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Asia-Pacific Journal of Regional Science

, Volume 2, Issue 2, pp 507–527 | Cite as

Impacts of multi-functionality of urban agriculture on the CCs in Japan

  • Lily Kiminami
  • Akira Kiminami
  • Shinichi Furuzawa
Article
  • 249 Downloads

Abstract

The purpose of this study is to build a model on the relationship between multi-functionality of urban agriculture (MFA) and the creative classes (CCs) focusing on those who are thinking creatively (TC) through examining Tokyo metropolis in Japan. The impacts of multi-functionality of urban agriculture on the CCs are clarified empirically based on a questionnaire survey targeting at the residents in Tokyo. The results of questionnaire survey clarified that multi-functionality of urban agriculture are preferred by the CCs especially those who are TC. Furthermore, among the CCs, those who are TC have a high level of social capital (SC) and are favorable to diversified lifestyle as well. Therefore, it is concluded that the multi-functionality of urban agriculture has positive impacts on the residence of the creative classes with a high level of SC and the diversity of lifestyle which is thought to be important for the development of cities. Policy implication drawn from the research encourages the city leaders to put more attention on the MFA in Japan, because it is favorable to the CCs especially those who are thinking creatively.

Keywords

Multi-functionality of urban (MFA) Creative classes (CCs) Thinking creatively (TC) Japan 

JEL Classification

R11 

1 Introduction

In 2016, the city of Tokyo was ranked as third among the power cities following the cities of London and New York among the 42 selected cities in the world1, and is also ranked as the forty-fifth among the 100 global sustainable cities due to its weak points such as aging problem, lack of green space and environmental risks.2 However, creativity is considered to be one of the most important driving forces for the sustainable development of the cities in knowledge-based society; its relationship with social–economic systems have been conducted mainly at the three levels of individual/economic entity, industry/economy and region in the theoretical and empirical researches, and in the formations of policy framework as well. From the viewpoint of sustainable development of the cities, it is effective to grasp the concept of creativity as a network of economic entities, organizations and regions (Landry 2008; UNCTAD 2010).

On the other hand, multi-functionality of agriculture (MFA) composed of economic, environmental, and social aspects supplies not only food but also services such as environmental conservation, good scenery and farm experience. These functions and relationships are changing during the process of development of cities under the influence of urban policy and planning such as the land taxation and zoning system. For example, it is considered that the promotion of multi-functionality of agriculture in the urban area would improve the amenity, attract the people with high creativity so called as “the creative classes” (CCs) by Florida et al. (2011) and bring about growth of the region.

According to the survey by Tokyo Metropolitan Government (2015), most of residents in Tokyo are willing to keep their farmland and experience farming (see Figs. 1, 2). Under the circumstance, the Basic Law on the Promotion of Urban Agriculture was executed in 2015, for realizing the co-existence of urban areas and agriculture.
Fig. 1

Residents’ willingness to keep doing agriculture and to maintain farmland in Tokyo. Source: “Agriculture in Tokyo 2015” Tokyo Metropolitan Government. Internet questionnaire survey for 500 residents

Fig. 2

Residents’ willingness to experience farming in Tokyo. Source: “Agriculture in Tokyo 2015” Tokyo Metropolitan Government. Internet questionnaire survey for 500 residents

Therefore, the purpose of this study is to build a model on the explanation of the relationship between multi-functionality of urban agriculture (MFA) and the creative class (CC) focusing on the people who are thinking creatively (TC) through examining Tokyo metropolis in Japan. We will first make a brief review on the researches of concept and theory of the CCs and the multi-functionality of urban agriculture. Second, we will build a theoretical model to explain the purpose of our research and propose a hypothesis based on the literature review. Third, we will clarify the relationship between the CC’s preference for the multi-functionality of urban agriculture (MFA), the situation of their social capital and their favorable lifestyle with a questionnaire survey targeting at the residents in Tokyo to verify the proposed hypotheses to achieve our research purpose.

2 Survey of existing research

2.1 Concept and theory of the CCs

Although there is no standardized definition about the creative classes (CCs), and there are issues over organizing data to empirically analyze the phenomenon, Florida (2002) defined the CCs as the people who are engaged in the work of “creating meaningful new forms” and who are classified into “super creative core” and “creative professionals”. The “super creative core” incorporates the likes of scientists, technicians, university professors, poets, novelists, artists, entertainers, actors, designers, and architects, while the “creative professionals” are made up of professional occupations like tech or finance workers, lawyers, doctors, managers and so forth and tends to reside in areas with a high level of amenities. The CCs is thought to be a key driving force for economic growth in the deindustrialized cities.

