2003.22

Industrial Diversity andMetropolitan Unemployment Rate
Keizo Mizuno
Fumitoshi Mizutani
Noriyoshi NakayamaIndustrial Diversity and Metropolitan Unemployment Rate

By

Keizo MIZUNO, Fumitoshi MIZUTANI, and Noriyoshi NAKAYAMA*

Correspondence:
Fumitoshi MIZUTANI
Graduate School of Business Administration
Kobe University
2-1 Rokkodai, Nada-ku,
Kobe 657 -8501, Japan(Tel) +81-78-803-6905(Fax) +81-78-803-6977(E-mail) toshi@rokkodai.kobe-u.ac.jp

September 8, 2003

* Keizo MIZUNO (School of Business Administration, Kwansei Gakuin University); Fumitoshi MIZUTANI(Graduate School of Business Administration, Kobe University); Noriyoshi NAKAYAMA (Faculty of Commerce,
The University of Marketing and Distribution Sciences)

1

Industrial Diversity and Metropolitan Unemployment Rate**[Abstract]:

The main goal of our study is to evaluate whether or not industrial diversity

helps reduce the unemployment rate of a metropolitan area. We used a data set from Japan’s 118
metropolitan areas from the year 1995 and determined from our analysis that although industrial
diversity might reduce the unemployment rate of a metropolitan area, it is only one of several factors
and that other factors might have a stronger impact on unemployment rate.

Second, it was foundthat for both the manufacturing and the construction industry, location quotient has a negative
relationship with the unemployment rate of a metropolitan area. We also discovered that the more
highly educated a metropolitan population is in terms of the percentage of graduates of institutionsof higher learning, the lower will be its unemployment rate.

[JEL Classification]: J6, R1, R5[Key Words]: Unemployment Rate, Industrial Diversity, Frictional Unemployment, Structural
Unemployment, Location Quotient, Metropolitan Area

1. Introduction
Although it has for years had a lower unemployment rate than other industrializedcountries, Japan has begun to see an increase in unemployment since its economy was hit by therecession of the late 90’s.

The level of a nation’s unemployment is commonly seen as a barometerof its economy’s health, so that Japan’s increased unemployment has worried the government andprompted consideration of several policy options.

Although previous studies analyze

unemployment rate in general (e.g. Rosen (1984) and Partridge and Rickman (1997)),
unemployment rate in Japan remains a topic of much interest because it varies so much by region,** The earlier version of this study was accomplished within the organization Urban Revitalization of Kansai Region

in FY2002, sponsored by Nihon Keizai Shinbun. We would like to thank the project members for their discussions
and comments on the earlier version. Especially, we would like to thank Hitoshi Akiyama (UFJ Research Institute),
Kyoichi Futagami (Osaka University), Takenori Inoki (International Japanese Culture Research Center), Yoshiaki
Shikano (Doshisya University) and Michihiko Tachi (Nihon Keizai Shinbun).

2

with large metropolitan areas such as Tokyo having lower unemployment rates than smaller cities,
with the puzzling exception of the nation’s second largest city, Osaka, which is suffering from a highunemployment rate.

In October 2002, the Kansai region, including the Osaka metropolitan area,

recorded an unemployment rate of 7.2%, much higher than the average rate of 5.5%.

Theoretically,

regional differentials of the unemployment rate are attributed to the friction resulting from adjustingfor the mismatch between demand and supply of labor markets among regions.

These structural

factors consist of the costs of exchanging information, moving, conducting transactions related tohousing, and dealing with psychological effects.

These components are important, but they do not

fully account for regional differentials in unemployment rate.

Industrial structure surely affects

unemployment rate, and this paper examines the relationship between the two, with the aim of
testing the hypothesis that more industrially diversified metropolitan areas have lower
unemployment rates.
This paper follows the theoretical justification of Simon (1988), who argues that industrial
diversity attains a lower unemployment rate by assuming that the frictional component of
employment fluctuations is a random variable and independent across industries.
empirical analysis of 91 large U.S. SMSAs strongly supports the hypothesis.

Simon’s

Although Simon’s

study makes important contributions to the study of the relationship between unemployment rate andindustrial diversity, it fails to clarify the structural factors affecting unemployment rate.

In thepresent study, by analyzing 118 metropolitan areas in Japan for the year 1995, we seek to make therelationship more clear.
What we hope to contribute can be stated as follows.

