Social Design Engineering Series

SDES-2017-21

Social value orientation and topography in urbanization:
A case of Beijing, China

Zhang Jingchao
School of Economics and Management, Kochi University of Technology
Beijing Association of Sustainable Development

Koji Kotani
School of Economics and Management, Kochi University of Technology
Research Center for Future Design, Kochi University of Technology

Tatsuyoshi Saijo
School of Economics and Management, Kochi University of Technology
Research Center for Future Design, Kochi University of Technology
Research Institute for Humanity and Nature
Urban Institute, Kyusyu University

30th October, 2017

School of Economics and Management
Research Center for Future Design
Kochi University of Technology
KUT-SDE working papers are preliminary research documents published by the School of Economics and Management jointly with the ResearchCenter for Social Design Engineering at Kochi University of Technology. To facilitate prompt distribution, they have not been formally reviewed
and edited. They are circulated in order to stimulate discussion and critical comment and may be revised. The views and interpretations expressedin these papers are those of the author(s). It is expected that most working papers will be published in some other form.

Social value orientation and topography in urbanization:
A case of Beijing, China
Zhang Jingchao*,† Koji Kotani*,‡,§,¶,|| Tatsuyoshi Saijo*,†,‡, **November 2, 2017

Abstract
Urbanization leads to cultural changes that shape social values and behavior. Topographicalvariation in mountainous, hilly and plains areas is considered one of the main indicators of thedegree of urbanization, following distance to urban cities. Therefore, it is hypothesized that there
may be a topographical difference in distributions of social value orientations (SVOs), which
categorize people’s social preferences into the prosocial, the proself and unidentified. To examine
this hypothesis, we conduct field surveys and experiments in mountainous, hilly and plains areasof Beijing, collecting the sociodemographic information and SVOs of 596 samples. We find that
proportions of proself people are higher in plains and hilly areas than in mountainous areas as
the distance to the center of Beijing becomes shorter. In addition, the proportion of unidentified
people is prominent in hilly areas, as they represent transitional societies. Overall, this result
suggests that social preferences transition from the prosocial to the unidentified and then to theproself with topographical changes, implying that new social mechanisms are necessary to shiftpeople’s social preferences toward prosocial behavior in the urbanization process. Otherwise,
important social problems such as air pollution and sustainability, which require cooperation, willpose more danger in the future.

Key Words: Social value orientation; topography; urbanization* School of Economics and Management, Kochi University of Technology†

Beijing Association of Sustainable Development
Research Institute for Future Design, Kochi University of Technology§
Urban Institute, Kyusyu University¶
College of Business, Rikkyo University||
Corresponding author, E-mail: kojikotani757@gmail.com** Research Institute for Humanity and Nature‡

1

Contents
Nomenclature

2

1

Introduction22

Data and Methodology

4

3

Result and discussion

94Conclusion

13

5

Bibliography

15

List of Figures

18

List of Tables

20

Nomenclature
SD Standard deviation
SVO Social value orientation

1

1

Introduction

2

As the second largest economy in the world, China has been recognized as an important economic

3

contributor to world development in the 21st century (Fang et al., 2015). From 1985 to 2015, China’s

4

population ratio in rural areas dramatically declined from 76.29 % to 43.90 % in contrast to the high5

population growth in urban areas (National Bureau of statistics of China, 2016). Along with this urban

6

expansion, China faces a series of challenges such as environmental deterioration and air pollution

7(Chan and Yao, 2008, Chen et al., 2017). Many scholars suggest that individual voluntary contri-

8

butions and efforts are essential to address such environmental and sustainability problems together

9with government interventions (See, e.g., Van Vugt et al., 1995, Van Lange et al., 2007). For instance,

10

Sovacool (2009) reports that an individualistic or selfish orientation in people’s personalities becomes11

an obstacle for public acceptance of renewable energy. Thus, it is claimed that proenvironmental and12cooperative behaviors must be promoted at the individual levels for the solutions of various social

2

13

problems that arise in the progress of urbanization (Ostrom et al., 2002, Dawkins, 2006, Wilson et al.,14

2009, Shahrier et al., 2016).

