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2 RQ 2: Do the Degrees of Incidence of Each Contributing Factor Differ??

2 RQ 2: Do the Degrees of Incidence of Each Contributing Factor Differ??

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A Questionnaire Assessment of the Contributing Factors to Empathy



383



4 Discussion

The purpose of the present study was to clarify the influence of multiple contributing factors related to the occurrence of empathy. For the current study, contributing factors of empathy, and the degree of each factor’s influence, were

investigated.

In terms of sex, results were similar to past studies [26, 27] showing that women

report more empathy or empathic behaviors compared to men.

With regard to age, overall, we did not observe age effects. However, when we

examined differences between specific age groups, significant results emerged.

Previous studies reported that there is an element that is related to age and an

element that is not so related with the element of empathy [26, 27]. We confirmed

that empathy had a significant difference by comparing each generation. We

considered that there are differences between the generations when it comes to

the elements of empathy that are of interest to them.

We also observed similar findings as past studies concerning the relationship

between personality and empathy [25]. The validity of the result was demonstrated

because previous studies reported the same results.

Regarding the influence of family variables on empathy, previous studies have

reported conflicting relationships [25]. This was also the case in the current study.

No previous study had examined the role of frustration on empathy. We considered that the frustration experience did not influence empathy because there is

nothing concerning remembrance of experiences in the four elements of empathy.

Furthermore, no previous study had examined how the desire to be empathic

affects empathy. Not surprisingly, we observed that a desire to be empathic was

influential on reports of empathy.

A multiple linear regression analysis showed that this desire was most influential

on empathy. Specifically, a desire to affect others emotively was most predictive of

empathy. We also observed that a connection between prosocial behavior and a

desire to affect others emotively was a strong influence on empathy. One potential

reason for why empathic desire had an influence on empathy is that empathic desire

motivates empathic behavior. This analysis also revealed that specific personality

characteristics also influenced empathy. We argue that specific personality predispositions strengthen empathic concerns, which lead to empathic behaviors. Since

such predispositions include curiosity with others and receptiveness toward external stimuli, empathic behaviors would likely result. Finally, in terms of sex, it is

likely that women are more socially responsive, which strengthens women’s

empathic concern and empathic behaviors. Since a woman generally has high social

responsiveness, there are the receptiveness and attentiveness to a stimulus.

Overall, the current study investigated the influence of various contributing

factors on empathy. Results showed that some factors were influential while others

were not. Moreover, we revealed a rank-order of factors predicting empathy. The



384



M. Nishio and T. Maeno



present findings are useful for effectively designing models of empathy that include

the myriad possible factors related to promoting prosocial and adaptive behaviors.



5 Conclusion

The current study assessed various contributing factors to empathy, as well as the

magnitude of influences for each factor. We observed that the following four factors

significantly contributed to empathy: sex, personality, experience, and desire.

Although age did not influence empathy globally, age differences were observed

when assessing specific age groups. We also observed that an individual’s frustration experience did not influence empathy. Regarding the magnitude of influence

for each factor, empathic desire was the most predictive of empathy, followed by

personality factors and sex. We considered that it was effective to understand such

rank when guiding empathy of the others.



References

1. Plutchik R (1990) Evolutionary bases of empathy. In: Empathy and its development.

Cambridge University Press, Cambridge, p 38

2. Davis MH (1994) Empathy: a social psychological approach. Westview Press, Boulder

3. Batson CD, Powell AA (2003) Altruism and prosocial behaviour. In: Handbook of psychology.

Wiley, Hoboken

4. Eisenberg N, Fabes RA, Murphy B, Karbon M, Maszk P, Smith M, Suh K (1994) The relations

of emotiveity and regulation to dispositional and situational empathy-related responding.

J Personal Soc Psychol 66(4):776.

