zOSIPP Discussion Paper : DP-2001-E-003

The Impact of Sectoral Shifts on the Unemployment Rate ofDifferent Age Groups

30 November 2001

Kei SakataResearcher, Institute for Social and Economic Research, Osaka University
C.R. McKenzieProfessor, Osaka School of International Public Policy, Osaka University【Keywords】Unemployment; Sectoral Shifts Hypothesis; Age cohort effects; Time Series Analysis;
Generated regressors.

【Summary】This paper examine Lilien’s sectoral shifts hypothesis for Japan for different age cohorts.
Previous studies of the sectoral shifts hypothesis for Japan have for the most part concentrated on therelationship between aggregate unemployment and sectoral shifts, and are typically not supportive of thehypothesis. However, recent increases in the unemployment rates of the young and the aged suggest a need toreexamine the hypothesis for different age groups. It is found that sectoral shifts have a short-term positiveeffect on the unemployment of aged male workers and the effects increase in times of recession.

The Impact of Sectoral Shifts on the Unemployment
Rate of Different Age Groups

Kei Sakata aabandC.R. McKenzieb

Institute for Social and Economic Research, Osaka University, Japan (ksakata@osipp.osaka-u.ac.jp)
Osaka School of International Public Policy, Osaka University, Japan (mckenzie@osipp.osaka-u.ac.jp)

Abstract: This paper examine Lilien’s sectoral shifts hypothesis for Japan for different age cohorts. Previous
studies of the sectoral shifts hypothesis for Japan have for the most part concentrated on the relationshipbetween aggregate unemployment and sectoral shifts, and are typically not supportive of the hypothesis.
However, recent increases in the unemployment rates of the young and the aged suggest a need to reexamine
the hypothesis for different age groups. It is found that sectoral shifts have a short-term positive effect on theunemployment of aged male workers and the effects increase in times of recession.

Keywords: Unemployment; Sectoral Shifts Hypothesis; Age cohort effects; Time Series Analysis; Generatedregressors.
1.

INTRODUCTION

Lilien’s [1982] sectoral shifts hypothesis asserts

Abraham and Katz [1984, 1986] argue thatthat sectoral shifts of demand affect unemployment

aggregate

because labour reallocation across sectors is a time

countercyclical movements in Lilien’s dispersion

consuming process. Using annual postwar data formeasure. Abraham and Katz [1984] find that when

the United States, Lilien [1982] estimated a reduced

Lilien’s sigma is purged of the influence ofform unemployment equation that included current

aggregate demand shocks, this purged proxy

and lagged values of a cross-sectoral dispersion

variable can explain only a small fraction ofmeasure

unemployment fluctuations in the United States.

(Lilien’ssigma)

andamonetarydemanddisturbances

caninduce

disturbance measure. Lilien found a statisticallysignificant

positive

relationshipbetween

dispersion measure and the unemployment rate.

theDavis [1987] suggests that Lilien fails to take into
account the possibility of stage-of-business-cycle
effects on the sectoral shifts hypothesis. If the value

withthe age groups 15-24 and 55-64 are particularly

unemployment is procyclical, then there will besurging. In the context of the aging population andincentives for unemployment spells to be shorter

casualisation of the youth workforce, it is importantduring expansions and longer during recessions.

to scrutinize the effects of sectoral shifts on theseTherefore, a given amount of labour reallocation

two groups.

offoregone

production

associatedmay lead to underestimating unemployment whenaggregate macroeconomic conditions are improving

In this paper, incorporating criticisms made ofand vice versa. Taking these suggestions into

Lilien’s work by previous studies, the sectoral shifts

consideration, Mills et al. [1995] provide evidence

hypothesis is tested for males aged 15-24 and 55-64

to support the sectoral shifts hypothesis for theusing Lilien’s dispersion index and a purged index,

United States.

and tests for stage-of-business-cycle effects areconducted.

