these cross-sectional U.S. findings
Federal Reserve Bank of Chicago
and the industry structure of EMU countries to speculate on the long-run regional impact of monetary
policy within the EMU. Their results suggest that
monetary policy will have a differential impact on
EMU countries. This implies that the EMU is not an
OCA, since it fails to meet the symmetric responses
criterion. In a competing study, Dornbusch, Favero,
and Giavazzi (1998) test this hypothesis directly using
time-series methods and find that the effect of monetary policy is not statistically different across EMU
countries. Their study suggests EMU countries have
similar responses to monetary policy shocks, which
is necessary for a region to be an OCA. An obvious
limitation of this work is that it is silent on the incidence of other disturbances affecting the EMU countries and the broader question of whether the EMU
will be viable in the long run.
Eichengreen has approached the question of
whether the EMU is an OCA from a number of interesting directions. Eichengreen (1992) joins others in
gauging the importance of country-specific shocks
by computing the variability of bilateral EMU real
exchange rates, for example, the real exchange rate
between Germany and France. The basic idea is that
these relative price fluctuations reflect shifts in demand
and supply affecting one EMU country relative to
another, so countries with more highly correlated
disturbances will have less volatile bilateral real
exchange rates. The typical approach of this type of
study is to compare the volatility of bilateral EMU
real exchange rates with the volatility of relative output prices of U.S. BEA regions. A common finding is
that the bilateral real exchange rates of EMU countries
are considerably more volatile than the relative output
prices of U.S. regions. This suggests that the EMU is
further than the U.S. is from being an OCA. An obvious weakness of this type of analysis is that it does
not directly compare the EMU and the U.S. using the
OCA criteria outlined earlier.
Observing this limitation, Eichengreen and
Bayoumi (1993) approach the issue in a more direct
way. They estimate individual models for U.S. BEA
regions and EMU countries using a technique developed by Blanchard and Quah (1989), which allows
them to extract unobserved components from the
data that describe so-called demand and supply
shocks. Demand and supply shocks are distinguished
by the fact that demand shocks are assumed to have
a temporary impact on the economy, while supply
shocks are assumed to have a permanent effect on
the economy. Eichengreen and Bayoumi (1993) then
compare the correlation coefficients of German supply
(and demand) shocks and those of other EMU countries
3
with the correlation coefficients of U.S. Mideast supply
(and demand) shocks and those of other U.S. regions.
They show that U.S. regional supply (and demand)
shocks tend to be more highly correlated than EMU
regional supply (and demand) shocks. The final step
of their analysis is to compare regional responses to
demand and supply shocks. Their results suggest that
the response functions of U.S. regions are more alike
than those of EMU countries. On the basis of this
analysis, they conclude that the EMU is further than
the U.S. is from being an OCA, which leads them to
argue that the EMU may find it more difficult than
the U.S. to operate a monetary union.
My empirical analysis builds on Eichengreen
and Bayoumi (1993) along two dimensions. First, I
update their work by analyzing more recent data.
Eichengreen and Bayoumis data spanned the years
from 1963 to 1986, while I consider data covering
the years from 1969 to 1997. These data are likely to
be more informative about the behavior of countries
under the EMU, since they include a greater number
of years over which the EMU countries were part
of the forerunner to the EMU, the EMS. Second, I
adopt a different way of decomposing the data that
allows me to directly measure the extent to which
regional business cycles are driven by common and
country-specific shocks. My conclusions differ from
Eichengreen and Bayoumis. In contrast to their findings, I show that with the exception of two relatively
small countries, Finland and Ireland, the euro zone
shares many of the regional business cycle characteristics of the U.S. In other words, the EMU comes as
close to being an OCA as the U.S. does. I argue on the
basis of these results that the long-run viability of the
EMU is similar to that of the U.S. monetary union.
A weakness of all the foregoing empirical research
is that historical data may be an unreliable guide to
the way euro-zone countries will behave under the
EMU. This observation is a simple application of the
Lucas critique. The basic idea is that historical data
may be uninformative since the structure of euro-zone
economies (and possibly the world economy) will
likely undergo significant change after the EMU
adopts a common currency. Frankel and Rose (1998)
find empirical support for this proposition by showing
that one form of structural change that may occur
under the single currency, greater trade flows between
countries, leads to more highly correlated business
cycles. A consequence of their work for all EMU
studies is that countries that may appear from historical
data to be poor candidates for inclusion in the euro zone
may indeed turn out to be suitable candidates after
joining the union. This clearly has implications for
4
earlier work that argued against the long-run viability
of the EMU. I argue that the EMU will be viable in
the long run, so Frankel and Roses results merely
reinforce my conclusions.
How similar are EMU country
business cycles?
A simple and direct way of assessing the similarity
of regional business cycles is to calculate the correlation
between aggregate and individual region business
cycles. High correlations are indicative of common
sources and responses to disturbances. In figure 1,
I plot cyclical movements in U.S. aggregate and
regional real income.2 The underlying data are BEA
annual state personal income from 1969 to 1997.
These data are deflated by the national consumer
price index.3 I use personal income rather than gross
state product because the former span a longer period.4
The eight BEA regions are the Great Lakes, Plains,
New England, Mideast, Southeast, Southwest, Rocky
Mountains, and Far West.5 The lowest correlation
between a region and the U.S. aggregate is 0.76 for
the Southwest, with the highest at 0.98 for the Southeast and Great Lakes. This suggests that common
shocks explain a large share of the variation in U.S.
regional income.
I repeat this exercise for the EMU. Figure 2
plots the cyclical fluctuations of aggregate and regional
EMU income. The underlying data are International
Monetary Fund (IMF) estimates of real annual GDP
from 1969 to 1997. The correlations between regional
and aggregate activity can easily be divided into
two groups. The first groupAustria, BelgiumLuxembourg, France, Germany, Italy, the Netherlands,
Portugal, and Spainresemble the U.S. regions, with
correlations ranging from 0.72 (Spain) to 0.90
(Germany and Italy). The second group of Finland
and Ireland, with correlations of 0.45 and 0.58, respectively, appear to have business cycles that are quite
different from the rest of the euro zone.
With the exception of Finland and Ireland, the
coherence between EMU regional business cycles
appears to be as high as that of U.S. BEA regions.
On the basis of these results, a subset of the EMU
can not be ruled out as a viable currency union. An
obvious weakness of this approach is that it does not
allow for a comparison of the sources of disturbances
or responses to disturbances across regions. Next,
I describe a statistical technique that overcomes this
limitation. Using these results, I can more closely
gauge the extent to which the EMU and the U.S. meet
the OCA criteria described earlier.
Economic Perspectives
Are the sources of shocks and responses to
them similar across EMU countries?
Methodology
My starting point for isolating the sources of regional shocks and responses to them is recent work
analyzing the regional effects of U.S. monetary policy.
The typical approach is to use a structural vector autoregression (VAR). A VAR is a statistical method
that allows one to estimate how an unpredictable
change (or disturbance) in one variable affects other
variables in the economy. For example, one of the
questions raised by theoretical research is whether a
change in monetary policy has a stronger effect on
regions that devote a larger share of activity to industrial production. A VAR allows one to estimate the
way that an unpredicted change in monetary policy
FIGURE 1
Cyclical movements of U.S. regional output
(percent)
Great Lakes
Plains
Great Lakes
Plains
U.S.
U.S.
New England
Mideast
New England
U.S.
U.S.
Southeast
Mideast
Southwest
Southeast
Southwest
U.S.
U.S.
Rocky Mountains
Far West
U.S.
Rocky
Mountains
Far West
U.S.
Note: Personal income data are filtered using the annual “business cycle” band-pass filter described
in Baxter and King (1995).
Source: Author’s calculations from U.S. Department of Commerce, Bureau of Economic Analysis, 1969–97,
“State personal income,” database.
Federal Reserve Bank of Chicago
5
FIGURE 2
Cyclical movements of EMU regional output
(percent)
Austria
Belgium-Luxembourg
Belgium-Luxembourg
EMU
EMU
Austria
Finland
France
France
EMU
EMU
Finland
Germany
Ireland
EMU
EMU
Germany
Italy
Ireland
Netherlands
Italy
EMU
EMU
Portugal
Netherlands
Spain
Portugal
EMU
EMU
Spain
Note: Gross domestic product filtered as described in figure 1.
Source: Author’s calculations from International Monetary Fund, gross domestic product data.
6
Economic Perspectives
affects the output of regions with relatively large and
small industrial sectors.
There is a wide range of variables one can use
in analyzing regional business cycles. I follow the
approach of Carlino and DeFina (1998a) by limiting
the analysis of U.S. regional business cycles to eight
VARs, which essentially study interaction between
the U.S. and a given region, in this case the eight
BEA regions. I adopt a slightly different structural
model by drawing on the approach of Christiano,
Eichenbaum, and Evans (1994) in their work on identifying and measuring the aggregate effects of U.S.
monetary policy shocks. Each U.S. regional VAR is
designed to study how unpredicted changes in world
oil prices, aggregate U.S. and regional income, and
U.S. monetary policy (U.S. federal funds rate) affect
the regions income.
VAR studies of international business cycles take
a somewhat similar approach to the U.S. regional
business cycle literature. International research has
focused almost exclusively on the relationship between
U.S. and G-7 (Group of Seven) business cycles under
different exchange rate regimes.6 This type of analysis
is generally restricted to bilateral VARs involving the
U.S. and a G-7 country. I adapt this approach to the
EMU. I employ 10 VARs. Just as in the U.S. regional
case, each EMU VAR is designed to study how unpredicted changes in world oil prices, aggregate EMU
and country of interest income, and EMU region
monetary policy (German short-term
interest rate) affect the EMU countrys income.
I estimate the U.S. and EMU VARs using annual
data over a common period spanning 1969 to 1997.
I limit the U.S. and EMU VARs so that they estimate
relationships between the four variables (world oil
prices, aggregate income, regional income, and a
regional short-term interest rate) with data from the
last two years. In other words, I estimate the link
between movements in aggregate and regional income
that occurred within the last two years.
Before I can shed light on the nature of regional
disturbances and responses to them, I need to impose
some structure on the system of equations described
by the VARs. There are numerous forms of identifying restrictions in the literature. In their work on the
EMU, Eichengreen and Bayoumi (1993) impose longrun restrictions on the data motivated by a theoretical
model. I use a recursive structure popularized by
Sims (1972). This approach imposes restrictions on
the covariance structure of the disturbances of the
model. In particular, structural disturbances are identified by imposing a recursive information ordering.
Throughout the analysis, I impose the following
Federal Reserve Bank of Chicago
information ordering: world oil prices; aggregate
regional income; indicator of regional monetary policy;
and regional or country income. This approach assumes, as in Christiano, Eichenbaum, and Evans
(1994) that the monetary authority chooses the value
of the monetary policy instrument after observing
contemporaneous movements in oil prices and aggregate output.7 In this setting I can conveniently refer
to the structural disturbances as an oil price or global
shock, aggregate output shock, monetary policy shock,
and region- or country-specific output shock.
With these models in hand, I am able to assess
the similarity of EMU and U.S. regional business
cycles along two dimensions. First, by studying the
sources of regional economic disturbances in the U.S.
and EMU, I can determine the extent to which fluctuations are caused by common and idiosyncratic
shocks. In the U.S. case, common shocks include
unpredicted changes to world oil prices, aggregate
U.S. income, and U.S. monetary policy (U.S. federal
funds rate). Similarly, in the case of the EMU, aggregate shocks include unpredicted changes to world oil
prices, aggregate EMU income, and EMU monetary
policy (German short-term interest rate). Idiosyncratic
shocks are captured by U.S. region-specific and
EMU country-specific output shocks. The relative
importance of the various sources of disturbance
will be revealed by the share of the one-step-ahead
forecast error of U.S. region or EMU country income
that is due to unpredicted changes in the disturbance.
In a perfectly symmetric case, regions would have
none of their forecast error explained by regionspecific shocks and the same shares for the various
common shocks.
Second, by studying the responses to economic
disturbances, I can assess whether regions have similar
responses to common shocks and determine the time it
takes regions to respond to idiosyncratic shocks. The
way that region and country income responds to various disturbances will be embodied in the estimated
parameters of the VAR and revealed through the shape
and size of the models impulse response function.
For a description of the methodology in greater
detail, see the appendix.
Do U.S. regions have similar economic
disturbances?
Tables 1 and 2 report decompositions of the
forecast errors of income for U.S. regions and EMU
countries, respectively. These decompositions indicate the share of the error attributable to a particular
disturbance for a given forecast horizon. The onestep-ahead errors are informative about the similarity
of disturbances across regions within a currency area,
7
while step lengths of greater than one contain joint
information about the similarity of disturbances and
responses to disturbances.
Table 1 reveals that a large share of the disturbance to U.S. regions is due to common shocks (that
is, unanticipated shocks to world oil prices, aggregate
U.S. income, and U.S. monetary policy). For example,
common disturbances explain a large share of the
variation in the Southeast, Great Lakes, Mideast, and
Far Wests one-step-ahead forecast error (84 percent
to 95 percent). The Rocky Mountains and Plains
appear to have the largest region-specific influences,
with 60 percent and 64 percent, respectively, of the
variation in their one-step-ahead forecast errors
explained by common disturbances. New England
and the Southwest fall somewhere in between, with
common disturbances explaining a little more than
70 percent of the variation in their one-step-ahead
forecast errors. The relative importance of different
common shocks is also similar across U.S. regions.
Shocks to aggregate U.S. income are a more important source than shocks to world oil prices and
TABLE 1
Forecast error variance decompositions for real personal income of U.S. regions
Great Lakes
Plains
Percentage of forecast error due to
Years
ahead
1
2
5
10
Oil
prices
U.S.
income
Fed funds
rate
35
39
21
26
58
53
21
20
0
5
57
51
Percentage of forecast error due to
Great Lakes
income
6
3
1
3
Years
ahead
1
2
5
10
Oil
prices
U.S.
income
16
25
18
23
47
54
33
29
New England
1
2
5
10
Oil
prices
U.S.
income
Fed funds
rate
35
38
33
33
36
14
4
8
0
5
26
29
New England
income
29
44
37
29
Years
ahead
1
2
5
10
Oil
prices
U.S.
income
Fed funds
rate
12
16
24
26
74
42
15
17
1
11
33
31
Southeast
1
2
5
10
14
31
27
25
Southwest
Oil
prices
U.S.
income
Fed funds
rate
Southeast
income
41
58
39
38
54
36
14
14
0
2
37
39
5
4
10
9
Years
ahead
1
2
5
10
Oil
prices
2
1
3
2
Rocky Mountains
1
2
5
10
Mideast
income
Percentage of forecast error due to
U.S.
income
Fed funds
rate
Southwest
income
72
68
50
48
0
2
16
26
26
30
31
24
Far West
Percentage of forecast error due to
Years
ahead
36
18
15
19
Percentage of forecast error due to
Percentage of forecast error due to
Years
ahead
0
2
33
29
Plains
income
Mideast
Percentage of forecast error due to
Years
ahead
Fed funds
rate
Oil
prices
U.S.
income
Fed funds
rate
Rocky Mtns.
income
20
24
10
9
40
30
17
19
0
2
32
46
40
44
40
26
Percentage of forecast error due to
Years
ahead
1
2
5
10
Oil
prices
U.S.
income
Fed funds
rate
26
40
42
43
57
42
32
31
1
0
7
13
Far West
income
16
18
18
13
Notes: Each panel describes the decomposition of the forecast error for the region of interest’s
income. The first column in each block refers to the number of years (s = 1, 2, ..., 10) ahead for
the forecast. Columns indicate the percentage of the s-step-ahead forecast error arising from
a particular structural disturbance.
Source: Calculations from author’s statistical model, using the following annual data series: IMF—world crude
oil prices; BEA—personal income by state; and Federal Reserve Board of Governors—federal funds rate.
8
Economic Perspectives
U.S. monetary policy. Overall, these results suggest
that U.S. regions have similar sources of economic
disturbances.
Table 1 also provides some indication of the similarity of responses to disturbances. Looking at horizons
of greater than one year, the relative importance of
TABLE 2
Forecast error variance decompositions for real gross domestic product of EMU countries
Austria
Belgium-Luxembourg
Percentage of forecast error due to
Years
ahead
1
2
5
10
Percentage of forecast error due to
Oil
prices
EMU
GDP
EMU interest
rate
Austrian
GDP
17
12
13
22
43
50
23
13
1
15
50
56
39
24
13
9
Years
ahead
1
2
5
10
Oil
prices
EMU
GDP
EMU interest
rate
Bel-Lux
GDP
24
19
17
26
56
52
21
18
0
13
55
49
20
16
7
7
Finland
France
Percentage of forecast error due to
Years
ahead
1
2
5
10
Oil
prices
3
1
17
19
EMU
GDP
EMU interest
rate
0
4
12
14
Percentage of forecast error due to
Finnish
GDP
4
2
5
7
93
94
66
60
Years
ahead
1
2
5
10
Oil
prices
EMU
GDP
EMU interest
rate
French
GDP
1
15
20
16
80
57
21
23
0
5
48
47
20
23
11
13
Germany
Ireland
Percentage of forecast error due to
Years
ahead
1
2
5
10
Percentage of forecast error due to
Oil
prices
EMU
GDP
EMU interest
rate
German
GDP
1
1
6
10
77
59
34
35
0
15
43
42
22
25
17
14
Years
ahead
1
2
5
10
Oil
prices
EMU
GDP
0
0
2
1
1
2
5
10
95
85
80
91
Netherlands
Oil
prices
EMU
GDP
EMU interest
rate
Italian
GDP
15
14
17
19
33
32
10
8
13
15
49
44
39
39
25
28
Years
ahead
1
2
5
10
Oil
prices
EMU
GDP
EMU interest
rate
Dutch
GDP
12
6
3
2
49
41
18
20
6
20
41
42
33
33
37
35
Portugal
1
2
5
10
2
9
16
7
Percentage of forecast error due to
Spain
Percentage of forecast error due to
Years
ahead
Irish
GDP
3
7
2
2
Italy
Percentage of forecast error due to
Years
ahead
EMU interest
rate
Oil
prices
EMU
GDP
EMU interest
rate
1
1
5
7
47
44
18
21
14
9
46
41
Percentage of forecast error due to
Portuguese
GDP
38
46
32
30
Years
ahead
1
2
5
10
Oil
prices
2
1
6
11
EMU
GDP
45
36
22
21
EMU interest
rate
15
27
46
45
Spanish
GDP
38
36
25
24
Notes: Each panel describes the decomposition of the forecast error for the country of interest’s
GDP. The first column in each block refers to the number of years (s = 1, 2, ..., 10) ahead for the
forecast. Columns indicate the percentage of the s-step-ahead forecast error arising from a
particular structural disturbance.
Source: Calculations from author’s statistical model, using the following annual data series:
IMF—world crude oil prices, interest rates, and gross domestic product.
Federal Reserve Bank of Chicago
9
common and idiosyncratic disturbances is largely
unchanged. This suggests that responses are fairly
similar. A common finding is that unanticipated
shocks to aggregate U.S. income are less important
at longer horizons.
Are EMU country economic disturbances more
alike than those of U.S. regions?
Table 2 reports forecast error decompositions for
the income of EMU countries. Concentrating on the
one-step-ahead forecast error, countries fall into three
groups. Common shocks explain about 80 percent
of the one-step-ahead forecast errors of income in
Belgium-Luxembourg, France, and Germany. This
share is a little above 60 percent for Austria, Italy,
the Netherlands, Portugal, and Spain. The outliers
are Finland and Ireland, where this share falls below
10 percent.
The decompositions of the first EMU group are
similar to the U.S. group comprising the Great Lakes,
Southeast, Mideast, and Far West. The second EMU
group has forecast error decompositions that are close
to those of the U.S. Rocky Mountain and Plains
regions. In both cases, oil price shocks are relatively
less important than in their U.S. counterpart, while
interest rate shocks are relatively more important
than in the U.S. regions. Just as in the U.S., aggregate
income shocks are the most important economic disturbance to EMU country income. The findings suggest that, with the exception of Finland and Ireland,
EMU country economic disturbances are as alike as
those of U.S. regions.
Again, ignoring Finland and Ireland, the longhorizon picture of EMU disturbances is also similar
to the U.S. This suggests that EMU responses to disturbances may well be as alike as U.S. responses.
Do U.S. regions have similar responses
to economic disturbances?
Figures 36 describe in detail the responses of
the eight BEA regions to common and idiosyncratic
shocks. The black lines trace the impulse response
functions of regional income: the way regional income
responds over time to a one standard deviation shock
to world oil prices, aggregate output, U.S. monetary
policy, and regional income, respectively. (The colored
lines are the 95 percent confidence bands of these
impulse response functions.) These figures show that
U.S. regions have similar responses to common disturbances (unanticipated shocks to world oil prices, aggregate U.S. output, and U.S. monetary policy) and
that they adjust to idiosyncratic shocks over a period
of about two years.
10
Figure 3 shows that an unanticipated increase in
the growth rate of world oil prices has a significant
negative impact on the income of seven of the eight
U.S. regions, which persists for about one year. The
exception is the Southwest, which is the largest oil
producing region of the U.S. Although the result is
not statistically significant, an increase in the growth
rate of world oil prices raises Southwest real income.
In contrast, figure 4 reveals that an unexpected
positive shock to aggregate U.S. income has an immediate positive impact on the income of all U.S.
regions. The effect of this shock on regional income
is generally not statistically significant beyond two
years. The only exception is the Southwest, where the
aggregate income shock has a statistically significant
effect six years after the shock.
Figure 5 shows that an unexpected tightening of
U.S. monetary policy (an unexpected rise in the U.S.
federal funds rate) tends to have a statistically significant effect on U.S. regional income two years after
the shock. The exceptions are the Southwest and Far
West. In both cases, the impulse response function is
virtually identical to those of other U.S. regions, but
not statistically different from the zero line.
Turning to idiosyncratic shocks, figure 6 reveals
that U.S. regions adjust quickly to region-specific disturbances. The regions can be divided into two groups.
The first group, consisting of the Great lakes, Plains,
Southeast, and Far West, have responses that are not
statistically significant beyond the year in which the
shock occurs. The second group, comprising New
England, Mideast, Southwest, and Rocky Mountains,
have responses that are statistically significant for no
more than three years after the shock.
Do EMU countries have responses that are more
alike than those of U.S. regions?
Figures 710 (pages 1518) describe in detail
the response functions of the EMU countries to common and idiosyncratic disturbances. These figures
suggest that, with the clear exceptions of Finland and
Ireland, the response functions of EMU countries are
at least as alike as those of U.S. regions. In addition,
the response functions imply that contrary to the general view, EMU countries adjust to idiosyncratic shocks
at the same speed or faster than U.S. regions.
In contrast to the U.S. result, figure 7 shows that
an unexpected positive shock to the change in world
oil prices does not have a statistically significant effect
on the income of all EMU countries.
However, figure 8 shows that an unanticipated
positive shock to aggregate EMU output has a statistically significant positive effect on the output of
most EMU countries that dies out one year after the
Economic Perspectives
shock. Again, the exceptions are Finland and Ireland,
where the effects of the aggregate output shock are
not statistically significant.
Turning to the regional monetary shock, we see
in figure 9 that EMU responses are not only similar
across countries, but also quite similar to the U.S.
response functions. As in the U.S., an unanticipated
tightening in regional monetary policy (an unanticipated increase in the German overnight money market
rate) leads to a contraction in regional income two
FIGURE 3
U.S. output response: Shock to world oil prices
(percent)
Great Lakes
Plains
New England
Mideast
Southeast
Southwest
Rocky Mountains
Far West
number of years after shock
number of years after shock
Notes: All figures report changes in the region of interest’s real personal income following a one standard
deviation shock to the given variable. The black line represents the point estimates of the impulse response
function for the region of interest. The colored lines are the 95 percent confidence bands, computed by
Monte Carlo simulation using 1,000 independent draws.
Source: Calculations from author’s statistical model, using the following annual data series: International
Monetary Fund—world crude oil prices; Bureau of Economic Analysis—personal income by state; and
Federal Reserve Board of Governors—federal funds rate.
Federal Reserve Bank of Chicago
11
years after the shock. It is important to note that
Finland and Ireland have similar responses to the rest
of the EMU, but their responses are not statistically
different from zero.
Finally, figure 10 describes the rate at which EMU
countries adjust to country-specific shocks. Ignoring
Finland and Ireland, there are essentially two groups,
just as there are in the U.S. case. The first group,
consisting of Austria, Belgium-Luxembourg, France,
Germany, and Italy, have response functions that are
not statistically different from zero a year after the
shock. The second group, the Netherlands, Portugal,
and Spain, adjust in under three years. The response
functions of Finland and Ireland display considerably
longer adjustment periods. In the case of Ireland,
idiosyncratic shocks appear to be highly persistent.
FIGURE 4
U.S. output response: Shock to aggregate U.S. income
(percent)
Great Lakes
Plains
New England
Mideast
Southeast
Southwest
Rocky Mountains
Far West
number of years after shock
number of years after shock
Notes and source: See figure 3.
12
Economic Perspectives
The lessons learned from the simple business
cycle analysis of the previous section carry over to the
VAR analysis. The EMU is characterized by a highly
symmetric centerAustria, Belgium-Luxembourg,
France, Germany, Italy, the Netherlands, Portugal, and
Spainand an asymmetric peripheryFinland and
Ireland. As noted earlier, the center countries have
highly correlated business cycle fluctuations. The
VAR analysis shows that these correlations are supported by common sources of disturbance and similar responses to these shocks. The VAR analysis also
reveals that EMU countries and U.S. regions behave
similarly along both these dimensions. Finally, in
contrast to anecdotal evidence, the VAR analysis
suggests that EMU countries adjust to idiosyncratic
shocks at roughly the same speed as U.S. regions.
FIGURE 5
U.S. output response: Shock to U.S. federal funds rate
(percent)
Great Lakes
Plains
New England
Mideast
Southeast
Southwest
Rocky Mountains
Far West
number of years after shock
number of years after shock
Notes and source: See figure 3.
Federal Reserve Bank of Chicago
13
Conclusion
The answer to the question of whether a currency
union will be viable in the long run depends to a
large extent on how far the union is from being an
OCA. With this in mind, I assess the long-run viability
of the EMU by comparing the EMU with a viable
currency union (the U.S.) based on critical OCA
criteria. My working hypothesis is that if the EMU is
as close as the U.S. is to being an OCA, then there
could be no presumption that the EMU would not be
viable in the long run. Alternatively, if the EMU is
much further from being an OCA than the U.S. is,
then the adoption of a single currency could be problematic for some EMU countries and would call into
question the viability of this monetary union. My
analysis suggests that the behavior of countries at
the center of the EMU is very similar to that of U.S.
regions for all OCA criteria. In contrast, I find that
FIGURE 6
U.S. output response: Shock to U.S. regional income
(percent)
Great Lakes
Plains
New England
Mideast
Southeast
Southwest
Rocky Mountains
Far West
number of years after shock
number of years after shock
Notes and source: See figure 3.
