Occupational Career Types of Baby-Boomers And

8/6/2019 Occupational Career Types of Baby-Boomers And

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The Japanese Economy, vol. 35, no. 4, Winter 20089, pp. 64106. 2009 M.E. Sharpe, Inc. All rights reserved.ISSN 1097203X / 2009 $9.50 + 0.00.DOI 10.2753/JES1097-203X350403

Ma XinXin

Occupational Career Types ofBaby-Boomers and Their Effectson Employment Pattern Selection

Abstract: In this article, individual data rom the Survey on the

Baby-Boomer Generations Work and Lie Vision conducted inOctober 2006 by the Japan Institute or Labor Policy and Training

(JILPT) are used to perorm a quantitative analysis on the eects

o occupational career type on employment pattern choices among

baby-boomers. The main conclusions are: frst, an analysis o the

eects on employment pattern choice beore age sixty shows that

Translation 2009 M.E. Sharpe, inc., rom the Japanese original, Dankai nosedai no shokugyou kiayria noltaipu oyobi sono shuugyou keitai no sentakli niataeru eikyou. Translated by Stacey Jehlik. Ma Xinxin is a COE Researcher atKeio University, and specializes in labor economics and econometrics. Major recentpublications include Nicchu ni okeru danjokan chingin kakusa no sai ni kansuruyouin bunseki [Factor Analysis o the Wage Disparity Between Men and Womenin Japan and China],Nihon roudou kenkyu zasshi, 2007, no. 560, pp. 96107.

The author is grateul or having been a participant in the project Survey onthe Baby-Boomer Generations Work and Lie Vision, conducted by the WorkEnvironment and Work-Lie Balance Department (ormerly the Work Environ-ment and Labor Conditions Department) o the Japan Institute or Labor Policy

and Training (JILPT) as part o my eorts to prepare this article. The authorthanks Proessor Atsushi Seiki in the Faculty o Business and Commerce at KeioU i it d P At hi S t S i R h Y t k A d L d


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WINTER 20089 65

specialists have a higher probability than generalists o workingin part-time jobs or being sel-employed; and second, an analysis

o the eects on preerred employment patterns at age sixty-fve

shows that specialists are more likely than generalists to preer

sel-employment. This analysis suggests that to promote the em-

ployment o seniors, it will be important to provide occupational

retraining to generalists who are in middle age, which includes

baby-boomers, and to respond to the occupational needs o special-

ists with specialized skills by providing a social environment thatmakes it easier or them to start their own businesses.

With the baby-boom generation approaching retirement age, thereis a growing magnication o such problems as labor shortagesand welare nancing challenges (Seike and Yamada 2004). Sev-eral steps are being taken to address these problems: the manda-tory retirement age is being raised, the revised Law Concerningthe Stabilization o the Employment o Older Persons has been

enacted (April 2006), and employers are being required to adoptemployment security measures or seniors, such as retirement ageextensions and continuing employment systems. With seniors want-ing to make use o the skills and experiences they have acquiredover the course o their careers, companies acing problems oskill succession need to take advantage o seniors who have botha strong desire to work and a wealth o experience and specializedknowledge and skills. Thus, it is important to conduct empirical

studies on what kinds o work is being done by the baby-boomersnow entering older age, and on what kinds o work patterns theyare hoping to have in the uture.

Previous research on the labor supply o seniors includes stud-ies on dichotomous empirical analysis (whether or not seniorswill withdraw rom the labor market),1 but little research has beendone on the selection o employment patterns among seniors.2Such research is important or the ollowing our reasons. First,

employment patterns are becoming increasingly diversied in thecurrent Japanese labor market (Japan Institute or Labor Policy


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66 THE JAPANESE ECONOMY

seniors has an impact on their employment patterns. This sug-gests that dierences in health will lead to dierent selectionso employment patterns among seniors. Third, because seniorshave diverse leisure preerences, even those who have the sameeconomic means (i.e., pensions and labor income) may make di-erent employment pattern selections (Ruhm 1990). Fourth, evenamong senior citizens, dierent employment patterns are chosenby people in dierent age groups (Japan Institute o Labor [JIL]

1995, 1998a).The determining actors in employment pattern selection among

baby-boomers include labor income, pensions, and other actorssuch as their health and household structure, but their occupationalcareer type has also been shown to be a major actor (Chioi 2002;Kim and DeVaney 2005; Seike and Yamada 2004).3 Koike andInoki (2002) used survey research and interview research on oc-cupational career types to show that in European and American

companies, white-collar workers possess a narrow range o oc-cupational skills (or tend to be specialists), while in Japanesecompanies, white-collar workers tend to have multiple job skilltypes (or tend to be generalists) due to the worker rotationsystem.4 With regard to the impact o occupational career typeon the employment status o seniors, JIL (1998b) points out thatcompanies are extremely likely to recruit highly skilled workers(specialists) in mid-career, and that highly skilled workers are

more likely than less-skilled workers to want to continue workingbeyond age sixty. Nonetheless, because there has thus ar beenvery little detailed quantitative analysis conducted on occupationalcareer types, it is unclear what kind o impact the occupationalcareer types o the baby-boomers might have on their selectiono employment patterns.

In this article, I use quantitative analysis to shed light on the ol-lowing three issues. First, what actors infuence the ormation ooccupational career type? Second, what eect does occupationalcareer type have on wages? And third, what eect does occupationalcareer type have on the selection o employment patterns beore age


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Prior Research and the Characteristics of This Study

Theories Regarding Wages and Employment Decisions

According to the theory o human capital (Becker 1964), wages aredetermined by the marginal labor productivity o employees, and hu-man capital refects labor productivity. Wages are raised to improvethe labor productivity o employees by raising both the general hu-man capital ormed through education, and the rm-specic humancapital ormed through skill development opportunities on the job orthrough education and training provided in the workplace.5

With regard to decisions about employment and labor hours, theneoclassical theory o labor economics suggests that the workerschoice o employment is determined by the reservation wage6 and themarket wage. I the market wage rate exceeds the reservation wagerate, the worker will choose employment. Under conditions wherethe workers leisure time is a superior good, and nonlabor income7

rises, labor hours will decline. In addition, while an increase in themarket wage rate will cause an increase in labor hours because othe substitution eect, i leisure time is a superior good, then laborhours will instead all as a result o the income eect. That is, theselection o labor hours is aected by both the substitution eect andthe income eect. According to these theories o agent equilibrium,because the market wage rates and reservation wage rates o indi-vidual workers dier, their employment pattern selections will also

dier. Likewise, because individual preerences or leisure dier,even people with the same market wage rate may select dierentlabor hours, and thus may select dierent employment patterns.

Based on the theories outlined above, several empirical analy-ses have been conducted on the selection o employment patternsamong seniors. The ollowing section summarizes the results oprevious studies on the United States/Europe and Japan.

Previous Studies on the Employment Patterns ofSeniors and the Position of This Study Among Them


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yoPr

eviousResearchandPos

itionoPresentStudy

Yearo

ion

Model

Employment-type

classifcation

Allotmentothe

wagesotheunemployed

Target

respondents

dy(2007)

Multinomiallogit

Actu

alanddesired:

ull-time,part-time,sel-

emp

loyed,unemployed

EstimateswagesusingHeckmanstw

o-step

wage

unction

Menandwomen,

5559

and

moto(2

002)

Multinomiallogit

Actu

al:ull-time,part-time,

emp

loymentseekers,

unem

ployed

EstimateswagesusingHeckmansw

ageunc-

tion

Men,

5569

2001)

Multinomiallogit

Actu

al:Employee,sel-em-

ployed,unemployed

EstimateswagesusingtheOLSwag

e

unction

Men,

6064

Oishi,

and

ami(19

95)

Multinomiallogit

Actu

al:ull-time,part-

time,sel-employed,

unem

ployed

Wage

sinfrmswith10-99employeesor

men6

0-64,

bypreecture

Men,

6064

nakian

d

no

Multinomiallogit

Actu

alanddesired:ull-time,

part-time,sel-

emp

loyed,unemployed

Part-timewages

Menandwomen,

5569

05)

Multinomiallogit

Actu

al:ull-time,

part-time,unemployed

Paneldata:wageswhenrespondentswere

emplo

yedfveyearsprior

Menandwomen,

5161

002)

Multinomiallogit

Actu

al:ull-time,


Totalindividualincome

Menandwomen,

5161

985)

Multinomiallogit

Actu

al:ull-time,


Uses

educationalbackgroundandye

arso

experienceasproxyindicators

Women,

6267


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WINTER 20089 69

Japan, and shows where the present study ts in among them.Honig (1985) studied women age sixty-two to sixty-seven in the

United States/Europe, divided employment patterns into ull-time,part-time, and not employed categories, and used educational back-ground and years o experience as proxy indicators. She ound thatamily composition actors (marital status, age o children, presenceo children, presence o children enrolled in school) have an impacton employment pattern selection, and that the older ones children,

and the lack o children or lack o children enrolled in school, thehigher the rates o unemployment.