In contrast, Brunello’s [1991] study for theaggregate unemployment rate in Japan does not

The plan of this paper is as follows. In section 2,

support the sectoral shifts hypothesis. However,the data used are described. In order to obtain

Sakata [2001] finds that sectoral shifts affect maleestimates

unemployment, but not female unemployment. He

components of money growth, a money growth

speculates

theequation is specified and estimated in section 3.

accumulation of human capital may play a crucial

Tests of the sectoral shifts hypothesis for thepart in explaining these differences. Sakata’s [2001]

aggregate unemployment rate and the malefindings suggest that it is important to examine

unemployment rate of two age groups are

Lilien’s hypothesis for different cohorts, and that

conducted in section 4. Section 5 explores thethe findings for the aggregate unemployment ratepossibility that the stage of the business cycle has

can be deceptive.

an effect on labour reallocation. Section 6 provides

thatgenderdifferences

inof

theexpectedandunexpected

some concluding remarks.
If as Sakata [2001] speculates the effects of sectoral
shiftson

unemployment

dependontheaccumulation of industry-specific human capital by2.

DATAworkers, then sectoral shocks may have a large
impact on older workers who have accumulated

Employment data to compute the measures oflarge amounts of human capital. In contrast,

sectoral shifts are taken from The Monthly Report

sectoral shocks may have a small (or no) impact onon Labour Force Survey (Roudouryoku Chousa

young workers who have yet to accumulate very

Houkoku) (LFS). LFS provides a thirteen-industry

much human capital. Sakata [2001] indicates thatdivision of monthly nonagricultural employment.

there are gender differences in the effects ofThe thirteen sectors are: Fisheries; Mining;

sectoral shocks on unemployment, and therefore,

Construction; Manufacturing: Textile mill products;

the analysis will focus on male unemployment.

Manufacturing: Chemical and related products;

Currently, the unemployment rates of workers in

Manufacturing: Metal and machinery;Manufacturing: Other; Electricity, gas, heat andM2+CD and real GDP, respectively, and R and i bewater supply; Transport and Communication;

the long-term interest rate and the inflation rate. In

Wholesale Trade; Retail trade, restaurants and pubs;

order to construct the expected and unexpected

Finance, insurance and real estate; and Services.

components of money growth, no contemporaneous

This categorization of industries began in January

variables appear in the money growth equation.

1973. As a result, the sample is from the firstquarter of 1973 to the fourth quarter of 1999. Data

Unit root tests suggest that all variables in theon the aggregate unemployment rate and themoney growth equation are non-stationary I(1), andunemployment rates for males aged 15-24 anda Johansen test suggests that there is one

males aged 55-64 are also obtained from LFS.

cointegrating relationship between m, y, R, and i.
As a result, the money growth equation is estimated

The short-term and long-term interest rates, theas an error correction model with four lags. The

three month Gensaki rate and the 10 year

residual from this money growth regression,

government bond yield, are taken from the NEEDSDMRt , is then used as a measure of unanticipateddatabase. Other macrodata are taken from themoney growth, and anticipated money growth isOECD Main Economic Indicators (MEI) in thedefined as

OECD Statistical Compendium. Money is measured∆ denotes the first difference of a variable. DMEt

as M2+CD. The real values of money and GDP are

and DMRt are used as explanatory variables in ancomputed by deflating by the consumer price index

unemployment equation to test the sectoral shifts(CPI). Energy prices are measured using the Fuelhypothesis.

DME t Δ= m t - DMR t ,whereand Electricity Price Index. The inflation rate iscomputed using the CPI. All the macroeconomic
data (except the interest rates) are seasonallyadjusted.

4.

TESTING THE SECTORAL SHIFTSHYPOTHESISIn this section, the proxy variables for sectoral
3.

MONEY GROWTH EQUATION

shocks are introduced. To check the robustness of
the results obtained, two different measures,

In order to test the implications of the sectoral shifts

Lilien’s [1982] measure and Mills et al.’s [1995]

hypothesis,

unemployment

measure which takes account of Abraham andequation is required. Expected and unexpected

Katz’s [1984] criticism, are used. Lilien’s measuremoney demand shocks computed as the fitted

has the following form:

values and residuals from a Barro [1977] typeσ t2 =awell-specified

money growth equations are included in theunemployment equation as explanatory variables.