14
Economic Perspectives
FIGURE 7
EMU output response: Shock to world oil prices
(percent)
Austria
Belgium-Luxembourg
Finland
France
Germany
Ireland
Italy
Netherlands
Portugal
Spain
number of years after shock
number of years after shock
Notes: See figure 3.
Source: Calculations from author’s statistical model, using the following annual data series:
International Monetary Fund—world crude oil prices, short-term interest rates, and gross domestic product.
Federal Reserve Bank of Chicago
15
FIGURE 8
EMU output response: Shock to aggregate EMU output
(percent)
Austria
Belgium-Luxembourg
Finland
France
Germany
Ireland
Italy
Netherlands
Portugal
Spain
number of years after shock
number of years after shock
Notes: See figure 3.
Source: See figure 7.
16
Economic Perspectives
FIGURE 9
EMU output response: Shock to EMU interest rates
(percent)
Austria
Belgium-Luxembourg
Finland
France
Germany
Ireland
Italy
Netherlands
Portugal
Spain
number of years after shock
number of years after shock
Notes: See figure 3.
Source: See figure 7.
Federal Reserve Bank of Chicago
17
FIGURE 10
EMU output response: Shock to EMU country income
(percent)
Austria
Belgium-Luxembourg
Finland
France
Germany
Ireland
Italy
Netherlands
Portugal
Spain
number of years after shock
number of years after shock
Notes: See figure 3.
Source: See figure 7.
18
Economic Perspectives
countries in the periphery of the EMU, Finland and
Ireland, are quite different from their EMU partners
with regard to the OCA criteria. On the basis of this
statistical analysis, I conclude that the EMU will
likely be a viable currency union for the center countries, but question the viability of a union with countries in the periphery.
APPENDIX
A VAR analysis of regional business cycles
This appendix describes my methodology in greater
technical detail. To isolate the various exogenous
shocks, including monetary policy shocks, I use the
vector autoregression (VAR) procedure developed by
Christiano, Eichenbaum, and Evans (1994). Let Zt
denote the 4 × 1 vector of all variables in the model
at date t. This vector includes changes in the log of
world oil prices (POIL), log levels of aggregate U.S.
(or euro-zone) income (YA), log levels of one of the
eight U.S. regions (or 10 euro-zone countries) income
(YR), and the level of the U.S. federal funds (or German
overnight money market) rate (R), which I assume is
the U.S. (or euro-zone) monetary policy indicator.
The order of the variables is:
1) Zt = (POILt, YAt, Rt, YRt).
I assume that Zt follows a second-order VAR:
2) Zt= A0 + A1Zt1 + A2Zt2+ ut,
where A0, A1, and A2 are 4 × 4 coefficient matrices,
and the 4 × 1 disturbance vector ut is serially uncorrelated. I assume that the fundamental exogenous
process that drives the economy is a 4 × 1 vector
process {εt} of serially uncorrelated shocks, with a
covariance matrix equal to the identity matrix. The
VAR disturbance vector ut is a linear function of a
vector εt of underlying economic shocks, as follows:
ut = C ε t ,
where the 4 × 4 matrix C is the unique lower-triangular decomposition of the covariance matrix of ut:
CC′ = E [ ut ut′ ].
This structure implies that the jth element of ut
is correlated with the first j elements of εt, but is orthogonal to the remaining elements of εt.
In setting policy, the U.S. Federal Reserve (or the
euro-zone member central banks) both reacts to and
affects the economy; I use the VAR structure to capture these cross-directional relationships. I assume that
the feedback rule can be written as a linear function,
Federal Reserve Bank of Chicago
Ψ, defined over a vector, Ωt, of variables observed at
or before date t. That is, if I let Rt denote the U.S.
federal funds rate (or German overnight money market rate), then U.S. (or euro-zone) monetary policy
is completely described by:
3) Rt = Ψ (Ωt) + c3,3ε3t,
where ε3t is the third element of the fundamental
shock vector εt, and c3,3 is the (3, 3) element of the
matrix C. (Recall that Rt is the third element of Zt.) In
equation 3, Ψ (Ωt) is the feedback-rule component of
U.S. (or euro-zone) monetary policy, and c3,3 ε3t is the
exogenous U.S. (or euro-zone) monetary policy shock.
Since ε3t has unit variance, c3,3 is the standard deviation of this policy shock. Following Christiano,
Eichenbaum, and Evans (1994), I model Ωt as containing lagged values (dated t 1 and earlier) of all variables in the model, as well as time t values of those
variables the monetary authority looks at contemporaneously in setting policy. In accordance with the
assumptions of the feedback rule, an exogenous
shock ε3t to monetary policy cannot contemporaneously affect time t values of the elements of Ωt. However, lagged values of ε3t can affect the variables in Ωt.
I incorporate equation 3 into the VAR structure
described by equations 1 and 2. Variables POIL and
YA are the contemporaneous inputs to the monetary
feedback rule. These are the only components of Ωt
that are not determined prior to date t. With this structure, I can identify the right-hand side of equation 3
with the third equation in VAR equation 2: Ψ (Ωt)
equals the third row of A0 + A1 Zt1 + A2Zt2, plus Σ 2i=1
c3,i εit (where c3,i denotes the (3, i) element of matrix
C, and εit denotes the ith element of εt ). Note that Rt
is correlated with the first three elements of εt. By
construction the shock c3,3ε3t to U.S. (or euro-zone)
monetary policy is uncorrelated with the monetary
policy feedback rule Ωt.
I estimate matrices A0 , A1, A2 and C by ordinary
least squares. The response of any variable in Zt to
an impulse in any element of the fundamental shock
vector εt can then be computed by using equations
1 and 2.
The standard error bounds in figures 3 through
10 are computed using the following bootstrap Monte
19
Carlo procedure. First, I construct 1,000 time series
of the vector Zt each of length T, where T denotes the
T
number of observations in my data sample. Let {ξt}t=1
denote the vector of residuals from the estimated VAR.
I construct 1,000 sets of new time series of residuals,
T
T
{ξt(j)}t=1
, j = 1, ..., 1,000. The tth element of {ξt(j)}t=1
is selected by drawing randomly, with replacement,
T
from the set of estimated residuals vectors {ξ t}t=1
.
T
For each {ξ t( j)}t=1, I construct a synthetic time series
Zt, denoted {Zt( j)}Tt=1, using the estimated VAR and
the historical initial conditions on Zt. Next, I reestimate
T
the VAR using {Zt(j)}t=1
and the historical initial conditions and calculate the implied impulse response
functions for j = 1, ..., 1,000. For each lag, I calculate
the 25th lowest and 975th highest value of the corresponding impulse response coefficient across all 1,000
synthetic impulse response functions. The boundaries
of the confidence intervals in the figures correspond to
a plot of these coefficients.
NOTES
See Corden (1993), chapters 79, for an extended discussion of
the EMS and events surrounding the 1992 breakdown of the system.
1
In general, time-series data are nonstationary. Nonstationary data
do not have well-defined standard deviations or correlations. One
way of overcoming this problem is to filter the data using a filter
that removes the nonstationary components and renders the data
stationary. There is a range of filtering techniques available, including linear time trends and first differencing. Baxter and King (1995)
have designed a filter that isolates components of the data that policy
analysts are interested in, the so-called business cycle frequencies
of one and a half to eight years. I use a BaxterKing filter to isolate
cyclical movements in U.S. and EMU time series.
2
Consumer price indexes do exist for metropolitan areas in the
various BEA regions. However, there is a very high degree of
3
correlation in consumer price fluctuations across these metropolitan areas. In addition, using region-specific price series would
impose a further limit on the analysis since many metropolitan
indexes are not available after 1986.
4
The gross product by state is available from 1977 to 1997.
See Carlino and DeFina (1998a), appendix A, for a listing of
states by BEA region.
5
6
For examples, see references in Kouparitsas (1998).
Carlino and DeFina (1998a) assume a similar recursive information ordering in their analysis of the regional impact of U.S.
monetary policy.
7
REFERENCES
Baxter, M., and R. G. King, 1995, Measuring
business cycles: Approximate band-pass filters for
economic time series, National Bureau of Economic
Research, working paper, No. 5022.
Blanchard, O., and D. Quah, 1989, The dynamic
effects of aggregate demand and supply disturbances,
American Economic Review, Vol. 79, No. 4, pp.
655673.
Carlino, G. A., and R. DeFina, 1998a, The differential regional effects of monetary policy, The Review of
Economics and Statistics, Vol. 80, No. 4, pp. 572587.
, 1998b, Monetary policy and the U.S.
states and regions: Some implications for European
monetary union, Federal Reserve Bank of Philadelphia, working paper, No. 98-17.
Christiano, L. J., M. Eichenbaum, and C. L. Evans,
1994, Identification and the effects of monetary
policy shocks, Federal Reserve Bank of Chicago,
working paper, No. 94-7.
Corden, W. M., 1993, Economic Policy, Exchange
Rates, and the International System, Chicago:
University of Chicago Press.
Dornbusch, R., C. A. Favero, and F. Giavazzi,
1998, The immediate challenges for the European
20
Central Bank, National Bureau of Economic
Research, working paper, No. 6369.
Eichengreen, B. J., 1992, Is Europe an optimum
currency area?, reprinted in European Monetary
Unification: Theory, Practice, and Analysis, B. J.
Eichengreen (ed.), Cambridge: MIT Press, pp. 5171.
Eichengreen, B. J., and T. Bayoumi, 1993, Shocking aspects of European monetary unification,
reprinted in European Monetary Unification: Theory,
Practice, and Analysis, B. J. Eichengreen (ed.),
Cambridge: MIT Press, pp. 73109.
Frankel, J. A., and A. K. Rose, 1998, The endogeneity of the optimum currency area criteria, Economic
Journal, Vol. 108, July, pp. 10091025.
Kouparitsas, M. A., 1998, Are international business cycles different under fixed and flexible exchange
rate regimes? Economic Perspectives, Federal
Reserve Bank of Chicago, Vol. 22, No. 1, pp. 4664.
Mundell, R. A., 1961, A theory of optimum currency
areas, American Economic Review, Vol. 51, No. 4,
pp. 657665.
Sims, C., 1972, Money, income, and causality, American Economic Review, Vol. 62, No. 4, pp. 540552.
Economic Perspectives
Regional employment growth and the business cycle
Ellen R. Rissman
Introduction and summary
The purpose of this article is to study the sources of
regional employment fluctuations in the U.S. and to
shed light on the interactions of these regional fluctuations with the aggregate economy. Many studies of
regional employment growth have analyzed the effect
of regional differences in a number of underlying
factors, such as local government expenditures and
tax policy, while controlling for aggregate economic
activity. My analysis focuses alternatively on the role
of regional fluctuations in determining aggregate
economic activity.
Macroeconomists have tended to concentrate on
the impact of changes in aggregate factors in determining the business cycle.1 Such aggregate factors
have included, for example, fiscal and monetary policy,
the role of consumer confidence, aggregate supply
and demand, and productivity. Yet there is a growing
literature that suggests that aggregate disturbances are
the result of a variety of influences.2 In the work introduced here, I explicitly consider the role of regional
employment fluctuations in determining the business
cycle. I do not specifically identify the sources of such
regional shocks. They could be the result of changing
federal governmental policies, for example, immigration or defense spending, that impinge upon certain
areas of the country more than others. They could also
reflect changes in local welfare programs or shifts in
local fiscal and tax policy.
The analysis is complicated by the fact that while
regional fluctuations may have aggregate repercussions, aggregate factors influence regional growth as
well. For example, general productivity shocks are
likely to have broad consequences across a variety of
industries and geographical areas that are reflected in
regional employment growth. Ascertaining what movements in employment growth are common across
regions and what are region-specific would be helpful
for policymakers. If, for example, regional employment
Federal Reserve Bank of Chicago
growth is largely unrelated to employment growth in
other regions, a more regional policy focus might be
appropriate. Examples of more localized policy would
include differential taxation and spending programs
that are coordinated within a region or a more geographically targeted approach to federal government
spending. If, however, most regional employment
growth is common across regions, a more centralized
policy process is warranted.
The business cycle has been conceptualized as
expansions occurring at about the same time in many
economic activities, followed by similarly general
recessions, contractions, and revivals which merge
into the expansion phase of the next cycle.3 Thus,
the business cycle is characterized by comovements
among a variety of economic variables and is observable only indirectly. Only by monitoring the behavior
of many economic variables simultaneously can one
quantify the business cycle. For example, recessions
are typically associated with declining output and
employment across broadly defined industries. It is
this notion of comovement that has supplied the foundation for measuring cyclical activity. This is the practice behind the widely publicized National Bureau of
Economic Researchs (NBER) dating of business
cycles and Stock and Watsons (1988) index of coincident economic indicators.
Ellen R. Rissman is an economist in the Economic
Research Department of the Federal Reserve Bank of
Chicago. The author would like to thank Ken Housinger
for his research assistance. She is particularly indebted to
Ken Kuttner for his insight and for providing the basic
statistical programs. Dan Sullivan, David Marshall,
Joe Altonji, and Bill Testa provided many thoughtful
comments. The author would also like to thank the
seminar participants at the Federal Reserve Bank of
Chicago for their patience and suggestions.
21
While most analyses of the business cycle focus
on the notion of comovement in employment or output across industries, a great deal of comovement
exists across geographical regions as well. Yet, until
recently this regional cyclicality has gone largely
unexplored, with a few notable exceptions such as
Altonji and Ham (1990), Blanchard and Katz (1992),
Clark (1998), and Clark and Shin (1999). The reason
for the lack of interest in the regional cycle has largely
been the belief that whatever cyclicality a geographical region experiences is due in large part to its industrial mix and to common aggregate shocks. In fact,
regional shocks are typically not considered in assessing the business cycle.
Altonji and Ham (1990) investigate the effect
of U.S., Canadian national, and sectoral shocks on
Canadian employment fluctuations at the national,
industrial, and provincial level. They find that sectoral shocks account for only one-tenth of aggregate
variation, with two-thirds of the variation attributable
to U.S. disturbances and one-quarter to Canadian
shocks. The relatively small importance of sectoral
fluctuations in describing aggregate variation in
Canadian data suggests that regional shocks have little
effect on the business cycle. The conclusion holds
true for Canada but the study does not necessarily
apply to the U.S. economy, in which external shocks
presumably play less of a role.
In a model similar to Altonji and Ham (1990),
Clark (1998) attempts to quantify the roles of national, regional, and industry-specific shocks on regional
employment growth for U.S. data. Contrary to the
traditional view that regional fluctuations are unimportant in determining the aggregate and the results
of Altonji and Ham (1990) for Canada, Clark finds
that roughly 40 percent of the variance of the cyclical
innovation in any regions employment growth rate
is particular to that region.4 He goes on to show that
these regional shocks tend to propagate across regions.
Clarks conclusion is that heterogeneous regional fluctuations have possibly important implications for
business cycle study. Although valuable, the methodology he employs does not permit the construction
of actual estimates of regional disturbances, which
hampers his ability to clarify the underlying causes
of the regional shocks.
In this article, I develop and estimate a model of
regional employment growth aimed at understanding
the role of the aggregate economy. Each regions employment growth is assumed to depend upon a common
factor, thought of here as the business cycle.5 This
common factor is not directly observable, but is inferred
through the comovements of employment growth
22
across a number of regions simultaneously. This does
not mean that each region responds in the same manner to cyclical fluctuations. Some regions will be more
cyclically sensitive while others are less. Accordingly,
the methodology permits the cycle to have a differential impact on regional employment growth.
The methodology I employ is similar to that in
Rissman (1997) and utilizes a statistical technique
known as the Kalman filter. The research here is akin
to Clarks in that it is an attempt to isolate the effects
of the business cycle and regional disturbances on
regional employment growth. However, I expressly
model the business cycle as a common factor affecting all regions and some more than others. A measure
of the business cycle develops naturally from the estimation of the model and is based solely upon the
comovements in employment growth across census
regions. In addition, I estimate regional employment
shocks, which are useful for elucidating the reasons
behind regional differences in economic growth.
In summary, while aggregate fluctuations are an
important force behind regional employment growth,
local disturbances contribute significantly as well.
The role of such local shocks is not uniform across
regions. My estimates indicate that almost 60 percent
of the steady state variance in employment growth in
the West South Central region is attributable to local
fluctuations. This compares with only about 10 percent
in East South Central, where aggregate conditions
are the driving force.
My results suggest that regional employment
growth can be described remarkably well by a simple
model in which a common business cycle has a differential impact upon the various regions. Measures of
the business cycle from this approach are quite consistent across models and agree quite well with more
typical measures of the business cycle. The main difference between this measure and other such measures
is that this one relies upon regional employment data
alone, while other measures may take into consideration
a wide variety of other factors, such as productivity.
Interestingly, errors made in forecasting employment growth in the West South Central region appear
to have some predictive content for forecasting employment growth in most other regions. This suggests that
there is something unique about this regions economy
that is not currently captured by the model but that
does have aggregate repercussions. This might be
due to the regions reliance on the oil industry. My
analysis implies that regional policies may be an important tool in managing the economy. However, more
research on the nature of the spillovers across regions
would be required to support economic policy targeting
specific regions.
Economic Perspectives
Data
In formulating a model of regional employment
growth, a necessary first step is to observe the patterns
in the data. The Bureau of Labor Statistics (BLS)
collects regional employment statistics from its
Employment Survey for the following nine census
regions: New England, Mid-Atlantic, East North
Central, West North Central, South Atlantic, East
South Central, West South Central, Mountain, and
Pacific.6 Figure 1 shows annualized quarterly employment growth for each of the nine census regions from
1961:Q1 to 1998:Q2. (The construction is explained
in box 1.) It is clear from the figure that some regions
consistently exhibit high employment growth (for
example, South Atlantic, East South Central, West
South Central, Mountain, and Pacific), while other
regions consistently exhibit below-average employment growth (New England, Mid-Atlantic, East North
Central, and West North Central).7
In addition to differences in mean employment
growth, regional employment growth exhibits an
apparent cyclical pattern. Typically, employment
growth declines during a recession (shaded areas in
figure 1) and increases in an expansion.8 This cyclical
pattern shows up quite clearly in all regions but is less
pronounced in some. Specifically, the Pacific and
Mountain states appear to be less affected by the
business cycle than a more typical Rust Belt region
such as East North Central. This is not to say that
employment growth does not decline here as well,
but in these regions contractions are associated with
smaller declines.
Closer inspection of figure 1 shows that regional
employment growth appears to have a random component in addition to a cyclical one. For example, the
West South Central region experienced a marked
decline in employment growth in the mid-1980s. This
decline was echoed in a few other regions, but was
nowhere as pronounced as in West South Central. In
fact, regions such as the Mid-Atlantic, East North
Central, South Atlantic, and Pacific experienced relatively little negative impact at that time.
In modeling the effect of the business cycle on
regional employment growth it is useful to know how
the business cycle affects the regional economy
through other less-direct avenues. For example, the
cycle may affect the distribution of employment
across regions. Figure 2 exhibits regional employment
growth net of aggregate employment growth. A negative number for a region indicates that that regions
employment share of the aggregate is shrinking.
Conversely, a positive number shows that that regions
employment is growing relative to the aggregate. The
Federal Reserve Bank of Chicago
BOX 1
Annual employment growth and net annual
employment growth
Employment growth in region i at time t, yit, is
calculated as:
yit ≡ log(eit / eit − 4 ) × 100,
where eit is employment in region i at time t.
Define net employment growth nit as the difference between regional employment growth and
aggregate employment growth. Specifically,
nit ≡ yit − yt
nit = [log(eit / eit − 4 ) − log(et / et − 4 )],
where et is defined as aggregate employment at
time t and yt is aggregate employment growth.
figure shows that trends in employment growth seem
to persist for long periods. For example, the Rust
Belt New England region experienced below national average employment growth for most of the earlier
part of the data period. This decline was temporarily
reversed in the 1980sthe much-vaunted Massachusetts miracle. However, the New England recovery was short-lived, as shown by the subsequent
pronounced decline in New Englands employment
share. The Mid-Atlantic states lost ground as well
over most of the period. In contrast, employment
growth in the Mountain states was above the national
average, with the exception of a brief period in the
mid-1960s and again in the mid-1980s.
The employment shares in figure 2 do not appear,
at least by casual observation, to behave cyclically.
It is not the case that a given regions relative importance in the composition of aggregate employment is
affected systematically by the business cycle. This is
in direct contrast to the evidence on industries, where
the composition of total employment shifts away from
goods-producing and toward service-producing industries during contractions. Although regions show
periods of expansion and contraction, at first blush
the timing of these regional cycles is unlike the
timing of the familiar business cycle. If a business
cycle is described by comovements in a number of
series, it is difficult to describe what these comovements might be from looking at net regional employment growth alone.
At times, statistical relationships can be difficult
to ascertain by casual observation of the data at hand.
To investigate a more complex model of the cyclicality of net regional employment growth, I perform a
23
FIGURE 1
Regional employment growth, 1961:Q198:Q2
(percent)
New England
East South Central
Mid-Atlantic
West South Central
East North Central
Mountain
West North Central
Pacific
South Atlantic
Notes: See box 1 for details of calculations. Shaded areas indicate recessions, as defined by the
National Bureau of Economic Research.
Source: U.S. Department of Labor, Bureau of Labor Statistics, 1960–98, employment database available
at ftp://ftp.bls.gov/pub/time.series and author’s calculations.
24
Economic Perspectives
FIGURE 2
Employment growth, regional less aggregate, 1961:Q198:Q2
(percent)
New England
East South Central
Mid-Atlantic
West South Central
East North Central
Mountain
West North Central
Pacific
South Atlantic
Notes: Regional employment growth less total employment growth, quarterly from previous year.
Growth rates measured as four-quarter log differences. See box 1 for details. Shaded areas
indicate recessions, as defined by the National Bureau of Economic Research.
Source: U.S. Department of Labor, Bureau of Labor Statistics, 1960–98, employment database
available at ftp://ftp.bls.gov/pub/time.series and author’s calculations.
Federal Reserve Bank of Chicago
25
regression exercise in which net regional employment
BOX 2
growth is assumed to depend upon lags of net regional
OLS regression testing effect of contractions
employment growth and whether the economy is in
on net employment growth
a contraction as defined by the NBER. (The form of
Let CONTRACT be a dummy variable taking
the regression is shown in box 2.) Table 1 shows the
on
the
value 1 during an NBER contraction and 0
results of these simple ordinary least squares (OLS)
elsewhere. The OLS regression equation is of the
regressions. A significant negative or positive number
form:
in the CONTRACT column indicates that, even after
accounting for dynamics through lags of own-region
nit = c + a( L )nit −1 + b * CONTRACT + ε it .
net employment growth, the state of the aggregate
Four lags of the dependent variable have
economy has an additional impact upon net employbeen included and are generally enough to ensure
ment growth. In the case of a negative number, the
that the error term is serially uncorrelated.
regions employment share shrinks during a contraction. Conversely, a positive number suggests that
the regions employment share expands during a
mirror the effects of the business cycle on the regional
contraction.
industry mix and, thus, there is relatively little role
From table 1, clearly business cycle contractions
for regional fluctuations or shocks to explain the patas defined by the NBER are not particularly good
terns in the data. Box 3 shows how state industry
at explaining regional net employment growth after
employment data can be used to evaluate this issue.
accounting for serial correlation in the dependent
Changes in state employment are dominated by
variable. Most of the estimates are not significantly
two effects. First, there is the effect of shifting industry
different from zero. The exceptions are Mid-Atlantic,
employment on employment within the state, holding
East North Central, and Mountain. In the East North
the contribution of the state in employment within
Central region, comprising Ohio, Indiana, Michigan,
the industry constant. The second effect measures the
Wisconsin, and Illinois, employment shares typically
importance of shifting the states contribution to each
decline in a recession. Furthermore, the estimated
industry, holding aggregate industry employment
effect for East North Central is quite large compared
with the other regions. In the Mid-Atlantic
and Mountain regions, employment shares
TABLE 1
tend to rise during a contraction. The R2
Effect of timing of NBER contractions on
statistic is a measure of the fit of the regresregional employment growth less aggregate
sion. The closer this number is to unity, the
employment growth, OLS
better the data fit the estimated equation.
2
–
The high values of R suggest that most of
Region
CONTRACT
R2
the variation in net regional employment
New England
0.1170
0.9281
growth is accounted for by lags in the deMid-Atlantic
0.1345**
0.8851
pendent variable.
Industry effects
To summarize, the data on regional
employment growth suggest that the business cycle affects regional employment
growth directly and to a far lesser extent
through its effect on the distribution of
employment across regions. It has long
been observed that the business cycle systematically affects the distribution of employment across industries.9 One possible
explanation for the cyclicality of regional
employment growth is that certain regions
are dominated by specific industries. To the
extent that this is true, then the regional
cycles found in employment growth merely
26
East North Central
West North Central
South Atlantic
East South Central
West South Central
Mountain
Pacific
–0.3581***
0.0272
0.0540
–0.0458
0.1444
0.1561*
–0.0308
0.8541
0.8207
0.8686
0.8393
0.9394
0.9267
0.8436
Notes: The regression equation estimated by OLS is:
nit = c +a (L)nit–1 + b*CONTRACT + ε i t,
where CONTRACT takes on the value of 1 during an NBER contraction and
is 0 otherwise; a(L) is a polynomial in the lag operator with a maximum
lag length of four. ***Indicates significance at the 1 percent level;
**indicates significance at the 5 percent level; and *indicates
significance at the 10 percent level.
Source: Author’s calculations based on data from the U.S. Depar tment
of Labor, Bureau of Labor Statistics, database at ftp://ftp.bls.gov/pub/
time.series and the National Bureau of Economic Research database
available on the Internet at www.nber.org.
Economic Perspectives
constant. The first effect can be thought of as an industry effect while the second can be thought of as a state
effect. If state effects are not important, then an analysis of employment growth by geographical region is
unlikely to yield any insight into business cycles. If,
however, a significant portion of the change in employment within a state is state-specific, a regional
analysis is likely to provide further information.
