Social Design Engineering Series

SDES-2018-1

Time preferences between individuals and groups in thetransition from hunter-gatherer to industrial societies

Yayan Hernuryadin
School of Economics and Management, Kochi University of Technology
Ministry of Marine Affairs and Fisheries, Republic of Indonesia

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

Yoshio Kamijo
School of Economics and Management, Kochi University of Technology
Research Institute for Future Design, Kochi University of Technology

4th June, 2018

School of Economics and Management
Research Institute 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.

Time preferences between individuals and groups in thetransition from hunter-gatherer to industrial societies
Yayan Hernuryadin*,† Koji Kotani*,‡,§,¶,|| Yoshio Kamijo*,‡June 2, 2018

Abstract
Three societies of the hunter-gatherer, the agrarian and the industrial represent the courseof human history for cultural and economic development. In this course, each society exhibits distinct cultures and daily life practices that shape human behaviors and preferences,characterizing temporal actions and consequences at individual and group levels. We examine
individual and group time preferences as well as their relation across the three societies. To
this end, we conduct a field experiment of eliciting individual and group discount factors in thesocieties of Indonesia: (i) the fisheries, (ii) the farming and (iii) the urban ones as a proxy of
the hunter-gatherer, the agrarian and the industrial, respectively. We find that both individualand group discount factors are the lowest (highest) in the fisheries (agrarian) society among thethree, while those in the urban are in the middle. We identify that the determinants of group
discount factors differ across societies; members of the lowest and middle discount factors ina group play an important role in forming a group discount factor in fisheries societies, while
only the member with the middle discount factor is a key in agrarian and urban societies. Overall, our results suggest that individual and group discount factors non-monotonically change
as societies transition from fisheries to agrarian and from agrarian to urban ones, and comparatively shortsighted people (the lowest and middle) are more influential than farsighted people
in forming group time preferences.

Key words: discount factors; individual and group time preferences; fisheries; farming; urban* School of Economics and Management, Kochi University of Technology†

Ministry of Marine Affairs and Fisheries, Republic of Indonesia
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‡

1

Contents
Nomenclature

2

1

Introduction

2

2

Methods and materials
2.1 Study areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 A discounting elicitation experiment . . . . . . . . . . . . . . . . . . . . .
2.2.2 Social value orientation games . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Experimental procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Empirical method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4
4
5
5
7
8
9

3

Results

11

4

Conclusion

18

5

Bibliography

20

List of Figures

22

List of Tables

26

Nomenclature
BPS

Badan Pusat Statistik

DKI

Daerah Khusus Ibukota

SVO Social value orientation

1

1

Introduction

2

Three societies of the hunter-gatherer, the agrarian and the urban have shaped the course of3

human history through economic and cultural development (Massey, 2002). In this course, each4society exhibits distinct cultures and daily life practices that characterize temporal actions and5consequences at individual and group levels. Ma et al. (2015), Shahrier et al. (2016, 2017) and6

Timilsina et al. (2017) suggest that a transition of societies from the rural to the urban affects social

7

preferences and behaviors. Moreover, such changes in preferences and behaviors are claimed to be2

8related to people’s temporal actions and consequences at individual and group levels. For example,

9

Indonesian fishermen work in a group to spot fishing grounds and catch fish in a competitive10

and harsh environment, farmers coordinate their efforts with other farmers for irrigation, planting,

11

growing and harvesting in an uncertain climate condition, and urban people live or work in an

12environment which is surrounded by technologies and detached from nature. This paper addresses

13

individual and group time preferences as well as their relation across different societies.

14

Several works have examined how sociodemographic and environment factors characterize

15time preferences (Harrison et al., 2002, Casse and Nielsen, 2005, Reimers et al., 2009, Tanaka

16

et al., 2010, Nguyen, 2011, Duquette et al., 2011, Johnson and Saunders, 2014). Harrison et al.

17(2002), Reimers et al. (2009) and Tanaka et al. (2010) demonstrate that age, income and education18

are correlated with time preferences. Another group of researches show that individual time prefer-

19

ences are explained by environments and occupations. Nguyen (2011) presents that fishermen with

20

experiences of participating in resource conservation programs are more future-oriented than those21

with other occupations. Johnson and Saunders (2014) demonstrate that divers are more future-22

oriented than fishermen since divers are required to be patient for maintaining healthy ocean for23

sustainability in their daily job. In addition, Casse and Nielsen (2005) and Duquette et al. (2011)24

examine farmers’ time preferences, suggesting that farmers with more future-oriented preferences

25

tend to adopt the best management practices in earlier stages or never perform slash-and-burn26

agriculture.