Chioi (2002) and Kim and DeVaney (2005) studied the agegroups ty-one to sixty-two and sixty-two to sixty-seven, anddivided employment patterns into ull-time, part-time, and notemployed categories. When controlling or the market wage rate,age, and gender had about the same impact on the probability oselecting ull-time or part-time employment. Investment assets,

pension income, employment health insurance, and health allhad a signicant impact on the selection o ull-time or part-timeemployment, and the respondents health status and educationallevel also had a signicant impact on the selection o part-timeemployment.

In Japan, Higuchi and Yamamoto (2002) studied male seniors agety-ve to sixty-nine, and divided employment patterns into ourtypes: ull-time, part-time, employment seekers, and not employed.

While the market wage rate had a signicant positive impact onull-time and part-time employment probability, various types opension benets and household income earned by others in therespondents household had a negative impact on the probabilityo selecting ull-time or part-time employment.

Mitani (2001) analyzed male seniors age sixty to sixty-our whowere employed at the age o ty-ve, and divided employmentpatterns into three categories: employed outside, sel-employed,and not employed. This study showed that the market wage rate hada signicant positive impact on the probability o being employedull-time rather than part-time Furthermore good health being


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the probability o employment, while larger real public pensionbenets lowered the probability o employment. In addition,while higher real job earnings lead to a higher probability ooutside employment, they lead to a lower probability o sel-employment.

Tachibanaki and Shimono (1994) studied male and emaleseniors (age ty-ve to sixty-nine), and divided employment pat-terns into our types: ull-time, part-time, sel-employed, and not

employed. They showed that the pensions most likely to encour-age retirement were public pensions and private pensions. Theyalso showed that retirement probability is higher among those inull-time employment than those in other employment patterns,that older respondents tend to have larger savings, and that thelarger the incomes o other household members, the higher theprobability that the respondent will be not employed. To studythe impact o occupational career, they divided these into three

types at the age o ty-ve: white-collar, blue-collar, and gray-collar, but they concluded that there are no dierences in behaviorbetween these three groups.

However, there are three limitations that must be noted regardingthese previous studies.

First, because quantitative analyses have not really ocusedon the baby-boomer generation, the determining actors in theselection o employment pattern in that generation have not been

clearly identied.Second, because there have not been any empirical analysesconducted on occupational career type, it is unclear what kindso actors have an impact on the ormation o occupational careertype.

Third, the eects o occupational career type are not evident.That is, it has not been made clear what kind o impact occupa-tional career type has on wages, or what kind o impact it has onthe selection o particular employment patterns.

In this article, I conduct an empirical analysis on the determin-ing actors in the selection o various employment patterns among


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presented in this article were established to acilitate the investiga-tion o the issues mentioned above.

Setting the Hypotheses

I established my hypotheses as ollows. First, the baby-boomerswere employed in large numbers during the high growth period,a period when Japanese manuacturing grew considerably. Topursue economies o scale through mass production, the cultiva-tion and retention o talented personnel became an important issuein corporate human resource management (Inoki 2000; Yashiro1997, 1999). For this reason, the lietime employment system wasestablished, and a human resource training system that promotedthe ormation o generalists, primarily among male white-collarworkers, was implemented. Thus, one might predict that amongmales, the longer their length o service with a company, the highertheir probability o being a generalist with rm-specic human

capital (Hypothesis 1).Next, because more generalists than specialists tend to be in

white-collar management positions, and because the wage systemis based on seniority, a generalist is likely to earn higher wagesthan a specialist, even i both employees have the same level olabor productivity (Hypothesis 2).8

I Hypothesis 2 is supported, then since the wages o generalistsare likely to rise in larger increments than the wages o specialists,

wages may greatly exceed labor productivity. This will make it di-cult to implement continued employment (Association o Employ-ment Development or Citizens, 1994). Thus, it is possible to predictthat specialists will have a higher probability than generalists obeing hired in a ull-time or part-time capacity through continuedemployment programs beore the age o sixty (Hypothesis 3).

These hypotheses are summarized below.

Hypothesis 1: Even among workers with the same level olabor productivity, the longer the employees length o service

with a company the higher his probability o being a general


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Hypothesis 2: Even when labor productivity is equal, wageswill be higher among generalists than specialists.

Hypothesis 3: The probability o obtaining ull-time employ-ment beore the age o sixty is higher among specialists than

generalists.

An examination o preerred employment patterns at the age osixty-ve showed that the higher the preerence or leisure time

among generalists, the higher the probability o their preerenceor being not employed.10 On the other hand, i an individual useshis labor income as capital to start a new business or utilizes hismanagement skills or proessional contacts, his probability o pre-erring sel-employment increases. Because the specialized skillsthat specialists possess make it relatively easy or them to eithercontinue their employment with the same company or accept a jobwith another company, they are more likely to preer ull-time orpart-time employment. However, i they are utilizing their special-ized skills, the probability that they will preer sel-employmentmay increase. Thus, it is dicult to clearly predict what impactoccupational career type will have on preerred employment pat-terns at age sixty-ve. These questions will be addressed based onthe results o my empirical analysis.

Analytical Framework

Estimation Model

The procedures used to conduct the quantitative analysis presentedin this article are described below. First, I used a multinomial logitmodel to analyze the determinants o occupational career type.Next, I calculated the wage unction or the eect o occupa-tional career type on wages. Finally, I inserted the wage unctionvalue into the structural multinomial logit model, and perormed

a quantitative analysis o the eect o occupational career type onemployment pattern.


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determination in the market wage rates and employment patternselection. To solve this problem, this study uses estimated wageunction values rather than actual wage rates. The second issue isthe possibility that sample selection bias may exist in the estimationo the wage unction. To correct this bias, I have used Heckmanstwo-step selection model (Heckman 1979). The third issue is theproblem o simultaneous determination in the pension system andemployment pattern selection. To solve this problem, I employ

the method presented by Seike (1993), using the pension systembenet qualication dummy as an explanatory variable. Fourth,there may be a discrimination problem in the structural multinomiallogit model.11 To address this, I adopt a strategy o establishing anexplanatory variable in the reduced orm and structural equations,and add explanatory variables refecting the amily background,work history, and peak proessional period to the reduced ormwage unction explanatory variable in the structural multinomial

logit analysis.The equations are shown below. First, Equation (1) shows themultinomial logit analysis ormula or occupational career typeprobability.12

Pr( )

exp( )

exp(

Y n

x

x

i

ij jn

j

p

ij jm

j

p

m

r= =

=

==

0

01

)

(1)

j: Pension system dummy, amily background dummy, non-health dummy, years o experience dummy, years o experiencesquared, educational background dummy, male dummy, occupationdummy, rm size dummy, industry dummy.

In Equation (1), Pr(Yi= n) is the probability that individual i will

select occupational career type n, and ij

expresses each actorjthat has an impact on the ormation o the occupational career type

o individual i. Value m represents each occupational career type(e.g., generalist, specialist, or other), while n is the one occupational


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Next, Equation (2) calculates Heckmans two-step wageunction.