N∑ (e / E ) ⋅ (∆ log(e ) − ∆ log(E )) ,2

itt

itt

where eit is the employment level in sector i , i=1,
N

2,…13,The money growth equation is estimated in a
simple form. Let m and y be the logarithms of real

(1)

i =1

at

timet,Et =∑ e is aggregate
iti =1

employment at time t, and N=13.

Mills et al. [1995] purge the index of monetary

theshocks, regarding these as the main driving force onintertemporal substitution of leisure. Imports can be

the demand side. Each sectoral growth rate,

assumed to reflect the effect of shocks from the∆ log(eit ) − ∆ log( Et ) , was regressed on the current

globalvalue and four lags of expected and unexpected

unemployment. The fuel and electricity price index

money growth, DME and DMR, as follows:

is used to control for the impact of oil shocks in theΔ log(e Δit ) - log(E t ) =a∑+α4

j= 04∑βj DME t - j +j= 0working-capital

economic

costsofenvironment

firms

onor

thedomestic

sample period.
εj DMR t - j +(2)it .

As with the specification of the money growth

Equation (2) was estimated for each i by ordinary
least squares, and the residuals,εequation, a vector autoregression (VAR) is firstestimated to determine the order of VAR, and then,~it , were

Johansen tests are conducted to determine thecombined to obtain the purged dispersion index

number of cointegrating relationships. In choosing

defined as:

the order of VAR and in testing cointegration, thecontemporaneous values and up to four lags of~s 2t ∑= (e it / E t )ε it2 .
N(3)

i =1

DME, DMR and σ t are included as exogenousvariables.

Augmented Dickey-Fuller (ADF) tests applied toσ t and s t reject the null hypothesis of a unit root

When the variable s t and the business cycle

suggesting that they are both stationary. In contrast,variables are added, it is assumed that thethe unemployment rates of all three groups namely,unemployment equations used for testing theall workers, U t , males

aged 15-24 , U15 t , andsectoral shifts hypothesis have the same structure as

males aged 55-64, U 55 t , are found to be non-the model specified when σ t is used. The test

stationary.

areresults for σ t and s t are shown in Tables 1 and 2,stationary, they cannot be cointegrated with therespectively. To save the space, the reported results

non-stationary U t , U15 t , and U 55 t . Thus, there

are limited to estimates directly related to thecan be no long-term relationship between thedispersion measures. Furthermore, as generated

unemployment

regressors, DME and DMR, are included in theAs

thedispersionvariables

andmeasuresthedispersionmeasures.

estimated model, t-statistics are computed using
Newey-West standard errors (Parzen weights).

Following Wallis [1987], U, U15, and U55 are thelogistic transformation of the unemployment rate of

For aggregate unemployment, the results in Tables

each group. The unemployment equation for each

1 and 2 are ambiguous in that there are bothgroup includes the short-term interest rate, r, thepositive and negative significant coefficients. Aslogarithm of the fuel and electricity price index,

shown in Sakata [2001], this may be due to treating

FEPI, and the ratio of imports to GDP, IMP. The

the labour force as a homogeneous group.

inclusion of the short-term interest rate aims tocapture the impacts on unemployment of changes inTable 1. The impact of σ t on Unemployment.

On the other hand, for males aged 55-64 the impact

σt

Ut

U15 tof the dispersion index is positive and significant

T-0.315**-0.494

for both σ t and s t . The results are in line with

(3.24)(0.45)-0.675the hypothesis that the groups that have relatively

t-1

U 55 tmore industry- and firm- specific human capital are(0.67)
t-2

more affected by sectoral shocks.

1.025(1.02)

t-3

t-4

R2

0.241*

1.181

2.796**(2.43)(1.22)(3.46)-0.223*-1.526(2.37)(1.28)

0.36

0.24

5.

The stage-of-business-cycle effects are modeled
here using the indicator of the stage of the business

0.32

cycle (Keiki Kijyun Hiduke) announced by theT=100 (Sample Period: 1975Q1-1999Q4)

Economic Planning Agency. A dummy variable,

Absolute t-values are presented in parentheses.
* and ** indicate the variable is significant at the 5% and 1%
significance levels, respectively.