15

Urbanization together with technological advancement or economic development brings about

16

cultural changes, potentially changing people’s values and behaviors (Zeng and Greenfield, 2015).

17

At the same time, it is suggested that the progress or degree of urbanization could be captured by

18

topographical variation, and such topographical environments can explain human psychology, prefer-

19

ences and behaviors (Rentfrow et al., 2013, 2015, Bach et al., 2016, Huggins and Thompson, 2016).

20

For the betterment of environmental and sustainability problems that arise in the process of urbaniza-

21

tion, it is valuable to understand how people’s preferences and behaviors change with topographical22

differences. Therefore, this paper addresses individual social preferences in relation to topography.

23

Geographical differences play a role in the distribution of personality traits. Rentfrow et al. (2013)24

reveal that people in the United States exhibit three state-level personality traits: “friendly and con-25

ventional” in the central US, “relaxed and creative” in the west coast, mountainous and sunbelt re-26

gions, and “temperamental and uninhibited” in the Mid-Atlantic and the northeast. Rentfrow et al.

27(2015) replicates the previous research on geographical psychology and find that personality changes

28

with geographical differences, and people in neighboring regions share a similar personality in Great29

Britain. These studies focus on personality differences by region that are characterized by north or

30

south, east or west, state or city at national level.

31

Other works focus on value shifts shaped by cultures in combinations of different environments.

32

Zeng and Greenfield (2015) demonstrate that people tended to be more individualistic than collec-

33

tivistic during ecological changes in China from 1970 to 2008.1 Talhelm et al. (2014) and Henrich34(2014) document that people in wheat-planting regions of China and in Europe exhibit more indi-

35

vidualistic traits than those in rice-planting regions of China and Japan owing to different patterns of36

farming culture. Ockenfels and Weimann (1999) and Brosig-Koch et al. (2011) conduct a solidarity

37

experiment, reporting that people in East Germany are more selfish than West Germany. Shahrier

38

et al. (2016) compare individual social preferences among three regions of Bangladesh, finding that1

Ecological change means a change in economic development, urbanization, technology development and educationlevels.

3

39people tend to be more competitive as societies become capitalistic. Overall, these papers capture the40

tendency of changes in individual social values under various cultures such as political, economic or

41

technological environments.

42

None of these previous works have analyzed how social preferences and behaviors are charac-

43

terized by topographical characteristics together with ongoing urban expansion at the micro level.

44

As noted by Bach et al. (2016), geographical variation in personalities, preferences and behaviors

45

needs to be examined from not only the macro level (i.e., country or state) but also the micro level

46(i.e., within-state); otherwise, important variations might be neglected. In most cases, topographical47

variation in mountainous, hilly and plains areas is believed to play a large role in different degrees

48

of urbanization, following distance to urban cities. Therefore, it is hypothesized that there may be a49

topographical difference in distributions of social value orientations (SVOs) that categorize people’s

50social preferences as prosocial, proself and unidentified. To examine this hypothesis, we conducted

51

field surveys and experiments in the mountainous, hilly and plains areas of Beijing, China, collecting

52

sociodemographic information and SVOs of 596 samples.

53

2

54

Study area

Data and Methodology

55

China has the largest population and highest economic growth in the world. As China’s capital

56

city and also the political, economic and cultural center, Beijing embodies China’s rapid urbanization57

and economic development. Beijing’s population reached 19.6 million in 2010, a 44.5 % rise as re-

58

ported at the time of 2000 (Beijing Municipal Bureau of Statistics, 2016b). Overall, Beijing has an

59

area of 16 410.5 km2 , comprising 6 urban and 10 suburban and rural districts. In addition to relatively

60

smaller urban areas (1368.3 km2 ), 92 % of Beijing belongs to suburban and rural areas (Beijing Mu-

61

nicipal Government, 2012). Another important fact is that there is a wide variation among the districts

62

of Beijing with respect to the topography and the degree of urbanization, while they share similar cli-

63

mate and culture (Beijing Municipal Bureau of Statistics, 2016a). Hence, we consider Beijing an4

64

appropriate field for the micro-level analysis in our research.