While Simon does not clearly

outline structural factors, we investigate the relationship between unemployment rate and industrial
diversity by considering both frictional and structural factors.

3

Second, we incorporate into ouranalysis of unemployment the location quotient of industries, a factor which has heretofore not been
included in analyses of unemployment rate.

Third, as for studies of metropolitan unemployment

rate, ours is the first in Japan because there has up to now been no authoritative definition in Japan ofa metropolitan area.

We thus began our study by defining metropolitan areas and collecting data

for each.

2. Unemployment, Industrial Diversity, and Location Quotient
2.1 Kinds of Unemployment
Before we present the points of argument concerning the relationships among
unemployment rate, industrial diversity, and industry-specific factors, we will summarize kinds of
unemployment.

Based on labor economics literature (e.g. Ehrenberg and Smith (2003)),

unemployment can be categorized into four types: frictional, structural, cyclical and seasonal.
Frictional unemployment occurs even in a full-employment labor market because labormarkets are in a natural state of constant change, and because information about job opportunities islimited, so that it often takes considerable time for unemployed workers to locate employers who
might be able to offer them suitable work.
Structural unemployment occurs when there is a mismatch between demand for and supply
of workers in a given area.

If wages were completely flexible and if costs of occupational or

geographic mobility were low, market adjustments would eliminate this kind of unemployment, but
the fact is that mobility costs between different occupations are generally not low because it requires
time and money to change jobs.

Geographical mobility is expensive because of the costs of job

searches in unfamiliar territory and the concomitant necessary financial transactions such as the saleof a house or other property.
Cyclical unemployment occurs during fluctuations in the business cycle, when a decline in4

aggregate demand in the output market causes a decline in demand for labor.

Seasonal

unemployment, is similar to cyclical unemployment but fluctuations in seasonal unemployment canbe regularly anticipated and follow a more systematic pattern over a period of time.

2.2 Industrial Diversity and Location Quotient
There have been studies about the effect of industrial diversity, such as one by Kort (1981),
who investigates the relationship between industrial diversity and regional economic instability in
U.S. cities. Kort finds that industrial diversity increases economic stability, although diversification
is not the sole explanatory factor.
Simon (1988) more directly investigates whether or not industrial diversity reduces
unemployment rate, by examining whether differences in frictional unemployment among cities isrelated to differences in industrial diversity, while assuming that labor mobility between cities is notfree.

Simon (1988) argues that industrial diversity attains a lower unemployment rate by assumingthat the frictional component of employment fluctuations is a random variable and independent
across industries.

Because fluctuations are uncorrelated across industries, frictional hiring in someindustries may coincide with frictional layoffs at others.

Unemployed individuals can fillconcurrently occurring vacancies.
Although his research is important in the study of the relationship between industrial
diversity and unemployment rate, his study also has some weaknesses, in that while he succeeds inseparating the frictional factor from the cyclical factor of unemployment, he fails to separate thestructural factor. In his study, the effects on unemployment of each city’s particular composition of
output are based on each city’s industry mix.
unemployment he means.

He does not clearly pinpoint what kind of

In fact, the net separation rate, which is defined as the difference

between the job separation rate and the job accession rate in the city, is taken as a proxy variable of5

industry mix.

As the net separation rate is often used as a measure of the degree of employment

adjustment by the business cycle, the net separation rate of a given industry could be considered ameasure of cyclical unemployment.
The Herfindahl index does not distinguish the components of industrial structure. Even if
the index is the same in different metropolitan areas, the meaning could be different.

For example,

consider two metropolitan areas, A and B, which are identical in the composition of employment,
except for two industries. Metropolitan area A has 10% of its employment in the manufacturingindustries but 5% in the service industry.

On the other hand, metropolitan area B has 5% in the

manufacturing industry but 10% in the service industry.

In this case, the two metropolitan areas

have the same index value but in reality the industrial factor is completely different.

In our study,

we try to separate the structural factor by using the location quotient of an industry.
Although we define location quotient in the next chapter, we can say now it measures theextent to which a metropolitan area is providing employment in an industry compared with thenational average.

By evaluating what kinds of industries show a large reading in location quotient,

we can delineate the characteristics of structural factors.

Furthermore, this measure also includes

the cyclical effect.
The meaning of location quotient of an industry can be elucidated by examining two
industries: construction and manufacturing.