Table 2 shows the relative importance of each of
these two factors for all states except Hawaii. Specifically, the table shows the portion of the normalized
change between 1985:Q1 and 1998:Q2 in employment
in state s attributable to changing industry employment and changing employment shares, respectively.10 The industry categories are mining, construction,
manufacturing, trade, services, transportation and
public utilities, government, and finance, insurance,
and real estate. The goal is to analyze how important
state and industry effects are in explaining state employment changes. A full set of data on all states with
the exception of Hawaii is available from 1982:Q1
forward. To avoid evaluating employment over two
BOX 3
Effect of industry composition on state employment
Define eis(t) as employment in industry i in
state s at time t. Define
s
ki (t ) ≡
s
ei (t )
ei (t )
as the share of industry is employment in state s.
These numbers sum to unity over all states. The
larger the share in a given state, the more important that state is in the employment of that particular industry. Employment in state s at time t,
es(t), can be calculated as:
e s (t ) = ∑ ki (t )ei (t ),
s
i
which says that total state employment is the sum
of employment in each industry within that state.
Now define the difference operator ∆τ as:
∆ τ x(t ) ≡ x (t ) − x(t − τ).
Applying the difference operator to the
expression for state employment yields:
declines, this effect calculates the effect of declining aggregate manufacturing employment on
employment within a given state, holding the
share of that states contribution to total manufacturing employment constant. No secondary
effects are permitted whereby the distribution
of manufacturing across states has been altered.
The second term captures the effect of
changing employment shares in industry i in state
s while keeping total industry employment constant. Suppose that employment remains constant
over time but that the importance of a given state
in its contribution to the total changes. This second
term calculates the effect of this shift on employment within that state. Finally, the third term is
an interaction term that permits both state industry
employment shares and industry employment to
vary together. Because it is calculated by multiplying together two changes, it is smaller in magnitude
than the first two effects and will be dominated by
the first two terms in the expression.
Rearranging terms,
∆ τe s (t ) + ∑ ∆ τ ki (t )∆ τ ei (t ) =
s
∆ e (t ) = ∑ ∆ ei (t )ki (t ) + ∑ ∆ ki (t )ei (t ) −
τ s
τ
τ
s
i
i
s
∑ ∆ e (t )k
τ
i
∑∆ k
τ
i
s
(t )∆ τ ei (t ).
i
From this expression, the change in state employment between periods tτ and t can be separated into three different effects. The first term to
the right of the equal sign reflects the effect of
changing industry employment while keeping the
share of industry is employment in state s constant. An example will help clarify this construct.
Suppose aggregate manufacturing employment
Federal Reserve Bank of Chicago
i
s
i
i
(t ) + ∑ ∆ τ ki (t )ei (t )
s
i
or
∑ ∆ e (t )k
τ
1=
i
i
s
i
(t ) + ∑ ∆ τ ki (t )ei (t )
s
i
∆ τe s (t ) + ∑ ∆ τ ki (t ) ∆ τ ei (t )
s
.
i
This expression says that the normalized sum
of the two effects should be unity.
27
TABLE 2
Changes in employment in state s, 1985:Q198:Q2
Industry effect
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
0.80
1.20
0.58
0.63
1.20
0.76
7.74
0.74
0.73
0.65
n.a.
0.57
1.31
0.76
0.84
0.82
0.67
1.78
1.07
1.48
3.85
0.91
0.80
0.73
0.99
0.91
0.89
0.48
1.06
2.54
0.81
36.32
0.63
1.15
1.03
1.34
0.60
1.87
4.89
0.67
0.70
0.67
0.89
0.53
1.15
0.82
0.61
1.11
0.73
1.76
State effect
0.20
–0.20
0.42
0.37
–0.20
0.24
–6.74
0.26
0.27
0.35
n.a.
0.43
–0.31
0.24
0.16
0.18
0.33
–0.78
–0.07
–0.48
–2.85
0.09
0.20
0.27
0.01
0.09
0.11
0.52
–0.06
–1.54
0.19
–35.33
0.36
–0.15
–0.03
–0.34
0.40
–0.87
–3.89
0.33
0.30
0.33
0.11
0.47
–0.15
0.18
0.39
–0.11
0.27
–0.75
Notes: See box 3 for the exact calculations. n.a. indicates
not available.
Source: Author’s calculations based on data from the U.S.
Department of Labor, Bureau of Labor Statistics, database
at ftp://ftp.bls.gov/pub/time.series.
28
different phases of the business cycle, I analyze changes
in state employment between 1985:Q1 and 1998:Q2.
The evidence provided in table 2 supports Clarks
(1998) contention that location-specific shocks are
important. For example, about 58 percent of the increase in employment in Arizona is attributable to
within-industry employment growth. However, the
remaining 42 percent of the increase is the result of a
shifting industrial mix within the state. Although the
effect of changing aggregate industrial employment
dominates, the importance of the changing industrial
composition within the state is not insignificant in
most instances, most often leading to increases in
state employment.
Some states, notably Alaska, California, Connecticut, Illinois, Louisiana, Maryland, Massachusetts,
New Jersey, New York, North Dakota, Oklahoma,
Pennsylvania, Rhode Island, Vermont, West Virginia,
and Wyoming, would have experienced an even larger
increase in employment between 1985:Q1 and 1998:Q2
except that employment shares shifted adversely.
New York appears to be somewhat of an outlier with
employment gains being offset to a large extent by
shifts in employment shares: Manufacturing employment as a share of total state employment fell precipitously, while employment in finance, insurance, and
real estate grew quickly.
The state industry employment data suggest that
employment growth is only partly explained by industry effects and that a good portion of state employment
changes results from location-specific factors. It follows that changes in local employment do not simply
reflect the local industrial mix, but also have a significant location-specific component. This adds another
dimension to our understanding of regional employment growth.
The model
The evidence above indicates that regional employment growth is driven in large part by a common
business cycle. Furthermore, regional shocks are important even after accounting for changing aggregate
industrial composition. Let annual employment growth
in region i, yit, have the following specification:
yit = α i + β0 Ct + β1 Ct −1 + β 2 Ct − 2 + γ i yit −1 + ε it ,
i
i
i
where αi is a constant, Ct is a variable meant to capture the business cycle, β0i , β1i , β2i are coefficients
measuring the effect on yit of current and lagged values of the business cycle (that is, β i(L)Ct = β0i Ct + β1i
Ct1 +
+ βpi Ctp , where p = 2), γi is a coefficient on
lagged own-region employment growth, and εit is an
Economic Perspectives
independent and identically distributed random variable with mean 0 and variance σi2, i = 1, ..., I.
The business cycle is assumed to affect each
region differently in terms of both timing and magnitude. This differing effect is captured parsimoniously
by the coefficients β0i , β1i , β2i . Those regions that are
less cyclical have values of the βji parameters that are
closer to 0. Those regions that lag the cycle have
estimates of βji that are insignificantly different from
0 for small j.
Finally, I assume that one cannot observe the
business cycle directly, but instead must infer it
through its effects on regional employment growth
across all regions simultaneously.11 I assume that the
cycle follows an AR(2) specification so that:
Ct = φ1Ct −1 + φ2Ct − 2 + ut .
The error term ut is assumed to be serially independent
and identically distributed with mean 0 and variance
of σu2 . The imposition of an AR(2) process for the
business cycle provides a succinct way of allowing
for a business cycle that is characterized by recessions
followed by expansions.
To completely specify the model, it is necessary
to assume something about the two types of shocks,
ut and εit, where ut can be thought of as a business
cycle shock and εit is a regional disturbance. Specifically, I assume that the cyclical shock and the regional
disturbances are mean 0, serially uncorrelated, and
uncorrelated with each other. Box 4 provides a detailed
discussion of the estimation.
Results
As currently specified, the model is not identified
without additional restrictions.12 Neither the scale nor
sign of the business cycle is defined. To see this, suppose that the common cycle Ct is rescaled by multiplying it by some constant b, and define Ct* = bCt.
Then Ct* = φ1Ct* + φ2Ct* + ut*, where ut* = but and
var(ut*) = b2σu2. I fix the scale by setting σu2 to 1 and
choose the sign so that β0 is positive in the East North
Central region. In fact, the parameter β0 turns out to
be positive in all regions. This is the natural normalization because we define a boom to be a state when
economic activity is high.
Additional assumptions are required to pin down
the timing of the cycle. Following Stock and Watson
(1989), I normalize by restricting the business cycle to
enter only contemporaneously in at least one region j,
that is, βj1 = βj2 = 0. This region has been set arbitrarily
as East North Central.13
Federal Reserve Bank of Chicago
The results reported in table 3 are for the model
described above, in which two lags of Ct are included
(that is, βi(L) is second order). The estimation uses
quarterly data from 1961:Q2 to 1998:Q3 for the nine
census regions.14
According to the model, movements in the regional employment growth rate reflect macroeconomic conditions, local dynamics, and idiosyncratic
fluctuations that are specific to the region. What kind
of growth rates should the regions experience over
the long term in the absence of cyclical fluctuations
and regional shocks? The expected long-term regional growth rate depends upon both the constant αi and
the coefficient on the lagged dependent variable γi.
Specifically,
Ε ( yi ) =
αi
.
(1 − γ i )
From this computation, the West South Central,
South Atlantic, and Mountain regions have had the
highest growth rates on average, with mean growth
over this period of 3.05 percent, 3.09 percent, and
3.74 percent, respectively. The Rust Belt regions of
New England, Mid-Atlantic, and East North Central
have had the lowest employment growth, recording
annual percentage increases of 1.51 percent, 1.01 percent, and 1.98 percent, respectively.
The parameter β0i reflects the contemporaneous
effect of the business cycle on region is employment
growth. These estimated coefficients (reported in
column 2 of table 3) are positive and significant for
all regions. The East North Central and East South
Central regions are the most cyclically sensitive, exhibiting the largest estimated values for β0. The West
South Central region is by far the least cyclically
sensitive contemporaneously with an estimated β0
of only 0.8406, so that an increase in Ct of one unit
is associated with a less than 1 percent increase in
regional employment growth contemporaneously.
Technically, the Kalman filter and maximum
likelihood estimation provide a way to obtain estimates of the business cycle, Ct, conditional on information prior to time t. I apply a Kalman smoothing
technique that uses all available information through
the end of the sample period to generate smoothed
estimates of Ct. These estimates of the cycle are also
referred to as two-sided estimates since they reflect
both past and future data.15
The process generating the business cycle is
estimated as
Ct = 0.6036Ct −1 + 0.0123Ct − 2 + ut
(0.1029)
(0.0822)
29
BOX 4
Estimation details
The Kalman filter is a statistical technique that is
useful in estimating the parameters of the model
specified above. These parameters include αi , β ki ,
γi , φ1 , φ2 , σu2, σ2i for i = 1, ..., I and for k = 1, ..., p.
In addition, the Kalman filter enables the estimation
of the processes ut and εit and the construction of
the unobserved cyclical variable Ct. The Kalman
filter requires a state equation and a measurement
equation. The state equation describes the evolution
of the possibly unobserved variable(s) of interest,
zt, while the measurement equation relates observables yt to the state.
The vector yt is related to an m × 1 state vector,
zt, via the measurement equation:
yt = Czt + Dε t + Hwt ,
where t = 1, ..., T; C is an N × m matrix; εt is an
N × 1 vector of serially uncorrelated disturbances
with mean zero and covariance matrix IN; and w t is
a vector of exogenous, possibly predetermined variables with H and D being conformable matrices.
In general, the elements of zt are not observable.
In fact, it is this very attribute that makes the Kalman
filter so useful to economists. Although the zt elements
are unknown, they are assumed to be generated by a
first-order Markov process as follows:
zt = Azt −1 + But + Gwt
for t = 1, ..., T, where A is an m × m matrix, B is an
m × g matrix, and ut is a g × 1 vector of serially
uncorrelated disturbances with mean zero and covariance matrix Ig. This equation is referred to as the
transition equation.
The definition of the state vector zt for any particular model is determined by construction. In fact,
the same model can have more than one state space
representation. The elements of the state vector may
or may not have a substantive interpretation. Technically, the aim of the state space formulation is to
set up a vector zt in such a way that it contains all
the relevant information about the system at time
t and that it does do by having as small a number of
elements as possible. Furthermore, the state vector
should be defined so as to have zero correlation
between the disturbances of the measurement and
transition equations, ut and εt.
The Kalman filter refers to a two-step recursive algorithm for optimally forecasting the state
vector zt given information available through time
t1, conditional on known matrices A, B, C, D, G,
and H. The first step is the prediction step and
30
involves forecasting zt on the basis of zt1. The second step is the updating step and involves updating
the estimate of the unobserved state vector zt on the
basis of new information that becomes available in
period t. The results from the Kalman filtering algorithm can then be used to obtain estimates of the
parameters and the state vector zt employing traditional maximum likelihood techniques.1
The model of regional employment growth
proposed above can be put into state space form
defining the state vector zt = (Ct, Ct1, Ct2)′; yt =
(y1t, ..., yIt )′. The system matrices are given below:
φ1 φ 2
A= 1 0
0 1
σ1 0
0 σ
2
D=
0
L
α1
α
2
H =
α 9
M
β10 β11 β12
2
β β12 β 22
C= 0
9
9
9
β 0 β1 β 2
0
0
0
L
L
O
γ1
0
0
γ2
0
0
ε t = (ε1t ε 2 t
M
0
0
σ 9
Lε
L
L
O
L
9t
)′
σ u
B = 0
0
0
0
γ 9
G=0
wt = (1 y1t −1 y2t −1
Ly
9 t −1
) ′.
The Kalman filter technique is a way to optimally infer information about the parameters of interest and, in particular, the state vector zt, which in
this case is simply the unobserved cycle, Ct, and its
two lags. The cycle as constructed here represents
that portion of regional employment growth that is
common across the various regions, while allowing
the cycle to differ in its impact on industry employment growth in terms of timing and magnitude
through the parameters of βi(L). The model is very
much in the spirit of Burns and Mitchells (1946)
idea of comovement but the estimation technique
permits the data to determine which movements are
common and which are idiosyncratic.2
The interested reader may obtain further details in Harvey
(1989) and Hamilton (1994).
2
Stock and Watson (1989) is a recent illustration of the Kalman
filtering technique for constructing the business cycle.
1
Economic Perspectives
TABLE 3
Regional employment growth model with lagged dependent variable
Cycle
2 quarters
ago
Lagged
regional
employment
growth
Standard
deviation of
regional
shock
Constant
Current
cycle
Cycle
1 quarter
ago
New England
0.3711**
(0.1605)
1.1428***
(0.1207)
–0.1332
(0.1650)
–0.5495***
(0.1218)
0.7535***
(0.0559)
1.1183***
(0.0710)
Mid-Atlantic
0.3520**
(0.1668)
1.1286***
(0.1092)
–0.4275***
(0.1528)
–0.0985
(0.0980)
0.6529***
(0.0719)
0.9122***
(0.0633)
East North Central
1.2952***
(0.4102)
1.8330***
(0.1450)
0.0000
—
0.3457***
(0.0822)
1.1718***
(0.0869)
West North Central
1.8999***
(0.4076)
1.0579***
(0.1025)
0.1164
(0.0948)
0.8563***
(0.0614)
South Atlantic
1.8717***
(0.3251)
1.2708***
(0.1157)
0.3939***
(0.0514)
0.9549***
(0.0696)
East South Central
2.2168***
(0.5117)
1.7102***
(0.1359)
0.4925**
(0.2760)
–0.2914**
(0.1401)
0.1411
(0.1198)
0.9026***
(0.0757)
West South Central
0.7077***
(0.2087)
0.8406***
(0.1207)
–0.2395*
(0.1544)
–0.1880*
(0.1204)
0.7683***
(0.0526)
1.2456***
(0.0750)
Mountain
1.3379***
(0.2923)
1.0218***
(0.1271)
–0.1161
(0.1715)
–0.3058***
(0.1246)
0.6418***
(0.0642)
1.2519***
(0.0766)
Pacific
1.1861***
(0.2921)
1.0751***
(0.1327)
–0.2514*
(0.1720)
–0.0295
(0.1366)
0.5606***
(0.0721)
1.3016***
(0.0809)
Region
0.0000
—
0.5853***
(0.1570)
0.0050
(0.0632)
0.0000
—
0.0000
—
Notes: The dependent variable is measured as annualized quarterly regional employment growth rates.
Regional employment growth is assumed to depend upon a constant, the current and two lags of the state
of the economy, and a single lag of own-region employment growth. Maximum likelihood estimates are reported.
Standard errors are in parentheses. ***Indicates marginal significance below 1 percent; **indicates marginal
significance below 5 percent; and *indicates marginal significance below 10 percent. The mean log-likelihood
is 6.48760 at the maximum.
Source: See table 2.
and is shown in figure 3 for the smoothed estimates.
The estimated employment cycle roughly corresponds
to the timing of the NBER business cycle in the sense
that contractions occur at approximately the same
time as the NBER recessions. Interestingly, business
cycle peaks as measured here typically precede the
NBER-dated peaks and recoveries tend to precede
the NBER-dated recoveries. This is particularly notable in light of the fact that the measure of cyclical
activity constructed here is based upon employment
data alone. It is a well-known empirical regularity
that employment lags the business cycle. This can be
seen from carefully comparing real gross domestic
product (GDP) growth and aggregate employment
growth in figure 4. So cyclical measures constructed
from employment data alone might be reasonably
expected to lag as well. As figure 3 shows, however,
this hypothesis is not supported by the data.
Given the high real GDP growth rates of recent
quarters, as shown in figure 4, we might expect the
business cycle to be abnormally high over this period.
Federal Reserve Bank of Chicago
FIGURE 3
Smoothed estimates of business cycle,
1961:Q498:Q2
percent
Notes: See box 2 for details of calculations. The horizontal lines
represent a band of plus or minus two standard deviations.
Shaded areas indicate recessions, as defined by the National
Bureau of Economic Research.
Sources: U.S. Department of Labor, Bureau of Labor Statistics,
1960–98, employment database available at ftp://ftp.bls.gov/
pub/time.series and author’s calculations.
31
FIGURE 4
Alternative measures of the business cycle,
1961:Q498:Q2
FIGURE 4
percent
Alternative
measure of business cycle
percent
GDP
GDP
Employment
Notes: Growth rates were calculated as four quarter log
differences in the respective variable. Gross domestic product
(GDP) data in 1992 chain-weighted dollars; employment data
for total civilian nonfarm payroll employment. All data are
seasonally adjusted. Shaded areas indicate recessions, as
defined by the National Bureau of Economic Research.
Sources: U.S. Department of Commerce, Bureau of Economic
Analysis, 1961–98, National Income and Product Accounts, and
U.S. Department of Labor, Bureau of Labor Statistics, 1961–98,
employment database available at ftp://ftp.bls.gov/pub/
time.series.
Instead, the estimated cycle suggests business conditions are currently hovering around neutral. The reason for the apparent disparity is quite simple. The
business cycle as constructed here depends solely
upon comovements in regional employment growth.
However, employment growth has recently been close
to its long-term average, as is also apparent in figure
4. The employment-based measure of the business
cycle constructed here reflects this trend employment
growth as implying neutral economic conditions.
GDP has exhibited such strong growth in recent
quarters because of the increase in productivity of the
economy and not because of any substantive increase
in employment growth. High productivity growth will
tend to increase output without a concomittant rise in
employment. This is what appears to have happened
in the latter part of the sample. Conversely, when
productivity growth is low and employment growth
remains stable, output-based measures of the cycle
are likely to show deeper recessions than employmentbased measures.
What happens to regional employment growth
when the economy experiences an aggregate onetime shock, that is, a change in the common shock
ut? A positive cyclical shock of one standard deviation
in magnitude increases the cycle by a unit of 1 at the
time it occurs. This, in turn, affects regional employment growth contemporaneously. The following
quarter the shock disappears but its effects linger and
32
are felt in two ways. First, the shock has an evolving
effect on the business cycle through its autoregressive structure.16 This effect translates into movements
in regional employment growth that also evolve over
time. Second, the shock affects regional employment
growth through the lag of regional employment
growth (feedback).
Figure 5 traces the effect of a one standard deviation one-time aggregate business cycle shock on the
cycle and also on regional employment growth. The
effect of the aggregate disturbance on the business
cycle itself dissipates smoothly over time. The regions
responses show more complicated dynamics, with
the largest impact being felt at the same time the disturbance occurs and one quarter thereafter. The effect
then fades over time. (In the West South Central region,
the shocks initial effect is smaller but the effect lingers
slightly longer than in other regions.)
In East North Central, for example, the cyclical
shock contemporaneously increases employment
growth by 1.75 percent per annum relative to its longterm average. The following quarter as these other
feedbacks influence regional employment growth,
the effect remains about the same at 1.71 percent,
despite the value of the shock returning to 0. However, as time progresses, the cyclical shocks effect
fades so by the seventh quarter following the shock,
employment growth in the East North Central region
is only 0.14 percent higher per annum than it would
have been in the absence of the disturbance.
Recall that the variance of the cyclical shock has
been scaled to equal unity. Because the current state
of the economy depends upon past realizations of the
business cycle as well as the aggregate shock, its
variance will reflect these dynamics. The variance of
Ct is computed as
var(Ct ) =
(1 − φ2 )
= 1.596.
(1 + φ2 ) (1 − φ2 )2 − φ12
Consequently, a one unit increase in u corresponds
approximately to a one standard deviation shift in
the cycle of (1.596)1/2 = 1.263.
Table 4 illuminates the relative importance of the
business cycle and the regional idiosyncratic shocks
in explaining the variance of each regions employment growth. (The calculations are shown in box 5.)
Clearly, regional shocks are more important in some
regions than in others. In West South Central, for
example, the regional shock accounts for almost 60
percent of the variance in the regions employment
growth rate. Regional idiosyncratic shocks account
for a somewhat smaller but still sizable proportion of
Economic Perspectives
FIGURE 5
Effect of one standard deviation cyclical shock
(percent)
Cycle
South Atlantic
New England
East South Central
Mid-Atlantic
West South Central
East Nor th Central
Mountain
West North Central
Pacific
periods after shock
periods after shock
Note: The panels trace the effect of a one standard deviation shock of +1 in the cyclical disturbance
on the cycle and each region separately, taking into account the dynamics of the cycle and the dependence
of current regional employment growth on lagged regional employment growth.
Source: Author’s calculations.
Federal Reserve Bank of Chicago
33
the total variance in New England, Mid-Atlantic,
Mountain, and Pacific. This compares with East South
Central, where almost 90 percent of the regions total
variance is attributable to variance in the aggregate
shock. The East North Central, West North Central,
and South Atlantic regions appear to be influenced
in large part by the aggregate shock.
The model has been estimated under the assumption that the regional disturbances are uncorrelated
with each other for all leads and lags and are serially
uncorrelated. This is a strong assumption and a test is
useful to assess the validity of the estimated model.
According to the model estimated above, all comovement is ascribed to the common cyclical shock. If the
model is true, then errors made in forecasting regional employment growth in one region should not be
useful for predicting regional employment growth in
another region. One can construct a simple diagnostic
test in which the estimated one-step-ahead forecast
errors in a regions employment growth are regressed
against lags of the one-step-ahead forecast errors in
other regions.17 If the model describes the data well,
lags of another regions forecast errors should not
be significantly different from 0 in these regressions. In other words, errors made in forecasting
another regions employment growth should not
significantly aid in the prediction of a given regions
employment growth.
In table 5, p-values are reported for the regressions described above, testing for the significance of
forecast error lags. If the model fits the data well, the
p-values should be large. Small p-values indicate that
the independent variable has some predictive content
for the dependent variable. Because of natural variation,
we would expect about 10 percent of the regressions
(that is, eight or nine) to have p-values of less than
0.100 even if the hypothesis was true. Table 5 shows
that, in fact, ten of the regressions show significantly
low p-values. More significantly, most of these low
p-values are in regressions involving the predictive content of forecast errors in the West South Central region.
One obvious reason why the West South Central
region may wield such influence in regional employment growth stems from the industrial composition
of the area. The West South Central states are heavily
dependent on oil and gas production. Disturbances to
these industries, in turn, have repercussions for other
industries and regions of the country. My results imply that, in addition to the common cyclical factor
affecting all regions, there might be another factor involved in explaining regional employment growth patterns. This factor is likely related to oil price shocks.
Further research is necessary to test this hypothesis.
The main advantage of estimating a Kalman filter
model of the sort presented here is its ability to obtain
estimates of the underlying cyclical and regional disturbances, as shown in figure 6. The analysis suggests
that New England experienced some positive shocks
in the late 1970s and early 1980s, coinciding roughly
with well-documented growth in technology and
business services at that time. However, some time
in the late 1980s, the region experienced a series of
large negative shocks. These shocks correspond to
the timing of the S&L crisis and the credit crunch.
At about this time, computers were making the transition from mainframe to desktop and some larger
New England employers were cutting back their labor
force in large numbers. Employment growth in New
England has recovered to some extent
and is approximately in line with what is
TABLE 4
predicted by the model.18
Steady state regional employment growth variance
The Mid-Atlantic region is heavily
due to cycle and shock, 1961:Q298:Q3
influenced by New York. Regional employment growth has held fairly steady,
Steady state
Percent
Percent
employment
of variance
of variance
with the stock bust of 1987 causing lowgrowth
from cyclical
from regional
er employment growth. The East North
Region
variance
shock
shock
Central region experienced a large negative
New England
7.3284
60.5
39.5
disturbance during the period surrounding
Mid-Atlantic
4.6298
64.8
31.3
the first oil price shock and smaller negaEast North Central
10.7283
85.5
14.5
tive ones in 1978 and in 1980. For much
West North Central
4.9939
85.1
14.9
of the 1980s through mid-1990s, employSouth Atlantic
5.9623
81.9
18.1
ment growth shocks in this area were
East South Central
8.0531
89.7
10.3
small and tended to be positive. This
West South Central
6.3430
40.3
59.7
Mountain
5.9528
55.2
44.8
likely reflects the bottoming out of the
Pacific
5.7963
57.4
42.6
farm crisis in 1986 and strong export
Note: See box 5 for a discussion of the calculations.
growth. The farm crisis also appears to
Source: See table 2.
have had an effect on employment
34
Economic Perspectives
BOX 5
How important are regional shocks?
The steady state variance of regional employment
growth reported in table 4 is, in general, a complicated function depending upon the variance of the
idiosyncratic shock, the variance of the cyclical disturbance, the cross-correlation structure between
regions, and the dynamics of the model. To construct a measure of the steady state variance of regional employment growth, first rewrite the model
in terms of a vector AR(1) process. Specifically, let
z t = (y1t, y2t , ..., y9t, Ct+1, Ct , Ct1)′ and rewrite the
model as:
z t = Π z t −1 + v t ,
where v t = (ε1t, ε2 t, ..., ε9t, ut, 0, 0)′ and the matrix Π
is formed as follows:
Γ 9x 9 C9x3
Π=
,
0
A3x3
3x 9
growth in the West North Central region. The West
South Central region appears to have more volatility,
and experienced a large negative disturbance in the
mid-1980s. This shock is most likely the result of the
oil price bust, followed by a recovery in the industry.