65

In March of 2016, we implemented field surveys and experiments to collect people’s social value

66

orientations (SVOs) and sociodemographic information in suburban and rural Beijing. As described,

67

Beijing has a topographical difference in mountainous, hilly and plains areas, reflecting different lev-68

els of urbanization because of the distance to the center of Beijing (Beijing Municipal Bureau of

69

Statistics, 2016a). Our study areas cover five suburban and rural districts in Beijing (See figure 1 for70

the distance to the center of Beijing): Yanqing, Miyun, Pinggu, Fangshan and Daxing. Regarding71

topographical and socioeconomic differences, these districts are categorized into three groups: moun-72

tainous areas (Yanqing and Miyun), hilly areas (Pinggu and Fangshan) and a plains area (Daxing). To

73

clarify distance from the survey areas to the center of Beijing, we draw rings from the survey spots in

74

each district around the center of urban Beijing (the center of the ring circle) in figure 1. The bigger

75

the size of the ring, the further the distance is.

76

As shown in figure 1, these rings divide the five districts into plains, hilly and mountainous ar-77

eas. The smallest ring represents the plains area (Daxing), which is closest to the center of Beijing.

78

Fangshan and Pinggu are marked by inner and outer rings of hilly areas, respectively, and exhibit

79

the second closest distance to the center of Beijing. The inner and outer rings of mountainous areas

80

represent Yanqing and Miyun, reflecting the most remote distance to urban areas. Although the rings

81of Pinggu and Yanqing to the center of Beijing appear to be close, the transportation conditions are82

different. The main roads from urban areas to Yanqing are mountainous, but the roads from the urban

83

areas to Pinggu are flat. Subsequently, it is much more difficult and time-consuming to reach Yanqing

84

than Pinggu from the center of Beijing.

85[Figure 1 about here.]86

In 2015, Daxing had the highest population density of 1507 people km−2 , while Yanqing and87

Miyun had the lowest population density of 157 people km−2 and 215 people km−2 , respectively (Bei-88jing Municipal Bureau of Statistics, 2016a). As a transitional group, Fangshan and Pinggu have a89

population density of 526 people km−2 and 445 people km−2 , respectively. If we further compare the90change of population density from 2011 to 2015, the same tendency is observed (Beijing Municipal
5

91

Bureau of Statistics, 2012, 2016a). In this five-year period, Daxing led in growth of population density

92

by 128 people km−2 , which is in sharp contrast to the decline in population density by 3 people km−2

93

in Yanqing. Taking Fangshan as a case of hilly areas, population density grows by 40 people km−2 in-94

between. In summary, a distinct variation among plains, hilly and mountainous areas can be detected95

by the above statements. This distinction is in accordance with our expectation that due to the prox-

96

imity to urban areas and convenient transportation, the plains areas are influenced by the economic

97

development and urbanization in Beijing. Next, hilly areas could be considered a transitional society

98

in the process of urbanization and economic development. Remote mountainous areas fall into the99least-urbanized group due to their distance from urban areas and inconvenient transportation.

100

Field surveys and experiments

101

We conducted field surveys and experiments based on 605 respondents; sociodemographic infor-

102

mation and SVOs, respectively, were collected through face-to-face interviews in Beijing. The head103of household or the decision maker in the household was asked to be responsible for the interview.