The outstanding general characteristic of theconstruction industry is that, compared to other industries, it demands a great number of non-skilledlaborers who need have no education or specialized training.

Therefore, a metropolitan area, forexample metropolitan area A, with a higher location quotient in the construction industry, could
obtain workers easily at the business upturn in economic activities and could lay off workers easily
at the business recession. The construction industry has a relatively strong buffer effect against thebusiness cycle.

On the other hand, the fact that most laborers are non-skilled and non-specialized6

indicates that they are not easily employed by different industries. This is an important factor of
the structural unemployment of a metropolitan area.
As another example, the automobile manufacturing industry consists of many kinds ofparts industries and is comprised of many vertically arranged companies.
construction industry, the automobile industry requires skilled workers.

Compared with the

In a metropolitan area witha large measure in location quotient, employed workers have more opportunities to change their jobs
among manufacturing industries.

On the other hand, in a metropolitan area with a lower value inthis measure, there are fewer opportunities to get jobs for workers engaged in that industry. It is
more costly to reduce structural unemployment.

Furthermore, as the manufacturing industries withmore competition might be immune to unemployment.
Thus, we consider the effects of both structural and cyclical unemployment by using theindustry’s location quotient, which reflects both effects.

3. Model3.1 Empirical ModelIn this study, our basic empirical model is specified as follows:
Uc = α + β Hc + + Σiηisic + γ lnCSc + µ Z c + δ D
Where

(1)

Uc: Unemployment rate of metropolitan area
Hc: Herfindahl index of industry

sic: Location quotient of an industry – i
CSc: Size of metropolitan area
Z c: Percentage of higher education graduates
D: Okinawa dummy (1 if metropolitan area in Okinawa, 0 for others)
First, one important goal is to evaluate whether or not industrial diversification can reduce

7

unemployment rate.

Herfindahl index (Hc) is used for this measure.

Herfindahl index takes on a

maximum of 1 if all employment of a metropolitan area is concentrated in only one industry. On
the other hand, the index takes on a minimum of 1/N if employment in an industry is diversified
equally among N industries.

Therefore, as our hypothesis is that industrial diversity can reduce theunemployment rate, the Herfindahl index should show a positive relationship with the
unemployment rate.
Second, the location quotient for industry - i (sic) mainly represents the factor of structural
unemployment.

The location quotient is defined as the percentage of total employment in a

metropolitan area engaged in a particular industry divided by the corresponding percentage for thewhole country (e.g. see McDonald, 1997).

Therefore, if this variable of a particular industry ismore than one, then the metropolitan area is considered as having this industry stronger or more
specialized than in other areas.

The Harfindarl index does not distinguish the differences amongindustries, even if the index is the same among metropolitan areas.
variables, we can see the structural factor on unemployment.
show the differences of the effects of the business cycle.

But by including these

Furthermore, these variables also

If a metropolitan area specializes in the

more employing industry, then the area’s unemployment rate is lower.

The expected sign of mostindustries is negative but some industries might show a positive relationship with the unemployment
rate.
Third, as for the size of a metropolitan area, the larger city has more jobs so that theemployment opportunities increase.
demand for jobs also increase.

However, at the same time it is necessary to consider that the

According to Vipond (1974), as the larger cities have wider wage

dispersion, job seekers have incentives to prolong the job search. As a result, if other conditions
are held constant, larger metropolitan areas have larger unemployment rates.

Furthermore, larger

cities might have more programs for unemployed workers so that the larger cities attract more8

unemployed workers.

For these reasons, there is a positive relationship between this variable andthe unemployment rate.
Fourth, an individual who has higher education tends to protect a job against
unemployment pressures, compared with a less educated individual.

A more educated individual

could be more productive and skillful so that he/she should be prone to unemployment if otherconditions hold the same.
Finally, in this model the Okinawa dummy is included because the unemployment rate in
the metropolitan areas in Okinawa prefectures cannot be explained only by the variables mentioned
above.

The Okinawa dummy is expected to be positive.

Table 1 summarizes the overall expected

sign of coefficients for regression analysis.
Table 1

Expected Sign of Coefficients for Regression Analysis

Herfindahl index(Hc)

Location quotient
for industry (sic)Size ofmetropolitan area
(CSc)

Percentage ofhigher education
graduates (Z c)

Okinawa dummy
(D)

+

-/+

+

-

+

3.2 Relationship between Herfindahl Index and Location Quotient
An important characteristic of our study is that the location quotient for industry – i at a
metropolitan area (sic) is included for the explanation of the difference of unemployment rate of a
metropolitan area.