Reese et al. (2010) clarified the following three typical methods on grasping the characteristics of the CCs such as (i) analysis of census data and other indicators of economy, business, and quality of life, (ii) case studies and other descriptive efforts, and (iii) surveys on business leaders and other experts. It also concluded that “creative professionals” was one of the most reliable index of the CCs based on the analysis of interrelationships among those methods in the cities in Canada.

Most of studies on the CCs in U.S.A. used standard employment classifications and the population census of the USDA-ERS or American Community Survey data. Meanwhile, Hansen and Niedomysl (2009) showed a direct correlation between the CC and levels of education, and treated educational attainment as a proxy for the CC.

For international comparison, Boschma and Fritsch (2009) made a new category of “Bohemians” who are engaged in cultural and artistic occupations separated from “super creative core” defined by Florida (2002) using the International Standard Classification of Occupations (ISCO 88). They pointed out that it was unclear whether formal education has the stronger effect, although there was some evidence of a positive relationship among creative class occupation, employment growth, and entrepreneurship at the regional level in a number of European countries.

However, the occupation classification on the CCs is not fixed, while Schoales (2006) included clothing and jewelry industry employees, Westlund and Calidoni (2014) only used “High-Tech” industry employees. On the other hand, McGranahan and Wojan (2007) showed that some occupations in Florida’s creative class don’t have high creativity using “thinking creatively” element of the 2004 O*NET content model.

There have been a few studies analyzing the CCs in the framework of economic theories. Batabyal and Nijkamp (2013) regarded the CCs as a supplier of creative capital and theoretically clarified the relationship between the CC’s preference and urban economic growth. Due to the difficulty of data collection on the CC’s economic activities, empirical analysis has seldom been undertaken. Therefore, the researches on the CCs focusing on the people who are ‘Thinking Creatively’ (TC), which is the most important characteristic of CCs, have been insufficient.

2.2 Regional factors attracting the CCs and multi-functionality of urban agriculture

Florida et al. (2008) made a path analysis with variables of factors such as “tolerance”, “university”, and “consumer services”. Boschma and Fritsch (2009) undertook an empirical analysis of the CCs in seven European countries, demonstrating that the CCs were attracted by a region’s tolerance and openness. Marlet and van Woerkens (2005) clarified that urban amenities, job opportunities and esthetics of cities attracted the CCs, but tolerance didn’t do it in Netherland.

Recently, studies on the CCs have increased not only in large cities but also in small and medium sized cities and in rural areas (Lovett and Beesley 2007; Hatchera et al. 2011; Rickman and Rickman 2011; Argent et al. 2013). McGranahan and Wojan (2007) and McGranahan et al. (2011) showed that the CCs were attracted by the areas providing outdoor amenities in natural environments rather than large cities in the U.S.A. Therefore, following the results of above existing studies, urban agriculture with multi-functionality may contribute to the increase of the CCs in large cities as well.

On the other hand, multi-functionality of agriculture composed of economic, environmental, and social aspects that supplies not only food but also services such as environmental conservation, good scenery and farm experience. (see Table 1). Zasada (2011) has offered a survey of effects in each area, though it has been multiple, there has been no agreed-upon definition of what they are, but these varied effects range broadly across society and the environment. By comparing a number of valuation methods, Randall (2002) pointed out the difficulties of appropriate evaluation of these multiple functions.
Table 1

Multi-functionality of urban agriculture

Function

Contents

Production

Basic functions such as production of foods

Communication

Enjoyment of culture and creation of communication through exchange between citizens and between citizens and farmers

Welfare

Prevention of aging through agricultural work, healing effects of plants, gardening therapy

Education

Emotional and environmental education through nature and agriculture and learning agriculture and forestry

Recycling

Organically grown vegetables by turning kitchen garbage into organic fertilizer

Environmental protection

Preservation of biological resources and natural environment

Creation of landscapes

Creation of pleasant landscapes, scenery of Japanese fields and scenery through which people can enjoy changes of the seasons

Disaster prevention

Disaster evacuation sites and routes, green spaces for disaster prevention, spread of fire, spaces for temporary housing

History and culture

Preservation of groves of village shrines and continuation of harvest festivals

Support for building residential lands

Promotion of building residential lands, provision of gardens and vegetable gardens that support good rural living

Withholding or controlling of urbanization

Temporary withholding or controlling urbanization for a certain period of time

In Japan, many studies have adopted the Hedonic approach as applicable to urban agriculture, such as Hiromasa and Fukazawa (1992), Maruyama et al. (1995), Terawaki (1997) and Hara and Kato (2005). However, these studies showed that the existence of agriculture in cities drove down the prices of surrounding land. As for the research on the evaluation of amenities in cities, Shimizu et al. (2014) analyzed the correlation between urban amenities and rents as the price of housing using the same Hedonic approach.