104

In the end, 596 samples were used in the data set because of the missing observations in 8 question-

105

naires. The respondents’ sociodemographic information contains age, gender, education, occupation,

106

annual household income and the number of children in a household. Table 1 lists the detailed defini-107

tions for all the sociodemographic variables collected in surveys. Education is an ordered categorical108

variable from 1 to 4 representing the orders of education levels from low to high. Age and household

109

income are numerical variables to capture their influence on SVOs. The occupation and the number of110children in the household are hypothesized as important determinants of people’s social preferences,111

following Shahrier et al. (2016). In regard to occupation, we take it as a dummy variable by asking

112

whether they engage in farming as a main occupation. If “No,” it means that they do not engage in

113

farming, taking jobs in the business and service sectors.

114[Table 1 about here.]115

A decomposed social value orientation (SVO) game is employed to measure people’s social pref-116

erences, categorizing the social preferences into four types of SVOs: prosocial, competitive, individ6

117

ualistic or unidentified (Van Lange et al., 1997, 2007). This SVO game consists of 9 questions, each118

of which asks subjects to choose one option among three. Each option comes with two numbers as

119

in the enumeration of options A, B and C shown below, representing the payoffs for “oneself” (You)120

and “the other,” respectively. The oneself (You) and the other are considered a pair of two persons

121

where “the other” is unknown to the other. A specific example for one question in the SVO game is122

as follows:

123

Option A: You receive 500; the other receives 100.

124

Option B: You receive 500; the other receives 500.

125

Option C: You receive 550; the other receives 330.

126

Suppose that one subject chooses one option among three options A, B or C. Subjects who choose

127

option A are considered to be competitive since this option reflects the motivation to maximize the gap

128

between oneself and the other (500 − 100 = 400). Subjects who choose option B are considered to be

129

prosocial because they tend to maximize the joint outcome (500 + 500 = 1000). Option C represents

130

an individualistic orientation because of the highest personal outcome among the three options (550),

131

regardless of the outcome of the other.

132

When a subject makes at least 6 consistent choices of options with one orientation among the133

prosocial, the competitive and the individualistic over 9 questions, she is judged to have a specific134

orientation and is otherwise “unidentified” (Van Lange et al., 2007). For the explanation of the SVO

135

game, we distributed a written instruction and made the presentation to the respondents. They were

136

informed that all the numbers in the options of questions represent the payoffs for oneself and the137

other in a pair. The respondents are informed when they are randomly paired with another person

138

in this game, but the identity of the partner is never revealed. We explain that the payment to each

139

subject is calculated by summing the payoffs earned from 9 options selected by oneself for “oneself”

140

and 9 options selected by the partner for “the other.” The maximal individual gain is 10 RMB (≈141

1.54 USD) and the mean of the individual’s gain is 8 RMB (≈ 1.24 USD).2
2

In the March of 2016, the exchange rate is 1 USD ≈ 6.48 RMB.

7

142

Empirical method

143

To characterize people’s social value orientation, the multinomial logit model is employed taking

144

the SVO as a dependent variable and other sociodemographic variables as independent variables. As

145mentioned, the SVOs consist of the prosocial, the individualistic, the competitive and the unidentified,146

and only 20 samples (3.36 %) in our data are identified as competitive. Therefore, we decided to147

merge the individualistic and competitive orientations into the “proself” for simplicity of analysis.3

148

Subsequently, a dependent variable in the multinomial logit model becomes an unordered categorical149

variable to consider the three categories of prosocial, proself and unidentified, assuming that the base150

group is prosocial. The model is utilized to estimate the marginal probability of being in one of the151social value orientations when one independent variable increases by one unit. The multinomial logit

152

model can be described as:

Probi (j) = Prob(Sji ≥ SJi ),∀J 6= j(1)153

where i is the ID number of the respondents (i = 1, . . . , 596), j represents one orientation among154

three social value orientations J where J = {prosocial, proself, unidentified}. Probi (j) represents

155

the probability that respondent i falls into orientation j, and this predicted orientation j represents the156

outcome with the highest tendency for individual i among three orientations. Sji is assumed to be a157

linear function of independent variables:

Sji = β j Xi + ji ,

(2)

158

where Xi represents the vector of independent variables for respondent i, β j refers to the vector of

159

regression coefficients depending on orientation j, and ji is an error term. From equations (1) and (2),3

In the literature, prosocial and proself behaviors are widely used as two essential aspects of human behavior (Van
Lange et al., 1998, Bogaert et al., 2008, Wilson et al., 2009, Timilsina et al., 2016, Wei et al., 2016).