Finally, the Pacific region was hit by a series of negative shocks in the early 1990s due to cutbacks in
where the matrix Γ has γ1, ..., γ9 along the diagonal
and 0 elsewhere, and A is defined in box 4. Let the
variancecovariance matrix of vt and zt equal Σ and
Ω, respectively. Then
Ω = ΠΩΠ′ + Σ,
which has the following solution:
vec(Ω) = [I − (Π ⊗ Π)]−1 vec(Σ ).
In this case the total steady state variance of
a regions employment growth is the sum of two
terms, one reflecting the variance of the idiosyncratic regional shock, and the other reflecting the
variance of the cyclical disturbance. Calculating the
percentage attributable to each of the two shocks
follows easily.
defense spending.19 The Pacific region seems to have
recovered to a large extent.
Conclusion
The business cycle is not observable directly. Instead, it must be inferred from observing many data
series simultaneously. Casual observation suggests
TABLE 5
Significance of lagged regional employment growth forecast errors
MidAtlantic
East
North
Central
West
North
Central
South
Atlantic
East
South
Central
West
South
Central
Mountain
Pacific
New England
Mid-Atlantic
East North Central
West North Central
South Atlantic
East South Central
West South Central
Mountain
Pacific
0.083
0.423
0.294
0.997
0.693
0.934
0.219
0.706
0.501
0.288
0.063
0.863
0.973
0.766
0.612
0.214
0.380
0.026
0.652
0.699
0.161
0.769
0.735
0.410
0.007
0.538
0.271
0.650
0.450
0.304
0.834
0.767
0.209
0.070
0.942
0.339
0.381
0.551
0.074
0.273
0.698
0.931
0.003
0.713
0.744
0.639
0.385
0.438
0.885
0.987
0.721
0.008
0.885
0.692
0.189
0.639
0.316
0.250
0.860
0.693
0.749
0.403
0.190
0.762
0.500
0.200
0.878
0.854
0.970
0.031
0.599
0.480
0.336
0.786
0.678
0.839
0.330
0.651
0.048
0.345
0.382
→
→
New
England
j
i
Notes: The table reports p-values for OLS regressions of the form:
eit = c + β1ejt–1 + β2ejt–2+...+ β 6ejt –6 + υt ,
where eit and ejt are the estimated one-step-ahead forecast errors at time t for regional employment growth
and i, j = 1, ..., 9. The p- values reported in the table are the significance levels for the test of the null hypothesis
that the β coefficients are 0. Low p-values indicate that the hypothesis is not consistent with the data.
Numbers in bold indicate a p-value less than 0.100.
Source: See table 2.
Federal Reserve Bank of Chicago
35
FIGURE 6
Estimates of cyclical and regional shocks, 1961:Q498:Q2
(percent)
Cycle
South Atlantic
New England
East South Central
Mid-Atlantic
West South Central
East North Central
Mountain
West North Central
Pacific
Notes: The horizontal lines represent two standard error bands. Shaded areas indicate recessions,
as defined by the National Bureau of Economic Research.
Source: U.S. Department of Labor, Bureau of Labor Statistics, 1961–98, employment database available
at ftp://ftp.bls.gov/pub/time.series.
36
Economic Perspectives
that all regions experience some cyclicality in employment growth, despite the fact that some regions show
above-average employment growth over long periods
and other regions consistently report below-average
employment growth. The fact that these regions move
more or less in tandem over time provides a way to
construct a measure of the business cycle.
In this article, I define the business cycle as comovements in regional employment growth. I estimate
the cycle using the Kalman filter and maximum likelihood techniques. The estimates of the cycle obtained
from the model are quite consistent and conform with
more traditional measures of the business cycle, for
example, GDP growth or the unemployment rate.
Because employment growth is distinct from productivity growth, the estimates of the cycle do not exhibit the large expansion in the most recent period that
output-based measures do. In fact, current estimates of
the business cycle show that the economy is well balanced, in the sense that there are no cyclical shocks that
seem to be expanding or contracting regional employment growth above or below long-term averages. If
employment growth contributes to inflation, this balance in the economy seems to imply that, despite high
output growth, inflation is under control.
Sectoral disturbances appear to be an important
determinant of regional employment growthat least
in some regions. This is particularly true for the West
South Central, Mountain, Pacific, New England, and
Mid-Atlantic states. Regional shocks play a far less
important role in explaining regional employment
growth in the East North Central, West North Central,
South Atlantic, and East South Central regions,
where most of the movements are related to aggregate fluctuations.
There are obviously many ways one could define
the business cycle. The tack taken here is to define it
relative to regional employment growth patterns. This
is not to say that all other information should be excluded from the analysis. However, the focus on an
employment-based measure helps shed light on regional issues. Furthermore, a comparison of an employment-based cyclical measure versus an output-based
measure may aid in our understanding of productivity.
Finally, the methodology employed permits the
recovery of a series of regional employment shocks.
The timing of such disturbances may be helpful for
assessing what factors may explain regional declines
or expansions that are not anticipated by long-term
patterns or cyclical influences. Although speculative,
it appears that oil shocks and defense contracts might
help explain the origin of regional shocks. The model
estimated here is somewhat simplistic, in that it does
not allow for regional spillovers that are not accounted
for by the aggregate shock. By examining the regional
disturbances that the model estimates and formulating
a better notion of the underlying economics behind
these regional shocks, one could develop a richer
understanding of regional dynamics.
NOTES
A comprehensive list is outside the scope of this article. A few
references include Barro (1977, 1978), Mishkin (1983), Gordon
and Veitch (1986), and Litterman and Weiss (1985).
1
2
Blanchard and Watson (1986).
3
Mitchell (1927).
4
Clark (1998), p. 202.
A more appropriate nomenclature might be the employment
cycle since it is constructed by filtering out the common movements in employment across regions. In contrast, the business
cycle is typically modeled as comovements in less narrowly focused
series. For example, Stock and Watson (1989) construct their
Coincident Economic Index with reference to industrial production, total personal income less transfer payments in 1982 dollars,
total manufacturing and trade sales in 1982 dollars, and employees on nonagricultural payrolls.
5
The New England states are Maine, New Hampshire, Vermont,
Massachusetts, Connecticut, and Rhode Island. Mid-Atlantic contains New York, Pennsylvania, and New Jersey. East North Central
comprises Wisconsin, Michigan, Illinois, Indiana, and Ohio.
South Atlantic contains Maryland, Delaware, Virginia, West
Virginia, North Carolina, South Carolina, Georgia, and Florida.
East South Central states are Kentucky, Tennessee, Alabama, and
Mississippi. West South Central contains Oklahoma, Arkansas,
Louisiana, and Texas. The East North Central states are Minnesota, Iowa, Nebraska, Kansas, North Dakota, South Dakota, and
Missouri. The Mountain states are Montana, Idaho, Wyoming,
Nevada, Utah, Colorado, Arizona, and New Mexico. Pacific contains Alaska, Hawaii, Washington, Oregon, and California.
These trends have been noted by previous researchers, including
Blanchard and Katz (1992).
7
The timing of the cyclical upturns and downturns in regional
employment growth is somewhat different from that proposed
by the NBER dating. It is well known that employment reacts
with a small lag to cyclical events so, for example, the trough
of the recessions is typically a short time after the NBER dating
of the trough.
8
9
This observation was made by Mitchell (1927).
6
Federal Reserve Bank of Chicago
Seasonally unadjusted data are reported monthly by the BLS
and are available on the BLS Labstat website. Calculations were
carried out using quarterly data that have been seasonally adjusted using the PROC X11 procedure. Hawaii has been omitted
from the calculations due to a lack of data for mining.
10
37
A richer model might incorporate other cyclical series as well,
such as gross domestic product (GDP) or industry employment.
However, because the objective is to describe regional employment patterns, the business cycle is constructed by looking at
comovements in regional employment patterns alone.
15
The discussion here follows Harveys (1989) analysis of common trends.
17
11
12
A more subtle point is raised in Stock and Watson (1989). Given
three data series that are serially uncorrelated but are correlated
with each other, it is always possible to restructure the model
with a single index. This common factor captures the covariance
of the three series. Over-identification occurs when there are
more than three observable variables (there are nine here) or
when the variables are serially correlated.
Details of this procedure can be found in chapter 4 of
Harvey (1989).
The evolution of the business cycle following a temporary one
standard deviation shock is found in the first panel of figure 5.
16
eˆit ≡ yit − yˆit|t−1 ,
13
The BFGS algorithm was used in maximizing the likelihood
function. In practice, numerical difficulties arose in which the
Hessian matrix failed to invert when the model was estimated
with the sole restriction that lags of the cycle do not enter into the
East North Central Region. The problem was resolved by restricting the South Atlantic region to depend solely upon the contemporaneous cycle as well.
14
The one-step-ahead forecast error is simply defined as:
where the forecast error e^it is calculated as the difference between
the actual regional employment growth rate at time t and the
models prediction of regional employment growth based upon
information up to time t 1.
Bradbury (1993) examines employment over the 199091 recession and the recovery in New England.
18
See Gabriel et al. (1995) for a discussion of migration trends
in California.
19
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, 1977, Unanticipated money growth
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Bradbury, Katharine L., 1993, Shifting patterns
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Burns, Arthur F., and Wesley C. Mitchell, 1946,
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Clark, Todd E., 1998, Employment fluctuations in
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Clark, Todd E., and Kwanho Shin, 1999, The
sources of fluctuations within and across countries,
in Intranational Macroeconomics, Gregory D. Hess
and Eric van Wincoop (eds.), Cambridge, New York,
and Melbourne: Cambridge University Press, chapter 9.
Gabriel, Stuart A., Joe P. Mattey, and William L.
Wascher, 1995, The demise of California reconsidered: Interstate migration over the economic cycle,
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Gordon, Robert J., and John Veitch, 1986, Fixed
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in The American Business Cycle, Robert J. Gordon
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Hamilton, James D., 1994, Time Series Analysis,
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Harvey, Andrew C., 1989, Forecasting Structural
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Economic Perspectives
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Mishkin, Fredrick, 1983, A Rational Expectation
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Federal Reserve Bank of Chicago
39
The 36th Annual Conference on Bank Structure and Competition
Call For Papers
The Federal Reserve Bank
of Chicago invites the submission
of research and policy-oriented papers for
the 36th annual Conference on Bank Structure
and Competition, to be held May 3-5, 2000,
in Chicago. Since its inception, the purpose
of the conference has been to encourage an
ongoing dialogue and debate on current
public policy issues affecting the
financial services sector.
The Changing tool'Industry Structure And Regulation:
What are the implications of mergers for industry competition?
For current antitrust methodologies? For regulation? Do the
answers differ for "megamergers"? What impact might the
development of Internet banking have on industry structure? On
market definitions and related regulatory issues, such as the
CRA? With all these changes, has the role of smaller banks
been enhanced or diminished?
f he theme for 2000 will return us to the original
roots of the conference: financial industry struc
ture and competition. With the recent elimination
of geographic barriers to bank expansion, there has been a
significant increase in the number and size of bank mergers in
the U.S. Similar activity has been occurring in other countries.
We have also seen an increase in cross-border mergers. Why?
Is the consolidation occurring for the "right" reasons? In the
right way? Why do banking consultants and academics fre
quently disagree on the potential benefits and costs associated
with mergers? What determines a successful bank merger?
Perhaps even more importantly, in addition to bank mergers we
are also seeing cross-industry mergers and affiliations—a con
vergence of commercial banks, investment banks, and insur
ance firms into modern financial service providers. While still
"separate" in name, in reality, the boundaries between these
firms have been significantly decreased or eliminated. What's
driving this trend? Are there significant efficiency gains from
universal banking or one-stop shopping? Should consideration
also be given to allowing commercial banks to take equity
positions in nonfinancial firms?
With respect to both merger trends, is regulation in the finan
I
reforming the international financial institutions
cial services industry keeping pace? Have the regulatory goals
(i.e., the IMF, World Bank, BIS, and regional
and structure adjusted appropriately? Most would argue that
development banks);
the SEC's mission and method of operation concerning invest
I
the implications of Internet banking;
ment bank regulation remains very different from that of com
I
technology and payment innovations;
mercial bank regulators. Furthermore, the insurance industry is
I
risk-based pricing of loans (particularly mortgage
lending); and
regulated on a state-by-state basis. Are these differences
desirable for the future? What should the goals be for financial
I
fair lending issues.
regulation in the new environment? Is it possible for invest
ment and commercial bank regulators to have conflicting man
Continuing the format of recent years, the final session of the
dates? Which, if either, takes precedent? Is there a need for a
conference will feature a panel of industry experts who will
single financial regulator for all financial products or should
discuss the purpose, structure, problems, and proposed changes
there be several competing regulators each covering a broad
associated with an important banking regulation. A record of
range of financial services? Should regulators be organized
the panel discussion will be included in the Proceedings of the
around function or different customer classes, such as whole
Conference. Past topics discussed at this session include bank
sale versus retail? Should they be organized around different
antitrust analysis, bank capital regulation, optimal regulatory
public policy goals, such as consumer protection versus safety
structures, and the appropriate role of the lender-of-last-resort.
and soundness? Is there a need for an "umbrella" regulator?
Proposals for this session are also welcome.
Given the accelerating rate of globalization in financial services,
Bridging States, Countries, find Industrie^ In The Betti
is there a need for more international harmonization of regula
If you would like to present a paper at the conference, please
tions? Similarly, should there be more international coordina
submit four copies of a completed paper or a detailed abstract
tion among regulatory agencies?
(the more complete the paper the better) with your name,
address, affiliation, telephone number, and e-mail address, and
The 2000 conference will focus on these and related questions.
those of any co-authors, by December 17, 1999.
Although much of the program will be devoted to the primary
Correspondence should be addressed to:
conference theme, there will be a number of additional sessions
on industry structure and regulation such as:
Conference on Bank Structure and Competition
I
Basel capital standards;
Research Department
I
measuring and managing risk, particularly for
Federal Reserve Bank of Chicago
transnational/global financial services companies;
230 South LaSalle Street
I
alternative approaches to dealing with financial crises;
Chicago, Illinois 60604-1413
I
the role and structure of the lender of last resort;
I
financial modernization in the U.S.;
For additional information contact:
I
safety net reform;
Douglas Evanoff at 312-322-5814 (devanoff@frbchi.org) or
Portia Jackson at 312-322-5775 (portia.jackson@chi.frb.org).
May 3-5, 2000
Child care costs and the return-to-work decisions
of new mothers
Lisa Barrow
Introduction and summary
Womens labor force participation has nearly doubled
over the last 50 years, from 31.0 percent in January
1948 to 60.6 percent by March 1999 (based on
monthly data from the Current Population Survey).
For women with young children, the increases have
been even more dramatic. From 1947 to 1996, the labor
force participation rate of women with preschool-aged
children increased by more than a factor of five, rising
from 12.0 percent to 62.3 percent (U.S. House of
Representatives, 1998). The rapid increase in participation of women with young children indicates that
women are spending less time out of the labor force
for child bearing and rearing. Indeed, looking at new
mothers in the National Longitudinal Survey of Youth
(NLSY), of those who were working prior to the birth
of their first child, three-quarters were back at work
within a year of the birth.
An important consequence of the trend toward
more rapid reemployment of new mothers is that
recent generations of women will have more actual
labor market experience (at each age) than their predecessors.1 In labor economics, a standard analysis
of the relationship between wages and education and
age (reflecting potential experience) shows that wages
increase with years of potential experience. For women,
potential experience is likely to exceed actual experience by more than for men. Thus, the increase in
womens actual work experience should be reflected
in a narrowing of the gender earnings gap. In fact,
despite the growing wage inequality of the 1980s, the
malefemale earnings gap has been closing steadily
since the late 1970s. From 1978 to 1990, the ratio of
female to male earnings rose from 0.73 to 0.85 for
whites and from 0.60 to 0.70 for African-Americans.2
According to ONeill and Polachek (1993), about onequarter of the closing of the malefemale wage gap
over the 197687 period can be attributed to changes
in the actual labor force experience of women and an
42
additional 50 percent can be accounted for by changes
in returns to experience for women relative to men.
Realistically, working women who choose to have
children will have to take some time off of work either
by taking family, sick, or vacation leave or by exiting
the labor market entirely. However, given the importance of experience in determining wages, the faster
women return to work following childbirth, the closer
their actual experience will be to their potential experience and the smaller the average earnings penalty
for women who have children.
In this article, I examine the economic determinants of a womans decision to return to work quickly
following childbirth. I consider three key factors in
this decision: the opportunity cost of taking time out
of the labor force (that is, the potential wage rate available to a woman), the wealth effect of other family income, and most particularly, the opportunity cost of
working outside the home in terms of child care costs.
I first describe a simple theoretical model of a
new mothers return-to-work decision. The model
predicts that the decision to return to work will depend
on a womans wage net of hourly child care costs
and other family income (including spouse or partner
income). I then test the theoretical model as closely
as possible. In order to get a measure of child care
costs faced by women as they decide whether to return
to work, I calculate average child care worker wages
across states and over time to proxy for variation in
child care cost across states and over time. I find that
women with higher wages are significantly more
Lisa Barrow is an economist in the Economic Research
Department of the Federal Reserve Bank of Chicago. The
author would like to thank Paul Huck, David Marshall,
Dan Sullivan, and seminar participants at the Federal
Reserve Bank of Chicago for helpful comments and
suggestions.
Economic Perspectives
likely to return to work, and women facing higher
child care costs or having greater other family income
are significantly less likely to return to work after
first birth. I also find that older women, women with
more education, and women whose adult female role
model was working when they were teenagers are more
likely to return to work.
Additional interest in womens labor force participation has been generated by the reforms to welfare
programs that have a primary goal of getting recipients
off of welfare and into the work force. Because the
majority of welfare recipients are women with children, child care costs may have important effects on
getting these women into the labor force. Therefore,
I look for greater sensitivity to child care costs
among women with less than a high school education
who are not married or do not have a spouse present.
I find no evidence that these womens labor force
participation decisions are more sensitive to child
care costs. Additionally, I find that for these women
the decision to return to work is also no more sensitive to the
unemployment rate of their home county than for
other women.
While this study was not designed to test alternative policies, several inferences may be drawn.
First, the results suggest that delayed child bearing
may have a greater impact on increasing labor force
participation of women with young children than
increases in wages or decreases in child care costs.
Second, while access to reliable child care is likely to
be a necessity for successfully moving mothers from
welfare to the labor force, this research
shows no
evidence that welfare recipients will be
more responsive to changes in child care
costs than other women. Finally, the inpercent
creased probability of a woman working
after childbirth associated with her female
role model having worked suggests that
we should expect to see continuing increases in the labor force participation rate
of women, thus increasing the size of the
labor force.
while employed women with at least one child under
age one spend an average of $88.60 per week.4
Since, on average, these women work about 36 hours
per week, child care costs represent a $2.00 to $2.50
per hour tax on the work effort of mothers with
young children.
Anderson and Levine (1998) provide a good
overview of much of the empirical literature examining
the relationship between child care and mothers
employment decisions. They note that while many
studies find the expected negative relationship between
child care costs and womens labor force participation
decisions, there is much variability among the estimates
in how responsive women are to changes in child
care costs.
My approach builds on several of the earlier
studies using the relatively detailed information
available in the NLSY. Although some of the earlier
studiesBlau and Robins (1991), Leibowitz, Klerman,
and Waite (1992), and Klerman and Leibowitz
(1990)use NLSY data as well, their data are less
current and hence less representative of women at
first birth. In addition, I use the subset of new mothers
who were working in the period before their first
birth in order to focus specifically on the return-towork decision.5
Women’s labor force participation
As mentioned in the introduction, womens labor
force participation rate has increased dramatically in
the last 50 years. Labor force participation rates for
women, men, and subgroups of women with children
FIGURE 1
Labor force participation rates
Men 16
years old +
Women with
children 6–17
years old
Previous research
Much of the previous literature on the
labor supply behavior of women with
young children has focused on the effect
of child care costs.3 Looking at Census
Bureau estimates from the Survey of
Income and Program Participation (SIPP)
1988 Panel, employed mothers spend an
average of $73.30 per week on child care,
Federal Reserve Bank of Chicago
Women 16
years old +
Women with
children
< 6 years old
Source: U.S. Department of Labor, Bureau of Labor Statistics,
1948–99, Current Population Survey, Washington, DC.
43
are displayed in figure 1. The labor force participation rate for all women ages 16 and over has nearly
doubled from 32 percent in 1948 to 60 percent in
1999. In comparison the labor force participation rate
for men ages 16 and over decreased from 87 percent
in 1948 to 75 percent in 1999. Over the same period,
participation rates for women with preschool-aged
children and women with school-aged children have
increased even more dramatically. For women with
children under six years old, labor force participation
increased from 14 percent in 1950 to 65 percent in
1997. Similarly, women with children ages six to 17
increased labor force participation from 33 percent in
1950 to 78 percent in 1997.6
Child care worker wages as a measure of
child care costs
Because I cannot observe the actual price of
child care faced by the women in my sample, I use
average child care worker wages across states and
over time as a proxy for child care costs. Child care
worker wages are likely to be a major portion of the
cost of providing child care. One would expect to see
differences in the cost of child care across states due
to differences in minimum wage levels and in the
supply of low-wage labor, among other factors. Because these differences may change over time, I calculate measures of child care costs by state and year.
Differences in child care costs across states could
also arise because of differences in demand for child
care. However, if states in which more women work
have higher child care costs because there is more
demand for child care, this will bias the estimates
against finding the expected negative effect of child
care cost on the probability a woman returns to work
after first birth.
I calculate average hourly wages for child care
workers by state and year for 1979 to 1993 from the
National Bureau of Economic Researchs Current
Population Survey (CPS), Labor Extract, Annual
Earnings File Extracts (National Bureau of Economic
Research, 197993). The average is the weighted
average of hourly earnings of all surveyed workers
who report a three-digit occupation code for child
care workers, private households, or for child care
workers, except private households.7 Hourly earnings
are calculated as edited hourly earnings when paid
hourly and edited or computed usual weekly earnings
divided by edited usual weekly hours otherwise.
Hourly earnings less than $0.50 and above the 99th
percentile in each year are dropped.8
Nationally, real average child care worker wages
increased over the period 197993. Average child
44
FIGURE 2
Average hourly wages
1977 dollars
All women
Child care
workers
Source: U.S. Department of Labor, Bureau of Labor Statistics,
1979–94, Current Population Survey, Washington, DC.
care worker wages and average wages for all women
are shown in figure 2. Wages for child care workers
and average wages for all women both increased in
real terms from 1979 to 1993. From figure 2, one
can see that average child care worker wages were
increasing faster than average wages for women, particularly over 198491. From 1979 to 1993, average
womens wages increased by 9 percent, adjusted for
inflation, while average child care worker wages rose
by 22 percent.9
Table 1 lists average child care worker wages by
state for 197993. As one might expect, states or districts that had state minimum wages above the federal
minimum wage throughout the 1980s such as the
District of Columbia, Alaska, and Connecticut have
higher than average child care worker wages over the
period. Hawaii, Massachusetts, Rhode Island, and
California did not raise their state minimum wages
above the federal minimum wage until 1988, but they,
too, have above-average child care worker wages over
the period. Likewise, it is not surprising to find that
West Virginia, Indiana, Idaho, and North Dakota,
where wages are relatively low, have below-average
child care worker wages.
Model description
To model womens return-to-work decisions, I
assume that each woman has a reservation wage, that
is, a threshold wage at which she would be willing
to go back to work.10 The probability that a woman
returns to work is the probability that her wage offer
net of child care costs exceeds her reservation wage.
Thus, higher child care costs and lower wage offers
will decrease the probability that a woman will go
back to work. In addition, assuming that increases in
Economic Perspectives
TABLE 1
Average child care worker wages by state, 197993
State
District of Columbia
Alaska
Hawaii
New Jersey
Massachusetts
Rhode Island
Connecticut
New York
California
Nevada
New Hampshire
Maryland
Georgia
Florida
Texas
Oklahoma
Illinois
Delaware
New Mexico
Pennsylvania
Louisiana
Washington
Wyoming
Colorado
South Carolina
North Carolina
Average
wage
6.45
6.19
5.82
5.79
5.44
5.42
5.38
5.37
5.31
5.23
4.98
4.98
4.94
4.94
4.80
4.78
4.64
4.60
4.56
4.54
4.53
4.46
4.43
4.42
4.39
4.35
State
Mississippi
Vermont
Kentucky
Minnesota
Arizona
Tennessee
Alabama
Missouri
Ohio
Utah
Arkansas
Virginia
Kansas
Michigan
Oregon
South Dakota
Maine
Montana
Wisconsin
Nebraska
Iowa
West Virginia
Indiana
Idaho
North Dakota
All states
Average
wage
4.35
4.34
4.33
4.32
4.28
4.25
4.22
4.20
4.19
4.13
4.08
4.07
4.04
3.99
3.91
3.91
3.78
3.72
3.62
3.61
3.50
3.48
3.44
3.40
3.38
4.58
multiple children at various ages. Limiting the sample to women who worked
in the year before birth defines a more
homogenous group of women, since they
all exhibit at least some attachment to the
labor force prior to their first birth. This
also allows me to use pre-birth wage
information as a proxy for post-birth
offered wages.