The location quotient shows how concentrated a given industry of a

metropolitan area is compared with the standard of whole country.

As Herfindahl Index of themetropolitan area also shows the degree of diversification in the industry, someone might think that
there is a linear relationship between these variables. In the empirical analysis, if there is a linear
relationship between explanatory variables, the multicollinearity problem might arise.
we will show that there is no linear relationship between these variables.

9

Therefore,If we take the case that there are N industries, Herfindahl index of a metropolitan area isdefined as follows:N

H C ≡ ∑ wiC2

(2)

i =1

Where

L
wiC ≡ N iC
∑ LiC
i =1

L ic : Numbers of employment in industry – i
On the other hand, the location quotient is defined as follows: LiC
s iC ≡ 

 Li

Where



N

∑ L 
i =1

iC


Li 
∑
i =1

N

=

wiC

 Li


(3)


Li 
∑
i =1

N

Li : Total number of employment in industry – i in whole country

Therefore, from the equation-(2) and (3), we can obtain the following relationship.
2





−2
N
N
Li 
 N  N22=  ∑ Li  ∑ (siC Li )H C = ∑ wiC = ∑ siC N


i =1
i =1
 i =1  i =1
 ∑ Li 
i =1



(4)

From equation-(4), we can see that there is no linear relationship between the Herfindahl index andthe location quotient of industry – i.

Therefore, the multicollinearity problem would not occur

between these variables.

4. Empirical Analysis
4.1

Definition of Metropolitan Areas
All variables used in this study are summarized based on metropolitan areas.

Therefore,

the observations used here, from which we estimate the unemployment rate function, are a data setof 118 metropolitan areas for the fiscal year 1995. A metropolitan area used here is defined as an10

employment area by Kanemoto and Tokuoka (2001). Although in the U.S., statistical metropolitan
areas are defined by the government, in Japan there is no authoritative definition of a metropolitan
area so we follow the definition found in Kanemoto and Tokuoka’s study.
Furthermore, the main reason we have used a cross-section data set rather than a timeseries data set for our analysis is that we wish to focus on regional differences in the unemployment
rate.

In Japan, variations in unemployment rate according to year are smaller than variations byregion, causing us to consider a cross-section analysis of unemployment rate more appropriate.

4.2

Definition of Variables
The variables used in this study are defined as follows and the statistical information for

the variables in summarized in Table 2.

First, unemployment rate of a metropolitan area (Uc) is

obtained by dividing the total number of unemployed workers by the total number in the labor force
in a metropolitan area.

Both total numbers of unemployed workers and total numbers of labor

force are obtained from the Population Census of Japan by the Statistics Bureau, Management andCoordination Agency from 1995.

These numbers are reported on a municipality basis so that we

use the sum of these numbers for a metropolitan area.
As for a variable expressing industrial diversity, the Herfindahl (Hc) index is used.

Asshown in equation-(2), the Herfindahl index is defined as the sum of squares of each industry’s
numbers to total numbers of all industries.

In this study, we calculated the Herfindahl index basedon the numbers of employees for ten industries, which are (1) agriculture, forest and fishing industry,(2) mining industry, (3) construction industry, (4) manufacturing industry, (5) public utilities (electric
power, gas, water supply and heat supply) industry, (6) transport and telecommunication industry, (7)wholesale and retail industry, (8) banking and insurance industry, (9) real estate industry, and (10)service industry.

Therefore, we calculate the index based on ten industries.

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The data source ofnumbers of employees for ten industries is the Establishment and Enterprise Census by the Statistics
Bureau, Management and Coordination Agency.

As the Establishment and Enterprise Census was

not reported in 1995, we estimate it by using two time periods, 1991 and 1997.
As for a variable of metropolitan size, we choose daytime population of a metropolitan
area.

Because daytime population shows more than a nighttime population the attractiveness of theeconomic activities of a metropolitan area, we use it to show the size of a metropolitan area. The
data source for this variable is The Population Census of Japan by the Statistics Bureau,
Management and Coordination Agency in 1995. In this study, we take a natural logarithm of thisvariable.
The location quotient of ten industries is used here to reflect the regional differences incompetitiveness by industries.

