The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
L A B O R M O B IL IT Y , U N E M P L O Y M E N T A N D
S E C T O R A L S H IF T S : E V ID E N C E F R O M
M IC R O D A T A
Prakash L ou n gan i, R ich ard R ogerso n
and Y a n g -H o o n Sonn
W o rk in g P aper Series
M a cro E c o n o m ic Issues
R esearch D epartm ent
Fed eral R ese rve B a n k o f C h ic a g o
N o vem b er, 19 89 (W P -8 9 -2 2 )
L a b o r M o b ilit y , U n e m p lo y m e n t a n d
S h if t s :
E v id e n c e
fro m
M ic r o
S e cto ra l
D a ta
Prakash Loungani, Richard Rogerson and Yang-Hoon Sonn*
I . In t r o d u c t io n
Economists distinguish conceptually among three types of unemploymentfrictional, cyclical and structural. Cyclical unemployment is associated with
declines in aggregate demand; in theory, such unemployment can be
alleviated through the macro tools of monetary and fiscal policy. Frictional
unemployment is attributed to the process of normal labor turnover.
Structural unemployment is caused by shifts in the com position of labor
demand across industries or regions in the economy. Since the reallocation of
workers across sectors cannot be instantaneously accomplished, these shifts
cause unemployment in some sectors. The extent of this unemployment
depends on many factors such as the nature of the compositional shift, the
opportunities for, and costs of, retraining workers and whether or not the shift
was anticipated. Structural unemployment can be combated through ’micro'
policies such as employment and training programs, the provision of
information about jobs in other sectors and the provision of relocation
allowances. The frictional-cum-structural component is often referred to as
the natural rate of unemployment.
It has been standard practice in macroeconomics to assume that the natural
rate of unemployment behaves in a fairly predictable way, and then to focus
on the causes and consequences of cyclical unemployment. For instance, in
the work of Robert Barro (1981), the natural rate for the U.S economy in the
post-WWII period is modeled as a simple time trend; the deviation of actual
unemployment from this trend is considered to be the ’cyclical' component.
Barro then considers the extent to which ’cyclical’ unemployment can be
explained by shocks to aggregate demand.
♦ P
rakash Loungani is assistant professor in the D
epartm of Econom University of Florida,
ent
ics,
and Consultant to the Federal Reserve Bank of Chicago; Richard Rogerson is assistant professor
in the Graduate School of Business, Stanford U
niversity; Yang-Hoon Sonn is a graduate student
in the D
epartm of Econom University of Florida. The authors thank w
ent
ics,
orkshop participants
at NBER and the Federal Reserve Bank of Chicago for their help.
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, WP-1989-22
1
However, as a result of innovative work by Black (1982) and Lilien (1982), a
new view of recessions has begun to emerge. Lilien noted that most of the
shocks experienced by the U.S. economy in the 1970’s--the curtailment of
defense expenditures due to the end of the Vietnam War, the oil price shocks,
the increase in import competition-affected some sectors of the economy
more than others.
While these shocks undoubtedly had an impact on
aggregate demand, their impact on the com position of demand was perhaps
equally important. Stated differently, Lilien suggested that much of what
macroeconomists thought of as cyclical unemployment was in fact better
thought of as structural unemployment. Lilien's view is commonly referred to
as the sectoral shifts hypothesis .
Lilien's empirical evidence on the extent of structural unemployment in the
1970's, though suggestive, was not entirely convincing. What Lilien needed
was a measure of the amount of sectoral reallocation of labor carried out by
the economy over a period of time. As a proxy for such a measure, he
constructed an index, a t, which measured the variance, or dispersion, of
employment growth across different industries in the economy. For instance,
if employment in all industries grew at the same rate over a given year, a for
that year would be zero; dispersion in growth rates would lead to positive
values of a. Lilien then demonstrated that, during the 1970's, years of high
unemployment coincided with years in which a was high.
At first sight, it may seem that the positive correlation between a and
aggregate unemployment, U, supports the sectoral shifts hypothesis.
However, in an influential critique of Lilien's work, Abraham and Katz (1984,
1986) demonstrated that the a-U correlation was consistent with other
hypotheses, including the aggregate demand hypothesis. Their basic point is
that this correlation can arise even in the absence of any labor reallocation.
All that is needed is that aggregate demand shocks trigger temporary layoffs
within each sector and the magnitude of this response varies across sectors.
More recent work in this area has proceeded along three lines. First, there
have been some attempts to extend the theoretical apparatus underlying the
sectoral shifts hypothesis.
The most prominent of these efforts is the
"reallocation timing" or "bunching" hypothesis of Rogerson (1986) and Davis
(1987a, 1987b). In these models the economy is subject to aggregate shocks
(e.g., money shocks, oil shocks) at the same time that it is in the process of
carrying out structural change (i.e, sectoral reallocation of labor). What these
models emphasize is that there is likely to be an interaction between aggregate
shocks and labor reallocation. For instance, a recession, even if it is induced
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
2
by a money shock, may be ’’used" by agents in the economy to carry out
rellocations that were going to be made later any way. Loungani and
Rogerson (1989) provide some empirical evidence in favor of these models.
They find that the reallocation of labor from the goods-producing industries to
the service-producing industries is higher during recessions than in a 'normal'
year. In other words, recessions are marked by an acceleration in the rate of
structural change in the economy.
Second, there has been an attempt to create dispersion indices that are less
vulnerable to the Abraham-Katz critique. Topel and Neumann (1985) and
Rissman (1987) decompose each industry's employment share into a
permanent (structural) and temporary (cyclical) component and then construct
a measure of structural change using only the permanent components. They
find that this measure is significantly correlated with unemployment.
Loungani, Rush and Tave (1989) use industry stock price indices to create a
Lilien-type dispersion index.
The main advantage of a stock market
dispersion measure over Lilien's employment-based measure is that sectoral
stock prices largely react to disturbances which are perceived to be
permanent, which need not be true of sectoral employment changes.
Loungani et. al. find that unemployment depends on up to two and three
annual lags of the stock market dispersion measure. This line of research is
discussed in greater detail in a companion working paper.
The third line of research, which is the focus of this working paper, uses
micro data in order to test the validity of Lilien's view. The goal of the micro
studies is to provide direct evidence on the link between sectoral mobility and
unemployment. Specifically, they seek to answer the following questions:
(1) Is sectoral mobility higher than average during recessions?
(2) What fraction of total weeks of unemployment experienced by all workers
is attributable to workers who switched sectors? Does this fraction increase
significantly during recessions?
The recent paper by Murphy and Topel (1987), which uses data from the
March Current Population Survey (CPS), is the first to use micro data to test
the sectoral shifts hypothesis. Their paper provides a wealth of information
about unemployment during the period 1968-1985, but pays particular
attention to the contribution of industrial mobility to unemployment over this
period. Murphy and Topel compare the incidence of unemployment among
those who change industries ("switchers") and those who do not ("stayers").
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
3
They conclude that the contribution of switchers to the incidence of
unemployment is virtually constant over the post-1970 period.
Their
conclusion appears to have been widely accepted as evidence against the
sectoral shifts hypothesis [see, e.g., Blanchard and Fischer (1989, p.355) and
Mankiw (1989, p. 87)].
This paper describes preliminary results from an alternate micro data set. We
use data from the Michigan Panel Study of Income Dynamics (PSID) for the
period 1974 to 1985. Unlike the CPS, the PSID follows individuals for
several years and hence offers great flexibility in defining and investigating
alternate concepts of sectoral mobility. One of our main findings is that
Murphy and Topel's conclusion is sensitive to alternate definitions of industry
switching. If the definition of a switcher is extended to include workers who
spend a year or more unemployed in the interim, the sectoral shifts hypothesis
receives greater empirical support. When this broader concept of sectoral
mobility is adopted, the contribution of industry switchers to total weeks of
unemployment is higher during recessions than during booms. In addition to
the cyclical importance of mobility, our results indicate that over 40% of all
unemployment is accounted for by individuals who are switching industries.
Another of our main findings is that mobility-based models of unemployment
which focus entirely on the volume of mobility may be missing an important
feature of the data. We find that different types of mobility have significantly
different amounts of unemployment associated with them. For example, a
typical transition from the goods producing sector to the service producing
sector involves over 50% more unemployment than a transition in the
opposite direction.