InWi=

i+

ik

ik+ u

1i(2)

ih + u

2i> 0 (3)

u1

~N(0, )u

2~N(0,1)

corr(u1u

2) =

k:Occupational career type (generalist dummy, specialistdummy, other dummy), years o experience, years o experiencesquared, educational background dummy, male dummy, job chang-ing experience dummy, occupation dummy, rm size dummy,industry dummy.

h: Occupational career type (generalist dummy, specialist

dummy, other dummy), years o experience, years o experiencesquared, educational background dummy, male dummy, job chang-

ing experience dummy, nonhealth dummy, pension system dummy,marital status dummy, number o children, number o childrenenrolled in school dummy, co-residence with parents dummy.

In Equation (2), ik

indicates each actor kthat has an impacton the wage rate o individual i. In Equation (3),

ihindicates each

actor h that has an impact on the employment decision o individuali. I test Hypothesis 2 using the estimated value o the occupationalcareer type in

k.

To analyze the impact o occupational career type on employmentpattern selection, the estimated value (In ) o the wage unctionound using Equations (2)(3) is inserted into the multinomial logitmodel. Equation (4) shows the calculation or this kind o structuredmultinomial logit analysis.

(4)

W

Pr( )

exp( )

exp( )

Y s

x In

x In

i

iL Ls s s

h

t

iL Ln n n

L

t

n

= =

+

+

=

=

0

0==1

q

W

W


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achievement dummy, cohort dummy, educational background dum-my, male dummy, marital status dummy, nonhealth dummy, numbero children, number o children enrolled in school, co-residencewith parents dummy, amily background dummy, unemploymentexperience dummy, desire to continue working dummy, job chang-ing experience dummy, peak proessional period dummy.

In Equation (4), Pr(Yi= s) is the ratio at which the individual i

selects employment pattern s beore the age o sixty (or the prob-

ability o the employment pattern being preerred by individual iat the age o sixty-ve).

iLis each actor other than the wage rate

(In ) that impacts the actual or preerred employment pattern oindividual i.

Lis the estimation coecient o each actorL when

selecting employment pattern s. In is the estimated value o thewage rate and is the estimation coecient o the wage rate. I testHypothesis 3 using the estimated value o the occupational careertype in

L.

Explanation of Data and Variable Creation

This article uses individual data rom the Survey on the Baby-Boomer Generations Work and Lie Vision conducted in October2006 by the Japan Institute or Labor Policy and Training (JILPT).This large-scale survey was conducted based on the standards othe Employment Status Survey among baby-boomers (individualsborn between 1947 and 1951).13 The total sample size was 2,722men and women combined. Table 2 shows the narrative statistics oeach variable except those that had missing values. The variableswere established as ollows.

First, to obtain the dependent variable, I took the annual employ-ment income (the median value o each annual income category)and divided it by the corresponding number o hours worked(calculating the annual number o hours worked based on weeklywork hours), to nd the wage rate per hour.

For the variable or the category related to occupational careertype, I established three groups based on the occupational career

W

W


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Table 2

Descriptive Statistics

VariablesSample

size MeanStd.dev.

Lowestvalue

Highestvalue

Dependent variables

Wage rate lnw 2,479 7.735 0.571 6.543 9.588

Occupational

career Generalist 2,652 13.7% 0 1

Specialist 2,652 39.8% 0 1

Other 2,652 46.5% 0 1

Employmentpattern(current) Full-time 2,511 15.9% 0 1

Part-time 2,511 57.9% 0 1

Sel-employed 2,511 21.0% 0 1

Not employed 2,511 5.2% 0 1

Employmentpattern(preerred) Full-time 2,538 31.8% 0 1

Part-time 2,538 22.3% 0 1

Sel-employed 2,538 20.6% 0 1

Not employed 2,538 25.3% 0 1

Explanatory variables

Personalattributes Male 2,722 60.5% 0 1

Married 2,700 89.7% 0 1Non-health 2,679 13.7% 0 1

Age (years) 2,722 56.977 1.439 55 59

Humancapital Years o experience 2,491 37.843 2.709 32 44

Junior high school 2,491 9.4% 0 1

High school 2,491 47.2% 0 1

Vocational school 2,491 7.0% 0 1

Junior college 2,491 3.7% 0 1

University andhigher 2,491 32.7% 0 1


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VariablesSample

size MeanStd.dev.

Lowestvalue

Highestvalue

Pensionbenefteligibility Public pension 2,722 78.1% 0 1

Corporate pension 2,722 11.8% 0 1

Other privatepension 2,722 14.0% 0 1

No pension + no

response 2,722 20.8% 0 1Family

composition No. o children 2,697 3.308 1.615 0 9

Have childrenenrolled in school 2,722 28.1% 0 1

Co-residence withparents 2,669 29.9% 0 1

Familybackground

Sel-employedamily 2,671 44.4% 0 1

Employee amily 2,671 49.2% 0 1

Other 2,671 6.4% 0 1Employment

statusDesire to continueworking 2,722 91.7% 0 1

Unemploymentexperience 2,722 35.7% 0 1

Firm size 199 employees 1,842 39.7% 0 1

100999 employees 1,842 30.2% 0 1

1,000+ employees 1,842 30.1% 0 1

Presence oa union Labor union 1,880 20.0% 0 1

Industry Primary industry 2,218 1.2% 0 1

Secondary industry 2,218 38.4% 0 1

Tertiary industry 2,218 60.4% 0 1

Region Large city 2,664 42.1% 0 1

Suburb 2,664 47.2% 0 1

Rural town orvillage 2,664 10.7% 0 1

Source: Survey on the Baby-Boomer Generations Work and Lie Vision (JILPT)

(2006).Note: The sample sizes refect the rst sample size or the rst o each variable inTable 2 Because the variables used in each quantitative analysis dier the sample


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groups based on the question items related to employment status andlabor hours: ull-time, part-time, sel-employed, and not employed.Employees who worked less than thirty-ve hours per week werecategorized as part-time, while those who worked thirty-ve hoursor more were categorized as ull-time.

With regard to the preerred employment pattern at age sixty-ve,respondents were grouped into our categories (ull-time, part-time,sel-employed, and not employed) based on their preerred employ-

ment pattern at the age o sixty-ve, as indicated in their responsesto the question What kind o work or social activities would youlike to be doing each year ater the age o sixty?

The explanatory variables were established as ollows. Variousactors have an impact on wages and the selection o employmentpattern, but in this study, I established explanatory variables bydividing these into primarily supply-side actors (income, humancapital, occupational career, amily background, amily composi-

tion, individual attributes, and employment history) and primarilydemand-side actors (rm size, industry).I established three dummy variables to refect the occupational

career type actors: generalist, specialist, and other. The dummyvariable or the generalist category was set at 1 when the responseto the question Which o the ollowing types best describes youright now? was 1. I am a generalist with skills that can be usedin a variety o elds and 0 or other responses. The dummy vari-

able or the specialist category was set at 1 when the responseto the question was 2. I am a specialist with skills that are espe-cially useul in a particular eld or 3. I am a skilled laborer whoworks alone in a eld that involves making goods and 0 or otherresponses. The dummy variable or the other category was setat 1 when the response to the question was 4. Neither and 0 orall other responses.

The income actor was divided into three components: marketwage, pension system, and savings. The market wage was calculatedusing the wage unction. Because the correlation between savingsand labor income is high entering both at the same time could cre-


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pension system were divided into six groups, and dummy variableswere established or each: No pension, Eligible to receive a wel-are pension, Eligible to receive a mutual-aid pension, Eligibleto receive a national pension, Eligible to receive a corporate pen-sion, and Eligible to receive some other private pension.

With regard to human capital, the number o years o experiencewas calculated as age no. o years o education 6. Five dummyvariables or educational background were established, represent-

ing junior high school, high school, vocational school, junior col-lege, and university. Responses related to occupation were dividedinto ten types, and a dummy variable was established or each:specialized or technical work, management work, administrativework, sales work, service work, security work, transportation andcommunications work, skilled labor or production process work,unskilled labor, and other.