5. Eisenberg N, Miller PA (1987) The relation of empathy to prosocial and related behaviors.

Psychol Bull 101(1):91

6. Eisenberg N, Mussen PH (eds) (1989) The roots of prosocial behavior in children.

Cambridge University Press, Cambridge/New York

7. Ministry of Economy, Trade and Industry (2013) White paper on international economy and

trade

8. Volvic Drink 1 (2013) Give 10 Campaign, Volvic. http://www.volvic.co.jp/csr/1lfor10l/index.

html

9. Kotler P (2000) Marketing management: the millennium edition. International edition.

Prentice Hall, London

10. Kotler P, Lee N (2008) Corporate social responsibility: doing the most good for your company

and your cause. Wiley, Hoboken

11. Schmitt B (1999) Experiential marketing. J Mark Manag 15(1–3):53–67

12. Schmitt BH (2010) Customer experience management: a revolutionary approach to connecting

with your customers. Wiley, New York

13. Cohen JB, Areni CS (1991) Affect and consumer behavior. Handb Consum Behav 4(7):

188–240

14. Thompson CJ, Locander WB, Pollio HR (1989) Putting consumer experience back into

consumer research: the philosophy and method of existential-phenomenology. J Consum Res

16(2):133



A Questionnaire Assessment of the Contributing Factors to Empathy



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15. Tokaji A (2001) The mechanism of impression arouse. Cogn Stud Bull Jpn Cogn Sci Soc 8(4):

360–368

16. Maslow AH (1959) Cognition of being in the peak experiences. J Genet Psychol 94(1):43–66

17. Maslow AH (1961) Peak experiences as acute identity experiences. Am J Psychoanal 21(2):

254–262

18. Nishio M, Makino Y, Shirasaka S, Maeno T (2014) System structure of “being emotively

moved” by analyzing emotive causes. In: Business systems laboratory second international

symposium, January 2014, Roma, Italy

19. Feshbach ND (1975) Empathy in children: some theoretical and empirical considerations.

Couns Psychol 5(2):25–30

20. Hoffman ML (1984) Interaction of affect and cognition in empathy. In: Emotions, cognition,

and behavior. Cambridge Universtiy Press, Cambridge/New York, pp 103–131

21. Davis MH (1980) A multidimensional approach to individual differences in empathy.

American Psychological Association, Washington, DC

22. Davis MH (1983) Measuring individual differences in empathy: evidence for a multidimensional approach. J Pers Soc Psychol 44(1):113

23. Mehrabian A, Epstein N (1972) A measure of emotive empathy1. J Pers 40(4):525–543

24. Eisenberg N, Fabes RA, Schaller M, Miller P, Carlo G, Poulin R, Shell R (1991) Personality

and socialization correlates of vicarious emotive responding. J Personal Soc Psychol 61(3):459

25. Wise PS, Cramer SH (1988) Correlates of empathy and cognitive style in early adolescence.

Psychol Rep 63(1):179–192

26. Davis MH, Franzoi SL (1991) Stability and change in adolescent self-consciousness and

empathy. J Res Pers 25(1):70–87

27. Gough HG, Heilbrun AB (1983) The adjective check list manual. Consulting Psychologists Press,

Palo Alto



Part VI



Regional Development and Policymaking



Evaluation of Countermeasures for Low

Birthrate and Aging of the Population

in a Suburban New Town

Yoshiki Ito, Tomomi Nonaka, and Masaru Nakano



Abstract Tama City is engaged in two measures to counter the declining birthrate

and aging of its population: an incentive scheme that encourages companies to

invite young people and an action plan to support the development of the next

generations. But, the exact process to estimate policy effects and identify measures

that lead to good results is unknown. This paper articulates the causes of Tama

City’s declining birthrate and aging population, and reveals measures derived from

a cohort-component method that are suitable for measuring the future impact of

policies intended to reduce these trends.

Keywords Urban service • Public service • Sustainable development • Policy

analysis • Social system design



1 Introduction

In recent years, in a number of advanced nations, the population has aged and fewer

babies have been born due to falling birthrates and longer lifespans. While the

working age population can increase in urban areas, populations can shrink dramatically in rural areas [1]. This is a global trend observed in urban areas such as

New York, Munich, Beijing, and Tokyo. Aging populations have significant

impacts on the economy and society as a whole and thus cannot be ignored in the

development of a sustainable urban infrastructure.

Sustainability of urban infrastructures has been evaluated with economic, environmental, and social axes [2–6]. Issues such as energy saving, renewable energy,

and efficiency of urban development have also been studied [7–14]. However, there

have been few studies on aging issues [15].