8

160

the multinomial logit models can be specified as:
Probi (j) = Prob(β j Xi + ji ≥ β J Xi + Ji ),∀J 6= j(3)

= Prob(β j Xi − β J Xi ≥ Ji − ji ).
161

The reduced form of equation (3) is:
exp β j Xi.
Probi (j) = P
J exp β J Xi

(4)

162

where the vector of regression coefficients β j are estimated from a standard maximum likelihood

163

method. The set of independent variables Xi contains age, gender, education, number of children in164

a household, occupation, annual household income and area dummies.4

165

Ethics statement
This study was approved by the research ethics committee of Kochi University of Technology.

166

167

Subjects provided their written consent to participate in this study.

168

3

Result and discussion

169

Table 2 describes the distributions of social value orientations (SVOs) in three areas of Beijing.

170

In general, 59.90 % of the respondents are identified as prosocial, and only 30.54 % and 9.56 % are

171

proself and unidentified, respectively. This result indicates that a majority of people in Beijing’s

172

suburban and rural areas are prosocial. In particular, a proportion of prosocial people is high in

173

mountainous areas (75.56 %), and consequently a proportion of proself people is the lowest (19.26 %).

174

The proportion of proself people reaches the highest in plains areas (35.33 %), followed by hilly
4Some researchers may claim that there might be reverse causality in our regression; i.e., competitive people move toand live in urban cities. However, regarding rural-urban migration, many studies establish that poor economic conditions
mainly push people to migrate from rural to urban areas (Dudwick et al., 2011, Young, 2013, Brueckner and Lall, 2015),
or a wide variety of opportunities including health and employment in urban areas are found to be the main motivations(Todaro, 1996, Zhang and Song, 2003). In other words, none of these studies suggest that more competitive or individualistic people tend to migrate to more urbanized or competitive societies. Shahrier et al. (2016) further illustrate that there
is no reverse causality between area dummies and SVOs in Bangladesh.

9

175

areas (33.12 %). A proportion of unidentified people is found to be high in hilly areas (12.86 %). In

176

summary, table 2 reveals that distributions of SVOs correlate with the topography or the degree of177

urbanization.

178[Table 2 about here.]

179

Table 3 provides summary statistics for the independent variables. On average, annual household

180

income is the highest in plains areas, and the lowest is in mountainous areas, and the income gap

181

between these two areas is considerably large. Average household income in hilly areas is lower but

182

closer to that in plains areas. However, the highest household income is found in hilly areas followed

183

by plains and mountainous areas. This result exhibits evidence of economic development across the184

suburban and rural areas of Beijing. Plains and hilly areas take the lead in economic development due

185

to shorter distance from urban areas in Beijing, while mountainous areas are the least developed due

186

to distance and transportation limitations.

187

On average, the age of the subjects across the three areas are close each other. The youngest group188

is represented by people in plains areas, followed by those in hilly and mountainous areas. Subjects

189

have reached the primary school level on average. Among the three areas, people in mountainous

190

areas have the lowest education level. The number of household children below 15 years old is the191highest in plains areas and the lowest in mountainous areas. With respect to the occupation dummy,192

the proportion of farmers (95 %) is highest in mountainous areas, with a significant decline (71 %)193

in hilly areas, and it turns out to be the lowest (69 %) in plains areas. This result verifies that with194

locations closer and more convenient to city areas in Beijing, societies are influenced by urbanization

195

with more business or job engagement other than farming activities.

196[Table 3 about here.]

197

Table 4 reports the marginal effects of each independent variable on the probability that a subject is

198

proself or unidentified, taking the prosocial as the reference group in the multinomial logit regression.