Another feature which has been overlooked in much of the mobility literature
is the case of workers who are displaced from one sector and seem to remain
without a strong employment connection for an extended period. Recent
papers by Lilien (1988) and Rogerson (1989) have considered this type of
outcome. Our results indicate that although this group is small in number, it is
significant in a discussion of aggregate unemployment. Hence, identification
of the factors that causes some workers to require an extended period to
relocate seems important in understanding unemployment.
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
4
n.
T h e S p e c if i c a t io n o f S e c t o r a l M o b ilit y
The PSID interviews individuals in the spring of each year, gathering
information about their labor force status, current occupation and industry if
applicable, and weeks of unemployment experienced by the individual during
the last calendar year. Because the PSID is a panel data set, this information
can be used to create a work history for an individual at yearly intervals. The
Annual Demographic File of the CPS, which Murphy and Topel used,
interviews individuals in March and collects information on current
employment status, industry and occupation if applicable, industry and
occupation of longest job held during the previous calendar year, and
unemployment during the last calendar year. However because individuals
are interviewed only once, the available information on an individual pertains
to at most a 15 month period.
The potential difficulty of this situation is that one cannot determine whether
or not individuals who report themselves as currently unemployed are in the
process of switching industries. Our results indicate that this is an important
limitation of the CPS because individuals who experience a large amount of
unemployment while changing industries tend to be concentrated in this
group.
Though small in absolute numbers, this group makes a large
contribution to total weeks of unemployment.
Although this aspect favors the use of the PSID over the CPS in analyzing
unemployment and mobility, it would be misleading to ignore some other
differences between the two data sets. The CPS has the advantage of being
significantly larger (Murphy-Topel have ten times the number of yearly
observations we have in this paper), and the CPS interview has a special
question about mobility which acts as a check against spurious mobility
caused by misreporting of industry.1
Finally, some limitations of the PSID-but not unique to the PSID-are that
there is essentially no information about what industries (if any) an individual
may have worked for between two consecutive interviews and unemployment
is measured over the calendar year rather than the interval between successive
interviews.
Abstracting from details for the moment, the nature of the exercise we wish to
carry out is straightforward. Pick two dates, tj and t^ For a given set of
workers we can observe their industry of employment at these two dates, thus
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
5
partitioning them into stayers and switchers. We can then analyze the
unemployment experienced by each group over the chosen interval.
Repeating this procedure for several intervals [t^, t^L where some of the
intervals correspond to expansions and others coincide with recessions would
allow us (i) to determine the correlation between mobility and the cycle and
(ii) to determine the correlation between the unemployment associated with
mobility and the cycle.
Although this procedure is straightforward in
principle, there are a number of issues which arise in the context of providing
a more detailed specification. In fact, one of the points that we wish to
emphasize is that one's interpretation of the "facts" depends crucially on the
specification of sectoral mobility.
We study three cyclical episodes of equal length: the recessionary periods of
1974-76 and 1981-83 and the expansionary period of 1977-79. The first year
of each episode is referred to as the base year. For each episode, a sample is
chosen consisting of workers who were heads of their households and who
were in the labor force at the time of the three interviews conducted during
that episode. We also require that the individuals be employed at the time of
the interview in the base year. This produces sample sizes of 2150, 2518 and
2708 for the periods beginning in 1974,1977 and 1981 respectively.2
We partition each sample into stayers and switchers using the following
specification of sectoral mobility. Industries are classified into 26 sectors,
twelve of which are goods producing and 14 are service producing. Let t
correspond to one of the base years. In order to be classified as a switcher in
the episode with base year t an individual must be employed in a different
sector or unemployed in period t+1 and must not have returned to
employment in the base year sector as of period t+2. All other individuals are
counted as stayers. The definition that we adopt has three salient features.
First, individuals who "switch-and-retum" are counted as stayers. Second, an
individual who does not change sectors between t and t+1 but who changes
between t+1 and t+2 is classified as a stayer; hence the focus is on separations
that are intitiated between t and t+1. Finally, individuals who are employed in
the base year and then unemployed in t+1 and t+2 are counted as switchers.
We emphasize that while we call this our primary specification we will
present some results for other specifications in a later section.
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
6
Once the workers have been partitioned into switchers and stayers we
compute weeks of unemployment for each group over the two year duration
of each episode. (Ideally, this would correspond to the total weeks of
unemployment between the interviews at date t and t+2 but because the PSID
measures unemployment between calendar years this is not exactly the case.)
H I . M o b ilit y a n d U n e m p lo y m e n t : R e s u lt s f o r th e P r im a r y
S p e c if i c a t io n
1 General Patterns
.
Table 1 provides some summary information about the unemployment
experiences of stayers and switchers during the three episodes. The table
shows the average weeks of unemployment per individual [E(w)], the average
weeks of unemployment per individual conditional on experiencing
unemployment [E(w|w>0] and the distribution of total weeks unemployed
across individuals. Although the weeks of unemployment do not necessarily
correspond to a single spell of unemployment we will sometimes refer to
E(w|w>0) as the duration of unemployment, for lack of a better term.
noted earlier, this data covers unemployment over a two year period.3
As
Several patterns are easily recognized from Table 1:
(i) Stayers experience less unemployment per individual than switchers in
each episode. For both groups the average is lowest in the expansion.
Moreover, the gap between the average unemployment of stayers and the
average unemployment of switchers increases during recessions (See row 1 in
each episode.)
(ii) Among individuals who report some unemployment, it is still true that the
average duration of unemployment is smaller for stayers than for switchers
during all three episodes and for each group the average is lowest during the
expansionary episode.
Also, note that once again the absolute increase in duration over the course of
the cycle is significantly larger for switchers than it is for stayers. (See row 2
in each episode.)
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
7
T a b le 1
U n em p lo ym en t E xp e rie n c e o f S w itc h e rs and S tayers
1974-76
Row
1.
2.
Averaqe Weeks
E(w)
E(w|w>0)
3.
4.
5.
6.
Weeks Unemployed
0
1-26
27-52
>52
Stayers
3.2
13.6
% workers
76.2
20.2
3.0
0.6
% weeks
0.0
53.0
34.4
12.5
Switchers
8.7
19.2
% workers
54.5
34.4
7.6
3.5
% weeks
0.0
39.5
33.5
27.0
1977-79
Row
1.
2.
Averaqe Weeks
E(w)
E(w|w>0)
3.
4.
5.
6.
Weeks Unemployed
0
1-26
27-52
>52
Stayers
2.1
11.1
% workers
81.6
16.7
1.4
0.3
% weeks
0.0
65.3
25.3
9.5
Switchers
5.5
15.5
% workers
64.6
28.0
7.0
0.5
% weeks
0.0
48.5
44.9
6.5
1981-83
Row
1.
2.
Averaqe Weeks
E(w)
- E(w|w>0)
3.
4.
5.
6.
Weeks Unemployed
0
1-26
27-52
>52
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
Stayers
3.8
15.5
% workers
75.6
19.8
3.9
0.6
% weeks
0.0
52.4
37.3
10.2
Switchers
12.9
27.1
% workers
52.4
29.6
9.9
8.2
% weeks
0.0
23.1
30.6
46.3
8
(iii) Stayers are much less likely to experience unemployment than are
switchers and for both groups the probability of experiencing unemployment
is lowest during the expansionary episode. Although the higher incidence of
unemployment among switchers was to be expected, it is of some interest to
note that in all three episodes a majority of switchers do not experience any
unemployment (See row 3.)
Economists have known for quite some time [see Clark and Summers (1979)
and Summers (1986)] that although high unemployment individuals are few in
number they account for a disproportionately large amount of unemployment
Table 1 reveals that this is true for each of the two groups separately.
However, several additional patterns are present in the table (see rows 5 and
6):
(iv) High unemployment individuals (those with w>27) are more significant
in accounting for unemployment among switchers than among stayers.
(v) The importance of the extremely high unemployment group (w>52) is
very sensitive cyclically in the switcher group; much less so in the stayer
group.
Table 2 provides information on the fraction of individuals classified as
switchers and the fraction of unemployment accounted for by switchers in
each episode. Two observations are apparent:
(vi) Mobility is highest during the two recessionary periods.