27

The relationships between individual and group time preferences have been studied by several

28

researchers. Charlton et al. (2013), Denant-Boemont et al. (2017), Gillet et al. (2009) and Sutter

29(2007) show that people tend to be more impatient in individual decisions than in group ones.

30

However, Yang and Carlsson (2016) find that individual decisions are not different with joint de-31

cisions in terms of time preferences. Another group of works such as Ito et al. (2011), Ma et al.

32(2015) and Osinski and Karbowski (2017) examine time preferences and social preferences, pre-33

senting that more patient subjects are likely to share payoffs with other people in a social-dilemma

34

situation. Ambrus et al. (2015) and He and Villeval (2017) demonstrate that a “median” member3

35(who has a median social preference in a group) has a significant influence on group decisions

36

since the highest and the lowest ones tend to be attracted to the median.

37

None of the past studies have addressed individual and group time preferences focusing on the38

transition of societies in cultural and economic development. We examine individual and group39

time preferences as well as their relation across the hunter-gatherer, agrarian and industrial soci-40

eties reflecting the course of human history. To this end, we conduct a field experiment regarding

41

individual and group discount factors for three societies of Indonesia: (i) the fisheries, (ii) the farm-42

ing and (iii) the urban as a proxy of the hunter-gatherer, the agrarian and the industrial societies,

43

respectively.1 Our empirical analysis yields two main results. First, we find that both individual44

and group discount factors are the lowest (highest) in fisheries (agrarian) societies among the three,45

while those in urban ones are in the middle. Second, we also identify that the determinants of group46

discount factors differ across three societies; members with the lowest and middle discount factors

47

in a group play an important role in making a group discount factor in fisheries societies, while48

only the member with the middle discount factor is a key in agrarian and urban societies. Overall,49

our results imply that individual and group discount factors non-monotonically shift as societies

50

change from fisheries to agrarian and from agrarian to urban societies, and comparatively short-51

sighted people (the lowest and middle) are more influential than farsighted people in determining

52

group time preferences.

53

2

Methods and materials

54

2.1

Study areas

55

The questionnaire surveys and experiments were conducted in Karawang and Jakarta with56

three different societies, fisheries and agrarian villages in Karawang and an urban city in Jakarta.

57

Karawang regency is located in the north part of Jawa Barat Province. Karawang is located be1

Barry et al. (1959) and Uskul et al. (2008) characterize fisheries societies as hunter-gatherer societies because of
their daily life practices.

4

58

tween 107°20 and 107°400 east longitude, and 5°560 and 6°340 south latitude. The population in

59

2015 is 2 273 579 with its density of 1094 km2 (BPS-Statistics of Karawang Regency, 2016), and60

168 901 or 18.15 % of the working population work at agriculture and fishery sectors (Karawang

61

Regency Government, 2015). Jakarta is the most densely populated and capital city in Indonesia

62

where a majority of people engage in government, business and service sectors. It is located be-

63

tween 6°120 south latitude and 106°480 east longitude. The population in 2016 is 10 277 628 with64

its density of 15 517 km2 , and 3 136 531 or 64.51 % of the working population work as a regular

65

employee in public and formal private sectors (BPS-Statistics of DKI Jakarta Province, 2017).
[Figure 1 about here.]

66

67

2.2

Experimental setup

68

2.2.1

A discounting elicitation experiment69

Several studies elicit individual time preferences through a multiple-price list procedure (Coller

70

and Williams, 1999, Harrison et al., 2002, Tanaka et al., 2010). The procedure normally requires

71

subjects to provide bank account or to arrange another meeting for receiving experimental pay-72

ments. However, this procedure is difficult to be implemented for fishermen, farmers and urban

73

people in Indonesia due to environmental and traffic conditions. We observe that subjects in our

74pilot experiments have difficulty to follow a multiple-price procedure in previous studies since

75

subjects’s education is limited in fisheries and agrarian villages. Therefore, we employ a different

76experimental procedure to elicit individual time preferences in the fields, we call a discounting

77

elicitation experiment.

78

We conduct the discounting elicitation experiments for each subject and a group of 3 subjects

79

so that time preferences at both individual and group levels are estimated as individual and group80

discount factors, respectively. First, we elicit individual time preferences through the discounting

81

elicitation experiment where subjects are asked to make a decision about whether to receive money

82

today or to receive money in the future. As most subjects are not educated, have limited literacy5

83

and cannot come back to the experimental field at specified time and date, we institute simple

84

experiments our subjects can understand and the associated experimental payments can be received85

on the spot. The discounting elicitation experiment begins by asking a question of whether each

86

subject chooses options A or B in the following manner:

87

Option A: You get 20000 Rp today.