Kiminami and Kiminami (2006, 2007) analyzed the awareness of urban residents regarding urban agriculture in Tokyo and Shanghai. The results showed that residents’ subjective impressions towards urban agriculture influence their consciousness of settlement in large cities. The study evaluated the multi-functionality of agriculture by showing that urban agriculture enhances the quality of urban life and the settlement of its residents. Furthermore, Peng et al. (2015) evaluated the multiple functions of urban agriculture in Beijing using proxy data for the economic, social and environmental effects of agriculture. However, researches on the relationship between regional factors attracting the CCs focusing on the people who are thinking creatively (TC) and multi-functionality of urban agriculture are insufficient.

Which theory has stronger explanatory power among CC (creative class), SC (social capital) and HC (human capital) in the regional economic development has been debated for some years (Florida 2003; Hoyman and Faricy 2009), but there are arguments especially both for and against the role of Bonding SC on regional economic development. However, Rich (2012) pointed out that small cities attempting to achieve economic and population stability should focus on their strengths: city livability and the thick social ties that maintain communities. Furthermore, regarding to the relationship between the CCs and social capital, although Westlund and Calidoni (2014) conducted an empirical study on Japan, they could not find a clear impact of civil society on regional development.

3 Theoretical model and hypotheses

Based on the existing researches on the creative classes, the multi-functionality of agriculture and the social capital as given above, we consider a theoretical model which is as follows. Here, the members of a city (N) are made up of two types, creative class (CC) and other general (NC).
$$N = {\text{CC}} + {\text{NC}}$$
(1)
Let r be the ratio of CC to N.
$$r = {{\text{CC}} \mathord{\left/ {\vphantom {{\text{CC}} N}} \right. \kern-0pt} N}\quad 0 < r < 1$$
(2)
The utility function U is assumed to be a function of consumer goods X and public goods G.
$$U = U\left( {X,G} \right)$$
(3)
CC has stronger G preference than NC. Therefore, the utility function of N can be written as follows,
$$U_{N} \, = \,U_{N} \left( {X,G;r} \right)$$
(4)
Member N of the city shall take the behavior of utility maximization.
$${\text{Max }}U_{N} = U_{N} \;\left( {X,G;r} \right)$$
(5)
Members of the city produce consumer goods X and public goods G (including local public goods supplied by urban agriculture) using their own labor and social capital.
$$X = X\left( {{\text{CC}}_{X} ,{\text{NC}}_{X} ,{\text{SC}}} \right)$$
(6)
$$G = G\left( {{\text{CC}}_{G} ,{\text{NC}}_{G} ,{\text{SC}}} \right)$$
(7)
$${\text{CC}}\,{ = }\,{\text{CC}}_{X} + {\text{CC}}_{G}$$
(8)
$${\text{NC}}\,{ = }\,{\text{NC}}_{X} \, + \,{\text{NC}}_{G}$$
(9)
However, CC and NC put their labor into the production of X and G in the same proportion.
$${{{\text{CC}}_{X} } \mathord{\left/ {\vphantom {{{\text{CC}}_{X} } {{\text{CC}}_{G} = {{{\text{NC}}_{X} } \mathord{\left/ {\vphantom {{{\text{NC}}_{X} } {{\text{NC}}_{G} }}} \right. \kern-0pt} {{\text{NC}}_{G} }}}}} \right. \kern-0pt} {{\text{CC}}_{G} = {{{\text{NC}}_{X} } \mathord{\left/ {\vphantom {{{\text{NC}}_{X} } {{\text{NC}}_{G} }}} \right. \kern-0pt} {{\text{NC}}_{G} }}}}$$
(10)
In addition, it is assumed that CC has higher labor productivity than NC, which is (1 + α) unit of NC labor in the production of X and the production of G.
$${\text{CC}}\,{ = }\,\left( {1 + \alpha } \right) \cdot {\text{NC}}\quad \alpha \,{ > }\, 0$$
(11)
Here, let SC be dependent on creative class (CC).
$${\text{SC}}\, = \,{\text{SC}}\,\left( {\text{CC}} \right)\,{ = }\,{\text{SC}}\,\left( {r \cdot N} \right)$$
(12)
The production functions of X and G can be transformed as follows.
$$X = X\left( {\left( {1 + \alpha } \right) \cdot r \cdot N_{X} + \left( {1 - r} \right) \cdot N_{X} ,{\text{SC}}\left( {r \cdot N} \right)} \right) = X\left( {\left( {\alpha r + 1} \right) \cdot N_{X} ,{\text{SC}}\left( {r \cdot N} \right)} \right)$$
(13)
$$G = G\left( {\left( {1 + \alpha } \right) \cdot r \cdot N_{Y} + \left( {1 - r} \right) \cdot N_{Y} ,{\text{SC}}\left( {r \cdot N} \right)} \right) = G\left( {\left( {\alpha r + 1} \right) \cdot N_{Y} ,{\text{SC}}\left( {r \cdot N} \right)} \right)$$
(14)

From the above production function, we can draw a production possibility curve (FF) under N. The utility of N is maximized at the contact point (X*, G*) of the production possibility curve and the social indifference curve of the utility function (II).