(vii) The fraction of total unemployment accounted for by switchers is highest
during the two recessionary periods.
T a b le 2
P e rcen tag e
of S w itc h e rs
and th e ir S hare o f U n em p lo y m e n t
1974-76
% of Switchers
Switchers Share
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
1977-79
1981-83
17.1
35.6
15.4
32.8
17.2
41.6
9
These two conclusions are apparently at odds with the results of MurphyTopel; a detailed discussion of this point is postponed until Section IV when
we consider alternate definitions of sectoral mobility.
Although the cyclical pattern in switchers share of unemployment is easily
ascertained, assessing the quantitative importance of this effect is much less
so. Average unemployment rates for the three samples are 3.9%, 2.4% and
5.2% respectively.
In passing from the second to the third episode
unemployment increases by 2.8% and unemployment accounted for by
switchers increases by 1.4%.
Hence, one half of the increase in
unemployment is accounted for by the increase in unemployment due to
switchers. In moving from the second episode to the first episode the
corresponding number is 40%.
Changes in the share of unemployment accounted for by switchers can really
be thought of as the combined result of changes in three components:
(a) direct effect: changes in the number of switchers relative to stayers
(b) incidence effect: changes in the incidence of unemployment among
switchers relative to stayers, (c) duration effect: changes in the amount of
unemployment per individual experiencing unemployment for switchers
relative to stayers.
Using "sw" subscripts to denote switchers, total weeks of unemployment
experienced by switchers are given by
where N denotes the total number of switchers, f denotes the fraction that
experience unemployment and d denotes the amount of unemployment
conditional on experiencing unemployment. Using "st" subscripts to denote
stayers, it follows that the share of total unemployment accounted for by
switchers during a given period is given by
(1)
(^ sw * ^SW * ^sw) +
* ^St * ^St)
The following calculation evaluates the importance of each factor. Take the
values of Nst/Nsw, W^sw» dst/dsw corresponding to the expansionary period,
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
10
1977-1979. For each of the recessionary periods insert the actual value for
one of the three ratios, leaving the other two unchanged and evaluate
expression (1). The results are reported in Table 3.
T ab le 3
D eco m p o sitio n o f S w itc h e rs S hare o f Total U n em p lo y m e n t units: %
Direct Effect only
Incidence Effect only
Duration Effect only
1974-76
35.4
32.7
33.1
1981-83
35.6
32.9
38.0
In 1977-79, switchers accounted for 32.6% of all unemployment
This table shows that in neither case is the incidence effect important and that
in 1974-76 only the direct effect is important. These results are of substantial
interest. Many discussions of the aggregate importance of sectoral shifts
implicitly or explicitly assume that it is the volume of mobility that is of
central importance. For example, the model of Lucas and Prescott (1974),
which was used by Lilien and others as the basis for the sectoral shifts
hypothesis, assumed that the time required to switch was fixed exogenously.
The above results suggest an important role for the duration effect in
influencing the contribution of switchers to aggregate unemployment. It is
also of interest that the incidence effect is nonexistent since the study of
Murphy and Topel (1987) concentrated on incidence of unemployment in
their discussion on mobility, another point that we shall return to in Section
IV.
2 Detailed Patterns
.
There is more to be learned by studying the pattern of reallocation in greater
detail. What types of transitions lie behind the statistics? What types of
transitions involve the most unemployment? To address questions such as
these, the switchers in each sample are classified into one of five categories:
those who move from one goods producing industry to another (Gj-Gj), those
who move from goods to services (G-S), with (Sj-Sj) and (S-G) defined
analogously, and finally the category (E-U) which refers to those individuals
who were employed in the base year t but were unemployed at both t+1 and
t+2. Stayers are classified into two categories: those who stay in a particular
goods producing industry (Gj-Gj) and those who stay in a particular service
producing industry (Sj-Sj).
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, WP-1989-22
11
Table 4 presents the percentage of total weeks of unemployment accounted
for by each group during each of the episodes. Several points are worth
noting. First, the category Gj-Gj is by far the most significant in accounting
for total weeks of unemployment. Second, while the two polar transitions GS and S-G are both contributing during all three episodes, the relative size of
the G-S flow is significantly larger during recessions. Third, it is easily seen
that the category E-U is critical in explaining why switchers account for an
increased fraction of unemployment during recessions.
This important
finding is discussed in more detail in the next section in the context of
analyzing differences between results reported here and those reported by
Murphy and Topel.
T a b le 4
U n e m p lo y m e n t S h a re s b y M o b ility T y p e
Cateqorv
1974-76
1977-79
1981-83
7.7
6.3
10.2
2.9
8.5
8.1
8.5
4.8
7.6
3.6
6.2
8.3
4.2
2.3
20.6
35.6
32.8
41.6
43.6
20.8
41.1
26.1
40.4
18.0
64.4
67.2
58.4
Switcher Categories
Gi - Qj
S i- S j
G -S
S -G
E -U
Switchers Share
Stayer Categories
Gi ■
Gi
^-Sj
Stayers Share
Table 5, which provides information on average unemployment per individual
for each of the seven categories discussed above also reveals some interesting
patterns. In both of the recessionary periods it is the switcher categories G^-Gj
and G-S which have the highest average unemployment of all the categories
ending in employment. This suggests that these types of transitions have
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
12
some distinguishing characteristics that cause them to be associated with high
unemployment. By contrast, the category Sj-Sj is relatively important from an
aggregate point of view but the unemployment experience at the individual
level is not very severe.
The last column of Table 5 shows the average unemployment per switcher for
each of the four switching categories ending in employment, when all three
samples are combined. The variation across categories is quite striking. The
average for the transition G-S is over 50% larger than those for either S^-Sj or
S-G.
This further illustrates the earlier statement that the amount of
unemployment associated with a particular switch depends on the nature of
the switch being considered. Moreover it highlights the earlier conclusion
that concentrating on the volume of mobility may be misleading. Given the
numbers in Table 5 it is clearly possible for the volume of mobility to be
constant at the same time that the share of unemployment accounted for by
mobility is increasing because of a change in the composition of mobility
types.4
T a b le 5
W e e k s o f U n e m p lo y m e n t Per In d ivid u al by M o b ility T yp e
Cateaorv
1974-76
1977-79
1981-83
pooled sample
Switcher Categories
Gi - Qj
Si-Sj
G -S
S -G
E -U
6.68
5.02
11.01
5.34
31.35
5.77
3.40
6.35
6.56
23.30
11.84
6.73
7.52
4.64
51.56
7.83
5.12
8.60
5.55
43.22
Switchers
8.57
5.47
12.94
9.24
5.33
1.74
3.05
1.36
6.95
1.87
5.07
1.66
3.20
2.05
3.78
3.01
Stayer Categories
G i- G i
Si - Sj
Stayers
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
13
Additional evidence is provided by Table 6 which shows the distribution of
total unemployment by category for the three samples combined.
The
switcher categories Gj-Gj and G-S are not only prone to higher average
unemployment but they are also the categories where the distributions are
most heavily weighted in favor of high duration (over 36 weeks) as opposed
to short duration (under 24 weeks).
There are many factors that may be relevant in producing the pattern of
average unemployment across mobility types. Cross-industry differences in
sector specific or firm specific skills may be important because workers may
choose to hold out for a job in their initial industry (or firm) for a longer
period of time before deciding to actually switch sectors. This is consistent
with the theory suggested by Murphy and Topel (1987) as well as with the
finding of Katz and Meyer (1988) that some of the highest unemployment
individuals are those who ex ante believe they are temporarily laid off but
who ex post are not recalled. Differences in savings or unemployment
insurance benefits that are correlated with industry may also be a factor, as a
simple search model would predict.
T ab le 6
D istrib u tio n o f U n e m p lo y m e n t By D uration an d M o b ility T yp e,
P ooled S am p le
Category
Weeks Unemployed
0 to 24
36 and over
Switcher Categories
Gi-Gj
Si-Sj
G -S
S -G
31.8
45.7
27.9
58.7
52.3
29.2
36.3
17.7
48.4
30.1
50.2
26.5
Stayer Categories
G i- G i
Si-Sj
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
14
To elaborate further on the connection between high unemployment
individuals, switching and the cycle, Table 7 presents evidence on the share of
total unemployment accounted for by switchers and stayers. The message
from the table is very straightforward. The share of unemployment due to
high unemployment individuals that is accounted for by switchers is strongly
countercyclical.