As shown in equation-(3), the location quotient is defined as a ratioof the given industry’s percentage in a metropolitan area to the given industry’s percentage in thewhole country.

In this study, we calculate the location quotient of each industry by the numbers of

employees. The data source of the employees is The Establishment and Enterprise Census. First,
we calculated the location quotient for all ten industries.

But finally six industries such as (1)construction, (2) manufacturing, (3) public utilities, (4) transport and telecommunication, (5)wholesale and retails, and (6) real estate, are used for the analysis by considering themulticollinearity problem.
In order to distinguish the quality of workers, we define a variable of the percentage ofhigher education graduates.

Higher education means junior colleges, technical colleges,

universities, and graduate schools of universities. The percentage of the higher education graduates
is defined by dividing population with higher education graduates by the total population of over
15-year-olds in a metropolitan area.

This data is only available in 2000 Population Census ofJapan by the Statistics Bureau, Management and Coordination Agency.

12

Therefore, we use it byassuming that the percentage is not much different from that of 1995.

We assume that the number

for the central city of each metropolitan area represents the number for the metropolitan area.
Finally, we use the Okinawa dummy variable in this study.

In this analysis, there arethree metropolitan areas from Okinawa prefecture. The unemployment rate of metropolitan areasin Okinawa is much higher than that in metropolitan areas on the Japanese main islands.

Okinawa

is far away from main islands of Japan and there are many specific conditions. Therefore, we use
the Okinawa dummy to reflect uncontrolled factors on the unemployment rate.
Table 2

Statistics of Used Variables

Variables

Mean

Unemployment Rate
Herfindahl Index
Log of Daytime Population
Location Quotient of Construction
Location Quotient of Manufacturing
Location Quotient of Public Utitities
Location Quotient of Transport & Telecommunication
Location Quotient of Wholesale & Retails
Location Quotient of Real Estate
Percentage of Higher Education Graduates
Okinawa Dummy

4.105
0.235
12.837
1.144
1.126
1.076
0.916
0.939
0.619
24.0970.169

4.3

Standard
Deviation
1.380
0.024
0.961
0.241
0.462
0.433
0.210
0.113
0.252
5.133
0.130

Minimum

Maximum

2.181
0.197
11.084
0.609
0.268
0.389
0.311
0.630
0.233
12.5000.000

13.0770.334
17.253
1.810
2.435
3.475
1.879
1.237
1.593
38.400
1.000

Empirical Results
We apply the regression for the unemployment rate function shown in equation-(1).

Theestimation method is the OLS method and a summary of estimation results is shown in Table 3.

As

the table shows, estimation results seem acceptable: adjusted R-squares are rather high, between
0.675 and 0.732 and coefficients of explanatory variables show the correct sign. Furthermore, theassumption of homoscedasticity of the variance of disturbance term is not rejected, based on the
Koenkar test (see Koenkar (1981)).

Although the Breusch-Pagan test is more commonly used, weused the Koenkar test because the Breusch-Pagan test is quite sensitive to the assumption of13

normality and the Koenkar test is a more robust test (see for example, Greene (2000, p.510).

Insummary, therefore, we decided to use these regression results to evaluate the unemployment rateand its factors.
Table 3
Herfindahl Index
Log of Daytime
Population
Location Quotient ofConstruction
Location Quotient of
Manufacturing
Location Quotient of
Public Utilities
Location Quotient ofTransport &Telecommunication
Location Quotient of
Wholesale & Retailes
Location Quotient ofReal Estate
Percentage of Higher
Education Graduates
Okinawa Dummy
Constant

Estimation Results: Coefficients and Standard Errors

Regression 1
6.559(4.618)0.329***(0.117)-2.006***(0.539)-1.528***(0.534)-0.024(0.173)0.716*(0.411)-0.374(1.636)0.010(0.447)-0.103***(0.019)6.557***(0.612)4.457(3.136)0.725
11.354

Regression 2
6.495(4.589)0.325***(0.115)-1.937***(0.446)-1.423***(0.271)-0.018(0.107)0.733*(0.402)

Regression 3
6.611(4.372)0.328***(0.098)-1.940***(0.431)-1.424***(0.238)Regression 4
5.022(4.340)0.352***(0.098)-2.016***(0.434)-1.572***(0.228)Regression 512.742***(4.362)0.446***(0.104)---0.733*(0.388)