Six dummy variables or the amily background actors were

used: amily o a salaryman working in a company, actory, oroce, amily o a company president or executive, amilyengaged in arming, orestry, or sheries, a sel-employed am-ily operating a commercial business or actory, amily o aspecialized reelance proessional, such as a lawyer, accountant,tax accountant, or doctor, and other. In the multinomial logitanalysis related to employment pattern, the responses amily oa salaryman working in a company, actory, or oce and am-

ily o a company president or executive were integrated intothe dummy or employee amily, while the responses amilyengaged in arming, orestry, or sheries, a sel-employedamily operating a commercial business or actory, amily o aspecialized reelance proessional, such as a lawyer, accountant,tax accountant, or doctor, and other were integrated into adummy or sel-employed amily.

Within the personal attributes, the gender dummy was set asmale = 1 and emale = 0. The poor health dummy was based on theresponse to the instruction Please rate your ordinary health statuson a scale o 1 to 5 Those who selected (1) poor and (2) somewhat


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Employment Pattern by Occupational Career Type

Figure 4 shows the respondents employment patterns beorethe age o sixty by occupational career type. Overall, the highestpercentage o respondents were engaged in ull-time employ-ment. Generalists had the highest proportion o respondents inull-time employment, at 67.4 percent, while the specialists hadthe highest proportion o sel-employed respondents. This shows

that employment patterns beore age sixty dier by occupationalcareer type.The employment patterns preerred by respondents at the age

o sixty-ve, by occupational career type, are shown in Figure5. Overall, about 30 percent o respondents in each occupationalcareer type preer part-time work. The proportion o respondentspreerring sel-employment was highest among specialists, at 24.8percent. This shows that preerred employment patterns at age

sixty-ve dier by occupational career type.

Preerred Employment Pattern at Age Sixty-fve by

Occupational Career Type Beore Age Sixty

Table 3 shows the preerred employment patterns at age sixty-veby employment pattern beore age sixty. The preerred employ-ment pattern at age sixty-ve was the same as the respondentsemployment pattern beore age sixty or 45.9 percent o ull-time

employees, 43.6 percent o part-time employees, 63.7 percent othe sel-employed, and 40.2 percent o the not employed.17 Theemployment pattern beore age sixty strongly aects the preerredemployment pattern at age sixty-ve, and this infuence is strongestamong the sel-employed.

The results o this cross-tabulation show that occupational ca-reer type has an impact on wage distribution employment patternselection beore age sixty, and preerred employment pattern at age

sixty-ve, but they do not specically show a causal relationship.To address this problem, I conducted the ollowing quantitative


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Figure 3. Wage Rate Distribution (Generalists)

Source: Compiled rom individual data rom the Survey on the Baby-Boomer Gen-erations Work and Lie Vision (JILPT) (2006).

Figure 4. Employment Pattern Beore the Age o 60 by OccupationalCareer Type


67.4%

10.8%

16.9%

4.9%

61.7%

11.1%

23.6%

3.6%

50.7%

21.9%

20.5%

6.9%

0%

10%

20%

30%

40%

50%

60%70%

80%

90%

100%

Generalist Specialist Other

Not

employed

Self-

employed

Part-time

Full-time

0

5

10

15

6 7 8 9 10

Generalist lnW

Percent


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Results

Analysis of Determinants in the Formation of

Occupational Career Type

Table 4 shows the results o my analysis o the determinants o

occupational career type.Ater controlling or the human capital actors o educational

background and length o service, the results show that a length oservice o our years or longer has an overall positive work continu-ation eect. Also, over the long term, the longer ones length oservice, the higher the probability that one is a generalist.

Results regarding gender show that men have a higher probabilitythan women o being generalists. These results support Hypothesis

1, that Even among workers with the same level o labor produc-tivity, the longer the employees length o service with a company,

Figure 5. Preerred Employment Pattern at Age Sixty-fve byOccupational Career Type


(2006).

18.1%

36.1%

14.3%

31.5%

19.6%

33.6%

24.8%

21.9%

26.0%

28.8%

18.9%

26.3%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Generalist Specialist Other

Full-time

Part-time

Not employed

Self-employed


22/44

WINTER 20089 85

The results regarding occupation show that specialists or engi-

neers have a higher probability o being specialists than people inother occupations.

The results related to industry show a tendency or individualsin tertiary industries to be less likely than people in primary andsecondary industries to be specialists.The survey data indicate thatthe proportion o respondents in secondary industries (38.4 percent)is considerably higher than that in primary industries (1.2 percent),and these analytical results thereore suggest that the development

o secondary industries during Japans high economic growthperiod is linked to the ormation o generalist and specialist job

Table 3

Preerred Employment Pattern at Age Sixty-fve by EmploymentPattern Beore Age Sixty

Age 60/Age 65 Full-time Part-time

Sel-employed

Notemployed Total

Full-time Number 629 306 90 346 1,371

% 46.9 22.3 6.6 26.2 100

Part-Time Number 82 161 18 108 369

% 22.2 43.6 4.9 29.3 100

Sel-employed Number 66 33 319 93 601

% 11.2 6.6 63.7 18.5 100

Part-time Number 82 161 18 108 369

% 22.2 43.6 4.9 29.3 100

Sel-employed Number 66 33 319 93 501

% 11.2 6.6 63.7 18.6 100

Not employed Number 18 37 12 45 112% 16.1 33.0 10.7 40.2 100

Total Number 785 537 439 592 2,353

% 33.4 22.8 18.7 26.1 100

Source: Survey on the Baby-Boomer Generations Work and Lie Vision, JILPT2006.


23/44


sisoftheDeterminantsofOccupationalCareerType(mult

inomiallogisticregressionm

odel)

Generalistvs.other

Specialistvs.other

Coefcient

z-value

Coefcient

z-value

nsyste

m

(nopension)

Publicpension

0.5

88+

1.7

9

0.211

0.9

4

Mutualaidpension

0.6

80**

2.0

0

0.0441165

0.1

7

Nationalpension

0.1

885703

**

0.7

6

0.1431744

0.8

1

Corporatepension

0.5

09*

2.0

5

0.1626566

0.7

8

Otherprivatepension

0.0

20

0.0

8

0.112

0.5

7

background

(salaryman+companypresident+directors)

Sel-employedamilyinarming,

orestry,orfsheriesin

dustry

0.1

771382

0.5

9

0.311

1.5

9

Sel-employedamilywithaactory

orcommercialbusiness

0.0

34

0.1

4

0.249

1.3

4

Sel-employedamilyoalawyer

ordoctor

2.0

91+

1.6

9

2.229*

1.9

7

Otherandnoresponse

0.1

48

0.3

3

0.259

0.7

6


24/44

WINTER 20089 87

status

Poorhealth

0.2

961185

**

2.8

7

0.0380978

0.4

9

capita

l

Yearsoexperience

0.0

765805

*

2.3

3

0.0346633

1.4

9

Yearsoexperiencesq

uared

0.0

02*

2.2

5

0.001

1.7

1

(Juniorhighschool)

Highschool

0.4

371587

0.9

4

0.032903

0.1

2

Vocationalschool

0.4

35

0.7

6

0.124

0.3

3

Juniorcollege

0.2

62

0.4

1

0.3685999

0.7

5

University

0.3

93

0.7

9

0.363

1.1

9

Male

0.8

48**

2.7

3

0.930**

4.5

3

tion

(Specialist,technician)

Management

0.0

76

0.2

7

1.322326**

5.5

6

Administration

0.8

103847

*

2.3

2

1.704863**

6.5

7

Sales

0.5

142969

1.4

2

1.25442**

4.6

7

Service

0.4

444413

1.0

8

0.9754646**

3.3

9

Security

2.1

61764*

1.9

8

1.811413**

3.4

5

Transportationandcommunication

2.4

47405**

3.0

8

1.935192**

5.0

6

Skilledlabor,productio

nprocessing

1.2

76697*

2.3

0

0.8445183**

2.8

4

Unskilledlabor

1.9

39978**

2.4

8

2.160351**

5.0

7

Other

-0.6

742173

1.1

7

1.844975**

4.0

4(continued

)


25/44


Generalistvs.other

Specialistvs.other

Coefcient

z-value

Coefcient

z-value

e

(Smallenterprise)

Medium-sizefrm

0.0

01

0.0

0

0.2373985

1.3

2

Largefrm

0.2

90

1.1

4

0.142

0.7

4

y

(Primary,

Secondary)

Tertiary

0.3

982738

+

1.7

5

0.4832157**

2.7

4

nt

0.2

209748

0.3

1

0.741**

1.5

1

bs.