During recessions it appears that high unemployment
individuals are disproportionately composed of switchers. The importance of
this observation is brought out by the fact that if the over 52 week group is
discarded then the cyclical pattern for switchers' share of total unemployment
depicted in Table 2 will disappear. This is one of the major findings we wish
to emphasize: the extent to which the data support the sectoral shifts theory of
unemployment fluctuations is due entirely to the fact that the small number of
individuals who tend to have very large amounts of unemployment are
disproportionately composed of switchers during recessions.
Table 7
Distribution Between Switchers and Stayers Conditional on Duration
1974-76
1977-79
1981-83
54.4
45.6
25.0
75.0
76.2
23.8
44.0
56.0
32.3
67.7
62.6
37.4
More than 52 weeks
Switchers
Stayers
More than 36 weeks
Switchers
Stayers
It is important to note that our findings do not bear on the issue of causation: it
is not clear if switching is the cause of high unemployment, high
unemployment tends to cause switching or if both are caused by some third
factor. While Lilien argued that it is sectoral shifts-and the switching that
they entail-that trigger the cycle, some other models in this literature do not
rely on his argument. For instance, in the models by Rogerson (1986) and
Davis (1986), recessions-whatever their cause-are a good time to carry out
switches that were going to be made later any way. Hence, there is a
correlation between switching and the cycle but one does not cause the other.
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
15
I V . A lt e r n a t e S p e c if ic a t io n s o f M o b ilit y :
A C o m p a r is o n W it h
M u rp h y -T o p e l
As mentioned in the last section, some of our findings appear to be at odds
with related findings by Murphy-Topel. In particular, their findings over the
time period studied here were that
(MT-1) Industrial mobility is procyclical with a declining trend.
(MT-2) The aggregate incidence of unemployment associated with mobility
was essentially acyclical.
They suggested that these findings were very damaging to the sectoral shifts
hypothesis as a theory of unemployment fluctuations.
The analysis reported in this section suggests that there is no real discrepancy
between the results presented here and those of Murphy and Topel. Rather
the differences are largely accounted for by differences in definitions and
accounting rules used in summarizing the data. Before demonstrating this,
there are two issues concerning their findings that we wish to discuss. First is
that in view of the results in Section III, the cyclicality of mobility, although
an important element of the sectoral shifts story, need not be the major
element. In particular, changes in the duration of unemployment associated
with switching due to compositional changes in the pattern of switching may
also be an important factor. Second is that their result on incidence is in fact
exactly what our results in Section III suggest, namely that the incidence
effect has played no role in the recessionary periods.
Recalling the discussion of Section II, the data used by Murphy-Topel is
produced by interviewing individuals once, at which time they provide
information on current status and industry of longest job held during the last
calendar year. Individuals whose current industry is different from the one
reported for the previous year are counted as switchers. This leaves the group
which is currently unemployed as unassigned since they have no current job.
The procedure used by Murphy-Topel was to divide this group evenly
between stayers and switchers.5 This procedure differs from that followed by
us because we assigned this group into the switcher category, although note
that our horizon is two years instead of one year. Looking back to Table 3 it
is clear that this difference in accounting rules is quite significant. If the 5050 rule is used in Table 3, then the share of unemployment accounted for by
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
16
switchers has the acyclical pattern of 31.4, 31.5 and 31.3 for the three
episodes in chronological order.
A natural question to ask, of course, is: how do we distinguish between the
appropriateness of these two accounting procedures? With the PSID it is
possible to follow individuals for additional periods to find out what
eventually happens to them, suggesting that one way to decide the issue is to
simply extend the analysis through time. Because the available data limits
this extension to one additional year for the 1981-83 period, we consider one
additional year for each episode. In this additional year, some individuals
report themselves as employed, others report themselves as still unemployed,
and a small number have left the sample and hence no record is available.6
For individuals who are employed in t+3, we can then compare their sector of
employment in t+3 with that at t to classify them as switcher or stayer. We
then take the weeks of unemployment during t and t+1 (as before) and
distribute them accordingly. Some weeks remain unclassified. Table 8
summarizes the information. Several important points emerge. First, a
significant fraction of the weeks remain unclassified when the analysis is
extended by one period. Second, in both of the recessionary periods, the
weeks that are accounted for are roughly on the order of two-thirds switchers
and one-third stayers. Third, during the expansionary episode almost all of
the classified weeks are in the stayer category. Hence, the 50-50 rule is
clearly inappropriate.
Table 8
Decomposition of Total Weeks of E-U Group
1974-76
Switchers
Stayers
Other
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
1977-79
1981-83
32.1
19.0
48.9
2.6
31.5
65.9
39.4
19.0
46.6
17
As noted in the previous discussion, there is a certain degree of arbitrariness
in using a two-year horizon to define switches. Using the numbers in Table 8,
we can calculate the effect of moving from a two-year criterion to a three-year
criterion, i.e., the E- U-U individuals who return to their period t industry in
t+3 are counted as stayers.
The resulting figures for the fraction of
unemployment accounted for by switchers are 33.5, 31.6 and 37.0 for the
three episodes in chronological order. Although the numbers are slightly
affected by the change in definition, the cyclical pattern is the same as
before.7
At the risk of belaboring the point, we present a somewhat extreme case to
illustrate the importance of specification. This specification uses the same
three base years as before, the same samples, but the horizon is limited to one
year and the rule for determining a switcher is that the individual must be
employed at both t and t+1, but in different industries. Based on the evidence
presented in this paper this is clearly an inappropriate choice of specification;
however, without this evidence it might have appeared to be a reasonable
specification if one imagined the sectoral shifts hypothesis entailing a lot of
sectoral mobility with reasonably small duration. Tables 9 and 10, which are
analogous in their format to Tables 1 and 2, present the results for this
specification. The contrast between the results in the two tables is striking.
Stayers now have more unemployment conditional on having some
unemployment; the duration figures for switchers are lower during recessions;
long duration is more important in explaining the unemployment of stayers
than of switchers; there is no cyclical pattern to the volume of switching; and
the fraction of unemployment accounted for switchers is highest during the
expansionary episode. All of these observations are opposite to those found in
Tables 1 and 2. The reason for this is quite simple. To be counted as a
switcher in this table, a worker has to complete the switch between successive
interviews. As shown earlier, much switching requires a relatively long
period, especially during recessions. As a result, the procedure generating
Table 9 inappropriately excludes many workers who simply are taking longer
in the process of switching.
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
18
Table 9
Unemployment Experience of Switchers and Stayers:
Effect of Alternative Specification on Table 1
1974-76
Row
1.
2.
3.
4.
5.
E(w)
E(w|w>0)
W eeks Unemoloved
0
1-26
27-52
Switchers
Stavers
2.3
11.5
Averaae W eeks
% workers
79.9
17.9
2.1
% weeks
0.0
63.1
36.9
3.7
10.9
% workers
65.9
31.1
3.0
% weeks
0.0
74.8
25.2
1977-79
Row
1.
2.
3.
4.
5.
Averaae Weeks
E(w)
E(w|w>0)
W eeks Unemoloved
0
1-26
27-52
Stavers
Switchers
3.5
11.8
2.1
13.1
% workers
83.9
13.9
2.1
% weeks
0.0
62.4
37.7
% workers
70.2
26.0
% weeks
0.0
65.0
3.8
35.1
1981-83
Row
1.
2.
Averaae W eeks
E(w)
E(w|w>0)
3.
4.
5.
W eeks Unemoloved
0
1-26
27-52
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
Stavers
3.6
16.7
% workers
78.2
17.5
4.3
% weeks
0.0
48.2
51.8
Switchers
3.6
10.7
% workers
65.8
31.4
2.8
% weeks
0.0
75.6
24.4
19
Table 10
Percentage of Switchers and their Share of Unemployment:
Effect of Alternative Specification on Table 2
1974-76
% of Switchers
Switchers Share
V.