88

Option B: You get 20000 + m Rp one month later.

89

The value of m in option B begins with m0 = 4000. When the subject chooses option A, we

90proceed to the next question in which the value of m is added by 4000 for option B, i.e., m =91

m1 = m0 + 1 · 4000 = 4000 + 4000 = 8000. Then, the subject is asked whether she prefers option

92

A or B. As far as the subject keeps choosing option A, she proceeds to the next question where93

the value of m for option B is increased by 4000. This updating procedure for m in option B to be94

mk = 4000 + k · 4000 continues arbitrary k times as far as a subject prefers option A to B. Now,95

suppose that the subject chooses option B to A at the nth trial. Then, we end the updating process

96

and ask the subject a series of questions to identify her threshold value of m between mn−1 and97

mn to be indifferent from receiving 20 000 Rp today.

98

We paid each subject the experimental reward just after the experiment. For this, we prepare a99mlottery where 20 cards are red and 20+ 1000
cards are yellow. When the subject picks a yellow card,100

she can get the reward of 20000 + m Rp, otherwise zero. In other words, the lottery is considered

101

20000
to have the probability ρ = 20000+mof successfully getting the value of 20000 + m Rp by picking

102a yellow card and the probability 1 − ρ of getting nothing by picking a red card. As most subjects

103

in this research do not understand the concept of probabilities, the number of yellow and red cards

104

are counted in front of each subject before inserting them into the bag. After seting up the number

105

of cards in the bag, we request each subject to choose between surely receiving 20 000 Rp and106

going for the lottery to possibly get 20000 + m Rp. A subject who chooses the lottery receives the107

payment according to the outcome of a random draw from the bag on the spot. A subject who does108

not choose the lottery gets 20 000 Rp. In preparing the lottery and asking each subject to choose6109

whether to go for the lottery or not, we also identify subjects’ risk preferences such as risk-averse

110

or risk-taking behaviors.

111

After finishing a discounting elicitation game at individual level, we proceed to the experiment112

at group level. We randomly choose 3 subjects and assign them as a group. We now implement113

the same procedures of asking a group of three people whether to choose option A or B as we114

did at individual level. The difference at group level is that the decision between options A and115

B at every trial and whether to go for the lottery or not must be discussed among group members.

116

We ask group members to reach concensus through discussion for every group decision without

117

relying on majority voting. After the group decisions between options A and B as well as whether

118

to going for the lottery or not, the group members was asked to decide how to split the payment

119

among the members in the same group.

120

2.2.2

Social value orientation games

121

We use a social value orientation (SVO) game suggested by Murphy et al. (2011) to measure

122

subjects’ social preferences. This method categorizes an individual value orientation into altruism,

123

prosociality, individualism or competitiveness depending upon their choices in the SVO game. In

124this game, subjects are asked to choose among nine options in each of six primary questions (See125

figure 2). Subjects are randomly paired where the subjects do not know each other. Each question

126

consists of a problem where a subject decide to allocate points to herself and to the other in her

127

pair by choosing one among nine options. After each subject has made her choices in all of the six

128

questions, she is asked to write the resulting distributions of money between oneself (you) and the129

other on the spaces provided on the right-hand side of the SVO instruction sheets (figure 2).

130

Subjects are informed that they get paid on the basis of their earnings from the SVO game in131

the following manner. The total amount of points a subject is allocated by herself and by the other132

in her pair are calculated by summing the points from all twelve items (six items from each person

133

in a pair). The points are converted into real money with an experimental exchange rate. In our

134experiment, we use 1 points equivalent to 200 Rp. The average payment in the SVO game was

7

135

28 000 Rp (approximately 2.10 USD). After the game, we identify a subject’s SVO by computing

136

the mean allocations for oneself Ās and for the other Āo , respectively, from her choices of six

137

items. Then, 50 is subtracted from each of Ās and Āo , and the inverse tangent of the ratio between

138

o −50
. The subject canĀs − 50 and Āo − 50 is calculated as her SVO angle, i.e., SVO = arctan Ā
Ās −50139

be identified as the altruist if her SVO angle is greater than 57.15◦ , the prosocial if it is between

140

57.15◦ and 22.45◦ , the individualist if it is between 22.45◦ and −12.04◦ , and the competitive if it

141

is less than −12.04◦ .
[Figure 2 about here.]

142

143

2.3

Experimental procedures

144

We implemented field experiments and surveys by employing different approaches of random

145

sampling to fisheries and agrarian societies in Karawang as well as urban societies in Jakarta be-146

cause they have different economic and socio-demographic characteristics. In Karawang, we first147

contacted the local government office to get approval for conducting the field research where 3

148

fisheries and 9 agrarian villages approved us, respectively. We obtained a list of residents from149

their local government offices and randomly chose a required number of households based on the150

population of each village. Subsequently, we invited an income-earning member from each house-151

hold to participate in our experiments by sending them invitation letters. In total, 200 fishermen152

and 197 farmers participated in our field research.