On the other hand, in order for N to do activities in the next term, there are the necessary levels of XS and GS per capita for X and G produced in this term. The CC is (1 + β) times higher than NC in GC level and the level of XS shall be the same.
$${\text{XS}}_{\text{CC}} = {\text{XS}}_{\text{NC}}$$
(15)
$${\text{GS}}_{\text{CC}} = \left( { 1+ \beta } \right) \cdot {\text{GS}}_{\text{NC}}$$
(16)
Then,
$${\text{XS}}_{N} = {\text{XS}}_{\text{CC}} \, = \,{\text{XS}}_{\text{NC}}$$
(17)
$$\begin{aligned} {\text{GS}}_{N} & = r \cdot {\text{GS}}_{\text{CC}} + \left( { 1- {\text{r}}} \right) \cdot {\text{GS}}_{\text{NC}} = r \cdot {\text{GS}}_{\text{CC}} + \left( { 1- {\text{r}}} \right) \cdot {\text{GS}}_{\text{NC}} \\ & = r \cdot \left( { 1+ \beta } \right){\text{GS}}_{\text{NC}} + \left( { 1- {\text{r}}} \right) \cdot {\text{GS}}_{\text{NC}} \\ & = (r \cdot \beta + 1) \cdot {\text{GS}}_{\text{NC}} \\ \end{aligned}$$
(18)
In order for the production activity of N to be sustainable, the selected volume of production (X0, Y0) in this term must satisfy the following conditions.
$$X_{0} \ge {\text{XS}}_{N}$$
(19)
$${\text{G}}_{0} \ge {\text{GS}}_{N} = \left( {r \cdot \beta + 1} \right) \cdot {\text{GS}}_{\text{NC}}$$
(20)
To clarify the relationship between the multi-functionality of urban agriculture and the CCs in the development of cities, the analytical framework of our research is shown in Fig. 3 and the following hypotheses are put forth.
Fig. 3

Analytical framework of this study

H1: The multi-functionality of urban agriculture is preferred by the TC, i.e., those who are thinking creatively.

Additionally, there have been heated debates on the relationship between social capital and the CCs in regional development. We would like to add the following hypothesis to make contribution to this issue.

H2: People who are thinking creatively (TC) have a high level of social capital as well.

Furthermore, to clarify the relationship between the CCs and the development of cities, we add the following hypothesis.

H3: Diversified life style preferred by the TC contributes to the development of cities.

4 Empirical analysis

4.1 Basic outline of the questionnaire survey and variable setting

For verifying our hypotheses of H1, H2 and H3, we carried out a questionnaire survey targeting at the residents in Tokyo as shown by Table 2. The internet usage questionnaire survey was conducted on September 22–24, 2017 through Macromill Co., Ltd. The number of collected samples is 1029.
Table 2

Socioeconomic attributes and basic results of the questionnaire (%)

 

Item

Distribution of the ratio of respondents

Socioeconomic attributes

Sex

Male

Female

Total

 

76.3

23.7

100.0

(57.1)

(42.9)

(100.0)

Age (year old)

20–24

25–29

30–34

35–39

40–44

 

1.0

5.4

9.5

7.6

12.2

(6.7)

(7.7)

(8.7)

(9.3)

(10.3)

45–49

50–54

55–59

60 and above

Total

14.9

15.5

16.1

17.8

100.0

(9.4)

(8.0)

(6.5)

(33.4)

(100.0)

Household income (million yen)

< 2

2 to < 4

4 to < 6

6 to< 8

8 to < 10

10 to < 12

0.8

11.6

17.5

14.5

13.4

8.5

12 to < 15

15 to < 20

20 and above

Don’t know

No answer

Total

8.6

5.6

5.0

5.2

9.3

100.0

Farmland

There is a lot

There is a bit

There is not much

There is not any

Total

 

6.9

29.2

32.9

31.0

100.0

Creative class

Occupation

Executive in a company

Professional/technical worker

The other worker

Total

 

33.3

33.3

33.3

100.0

Thinking creatively

Extremely important

Very important

Important

Somewhat important

Not important

Total

18.7

24.5

22.5

18.5

15.8

100.0

The value in parenthesis of sex item indicates the ratio calculated from the Labor Force Survey (employee with the age of 15 and above: Tokyo). The value in parenthesis of age item indicates the ratio calculated from the National Census: 2015 (Tokyo). Null hypotheses that ‘the ratio of the response by the questionnaire survey is equivalent to the ratio calculated from a population’ were rejected as a result of the test of the goodness of fit