1977-79
1981-83
13.5
20.0
13.1
20.1
12.4
12.4
I n d i v id u a l C h a r a c t e r i s t ic s a n d U n e m p lo y m e n t
In this section we consider the question of what, if any, personal
characteristics are associated with mobility and weeks of unemployment
experienced. As a byproduct we will produce some results which provide
evidence that some of the differences between booms and recessions that we
have discussed earlier are in fact statistically significant. A related issue is
whether or not the differences we have detected between the three epsiodes is
partly caused by "statistical aggregation." Because the three samples vary in
characteristics this is theoretically possible. Table 11 shows how some of the
characteristics of the sample have changed over time.
Table 11
Selected Characteristics of the Samples
Characteristics
age < 25
female
tenure < 6 months
tenure > 10 years
durables industries
skilled occupations
1974-76
1977-79
1981-83
16.3
19.7
22.2
24.4
20.7
50.7
16.0
19.5
21.0
12.6
21.0
21.5
18.2
18.8
51.4
18.5
16.3
52.6
Our basic strategy is to pool the individual data on total weeks for the three
samples; then we regress the individual’s total weeks on individual
characteristics and on dummy variables which pick up the effects of the
business cycle (i.e., whether the individual moved during a boom or a
recession). We estimate a set of two equations:
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
20
(2)
(3)
I = a + Zjp + D78t! + D 8 2 t 2 + e;
j
WMi = a m + Xmipm + D7881 + D 8 2 8 2 + emi
The following notation is used. WMj is the total weeks of unemployment of
switcher i, and Ij is a (0,1) variable indexing whether individual i is a switcher
or a stayer. D78 is a (0,1) dummy variable which takes on the value 1 if the
switcher belongs to the 1977-79 sample. D82 is a (0,1) dummy variable
which takes on the value 1 if the switcher belongs to the 1981-83 sample. The
X's and Z ’s are sets of individual characteristics. They include a constant, the
individual’s age, sex (FEMALE, 1 if female) and education (EDU).8 The
variable TENRS takes the value 1 if the individual has been in his current job
less than 6 months, and is zero otherwise; the variable TENRL is 1 if the
individual has been in his current job more than 10 years. The variable DUR
takes the value 1 if the individual was in a durables goods industry in t. The
variable SKILL takes on the value 1 if the individual is either a manager or a
professional or a craftsman; these occupations are commonly considered to be
more skill-intensive than the other 1-digit occupations [see Duncan and
Hoffman (1979), Shaw (1984, 1989) and Shapiro and Hills (1986, p.47)].
Finally, the variable FRQ denotes the number of job switches the individual
has made in the three year period prior to the base year.
The estimation procedure for this system is described in Lee(1978) and
Maddala(1986, p. 223-28). The total weeks equations cannot be consistently
estimated using ordinary least squares because E fe ^ | Ij = 1) =\ 0. Hence, a
two-step procedure is used in which the first step is to estimate equation (2)
by probit analysis. Then, in the second step, the parameters of the total weeks
equation for switchers are consistently estimated from an OLS regression of
WMi on
and a selectivity variable, L. The variable L is equal to
[-f(Ii)/F(Ii)] where Ij = a + Zj|3, F and f are the cumulative distribution
function and the density function, respectively, of a standard normal random
variable.
The results from estimating this system are reported in Table 12. The
coefficient estimates on the time dummies-the x's and the 8’s-are of
particular interest. The x estimates capture cyclical patterns in the volume of
mobility while the 8’s reflect cyclical patterns in the duration of
unemployment for a switcher.
If the differences between booms and
recessions documented in previous sections of this paper are statistically
significant, the estimates of xj and
should be negative and the estimates of
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P -1989-22
21
x2 and 52 should be zero or positive. (They could be positive if the 1981-82
recession was more ’severe' than the 1974-75 one.)
The first column gives the probit estimates; the dependent variable is a (0,1)
dummy that indicates whether the individual is a switcher or a stayer. The
estimates of x1 and x2 are negative and positive, respectively, but neither one
is significantly different from zero. This means that, once the individual
characteristics that influence mobility are held constant, the volume of
mobility is acyclic.
The effects of individual characteristics on mobility accord with intuition.
The probability of switching declines with age and education, but the standard
error on the estimate of education is quite large. Females have a lower
probability of switching. There is more switching among individuals with
short tenure (but the estimated coefficient is not significantly different from
zero) and less among those with very long tenure. Individuals in the durables
goods sector show a higher incidence of switching while individuals in skill
intensive occupations show a lower incidence. Finally, those who switched
more often in die past have a higher incidence of switching. This last result is
similar to that reported by Mincer and Jovanovic (1981).
The second column gives the results for the total weeks equation. The
dependent variable is the total number of weeks of unemployment reported by
a switcher over the two year period (i.e., 1974-76 or 1977-79 or 1981-83).
Some additional explanatory variables are included in this equation. The
variable NE takes the value 1 if the individual's region in the base year is the
northeast, and is zero otherwise. The variable CITY takes the value 1 if the
population of the largest city in the individual's SMSA is greater than 50,000.
Note that the variable FRQ appears in the probit equation but not in the total
weeks equation; such an exclusion restriction is needed to identify the second
equation.
The estimated coefficients on the dummy variables D78 and D82 strongly
support the results of Section II. The coefficient of D78 is negative and
significantly different from zero; the duration of unemployment for a switcher
declines during a boom. The coefficient on D82 is positive, supporting the
impression conveyed earlier that the 1981-82 recession was more severe than
the 1974-75 one.
The results also show that the total weeks of unemployment experienced by a
switcher decline with age, education, length of tenure and proximity to a large
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
22
city.
Individuals in skilled occupations have lower total weeks of
unemployment. Being in the northeast or in a durables goods industry
increases total weeks, though only the former effect is statistically significant.
The estimated coefficient on the selectivity variable, L, is not significantly
different from zero. In light of this result, we also estimate a single equation
for total weeks without the selectivity correction. The results, reported in
column (iii), are similar to those in column (ii).
For purposes of comparison, we estimate a total weeks equation for stayers
[see column (v)]. The estimated coefficients of the dummy variables D78 and
D82 show the same cyclical pattern as in the switchers' equation; however,
consistent with the results of the earlier section, the pattern is much less
pronounced for stayers. Another interesting difference between switcher and
stayer unemployment is the following. Skilled individuals are less likely to
switch, but, conditional on switching, skill decreases total weeks of
unemployment. On the other hand, conditional on staying, skill increases
total weeks of unemployment. This suggests that skilled individuals typically
wait around for re-employment, possibly because some part of their human
capital is industry-specific (or firm-specific). However, some part of their
capital is useful in other industries; hence, if they do decide to look elsewhere
for employment, their weeks of unemployment are lower than for unskilled
switchers.
In addition to these results we have tried several specifications with
interaction terms, in particular interactions involving individual characteristics
and the time dummies to ascertain whether any particular characteristics had
different impacts on unemployment across the three episodes. Columns (iv)
and (vi) present results for the case where education and skill are interacted
with the time dummies. An interesting pattern that emerges for the 1981-83
period is that conditional upon staying, skilled individuals suffered higher
unemployment and that less educated individuals also did. This is suggestive
of less educated skilled workers being laid-off and simply waiting to get their
old jobs back. This also suggests a selection process involving mobility.