153

In Jakarta, we randomly chose subjects based on occupations. First, we collected informa-

154tion about a proportion of each occupational category in total population of the Jakarta areas by155

referring to BPS-Statistics of DKI (Daerah Khusus Ibukota) Jakarta Province. Hereafter, we ran-156

domly chose a number of organizations or companies for each category, and contacted their office157

to get approval for conducting our field research. We invited individuals from these companies

158

and organizations based on their compliance. In total, 200 urban people participated in our field159

research, and the experiments were conducted at community halls in each area of Jakarta. Overall,
8

160

597 subjects participated in our experiment (197 farmers, 200 fishermen and 200 urban people).

161

We asked each subject to leave the experimental site soon after completing all the tasks to prevent

162

unnecessary interactions among subjects.

163

In each session of our field experiments, we prepared a printed experimental instruction (a164discounting elicitation experiment and the SVO game) to subjects in the Indonesian language (Ba-165

hasa). Furthermore, we explained the experimental procedures and rules by verbal presentation

166

and ensured that subject understood. We first conducted the SVO games and then proceed to dis-167

counting elicitation experiments at individual and group levels with questionnaires. To motivate

168

subjects to seriously participate in our experiments, we stated that subjects would get paid with the169real money based on their performances. Each subject earned the average experimental earnings

170

from SVO games, discounting elicitation experiments and the participation fee was 90 000 Rp (≈171

6.5 USD). Approximately, 15 ∼ 20 subjects participated in each session of our experiment and172

took 3 ∼ 4 hours.

173

2.4

174

175

Empirical method

We employ betafit regressions to identify factors that characterize group discount factors. The
betafit models can be mathematically expressed as::gi = β0 + β 1 xi + β2 zi + i ,

(1)

176

where subscript i represents each group’s ID, gi is a group discount factor estimated, xi is a vector

177

of independent variables of categories of individual discount factors (the lowest, middle, and high-178est), and sociodemographic information such as age, education, household income and a number

179

of household members, and occupation dummy. In addition, since the SVOs is categorized as the180

altruist, the prosocial, the individualistic and the competitive, and only 18 samples or 3.01 % in181

our data are identified as the altruist and competitive. We merge the individualistic and competi-

182

tive orientations into the “proself,” and altruist and prosocial into the “prosocial” for simplicity of

9

183

analysis. Therefore, zi is a dummy variable of SVOs that takes 1 when subject i is a proself and is184

otherwise 0. The β0 (β 1 ) and β2 are the associated parameters (of vectors) to be estimated. Table 1

185

presents the definitions of the variables used in the regression analysis.

186

The betafit regression developed by Ferrari and Cribari-Neto (2004) accommodates a group187

discount factor that is bounded between 0 and 1 as a dependent variable with the assumption that188

group discount factors gi s follow a beta distribution:f (gi ; µ, φ) =Γ(φ)
g µφ−1 (1 − gi )(1−µ)φ−1 ,ΓµφΓ((1 − µ)φ) i

gi ∈ (0, 1),

189

where E(gi ) = µ, Var(gi ) = µ(1−µ), φ is an accuracy parameter and φ − 1 is a distribution param1+φ

190

eter. Various combinations of µ and φ determine types of beta densities such as J shaped, inverted

191

J shaped and U shaped (Ferrari and Cribari-Neto, 2004). Application of betafit regressions appear

192

to be valid because the distributions of group discount factors estimated in our experiments are

193

identified to be U shaped and inverted J shaped (see figure 2(a) and figure 2(b)). The maximum

194

likelihood method is used to recognize the unknown parameters β0 , β 1 , β2 in equation (1) with

195

which a marginal effect of an independent variable on the group discount factors, gi s is obtained.

196[Table 1 about here.]

197

Table 1 presents the definition of the variables that are hypothesized to affect group discount198

factors. We rank individual discount factors of three subjects in a group into the lowest, the middle

199

and the highest discount factors. A group discount factor is the elicited value of discounting the fu-

200

ture value at group level, as described in section 2.2.1. We are interested in how individual discount201

factors and the associated rankings shall affect group discount factors. The average age, income,202

household members at group level are also considered to affect group time preferences, following

203

Harrison et al. (2002) and Reimers et al. (2009). In addition, a number of proself members in a

204

group are included in the models to capture how individual social preferences influence group time

205

preferences. Finally, we define a dummy variable for agrarian and urban societies, resepectively,

206

taking the reference group as the fishery society. We consider the dummy variables to see how a
10

207

transition of societies from the fisheries to the farming and from the farming to the urban may have208

affected individual and group time preferences as well as their relations.