-

-

-

-

-

-

-

-

-

-0.103***(0.018)6.541***(0.606)3.864*(1.990)
0.732
7.594

-0.107***(0.018)6.492***(0.593)4.955**(1.926)
0.726
5.564

-0.088***(0.019)6.403***(0.644)-1.449(1.464)
0.675
3.081

0.010
(0.445)-0.103***(0.018)6.542***(0.606)3.934*(2.140)0.727
11.034

Adjusted R-squares
Koenkar Test
(Note):(1) Numbers in parenthesis are standard errors.
(2) These are statistically significant at 1% (***), 5% (**) and 10% (*).

Regression 1 represents a case using all explanatory variables.

-1.121***(0.224)

Because the coefficient of

the Herfindahl index shows the positive sign, industrial diversity has a negative relationship with the
unemployment rate.

That is, the more diversified the industries in a metropolitan area, the lower

the unemployment rate.
Attran (1986)).

This result is very similar to that in previous studies (see for example,

However, the value of the variable is not statistically significant so that in Japan it

is not as strong a factor as in the U.S.

14

On the other hand, the location quotient of some industries has a statistically significant
effect on the unemployment rate.
of unemployment.

This means that individual industrial structure affects the degree

The most significant industries are construction and manufacturing.

The

coefficients of these variables show the negative sign so that the metropolitan areas which have more
employment in these industries have lower unemployment rates.
The construction and manufacturing industries are generally considered to have job
security.

First, manufacturing, an example of which is the automobile industry, has a wide range ofmanufacturing and provides many kinds of job opportunities to its workers.

As a result,metropolitan areas with strong manufacturing industries have low unemployment rates.
construction industry absorbs many redundant workers during recession.

The

Therefore, metropolitan

areas with strong construction industries have lower unemployment rates.
As the location quotient of the wholesale and retail industry is strongly correlated with thelocation quotient of the manufacturing industry (i.e. the correlation coefficient is –0.858), we apply
the regression again excluding the location quotient of the wholesale and retail industry (seeRegression 2).

As seen in the result, estimated coefficients are not much different from those in

Regression 1.
Third, we apply the regression again excluding both the location quotient of the public
utilities and the location quotient of the real estate industry, which are not statistically significant inRegression 1.

Again, this result (Regression 3) does not change much either.

However, if we exclude the location quotient of the transport and telecommunicationindustries (Regression 4), then the coefficient of the Herfindahl index becomes slightly smaller andthe coefficients of the location quotients of both construction and manufacturing industries becomelarger negatively.
Finally, if we exclude the location quotient of the construction industry, the coefficient of15

the Herfindahl index becomes larger and has statistical significance.

As the correlation coefficient

between these variables is not large (i.e. –0.620), we cannot find a clear multicollinearlity problem.
Although we need more detailed investigation, we think so far that the location quotient of theconstruction industry, which shows the specific structure of the construction industry itself, is an
important factor in explaining the difference in unemployment rate.

5. Conclusion
The main goal of our study is to evaluate whether or not industrial diversity helps toreduce the unemployment rate of a metropolitan area.
industry-specific factors affect the unemployment rate.
metropolitan areas for the year 1995.

We also investigate to what extent

The data set for this study is Japan’s 118

From our analysis, our conclusion is summarized as follows:First, industrial diversity might reduce the unemployment rate of a metropolitan area inJapan.

This result certainly supports Simon’s (1981) argument.

However, industrial diversity is

not as strong factor as others: the industrial diversity factor as measured by the Herfindahl index isnot statistically significant in our analysis.
Second, according to the location quotient of each industry, the manufacturing industry
has a negative relationship with the unemployment rate of a metropolitan area.

In general, themanufacturing industry characteristically has vertical business connections among manufacturing
companies and furthermore, the industry is widely dispersed so that job security is strong. In fact,
another study by Mizuno (1992) shows that a higher ratio of manufacturing sectors to the service
sectors lowers the unemployment rate.
Third, the location quotient of the construction industry also has a negative relationshipwith the unemployment rate.

Because the public works projects have often been used for theemployment promotion policy in Japan, the construction industry is considered as a buffer against

16

unemployment.
Last, as for other findings, the higher the percentage of highly educated people there arein a population, the lower its unemployment rate will be.