Data and estimation
NLSY data
The original NLSY sample contains
5,842 women, excluding the military
sample that was dropped in 1985.11 In
this study, I primarily use the 1994 NLSY
child file, which provides detailed information on the children of the original
NLSY sample women, including some
relevant information on their mothers. In
addition, I use the 1993 NLSY youth file
to get geographic and family income information for the mothers. According to
the 1994 child file, there are 3,468 women
whose first child was born between 1979
and 1994 and resided in the mothers
household the first year of birth.12 Characteristics of these women are reported
in the first column of table 2.13
The NLSY reports the number of
Notes: Averages are reported in real 1997 dollars. Averages are the
weeks before and after birth that a woman
weighted average by state (or over all states) of hourly earnings of all
left and resumed employment. The women
surveyed workers in the 1979–93 NBER CPS Annual Earnings File Extracts
who report a three-digit occupation code for child care workers, private
of the NLSY have high employment rates
household or for child care workers, except private households. Hourly
before giving birth; 76 percent of all
earnings less than $0.50 and above the 99th percentile for each year are
excluded.
mothers were working within 51 weeks
Source: Author’s estimates from National Bureau of Economic Research,
prior to their first childs birth. Although
1979–93, CPS Annual Earnings File Extracts.
the participation rate is high relative to
the overall participation rate for women,
this reflects in part the relatively young
income increase the number of hours of leisure a
age of the NLSY women and, more generally, the age
person wants to consume, higher other family inof women at the time of their first birth. The national
come will also decrease the probability of returning
rates are calculated for women ages 16 years and
to work.
over, while the average age at first birth for NLSY
My empirical strategy is to study the determinants
women is 23 years. Nationally, the labor force particof the return-to-work decision for new mothers who
ipation rate for women in their early twenties is
around 73 percent.14
were working prior to the birth of their first child. I
limit the sample to women giving birth to their first
Means and standard deviations for characteristics
child for simplification of the return-to-work decision.
of the regression sample are presented in column 2
This group is more uniform in the sense that all
of table 2. The sample is limited to women who were
mothers face a first birth but not all will face a subseworking before the birth of their first child and women
quent birth. Additionally, these women are all facing
with complete data on variables used in the regression
the decision to return to work with the need to hire
analysis. The women who were working prior to givchild care for a child under age one only, not for
ing birth tend to have higher other family income and
Federal Reserve Bank of Chicago
45
are older (24 versus 21 years old) and better educated
(12.9 versus 11.2 years of education).
As shown by the variable in row 2 of table 2, 76
percent of the mothers who were working returned to
work within 51 weeks following their childs birth. A
more detailed picture of the process is provided in
figure 3, which shows the fraction of the sample
from column 2 of table 2 who were working in each
week before and after childbirth. Expectant mothers
gradually withdraw from employment in the months
before their delivery and then gradually return.15
The pattern for the full sample of NLSY women
in column 1 of table 2 is very similar to that of the
regression sample.
In addition to the standard variables included in
a labor force participation equationwages, unemployment rates, age, education, and raceI include
an indicator for the mother having had a working
female role model when she was 14 and one for the
presence of a womans parent, step-parent, or grandparent in the household around the birth year. The
role model variable is intended to help capture a
womans attitude about being a working mother.
Although a woman may have different feelings about
TABLE 2
Mean characteristics for returners and non-returners
Description
Full sample
Regression
sample
Return: Yes
Return: No
t-value
Worked within 51 weeks
before first birth
0.765
[0.424]
1
[0]
1
[0]
1
[0]
—
Working within 51 weeks
after first birth
0.616
[0.486]
0.762
[0.426]
1
[0]
0
[0]
—
State average wage
for child care workers
4.506
[0.858]
N = 3,302
4.559
[0.890]
4.560
[0.897]
4.555
[0.869]
0.1
Hourly wage fourth quarter
before birth
9.274
[5.040]
N = 2,237
9.221
[4.954]
9.666
[5.177]
7.797
[3.836]
8.4***
Spouse or partner present
0.781
[0.414]
0.820
[0.384]
0.839
[0.368]
0.760
[0.428]
3.6***
19,430
[32,625]
N = 3,207
23,840
[35,558]
23,970
[32,623]
23,422
[43,677]
23.234
[4.201]
24.190
[3.990]
24.512
[3.918]
23.159
[4.047]
6.4***
12.416
[2.293]
N = 3,466
12.942
[2.129]
13.143
[2.118]
12.298
[2.038]
7.7***
0.524
[0.499]
0.537
[0.499]
0.552
[0.497]
0.491
[0.500]
2.3**
0.300
[0.458]
N = 3,395
0.245
[0.430]
0.223
[0.417]
0.313
[0.464]
3.7 ***
0.228
[0.420]
0.194
[0.395]
0.195
[0.397]
0.189
[0.392]
0.3
8.066
[3.327]
N = 3,159
7.793
[3.288]
7.620
[3.166]
8.343
[3.598]
3.9 ***
3,468
1,956
1,490
466
Spouse or partner income
Mother’s age in years
at child’s birth
Mother’s education in years
by birth year
Adult female role model
worked when mother was 14
Parent, step-parent, or
grandparent of mother resides
in household in birth year
African-American
County unemployment rate
in year following birth
Observations
0.3
Notes: All means are unweighted. The number of observations, N, is noted where different from the base
sample size. Wages and income are in real 1997 dollars. Standard deviations are in brackets. ***Indicates
statistically different from 0 at the 1 percent significance level; and ** indicates statistically different from 0
at the 5 percent significance level.
Source: Author’s calculations using data from the Center for Human Resource Research, 1993 and 1994,
National Longitudinal Survey of Youth , Columbus, OH
46
Economic Perspectives
working when she has young children verFIGURE 3
sus when her children are teenagers, this is
Labor force participation, NLSY women
the only information available on whether
proportion of women working
a woman lived in a household with a
working female role model. The grandparent indicator is included to reflect a
woman having greater access to low-cost
Regression
child care. As shown in rows 9 and 10 of
sample
table 2, 52 percent of the NLSY womens
role models worked when they were 14,
and 30 percent of the overall sample of
Full sample
new mothers lived with their own parent,
step-parent, or grandparent.
Columns 3 and 4 of table 2 show the
characteristics of women in the regression
sample who were and were not back at
work within a year of childbirth. A simple
comparison across the columns suggests
time surrounding birth
that women with higher wages, those with
Source: Author’s calculations using data from the Center for Human
a spouse or partner, older women, those
Resource Research, 1993 and 1994, National Longitudinal Survey
of Youth, Columbus, OH.
with more education, and those whose
mother worked are more likely to return to
work quickly. Column 5 presents absolute
t-values for the hypothesis that the means in column
to work. As modeled, the offered wage and child care
3 equal the means in column 4. As predicted by the
costs affect the net wage and thus the probability that
model, women who return to work have higher wages
the net offered wage exceeds the reservation wage,
on average; however, differences in average child
while some of the other characteristics are expected
care costs and in average other family income for
to affect a womans reservation wage. The probit
returners and non-returners are not statistically signifimodel estimates the probability of returning to work
cant. The differences in age, education, working female
as a function of offered wage, child care costs, other
role model, and unemployment rates are statistically
family income, and demographic and labor market
significant. Women who return to work are older,
characteristics. The estimation equation is as follows:
more educated, more likely to have had a working
role model, less likely to live with a parent or grand1) Pr[working 1 year after birth] = β0 + β1 wage +
β2C + Zβ3 + β4UR ε ,
parent, and are living in counties with lower average
unemployment rates.
where wage is the wage in the fourth quarter before
The employment pattern illustrated by figure 3
birth,16 C is the child care cost variable, Z is a matrix
suggests estimating a more dynamic model of weeks
including age, education, other family income, and
to return to work following birth such as a tobit or
indicator variables for having a spouse or partner,
hazard model. The results from estimating a tobit
having a working female role model, being Africanmodel of weeks to return to work censored at 52 weeks,
American, and having one of the childs grandparents
although not reported in this article, are consistent
in the household, and UR is the county unemploywith the probit estimates discussed below. Women
ment rate in the year following the birth year.
with higher wages and more education return to work
First, I estimate the model specified in equation
more quickly following birth, and women facing high1. These results are presented in table 3. I report the
er child care costs and having higher other family inchange in probability of returning to work within one
come delay their return to work longer after birth. This
year of birth associated with a change in each indepenshould not be surprising, however, since none of the
dent variable.17 For example, increasing the average
variables vary over the weeks following birth.
child care worker wage by $1 decreases the probabilProbit estimation of the probability a woman
ity that the average woman will return to work within
returns to work following first birth
one year of her childs birth by 0.038, from 0.778
As discussed above, I assume each woman has a
to 0.740.18 Thus, as predicted by the simple utility
reservation wage at which she is willing to go back
Federal Reserve Bank of Chicago
47
in pretax dollars while child care expenditures are in after-tax dollars. In addition,
Probit estimates of labor force participation model
the child care cost measure is the hourly
Associated change
child care worker wage rather than the
in probability of returning
hourly price. Given the Census Bureau
Independent variable
to work within 1 year
estimates from the SIPP cited above, one
would expect hourly child care costs to
Child care worker wage
–0.038***
be at most 54 percent of average child
(0.012)
care worker wages.20 Assuming that the
Pre-birth wage
0.017***
hourly cost of child care equals 54 percent
(0.003)
of average child care worker wages, the
Spouse or partner income
–0.012***
tax rate would have to be in excess of 75
divided by 10,000
(0.003)
percent to generate the observed change
Indicator for spouse or partner
0.089***
in probability associated with a $1 change
(0.035)
in the offered wage. This result can be
partially reconciled if other costs of
Mother’s age in birth year
0.007**
(0.003)
working are correlated with child care
costs. If other costs of working are posiMother’s education at birth year
0.017***
tively correlated with child care costs, then
(0.006)
the effect of child care costs on the probaRole model work
0.041**
bility of returning to work is overstated.
(0.019)
Spouse/partner income affects womGrandparent
0.001
ens probability of returning to work as
(0.027)
predicted by the model: The higher a
African-American
0.049*
womans spouse/partner income, the less
(0.026)
likely she is to return to work. If other
Unemployment rate in year
–0.007**
income is allowed to enter separately for
following birth
(0.003)
women with spouses and women with
partners, the decrease in probability assoNote: The dependent variable is an indicator for returning to work within
one year of giving birth to the first child. The probability of returning to
ciated with a $10,000 increase in spouse
work predicted at the mean characteristics of the women in the sample is
income is 0.011 with a standard error of
0.778. The reported estimate is the change in probability of returning to
work associated with a one unit change in a given variable, evaluated at
0.003; that is, the probability a woman
the mean of the characteristics. For example, a $1 increase in the average
will return to work falls from 0.778 to
child care worker wage is associated with a 0.038 decrease in the
probability a woman returns to work, a decrease from 0.778 to 0.740.
0.767. Similarly, a $10,000 increase in
There are 1,956 observations. Standard errors are in parentheses.
***
partner income is associated with a deIndicates statistically different from 0 at the 1 percent significance
level; **statistically different from 0 at the 5 percent significance level;
crease in the probability of returning to
and *statistically different from 0 at the 10 percent significance level.
work from 0.778 to 0.752. Finally, 66
women with spouses or partners have
other income calculated to be $0. When
maximizing model described above, women who live
these observations are excluded, average child care
in states with higher child care costs, proxied by child
worker wages becomes slightly more important. The
care worker wages, are significantly less likely to rechange in probability associated with a $1 change in
turn to work within one year of giving birth to their
child care worker wages falls to 0.040 with a standard
first child. In addition, lower wage women are less
error equal to 0.012; that is, a decrease in probability
likely to return to work within one year of giving birth,
from 0.778 to 0.738 is associated with a $1 increase in
as are women with higher partner or spouse income,
the average child care worker wage. The changes assocontrolling for the presence of a spouse or partner.19
ciated with other income, the spouse/partner indicator,
Older women, women with more education, and those
age, female role model, the grandparent indicator, and
who had a working female role model are all more
African-American increase in magnitude, and the edulikely to return to work after giving birth.
cation coefficient decreases slightly.
The theoretical model predicts that offered wage
The results presented in table 4 explore the posand hourly child care price should have coefficients
sibility that women who are most like welfare recipiequal in magnitude and opposite in sign. In comparing
ents may differ from other women in their sensitivity to
the wage and cost coefficients, the wage is measured
child care costs as well as to other economic variables,
TABLE 3
48
Economic Perspectives
in particular, the unemployment rate. I try two measures for similarity to welfare recipients: education
less than 12 years at childs birth and the combination
of both being unmarried and having fewer than 12
years of education at childs birth. Columns 1 and 2
of table 4 list probit estimates using the education indicator only, while columns 3 and 4 use the joint
indicator of education and marital status. Columns 1
and 3 present the results allowing for differing sensitivity to child care costs. In both specifications there is
little evidence that either less educated women or
less educated women without a spouse present are
any more sensitive to child care costs than all women
in the sample. While the estimated change in probability of returning to work associated with a $1 change
in hourly child care costs for the women most like
welfare recipients is smaller than for all other women,
the difference is not statistically significant at conventional levels. Similarly, their probability of returning
to work is not significantly more responsive to higher
unemployment rates as shown in columns 2 and 4.
The calculated child care cost, wage, and family
income elasticities of employment provide one way
to compare the results of this study to others. 21 The
elasticity is the percent change in probability associated with a 1 percent change in a given variable. The
specification of table 3 implies a child care cost elasticity of 0.23. In other words, a 1 percent increase
in child care cost is associated with a 0.23 percent
decrease in the probability of returning to work.22
This estimate is similar to the average price elasticity
of employment of 0.20 estimated by Connelly
(1992a), but somewhat smaller than estimates from
many other studies. Blau and Robins (1988) calculate
a price elasticity of employment of 0.38 over a range
of child care costs, Kimmel (1993) calculates an
elasticity of 0.31 for married women using her preferred child care cost measure, and Powell (1997)
calculates an elasticity of 0.38 for married women
using predicted cost of child care. The elasticities
calculated by Anderson and Levine (1998) for women with children under six years are also much larger,
between 0.46 and 0.59. Ribar (1995) calculates a
much smaller elasticity of 0.09, while that of Ribar
(1992) is much higher at 0.74. The wage elasticity
of labor force participation is much smaller, at 0.21,
TABLE 4
Probit estimates of labor force participation model,
by education and marital status
No high school diploma
No spouse and no high
school diploma
Indicator
–0.194
(0.167)
–0.073
(0.078)
–0.203
(0.219)
–0.060
(0.112)
Child care worker wage
–0.040***
(0.013)
–0.036***
(0.012)
–0.036***
(0.013)
–0.036***
(0.012)
Child care worker wage
interacted with Indicator
0.021
(0.030)
Unemployment rate in year
following birth
–0.007**
(0.003)
Unemployment rate
interacted with Indicator
—
—
–0.007**
(0.003)
–0.001
(0.007)
0.010
(0.036)
–0.007**
(0.003)
—
—
–0.006**
(0.003)
–0.008
(0.011)
Pre-birth wage
0.017***
(0.003)
0.017***
(0.003)
0.017***
(0.003)
0.017***
(0.003)
Spouse or partner income
divided by 10,000
–0.011***
(0.003)
–0.011***
(0.003)
–0.011***
(0.003)
–0.011***
(0.003)
Indicator for spouse or partner
0.086***
(0.035)
0.085***
(0.035)
0.055
(0.036)
0.054
(0.036)
Note: The dependent variable is an indicator for returning to work within one year of giving birth to the first child.
Each equation also includes the additional covariates listed in table 3. The reported estimate is the change in
probability of returning to work associated with a one unit change in a given variable, evaluated at the mean of
the characteristics. Columns 1 and 3 present the results allowing for differing sensitivity to child care costs.
There are 1,956 observations. Standard errors are in parentheses. ***Indicates statistically different from 0
at the 1 percent significance level; and ** indicates statistically different from 0 at the 5 percent significance level.
Federal Reserve Bank of Chicago
49
than those estimated by Ribar (1992 and 1995) of 0.68
and 0.53, Kimmel (1993) of 0.58, Powell (1997) of
0.85, and Anderson and Levine (1998) of 0.58, but
larger than the 0.04 calculated by Michalopoulos,
Robins, and Garfinkel (1992).23 Finally, the other
income elasticity of 0.04 is very similar to the estimates of Michalopoulos, Robins, and Garfinkel (1992)
and Ribar (1995), of 0.01 and 0.05, respectively.
Although more education seems to increase the
probability that a woman will return to work after
first birth, this result has several possible interpretations. It may be that women who get more education
do so because they are more committed to the labor
force and thus are more likely to go back to work.
Alternatively, it may be that women with more education are more likely to hold jobs from which they
can take leave as opposed to having to quit and, hence,
they face lower costs of returning to work after birth.24
Finally, this may be reflecting part of the wage effect
due to the high correlation of education with wages
and possible measurement error in the wage variable.
I include the working female role model variable
to capture the idea that women may have different
views about the appropriateness of working when they
have children. Although a woman may view working
when she has a young child differently than when she
has a child aged 14, this is the only role model information available. Across all estimated equations, this
variable has a consistent positive and significant coefficient. One might be concerned that this variable is
reflecting an inter-generational correlation in income
status rather than a role model effect per se. For example, poor women may be more likely to work, and their
children may be more likely to be poor and, hence, also
more likely to work. However, including other family
income should help control for wealth, and the role
model coefficient remains virtually unchanged when
unearned income is excluded.
As for other variables in the model, older women
are more likely to return to work after birth, although
again this may partially be picking up the wage effect.
Contrary to expectations, having a parent or grandparent in the household does not seem to affect the
reemployment rate, suggesting that parents and grandparents may not serve as a major source of child care.
While having a parent or grandparent in the home and
the decision to return to work may be simultaneously
determined, omitting the grandparent indicator does
not change the coefficient estimates significantly.
A better indicator of access to lower cost child care
would be a measure of having relatives in close proximity, but this information is only available for one
year of the NLSY. Finally, at the 10 percent level of
50
significance, African-American women in this sample are more likely than other women to go back to
work, and higher county unemployment rates reduce
the probability that a woman returns to work after
first birth.
Implications of the estimates
Using the table 3 results to explore some of the
implications of the estimates, I simulate the effects of
various factors on the probability of returning to work.
Based on SIPP data, weekly expenditures on child
care for families with a preschool-aged child increased
23 percent from 1986 to 1993. Considering a potential increase in child care subsidization that would
reduce hourly costs by 20 percent, the probability of
returning to work increases by 3 percentage points.
If I assume these results hold for all women of childbearing age, this would lead to an expected increase
in the labor force of 1.8 million workers.25
Next, as women delay child bearing they are more
likely to return to work quickly, holding wages constant. Since wages generally increase over those years
of delayed child bearing, older mothers will have an
additional tendency to return to work quickly due to
the higher opportunity cost of not working. On average the probability of returning to work is 0.78. The
probability of returning for a 24-year-old (the median
age at first birth) earning the average wage of 24-yearold mothers in this sample is 0.77. For a 27-year-old
mother (the seventy-fifth percentile age at first birth
in the sample) earning average wages for a 27-yearold in this sample, the probability increases to 0.83.26
From 1988 to 1991 the proportion of preschoolers being cared for by their fathers rose from 15 percent to 20 percent.27 This number fell back to 16
percent in 1993, according to the most recent census
report.28 As suggested by the Census Bureau, this
temporary rise in the percentage of children being
cared for by their fathers in 1991 may be attributed
to higher unemployment and underemployment of
fathers. This is consistent with the possibility that
worsening employment opportunities for womens
spouses and partners during part of the sample period
encouraged more women to return to work sooner
after childbirth. For a high-wage woman (wage at
the seventy-fifth percentile) with a high level of other
family income (at the seventy-fifth percentile), the
probability of returning to work in the first year is
0.80. If instead she faces low other family income
(in the twenty-fifth percentile), the probability she
returns within a year rises to 0.83.
Finally, from January 1992 to January 1999, t
he unemployment rate in the U.S. dropped from 7.3
percent to 4.3 percent. The probability the average
Economic Perspectives
woman returns to work when the unemployment rate
is 7.3 percent is 0.78. When the unemployment rate
drops to 4.3 percent, the probability of returning to
work rises to 0.80.
These estimates suggest that delayed child bearing
will play a much more important role in increasing
womens labor force participation shortly after childbirth, and, hence, their overall actual work experience
accumulation, than small increases in child care cost
subsidization, the effects of changing employment
opportunities for their spouses and partners, or decreases in the overall unemployment rate. Another
interesting long-term implication of the increased
labor force participation of mothers today is that their
daughters may also be more likely to participate in
the labor force. Thus, we should expect to see continued participation rate increases with new generations
of women entering the labor force.
Conclusion
This article examines the effects of child care
costs, potential wages, and other family income on a
womans decision to return to work shortly following
the birth of her first child. Utility maximization predicts that child care costs and other family income will
have a negative effect on the probability of returning
to work, while potential wages will have a positive
effect. A simple comparison of means of cost, wages,
and other income for returners and non-returners shows
differences as predicted by the model that are significant for the wage measure. Further multivariate analysis confirms these results for wages and indicates
that child care costs and other family income also have
statistically significant effects on the probability of
returning to work. The estimates suggest that the
elasticity of the reemployment rate for new mothers
with respect to child care costs is about 0.23, while
the elasticity with respect to other family income is
about 0.04. Finally, the elasticity with respect to the
mothers wage is about 0.21. Additionally, age and
education, having a spouse or partner, having had a
working female role model, and living in areas with
lower unemployment rates have statistically significant, positive effects on the probability that a woman
returns to work.
As mentioned in the introduction, the results of
this study have implications for evaluating policy.
The results suggest that delayed child bearing may
have a greater impact on increasing labor force participation of women with young children than increases
in wages or decreases in child care costs. Additionally,
while access to reliable child care is likely to be a
necessity for successfully moving mothers from welfare to the labor force, this research shows no evidence
that welfare recipients will be more responsive to
changes in child care costs than other women. Moreover, the overall estimate of responsiveness to changes
in child care costs does not indicate that such changes
will lead to large changes in labor force participation.
Thus, increasing subsidization of child care without
additional programs and incentives is not likely to
have large effects on labor force participation among
the welfare population. Finally, the increased probability of a woman working after childbirth associated
with her female role model having worked suggests
that we should expect to see continuing increases in
the labor force participation rate of women, thus increasing the size of the labor force.
APPENDIX
Theoretical model
I model a womans return-to-work decision as a utility
maximization problem with child care expenditures entering the budget constraint and, hence, affecting the
employment decision. First, I assume a woman makes
her labor force participation decision by maximizing
her utility, taking her husbands labor force participation and income as given.1 Her problem is to maximize:
U ( X , D, L) s...t (a ) px X + pd D ≤ wH + Y
(b) H + L = T
(c ) 0 ≤ H ≤ T , 0 ≤ L ≤ T,
where X is a composite good excluding day care and
leisure, px is the price of X, D is the hours of day care
demanded, pd is the hourly price of day care, H is the
number of hours the woman works, w is the wage
rate, Y is her husbands income plus other unearned
income, T is the total time constraint, and L is the
number of leisure hours.2 In modeling the decision
this way, I am implicitly assuming that maternal and
market child care are good substitutes.
Assuming additionally that H < T and D=H, the
optimization problem can be written,3
2)
ý = U ( X, L) − λ[ px X + (w − pd ) L −
(( w − pd )T + Y )] + δ (T − L),
Federal Reserve Bank of Chicago
51
with the associated conditions:
(a) U1 − λpx = 0,
(b) U 2 − λ (w − pd ) − δ = 0,
(c ) λ[ px X + ( w − pd ) L − (( w − pd )T + Y )] = 0, and
(d ) δ (T − L) = 0,
where λ > 0 is the marginal utility of wealth and δ is
a non-negative slack variable associated with the
womans hours of work decision. From condition
(b), w pd = U2/λ δ/λ. Calling U2/λ the reservation
wage, w*(H), the first-order condition can be rewritten
as w pd = w*(H) δ/λ. If the woman works, δ = 0,
the net wage exceeds the reservation wage evaluated
at H = 0, and hours of work are chosen such that
w pd = w*(H) when H > 0.
For simplicity, I assume a utility function consistent with linear labor supply,
3)
H i = β1 (wi − pdi ) + β 2Yi + Z i β3 + γ i ,
for individual i, where Z is a vector of demographic
characteristics and γ is an error term. The linear labor
supply function restricts the coefficient on the wage
net of child care costs to be the same regardless of
the level of the wage. This is the easiest form to model
empirically; however, given that my measure of cost
is an index of the true cost of child care, I do not impose the additional restriction during estimation that
the coefficients on wages and costs are equal. Substituting the budget constraint into equation 3 and solving for the reservation wage,
wi* (0) = α1Yi + Z iα 2 + µ i ,
where α1 = β2/β1, α2 = β3/β1, and µi= γi/β1. The
probability that a woman works can be represented by
Pr( H i > 0) = Pr ( wi − pdi ) > wi* (0) =
Pr [µ i < (wi − pdi ) − α1Yi − Zi α 2 ].
Thus, higher child care costs and lower wages
decrease the probability that a woman will go back to
work. Assuming that leisure is a normal good, higher
other family income also decreases the probability of
returning to work.
An important consideration is that there may be
unobserved taste shifters that have not been specified
in the model. For example, let τ reflect taste for work
52
and enter the model by affecting the marginal rate of
substitution between leisure and money, that is, let
U = U(X, τ 1L). Condition (b) of equation 2 then
becomes w pd = (τ 1)U2/λ δ/λ, where δ = 0 if a
woman works. The greater the taste for work (the
greater τ), the lower the net wage needed to exceed
(τ 1)U2/λ. Thus, correlations between τ and wages or
child care costs can lead to biased estimates of their
effects on the probability of returning to work.
Data
Child care cost measure
The state average child care worker wage is the
weighted average by state and year of hourly earnings
of all surveyed workers in the 197993 NBER CPS
Annual Earnings File Extracts who report a threedigit occupation code for child care workers, private
households, or for child care workers, except private
households. Hourly earnings are calculated as hourly
earnings where reported and as edited usual weekly
earnings divided by edited usual weekly hours, otherwise. Hourly earnings less than $0.50 and above the
99th percentile for the year are dropped. Weights used
are the earnings weights provided in the CPS data.
NLSY data
The wage and employment data before and after
birth and mothers age at birth come from the NLSY
1994 child file and were constructed or measured in
relation to the birth of the child. The pre-birth wage
is the wage recorded for the fourth quarter before
birth, and the post-birth wage is the wage recorded
for the fourth quarter after birth. All wages are in real
1997 dollars. Wages less than $1 and greater than $160
are recoded to missing. Other variables are from the
youth file and relate to the survey year which may or
may not match up well with the birth year, depending
on the month of birth. For determining the usual residence of the child, I count the child as living with the
mother if his or her usual residence is coded as in the
mothers household either in the survey year of the
birth year or in the survey year after the birth year.
Similarly, a spouse or partner or mothers mother,
grandmother, stepmother, father, grandfather, or stepfather is present if the mother reports so either in the
birth year or in the survey year following the birth
year. Mothers education is the highest grade completed in the survey year of the birth year or the most
recent available record from previous years, since the
variable is missing unless the status has changed from
the previous year. If highest grade completed is
ungraded, it is considered missing.
Economic Perspectives
The unemployment rate data in the youth geographic
data are county unemployment data from the County and
City Data Book. The unemployment rate at birth is measured as the unemployment rate in the birth year, and the
unemployment rate after birth is measured as the unemployment rate in the survey year after the birth year. The
state of residence is the residence reported in the survey
year of the birth year unless the code is missing, in which
case it is the state reported in the survey year following the
birth year. The child care cost variable is then matched by
these state codes.
From 1979 to 1989, respondents were asked for
total income for their partner in the previous year.