209

3

Results

210

Table 2 provides the summary statistics of individual discount factors, group discount factors

211

and other variables used in the analysis. The median individual (group) discount factors of fish-212

eries, agrarian and urban societies are 0.100 (0.045), 0.500 (0.417) and 0.333 (0.278), respectively.

213

These results reveal that both individual and group discount factors are the lowest in the fisheries214

society, and those in the agrarian society are higher than those in the urban society. In other words,

215

both individual and group discount factors non-monotonically change as societies transition from216

fisheries to agrarian and from agrarian to urban ones. The result also shows that the overall me-

217dian (average) of group discount factors is 0.111 (0.353), while the overall median (average) of

218

individual discount factors is 0.317 (0.414). This result implies that group discount factors tend to219

be lower than individual discount discount factors. The median (average) group discount factors

220of group members with the lowest, middle and the highest are 0.040 (0.134), 0.100 (0.322) and

221

0.598 (0.556) in the fisheries society, 0.091 (0.184), 0.500 (0.505) and 0.909 (0.809) in the agrarian222

society, and 0.067 (0.154), 0.352 (0.397) and 0.727 (0.646) in the urban society, respectively. This

223

reflects the fact that individual discount factors in the fisheries society are consistently the lowest

224

for every rank of individual discount factors in a group (the lowest, middle and the highest group225

members).

226

Regarding age, the overall average age of the subjects is 43 years old. The average age of227

farmers is the oldest because farmers tend to work longer than fishermen and urban people. This228

finding can be seen in the “max” row under age in table 2 where the maximum age of farmers is 68229

years old. Moreover, the average ages of fishermen and urban people are not significantly different

230

from one another since fishermen need to work in a labor intensive manner and the urban society

231

attracts young people from rural areas to seek better jobs and opportunities. Table 2 also shows

11

232

the median household income is the highest (3.300) in the urban, the second-highest (3.100) in the233

agrarian and the lowest (2.500) in the fisheries society. The income range is the widest in the urban

234

society, which is consistent with the fact that the standard deviation (SD) of household income

235(2.773) in the urban is the highest among the three. This indicates that Jakarta is highly capitalistic236

to have high income gap. The average number of household members is the largest (4.875) in the237

urban, the second-largest (4.485) in the fisheries and the lowest (4.222) in the agrarian societies,

238

respectively. This reflects the fact that that most of farmers’ children do not live with their parents

239

since they usually move to urban areas for better jobs and opportunities. In summary, individual240

and group discount factors in the fisheries society are consistently the lowest, and fishermen are

241

relatively young and earn low income as compared with farmers and urban people.

242[Table 2 about here.]

243[Figure 3 about here.]

244

Figures 2(a) and 2(b) show the frequency distributions of the individual and group discount245

factors for fisheries, agrarian and urban societies, respectively. The vertical axis denotes the per-246

centage of frequencies and the horizontal axis denotes the discount factor. Regarding individual

247

discount factors, figure 2(a) demonstrates that the highest spike in the frequency distributions for248

the fisheries and urban societies occurs around 0, while the highest spike for the agrarian societies249

occurs around 1. On the other hand, figure 2(b) shows that the highest spike in the frequency distri-

250

bution of group discount factors occurs around 0 for every society, while the spike in the fisheries

251society are higher than those in the agrarian and urban societies. These findings in the frequency

252

distributions of individual and group discount factors across the three societies are in line with the253summary statistics in table 2. On the basis of the summary statistics, figures 2(a) and 2(b), we run

254

a Mann-Whitney test to examine whether the distributions of the individual and group discount255

factors for any pair of the fisheries, agrarian and urban societies are the same. The null hypothesis256

is that the distributions are independent of the three different societies. The results mostly reject
12

257

the null hypothesis for individual (group) discount factors at the 1 % (1 %), 5 % (1 %) and 5 %258(18 %) significance levels for the fisheries vs. the agrarian, the fisheries vs. the agrarian and the259

agrarian vs. urban societies, respectively. Overall, the individual and group discount factors can

260

be considered dependent on the three societies.