Sex: Chi square = 309.5 (p value = 0.000)

Age: Chi square = 155.0 (p value = 0.000)

To clarify the different influences between creative occupation and others on the results of answer, the condition of assignment including “manager and executives”, “professionals/technical workers”, and “company officers who are neither manager nor professional or technical worker” are equally allocated. Also, as a subjective indicator of the creative degree of respondents, the question of “How important is thinking creatively to the results of your current work?” is set up (see Table 3).
Table 3

Occupational CC and thinking creatively

 

Total

Executives in a company

Professional/technical worker

The other worker

Ratio (%)

 Extremely important (4)

18.7

29.7

17.5

8.7

 Very important (3)

24.5

27.7

29.4

16.3

 Important (2)

22.5

21.0

25.1

21.6

 Somewhat important (1)

18.5

13.4

16.3

25.7

 Not important (0)

15.8

8.2

11.7

27.7

Score (0–4)

2.1

2.6

2.2

1.5

As shown in Table 3, there is a positive correlation between creative occupation (CO) and thinking creatively (TC), but thinking creatively is considered to be important even in other occupations. Therefore, it is necessary to grasp the creative class from the view of “thinking creatively” rather than from “creative occupation”.

4.2 Results of the structural equation modeling (SEM)

Here, we apply the covariance structure analysis to the results obtained from the questionnaire survey to clarify the formation process of the relationships among the multi-functionality of urban agriculture, social capital, and the diversity of lifestyle.

Table 4 shows the basic statistics used for SEM. The values in parentheses represent each hierarchy. In stratum 1, there are variables of socioeconomic attributes and farmland existence, and stratum 2 includes the CCs and social capital. Variables relating to diversity of lifestyle and multi-functionality of urban agriculture (MFA) (level of importance), are set for stratum 3. Detailed information on the variable setting concerning multi-functionality of urban agriculture, social capital, and diversity of lifestyle is shown in Appendix.
Table 4

Descriptive statistics of variable

Variables

Layer

Number. of Obs.

Average

SD

Min.

Max.

Socio-economic attributes

 Sex

(1)

1029

0.76

0.43

0.00

1.00

 Age

(1)

1029

5.18

2.18

0.00

8.00

 Household income

(1)

879

3.71

2.07

0.00

8.00

Farmland

(1)

1029

1.12

0.93

0.00

3.00

Creative class

 Creative thinking

(2)

1029

2.12

1.34

0.00

4.00

 Executive

(2)

1029

0.33

0.47

0.00

1.00

 Prof./Tech. worker

(2)

1029

0.33

0.47

0.00

1.00

Social capital

(2)

1029

1.04

0.80

0.00

2.00

Diversity of lifestyle

 Self-actualization (high–low)

(3)

1029

0.76

0.58

0.00

3.47

 Self-investment (low-high)

(3)

1029

0.23

0.49

− 1.29

1.65

 Altruism (low–high)

(3)

1029

0.13

0.37

− 1.06

1.76

Multi-functionality of agriculture

 Economic function

(3)

1029

0.58

0.69

0.00

2.00

 Environmental function

(3)

1029

0.67

0.74

0.00

2.00

 Social function

(3)

1029

1.87

1.82

0.00

8.00

Details of each variable are as follows. Values in parentheses are given when applicable

Sex: male (1), female (0)

Age: 20–24 (0), 25–29 (1), 30–34 (2), 35–39 (3), 40–44 (4), 45–49 (5), 50–54 (6), 55–59 (7), 60 years old and above (8)

Income: < 2 (0), 2 to < 4 (1), 4 to < 6 (2), 6 to < 8 (3), 8 to < 10 (4), 10 to < 12 (5), 12 to < 15 (6), 15 to < 20 (7), 20 million yen and above

Farmland: there is a lot (3); there is a bit (2); there is not much (1); there is not any (0)

Thinking creatively: extremely important (4); very important (3); important (2); somewhat important (1); not important (0)

Executive: executive in a company (1); others (0)

Professional/technical worker: professional/technical worker (1); others (0)

4.2.1 Model of thinking creatively

Figure 4 and Table 5 are the path diagram and the path coefficient obtained from the result of covariance structure analysis on the model of thinking creatively. The main goodness of fit indices are Chi square (6) = 590.617 (p value: 0.000), RMSEA = 0.308, CFI = 0.597, which is a good result. According to the analysis results, the formation process of consciousness about the diversity of lifestyle (such as the types of “self-actualization”, “self-investment” and “altruism”) is complicatedly influenced by “thinking creatively”, social capital, socioeconomic attributes, farmland, multi-functionality of urban agriculture. We will look at the items that were significant at the 1 and 5% level as follows.
Fig. 4