1,1

77

lihood

1,0

11.8

4

2

0.0

000

R2

0.1

59

EstimatedbasedonSurveyontheBaby-BoomerGenerationsWorkandLieVision,JILPT2006.

Signifc

ancelevels:+10percent;*5

percent,and**1percent.Estimateswerecalculatedusingth

emultinomiallogitmodel.Th

e

ecategoryisOther.Intheanalysis

,thereerencecategorieswere

Nopension,Salaryman+companypresident+directors,

highschool,SmallandPrimary,

secondary.Therespondents

regionwasalsoincluded,butthecoefcientsarenotreported

(continued)


26/44

WINTER 20089 89

goods, and the development o the manuacturing industry, Japa-nese companies began to adopt unique, internal personnel trainingsystems (Inoki 2000).

Results related to the pension system show that eligibility toreceive a mutual-aid pension or corporate pension leads to a higherprobability o being a generalist. Those who work or large companiesor government agencies are also more likely to be generalists.

With regard to the amily background, respondents rom sel-

employed or specialist amilies have a higher probability o beingspecialists.

Analytical Results of the Effect of Occupational Career

Type on Wages

Table 5 shows the results o my analysis o the eect o occupa-tional career type on wages.

The analytical results produced using Heckmans two-stepmodel or the wage unction appear to be quite similar to the OLSresults. Thus, I will explain below only the results produced usingHeckmans model.

An examination o the actors in occupational career type showthat the gure or specialists is 0.112, and has a level o statisticalsignicance o less than 1 percent. When controlling or actorso human capital, the wages o specialists are 11.2 percent lowerthan the wages o generalists. These ndings are consistent with thendings shown in the wage rate distribution diagrams, and supportHypothesis 2, which states that Even when labor productivity isequal, wages will be higher among generalists than specialists.

The results show that longer lengths o service and higher lev-els o education predict higher wages. This is consistent with thetheory o human capital. They also show that men have higherwages than women, and that wages are higher or those workingin larger companies. These ndings are consistent with the previ-

ous research (Higuchi 1991; Ma 2007b; Ono 1989). The resultsurther indicate that the wages o union members are higher than


27/44


isotheEectoOccupational

CareerTypeonWages

mantw

o-stepselectionmodel)

Secondstage

estimates

F

irststageestimates

Coefcient

z-value

Coefcient

z-value

Male

0.2

62**

8.1

1

0.009**

0.0

7

capita

l

Yearsoexperience

0.0

14**

3.9

1

0.715

1.3

7

Yearsoexperiencesq

uared

0.0

00

0.7

7

0.0096347

1.4

0

(Juniorhighschool)

Highschool

0.0

92*

2.2

5

0.4660219*

2.1

5

Vocationalschool/jun

ior

college

0.1

55**

2.9

7

0.5060038*

1.9

3

University

0.2

88**

6.1

9

0.3370748

1.1

6

tionalcareer(generalist)

Specialist

0.1

1795**

3.3

7

0.0330706

0.2

2

Other

0.1

499449**

4.3

4

0.1269896

0.8

6

nging

Jobchangingexperien

ce

0.0

26807

0.8

6

0.216*

2.1

0

byindustry

(Specialist,technician)

Management

0.1

14**

3.2

7

Administration

0.0

740218+

1.8

7


28/44

WINTER 20089 91

Sales

0.1

152711**

2.8

4

Service

0.1

540444**

3.4

0

Security

0.3

313513**

3.7

1

Transportationandcommu-

nications

0.1

57376

2.4

9

Skilledlabor,productio

n

processing

0.1

997564**

4.2

5

Unskilledlabor

0.1

219761*

2.0

0

Other

0.0

07

0.1

0

ze

(Smallfrm)

Medium-sizefrm

0.0

62*

2.2

3

Largefrm

0.1

74**

5.7

5

y

(Primary,

Secondary)

Tertiary

0.0

16

0.6

1

Union

0.0

53+

1.6

7

nsyste

m

(Nopension)

Publicpension

0.1

131

0.1

2

Mutualaidpension

0.1

16

0.0

0

Nationalpension

0.9

45**

5.2

7

Corporatepension

0.6

059538**

5.9

6

Otherprivatepension

0.1

86

1.3

7

status

Poorhealth

0.0

674343

0.5

7

compo

sition

Married

0.0

529795

1.1

2(continued

)


29/44


Secondstage

estimates

F

irststageestimates

Coefcient

z-value

Coefcient

z-value

No.ochildren

0.245

1.5

1

Havechildrenenrolled

in

school

0.026

0.8

5

Co-residencewithparents

0.1243362

1.0

7

nt

7.3

59**

102.0

7

0.0497846

0.5

0

0.0

565*

0.8

0

roobs.

1,3

52

obs.

213

orobs

.

1,1

390

.000

EstimatedbasedonSurveyontheBa

by-BoomerGenerationsWork

andLieVision,JILPT2006.

JapanInstituteorLaborPolic

y

ining.2006.Tayounahatarakikatao

megururontenbunsekihouko

kusho[AnalyticalReporton

theDebatesRegardingVariou

s

atterns],LaborPolicyResearchReportno.70.

Signifc

ancelevels:+10percent;*5p

ercent,and**1percent.Estim

ateswerecalculatedusingthe

multinomiallogitmodel.Inth

e

,there

erencecategorieswereJuniorhighschool,Generalist,

Specialist,engineer,Small,

andNopension.Therespon

-

egionw

asalsoincluded,butthecoef

cientsarenotreportedhere.

(continued)


30/44

WINTER 20089 93

The Effect of Occupational Career Type onEmployment Pattern

Table 6 shows the results o my analysis o the eect o occupationalcareer type on employment patterns beore age sixty.

The probability o being a part-time worker is higher or spe-cialists than or generalists. Also, the probability o being sel-employed is higher among specialists than generalists. However,

there was no signicant dierence between generalists and special-ists in terms o their probability o being ull-time workers. Thispartially supports Hypothesis 3.

With regard to the pension system, eligibility to receive a publicpension or mutual-aid pension leads to a higher probability o hav-ing ull-time employment. However, eligibility to receive a nationalpension reduces the probability that one is a ull-time employee.Also, eligibility to receive a mutual aid pension or corporate pen-

sion reduces the probability o being sel-employed. The selectiono employment pattern was shown to dier depending on onespension type.

With regard to other actors, older respondents had a higher prob-ability o being sel-employed. The probability o being a ull-timeemployee or sel-employed was higher among men than women,but women were more likely than men to engage in part-time work.Experience being unemployed reduced the respondents probabilityo being a ull-time employee. The higher the respondents desireto continue working, the greater his likelihood o being employedull-time or sel-employed. Also, those who reported experienc-ing their peak proessional period in their twenties had a higherprobability o being employed ull-time or sel-employed, whilethose who reported experiencing their peak proessional period intheir thirties or orties had a higher probability o being employedpart-time.