Individuals whose skills are not conformable with alternate opportunities don't
switch industries resulting in long spells of unemployment whereas those who
do switch experience little unemployment. 9
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
23
Table 12
Regression Results
Equations
Variable
(i)
(i'O
Constant
-1.106**
(0.124)
-0.004*
(0.002)
-0.203**
(0.049)
-0.004
(0.007)
21.61**
(3.69)
-0.11*
(0.05)
2.44
(1.38)
0.99**
(0.19)
23.13**
(3.14)
-0.10*
(0.05)
2.67*
(1.35)
-0.97**
(0.19)
0.034
(0.044)
-0.168**
(0.058)
0.028
(0.049)
0.011
(0.041)
0.127**
(0.045)
-0.307**
(0.039)
5.95**
(1.14)
-2.83
(1.75)
2.40
(1.33)
-2.06
(1.08)
1.09
(1.19)
-3.45**
(1.17)
5.80**
(1.12)
-2.54
(1.71)
2.40
(1.33)
-2.09
(1.08)
0.92
(1.17)
-3.08“
(1.07)
-0.009
(0.046)
0.088
(0.045)
0.365**
(0.019)
-2.77*
(1.25)
5.54**
(1.20)
AGE
FEM A LE
EDU
(iii)
NE
C ITY
DUR
S K ILL
-2.67*
(1.24)
5.49**
(1.20)
-1.04**
(0.39)
2.90**
(0.39)
13.84**
(1.33)
-0.11**
(0.02)
1.46**
(0.41)
-0.77**
(0.10)
0.16
(0.13)
-0.59**
(0.14)
10.18**
(0.41)
-0.51
(0.44)
-0.41
(0.41)
0.94**
(0.35)
-0.06
(0.50)
2.40**
(0.64)
-0.38
(0.84)
4.13**
(0.82)
-2.71
(1.53)
7.90**
(1.62)
0.102
1220
0.105
1220
0.175
6539
0.182
6539
SK ILL78
SKILL82
D78
D82
FR Q
L
-Log Likelihood
R-square
Sample Size
10.29**
(0.41)
-0.51
(0.44)
-0.38
(0.42)
0.93**
(0.35)
0.18
(0.42)
3.85**
(0.34)
1.51
(1.92)
-3057.4
7759
0.102
1220
(i) Probit Regression
(ii) Total Weeks Equation for Switchers with L (selectivity correction)
(iii) Total Weeks Equation for Switchers without L
(iv) Total Weeks Equation for Switchers with Interaction Terms
(v) Total Weeks Equation for Stayers
(vi) Total Weeks Equation for Stayers with Interaction Terms
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, W P-1989-22
(Vi)
14.74**
(1.00)
-0.12**
(0.02)
1.56**
(0.41)
-0.91**
(0.06)
EDU82
TEN RL
(V)
23.37**
(4.28)
-0.10**
(0.05)
2.36
(1.35)
-0.92**
(0.31)
0.26
(0.44)
-0.46
(0.44)
5.63**
(1.12)
-2.22
(1.72)
2.27
(1.33)
-2.06
(1.08)
-1.01
(1.44)
-3.46
(1.86)
2.90
(2.59)
-1.56
(2.48)
7.08
(5.23)
11.21*
(5.27)
EDU78
TEN RS
(iv)
*5% significance level
‘ *1% significance level
24
V I . C o n c lu s io n s
This paper has analyzed the correlation between sectoral mobility and
unemployment using the PSID data for the years 1974-83. One basic goal has
been to test the sectoral shifts hypothesis advanced by Lilien (1982) and
others. Following Murphy and Topel (1987), we use individual data to
compute the number of industry switchers (i.e., the volume of mobility)
during booms and recessions.
We also measure the total weeks of
unemployment associated with industry switching. Dividing these total weeks
of unemployment by the number of switchers, we get a measure of the
average duration of unemployment associated with mobility.
One theme that emerges quite strongly from our work is the need to
distinguish between the volume of mobility and the duration of
unemployment associated with mobility. We find, as do Murphy and Topel,
that the volume of mobility is roughly constant across booms and recessions.
Hence, sectoral shifts models in which unemployment fluctuations arise
because of increases in the number of switchers, with the duration of
unemployment fixed, are likely to be inconsistent with the data.
What does display a cyclical pattern is the duration of unemployment
associated with mobility. We find that a typical switcher experiences a
significantly larger duration of unemployment during periods marked by
recessions than during booms. However, the role of those individuals who
take as long as two years to locate alternative employment is critical in
generating such a cyclical pattern. If these individuals are excluded, even the
duration of unemployment does not fluctuate much across booms and
recessions. This fact also explains why our duration results differ from those
of Murphy and Topel: the data set they used automatically excluded such
individuals.
To summarize, it is important to focus on workers who are displaced from one
sector and seem to remain without a strong employment connection for an
extended period. As stated earlier, recent papers by Lilien (1988) and
Rogerson (1989) have been proceeding in this direction.
Whatever the ultimate verdict on the sectoral shifts models, our results
indicate the importance of sectoral mobility in any discussion of
unemployment. Even during the boom of 1977-79, nearly a third of total
weeks of unemployment were attributable to industry switchers. Moreover,
F R B C H IC A G O W orking P a p e r
N ovem ber 1989, WP-1989-22
25
the duration of unemployment for the average switcher was three times that
for the average stayer; since, the costs of unemployment to the individual are
often assumed to rise with duration, this fact also points to the importance of
mobility.
Footnotes
^M
ellow and Sider (1983) com
pared CPS respondents' description of their jobs with those
provided independently by their em
ployers. They found a high level of agreem in w
ent
orker and
em
ployer responses to industry affiliation--92% at the one-digit SIC level and 84% at the threedigit level. W
hile we do not know if sim num
ilar
bers w
ould hold for our data set, note that the
industry classification we use corresponds roughly to a two-digit level. Also, a lot of our results
pertain to cyclical patterns in m
obility; there seem no reason to believe that there are system
s
atic
cyclical patterns in m
isreporting of industry.
7
‘‘'Though the PSID began collecting data on industry affiliation in 1971, we did not use the 197173 data since there appear to som coding errors in 1972 an 1973.
e
d
3The only exception is the second year unem
ploym of sw
ent
itchers w are able to accom
ho
plish
the sw
itch between t and t+1. Including such spells does not significantly alter any of our results.
^Loungani and Rogerson (1989) also found that the volum of m
e
obility is only mildly
countercylical. H
owever, the outflow of w
orkers from goods to services accelerates during
recessions, while the flow from services to stable em
ploym in durables accelerates during
ent
boom
s.
^This is not explicitly stated in their paper but w com unicated to us by one of the authors.
as
m
^As argued by Pam and King (1977), it is m likely that individuals leaving the sam are
es
ost
ple
experiencing continued instability in their em
ploym situation.
ent
7
'Although we do not report themhere, it is also the case that all of the other quantitative findings
from section IH are only m
arginally affected by this change in definition,
o
°The inform
ation on education w collected only during the 1975 interview hence, any
as
;
additional education acquired by the individual after 1975 is not reflected in the data. Axel
Borsch-Supan uses the PSID data to discuss the influence of education on m
obility in greater
detail.
V e also tried som other specifications, however none of them produced any statistically
e
significant results. For exam
ple, interacting durables w the tim dum ies resulted, as
ith
e
m
expected, in positive coefficients on the interaction term but they w
s
ere not significant at
conventional levels; for instance, in the 1981-83 episode the t-statistic w 1.5.
as
FRB CHICAGO Working Paper
November 1989, WP-1989-22
26
Bibliography
Barro, Robert, 1981, Money, Expectations and Business Cycles, Academic
Press.
Black, Fisher, 1982, General Equilibrium and Business Cycles, NBER
Working Paper No. 950.
Blanchard, Olivier Jean and
Macroeconomics, MIT Press.
Stanley
Fischer,
1989,
Lectures
on
Borsch-Supan, Axel, 1987, The Role of Education: Mobility Increasing or
Mobility Impeding?, NBER Working Paper No. 2329.
Clark, Kim and Lawrence Summers, 1979, Labor Market Dynamics and
Unemployment: A Reconsideration, Brookings Papers on Economic Activity,
1979:1,13-60.
Davis, Steve J., 1986, Sectoral Shifts and the Dynamic Behavior of
Unemployment: A Theoretical Analysis, University of Chicago Working
Paper No. 86-35.
Davis, Steve J., 1987a, Allocative Disturbances and Specific Capital in Real
Business Cycle Theories, American Economic Review Papers and
Proceedings, 77, 326-32.
Davis, Steve J., 1987b, Fluctuations in the Pace of Labor Reallocation,
Camegie-Rochester Conference Series on Public Policy, 27,335-402.
Duncan, Greg J. and Saul Hoffman, 1979, On-the-job Training and Earnings
Differences by Race and Sex, Review of Economics and Statistics, 16, 594603.
Hall, Robert E., 1970, Why is the Unemployment Rate So High at Full
Employment?, Brookings Papers on Economic Activity, 3, 369-402.