199

In general, age and education do not have a significant effect on people’s SVOs. Household income

200

and occupation dummy (farmer or not) are significant factors that affect the likelihood that a subject
10

201

is unidentified in SVOs. There is a negative relationship between number of children in a household

202

and the proself orientation. Gender positively influences proself orientation. With respect to area

203

dummies, people in plains and hilly areas are more likely to have a proself orientation compared to204

those in mountainous areas. More interestingly, people in hilly areas tend to be more unidentified in

205

SVOs.

206

[Table 4 about here.]

207

More specifically, an increase in household income by 10 000 RMB significantly leads to a higher

208

probability that a subject will be unidentified by 1.3 %, taking the prosocial orientation as a reference209

group (table 4). In Beijing’s rural area, 10 000 RMB (≈ 1543.2 USD) is a large amount of money that210

accounts for 39 % of the average household income in our survey. As a consequence, it is less likely

211

that such an increase in household income would materialize within a short period for a household.

212

Thus, the magnitude of the income effect can be considered practically insignificant because the213

marginal probability that a subject will be unidentified changes very slowly.

214

As described in Table 4, males appear to have a higher tendency to be proself than females by

215

7.1 %, which is consistent with the previous findings in Van Lange et al. (1997) and Eckel and Gross-216

man (1998). They report that females exhibit higher prosocial preferences than males. Our result

217

reflects a gender difference in the proself orientations in China. In reality, as in most other countries,

218

males are for the most part the head of household and bread earner in China. They need to work very219

hard for family survival or better living conditions. Given these conditions in China, it is expected

220

that males would have the higher probability of being proself than females.

221

Regarding the number of children under 15 years old, our results reveal that one more child in a

222

household brings about a 5.9 % decline in the likelihood that a subject is proself, taking the prosocial

223

as a reference group. On the contrary, Shahrier et al. (2016) report that people become more individ-

224

ualistic or unidentified with the increased number of children per household. Unlike other countries

225

such as Bangladesh, China has implemented the one-child and two-child policies since 1979 and226

2015, respectively. Hence, unlike in Bangladesh, it is neither substantially difficult nor affordable to227

raise one or two children in Beijing; having children is welcomed. In addition, Chinese parents place
11

228

particularly high expectations and importance on the next generation because of the one-child policy229

of the past three decades. Thus, people can be cooperative in sharing information or experience for230

the development of children in the future.

231

As shown in Table 4, farmers tend to have a higher probability than nonfarmers of being uniden-

232

tified by 10.9 %, relative to the probability of a subject being prosocial. This result indicates that233

farmers’ social preferences are more unpredictable than those of nonfarmers. In one sense, agri-

234

culture in China has gradually shifted from traditional to modern approaches due to the progress of235

agricultural technologies. Beijing has taken the lead in this transition, and the modernization of agri-

236

culture enables people to easily and independently engage in farming and the sale of products using

237

new technologies and online networks. On the other hand, traditional agriculture requires a larger

238

labor force or more engagement by local people, which might stimulate collaboration among farming

239

households. Based on these two aspects, farmers might have unstable or unidentified values owing to240

this transition in agriculture.

241

With respect to area dummies, the results reveal that in comparison with people in mountainous

242

areas, those in plains and hilly areas exhibit a greater tendency to be proself than prosocial by 14.3 %243

and 14.4 %, respectively. More interestingly, people in hilly areas are more likely to be unidentified

244

by 6.4 % compared with mountainous areas. These results illustrate our expectation that topograph-245

ical variation in mountainous, hilly and plains areas is an important determinant of people’s social

246

preferences regarding the distance to urban areas. As mentioned, plains areas (Daxing) are located247

the shortest distance from the center of Beijing, followed by hilly areas (Fangshan and Pinggu) and248

mountainous areas (Yanqing and Miyun), which are the furthest from the center of Beijing. Due to249

the advantages or disadvantages of each district in topography, urbanization in Beijing has been de-

250

veloped at different speeds, and thus, this result appears to suggest that topographical variation shapes

251

lives and people’s social preferences.