After 1989 respondents were asked for partner income
broken down into several categories. Spouse income
for all years is reported broken down into several
categories. Other income for women with partners
from 1979 to 1989 is partner income as reported in
the following survey year. Other income for women
with spouses for all years is calculated as annual
spouse income from wages and salary, plus any farm
or own business income, plus spouse unemployment
compensation, plus respondent or spouse income from
food stamps and other sources. Other income for
women with partners from 1990 to 1993 is calculated
as total partner income from wages and salary, plus
any farm or own business income, plus partners total welfare income. To minimize the loss of observations from missing information, other income is used
as calculated for the year of the birth or the year after
birth. All income is top-coded at $75,001 for 197984
and at $100,001 for 198593. Income is in real 1997
dollars.
The validity of this assumption is certainly debatable, and future
analysis could model the labor supply decisions of a woman and
her spouse/partner as a joint decision.
1
Below, I assume a linear labor supply function. See Stern (1986)
for a discussion of the form of the utility function and the implications of the assumption.
2
I assume that day care is specifically purchased to cover hours
worked and that a womans leisure time includes time she spends
caring for her children. Certainly, women may hire child care
during their leisure hours, but I consider these nonwork child
care hours to be a separate good included in the composite good.
3
NOTES
Shapiro and Mott (1994) provide some evidence that labor force
participation surrounding first birth is an important predictor of a
womans later labor force participation behavior, and hence greater
actual work experience at all points in life.
1
2
Blau and Kahn (1992).
See Nakamura and Nakamura (1992) for a review of some of the
literature analyzing the effect of children on female labor supply
more generally. See Leibowitz and Klerman (1995) for a more
recent paper looking at the effects of children on married mothers
labor supply over time.
3
10
See the appendix for a more formal description of the model.
The NLSY is a nationally representative sample of 12,686 men
and women who were between the ages of 14 and 21 in 1979, including a military sample and an oversample of African-Americans,
Hispanics, and poor non-African-Americans and non-Hispanics.
See Center for Human Resource Research (1989 and 1993) for
more information on the survey.
11
For the 918 women with first births before 1979, there are no
birth year data available.
12
The appendix contains more details of how the dataset is constructed.
13
U.S. Department of Commerce, Bureau of the Census (1992).
Mean expenditures are conditional on making positive child care
payments and have been converted to real 1997 dollars.
4
5
Much of this article is based on Barrow (1999).
While it appears that women with school-aged children have
higher labor force participation rates than men, this is a function
of the difference in the age distribution of all men versus women
with school-aged children. The participation rate for men with
school-aged children is 93 percent (U.S. Department of Labor,
Bureau of Labor Statistics, unpublished data).
U.S. Department of Commerce, Bureau of the Census (1998),
table No. 645. In 1997 the participation rate for women ages 16
to 19 was 51.0 percent, the rate for women 20 to 24 was 72.7
percent, and the rate for women 25 to 34 was 76.0 percent.
14
6
Although a larger percentage of NLSY women return to work
after first birth, the employment patterns are very similar to those
of the National Longitudinal Survey of Young Women presented
in McLaughlin (1982)
15
Pre-birth wage is the best approximation I have of the wage
women actually face when making their return-to-work decision.
Because I am looking at these women over such a short time
frame, I assume that there is minimal wage erosion.
16
7
Weights used are the earnings weights provided in the CPS data.
205 observations were dropped, leaving 20,080 wage observations
for child care workers in 50 states and one district over 15 years.
8
Approximately 95 percent of child care workers in the CPS data
are women.
9
Federal Reserve Bank of Chicago
For continuous variables, this is the change in probability associated with an infinitesimal change in the independent variable,
while for discrete variables it is the change associated with a one
unit change in the independent variable.
17
53
0.778 is the predicted probability of returning to work for a
woman with the characteristics of the average woman in the
sample. The predicted change in probability is calculated at
this mean.
18
Very few observations are affected by the income top-coding,
and including an indicator for the presence of a top-coded income
measure has no important effects on the results; however, women
whose spouse or partner income is top-coded are significantly
less likely to return to work.
19
As noted above, U.S. Department of Commerce, Bureau of the
Census (1992) estimates women with at least one child under age
one spend an average of $88.60 on child care per week and work
an average of 36 hours per week. This $2.46 per hour cost in 1997
dollars is 54 percent of the mean state average child care worker
wage of $4.58 per hour.
20
Elasticities are only available from a subset of the studies for
a subset of the elasticities of interest. In the text I cite all studies
for which an elasticity calculation is available.
Even if mothers age and education at childs birth are omitted
from the estimation, the wage coefficient is never large enough to
generate an elasticity as large as the cited studies.
23
The Family and Medical Leave Act of 1993 became effective
after most of the women in the NLSY sample gave birth to their
first child. This act allows workers at companies with more than
50 employees to take up to 12 weeks of job-protected leave to
care for a child or other immediate family member, lowering the
cost for many women of returning to the labor force after childbirth.
24
I use census population estimates of approximately 60.1 million
women aged 15 to 44 as of April 1, 1999.
25
The probabilities are evaluated at the mean values for all covariates other than the ones being changed for the simulations.
26
27
U.S. Department of Commerce, Bureau of the Census (1994).
28
U.S. Department of Commerce, Bureau of the Census (1996).
21
Elasticities are calculated at the mean employment rate and the
mean average child care worker wage across observations.
22
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Stern, Nicholas, 1986, On the specification of
labour supply functions, in Unemployment, Search,
and Labour Supply, Richard Blundell and Ian Walder
(eds.), New York: Cambridge University Press,
pp. 143189.
U.S. Department of Commerce, Bureau of the
Census, 1998, Statistical Abstract of the United
States: 1998, available on the Internet at www.census.
gov/prod/3/98pubs/98statab/cc98stab.htm.
, 1997, Statistical Abstract of the United
States: 1995, 115th edition, Washington, DC: U.S.
Government Printing Office.
Powell, Lisa M., 1997, The impact of child care
costs on the labour supply of married mothers: Evidence from Canada, Canadian Journal of Economics,
Vol. 30, No. 3, August, pp. 577594.
, 1996, Whos minding our preschoolers? (data from the Survey of Income and Program
Participation), Current Population Reports, Washington, DC: U.S. Government Printing Office, No. P70-53.
Ribar, David C., 1995, A structural model of child
care and the labor supply of married women, Journal
of Labor Economics, Vol. 13, No. 3, July, pp. 558597.
, 1994, Whos minding the kids? Child
care arrangements: Fall 1991 (data from the Survey
of Income and Program Participation), Current
Population Reports, Washington, DC: U.S. Government Printing Office, No. P70-36.
, 1992, Child care and the labor supply
of married women: Reduced form evidence, Journal
of Human Resources, Vol. 27, No. 1, Winter, pp.
134165.
Shapiro, David, and Frank L. Mott, 1994, Longterm employment and earnings of women in relation
to employment behavior surrounding the first birth,
Journal of Human Resources, Vol. 29, No. 2, Spring,
pp. 248275.
Federal Reserve Bank of Chicago
, 1992, Whos minding the kids? Child
care arrangements: Fall 1988 (data from the Survey
of Income and Program Participation), Current
Population Reports, Washington, DC: U.S. Government Printing Office, No. P70-30.
U.S. House of Representatives, 1998, The Green
Book, Washington, DC: U.S. Government Printing
Office.
55
Will a common European monetary policy
have asymmetric effects?
Luigi Guiso, Anil K Kashyap, Fabio Panetta,
and Daniele Terlizzese
Introduction and summary
The launch of the euro has been accompanied by a
vigorous debate. On the one hand, supporters of a
common monetary policy (for example, Lamfalussy,
1997) have argued that the move to a single currency
is necessary to fully exploit the obvious advantages
of a single market. On the other hand, skeptics have
argued that European Union (EU) economies are too
dissimilar to be subjected to a common monetary
policy. Feldstein (1997) goes so far as to predict that
the political tensions created by the common monetary
policy could lead to another European war.
The debate boils down to a disagreement over
how hard it will be to effectively run a common
monetary policy. There are at least three conditions
that must be met for a common policy to succeed
without causing frictions among the members of the
coalition. First, members must agree on the ultimate
goals to be achieved through monetary policy. This
issue was formally settled through the 1992 Maastricht
Treaty and the ensuing ratification process by national
parliaments, leading to the adoption of a goal of price
stability as the primary objective for the European
Central Bank (ECB).
Second, the common policy will be easier to
implement if the member countries business cycles
are aligned. Monetary policy instruments are macroeconomic variables that work across the board and,
therefore, cannot simultaneously be tailored to diverging cyclical conditions in the area of their jurisdiction.
However, if different countries or sizable regions are
at different points in the inflation cycle, then assessing the appropriate monetary policy stance becomes
a much more difficult task. Large countries such as
the U.S. constantly confront this problem, but the
degree of economic integration and the availability
of alternative policy instruments to redistribute the
burden of the adjustment are likely to be poorer in
the euro area than in the U.S.1
56
A third and perhaps more subtle issue is whether
the transmission mechanism operates in a similar fashion across all the countries in the union. In particular,
even if shocks hit all countries equally, their business
cycles are aligned, and there is no disagreement over
whether a response is needed, differences in the transmission mechanism could mean that the appropriate
size and timing of the response are difficult to assess.
Moreover, if the burden of adjustment is not equally
shared across countries, sizable distribution differences
are likely to create political tension.
The issue of how much the transmission mechanism differs across the member states of the monetary
union is just beginning to attract interest. One obvious difficulty with addressing the question is the possibility of a regime switch that could have occurred
with the creation of the ECB. It is possible that all
past evidence on the transmission mechanism is no
longer relevant because beliefs about policy will
now differ.
While we concede that this is possible, we doubt
that this institutional change has brought about behavioral changes in a sharp, discontinuous fashion.
Luigi Guiso is a professor of economics at the Università
di Sassari, Italy, and scientific coordinator at Ente Luigi
Einaudi for monetary, banking, and financial studies. He
is also a research fellow at the Centre for Economic
Policy Research, London. Anil K Kashyap is a professor
of economics at the University of Chicago, Graduate
School of Business, consultant to the Federal Reserve
Bank of Chicago, and research associate at the National
Bureau of Economic Research (NBER). Fabio Panetta
and Daniele Terlizzese are deputy directors of the
Research Department at the Bank of Italy. Mr. Kashyap
thanks the National Science Foundation for a research
grant, administered through the NBER, and the University
of Chicago Graduate School of Business for research
support. The views in this article do not necessarily reflect
those of the Bank of Italy.
Economic Perspectives
There is abundant evidence that people adjust their
behavior gradually. In this case, collecting evidence
on how agents operated in the past regime should
provide some information on how they will behave
in the present one.
Even in the absence of structural breaks, however,
trying to conduct the relevant cross-country aggregate
comparisons in the transmission mechanism is fraught
with difficulties. Research on how to identify the
response of a single economy to monetary disturbances
in a convincing and robust fashion is just becoming
available for some countries. There has been very
little work on doing this for multiple countries using
a common framework. In particular, to study the
effects of how a common monetary policy might
matter, one needs to impose a uniform monetary
policy reaction function across countries and to constrain exchange rate movements.
Our reading of the existing literature is that this
type of study has yet to be done. As a result, we are
left with a set of only partially comparable findings,
which prevents us from drawing any strong conclusions
about the similarities of the transmission mechanisms
across European countries. A full investigation of
this type would be quite valuable but is beyond the
scope of this article.
We believe, however, that the evidence from
studies conducted at the aggregate level should be
supplemented by systematic comparisons at the micro
level. The richness of the information available at the
micro level should allow us to identify differences in
behavior among different groups of agents in the
same country and similar groups of agents in different
countries. This is important because aggregate differences could arise for a variety of reasons. One possibility is that similar firms and individuals in different
countries could behave differently. In this case, one
might believe that as institutional arrangements converge, and the single market is fully realized, the differences could abate. Alternatively, similar firms and
individuals might act similarly, but the mix of these
agents across countries might differ.
Disentangling true behavioral differences from
differences that are the result of compositional effects
is important for several reasons: first, because doing
so is likely to enhance our understanding of the causes
of the differences; second, because this should lead
to a better assessment of the likely persistence of any
differences; and finally, because this might help identify policy actions that could be used to partially alleviate the differences. Of course, a full investigation of
these issues will require several detailed studies. Here,
we take a first step and present a sort of feasibility
Federal Reserve Bank of Chicago
analysis, aimed at assessing whether what appear to
be large structural differences in the economic and
financial structures of the various countries in the
euro area can be expected to lead to differences in
the transmission mechanism.
Our analysis follows three logical steps. First,
we try to identify the types of microeconomic heterogeneity that different theories of monetary transmission
suggest could be important. The goal here is not to
compile any evidence on which of these theories is
most important, but rather to use the union of the theories to guide our selection of which cross-country
data we ought to compare.
Next, we collect a number of indicators available
for multiple countries to demonstrate that, along the
dimensions identified in the previous step, there are
sharp cross-country differences in the underlying
microeconomic landscape of the different EU countries. Theoretically, these firm-level and institutional
differences could alter the aggregate impact of monetary policy.
Finally, having identified many potentially important factors suggestive of differences in the transmission mechanism, we turn to data on one specific
country, Italy, to see what these factors say about business cycle dynamics and the response to monetary
policy shocks in that country. If they were to possess
explanatory power in one country, we would read
this result as corroborating the basic idea that the
structural characteristics of the various economies
are relevant factors in explaining cross-country differences and similarities of the transmission mechanism. While the analysis is still preliminary and does
not go much beyond a descriptive level, our findings
suggest that microeconomic characteristics of Italian
firms do seem to have considerable predictive power
regarding cyclical fluctuations.
Summing up, we draw three main conclusions
from our analysis. First, there are several good reasons
why previous attempts to uncover the likely effects
of the shift to the common monetary policy have been
inconclusive. Second, looking at the micro data from
different countries can help resolve some of the questions left unanswered by the studies that have focused
on aggregate data. Finally, in the Italian recession
that followed Italys exit from the Exchange Rate
Mechanism (ERM) in 1992, a number of suggestive
differences in investment rates and profitability of
different sets of firms emerge, in line with existing
theories. The next step in our research will be to study
these differences further by refining our indicators,
controlling for the correlation among them, and dealing better with endogeneity problems.
57
Prior studies comparing monetary
transmission mechanisms in Europe
A number of recent papers have attempted to
gauge the differences and similarities among the monetary transmission channels of the EU countries. Almost
all these studies rely on aggregate data and analyze the
response to a monetary policy shock displayed by macroeconometric models of the different economies.2
An obvious, preliminary issue is whether anything at all can be learned from the past research.
Indeed, it is certainly possible that the final move to
European Economic and Monetary Union (EMU) is
such a big regime shift that past experience is no longer
a reliable guide.3 However, there is no clear evidence
nor is it likely on a priori groundsthat the regime
shift will lead to a sharp discontinuous break in relations in the economy. As behavior tends to adjust
gradually, past relationships are likely to retain some
of their predictive value for the near term.
Even in the absence of structural breaks, however,
considerable care is required to translate the knowledge
of the (past) differences and similarities among monetary policy transmission mechanisms in the EU
countries into an assessment of the (future) transmission mechanisms of the single monetary policy in
the different countries. The move to a single currency
changes significantly the conditions under which
monetary policy operates, making it difficult to interpret most of the empirical evidence on the past transmission mechanisms.
The ideal study, based on past experience,
which would be informative about differences across
countries in the transmission mechanism of a single
monetary policy, would consider the response of
the various EU economies to the same temporal
sequence of monetary policy shocks, holding fixed the
exchange rate among them. In addition, as stressed
by Dornbusch, Favero, and Giavazzi (1998), in the
ideal circumstances it should also be possible to test
the statistical significance of any difference found in
the transmission mechanism. With this benchmark in
mind, we can survey the existing empirical literature
on the European monetary transmission channel.
Studies based on large-scale
macroeconometric models
The existing literature can roughly be classified
into two main groups, depending on whether the evidence is obtained from models of the various economies that do or do not have a common structure. The
primary findings involving models that do not necessarily have the same structure come from the comprehensive Bank for International Settlements (BIS)
project on the transmission mechanisms in the principal
58
industrialized countries (Bank for International Settlements, 1995). The project simulated the response
of the central banks macroeconometric models to a
common, standardized monetary policy shock (an
increase of the policy rate by 1 percentage point for
two years, with the rate returning to the baseline path
immediately afterwards).
Importantly, the BIS research protocol envisaged
the simulations to be conducted both under unchanged
exchange rates and allowing the exchange rates to react to the move in the interest rate. In the latter case,
two variants were agreed upon: one allowing for an
independent response of each currency and a second
involving a coordinated response of the ERM currencies, with a common pattern of the exchange rate
vis-à-vis the rest of the world. Thus, in principle, the
evidence produced within the BIS study complies with
the two main requirements of the ideal experiment.
Unfortunately, however, not all countries in the
study implemented the protocol in its strict form.
Specifically, the variant corresponding to a coordinated response of the ERM countriesprecisely the
exercise that would have been necessary to comply
with the ideal defined aboveis missing for Germany, Spain, and the UK.4 In addition to this limitation, since the BIS study makes use of traditional
large-scale macroeconometric models, it is subject to
the standard criticisms of those models.
In particular, the sheer size of the models and the
lack of fully articulated and consistent foundations in
optimizing behavior can lead to simulation results that
are difficult to interpret. Moreover, one can argue that
many of the equations in these models would fail statistical tests aimed at assessing their specification.
Similarly, the modeling of the instruments of monetary
policy is often done in an ad hoc way. Collectively,
these problems could distort the picture of how monetary policy operates. Finally, the BIS study does not
allow formal statistical testing of the differences
found, since the models are estimated independently.
Bearing these caveats in mind, the evidence
from the BIS studysummarized for the main euro
area countries in table 15points to some differences,
particularly among large and small countries. In particular, the gross domestic product (GDP) response is
considerably more pronounced in the larger countries.
Among them, Italy exhibits a slightly larger and definitely longer lasting response. A second relevant difference concerns the price response, which is initially
non-negligible only in Italy, the Netherlands, and
Belgium; in Germany, it becomes sizable only after
the first two years, and keeps increasing over the
period; in Austria, the price response is basically nil.
Economic Perspectives
Favero, and Giavazzi, 1998; and Peersman
and Smets, 1998).
Compilation of simulation data from BIS study
The papers using structural vector
First
Second
Peak
Last
autoregressions (SVAR) all try to determine
year
year
effect
year (7th)
how a change to one of the variables being
analyzed influences the other variables
Italy
GDP
–0.18
–0.44
–0.44
–0.12
under consideration (for instance, how
PGDP
–0.13
–0.38
–0.51
0.07
interest rates might influence investment).6
France
GDP
–0.18
–0.36
–0.36
0.05
These papers run into two problems in this
PGDP
–0.04
–0.19
–0.31
–0.21
context. First, the shocks to the models
GDP
–0.15
–0.37
–0.37
0.11
Germanya
typically differ across countries, both in
PGDP
0.03
–0.02
–0.53
–0.53
terms of size and time path. These differNetherls.
GDP
–0.10
–0.18
–0.18
0.02
ences make it impossible to make legitiPGDP
–0.08
–0.36
–0.47
–0.16
mate comparisons among the responses.
Belgium
GDP
–0.03
–0.12
–0.23
0.02b
This problem is exacerbated because most
PGDP
–0.13
–0.51
–0.84
–0.55b
models embody different assumptions
Austria
GDP
–0.08
–0.14
–0.14
0.01
about the way in which the monetary
PGDP
0.02
–0.01
–0.05
0.00
authority responds to new developments
a
German data are not strictly comparable because the exchange rate was
(that is, the endogenous component of
not handled in exactly the same way as for the other countries.
monetary policy). Thus, even on the offb
Fifth year after the shock.
chance that the same initial disturbance is
Note: Responses of real GDP and the GDP deflator (PGDP) to a 100 basis
point increase in the policy rate for two years, followed by return of the rate
analyzed, the monetary policy responses
to the normal level (fixed exchange rate vis-à-vis ERM countries; deviations
would not be harmonized so that a symfrom baseline in percentage points).
Source: Bank for International Settlements (1995).
metric response across countries would
not be expected. Instead, the differences in
the assumed monetary reactions would
generate different economic responses, even if the
Overall, given that the BIS study comes someunderlying structure of the economies were similar.7
what close to satisfying two of the three conditions
characterizing the ideal empirical study, the differThe second problem in the SVAR literature is a
ences identified in this study should be taken seriously.
failure to properly account for the lock-in of the pariMoreover, the model used in the BIS study (central
ties among the currencies in the euro area, which
banks models) represents the insider wisdom of
implies a common response of the exchange rate.
the monetary policy authorities, which is interesting
Indeed, the SVARs often do not include the exchange
in itself. However, the lack of a common structure in
rate; when they do, the shocks are often inferred in
the models raises the question of whether any differdubious manner. For instance, the studies we have
ences one observes are simply an artifact of different
seen always assume that a disturbance to interest rates
and arbitrary modeling choices.
does not simultaneously influence exchange rates (or
vice versa). Such shocks are hard to imagine since
Studies imposing a common structure on the
they imply a free lunch, whereby investors could
models for different countries
move money towards high-interest countries without
The second group of papers studying the transexpecting to see some of the interest rate gains eroded
mission channels in Europe is more heterogeneous.
by changes in exchanges rates. With the shocks having
These studies include evidence from structural vector
been identified in this fashion, it is very likely that
autoregressions (Gerlach and Smets, 1995; Barran,
the so-called monetary policy shock is in fact a
Coudert, and Mojon, 1996; Ehrmann, 1998; Kieler
combination of shocks, including the endogenous
and Saarenheimo, 1998; Ramaswamy and Sloek, 1998;
response to movements of the exchange rate.
and Dedola and Lippi, 1999); from small structural
As a result of these two problems, much of the
models with a common structure (Britton and Whitley,
evidence produced by the SVAR literature is of only
1997); from relatively large multicountry models (the
limited relevance for the issue at hand, as it does not
U.S. Federal Reserve multicountry model in the BIS
appropriately represent the situation that is likely to
study; the models in International Monetary Fund [IMF],
prevail in the monetary union. A vivid example of
1996, and the European Commission; and Roeger and
the difficulties in interpreting the SVAR results is
Int Veld, 1997); and from prediction equations for
the Gerlach and Smets (1995) study, in which the
output, estimated for different countries (Dornbusch,
TABLE 1
Federal Reserve Bank of Chicago
59
responses to both a one standard deviation, one-period shock (reported in table 2 as variant 1), and a 100
basis point, two-year sustained increase of the interest rate (variant 2) are presented. In the first case the
response of GDP looks similar across Germany, France,
and Italy, while in the second case, German output
moves by almost twice as much as that of the other
two major countries of the euro area; in the latter
case the German result is also much more persistent
(although this is masked in the table).
Even taking the SVAR evidence at face value,
the results are often ambiguous.8 While many of the
studies tend to conclude that the differences in the
transmission mechanism are not large, the differences
they identify do not seem to be particularly robust:
As summarized in table 2, different studies present
somewhat different rankings of the potency of monetary policy.
The main regularities that do seem to emerge are
that Germany is almost always the country in which
monetary policy is most powerful, often followed by
France, and that monetary policy is always seen as
being more potent in Germany than in Italy, where
monetary policy appears to have the mildest effect on
output. These conclusions are almost the opposite of
the findings from the aforementioned BIS project.
One potential reconciliation is offered by Kieler and
Saarenheimo (1998), who show the extreme indeterminacy of the SVAR results: A very large set of widely
different responses of output to monetary policy, each
equally supported by the available data, can be produced by varying the assumptions used to identify
shocks. Restricting the identifying assumptions to
those that yield impulse responses bounded within a
sort of window of plausibility (for example, the
initial output and price response to a contractionary
shock should not be too positive) still leaves open a
very wide range of possibilities.
Looking at small structural models and multicountry models, both with essentially the same structure across countries, none of the studies quite comply
with the requirements set out above. In particular, the
common response of the exchange rate has not been
implemented. The evidence extracted from simulations
of these models points to relatively small differences
in the transmission channels across countries. Aside
from the U.S. Federal Reserve multicountry model
(which generates a much stronger initial response for
Germany and France than for Italy), the other models
show little or no difference in the impact on GDP.
Of course, the identifying assumptions that underlie
these models are subject to the same criticisms leveled
at the national macroeconometric models.
Finally, the studies based on prediction equations for output have the advantage of having been
devised precisely to provide the sort of ideal evidence
described above. The estimated equations allow the
path of both the monetary policy shock and the exchange rate to be common across countries, and the
estimation is done jointly so that formal statistical
testing is possible. On the other hand, the ad hoc
nature of these equations limits ones ability to interpret the results, and doubts can be raised about the identification of the monetary policy shock. Dornbusch,
Favero, and Giavazzi (1998) jointly estimate an equation for output growth in each country. The specifications predict output growth in each country as a
function of its past own values and of past and
present values of growth in the other countries, expected and unexpected components of interest rates,9
and the bilateral exchange rates with the dollar and
the deutschemark (DM). The specification of the
TABLE 2
Effect of monetary policy on output, using SVARs
Effect on GDP one year after shock
Study
Ramaswamy and Sloek (1998)
Barron, Coudert and Mojon (1996)
Gerlach and Smets (1995), variant 1
Gerlach and Smets (1995), variant 2
Ehrmann (1998)
Dedola and Lippi (1999)c
Germany
France
Italy
UK
Sweden
Netherlands
Strength of
responses a
–0.6
–0.6
–0.3
–1.0
–0.9
–2.2
–0.4
–0.4
–0.2
–0.5
–0.5
–1.4
–0.5
–0.3
–0.2
–0.5
–0.1
–1.1
–0.5
–0.4
–0.6
–0.7
0.2b
–1.4
–0.3
–0.4
n.a.
n.a.
–0.1
n.a.
–0.6
–0.3
n.a.
n.a.
0.0
n.a.
S<F<I=UK<G=NL
I=NL<F=UK=S<G
I=F<G<UK
I=F<UK<G
NL<I=S<F<G
I<UK=F<G
a
These orderings rank the responses according to their magnitude in each study.
Data are not comparable.