Y. Ito • M. Nakano (*)

Graduate School of System Design and Management, Keio University, 4-1-1 Hiyoshi,

Kohoku-ku, Yokohama, Kanagawa 223-8526, Japan

e-mail: nonaka@ise.aoyama.ac.jp

T. Nonaka

Dept. of Industrial and Systems Engineering, Aoyama Gakuin University, 5-10-1 Fuchinobe,

Chuo-ku, Sagamihara-shi, Kanagawa 252-5258, Japan

© Springer Japan 2016

T. Maeno et al. (eds.), Serviceology for Designing the Future,

DOI 10.1007/978-4-431-55861-3_27



389



390



Y. Ito et al.



Conditions of cities vary widely with respect to factors such as development

process, historical background, and geographical characteristics. The specific background of a city should be considered for the sustainability of its urban infrastructure. Thus, conducting urban studies to determine common and individual themes

among different urban areas, through case studies of typical cities and advanced

cities having new problems, is important.

Japan has the most serious aging population problems. Suburban new towns are

planned areas of agglomerated dwellings with no neighboring central cities

[16]. Tama New Town in Tama City is the largest suburban new town in Japan

and has the highest increase in the old-age index (65 years or older) in Japan. In this

article, a model is developed for evaluating sustainability of the suburban

new town.

In Tama City, an incentive scheme to attract enterprises was established in 2011

for the promotion of business relocation and expansion of job opportunities

[17]. Expanded job opportunities is expected to reduce the number of young people

moving out and encourage more young people to move in. An action plan to support

families raising children, “Action plan for Measures to Support the Development of

the Next Generation,” was also established in FY2011 to encourage families raising

children to remain in Tama City and increase the birthrate [18]. For both measures,

however, neither their effects on the current situation nor their impact on future

population composition have been clarified quantitatively.

Various studies have been conducted in Japan and overseas regarding the impact

of countermeasures to motivate women to bear children [19]. Few studies, however,

reveal the impact of those measures on the future population composition. Though

studies for population estimates in specific geographic areas have been made in

Japan and overseas, many simply estimate future population compositions

[20, 21]. They do not consider the effects of the current countermeasures to mitigate

the problem. Future population composition is estimated through studies of the

collective housing area, suggesting that measures to counter the declining birthrate

and aging population are needed [22].

This study analyzes Tama City’s population estimates from the past to clarify the

causes of population aging. Next, a population estimate model is developed to

assess the incentive scheme for attracting enterprises and the action plan to support

families raising children.

Section 2 analyzes population estimate models used in this study. Section 3

describes preconditions to the study in detail. In Sect. 4, causes of the problems in

Tama City are clarified through scenario analysis. In Sect. 5, population in the

future are estimated in consideration of countermeasures for the problems

of. Section 6 gives the conclusion of this study and future issues.



Evaluation of Countermeasures for Low Birthrate and Aging of the Population. . .



391



2 Population Estimate Model

2.1



A Cohort-Component Method



The purpose of this study is to clearly articulate the causes of Tama City’s declining

birthrate and aging population, and to evaluate the impacts of Tama City’s ongoing

measures for handling these issues on future population composition. The former is

clarified through scenario analysis of population estimates between 1970 (when the

initial residents of Tama New Town moved into the area) and 2010 (the year before

the countermeasures came into effect). The latter is evaluated through population

estimates between 2010 and 2035. For population estimates, a model based on a

cohort-component method [23], with demographic equations and census figures,

is used.

Using survival rates and move-in and move-out rates by gender and age in the

base year, five-year-later populations by gender and age are estimated. Ten-yearlater populations are derived by entering the five-year-later populations. The population estimate model is described in detail in subsequent sections.



2.2



Calculation Processes of the Cohort-Component Method



Mathematical formulae of the proposed model are given as follows:





M0, tỵ5 ẳ p0M, t ỵ m0M, t BtM

M



M

M85, tỵ5 ẳ p85

, t ỵ m85, t M80, t ỵ M85, t ị





Mx, tỵ5 ẳ pxM, t ỵ mxM, t Mx5, t , 5 x 80





F0, tỵ5 ẳ p0F, t ỵ m0F, t BtF

F



F

F85, tỵ5 ẳ p85

, t ỵ m85, t F80, t ỵ F85, t ị





Fx, tỵ5 ẳ pxF, t ỵ mxF, t Fx5, t , 5 x 80

Bt ẳ



45

X



Fx, t ỵ Fx, tỵ5 Þ bx, t Â



x¼15



ð2Þ

ð3Þ

ð4Þ

ð5Þ

ð6Þ

ð7Þ



BtM ¼ α Â Bt



ð8Þ



¼ β Â Bt



9ị



BtF



mxM, t ẳ mxM, t, in mxM, t, out

mxF, t



5

2



1ị







mxF, t, in







mxF, t, out



10ị

11ị



392



Y. Ito et al.



Mx,t, Fx,t: Male and female populations in the age segments ðx to x ỵ 4ị in year t