In our analysis, an increase in the ratio of

higher education by 10% contributes to a decrease in the unemployment rate by 1%.

And the larger

cities have higher unemployment rates.

[2003.9.8 660]
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17

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Discussion Paper
No.
2002・1

AuthorNobuyuki Isagawa

2002・2

Nobuyuki Isagawa

2002・3Mahito Okura

2002・4

Elmer Sterken
Ichiro Tokutsu
Mahito Okura
Mahito Okura
Nobuyuki Isagawa
Koji Okubayashi
Toshinori Takashina

2002・5
2002・6
2002・7
2002・8

2002・9Hajime Shimizu

2002・10 Nobuyuki Isagawa
2002･11

Nobuyuki Isagawa

2002･12Kenji Kutsuna
Richard Smith

2002・13 Kunio Miyashita
2002・14 Hajime Shimizu
2002・15 Hajime Shimizu
2002・16 Koji Okubayashi
2002・17 Fumitoshi Mizutani
Shuji Uranishi
2002・18 Kazuhisa Otogawa
2002・19 Atsuo Takenaka
2002・20 Tsuneo Nakano

2002・21 Tsuneo Nakano
2002・22 Fumitoshi Mizutani
Shuji Uranishi
2002・23 Atsushi Takao
Mahito Okura
2002・24 Fumitoshi Mizutani
2002・25 Fumitoshi Mizutani
Takuya Urakami
2002・26 Yasuyuki Miyahara
2002・27 Nobuyuki Isagawa
2002・28 Mitsutoshi Hirano

Title
Cross Holding of Shares, Unwinding Cross Holding of
Shares, and Managerial
Entrenchment
A Theory of Stock Price Behavior following Repurchase
Announcements
An Equilibrium Analysis of the Insurance Market
with Vertical Differentiation
What are the determinants of the number of bankrelations of Japanese firms?
Review Article：Adverse Selection in Insurance Market
Welfare Effect of Firm Size in Insurance Market
Does Investment Horison Affect Investment Strategy ?
Research Report of Careers and Employment Process ofWorkers over 60 years old :a Case of a Japnaese
Manufacturing Company
On the Existence of Tax Equilibrium : Incomplete Market
with Capital Income Tax
Cost of Financial Distress and Debt Restructuring :Debt Forgiveness and Equity-for-Debt Swap
Open-Market Repurchase Announcements, Actual
Repurchases, and Stock Price Behavior in Inefficient
Markets―revised version of No.2001･36―
Why Does Book Building Drive Out Auction Methodsof IPO Issuance?
Evidence and Implications from Japan
International Logistics and Modal Choice
Incomplete markets with capital income tax and public
goods
On the possibility of Pareto improving of tax-equilibrium
China-Japan Comparison of Work Organization
The Post Office vs. Parcel Delivery Companies:
Competition Effects on Costs and Productivity―revised version of No.2001･33―
Earnings Forecast and Earnings Management of JapaneseInitial Public Offerings Firms
The Classification of Foreign R&D Units
The Dutch East India Company and its Corporate
Governance―revised version of No.2000･40―
The British East India Company and its Corporate
Governance
Privatization Effects on TFP Growth and Capital
Adjustments
Some Notes on the Privatization of Postal Life Insurancein JapanPrivately Owned Railways’ Cost Function, Organization
Size and Ownership
A Private-Public Comaprison of Bus Service Operators
Principal-Multiagent Relationships with Costly
Monitoring
Unwinding of Cross Shareholding under Managerial
Entrenchment
Contingency Model for Personnel Grading System―Issues on Design and Management for Qualification
System and Job Grading System―

Date(in Japanese)

1／ 2002(in Japanese)

1／ 20022／20023／2002(in Japanese)(in Japanese)(in Japnaese)

3／20023／20023／20024／2002(in Japnaese)

4／2002(in Japnaese)

4／20025／20025／2002(in Japnaese)(in Japnaese)

6／20026／20026／20027／20027／20028／2002(in Japanese)(in Japanese)

8／20028／2002(in Japanese)

8／20028／2002(in Japanese)

9／20029／20029／2002
10／2002
10／2002(in Japanese)

11／2002

Discussion Paper
No.
Author
2002・29 Atsushi Takao
2002・30 Yasuhiro Shimizu
2002・31 Hideki Murakami
2002・32 Atsushi Takao