252

The plains area of Daxing experiences the first round of urbanization among these districts and253

is influenced by Beijing in the form of sharp increase in population, which is in sharp contrast to

254

mountainous areas. Under these conditions, industries are rapidly expanding in the plains area, at-255

tracting people to this area for job opportunities and a new life. Thus, villages in the plains area have
12

256

developed along the lines of “urban villages,” with sound infrastructure, services and housing condi-

257

tions. As a consequence, these rural residents can easily get access to goods, services or resources258

by themselves. Once people adapt themselves to such an independent life, they are more likely to

259

become individualistic (Henrich, 2014). Based on the above set of life changes in the plains area, it is260

plausible that these people tend to fall within the proself orientation.

261

Mountainous areas are now viewed mainly as important ecological assets or barriers to protect

262

nature and the Beijing environment. Due to the beauty of the wilderness and environment, eco-based263

services and tourism have grown, and to prevent natural disasters such as floods and mudflows, local

264

people voluntarily cooperate and help each other on a daily basis in both their jobs and private life.

265

Therefore, people in mountainous areas are usually friendly with not only neighbors but also other266

people, implying that cooperation in the culture remains, as shown by certain Chinese traditions such267

as Luoye Guigen, mutual cooperation among households is sustained to maintain each other’s houses.5

268

Hilly areas (Fangshan and Yanqing) could be viewed as having just begun the process of urbaniza-

269

tion, and they are now going through a transition from the mountainous and plains areas. Therefore,

270

people’s values and behavior may also be transitioning, resulting in individual social preferences to271

be the unidentified, which is consistent with Shahrier et al. (2016).

272

4Conclusion

273

Urbanization can lead to cultural changes that influence human values and behavior. Topograph-274

ical differences in plains, hilly and mountainous areas can reflect different degrees of urbanization

275

based on distances to city areas. Hence, it is likely that there are topographical differences in social

276

value orientation (SVOs). To examine this relationship, we conducted field surveys and experiments in

277

Beijing, finding that the proportions of proself people are higher in plains and hilly areas than in moun-278

tainous areas, where the distance to the center of Beijing is shorter. As a transitional society, people279

in hilly areas tend to be more unidentified in SVOs. Overall, we demonstrate that social preferences

280

transition from the prosocial to the unidentified and then to the proself with topographical changes.
5

Luoye Guigen refers to the traditional belief that human spirits and souls go back to their birth places for peaceful

rest.

13

281

This result implies that new social mechanisms are necessary to influence social preferences, inducing

282

prosocial behavior in the process of urbanization. Otherwise, important social problems such as air283

pollution or sustainability, which require further cooperation for solutions, will pose more danger in284

the future.

285

We note some limitations of our study and directions for future research. This study does not

286

take into account samples of people who live in the center of Beijing. In reality, it was extremely287

difficult for us to find and contact such people for data collection under time and budget constraints,

288

and thus, we have mainly collected samples in suburban and rural areas. However, we conjecture289

that the qualitative results will not change even if we add the sample of residents in the center of290

Beijing. As for future research, similar types of research could be explored in other countries that291

experience rapid urbanization and serious social problems. In that case, it is meaningful to confirm

292

the robustness of this analysis by comparing our results with those in different countries. Although293

we admit that our research has certain limitations and possibilities, it is our belief that this paper294

represents an important first step in addressing how social preferences are shaped by topographical295

differences in the urbanization process. Based on these findings, we suggest new social mechanisms

296

are needed to induce prosocial behavior in urban societies and to secure human sustainability; we also297

hope that the robustness of our results will be established in the near future.

14

5

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List of Figures
1

The survey areas in Beijing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

18

19

Figure 1: The survey areas in Beijing

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List of Tables
12
3
4

Descriptions of the independent variables . . . . . . . . . . . . . . . . . . . . . . .
Distribution of social value orientation (SVO) in three areas . . . . . . . . . . . . . .
Summary statistics for all the independent variables . . . . . . . . . . . . . . . . . .
Marginal effects of the multinomial logit regression . . . . . . . . . . . . . . . . . .