x ẳ 0, 5, . . . , 80 ; M85,t and F85,t indicate male and female populations of

people aged 85 years or older

bx,t: Birthrates by age of females in the age segments ðx to x þ 4Þ between years

t and t þ 5

Bt: Birthrates between years t and t ỵ 5

F

BM

t , Bt : Number of male and female babies born between years t and t ỵ 5

, : Ratio of male and female births

The sex ratio at birth in recent years has been 106 male births per 100 female

births. In this study, α ¼ 106=206 and β ¼ 100=206 are used [24].

F

pM

x;t , px;t : Rates of male and female populations in the age segments ðx À 5 to x À 1Þ

in year t who survive in the age segments x to x ỵ 4ị in year t ỵ 5

M

p0;t , pF0;t : Rates of male and female babies born between years t and t ỵ 5 who

survive in the age segments (04) in year t ỵ 5

F

pM

85;t , p85;t : Rates of males and females aged 85 years or older in year t who survive

in the age segments (85 years or older) in year t ỵ 5

F

mM

x;t , mx;t : Net migration rates of male and female populations in the age segment

ðx À 5 to x À 1Þ who fall into ðx to x ỵ 4ị in year t ỵ 5

F

mM

x;t;in , mx;t;in : Move-in rates of male and female populations in the age segment

ðx À 5 to x À 1Þ who fall into x to x ỵ 4ị in year t ỵ 5

F

mM

x;t;out , mx;t;out : Move-out rates of male and female populations in the age segment

ðx À 5 to x À 1ị who fall into x to x ỵ 4ị in year t ỵ 5

F

mM

0;t , m0;t : Migration rates of male and female babies born between years t and t þ 5

who fall into the age segment (0 to 4) in year t ỵ 5

F

mM

85;t , m85;t : Migration rates of male and females aged 80 years or older in year t who

fall into the age segment (85 years or older) in year t ỵ 5



2.3



Reflection of Effects of Measures in the CohortComponent Method



This section describes the method to analyze the effects of countermeasures for

declining birthrate and aging population in the calculation processes of the model.

The following two measures have been implemented by Tama City: an incentive

scheme to attract enterprises and an action plan to support families raising children.

The incentive scheme to attract enterprises is assumed to reduce the move-out

rates. Reasons for move-out are categorized into eight items, such as housing,

environmental issues, commuting to workplace, and living together with or apart

from parents or children [18]. The survey results are categorized by gender and age.

The implementation of the incentive scheme is assumed to encourage people to

have their workplace near their homes. Those who cited convenience of commute

as a reason for move-out are considered to all remain in Tama City.



Evaluation of Countermeasures for Low Birthrate and Aging of the Population. . .



393



Calculation processes of the cohort-component method, which reflect the effects

of the countermeasures, are formulated in Eqs. (12) and (13). Equations (12) and

(13) were obtained by adding the effects of the action plan to Eqs. (10) and (11).

È

À

ÁÉ

mxM, t ¼ mxM, t, in  1 À RxM, work, in

È

À

ÁÉ

À mxM, t, out  1 À RxM, work, out

È

À

ÁÉ

mxF, t ¼ mxF, t, in  1 À RxF, work, in

È

À

ÁÉ

À mxF, t, out  1 À RxF, work, out



ð12Þ

ð13Þ



F

M

F

RM

x;work;in , Rx;work;in , Rx;work;out , Rx;work;out : Percentage of male and female people

who cite commute as the reason for move-in and move-out to the total move-out

population in the age segment ðx À 5 to x 1ị who fall into x to x ỵ 4ị in year t ỵ 5

The Action Plan for Measures to Support the Development of the Next Generation is assumed to affect both move-out rates and birthrates. Similar to the study of

the incentive scheme, Tama City’s survey data of reasons for move-out are used to

include the move-out rates in the cohort-component method. Those who cited

childrearing circumstances as the reason for move-out are assumed to all remain

in Tama City due to the action plan. Calculation processes of the cohort-component

method, which reflect the effects of the measures, are formulated in Eqs. (14) and