261

The summary statistics, frequency distributions and Mann-Whitney test suggest that individual262

and group discount factors vary among three societies. To further characterize the relationship263

between group and individual discount factors, we run betafit regressions together with other in-

264

dependent variables. Table 3 presents the marginal effects of independent variables on the group265

discount factors with several model specifications. Model 1 in table 3 is considered a baseline re-266

gression including all the independent variables except society dummy variables. The result shows

267

that group members with the lowest discount factors, middle discount factors and the number of268

proself members exhibit statistical significances, playing important roles on forming group dis-269

count factors. In addition, a number of household members influences group discount factors to270

a certain extent. In particular, the results imply that a group discount factor decreases by 0.0185

271(0.054) when the lowest (middle) individual discount factor in a group declines by 0.100. Like-

272

wise, a group discount factor decreases by 0.024 (0.036) together with an increase in a number of273

household members (in a number of prosocial members in a group).

274[Table 3 about here.]275

To check the robustness of our results, we include the agrarian and urban as dummy variables

276

with the reference of the fisheries society as model 2 in addition to the baseline specification in

277

model 1. Model 2 is estimated to examine how the transition of societies from the fisheries to the278

agrarian and from the agrarian to the urban may influence group time preferences. In model 2,279

the same qualitative results are observed as in model 1 even with society dummy variables of the280

agrarian and urban. The results in model 2 of table 3 also consistently present that a group discount281

factor decreases by 0.021 (0.054) when the lowest (middle) individual discount factor in a group282

declines by 0.100, respectively. An increase of a number of household members in a group might283

lead to a 0.025 decrease in a group discount factor. Furthermore, the society dummy variables
13

284

are identified to be significant in the sense that a group discount factor in the agrarian (the urban)

285

is likely to be 0.096 (0.086) higher than that in the fisheries. This result shows that although we

286

include societies dummy variables in regression, the lowest and middle discount factors of group287

members remain to affect group discount factors, and the types of societies characterize group time288

preferences, being consistent with Nguyen (2011) and Johnson and Saunders (2014).

289

We separately run regressions as models 3, 4 and 5 in table 3 for the fisheries, agrarian and290

urban societies, respectively, because we find that farmers and urban dummy variables are signifi-

291

cant in model 2, enabling us to examine whether the determinants of group discount factors differ

292

across the three societies. Model 3 in table 3 exhibits a qualitatively identical result with model 1

293of the baseline specification, implying that a group discount factor decreases by 0.028 (0.056) in294

the fisheries society when when the lowest (middle) individual discount factor in a group declines

295

by 0.100. On the other hand, the results in models 4 and 5 are similar in that the middle discount296

factor in a group is the only significant determinant. The result of model 4 in table 3 shows that a297

group discount factor decreases by 0.048 when the middle discount factor in a group declines by298

0.100, and that an increase in a number of household members decreases a group discount factor299

by 0.055. Finally, model 5 demonstrates that a group discount factor decreases by 0.038 when the300

middle individual discount factor in a group declines by 0.100.

301

Overall, our statistical analysis demonstrates that individual and group discount factors in the302

fisheries (agrarian) society is the lowest (highest), while those in the urban society is the middle.

303

Table 3 demonstrates that comparatively short-sighted people with the lowest and middle individ-

304

ual discount factors in a group remain consistently significant in both models 1 and 2, while the305society dummy variables are statistically and economically significant in model 2. The fisheries306

society (model 3) exhibits the same qualitative result with models 1 and 2 of the baseline specifica-

307

tion in that the lowest and middle individual discount factors that play significant roles in forming

308group discount factors. The agrarian and urban societies (models 4 and 5) consistently show that309

the middle individual discount factor is the only significant variable to characterize group discount310

factors. Although we have tried a variety of different regression specifications, our results in mod-

14

311

els 1-5 remain consistent and robust with respect to the roles of individual discount factors on

312group discount factors. Some socioeconomic variables and other factors such as age, a number of313

household members, a number of proself members in a group and so on are also identified to be314

statistically and economically significant depending on the specifications of betafit regressions.

315

There are some possible explanations for our findings with respect to the roles of individual

316

discount factors in forming group discount factors. First, fishermen in our study region (Karawang)

317

are known to catch fish and earn income on a daily basis. They typically spend all of their daily

318

income within that day, and do not have motivations to save money for their future since they319

simply expect that they can go fishing the next day to generate money for living. Also, most320

fishermen in that region believe that fish stock is inexhaustible because God always provides fish321

in the sea (We find that 80.5 % of the fishermen believe so in our questionnaire survey). Therefore,322

the daily life practices, such belief about inexhaustible fish stock and their cultures shall nurture323

fishermen to be more shortsighted than farmers and urban people. This is in line with the argument324

in Johnson and Saunders (2014) demonstrating that fishermen are more shortsighted than divers

325

since divers are required to be patient for maintaining healthy ocean and environment.

326

Fishermen in Karawang work in a fishing vessel as a group of 3 to 20 fishermen. In this327

environment, fishermen face two types of competitions: intra-vessel and inter-vessel competitions.