Figures refer to the maximum elasticity to the shock of industrial production.
n.a. indicates data not available.
b
c
60
Economic Perspectives
output equations in Peersman and Smets (1998) is
similar, but they include the German real interest rate
and the differential with the German real rate instead
of the expected and unexpected components of interest rates, and they replace the bilateral exchange rate
against the dollar with the bilateral exchange rate
between Germany and the U.S.; in addition, they
allow no contemporaneous relationships. While the
quantitative results differ in the two papers, they both
point to significant differences in the output responses
of Italy, on one side, and Germany and France, on the
other. In particular, the Italian response is stronger,
a result that is similar to that in the BIS study but
sharply in contrast with the SVAR evidence.10
Summary
The main lesson we draw is that the evidence so
far available is not quite appropriate to assess whether
the single monetary policy will have a differential
impact on the euro area countries. Moreover, the results
are not robust: Methodological differences (such as
which variables are included in the models and how
shocks are identified) change the conclusions quite
substantially. With the relevant exception of the output equations, one regularity is that models with a
similar structure tend to yield small differences in the
transmission mechanisms, whereas models with a
more idiosyncratic structure tend to show larger differences. However, it is unclear whether, on the one
hand, the similarities in the former case are forced by
the choice to impose the same structure on (truly) different economies or whether, on the other hand, the
differences in the latter case result from the choice
of modeling as different economies that are (truly)
similar. It should nonetheless be acknowledged that,
though far from being conclusive, the two pieces of
evidence that most closely comply with the requisites
for the ideal experimentnamely the BIS study
and the output equationsprovide roughly consistent
results and point to noticeable differences in the
transmission mechanisms.
Microeconomic evidence on the structure of
European economies
The ambiguity of the macroeconometric findings
on differences in the transmission mechanism undoubtedly stems, at least in part, from the poor design of
the existing studies. Further work to remedy these
problems should help to substantially clarify matters.
We believe, however, that one additional reason for
the inconclusive findings of these studies is their reliance on aggregate data. Relevant differences in the response to a monetary shock might be observed among
different groups of agents in the same country, similar
Federal Reserve Bank of Chicago
groups of agents in different countries, or both. However, the relative weights of these groups could differ
across countries, in which case aggregation problems
will confound attempts to make sense of the evidence.
Therefore, we propose to supplement the macrolevel analysis with an exploration conducted at the
micro level. Focusing on micro data has two further
advantages. First, by identifying the behavioral responses of sets of agents that have been grouped according to different structural characteristics, this
approach provides the information needed to uncover
the causes of whatever differences might be present
at the macro level. Second, it might help identify
possible policy interventions or natural mutations
which, by altering the microeconomic landscape
in the relevant ways, could lead to more uniform
effects of the common monetary policy.
We consider four different theories of how monetary policy can affect the economy. These theories
identify the characteristics of the various economies
that should determine the potency of monetary policy.
While we recognize that these theories of monetary
transmission share some common featuresfor
instance, most require that prices do not instantly
adjust to changes in monetary conditionswe consider it useful to highlight the differences among the
theories rather than the similarities. Once we have identified the salient characteristics, we can see whether the
member countries of the monetary union differ along
these dimensions.
Theories of monetary policy transmission
The textbook model of monetary transmission
supposes that open market operations matter because,
in the presence of temporarily fixed prices, altering
the mix of money and bonds changes the real value
of the money supply. This leads to a shift in interest
rates to clear the money market and, subsequently, to
changes in spending on interest sensitive items. Since
this mechanism operates in a host of models ranging
from the IS/LM to cash-in-advance or limited-participation models, we refer to it as the conventional
mechanism. We take its central prediction to be that
the potency of monetary policy across countries will
depend on the cross-country variation in the interest
sensitivity of spending (see Kakes, 1999, for further
discussion).
A second theory of monetary transmission builds
on the interest rate mechanism by assuming that financing difficulties can amplify the impact of the initial
change in interest rates. Capital market distortions induce lenders to require collateral before they will make
funds available. Because any interest rate increase
lowers the value of future cash flows, collateral is
61
influenced by open market operations, and this is
assumed to alter the availability of funds and ultimately
spending. We call this the borrower-net-worth mechanism (see Bernanke and Gertler, 1995). We take the
central prediction of this theory to be that debt capacity will depend on borrowers net worth and this will
drive spending.11
A third, and closely related, theory emphasizes
the role of banks. This theory posits that an open
market sale matters because it removes reserves from
the banking system; this in turn impairs banks ability to make loans. For some customers a cut in bank
lending is assumed to translate into reduced spending.
Thus, the theory requires that both banks and bank
customers have financing problems that are exacerbated when a monetary tightening is undertaken
see Stein (1998) for a formal model and Kashyap
and Stein (1997) for a discussion in the context of
the EMU. This channel is really a special case of the
borrower-net-worth channel since it focuses on the
importance of the availability of funds from banks;
to highlight this we call it the bank-lending channel.
We take its central prediction to be that the potency
of monetary policy will depend on the degree to which
banks are able to raise alternative funds to offset reserve
fluctuations and the extent to which consumers and
firms must rely on banks for their financing.
A final mechanism, which has a long history in
discussions of monetary policy transmission, focuses
on the non-price methods of allocating credit. For
instance, Roosa (1951) argued that monetary policy
could be quite potent without moving interest rates
by influencing the availability of credit. The net-worth
and bank-lending mechanisms described above are
special cases of this theory, in that they assume that
contracting difficulties influence credit allocations in
a particular way. Alternative versions of the creditrationing hypothesis would permit factors beyond net
worth and collateral to influence credit availability.
For example, in the seminal Stiglitz and Weiss
paper (1981), equilibria in which credit is rationed
are possible because of asymmetric information between borrowers and lenders that leads to problems
of moral hazard and adverse selection. Williamson
(1987) studies the implications for lending of an imperfect ability to monitor borrowers. He shows that
a rationing equilibrium may exist in which interest
rates are no longer allocative; instead lenders adjust to
shocks by changing the amount of credit they extend.
Working out the precise implications for monetary policy transmission is difficult because the credit
allocations can differ depending on the modeling
assumptions. However, one robust prediction from
62
these models is that credit rationing becomes increasingly likely and widespread in economies with less
efficient legal systems, more opaque borrowers
activities, and weak enforcement of contracts.12 Thus,
we also report data comparing the economies along
these dimensions.
Microeconomic data describing different
economies in Europe
Collectively, these theoretical considerations
suggest a number of structural features that would be
useful to compare across the European economies
that are operating with a common monetary policy
(or, in the case of the UK, are considering joining the
union). Finding comparable data on the relevant indicators for all 11 countries that adopted the euro is quite
difficult, so our preliminary exploration focuses on
seven countries with readily available data.13 The
proxies shown in table 3 are intended to provide some
evidence on the differences in interest sensitivity,
collateral positions, importance and availability of
bank loans, and the costs of contract enforcement.
First, we review the findings for the different indicators. Then we draw some tentative conclusions about
individual countries.
One factor that is common to all the theories is
some form of imperfect price adjustment. If prices
adjust more quickly to monetary impulses in some
countries rather than others then this would lead to
different patterns of output adjustment. Thus, an
obvious starting point for comparisons would be
the degree of price rigidity across countries.
A major problem with this tack is the uncertainty
over how pricing practices may change once prices
in the euro area are quoted in the same units. One
of the benefits often cited by the advocates of the
single currency is that it will increase competitiveness of product markets, which will tend to equalize
prices and price-setting practices across countries.
To the extent this is true it raises questions about
how much faith to put in past evidence on pricing
policiesthis is one case where a sharp change in
behavior seems possible.
Nevertheless, we can probably gain some insight
into the price rigidity issue by looking at labor market frictions. Labor costs account for a major portion
of total costs and it is generally agreed that legislation governing the hiring and firing of workers in
Europe makes wages relatively rigid. Moreover, the
move to a single currency will not directly (or immediately) change the contractual framework governing
the labor market. Thus, we report data on labor markets
as a first measure of structural differences.
Economic Perspectives
The first row in table 3 shows summary information on employment protection legislation in different countries. Taken from the June 1999
Organization for Economic Cooperation and Development (OECD) Employment Outlook, the data represent a weighted average of indicators pertaining to
TABLE 3
Selected characteristics for European countries
Country
Variable
UK
Employment protectiona
(rank, 26 OECD countries)
Germany
Italy
France
Spain
Netherlands
Belgium
0.9
(2)
2.6
(20)
3.4
(23)
2.8
(21)
3.1
(22)
2.2
(13)
2.5
(16)
Capital output ratiob
(Investment/GDP)
1.99
(0.154)
4.0
(0.223)
3.2
(0.180)
3.0
(0.191)
n.a.
(0.212)
n.a.
(0.197)
3.0
(0.181)
Fraction of financing
that is short termc
0.960
0.593
0.838
0.893
0.925
0.620
0.882
0.47
0.44
0.45
0.38
0.29
0.25
0.29
63.1
(60.5)
52.0
(61.0)
52.3
(62.5)
46.3
(49.1)
53.5
(56.4)
43.9
(63.7)
51.4
(58.4)
1,128
406
251
357
267
205
363
1.020
0.779
0.314
0.510
0.580
0.649
0.415
12
15
48
11
36
2.5
24
4.75
6
19
15
10
11
19.5
22.5
14.5
2.9
4
1.5
14.3
0
Market capitalization
relative to GDP k
1.65
0.48
0.46
0.65
0.69
1.53
0.94
Average bank size,
billions of dollarsl
24.9
12.8
12.3
20.1
10.2
32.1
22.3
% of total deposits in
5 largest banksm
27.0
14.0
40.4
68.8
39.8
81.3
61.0
Exports outside EU-15
relative to GDP d
Firms’ leverage
(median) %e
Median number of
employees per firmf
Household indebtness
Months to repossess
g
h
Repossession cost
as % of house valuei
% of firms with single bank
j
a
OECD (1999b), summary indicators of strictness of employment protection, table 2.5.
Stock of capital at current prices divided by value added at current prices in 1996. The stock of capital is computed by
the perpetual inventory method from OECD, 1999; the investment to GDP ratio is calculated from the IMF’s International
Financial Statistics, using the reported data on gross investment and GDP, in current dollars, averaged from 1992 to 1996.
c
Ratio of current liabilities to total liabilities minus equity in 1996 from Enria (1999).
d
Openness of EMU members from Favero and Giavazzi (1999)
e
Firms’ leverage is total debt divided by total debt plus net capital in 1996 using the sample of firms from Amadeus
from Enria (1999).
f
Median of the mean of industry-level employment built by Kumar, Rajan, and Zingales (1999) using raw data from Eurostat.
g
1994 total household liabilities as a fraction of disposable income from BIS (1995).
h
Number of months (as of 1990) necessary to repossess collateral in case of default on a mortgage from European
Mortgage Federation.
i
Legal costs to repossess collateral in case of default on a mortgage as a percentage of the value of the house in 1990
from European Mor tgage Federation.
j
Share of firms entertaining only one bank relation from Ongena and Smith (2000).
k
Market value of firms listed on major exchanges as of year-end 1998 divided by GDP from Federation of European
Stock Exchanges Annual Repor t, with GDP data from the OECD.
l
IBCA Bankscope database for European banks; figures pertain to total assets as of 1997 year-end.
m
Share of deposits of five biggest credit institutions in 1996 from European Central Bank (1999).
n.a. indicates data not available.
b
Federal Reserve Bank of Chicago
63
regular labor contracts, temporary contracts, and collective dismissals. The levels of these averages therefore
have no direct economic interpretation, but the rankings
for the main 26 OECD members are informative.
The data confirm the well-known finding that
labor market institutions in the UK are much more
flexible than in the rest of Europe. The amount of
employment protection in the other countries (except
possibly in the Netherlands) is fairly similar. If one
believes that labor market frictions are going to be a
key determinant of future cross-country differences
in wage and price flexibility, it would appear that the
differences among the continental economies will not
be too large.14
Turning to the specific theories, trying to find
evidence on interest sensitivity of spending one runs
into many of the same econometric difficulties discussed in the last section. In particular, determining
whether results are driven by ad hoc specification
choices or true behavioral differences is not easy.
Therefore, the evidence we provide should only be
considered a first pass at the issue. We try, however,
to assess the robustness of any inferences that we might
draw by providing several indicators that should be
closely related to interest sensitivity.
One measure we consider is the ratio of fixed
capital to output. Countries with high levels of capital
to output will (assuming they are close to a long-run
desired level) have higher investment requirements.
We expect that interest rate changes should matter
more in high-investment countries. Looking at the
data in the table we find three groups of countries:
Germany, which has a very high level of capital; the
UK, which has a relatively low level; and the remaining
countries, which lie in between (although they are
closer to Germany than to the UK). The numbers in
parentheses below the capital-to-output ratio are average levels of investment to GDP between 1992 and
1996 from national income account data. These numbers essentially confirm that the British and German
differences are not due to the vagaries involved in
estimating the stock of capital. By this metric, monetary policy should have strong output effects in
Germany, while it should have much more modest
effects in the UK. The other countries, except possibly
the Netherlands, should be in between.
As a second indicator, we look at data on the
maturity structure of debt. Countries with mostly shortterm debt can expect changes in interest rates to affect
borrowing costs more rapidly than countries with
mostly long-term debt. The data again show that
Germany and the UK are the two polar cases, although
the ranking of monetary policy potency is reversed,
64
with German firms having much more long-term debt
than British firms.15 Aside from the Netherlands, which
also has a relatively low fraction of short-term debt,
most of the other countries debt-maturity structures
are closer to the UK than to Germany.
The negative correlation between the debt maturity
and the capital-to-output ratio is not too surprising. If
there are any frictions in borrowing and lending, then it
may be desirable to match the maturity of any debt to
the life of the asset. Therefore, it makes sense that in
Germany, with its higher level of fixed (long-term)
assets, the fraction of long-term debt is also higher.
A slight extension of the conventional model
would allow interest rates to be important because of
their impact on exchange rates. With a single monetary policy this channel no longer directly matters for
trade within the euro area. However, it will retain its
relevance if there are differences in trading patterns
with countries outside the euro area. Data on the ratio
of exports to GDP outside of the 15 countries in the
EU are reported in table 3. It appears that the four
large countries are much more likely to trade outside
of the EU than the smaller countries. This pattern is
probably going to persist and should mean that, all
else equal, monetary policy should have more potency
in the larger countries than in the smaller countries.16
The net-worth channel suggests that we look for
differences in collateral levels. We consider three
proxies. One measure is the leverage of firmsin particular, the ratio of debt to debt plus equity. The data in
the table show that there is relatively little variation
across countries in this dimension. Except for France,
the median firm has a leverage ratio of between 0.56
and 0.64. The French firms have less debt, and one
possible interpretation of this observation is that they
have more borrowing capacity. Alternatively, the lack
of debt may reflect problems with contract enforcement; we discuss this interpretation below.
The data on leverage are for a sample of large
firms, including those listed on public stock markets.
It is quite plausible that borrowing frictions are more
important for smaller, non-publicly traded companies.
Therefore, we also report data from Kumar, Rajan,
and Zingales (1999) on firm size (in which firms are
weighted according to the total employment in enterprises of a given size).17 In terms of the size of the
median firm, there are three groups of countries.
The typical UK enterprise is much larger than those
found on the Continent. The Italian, Dutch, and
Spanish firms are relatively small, while the remaining
countries have middle-sized firms. These figures suggest that collateral considerations should be strong
in Italy, the Netherlands, and Spain and much weaker
in the UK.
Economic Perspectives
The last of the proxies we consider is household
debt levels, more specifically the ratio of household
liabilities to disposable income. Once again, the UK
stands out, with borrowing levels far exceeding those
found elsewhere. Italy stands out as the country with
the lowest household borrowing, although Belgium
also shows quite low levels.
One possible interpretation of these data is that
Italian and Belgian households should at least be able
to borrow to make up any income shortfalls. But the
alternative interpretation is that households in these
countries are less willing to borrow. Past research
analyzing cross-country savings patterns, however,
favors the former interpretation (Jappelli and Pagano,
1989, and Guiso, Jappelli, and Terlizzese, 1994).
Furthermore, two proxies related to contract
enforcement suggest these patterns reflect differences
in the efficiency of credit markets, rather than differences in households willingness to borrow. One of
these indicators is the number of months needed to
repossess collateral in the event of a default. The
second measure is the estimated legal costs of repossessing a house in the event of a mortgage default
(expressed as a percentage of the value of the house).
Both variables suggest that enforcement costs are high
in Italy and low in the UK.
Thus, one would expect much less mortgage
debt in Italy than in the UK and, hence, much lower
overall borrowing. These considerations lead us to
interpret the debt data as a measure of the depth of
local capital markets. On the one hand, the Italians
are less able than the British to smooth out shocks to
consumption, since their capital markets are not as
well developed and will not be able to rely as much
on borrowing. On the other hand, being less leveraged
than the British, the Italians are less vulnerable to
shocks to interest rates.
Belgium and, to some extent, Spain also appear
to be countries where contract enforcement is relatively costly. Interestingly, the Belgian, Italian, and
Spanish legal systems are all derived from the French
legal system. As La Porta, Lopez-de-Silanes, Shleifer,
and Vishny (1997) note, creditors rights to reorganize
or liquidate firms are relatively weak in the French
system. In contrast, Germany appears to be relatively
efficient by these measureswhich also accords with
La Porta et al.s findings. This suggests that credit
rationing is more likely to occur in Belgium, Italy,
and Spain than in Germany or the UK. However, as
mentioned earlier, this could strengthen or weaken
the impact of monetary policy.18
Finally, as proxies for the bank lending channel
we report several measures of bank loan demand and
Federal Reserve Bank of Chicago
loan supply (see Cecchetti, 1999, for further data).
Our data show that in all the countries, it is typical
for large firms to have several banks. This should
help insulate them from a credit crunch that might
result if an individual bank gets into trouble. Smaller
firms appear to be more likely to rely on a single bank,
although, to the best of our knowledge, it is not possible to get comparable data for small firms. Therefore, the previously described data on the variation in
average firm size will be relevant for the lending
channel too. From the lending channel perspective,
this suggests that the reliance on bank funding is
likely to be highest in Belgium, Italy, and Spain and
lowest in the UK.
A second indicator of the importance of banks
for the funding of businesses is the size of the capital
market. Judging by the ratio of the value of shares
traded on the major public stock exchanges to GDP,
there is striking variation in the depth of capital markets across countries. Particularly in the UK, but also
in the Netherlands, there are many huge publicly
traded companies. These companies almost always
have access to some types of nonbank finance. In
contrast, in Germany and Italy the stock market capitalization is relatively low, a feature supporting the
commonly held view that the banks dominate the
financial system in these countries.
In terms of bank loan supply, Kashyap and Stein
(1999) find that in the U.S. smaller banks lending is
more closely tied to monetary policy than that of
large banks. This suggests that shifts in bank loan
supply are more likely if a countrys banking system
consists mostly of small rather than large banks. One
way to make this comparison is to look at differences
in the absolute size of banks in the different countries.
Table 3 shows the average size of the banks in the
IBCA Bankscope database for European banks in
each country in 1997. This database provides information on the largest banks in each country, covering
institutions that grant between 80 percent and 90 percent of domestic credit. By this yardstick the Belgian,
British, Dutch, and French banks are best positioned
to insulate borrowers from changes in credit availability; the German, Italian, and Spanish banks are
relatively small and therefore may not be so well
able to guarantee funding for their clients.
The data in table 3 also show the share of total
banking deposits in the top five banks. Focusing on
concentration may be appropriate if one believes that
the lack of integration of the banking markets is likely
to persist, and if the largest banks in each country are
expected to be able to attract funds during a credit
squeeze, even if some of the banks may not be large
65
in an absolute sense. Interestingly, except for the UK,
this size measure suggests the same classification of
countries as implied by the absolute measure of size;
in the UK the many nonbanking financing options
and the large absolute size of the leading British banks
lead us to suspect that shifts in bank loan supply
would be relatively less important.
Summary
Obviously, the data in table 3 are open to multiple
interpretations, and the connections between some of
our proxies and the ideal variables suggested by theory
are sometimes loose, but we feel that several general
conclusions are warranted. First, there do seem to be
fairly strong differences across the countries in several respects. Moreover, the indicators do not seem
likely to change quickly. Therefore, if these features
do matter for monetary transmission, it seems likely
that the differences will be in place for several years.
The Italian economy appears to be one in which
several of the theories would predict a strong effect
of monetary policy on the economy. In relative terms,
Italy has a fairly high fixed-capital stock, poor contractual enforcement, lots of small firms, rigid labor
markets, and many small banks operating within a
financial system that has been bank-dominated. All
of these factors suggest comparatively strong effects
of monetary policy.
The UK looks to be almost the opposite of the
Italian case. There is relatively little fixed capital, good
contract enforcement, very flexible labor markets, and
many large firms with genuine alternatives to nonbank
financing. The only common feature between the two
countries is that they both do a significant amount of
trading with non-European countries.
Most of the other countries sit in the middle, with
characteristics that, according to which theory of
monetary transmission one considers, indicate stronger
or weaker effects of monetary policy. For instance, in
Germany firms are relatively large and contract enforcement is pretty good, which should help to insulate firms from monetary policy. However, Germany
also has a high level of investment, fairly rigid labor
markets, and exports a significant amount of goods
to countries outside of Europe. France has more large
banks and a more developed stock market than
Germany, but corporate leverage and household
borrowing in France are much lower, and it is fairly
costly to repossess collateral.
Cross-firm differences in cyclical
performance in Italy
Ultimately, it will take a number of studies and a
considerable amount of work to determine which of
66
the factors identified above are most important for
the transmission of monetary policy. As a first step,
with the intent of providing a sort of benchmark and,
at the same time, assessing whether the characteristics highlighted above do indeed matter, we explore
how firms that differ along those dimensions have
fared in the wake of a monetary tightening. We focus
on the one country, Italy, in which a priori we are
most likely to observe strong effects of monetary
policy. We believe that subsequent work can try to
narrow the alternatives and, more importantly, pinpoint whether the factors that may have been significant in Italy are also relevant in other countries.
Macroeconomic conditions in Italy in the 1990s
Before we investigate the microeconomic evidence
in Italy it is necessary to describe the macroeconomic
environment. Table 4 shows a set of macroeconomic
indicators for 198997, the period for which we have
good firm-level data. The period is marked by considerable volatility, much of which is attributable to the
developments leading up to the adoption of a common
monetary policy. The year 1992 was a watershed year.
Growth in the three preceding years had been relatively rapid, although the economy was gradually
slowing down. While the primary deficit had improved, the overall deficit was still around 10 percent
of GDP. In 1991 the total deficit deteriorated slightly
and reached 10.8 percent in 1992. This situation put
downward pressure on the exchange rate (which was
fixed as part of the ERM).
Over the next year a number of policy changes
aimed to help ease the pressure on the lira. In July
the government adopted a 30,000 billion lire (about
2 percent of 1992 GDP) fiscal tightening, which ultimately proved to be insufficient to ease pressure on
the exchange rate. In September, the government
decided to abandon the attempt to maintain parity
with the DM and the exchange rate started floating
freely: It jumped from 756 lire to the DM in August
to 806 lire in September and 882 in October, a devaluation of 15 percent from the previous central parity.
From then on the exchange rate continued to fall,
though the devaluation had, overall, relatively small
effects on the price level.
To stabilize the exchange rate, interest rates were
sharply increased and (perhaps more importantly) a
second, remarkably large set of fiscal measures were
announced at the end of 1992. Collectively, these
changes reduced spending by approximately 92,000
billion lire (6 percent of GDP). The fiscal adjustment
marked a clear break: In 1993 the primary deficit
climbed to 2.6 percent of GDP. This was also a year
of deep recession, with industrial production falling
Economic Perspectives
TABLE 4
Macroeconomic conditions in Italy, 1989 to 1997
Variable
Lira/DM exchange rate
(% depreciation)
Real GDP growth, %
1989
1990
1991
Full year
1992
October
1992
1993
1994
1995
1996
729.7
(–1.54)
741.6
(1.63)
747.7
(0.82)
790.0
(5.67)
881.92
(16.1)
950.7
(20.33)
994.7
(4.63)
1,138.0
(14.41)
1,026.3
(–9.82)
982.2
(–4.29)
1997
2.9
2.2
1.1
0.6
n.a.
–1.2
2.2
2.9
0.7
1.5
3-month Treasury
bill rate, %
12.65
12.28
12.66
14.48
15.51
10.47
8.84
10.73
8.61
6.40
Domestic credit
growth, %
14.85
13.14
12.67
11.71
11.75
7.60
6.22
5.10
4.68
4.21
Government primary
deficit/GDP, %
1.1
1.7
–0.1
–1.9
n.a.
–2.6
–1.8
–3.9
–4.5
–6.7
Total government
deficit/GDP, %
9.8
11.1
10.1
9.6
n.a.
9.5
9.2
7.7
6.6
2.7
Notes: The exchange rate devaluation in October 1992 is with respect to the exchange rate in August 1992.
Credit growth for October 1992 is relative to October 1991. n.a. indicates not applicable.
Sources: Bank of Italy, 1997 and 1992, Annual Report.
by 2.4 percent and GDP down 1.2 percent. However,
recovery began quickly; in 1994 industrial production
increased by 5.2 percent and GDP by 2.2 percent.
Due to the combination of the sharp devaluation
(which greatly benefited export-oriented firms) and
the tight fiscal policy (which heavily affected firms
with a domestic market), the recession and the subsequent recovery were unevenly distributed. This is relevant in interpreting some of the latter results. As table
4 makes clear, 1993 also saw a marked slowdown in
credit availability. Total credit to the economy grew
by 7.6 percent, almost two-thirds its growth rate in
the previous year. Though this slowdown can partly
be explained by a reduction in demand, it is likely
that access to credit became more difficult.19 The
recovery continued in 1995, while at the same time
the exchange rate depreciated sharply. As the dollar
tumbled in the wake of the Mexican crisis, and concerns arose over the domestic political situation, the
lira depreciated sharply in February and March. Interest rates were then increased temporarily. The two
subsequent years saw a marked slowdown followed
by a mild recovery. At the same time, under pressure
to fulfill the Maastricht criteria for admission to the
monetary union, the government tried to speed up
Italys fiscal adjustment and, in 1997, the primary
surplus reached 6.7 percent of GDP, allowing a total
deficit of 2.7 percent.
Firm-level comparisons over the last
ten years in Italy
To further examine the potential importance of
microeconomic heterogeneity in the monetary
Federal Reserve Bank of Chicago
transmission mechanism we report some simple diagnostics about investment and profitability for different sets of Italian firms. On the one hand, this task
is complicated by the odd mixture of shocks, described
above, that have hit the Italian economy since 1992.
On the other hand, the shocks were very large and,
therefore, have the potential to yield some clearly
visible results. Ultimately, much more work will be
needed to carefully identify and quantify these disturbances and to keep track of their impact on firms
performances. In the meantime, we hope that these
exploratory tabulations may provide some guidance
about which contrasts deserve further investigation.