Path diagram (thinking creatively model)

Table 5

Estimated results of path coefficient (thinking creatively model)

     

Coef.

p > |z|

 

1

Self-actualization

(3)

Thinking creatively

(2)

0.110

0.000

***

2

 

(3)

Social capital

(2)

0.046

0.033

**

3

 

(3)

Farmland

(2)

− 0.043

0.017

**

4

 

(3)

Sex

(2)

− 0.257

0.000

***

5

 

(3)

Age

(2)

− 0.019

0.024

**

6

 

(3)

Income

(2)

0.006

0.555

 

7

Self-investment

(3)

Thinking creatively

(2)

− 0.059

0.000

***

8

(Opposite sign)

(3)

Social capital

(2)

0.017

0.396

 

9

 

(3)

Farmland

(2)

0.047

0.004

***

10

 

(3)

Sex

(2)

− 0.020

0.606

 

11

 

(3)

Age

(2)

− 0.015

0.047

**

12

 

(3)

Income

(2)

− 0.025

0.003

***

13

Altruism

(3)

Thinking creatively

(2)

0.008

0.390

 

14

(Opposite sign)

(3)

Social capital

(2)

− 0.043

0.005

***

15

 

(3)

Farmland

(2)

− 0.030

0.016

**

16

 

(3)

Sex

(2)

0.055

0.074

*

17

 

(3)

Age

(2)

− 0.003

0.626

 

18

 

(3)

Income

(2)

0.009

0.178

 

19

Economy: MFA

(3)

Thinking creatively

(2)

0.055

0.001

***

20

 

(3)

Social capital

(2)

0.027

0.333

 

21

 

(3)

Farmland

(2)

0.053

0.024

**

22

 

(3)

Sex

(2)

− 0.101

0.077

*

23

 

(3)

Age

(2)

− 0.032

0.004

***

24

 

(3)

Income

(2)

− 0.022

0.067

*

25

Environment: MFA

(3)

Thinking creatively

(2)

0.044

0.016

**

26

 

(3)

Social capital

(2)

0.006

0.833

 

27

 

(3)

Farmland

(2)

0.068

0.006

***

28

 

(3)

Sex

(2)

− 0.173

0.005

***

29

 

(3)

Age

(2)

0.015

0.204

 

30

 

(3)

Income

(2)

− 0.007

0.568

 

31

Sociality: MFA

(3)

Thinking creatively

(2)

0.152

0.001

***

32

 

(3)

Social capital

(2)

0.185

0.012

**

33

 

(3)

Farmland

(2)

0.092

0.130

 

34

 

(3)

Sex

(2)

− 0.895

0.000

***

35

 

(3)

Age

(2)

0.024

0.397

 

36

 

(3)

Income

(2)

− 0.027

0.406

 

37

Thinking creatively

(2)

Farmland

(1)

0.177

0.000

***

38

 

(2)

Sex

(1)

0.726

0.000

***

39

 

(2)

Age

(1)

− 0.021

0.311

 

40

 

(2)

Income

(1)

0.115

0.000

***

41

Social capital

(2)

Farmland

(1)

0.134

0.000

***

42

 

(2)

Sex

(1)

0.041

0.511

 

43

 

(2)

Age

(1)

0.050

0.000

***

44

 

(2)

Income

(1)

0.058

0.000

***

 

Covariance

   

45

Thinking creatively

(2)

Social capital

(2)

0.179

0.000

***

Results of the estimates between variables within layer 1 and 3 are abbreviated in the table

“***”, “**” and “*” indicate statistically significant at 1, 5, 10% level, respectively

First, the factors that influence “thinking creatively” are the social–economic attributes such as sex (+), income (+), and farmland (+).

Next, regarding the relationship between “thinking creatively” and “social capital”, a positive correlation (+) was confirmed according to the result of assuming covariance of error terms. Furthermore, the social economic attributes of age (+), income (+), and farmland (+) affected “social capital”.

Subsequently, looking at factors that influence the awareness toward the multi-functionality of urban agriculture, “thinking creatively” positively affects all aspects of the MFA and “social capital” positively affects the sociality of the MFA. Furthermore, “thinking creatively” has a positive influence on “self-actualization” and “self-investment”, and “social capital” has a positive influence on “altruism” in the diversity of lifestyle that is important for the sustainable development of cities. In addition, the existence of farmland has direct influence on “self-investment” in the diversity of lifestyle and environmental function of the MFA as well.