Table 7 shows the estimates o the eect o occupational career

type on preerred employment pattern at age sixty-ve. While special-ists are more likely than generalists to preer sel-employment there


31/44


Full-timevs.notemployed

Part-timevs.notemployed

Sel-employedvs.

notemployed

Coefcient

z-value

Coefcient

z-value

Coefcient

z-value

wages

Wagerate

(estimated)

2.4

25

0.1

1

12.1

74

0.5

2

24.6

00

1.0

7

ational

r

(Generalist)

Specialist

0.6

29

1.5

4

0.9

92*

2.0

7

0.7

10+

1.6

2

Other

0.4

37

1.0

9

0.9

59*

2.0

6

0.5

45

1.2

5

nsyste

m

(Nopension)

Publicpension

1.7

08**

5.6

1

0.5

11+

1.6

4

0.4

133315

1.3

7

Mutualaidpension

1.2

99**

2.6

5

0.3

21

0.5

8

2.3

9173**

3.7

2

Nationalpension

1.4

58772**

5.0

6

0.5

735402+

1.8

4

0.3

56

1.1

4

Corporatepension

0.1

54

0.3

7

0.0

37

0.0

8

1.2

62213**

2.5

5

Otherprivatepension

0.1

16

0.3

4

0.0

99

0.2

8

0.3

45

0.9

7

s

Achievemento

savingsgoals

0.6

206468

1.2

7

0.5

391381

0.9

6

0.3

933865

0.7

1

(Bornin1951)

1947

0.3

17

0.5

5

0.9

05

1.4

0

1.3

66*

2.2

4

ectoOccupationalCareerTyp

eonEmploymentSelectio

nBeoreAgeSixty(multin

omiallogisticregression


32/44

WINTER 20089 95

1948

0.0

084274

0.0

2

0.5

45

1.0

8

0.7

74+

1.6

2

1949

0.1

02

0.2

6

0.3

85

0.8

8

0.7

07+

1.6

5

1950

0.2

00

0.5

0

0.4

91

1.1

4

0.5

69

1.3

4

ion

(Juniorhighschool)

Highschool

0.8

117888

0.2

2

2.4

14141

0.6

0

3.3

68542

0.8

6

Vocationalschool/

juniorcollege

0.8

947956

0.1

9

2.8

12882

0.5

3

4.4

72664

0.8

7

University

1.9

81155

0.1

4

8.0

61483

0.5

3

14.9

8134

1.0

1

al

utes

Male

1.0

89**

3.3

2

1.0

41966**

2.8

3

1.1

18**

3.2

3

Married

0.5

725743

1.4

0.2

63

0.6

0

0.0

950571

0.2

2

Poorhealth

0.0

372692

0.2

8

0.0

00

0.0

0

0.0

969456

0.6

9

osition


inschool

0.2

10

0.6

0

0.0

11

0.0

3

0.4

49

1.2

3

No.ochildren

0.1

06

1.2

3

0.1

72+

1.8

5

0.0

86

0.9

4

Co-residencewith

parents

0.1

475783

0.5

4

0.0

777231

0.2

6

0.1

46

0.5

1

ground

Employeeamily

Sel-employedamily

0.0

24

0.0

9

0.1

034176

0.3

6

0.4

37

1.5

6

Other

0.3

166175

0.6

3

0.3

269459

0.6

2

0.6

221083

1.1

1

yment

ry

Experiencebeing

unemployed

0.6

571501*

2.3

4

0.0

285174

0.1

0

0.1

792927

0.6

0

(continued

)


33/44


Full-timevs.notemployed

Part-timevs.notemployed

Sel-employedvs.

notemployed

Coefcient

z-value

Coefcient

z-value

Coefcient

z-value

Desiretocontinue

working

1.0

03**

2.6

7

1.1

56

2.7

3

1.8

35**

3.7

5

Experiencechanging

jobs

0.0

38

0.1

1

0.2

11

0.5

8

0.4

651151

1.3

5

mployment

d

None

20s

1.7

25866*

2.1

9

2.0

3475+

2.5

7

2.4

98709**

3.1

3

30s

1.4

08149+

1.7

6

1.8

42481*

2.2

7

1.7

79264*

2.2

0

40s

1.1

2136

1.3

8

2.0

75565*

2.5

1

2.0

01694*

2.4

2

50s

0.1

58

0.1

5

0.9

00613

0.8

3

0.3

413944

0.3

2

ant

15.5

5071

0.1

0

89.5

4052

0.5

1

182.8

515

1.0

6

1,7

41

elihood

1,3

15.9

2

2

0.0

00

oR2

0.2

83

:Estim

atedbasedonSurveyontheB

aby-BoomerGenerationsWorkandLieVision,JILPT2006

.

Signif

cancelevels:+10percent;*5

percent,and**1percent.Estimateswerecalculatedusingth

emultinomiallogitmodel.Th

e

cecate

gorywasNotemployed.Intheanalysis,thereerencecategorieswereGeneralist,Nopension,Bornin1951,Junio

r

hool,

Employeeamily,None[peakproessionalperiod].Therespondentsregionwasalsoincluded,butthecoefcientsar

e

ortedh

ere.

6(continued)


34/44

WINTER 20089 97

Eligibility to receive a national pension was shown to reducethe probability o preerring ull-time employment. Eligibility toreceive some other type o private pension was shown to reducethe probability o preerring part-time employment. Also, eligibil-ity to receive a public pension, mutual aid pension, or corporatepension reduces the probability o preerring sel-employmentacross the board. Pension benet eligibility was shown to reducethe probability o preerring employment to nonemployment at

age sixty-ve.The probability o preerring ull-time or sel-employment was

higher among men than women, and the desire to work beyondthe age o sixty-ve was stronger among men than women. Thosewho come rom sel-employed amilies are more likely than thosewho come rom amilies o employees to preer sel-employment.A stronger desire to continue working led to a higher probabilityo preerring all employment patterns, ull-time, part-time, or

sel-employment, and this impact was most evident or ull-timeemployment.The actors that did not have a signicant impact on preerred

employment type at age sixty-ve were the wage rate, savings ori-entation, birth cohort, educational background, amily composition,and peak proessional period.

Summary and Future Challenges

In this article, I conducted a quantitative analysis o such issues asthe eect o occupational career type on the employment patternsselected by baby-boomers. My major conclusions are as ollows.

First, specialists around the age o sixty have a higher probabilitythan generalists o actually being employed and being employedin their preerred employment pattern. This is because the avor-able treatment that generalists receive under the seniority-basedwage system make it easy or discrepancies to emerge between an

employees level o contribution and their wages, and this in turnmakes it dicult or companies to continue the employment o


35/44


7 ecto

OccupationalCareerTyp

eonPreerredEmployme

ntPatternatAgeSixty-fv

e(mutinomiallogisticre-

onmodel)

Full-timevs.

notemployed

Part-timevs.

notemployed

Sel-employedvs.

notemployed

Coefcient

z-value

Coefcient

z-value

Coefcient

z-value

wages

Wagerate

(estimated)

3.9

15

0.3

4

4.3

85021

0.3

5

3.9

12212

0.2

9

ational

r

(Generalist)

Specialist

0.2

34

1.1

8

0.1

62

0.7

4

0.5

91*

2.3

5

Other

0.3

36+

1.6

1

0.2

11

0.9

3

0.4

18

1.5

8

nsyste

m

(Nopension)

Publicpension

0.5

29

2.8

1

0.2

76

1.4

9

0.7

806432**

4.2

2

Mutualaidpension

0.0

65

0.2

8

0.5

92

2.4

9

1.5

25932**

4.6

5

Nationalpension

0.3

587152*

2.2

5

0.2

29

1.4

1

0.9

13**

5.0

9

Corporatepension

0.2

694219

1.4

0

0.0

18

0.0

8

0.7

209943**

2.7

0

Otherprivatepension

0.1

18223

0.6

6

0.3

843351*

2.0

3

0.2

009732

0.9

7

s

Achievemento

savingsgoals

0.8

307026**

2.8

3

0.2

612586

0.9

2

0.5

104225

1.5

(Bornin1951)

1947

0.2

63

1.0

0

0.1

18

0.3

9

0.0

59

0.1

9

1948

0.0

702683

0.3

0

0.0

08

0.0

3

0.0

53

0.2

0


36/44

WINTER 20089 99

1949

0.0

88

0.4

1

0.1

08

0.4

7

0.1

83

0.7

4

1950

0.0

797839

0.3

8

0.0

884336

0.3

9

0.0

50

0.2

1

ion

(Juniorhighschool)