The data that we analyze are drawn from the
Italian Company Accounts Database, a large dataset
collecting balance sheet information and other items
on a sample of over 30,000 Italian firms. The data,
available since 1982, are collected by Centrale dei
Bilanci, an organization established in the early 1980s
jointly by the Bank of Italy, the Association of Italian
Banks, and a pool of leading banks to gather and
share information on borrowers. Besides reporting
balance-sheet items, the database contains detailed
information on firm demographics (including year
of foundation, location, type of organization, ownership status, structure of control, and group membership), employment, and flow of funds. It also reports a
firms credit score, computed directly at the Centrale
dei Bilanci to help banks in screening borrowers.
Balance sheets for the banks major clients (defined
according to the level of their borrowing) are collected
by the banks.
67
The focus on the level of borrowing skews the
sample toward larger firms (which also means that
trade and service sector firms are underrepresented,
while manufacturing firms are overrepresented). Furthermore, because most of the leading banks are in
the northern part of the country, the sample has more
firms headquartered in the North than in the South.
Finally, since banks are most interested in firms that
are creditworthy, firms in default are not in the dataset,
so the sample is also tilted towards higher than average
quality borrowers. Despite these biases, the sample
still has much broader coverage than most datasets
analyzed by economists since it includes thousands
of unlisted companies and many very small firms
for example, the median firm in the sample in the
early 1990s had only 26 employees.
The first panel in table 5 shows the evolution of
investment and return on assets (ROA) for the median
firm in the full sample. The major macroeconomic
developments described in the last section are clearly
reflected in this Company Accounts Database. In particular, profitability and investment were highest in the
late 1980s and early 1990s. The 1993 recession also
is easy to spot, as investment plunged and profitability
sagged. By the end of the period investment had recovered, although profitability remained depressed.
However, the data for the median firm mask some
stark differences across segments of the economy. The
size panel in the table contrasts small firms (defined
as having fewer than 50 employees) and large firms
(more than 500 employees). Small firms generally
have higher profit rates, as measured by return on
assets (ROA), than large firmsthis is not surprising
given the larger failure rates of such firms. The smaller
firms also have a lower investment rate, partly because
these firms are less likely to be in capital-intensive
industries.
For our purposes, however, the differences around
the 1993 recession are most relevant. For large firms
the recession was rather mild; the investment rate fell
by about 20 percent and profitability dipped slightly.
For small firms the declines were much steeper: Investment dropped by more than 40 percent and ROA also
declined by more than 1 percentage point. As late as
1996, small firms ROA had not returned to the 1992
level, whereas large firms profitability had recovered
by 1995. Thus, it appears that smaller firms fared
worse than larger firms in this episode.
The export propensity panel of the table compares firms based on their exports as a fraction of their
sales. Interestingly, prior to 1992 there was virtually
no difference in profitability (ROA) between the
high export sensitivity firms (whose exports account
68
for more than 30 percent of sales) and the low export
sensitivity firmsalthough the investment rates were
higher for the high-export firms. The two groups,
however, fared quite differently during the recession.
For the typical low-export firm, investment virtually
ceased in 1993 and was down nearly 25 percent in
1994; profits also dropped sharply. For the 10 percent
of firms that were heavily export-oriented, profits
were unchanged and investment dropped a bit but
had fully recovered by 1994.
Given the large devaluation it is not too surprising
that the exporters outperformed the domestic sellers,
but we find the magnitude of these differences surprising. We explore these differences further below.
Note that the strong exchange rate effects reinforce
the concerns raised earlier about the importance of
properly accounting for the impact of the single currency on the exchange rate when studying the transmission mechanism.
Another obvious contrast to consider is the degree
to which firms are dependent on banks for their funding. The interest rate spike in the fall of 1992 and the
subsequent recession severely affected the strength
of Italian banks balance sheets. For instance, the
percentage of nonperforming loans rose from about
14.6 percent in 1992 to 22.5 percent in 1993 and then
peaked at 31.1 percent in 1994, before dropping back
to pre-crisis levels. Given the degree of the banking
problems and the usual lending channel considerations,
studying borrowers bank dependence seems particularly appropriate.
Unfortunately, the institutional arrangements in
Italy make developing a measure of bank dependence
difficult. The standard approach in most studies is to
compare firms that have access to public capital markets (for example, firms that are listed on a stock
exchange or have publicly traded bonds) with firms
that have little or no access. However, the underdevelopment of Italian capital markets means that essentially all firms have been bank dependent (for
example, less than 0.5 percent of the firms in the
sample are listed and these firms account for less
than 8 percent of total sales in the sample). Thus, any
measure of the amount of bank borrowing scaled by
firm size tends to uncover relatively profitable and
creditworthy firms rather than high-risk firms that are
extremely reliant on banks. One challenge for further
work on monetary transmission in Italy and other
countries with underdeveloped capital markets will be
to find better proxies to study bank dependence.20
The bank-dependence indicator we use in this
study is whether a firm belongs to a corporate group.
These alliances are quite important in Italy. Our
Economic Perspectives
working definition of a group member is whether the
firm reports that it is controlled by a holding company.
The holding companies for these groups typically
have access to reliable funding through large banks
and the capital markets, and operate an internal capital market for their group members. For instance,
Bianco et al. (1999) find that member firms investment
is less sensitive to cash flow than that of nonmember
TABLE 5
Investment and profitability for different sets of Italian firms
(data for median firm)
Category
All firms
Size
Small
Large
Export
propensity
High
Low
Group
membership
Nonmember
Member
Interest
coverage
High
Low
Interest
sensitivity
High
Low
Location
North
South
1988
1989
1990
1991
1992
1993
1994
1995
1996
NF
I/A
ROA
34,379
2.29
8.52
36,009
2.02
8.52
37,436
2.05
8.14
37,326
2.07
7.35
36,883
1.77
7.39
39,280
1.12
6.44
42,814
1.43
6.22
34,772
2.00
7.18
32,114
2.30
6.53
NF
I/A
ROA
23,933
1.62
8.62
25,330
1.37
8.72
26,312
1.55
8.38
26,023
1.59
7.60
25,618
1.35
7.67
27,178
0.77
6.60
29,828
1.06
6.33
21,421
1.41
7.27
18,849
1.66
6.63
NF
I/A
ROA
780
4.83
7.88
804
4.74
7.36
797
4.18
6.53
842
3.85
6.16
793
3.43
6.00
782
2.79
5.76
780
2.84
5.58
798
3.74
6.43
847
3.72
6.17
NF
I/A
ROA
3,656
3.98
8.49
4,153
2.82
8.49
4,363
2.63
8.03
4,243
2.73
7.37
3,608
2.40
7.59
3,438
2.33
7.63
4,259
2.75
7.50
4,380
3.83
8.95
4,744
3.41
7.32
NF
I/A
ROA
30,723
2.20
8.53
31,856
1.91
8.52
33,073
1.98
8.15
33,083
1.99
7.35
33,275
1.70
7.37
35,842
0.10
6.31
38,555
1.30
6.05
30,392
1.75
6.92
27,370
2.10
6.39
NF
I/A
ROA
6,764
3.14
9.39
7,753
2.62
9.16
8,633
2.58
8.78
9,091
2.53
7.86
9,762
2.09
7.88
10,616
1.62
6.91
11,131
2.06
6.69
8,930
2.84
7.90
8,415
2.79
6.95
NF
I/A
ROA
5,184
3.15
8.36
5,683
3.03
8.21
6,344
2.75
7.48
6,732
2.53
6.69
7,134
2.17
6.54
7,906
1.53
5.74
8,383
1.76
5.78
8,003
2.20
6.76
7,385
2.36
6.25
NF
I/A
ROA
28,701
2.59
9.41
29,585
2.31
9.41
29,641
2.36
9.08
28,451
2.40
8.41
27,032
2.12
8.60
29,156
1.37
7.53
34,141
1.72
7.08
27,910
2.43
8.18
25,765
2.67
7.43
NF
I/A
ROA
5,678
1.02
2.04
6,424
0.99
2.63
7,795
1.04
2.53
8,875
1.19
1.85
9,851
0.97
2.17
10,124
0.57
1.42
8,673
0.57
1.01
6,862
0.71
1.70
6,349
1.08
1.79
NF
I/A
ROA
10,189
2.51
8.54
10,653
2.32
8.72
11,118
2.28
8.38
11,092
2.23
7.61
11,046
1.76
7.48
11,140
1.30
6.50
11,643
1.52
6.10
8,826
2.31
7.11
7,831
2.58
6.54
NF
I/A
ROA
11,070
3.11
8.19
11,459
2.74
8.05
11,894
2.72
7.70
11,823
2.69
7.07
11,598
2.35
7.14
12,170
1.51
6.41
13,440
1.90
6.15
11,168
2.37
6.84
10,358
2.71
6.22
NF
I/A
ROA
23,247
2.57
8.85
24,279
2.32
8.72
24,931
2.36
8.23
24,828
2.29
7.34
24,801
1.94
7.34
26,465
1.27
6.54
29,254
1.58
6.38
24,486
2.27
7.54
22,988
2.50
6.70
4,590
1.57
4,958
1.24
5,193
1.42
5,120
1.57
4,943
1.19
5,212
0.63
5,567
0.94
4,203
1.21
3,648
1.70
7.47
7.66
7.25
6.72
6.78
5.36
5.03
5.28
5.53
NF
I/A
ROA
Notes: I/A is investment in fixed capital during the year divided by year-end assets; ROA is return on assets;
and NF is the number of firms. Sample splits are defined in the text.
Source: Authors’ calculations based on data from the Italian Company Accounts Database.
Federal Reserve Bank of Chicago
69
firms. Thus, group membership may be an indirect
proxy for firms that are not susceptible to a bank
credit crunch. Conversely, the firms that classify
themselves as independent are likely to be highly
reliant on bank financing.
The group membership panel in table 5 compares member firms with nonmember firms.21 In terms
of investment, the typical member and nonmember
firms are almost identical until 1993; only in the last
three years of the sample do any differences appear
and in these years the member firms invest less. The
member firms also show consistently lower ROA than
the nonmember firms. However, it does not appear
that the member/nonmember distinction explains
very much of the movement in ROA around the 1993
recession. For both sets of firms, ROA drops (by fairly
similar percentages) and recovers by 1995. Overall, it
does not appear that splitting the sample based on
group membership is very informative.
One reading of the borrower-net-worth theory is
that balance-sheet conditions should determine the
cyclical sensitivity of different firms. We separated
the firms whose required interest payments exceed
their operating income (and operating income is positive)the most extreme evidence of an impaired
financial condition.22 When we compare them with
the remaining firms, the distressed firms show low
levels of investment and ROAundoubtedly these
firms have some real problems with operating efficiency beyond their financial troubles. The recession was
particularly harsh for the firms that had interest coverage problems. Investment dropped by more than
40 percent, while profitability was down by more
than one-third. Certainly, this is consistent with the
predictions of the net-worth models, but these firms
having been hit by real shocks (perhaps the same
ones driving the business cycle) might also be a plausible explanation.
According to the traditional theory of monetary
transmission, interest sensitivity is the key indicator
of which firms will adjust the most during a monetary
tightening. As a crude proxy for interest sensitivity,
we sort firms according to their industry. We classify
firms in the construction sector or that produce capital goods, durable goods, and intermediate goods
used in the production of investment goods as highly
interest sensitive. The low interest sensitivity firms
produce nondurable consumption goods or intermediate goods needed for nondurable consumption goods.
We exclude agricultural firms, service sector firms, utilities, and other firms for which we could not make a
clear classification based on their industrial code.
70
The interest sensitivity panel in table 5 shows
investment and profitability for these firms. There do
not appear to be noticeable differences for these two
sets of firms around the recession. For both types of
producers, investment and ROA drop noticeably in
1993. In percentage terms, the drop in investment is
larger for the low-sensitivity firms, but the opposite is
true for ROA. Furthermore, in the next year investment recovers more for the nondurables producers,
while the ROA drop is again bigger for the durable
good producers. By 1996, investment for both sets of
firms had moved back to early 1990s levels. Overall,
we see no clear pattern to the changes for these firms.
The location panel of table 5 compares firms
based on whether their headquarters are in the northern or southern part of the country.23 The southern
firms are generally considered to operate in an environment that is less conducive to efficiency, are more
generally dependent on government subsidies, and
are typically less export-oriented. We would expect
the combination of the fiscal contraction and high
interest rates during the recession to have a more
potent effect in the South than the North. The data
confirm our conjectures. The southern firms begin
with lower ROA and a lower investment rate, and
show extreme drops in investment and profitability
in 1993. The ROA for the southern firms remains
low through 1996.
While these simple comparisons can be misleading, we believe we can safely draw several overall
conclusions from table 5. First, information on export
sensitivity seems essential to understand the 1993
Italian recession. More than any other factor, export
sensitivity appears to isolate the firms that suffered
the most. In addition, firm size appears to be important. In line with many theories, small firms had a
more difficult time managing the recession. Similarly,
firm location seems to matter. For the other factors,
we consider the results rather mixed.
The obvious next step is to jointly control for the
various features that we have identified. A full-blown
regression analysis will eventually be needed; at this
point, we prefer to keep the analysis simpler and
shorter. As a robustness check and first step towards
simultaneously allowing for alternative factors, we
report several four-way sample splits. We first control for export propensity and then separate the firms
along other dimensions. These tabulations allow us
to see the extent to which all the table 5 results may
be driven by export patterns.
The results in table 6 confirm that while exports
are indeed important, they do not seem to be the
whole storyto save space the table only shows the
Economic Perspectives
four years around the recession. In particular,
we draw five conclusions from this table.
First, in all but one case (discussed further
below) the high-export firms do noticeably
better than comparable low-export firms.
Second, among the low exporters, small
firms fare worse than large firms. Hence,
size is not simply standing in for exporting
tendencies. Third, the previous ambiguous
results involving the comparisons of durable goods and nondurables producers do
not become any clearer after controlling
for exports. Among the domestically focused firms, both the interest-sensitive and
interest-insensitive firms experience comparable declines in investment and ROA.
Fourth, the group membership results
remain mixed. Perhaps one can conclude
that the low export group member firms
did slightly worse than comparable nonmember firms; however, these differences
are not very pronounced.
Finally, table 6 indicates that the results
for interest coverage appear to involve
more interesting interactions with exporting
patterns than the other comparisons. The
high-export firms with coverage problems
actually underperform the non-exporters
in terms of ROA, though their investment
is less affected by the recession. Also, the
drop in investment among non-exporting
firms is not too different in percentage
terms between the high- and low-coverage
firms. Further study of this interaction
is needed.
TABLE 6
Investment and profitability, controlling for
export propensities
(data for median firm)
Category
Size
Small firms
High export
Low export
Large firms
High export
Low export
Interest sensitivity
High
High export
Low export
Low
High export
High export
Group membership
Nonmember
High export
Low export
Member
High export
Conclusion
Our three main findings are as follows.
First, the existing attempts to assess the
likely effects of the shift to a common
monetary policy are not very informative.
The main problem is that no one has conducted a careful examination of what would
happen if the euro system countries were
subjected to the same temporal sequence
of monetary policy shocks, holding fixed
the exchange rate among them. This is the
key constraint that will be imposed by the
common monetary policy, and we simply
do not know how different the responses
would be across countries. Some work
to fill this gap in the literature would be
quite valuable.
Federal Reserve Bank of Chicago
Low export
Interest coverage
High
High export
Low export
Low
High export
Low export
1991
1992
1993
1994
NF
I/A
ROA
NF
I/A
ROA
2,191
1.80
7.69
23,832
1.56
7.59
1,932
1.68
7.98
23,686
1.32
7.64
1,612
1.52
8.23
25,566
0.72
6.49
1,861
1.88
7.90
27,967
1.00
6.22
NF
I/A
ROA
NF
I/A
ROA
178
4.91
4.20
664
3.62
6.39
111
3.96
4.46
682
3.42
6.29
161
3.36
5.83
621
2.64
5.73
202
3.63
6.92
578
2.51
5.22
NF
I/A
ROA
NF
I/A
ROA
1,786
2.84
7.60
9,306
2.10
7.62
1,519
2.36
7.66
9,527
1.67
7.45
1,441
2.27
7.55
9,699
1.14
6.32
1,768
2.52
7.40
9,875
1.35
5.86
NF
I/A
ROA
NF
I/A
ROA
1,502
2.57
7.19
10,321
2.71
7.03
1,529
2.47
7.57
10,339
2.33
7.05
1,148
2.26
7.71
11,022
1.42
6.22
1,472
2.75
7.72
11,968
1.78
5.95
NF
I/A
ROA
NF
I/A
ROA
1,278
3.13
7.77
7,813
2.40
7.88
1,175
2.66
8.07
8,587
2.01
7.85
1,216
2.56
7.94
9,400
1.51
6.78
1,478
2.92
7.73
9,653
1.95
6.53
NF
I/A
ROA
NF
I/A
ROA
976
3.26
6.90
5,756
2.41
6.76
835
2.81
7.13
6,299
2.05
6.47
970
2.72
7.10
6,936
1.37
5.53
1,321
3.07
2.38
7,062
1.50
5.52
NF
I/A
ROA
NF
I/A
ROA
3,655
2.96
8.04
26,261
2.27
8.32
3,164
2.54
8.17
27,201
1.9
8.32
3,156
2.44
8.17
29,349
1.16
7.26
3,983
2.86
7.87
32,318
1.49
6.84
NF
I/A
ROA
NF
I/A
ROA
588
1.43
–0.99
6,822
1.11
1.34
444
1.29
–1.23
6,074
0.90
–0.03
282
1.21
–2.84
6,493
0.46
–1.05
276
1.42
–2.52
6,237
0.46
–0.30
Notes: I/A is investment in fixed capital during the year divided by year-end
assets; ROA is return on assets; and NF is the number of firms. Sample splits
are defined in the text.
Source: Authors’ calculations based on data from the Italian Company
Accounts Database.
71
Second, there are good reasons to believe that
looking carefully at microeconomic data across
countries might provide some insights about the
transmission mechanism. Looking at some of the
microeconomic structural differences among several
European countries, these countries appear to differ
significantly along many dimensions that are potentially relevant for the transmission of monetary policy.
For instance, conditions in Italy and the UK look to
be very different.
Finally, drawing on micro data for a specific
country during a particular episode, we find that
differences among firms that are related to the observed differences across countries do matter for the
cyclical pattern and the response to shocks, including
monetary shocks. Our analysis is mainly descriptive.
Further work needs to be done to improve the methodology and obtain better measures of a number of
relevant firm characteristics. However, our exploratory findings suggest that similar exercises using
micro datapossibly extended to householdsfrom
other countries could be quite valuable in helping us
to understand the nuances of the monetary transmission mechanism.
NOTES
See Kouparitsas (1999) and Carlino and DeFina (1998) for some
statistical evidence on this point. Supporters of the monetary
union argue that the launch of the euro will result in an increase
in the degree of synchronization of the business cycles of the
member countries. However, there are theoretical arguments suggesting that synchronization could increase or decrease. For example, Krugman (1991) shows how synchronization can depend
on productive specialization. If the monetary union makes it easier for countries to specialize in production for certain sectors then
countries may become less harmonized. Alternatively, if intraindustry trade increases this can lead to greater synchronization.
1
Surveys of the literature can also be found in Kieler and Saarenheimo (1998), Dornbush, Favero, and Giavazzi (1998), Gambacorta
(1999), and Kouparitsas (1999).
2
For an interesting version of this argument, see Frankel and Rose
(1998), who discuss how the changing trade linkages that might
follow a shift to a single currency could alter the output correlations across countries.
3
We include the UK in the analysis since it may join the union
at a later date. The lack of comparable data forced us to drop
Greece from the analysis.
4
Data for Germany are not strictly comparable, as they refer to an
experiment in which the exchange rate moves vis-à-vis all countries.
However, owing to the specific pattern for the exchange rate assumed in the ERM-coordinated experiment, the changes in the
effective exchange rate are roughly the same as in the other countries (stronger in the last years of the experiment). Spain is not included in table 1 as the changes in the effective exchange rate in
the experiments performed are not comparable with those of the
other countries.
5
The SVAR relates a set of variables to lags of the variables. For
instance, investment and interest rates could be assumed to be
determined by past values of investment and interest rates. See
Kouparitsas (1999) for a further discussion of how the inference
is conducted.
6
The article by Gerlach and Smets (1995), among the first on the
subject, explicitly recognizes this point and complements the
standard impulse responses with responses to a prespecified path
for the interest rate (this is equivalent to hitting the model with a
sequence of shocks appropriately chosen). However, aside from
Kieler and Saarenheimo (1998), subsequent papers have ignored
the issue. As we argue below, this can be quite important.
7
72
We focus here on the output comparisons mainly for convenience; the price responses are often not reported. We would not,
however, expect them to be any more uniform than the patterns
for GDP.
8
In the preferred equation, only the expected part of interest rates
is retained. The expected rate is constructed to be near a target
level which is a function of exchange rate, GDP, and inflation
deviations from target levels that vary across countries.
9
Peersman and Smets find the response in Belgium is also stronger
than in other countries, contradicting the BIS study.
10
This theory is sometimes called the credit channel (or the broad
credit channel) of monetary transmission.
11
It is possible that a monetary policy contraction will be more
potent in countries with poor legal enforcement. For instance, in
the Williamson (1987) setup, low monitoring costs increase the
possibility that the equilibrium involves no rationing, and in these
equilibria interest rates on loans change but quantities do not respond to monetary policy. In rationing equilibria, which are more
likely with high monitoring costs, a tightening will affect loan
quantities but not prices.
12
See Cecchetti (1999) for a similar exercise that focuses more on
financial and legal differences.
13
There is considerable pressure and a countervailing strong
amount of resistance to reforming labor market institutions in
most European countries, including Spain, Italy, France, and
Germany. Reform is moving slowly so that in relative terms the
European labor markets are still fairly rigid. One factor for the
slow adjustment is the tendency to temporarily suspend a general
practice in a particular set of circumstances rather than completely
rolling back the general practice.
14
Rajan and Zingales (1995) show that the German treatment of
pension obligations can inflate the liabilities figures for German
firms. We do not believe that this effect is very important for
this sample.
15
For all the countries, the fact that some primary commodities
are priced in dollars could mean that a change the euro/dollar
exchange rate could cause fluctuations in input pricesof course,
this has been true historically as well.
16
These data are the medians across industries in each country. The
industry average levels of employment are calculated by weighting
firm size by the fraction of industry employment in each firm.
17
Economic Perspectives
Cecchetti (1999) conducts an intriguing exercise in which he
relates the La Porta et al. measures of shareholder rights, creditor
rights, and the ability to enforce contracts on measures of the impact of interest rates on output and inflation. He finds that variation in the legal code does seem to partially explain why the
potency of monetary policy varies. One difficulty for our purposes
is that the interest rate sensitivities he uses come from models
that do not account for the exchange rate restrictions discussed
in the last section. These correlations also involve non-European
countries. Nevertheless, the findings suggest that enforcement
costs and legal structure do matter for monetary transmission.
with a single bank do exhibit the characteristics that one might
expect for bank dependent borrowers. However, the propensity to
use multiple banks is very high, so it is possible that this screen
may not generalize to other countries. Within Italy using this
variable is also complicated by the need to merge the company
accounts data with another data source, which means many firms
end up being dropped from the analysis.
An annual Bank of Italy survey on a sample of manufacturing
firms collects information on the access to bank credit. Specific
questions are asked as whether firms applied for loans and were
rejected by the bank(s), even if they were willing to pay the market rate and possibly even accept an increase in the cost of credit.
Guiso (1998) shows that the share of firms that were turned down
at the end of 1992 and 1993 were 9 percent and 12.8 percent,
respectively, compared with an average of about 3 percent in
the previous years.
22
18
19
One proxy that we experimented with is the number of banks
with which a borrower has contact. In Italy it appears that firms
20
Unfortunately, many firms do not classify themselves as either
belonging to a group or as being independent, so we exclude
these firms from the comparison.
21
The exact classification is that low-coverage firms have a positive level of gross operating margin, but a ratio of gross operating
margin which is less than the interest payments on their outstanding debt.
Northern firms are located in one of the following regions:
Valle dAosta, Piemonte, Liguria, Lombardia, Veneto, Trentino
Alto Adige, Friuli Venezia Giulia, and Emilia Romagna. Southern firms are from the following regions: Abbruzzo, Molise,
Campania, Puglia, Basilicata, Calabria, Sicilia, and Sardegna.
The remaining firms are in the central region and are excluded
from this comparison.
23
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Federal Reserve Bank of Chicago
75
Index for 1999
Title & author
BANKING, CREDIT, AND FINANCE
Competitive analysis in banking: Appraisal of the methodologies
Nicola Cetorelli
Birth, growth, and life or death of newly chartered banks
Robert DeYoung
New facts in finance
John H. Cochrane
Portfolio advice for a multifactor world
John H. Cochrane
Issue
Pages
First Quarter
2-15
Third Quarter
18-35
Third Quarter
36-58
Third Quarter
59-78
ECONOMIC CONDITIONS
Agglomeration in the U.S. auto supplier industry
Thomas H. Klier
First Quarter
18-34
The impact of technology on displacement and reemployment
Daniel Aaronson and Kenneth Housinger
Second Quarter
14-30
Child care costs and the return-to-work decisions of new mothers
Lisa Barrow
Fourth Quarter
42-55
INTERNATIONAL ISSUES
Measurement errors and quality-adjustment methodology:
Lessons from the Japanese CPI
Shigenori Shiratsuka
Second Quarter
2-13
Is the EMU a viable currency area? A VAR analysis
of regional business cycles
Michael A. Kouparitsas
Fourth Quarter
2-20
REGIONAL ISSUES
Slow work force growth: A challenge for the Midwest?
Richard E. Kaglic and William A. Testa
Second Quarter
31—45
Small business finance in two Chicago minority neighborhoods
Paul Huck, Sherrie L. W. Rhine,
Philip Bond, and Robert Townsend
Second Quarter
46-62
MONEY AND MONETARY POLICY
The new view of growth and business cycles
Jonas D. M. Fisher
First Quarter
35-56
Third Quarter
2-17
Fourth Quarter
21-39
Fourth Quarter
56-75
State budgets and the business cycle: Implications for the federal
balanced budget amendment debate
Leslie McGranahan
Regional employment growth and the business cycle
Ellen R. Rissman
Will a common European monetary policy have asymmetric effects?
Luigi Guiso, Anil K Kashyap, Fabio Panetta,
and Daniele Terlizzese
To order copies of any of these issues, or to receive a list of other publications,
please telephone (312)322-5111 or write to:
Federal Reserve Bank of Chicago
Public Information Center, P.O. Box 834
Chicago, IL 60690-0834
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