Katz, Lawrence and Bruce Meyer, Unemployment Insurance, Recall
Expectations and Unemployment Outcomes, NBER Working Paper No. 2594,
1988.
Lee, Lung-Fei, 1978, Unionism and Wage Rates : A Simultaneous Equations
Model with Qualitative and Limited Dependent Variables, International
Economic Review, 19,415-33.
Lilien, David, 1982, Sectoral Shifts and Cyclical Unemployment, Journal of
Political Economy, 90,777-93.
FRB CHICAGO Working Paper
November 1989, WP-1989-22
27
Lilien, David, 1988, Frictional and Structural Unemployment in Equilibrium,
mimeograph, UC-Irvine.
Loungani, Prakash and Richard Rogerson, 1989, Cyclical Fluctuations and
Sectoral Reallocation: Evidence from the PSID, Journal of Monetary
Economics, 23,259-73.
Loungani, Prakash, Mark Rush and William Tave, Stock Market Dispersion
and Unemployment, working paper, July 1989.
Lucas, Robert E. and Edward Prescott, 1975, Equilibrium Search and
Unemployment, Journal of Economic Theory, 7,188-209.
Maddala, G.S., 1986, Limited-Dependent and Qualitative Variables in
Econometrics, Econometric Society Monographs No. 3, Cambridge
University Press, Cambridge.
Mankiw, N. Gregory, 1989, Real Business Cycles: A New Keynesian
Perspective, The Journal of Economic Perspectives, 3, pp. 79-90.
Mellow, Wesley and Hal Sider, 1983, Accuracy of Response in Labor Market
Surveys: Evidence and Implications, Journal of Labor Economics, 1, 331-44.
Mincer, Jacob and Boyan Jovanovic, 1981, Labor Mobility and Wages, in
Sherwin Rosen ed., Studies in Labor Markets, The University of Chicago
Press, 21-63.
Murphy, Kevin M. and Robert Topel, 1987, The Evolution of Unemployment
in the United States : 1968-85, in Stanley Fischer ed., NBER Macroeconomics
Annual 1987 (MIT Press, Cambridge).
Rissman, Ellen, What is the Natural Rate of Unemployment? Economic
Perspectives, Federal Reserve Bank of Chicago, 1987.
Rogerson, Richard, 1986, Sectoral
mimeograph, University of Rochester.
Shifts
and
Cyclical
Fluctuations,
Rogerson, Richard, 1989, Sectoral Shocks, Human Capital and Displaced
Workers, mimeograph, Stanford University.
Shapiro, David and Stephen M. Hills, 1986, Adjusting to Recession: Labor
Market Dynamics in the Construction, Automobile and Steel Industries, in
Stephen M. Hills (ed.), The Changing Labor Market, Lexington Books.
Shaw, Kathryn, 1984, A Formulation of the Earnings Function Using the
Concept of Occupational Investment, Journal of Human Resources, 19, 319340.
FRB CHICAGO Working Paper
November 1989, WP-1989-22
28
Shaw, Kathryn, 1989, Wage Variability in the 1970’s: Sectoral Shifts or
Cyclical Sensitivity ?, The Review of Economics and Statistics.
FRB CHICAGO Working Paper
November 1989, WP-1989-22
29
Federal Reserve Bank of Chicago
R E S E A R C H STAFF M E M O R A N D A , W O R K I N G PAPERS A N D STAFF STUDIES
The following l s s papers developed in recent years by the Bank’ research s a f Copies of those
it
s
tf.
materials that are currently available can be obtained by contacting the Public Information Center
(312)322-5111.
Working Paper Series— A s r e of research studies on regional economic issues relating to the Sev
eis
enth Federal Reserve D s r c , and on financial and economic t p c .
itit
ois
Regional Economic Issues
Donna Craig Vandenbrink
“The Effects of Usury Ceilings:
the Economic Evidence,” 1982
David R. Allardice
“Small Issue Industrial Revenue Bond
Financing in the Seventh Federal
Reserve Dis r c , 1982
tit”
WP-83-1
William A. Testa
“Natural Gas Policy and the Midwest
Region,” 1983
WP-86-1
Diane F. Siegel
William A. Testa
“Taxation of Public U i i i s S l s
tlte ae:
State Practices and the I l n i Experience”
lios
WP-87-1
Alenka S Giese
.
William A. Testa
“Measuring Regional High Tech
Activity with Occupational Data”
WP-87-2
Robert H. Schnorbus
Philip R. Israilevich
“Alternative Approaches to Analysis of
Total Factor Productivity at the
Plant Level”
WP-87-3
Alenka S Giese
.
William A. Testa
“Industrial R & D An Analysis of the
Chicago Area”
WP-89-1
William A. Testa
“Metro Area Growth from 1976 to 1985:
Theory and Evidence”
WP-89-2
William A. Testa
Natalie A. Davila
“Unemployment Insurance: A State
Economic Development Perspective”
WP-89-3
Alenka S Giese
.
“A Window of Opportunity Opens for
Regional Economic Analysis: BEA Release
Gross State Product Data”
WP-89-4
Philip R. Israilevich
William A. Testa
“Determining Manufacturing Output
for States and Regions”
WP-89-5
Alenka S.Geise
“The Opening of Midwest Manufacturing
to Foreign Companies: The Influx of
Foreign Direct Investment”
WP-89-6
Alenka S Giese
.
Robert H. Schnorbus
“A New Approach to Regional Capital Stock
Estimation: Measurement and
Performance”
•WP-82-1
••WP-82-2
•lim ited quantity available.
**Out of print.
2
Working Paper Series (corn'd)
WP-89-7
William A. Testa
“Why has I l n i Manufacturing Fallen
lios
Behind the Region?”
WP-89-8
Alenka S Giese
.
William A. Testa
“Regional Specialization and Technology
in Manufacturing”
WP-89-9
Christopher Erceg
Philip R. Israilevich
Robert H. Schnorbus
“Theory and Evidence of Two Competitive
Price Mechanisms for Steel”
WP-89-10
David R. Allardice
William A. Testa
“Regional Energy Costs and Business
Siting Decisions: An I l n i Perspective”
lios
WP-89-21
William A. Testa
“Manufacturing’ Changeover to Services
s
in the Great Lakes Economy”
Issues i Financial Regulation
n
WP-89-11
Douglas D. EvanofT
Philip R. I
srailevich
Randall C. Merris
“Technical Change, Regulation, and Economies
of Scale for Large Commercial Banks:
An Application of a Modified Version
of Shepard’ Lemma”
s
WP-89-12
Douglas D. EvanofT
“Reserve Account Management Behavior:
Impact of the Reserve Accounting Scheme
and Carry Forward Provision”
WP-89-14
George G. Kaufman
“Are Some Banks too Large to Fail?
Myth and Reality”
WP-89-16
Ramon P. De Gennaro
James T. Moser
“Variability and Stationarity of Term
Premia”
WP-89-17
Thomas Mondschean
“A Model of Borrowing and Lending
with Fixed and Variable Interest Rates”
WP-89-18
Charles W. Calomiris
“Do "Vulnerable'" Economies Need Deposit
Insurance?: Lessons from the U.S.
Agricultural Boom and Bust of the 1920s”
Macro Economic Issues
WP-89-13
David A. Aschauer
“Back of the G-7 Pack: Public Investment and
Productivity Growth in the Group of Seven”
WP-89-15
Kenneth N. Kuttner
“Monetary and Non-Monetary Sources
of Inf a i n An Error Correction Analysis”
lto:
WP-89-19
Ellen R. Rissman
“Trade Policy and Union Wage Dynamics”
•Limited quantity available.
••Out of print.
Working Paper Series (cont'd)
WP-89-20
Bruce C. Petersen
William A. Strauss
“Investment Cyclicality in Manufacturing
Industries”
WP-89-22
Prakash Loungani
Richard Rogerson
Yang-Hoon Sonn
“Labor Mobility, Unemployment and
Sectoral S i t : Evidence from
hfs
Micro Data”
♦Limited quantity available.
♦♦Out of print.
4
Staff Memoranda—A s r e of research papers in draft form prepared by members of the Research
eis
Department and distributed to the academic community for review and comment. ( e i s discon
Sre
tinued in December, 1988. Later works appear i working paper s r e )
n
eis.