Table 2. The impact of s t on Unemployment.
stT

Ut-0.294**(2.56)

U15 t-0.312(0.27)-0.977(0.95)
0.371(0.36)
1.249(1.13)-1.327(0.93)
0.17

t-1
t-2
t-3
t-4
R2
T=95

STAGE-OF-BUSINESS CYCLE EFFECTS

0.22

Bt , is constructed to take the value unity when theannounced business cycle is in a downturn, andzero otherwise. Interaction variables of the form

U 55 tBt σ t and its four lags are then also added to thebaseline unemployment equation of each groupwith significant dispersion variables. In addition, inorder to distinguish the effects of the recession from2.456**(3.33)

the stage-of-business-cycle effects, a recession
dummy variable is also included.

0.29
Table 3 summarises the results of testing for stage-(Sample period: 1976Q2-1999Q4)

of-business-cycle effects using σ t . Significant

The results for the males aged 15-24 show thatnone of the current or lagged values of either
dispersion variable, σ tsignificant.

Waldor s t , are individuallytests

confirm

insignificance (p-value=0.08 fortheirσtjointand p-value=0.62 for s t ). Although the Wald test for σ t
rejects the null hypothesis at the 10 % significancelevel, none of dispersion measures are significant
when the current and lagged values of σ t areadded individually.

stage-of-business-cycle effects are present for
aggregate

unemploymentandfor

theunemployment of males aged 55-64, indicating thatthe impacts of sectoral shocks on unemployment
increases in times of recession for these two groups.
In contrast, the results for males aged 15-24 show
no significant interaction terms. A Wald test also
accepts the null hypothesis that the current and fourlags of the stage of business cycle variables arejointly insignificant for this group (p-value=0.26).

Results consistent with those in Table 3 are also

may be blind to the actual impacts of sectoral shifts

obtained when the stage-of-business-cycle effects

on different cohorts.

are investigated using the purged index s t .
7.

REFERENCES

Table 3. Stage-of-Business-Cycle Effects.
Bt σ tT

Ut

t-1
t-2
t-3
t-4
R2

0.163*(3.47)
0.42

U15 t
1.314
(0.82)
0.297
(0.2)
0.143
(0.12)
0.868(0.75)-0.185(0.26)
0.23

U 55 tAbraham,K.G.,

andKatz,

L.F.,

Cyclicalunemployment: sectoral shifts or aggregate
disturbances?, NBER Working Paper No. 1410,
1984.
Abraham,
1.679**(3.21)

K.G.,

andKatz,

L.F.,

Cyclicalunemployment: sectoral shifts or aggregate
disturbances?, Journal of Political Economy,94, 507-522, 1986.

0.35

Barro, R.J., Unanticipated money growth and

T=100 (Sample period: 1975Q1-1999Q4)

unemployment in the United States, American
6.

Economic Review, 67, 101-115, 1977.

CONCLUSION

Brunello, G., Mismatch in Japan in Mismatch andIn contrast to the previous studies for the United

Labour Mobility (F. Padoa-Schioppa, Ed.), pp.

States, earlier studies for Japan do not support the140-179. Cambridge Univ Press, London/New

sectoral shifts hypothesis. However, Sakata [2001]

York, 1991.

provides evidence that in Japan, the findings for
aggregate

unemploymentcanbemisleading

because sectoral shocks have affects that differ
according to workers’ levels of industry- and firmspecific human capital accumulation.

Davis, S.J., Fluctuations in the pace of labor
reallocation, Carnegie-Rochester Conference
Series on Public Policy, 27, 355-402, 1987.
Lilien,D.M.,

Sectoralshifts

andcyclicalunemployment, Journal of Political Economy,
90, 777-793, 1982.

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Mills, T.C., Pelloni, G. and Zervoyianni, A.,

the unemployment of males in two age groups, 15-

Unemployment fluctuations in the United

24 and 55-64, whose accumulation of humanStates: further tests of the sectoral-shifts

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hypothesis, Review of Economics and Statistics,

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Sakata,K.

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unemployment in Japan, Journal of theIn addition, the aggregate unemployment rate andJapanese

the unemployment rate of males aged 55-64 areforthcoming, 2001.

andInternationalEconomies,

strongly influenced by the stage of the business

Wallis, K.F., Time series analysis of bounded

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