(15):



È

À

ÁÉ

mxM, t ¼ mxM, t, in  1 À RxM, child, in

È M

À

ÁÉ

À mx, t, out  1 À RxM, child, out

È

À

ÁÉ

mxF, t ¼ mxF, t, in  1 À RxF, child, in

È F

À

ÁÉ

À mx, t, out  1 À RxF, child, out



ð14Þ

ð15Þ



F

M

F

RM

x;child;in , Rx;child;in , Rx;child;out , Rx;child;out ,: Percentage of male and female people

who indicate childrearing circumstances as the reason for move-in and move-out to

the total move-out population in the age segment ðx À 5 to x À 1ị who fall into

x to x ỵ 4ị in year t ỵ 5

Next, the effects of the action plan on birthrates are formulated. In the action

plan, nurseries are built in respective city blocks and every applicant is able to enter

the nursery as he or she wishes. As shown in previous studies [19], implementation

of childrearing support measures to facilitate the availability of nurseries increases

the childbearing intention rate by 8.8 %; this rate is used in this study. The measures

are considered to have a similar impact on childbearing intention in the age segment

(15–49) in this study, although this assumption was not covered in previous studies.

In this context, calculation processes of the cohort-component method, which

reflect the effects of the countermeasures, are formulated in Eq. (16):



394



Y. Ito et al.



Bt ẳ



45

X



Fx, t ỵ Fx, tỵ5 ị fbx, t 1 ỵ W ịg



xẳ15



5

2



16ị



Bt: Birthrates between years t and t ỵ 5

bx,t: Birthrates by age of females in the age segments x to x ỵ 4ị between years

t and t ỵ 5

Fx,t: Population of females in the age segments x to x ỵ 4ị in year t

W: Rate of increase in childbearing intention by implementation of the Action plan

for Measures to Support the Development of the Next Generation

As described above, W is assumed to be 0.088 in this study. Equation (16) is

obtained by adding the effects of the action plan to Eq. (3).



2.4



Evaluation Index



In this study, the results of population estimates by the cohort-component method

are evaluated by old-age indexes. Old-age indexes are percentages of people aged

65 years or older in Japan or specific areas. Lower old-age indexes are considered

better. Old-age indexes are formulated in Eq. (17):

X1

I old index ẳ



xẳ65



Px



Pall



17ị



Iold index: Old-age index

Pall: Total population in Japan or a specific area

Px: Population of x–year old people in Japan or a specific area



3 Data and Preconditions

3.1



Standard Population



The population of Tama City as per the national census is used for the standard

population [25]. Census data of 1970 are used for the scenario analysis to analyze

causes for the declining birthrate and population aging, and that of 2010 is used for

the impact assessment of countermeasures for declining birthrate and population

aging.



Evaluation of Countermeasures for Low Birthrate and Aging of the Population. . .



3.2



395



Assumed Values for the Cohort-Component Method



Four assumed values are used in the cohort-component method: future survival

rates by gender and age, future net migration rates by gender and age, future

birthrates of females by age, and the male and female ratio at birth. Known data

are used as assumed values for the scenario analysis of population estimates

between 1970 and 2010. For the analysis, census data [25, 26] are used.



3.3



Impacts of the Measures for the Declining Birthrate

and Aging Population on the Move-Out Rate



As described in the preceding section, reasons for move-out shown by Tama City’s

survey are used to analyze impacts of the countermeasures for the declining

birthrate and aging population on the move-out rate. Populations between 2010

and 2035 are estimated, assuming that Tama City’s survey results in 2010 are

applicable until 2030.



3.4



Impacts of the Measures for the Declining Birthrate

and Aging Population on the Birthrate



As described in the preceding section, findings of previous studies are used to

analyze impacts of countermeasures for declining birthrate and aging population on

the birthrate. Populations between 2010 and 2035 are estimated, assuming that the

findings of the aforementioned previous studies are applicable between 2010

and 2035.



4 Factor Analysis for the Declining Birthrate and Aging

Population

This section describes simulation results of factor analysis for the declining birthrate and aging population.



4.1



Scenario Building



Table 1 shows scenarios used to analyze factors that have affected declining

birthrate and aging population. Populations are estimated by changing a factor



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