4.2.2 Model of creative occupation

Figure 5 and Table 6 are the path diagram and path coefficient obtained from the result of covariance structure analysis on the model of creative occupation. The main goodness of fit indices are Chi square (4) = 881.001 (p value: 0.000), RMSEA = 0.348, CFI 0.521. It is a good result although the degree of goodness of fit is generally lower than that of the model of thinking creatively.
Fig. 5

Path diagram (creative occupation model)

Table 6

Estimated results of path coefficient (creative occupation model)

     

Coef.

p > |z|

 

1

Self-actualization

(3)

Executive

(2)

0.099

0.035

**

2

 

(3)

Prof./Tech. worker

(2)

0.063

0.133

 

3

 

(3)

Social capital

(2)

0.077

0.000

***

4

 

(3)

Farmland

(2)

− 0.028

0.126

 

5

 

(3)

Sex

(2)

− 0.201

0.000

***

6

 

(3)

Age

(2)

− 0.027

0.004

***

7

 

(3)

Income

(2)

0.014

0.151

 

8

Self-investment

(3)

Executive

(2)

− 0.148

0.000

***

9

(Opposite sign)

(3)

Prof./Tech. worker

(2)

− 0.071

0.057

*

10

 

(3)

Social capital

(2)

0.000

0.988

 

11

 

(3)

Farmland

(2)

0.039

0.017

**

12

 

(3)

Sex

(2)

− 0.034

0.395

 

13

 

(3)

Age

(2)

− 0.007

0.367

 

14

 

(3)

Income

(2)

− 0.023

0.006

***

15

Altruism

(3)

Executive

(2)

0.006

0.853

 

16

(Opposite sign)

(3)

Prof./Tech. worker

(2)

− 0.039

0.180

 

17

 

(3)

Social capital

(2)

− 0.039

0.010

**

18

 

(3)

Farmland

(2)

− 0.029

0.021

**

19

 

(3)

Sex

(2)

0.063

0.041

**

20

 

(3)

Age

(2)

− 0.006

0.351

 

21

 

(3)

Income

(2)

0.010

0.143

 

22

Economy: MFA

(3)

Executive

(2)

− 0.055

0.358

 

23

 

(3)

Prof./Tech. worker

(2)

0.010

0.845

 

24

 

(3)

Social capital

(2)

0.043

0.128

 

25

 

(3)

Farmland

(2)

0.060

0.010

**

26

 

(3)

Sex

(2)

− 0.055

0.334

 

27

 

(3)

Age

(2)

− 0.030

0.010

**

28

 

(3)

Income

(2)

− 0.013

0.302

 

29

Environment: MFA

(3)

Executive

(2)

0.003

0.967

 

30

 

(3)

Prof./Tech. worker

(2)

0.037

0.520

 

31

 

(3)

Social capital

(2)

0.018

0.549

 

32

 

(3)

Farmland

(2)

0.074

0.003

***

33

 

(3)

Sex

(2)

− 0.146

0.017

**

34

 

(3)

Age

(2)

0.015

0.223

 

35

 

(3)

Income

(2)

− 0.002

0.885

 

36

Sociality: MFA

(3)

Executive

(2)

0.015

0.923

 

37

 

(3)

Prof./Tech. worker

(2)

− 0.021

0.881

 

38

 

(3)

Social capital

(2)

0.230

0.002

***

39

 

(3)

Farmland

(2)

0.113

0.063

*

40

 

(3)

Sex

(2)

− 0.790

0.000

***

41

 

(3)

Age

(2)

0.016

0.594

 

42

 

(3)

Income

(2)

− 0.009

0.777

 

43

Executive

(2)

Farmland

(1)

− 0.007

0.617

 

44

 

(2)

Sex

(1)

0.151

0.000

***

45

 

(2)

Age

(1)

0.066

0.000

***

46

 

(2)

Income

(1)

0.045

0.000

***

47

Prof./Tech. worker

(2)

Farmland

(1)

0.016

0.314

 

48

 

(2)

Sex

(1)

0.101

0.007

***

49

 

(2)

Age

(1)

− 0.052

0.000

***

50

 

(2)

Income

(1)

− 0.008

0.304

 

51

Social capital

(2)

Farmland

(1)

0.134

0.000

***

52

 

(2)

Sex

(1)

0.042

0.507

 

53

 

(2)

Age

(1)

0.051

0.000

***

54

 

(2)

Income

(1)

0.057

0.000

***

 

Covariance

   

55

Social capital

(2)

Executive

(2)

0.011

0.338

 

56

 

(2)

Prof./Tech. worker

(2)

0.031

0.013

**

Results of the estimates between variables within layer 1 and 3 are abbreviated in the table

“***”, “**” and “*” indicate statistically significant at 1, 5, 10% level, respectively

It is clarified that the creative occupation is neither affected by the degree of the existence of farmland nor affected by the consciousness toward the MFA.