Highschool

1.8

09198

0.9

3

0.0

829511

0.0

4

0.3

33

0.1

5

Vocationalschool/

juniorcollege

2.1

08768

0.8

3

0.0

59

0.0

2

0.7

26

0.2

5

University

3.8

37241

0.5

3

1.5

16

0.1

9

2.0

96

0.2

5

al

utes

Male

1.4

52**

7.3

7

0.1

87

0.9

5

1.2

15**

5.6

4

Married

0.3

490669

1.4

4

0.0

15034

0.0

6

0.0

812749

0.2

9

Poorhealth

0.0

275115

0.3

8

0.0

73

0.9

6

0.0

55

0.6

7

osition


inschool

0.2

43

1.4

4

0.2

585741

1.3

7

0.1

00

0.5

1

No.ochildren

0.0

01

0.0

3

0.0

29

0.5

6

0.0

45

0.8

2

Co-residencewith

parents

0.0

781916

0.5

2

0.0

888635

0.5

6

0.0

12

0.0

7

ground

Employeeamily

Sel-employedamily

0.1

37

0.9

6

0.1

418694

0.9

4

0.3

37*

2.0

8

Other

0.2

83

0.8

5

0.2

01

0.5

9

0.2

81

0.7

5

yment

y

Experiencebeing

unemployed

0.0

83

0.5

1

0.0

782388

0.4

7

0.1

93

1.0

5

Desiretocontinue

working

2.0

12**

5.7

1

1.6

82**

4.9

5

1.3

55**

3.7

6

(continued

)


37/44


Full-timevs.

notemployed

Part-timevs.

notemployed

Sel-employedvs.

notemployed

Coefcient

z-value

Coefcient

z-value

Coefcient

z-value

Experiencechanging

jobs

0.3

30*

2.0

3

0.2

04

1.1

80

0.0

389182

0.2

1

mploym

entNone

20s

0.0

916826

0.3

1

0.0

394552

0.1

3

0.4

997924+

1.6

4

30s

0.0

64

0.2

3

0.1

35

0.4

6

0.0

980566

0.3

3

40s

0.0

721162

0.2

5

0.1

823974

0.6

1

0.4

475596

1.4

7

50s

0.2

501417

0.7

6

0.5

095898

1.4

7

0.5

971788+

1.6

7

nt

30.9

1211**

0.3

6

31.0

84

0.3

4

26.9

65

0.2

7

1,7

61.0

00

lihood

2,1

42.4

6

2

0.0

00

R2

0.1

13

EstimatedbasedonSurveyontheBaby-BoomerGenerationsWorkandLieVision,JILPT2006.

Signifc

ancelevels:+10percent;*5percent,and**1percent.Estimateswerecalculatedusingth

emultinomiallogitmodel.The

ecateg

orywasNotemployed.Inth

eanalysis,thereerencecatego

rieswereGeneralist,Nope

nsion,Bornin1951,Junio

r

hool,Employeeamily,None[peakproessionalperiod].Ther

espondentsregionwasalsoin

cluded,butthecoefcientsare

rtedhere.

(continued)


38/44

WINTER 20089 101

employees have rm-specic human capital that cannot be usedwhen they change jobs, they are less likely to become reemployedat another company ater leaving their workplace. The continuedemployment and reemployment o generalists who want to keepworking is going to be an important challenge that will have to beaddressed in the uture.

To promote the employment o senior citizens, education andtraining will have to be provided or middle-aged and senior gen-

eralists, including the baby-boomers.It will also be important or companies to adapt to the continued

employment o seniors by revising their old seniority-based wagesystems. They are going to have to start transitioning to a peror-mance-based wage system in which wages refect the workers levelo contribution to the company (Ebisuno 2002; Yashiro 1999).

Second, there is a high probability that specialists will preer sel-employment at age sixty-ve. In this group, many people currently

working as employees are thinking about making the transition tosel-employment. It will be particularly important that eorts bemade to create a social environment where it is easy or workerswith specialized skills to start their own businesses.

Third, the proportions o people wanting to be engaged in ull-time, part-time, and sel-employment at age sixty-ve are aboutequal, and the share o people who hope to be engaged in part-timeemployment at age sixty-ve is much higher than the share o

workers actually working in part-time jobs beore age sixty. Theemployment needs o baby-boomers are clearly quite diverse. Thus,to create a society where lielong service is the norm, it will beimportant or society to create diverse employment opportunitiesthat meet the varied employment needs o seniors. Companies needto begin investigating the introduction o new work shit options(such as shortened workweek systems and fexible employmentsystems) or older employees (Seike 1992).

Finally, the analysis in this study claried several acts relatedto the determinants o occupational career type, and the eects ooccupational career type on wages employment pattern selection


39/44


study relied on individuals sel-reporting their own occupationalcareers, uture analyses should add an objective index o occu-pational career type. Second, because this was a cross-sectionalanalysis, issues still remain in terms o the dierences betweenindividuals. In the uture, panel data analyses should be conductedto address these issues. And third, the analysis in this article wasa quantitative analysis o the diverse employment pattern selec-tions made by ull-time workers, part-time workers, sel-employed

people, and the not employed, but it does not include any analysiso the eects o liquidity constraint actors on sel-employment.18A rigorous empirical analysis o sel-employment will surely needto be conducted in the uture.

Notes

1. For more on the dichotomous empirical analysis on the employment oseniors, see Oishi (2000), Seike (1993), Seike and Yamada (1996, 2004).

2. In this article, employment patterns will be divided into our types: part-time, ull-time, sel-employed, and not employed.

3. Chioi (2002) and Kim and DeVaney(2005) showed that the actors oincome, pension, health, and household composition have an impact on theemployment patterns o seniors. Seike and Yamada (2004) divided employedworkers into company people and work people based on their occupationalhistory, and showed that the continuing employment status o these two groupsdiers because o dierences in their accumulation o human capital.

4. The Nippon Keidanren ([Nikkeiren] 1995) categorizes occupational careertypes as ollows, based on the new era o Japanese-style management. Workers

are classied as (1) lietime-employed elite executives (generalists), (2) special-ized workers who move reely between companies (specialists), and (3) ordinaryworkers who work based on their own individual needs (other). In light o theKeidanrens classication system and the individual data, this article uses threeoccupational career types, generalist, specialist, and other.

5. According to a study by Mincer (1974), in quantitative analyses o hu-man capital, the level o education is used as a proxy or general human capital,while the number o years o experience is used as a proxy or rm-specichuman capital.

6. In the analysis below, explanatory variables such as marital status, numbero children, the presence o co-resident children and co-residence with parents

are used as indicators o the reservation wage.7. In this article, pension income is used as a proxy or nonlabor income.8 I hi i i l i h i l ( d i l l


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WINTER 20089 103

o service, years o experience) is an indicator that refects labor productivity,i we were to control or these human capital variables, the condition o equallabor productivity would be met.

10. My reasons or selecting the age o sixty-ve are as ollows. First, in thissurvey, respondents were asked about the employment they preer at each ageater the age o sixty, in one-year increments, but the response rate was highestamong sixty-ve-year-olds. Also, according to the revised Law Concerning theStabilization o the Employment o Older Persons, business owners are now beingrequired to raise the mandatory age to sixty-ve and to introduce employmentcontinuation systems. Further, pension system reorms will gradually raise theage at which pensions are payable rom sixty to sixty-ve. For these reasons, itseems that the employment status o seniors is likely to change signicantly atthe age o sixty-ve. I thereore chose to use sixty-ve as the age or analyzingpreerred employment patterns in this study.

11. The discrimination rule is as ollows: I the condition KJ> =H 1 isullled, then the structural equation process is overidentied. In this ormula,Kis the total number o exogenous variables in the model, Jis the number oexogenous variables in the structural equation, andHis the number o endogenousvariables in the structural equation.

12. For details about the multinomial logit model, see Green (2003).13. This generally reers to people born in 1947 to 1951, but is a ve-year

age cohort.14. For more inormation on the specic standards or establishing these

categories, see the section on Occupational Career Factors in the explanatoryvariables. However, while occupational career type as an explanatory variablewas established as a series o dummy variables, occupational career type as adependent variable is a category variable.

15. While savings might be seen as a proxy or nonlabor income, the surveydata show that the correlation between savings and labor income is high at0.8997. Thus, in this analysis o employment patterns, I will not use the amounto savings and will use savings target achievement instead as an indicator o

savings orientation.16. There were three dummy variables or industry, based on survey responses.Responses indicating a primary industry (arming, orestry, and sheries, ormining) = 1, other = 0. Responses indicating a secondary industry (construction,manuacturing, public utility industries like electricity and gas, transportation,telecommunications) = 1, other = 0. Responses indicating a tertiary industry(wholesaling, retailing, nance and insurance, real estate, eating and drinkingestablishments, inns and hotels, labor agency services, business service contrac-tors, services or oces other than business service contractors, services orindividuals, hospitals, schools, and other public service industries, public serviceor national, preectural, or municipal governments) = 1, other = 0.