**SM-81-2
George G. Kaufman
“Impact of Deregulation on the Mortgage
Market,” 1981
••SM-81-3
Alan K. Reichert
“An Examination of the Conceptual Issues
Involved in Developing Credit Scoring Models
in the Consumer Lending Field,” 1981
Robert D. Laurent
“A Critique of the Federal Reserve’ New
s
Operating Procedure,” 1981
George G. Kaufman
“Banking as a Line of Commerce: The Changing
Competitive Environment,” 1981
SM-82-1
Harvey Rosenblum
“Deposit Strategies of Minimizing the Interest
Rate Risk Exposure of S&Ls,” 1982
•SM-82-2
George Kaufman
Larry Mote
Harvey Rosenblum
“Implications of Deregulation for Product
Lines and Geographical Markets of Financial
I s i i i n , 1982
ntttos”
•SM-82-3
George G. Kaufman
“The Fed’ Post-October 1979 Technical
s
Operating Procedures: Reduced Ability
to Control Money,” 1982
SM-83-1
John J Di Clemente
.
“The Meeting of Passion and I t l e t
nelc:
A History of the term ‘
Bank’in the
Bank Holding Company Act,” 1983
SM-83-2
Robert D. Laurent
“Comparing Alternative Replacements for
Lagged Reserves: Why S t l for a Poor
ete
Third Best?” 1983
♦•SM-83-3
G. O. Bierwag
George G. Kaufman
“A Proposal for Federal Deposit Insurance
with Risk Sensitive Premiums,” 1983
•SM-83-4
Henry N. Goldstein
Stephen E. Haynes
“A C i ical Appraisal of McKinnon’
rt
s
World Money Supply Hypothesis,” 1983
SM-83-5
George Kaufman
Larry Mote
Harvey Rosenblum
“The Future of Commercial Banks in the
Financial Services Industry,” 1983
SM-83-6
Vefa Tarhan
“Bank Reserve Adjustment Process and the
Use of Reserve Carryover Provision and
the Implications of the Proposed
Accounting Regime,” 1983
SM-83-7
John J Di Clemente
.
“The Inclusion of Thrifts in Bank
Merger Analysis,” 1983
SM-84-1
Harvey Rosenblum
Christine Pavel
“Financial Services in Transition: The
Effects of Nonbank Competitors,” 1984
SM-81-4
••SM-81-5
♦Limited quantity available.
♦♦Out of print.
Staff Memoranda (corn'd)
SM-84-2
George G. Kaufman
“The Securities Activities of Commercial
Banks,” 1984
SM-84-3
George G. Kaufman
Larry Mote
Harvey Rosenblum
“Consequences of Deregulation for
Commercial Banking”
SM-84-4
George G. Kaufman
“The Role of Traditional Mortgage Lenders
in Future Mortgage Lending: Problems
and Prospects”
SM-84-5
Robert D. Laurent
“The Problems of Monetary Control Under
Quasi-Contemporaneous Reserves”
SM-85-1
Harvey Rosenblum
M. Kathleen O ’
Brien
John J Di Clemente
.
“On Banks, Nonbanks, and Overlapping
Markets: A Reassessment of Commercial
Banking as a Line of Commerce”
SM-85-2
Thomas G. Fischer
William H. Gram
George G. Kaufman
Larry R. Mote
“The Securities Activities of Commercial
Banks: A Legal and Economic Analysis”
SM-85-3
George G. Kaufman
“Implications of Large Bank Problems and
Insolvencies for the Banking System and
Economic Policy”
SM-85-4
Elijah Brewer, I I
I
“The Impact of Deregulation on The True
Cost of Savings Deposits: Evidence
From I l n i and Wisconsin Savings &
lios
Loan Association”
SM-85-5
Christine Pavel
Harvey Rosenblum
“Financial Darwinism: Nonbanks—
and Banks— Are Surviving”
SM-85-6
G. D. Koppenhaver
“Variable-Rate Loan Commitments,
Deposit Withdrawal Risk, and
Anticipatory Hedging”
SM-85-7
G. D. Koppenhaver
“A Note on Managing Deposit Flows
With Cash and Futures Market
Decisions”
SM-85-8
G. D. Koppenhaver
“Regulating Financial Intermediary
Use of Futures and Option Contracts:
Policies and Issues”
SM-85-9
Douglas D. EvanofT
“The Impact of Branch Banking
on Service Accessibility”
SM-86-1
George J Benston
.
George G. Kaufman
“Risks and Failures in Banking:
Overview, History, and Evaluation”
SM-86-2
David Alan Aschauer
“The Equilibrium Approach to Fiscal
Policy”
•Limited quantity available.
••Out of print.
6
Staff Memoranda (cont'd)
SM-86-3
George G. Kaufman
“Banking Risk in Historical Perspective”
SM-86-4
Elijah Brewer I I
I
Cheng Few Lee
“The Impact of Market, Industry, and
Interest Rate Risks on Bank Stock Returns”
SM-87-1
Ellen R. Rissman
“Wage Growth and Sectoral S i t :
hfs
New Evidence on the S a i i y of
tblt
the Ph l i s Curve”
ilp
SM-87-2
Randall C. Merris
“Testing Stock-Adjustment Specifications
and Other Restrictions on Money
Demand Equations”
SM-87-3
George G. Kaufman
“The Truth About Bank Runs”
SM-87-4
Gary D. Koppenhaver
Roger Stover
“On The Relationship Between Standby
Letters of Credit and Bank Capital”
SM-87-5
Gary D. Koppenhaver
Cheng F. Lee
“Alternative Instruments for Hedging
Inflation Risk in the Banking Industry”
SM-87-6
Gary D. Koppenhaver
“The Effects of Regulation on Bank
Participation in the Market”
SM-87-7
Vefa Tarhan
“Bank Stock Valuation: Does
Maturity Gap Matter?”
SM-87-8
David Alan Aschauer
“Finite Horizons, Intertemporal
Substitution and Fiscal Policy”
SM-87-9
Douglas D. EvanofT
Diana L. Fortier
“Reevaluation of the Structure-ConductPerformance Paradigm in Banking”
SM-87-10
David Alan Aschauer
“Net Private Investment and Public Expenditure
in the United States 1953-1984”
SM-88-1
George G. Kaufman
“Risk and Solvency Regulation of
Depository In t t t o s Past Policies
siuin:
and Current Options”
SM-88-2
David Aschauer
“Public Spending and the Return to Capital”
SM-88-3
David Aschauer
“I Government Spending Stimulative?”
s
SM-88-4
George G. Kaufman
Larry R. Mote
“Securities Activities of Commercial Banks:
The Current Economic and Legal Environment1
SM-88-5
Elijah Brewer, I I
I
“A Note on the Relationship Between
Bank Holding Company Risks and Nonbank
Activity”
SM-88-6
G. O. Bierwag
George G. Kaufman
Cynthia M. Latta
“Duration Models: A Taxonomy”
G. O. Bierwag
George G. Kaufman
“Durations of Nondefault-Free Securities”
•Lim ited quantity available.
••Out of print.
7
Staff Memoranda (cont'd)
SM-88-7
David Aschauer
“I Public Expenditure Productive?”
s
SM-88-8
Elijah Brewer, HI
Thomas H. Mondschean
“Commercial Bank Capacity to Pay
Interest on Demand Deposits:
Evidence from Large Weekly
Reporting Banks”
SM-88-9
Abhijit V. Banerjee
Kenneth N. Kuttner
“Imperfect Information and the
Permanent Income Hypothesis”
SM-88-10
David Aschauer
“Does Public Capital Crowd out
Private Capital?”
SM-88-11
Ellen Rissman
“Imports, Trade Policy, and
Union Wage Dynamics”
Staff Studies— A s r e of research studies dealing with various economic policy issues on a national
eis
lvl
ee.
SS-83-1
**SS-83-2
Harvey Rosenblum
Diane Siegel
“Competition in Financial Services:
the Impact of Nonbank Entry,” 1983
Gillian Garcia
“Financial Deregulation: Historical
Perspective and Impact of the Garn-St
Germain Depository Institutions Act
of 1982,” 1983
•Limited quantity available.
**Out of print.
@FedFRASER