2002・33 Nobuyuki Isagawa

2003・5

Takuji Hara
Katsuhiko Kokubu
Kazuhiro Tanaka
Fumitoshi Mizutani
Shuji Uranishi
Yang Jia Yin
Koji Okubayashi
Mitsutoshi Hirano

2003・6

Kazuhisa Otogawa

2003・7

Nobuyuki Isagawa
Tadayasu Yamashita
Kenji Kutsuna
Marc Cowling

2002・34
2003・1
2003・2
2003・3
2003・4

2003・8

2003・9

Noriko Masumura

2003・10 Nobuyuki Isagawa
Katsuhiko Okada
2003・11 Mitsutoshi Hirano
2003・12 Kenji Kutsuna
Richard Smith
2003・13 Kenji Kutsuna
Janet Kiholm Smith
Richard L. Smith
2003・14 Hideya Kubo
2003・15 Hideki Murakami
2003・16 Norio Kambayashi
2003・17 Nobuyuki Isagawa
2003・18 Nobuyuki Isagawa
2003・19 Huang Lin
2003・20 Koji Okubayashi
2003・21 Nobuyuki Isagawa

Title
On the Emergency Effect of the late FUKUZAWA,
Yukichi in Introducing of Modern Insurance into Japan
Amortization of Intangible Assets before Newark
Morning Ledger
An Economic Analysis of Duopolistic Competition
between Gulliver and Dwarf airlines:
The case of Japanese Domestic Air Markets
Simulation Model to Consider the Penetration of Modern
Insurance in Japan −Confirmation of Emergence andPhase Transition of New Japanese Risk-Bearing System
by Evolutional Economics
Mutual Shareholding and Unwinding of Mutual
Shareholding as Stockpile for Business Recovery
TLOs in Japan in the life science area : present and future
Environmental Accounting for Corporate Management
Embeddedness and Entrenchment of Managers
The Effects of Privatization on TFP Growth and Capital
Adjustments
Labour Turnover of Japan-Affiliated Companies inShanghai
Generation and Resolution of Asymmetric Information in
Human Resource Management –In the Cases of Job
Rotation in Two Retailers in JapanMarket Liquidity around Quarterly Earnings
Announcements
Lender’s Risk Incentive and Borrower’s Risk Incentive
Determinants of Small Business Loan Approval:
Evidence from Japanese Survey after 1997 Financial
CrisisVoluntary Corporate Disclosure and the Cost of Debt:
The Case of Quarterly Reporting
Corporate Financial Strategy and Stock Price Behavior
in a Noise Trader Model with Limited Arbitrage
Two Modes of Economic Organization in the Duality
Principle and Asymmetric Personal Information
Why Does Book Building Drive Out Auction Methods of
IPO Issuance? Evidence from Japan (Revised version)
Banking Relationships and Access to Equity CapitalMarkets:
Evidence from Japan’s Main Bank System
The Present Condition and Problem of JapaneseCorporate Pension Systems
An empirical analysis of the spillover and dynamic
effects of low-cost airline’s presence on rival’s price:
US duopoly case
Japanese Style of Management Education
Unwinding of Cross Shareholding for BusinessRestructuring
Unwinding of Cross Shareholding: A Signalling
Approach
Strategies and Store Development of Carregour in the
Chinese Continent
Emp lo yme nt Situation and Measur es for
In cr easing Emp lo yme nt Oppor tun ities;
Th e Cas e o f Jap an
L end er ’s Risk In cen tiv e and D eb t Forg iv ene ss(in Japanese)Date
11／2002(in Japanese)

11／2002
11／2002(in Japanese)

12／2002

12／2002(in Japanese)(in Japanese)(in Japanese)

12／2002
1／20032／20032／2003(in Japanese)

3／2003(in Japanese)

3／20033／2003(in Japanese)

3／20033／2003(in Japanese)

4／20034／2003(in Japanese)

5／20035／20035／2003(in Japanese)

6／2003(in Japanese)

7／2003(in Japanese)(in Japanese)

7／20037／2003(in Japanese)

7／2003(in Japanese)

8／20038／20038／2003

Discussion Paper
No.
Author
2003・22 Keizo Mizuno
Fumitoshi Mizutani
Noriyoshi Nakayama

Title
Industrial Diversity and Metropolitan Unemployment
Rate

Date
9／2003