17. See Ma (2007a) and the Japan Institute or Labor Policy and Training (2007)or the results o a crosstabulation o the selected employment pattern beore agei t ith i i il b k d d d k i l


41/44


18. Blanchfower and Oswald (1998), Coate and Tennyson (1992), and Stiglitzand Weiss (1981) have argued that liquidity constraints (e.g., o capital, prop-erty, liquid assets, home ownership, and past income) have a signicant impacton sel-employment, but this analysis did not address the eects o the variousliquidity constraint actors on sel-employment probability.

References

Association o Employment Development or Citizens. 1994. Koureika jidai nitekigou shita chingin taikei moderu ni kansuru chousa kenkyu houkokusho

[Survey Report on a Wage System Model Applicable to an Aging Society].Becker, G.S. 1964.Human Capital: A Theoretical and Empirical Analysis with

Special Reerence to Education. Chicago: University o Chicago Press.Blanchfower, D.G., and A.J. Oswald. 1998. What Makes an Entrepreneur?

Journal o Labor Economics 16: 2660.Chioi, N.G. 2002. Sel-Dened Retirement Status and Engagement in Paid

Work among Older Worker-Age Women: Comparison between ChildlessWomen and Mothers. Sociological Inquiry 71, no. 1: 4371.

Coate, S., and S. Tennyson, S. 1992. Labor Market Discrimination, imperectInormation and Sel-Employment. Oxord Economic Papers 44: 27288.

Ebisuno, Sumiko. 2002. Ch ukourensha no koyou mondai to koyou kikaikakuho/soushutsu ni tsuite [The Employment Problems o the Middle-Aged and Seniors: Securing and Creating Job Opportunities].Nihon roumugakkaishi 4, no. 1: 4655.

Green, W.H. 2003.Econometric Analysis. 5th ed. Upper Saddle River, NJ:Prentice Hall.

Heckman, J. 1979. Sample Selection Bias as Specication.Econometrica47, no. 1: 15361.

Higuchi, Yoshio. 1991.Nihon keizai to sh ugyou koudou [The JapaneseEconomy and Employment Behavior]. Tokyo: Toyo keizai shinposha.

Higuchi, Yoshio, and Isamu Yamamoto. 2002. Wagakuni dansei koureishano roudou kyoukyu koudou mekanizumunenkin / chingin seido no koukabunseki to koureisha sh ugyou no shouraizou [Japans Labor SupplyMechanisms o Senior Males: Analysis o the Eects o the Pension andWage Systems and a Future Vision or Senior Employment], in Institute orMonetary and Economic Studies, Bank o Japan, ed. Kinyu Kenkyu, no.10: 3177. Institute or Monetary and Economic Studies, Bank o Japan.

Honig, Marjorie. 1985. Partial Retirement Among Women.Journal oHuman Resources 20, no. 4: 61321.

Inoki, Takenori. 2000. Keizai seichou no kajitsu: 19551972 [The Fruits oEconomic Growth]. Chuo koronsha.

Japan Institute or Labor Policy and Training. 2006. Tayouna hatarakikatao meguru ronten bunseki houkokusho [Analytical Report on the DebatesRegarding Various Work Patterns] Labor Policy Research Report no 70


42/44

WINTER 20089 105

de-ta bunseki [Report on a Survey o Baby-Boomers Employmentand Liestyles: Analysis o Data rom the Survey on the Baby-BoomerGenerations Work and Lie Vision]. Labor Policy Research Report, no. 85.

Japan Institute o Labor. 1995. Jukunen raiu ni kansuru chousa [Survey oMature Liestyles]. Survey Report, no. 72.

. 1998a. Intai katei no arikata to intaigo no seikatsu ni kansurukenkyu [Study o the Retirement Process and Post-Retirement Living].Survey Report, no. 110.

. 1998b. Chukounensha no tenshoku jittai to koyou/shokugyou tenbou[Job Changing Behavior Among Middle-Aged and Senior Citizens andTheir Employment and Occupational Outlook]. Survey Report, no. 111.

Kim, H., and S.A. DeVaney. 2005. The Selection o Partial or Full Retirementby Older Workers.Journal o Family and Economic Issues 26, no. 3:37194.

Koike, Kazuo, and Takenori Inoki. 2002.Howaito kara-no jinzai keisei [TheFormation o White-Collar Human Resources]. Toyo keizai shinposha.

Ma, Xinxin. 2007a. Dankai no sedai no shokugyou kyaria no taipu toshgyou keitai no sentaku [Occupational Career Types o Baby-Boomersand Their Selection o Employment Patterns]. In Japan Institute or LaborPolicy and Training, ed., Dankai no sedai no shugyou to seikatsu nikansuru chousa kenkyu houkokudankai no sedai no shgyou to seikatsu

bijon chousa de-ta bunseki [Report on a Survey o Baby-BoomersEmployment and Liestyles: Analysis o Data rom the Survey on theBaby-Boomer Generations Work and Lie Vision]. Labor Policy ResearchReport 85: 3057.

. 2007b. Nicchu ni okeru danjokan chingin kakusa no sai ni kansuruyouin bunseki [Factor Analysis o the Wage Disparity Between Men andWomen in Japan and China].Nihon roudou kenkyuzasshi, no. 560: 96107.

Mincer, Jacob. 1974. Schooling, Experience and Earnings. New York:Columbia University Press.

Mitani, Naoki. 2001. Koureisha koyou seisaku to roudou juyou [SeniorEmployment Policies and Labor Demand]. In Koyou seisaku no keizaibunseki [Employment Policy and Economic Analysis], ed. Takenori Inokiand Fumio Otake, 33988. Tokyo: University o Tokyo Press.

Oishi, Akiko. 2000. Koureisha no sh ugyou kettei ni okeru kenkou youinno eikyou [Infuence o Health Factors on Employment Status o theElderly].Nihon roudou kenkyu zasshi 481: 5162.

Ono, Akira. 1989.Nihon no koyou kankou to roudou shijou [JapaneseEmployment Practices and the Labor Market]. Tokyo: Toyo keizaishinposha.

Ruhm, C.J. 1990. Bridge Jobs and Partial Retirement.Journal o LaborEconomics 8, no. 4: 482501.

Seike, Atsushi. 1992. Koureisha no roudou keizaigaku [The Labor Economicso Senior Citizens]. Tokyo: Nikkei.

1993 Koureika shakai no roudou shijou: sh ugyou koudou to kouteki


43/44


Seike, Atsushi. 1997. Shougai geneki shakai no joukenhataraku jiy u to intaino jiyu- [Conditions o a Lielong Service Society: Freedom to Work andFreedom to Retire]. Tokyo: Chuko Shinsho.

Seike, Atsushi, and Atsuhiro Yamada. 1996. Pension Rich no jouken[Pension Rich Conditions].Nihon keizai kenkyu 33: 3861.

. 2004. Koureisha shugyou no keizaigaku [The Economics o SeniorEmployment]. Tokyo: Nikkei.

Stiglitz, J.E., and A. Weiss. 1981. Credit Rationing in Markets with ImperectInormation.American Economic Review 71: 393410.

Tachibanaki, Toshiaki, and Keiko Shimono. 1994. Koureisha no sh ugyoukeitai no kibou to jissai [Preerred and Actual Employment Patterns oSenior Citizens]. In Kojin chochiku to raiu saikuru [Personal Savings andthe Lie Cycle], ed. Toshiaki Tachibanaki and Keiko Shimono, 87110.Tokyo: Nikkei.

Yashiro, Naohiro. 1997.Nihonteki koyou kankou no keizaigakuroudou shijouno ryuudouka to nihon keizai [The Economics o Japanese EmploymentPractices: Liquidation o the Labor Market and the Japanese Economy].Tokyo: Nikkei.

. 1999. Koyou kaikaku no jidaihatarakikata ha dou kawaru ka? [Ageo Employment Reorm: How Will Work Patterns Change?]. Tokyo: Chuokoronsha.


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Occupational Career Types of Baby-Boomers And