328

In intra-vessel competitions, each fisherman in the same vessel has different kinds of tasks and job

329

levels, competing each other to get promoted. On the one hand, inter-vessel competitions occur

330

when a group of fishermen in a vessel compete with other groups in different vessels for better

331

fishing spots and more harvests. Carpenter and Seki (2011) and Huang and Smith (2014) illustrate332

that groups of fishermen compete each other to catch more fish where the actions taken by groups

333

of fishermen depend on other groups’ actions. Because fishermen in our study region is under334

severe intra-vessel and inter-vessel competitions, they become familiar with being or tend to be335

shortsighted at individual and group levels in the way that comparatively short-sighted members

336

in a group are more influential in forming a group discount factor.

337

Farmers in Karawang need to have patience and consideration for future in nature, because

15

338

farmers must wait six months for a series of cultivating and growing to harvest crops as one cycle.

339

At the same time, they need to deal with huge uncertainty. The major sources of uncertainty for340

farmers are natural disasters since they can destroy all agricultural productions in the field. Al-

341

though fishermen face the same type of risks and uncertainty that come from natural disasters, they342

can go to sea and fish within a few days after natural disasters. This is a fundamental difference343

between farmers and fishermen. In addition, farmers need to maintain their arable land for culti-

344

vating and harvesting since the land is on their own property. Therefore, farmers in Karawang are345

motivated to save, invest and accumulate capital and wealth by saving gold as a preparation for346

an uncertain future. These daily practices and cultures appear to induce farmers to be patient or347

farsighted. Farmers typically work as a group to coordinate their efforts for irrigation, planting,348

growing and harvesting to tackle uncertain climate conditions. For example, a group of farmers

349

should cooperate, coordinate and wait based on an irrigation schedule for fairness, avoiding the350

shortage of water among other groups of farmers. In a nutshell, the aforementioned practices and351

cultures of the agrarian society in Karawang appear to induce farmers to be the most farsighted at352

individual and group levels.

353

Finally, urban people in Jakarta usually live or work in an environment which is surrounded by354

technologies and detached from nature. Urban people in Jakarta does not usually feel the limitation355

or constraints of basic needs on a daily basis such as food, electricity and water, while the fisheries356

and agrarian societies have some experiences of tackling nature and feeling the limitation of various

357

resources. In urban life of Jakarta, rice, meat and fish can be readily available in supermarkets or358

department stores, and such stores usually never run short of any product because of national and359

international trades. In addition, by simply switching on a button, every energy source such as360

electricity becomes effective. This type of life implies that basic needs urban people may want361

to demand tends to be readily available or becomes effective soon after their requests, compared362

to fisheries and agrarian societies. On the other hand, urban people need to wait one month to363

get salaries, and also need to study and grow themselves to be capable and competitive in the364

workplace of urban life. Therefore, urban life comes with a mixture of being shortsighted on

16

365

the basic needs and being farsighted on their career for survival. Therefore, we conjecture that366

individual and group discount factors in urban societies are in the middle between fisheries and367

agrarian societies.

368

Another interesting finding is that comparatively shortsighted people (the lowest and middle)369

are more influential than farsighted people in forming group time preferences in models 1, 2 and370

3. This result is in line with Ambrus et al. (2015) and He and Villeval (2017) to a certain extent

371

that elicit individual and group social preferences based on gift exchange, ultimatum and modified

372

dictator games by asking subjects to allocate the resources to themselves and others. In eliciting373

individual social preferences, each subject plays a series of the games indicated above. In eliciting374

group social preferences, a group of 5 members (Ambrus et al. (2015)) or 3 members (He and375

Villeval (2017)) is formed where each group member is ranked with respect to social preferences376

on the basis of his choices in individual games. Each group determines how to share resources377

between their groups and other groups. Ambrus et al. (2015) and He and Villeval (2017) find that378

a member with the median social preference in a group has a significant effect on group social

379

preferences because the highest and lowest subjects in a group tend to get attracted to the median380

member. In our case, however, the lowest individual discount factor is identified to be significant,381

which is different from Ambrus et al. (2015) and He and Villeval (2017). It is early to conclude382

that the unique result in our analysis on group time preferences is generalized, however, at least, it

383

may be the case that group time preferences are attracted to the relatively lower individual discount384

factors in a group.