We will look at the items that were significant at the 1 and 5% level as follows. First, as for socioeconomic attributes, the “creative occupation” is influenced by sex (+), age (+), and income (+) in “manager and executives”, and sex (+), age (−) and income (+) in “professionals/technical workers”.

Next, a positive correlation was confirmed between “professionals/technical workers” and “social capital”. Also, “social capital” was affected by the social–economic attributes of age (+), income (+) and farmland (+).

Subsequently, looking at the factors that influence the awareness toward the multi-functionality of urban agriculture, the factor of “social capital” positively affects the sociality of the MFA. Furthermore, as for the diversity of the lifestyle in the third hierarchy, “manager and executives” has a positive influence on the types of “self-actualization” and “self-investment”, while “social capital” has a positive influence on the type of “altruism”. In addition, it shows that the existence of farmland has direct influence on the diversity of lifestyle and the awareness toward the MFA as well.

Table 7 summarizes the results of questioning about what items of the MFA are important or not, and what items they are enjoying about. It is clarified that the items with high percentage of importance are also the items with high percentage to be enjoyed about, although there are 5.6 and 20.6% responded that “No function seemed important”, and “I don’t enjoy any role of urban agriculture”, respectively among the respondents.
Table 7

Importance and enjoyment about functions of urban agriculture (ratio of response: %)

 

Important (A)

Enjoyment (B)

(A) − (B)

Supply of fresh and safe agricultural and livestock products

54.8

39.9

14.9

Providing the employment

31.0

11.4

19.6

Conservation of the greenery and environment

47.5

28.7

18.8

Educational functions such as farming experience and food education

22.5

8.3

14.2

Revitalization of regional industry

29.2

9.2

20.0

Disaster prevention function such as evacuation place in case of disaster

22.4

10.6

11.8

Making a life tasteful and peaceful

30.3

21.4

8.9

Succession of local tradition and culture

18.9

7.3

11.6

Formation of good scenery

22.4

13.8

8.6

Place for local community

18.7

7.7

11.0

Medical and welfare functions such as horticulture therapy

11.0

2.5

8.5

Place of familiar recreation

11.0

4.6

6.4

Other

0.1

0.3

− 0.2

No function seemed important (I don’t enjoy any role of urban agriculture)

5.6

20.6

− 15.0

The bold emphasises are added to the top three items with the largest point in each column. Respondents answered there isn’t any farmland of urban agriculture in their residence are excluded

5 Concluding remarks

The above analytical results clarified that multi-functionality of urban agriculture has positive impacts on the residence of the TC, i.e., those who are thinking creatively (H1). It is also clarified that those who are “thinking creatively” have a high level of social capital (H2) and are favorable to diversified lifestyles (H3) that is thought to be able to contribute to the development of cities as well.

Policy implication drawn from the research encourages the city leaders to pay more attention to the multi-functionality of urban agriculture, because it is favorable to those who are thinking creatively. On the other hand, without a greater increase in the multi-functionality of agriculture in suburban areas, it might be difficult to attract a larger number of creative people to reside in it. However, a uniform policy for improving MFA without considering the regional diversification has brought on the results of the concentration of the CC in urban central areas and the declination in suburban areas under the circumstance of globalization. Because the economic advantage of agriculture in land endowment in suburban areas compared to that in central areas has obviously resulted in a decrease of the CC in these areas.

However, as pointed out by Berry and Portney (2016), for city policymakers who seem to think they can jump-start their economic growth simply by creating cultural and “green” amenities to attract larger numbers of the CC but without affecting the dominant city political ideology are probably misguided. To lead to a more universally applied study, a larger number of examined areas, the diversified scales and characteristics of cities including international comparative study will be taken into account in our next research agenda.

Footnotes

  1. 1.

    See “Global Power City Index 2016”, Institute for Urban Strategies (The Mori Memorial Foundation).

  2. 2.

    See “Sustainable Cities Index 2016: Putting people at the heart of city sustainability”, Arcadis.

Notes

Acknowledgements

This study was supported by JSPS KAKENHI Grant Number 17K07989 (study on the creativity of cities through improving agriculture amenity: comparative analysis between Japan and China), 15K07603 (study on the function of corporate social capital in farm business) and 15K18750 (study on the entrepreneurship of social business in rural and agricultural sector). We wish to express our gratitude for the support.

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Copyright information

© The Japan Section of the Regional Science Association International 2018

Authors and Affiliations

  • Lily Kiminami
    • 1
  • Akira Kiminami
    • 2
  • Shinichi Furuzawa
    • 1
  1. 1.Institute of Science and TechnologyNiigata UniversityNiigataJapan
  2. 2.Department of Agricultural and Resource EconomicsThe University of TokyoTokyoJapan

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