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21
22
23
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Table 1: Descriptions of the independent variables
Variable

Description

Education

An ordered categorical variable that takes 1 when a respondent is
educated at primary school level, 2 is middle high school level, 3 is highschool level and 4 is university level.
Household income Annual household income for the year of 2015 in 10 000 RMB.
AgeAge of the respondent
GenderA dummy variable that takes 1 when the respondent is male, otherwise 0.
Children
The number of the children in a household who are below 15 years old.
FarmerA dummy variable that takes 1 when a respondent engages in farming
as a main occupation, otherwise 0.
Area dummy
A dummy variable that takes 1 when a respondent resides in plains(hilly) areas, taking mountainous areas as a base group.

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Table 2: Distribution of social value orientation (SVO) in three areas
Prosocial

Proself

Mountain 102 (75.56 %) 26 (19.26 %)Hilly
168 (54.02 %) 103 (33.12 %)Plains87 (58.00 %) 53 (35.33 %)Overall

357 (59.90 %)

182 (30.54 %)22

Unidentified
7 (5.19 %)
40 (12.86 %)
10 (6.67 %)
57 (9.56 %)Table 3: Summary statistics for all the independent variables

Mountain

Areas
Hilly

Plains1.47 (1.00)
1.02
0.2
5

2.84 (3.00)
1.98
0.2
12

2.97 (3.00)
1.78
0.2
8

2.56 (2.00)
1.85
0.2
12

56.45 (56.00)
11.30
20
91

54.19 (56.00)
14.08
20
88

53.86 (54.00)
10.71
21
86

54.62 (55.00)
12.72
20
91

1.65 (2.00)
0.64
1
3

1.97 (2.00)
0.84
1
4

1.79 (2.00)
0.74
1
4

1.85 (2.00)
0.78
1
4

0.34 (0.00)
0.51
0
2

0.49 (0.00)
0.60
0
2

0.59 (1.00)
0.64
0
2

0.48 (0.00)
0.59
0
2

0.65 (1.00)
0.48
0
1

0.55 (1.00)
0.50
0
1

0.80 (1.00)
0.40
0
1

0.64 (1.00)
0.48
0
1

Average (Median)
SD
Min
Max

0.95 (1.00)
0.22
0
1

0.71 (1.00)
0.45
0
1

0.69 (1.00)
0.46
0
1

0.76 (1.00)
0.43
0
1

Sample size

135

311

150

596

OverallHousehold income in 10 000 RMB
Average (Median)1
SD2
Min
Max
Age
Average (Median)
SD
Min
Max
Education (categorical variables)
Average (Median)
SD
Min
Max
Children (< 15 year-old)
Average (Median)
SD
Min
Max
Gender (Male = 1)
Average (Median)
SD
Min
Max
Farmer (YES = 1)

1 Median in parentheses.
2 SD stands for standard deviation.

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Table 4: Marginal effects of the multinomial logit regression
Multinomial logit regression
Proself
Unidentified
Household income (in 10 000 RMB)
Education
ChildrenGender (base group = female)
Age
FarmerArea dummy (base group = mountainous areas)
Plains areas
Hilly areas

0.015(0.012)−0.046(0.030)−0.059*(0.035)0.071*(0.041)−0.003(0.002)−0.069(0.057)0.013**(0.006)0.010(0.016)0.003(0.016)0.002(0.020)0.000(0.001)0.109***(0.019)0.143**(0.068)0.144***(0.054)0.016(0.038)0.064**(0.030)

The regression takes the prosocial as the reference group.
Children refer to the number of children in a household whose age is below 15 years old.
***significant at the 1 percent level, **significant at the 5 percent level and *significant at the 10 percent level.
The LR χ2 value in the multinomial logit regression is 63.10 and significant at the 1 percent level.

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