385

In summary, our results reveal that individual and group discount factors non-monotonically386

change as societies transition following a course of human history through cultural and economic

387

development. More specifically, both individual and group discount factors increase as societies

388

transition from the fisheries to the agrarian, and then decrease as societies transition from the agrar-389

ian to the industrial in the way that individual and group discount factors are the lowest (highest) in390

the fisheries (agrarian) society, while those in the industrial one are in the middle. Our regression

391

results also show that comparatively shortsighted people (the lowest and the middle) play important

17

392

roles in characterizing group time preferences. These results can be considered one of important393

evidence for the factors to influence resources sustainability and economic development processes394

in each type of societies as well as further evolution of human time preferences in the future.

395

4Conclusion

396

Previous researches claim an importance to consider a transition of societies from the rural to397

the urban in order to analyze social preferences and behaviors, demonstrating that people in the398urban societies are becoming more proself (Ma et al., 2015, Shahrier et al., 2016, 2017, Timilsina

399

et al., 2017). This paper considers three societies of the fisheries, the farming and the urban as a

400

proxy of the hunter-gatherer, the agrarian and the industrial, well representing the distinct cultures

401

and daily practices that might shape human time preferences and behaviors. We have conducted402

a field experiment of eliciting individual and group discount factors in the three societies of In-

403

donesia. We find that both individual and group discount factors are the lowest (highest) in the404

fisheries (agrarian) society among the three, while those in the urban are in the middle. We also405

identify that the determinants of group discount factors differ across the three societies; members

406

of the lowest and middle discount factors in a group play crucial roles in forming a group discount407

factor in the fisheries society, while only the member with the middle discount factor is a key in408

agrarian and urban societies. Overall, our results suggest that individual and group discount fac-409

tors non-monotonically change as societies transition from the fisheries to the agrarian and from410

the agrarian to the urban, and comparatively shortsighted people (the lowest and middle) are more

411

influential than farsighted one in forming group time preferences.

412

We finally note some limitation and possibilities of future studies. In this research, statisti-

413

cal analysis is a main tool to characterize group time preferences through utilizing the ranking of414

individual discount factors in a group. However, we have not examined the details of how group415

members determine or agree on group discount factors through their discussions in our field experi-

416

ments. If we use a qualitative-deliberative analysis in psychology on transcribed group discussions,

18

417

we should be able to identify how group members reach an agreement or compromise about group418

discount factors. If such analysis is successfully conducted, we should be able to further clarify419

the detailed dynamic process of how people with the lowest or the middle discount factors in a

420

group influece group time preferences and to check the consistency with our statistical results.

421

These caveats notwithstanding, it is our belief that this field experiment is an important first step422

to examine individual and group time preferences as well their relation. Our results indicate that423

individual and group time preferences as well as their determinants evolve as societies change.

19

5Bibliography

Ambrus, A., Greiner, B., and Pathak, P. A. (2015). How individual preferences are aggregated ingroups: An experimental study. Journal of public economics, 129:1–13.
Barry, H., Chid, I. L., and Bacon, M. K. (1959). Relation of child training to subsistence economy.
American anthropologist, 61:51–63.
BPS-Statistics of DKI Jakarta Province (2017). Karawang regency in figures 2016. Technicalreport, BPS-Statistics of DKI Jakarta Province.
BPS-Statistics of Karawang Regency (2016). Karawang regency in figures 2016. Technical report,
BPS-Statistics of Karawang Regency.
Carpenter, J. and Seki, E. (2011). Do social preferences increase productivity? Field experimental
evidence from fishermen in Toyama bay. Economic inquiry, 49:612–630.
Casse, T. and Nielsen, U. (2005). Farmer strategies and forest conservation: A case study fromsouth-western Madagascar. International journal of social economics, 32:704–716.
Charlton, S. R., Yi, R., Porter, C., Carter, A. E., Bickel, W., and Rachlin, H. (2013). Now for me,
later for us? Effects of group context on temporal discounting. Journal of behavioral decision
making, 26:118–127.
Coller, M. and Williams, M. B. (1999). Elicting individual discount rates. Experimental economics,
2:107–127.
Denant-Boemont, L., Diecidue, E., and l’Haridon, O. (2017). Patience and time consistency incollective decisions. Experimental economics, 20:181–208.
Duquette, E., Higgins, N., and Horowitz, J. (2011). Farmer discount rate: Experimental evidence.
American journal of agricultural economics, 94:451–456.
Ferrari, S. L. P. and Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions.
Journal of applied statistics, 31:799–815.
Gillet, J., Schram, A., and Sonnemans, J. (2009). The tragedy of the commons revisited: The
importance of group decision-making. Journal of political economy, 93:785–797.
Harrison, G. W., Lau, M. I., and Williams, M. B. (2002). Estimating individual discount rates indenmark: a field experiment. American economic review, 92:1606–1617.
He, H. and Villeval, M. C. (2017). Are group members less inequality averse than individual
decision makers. Journal of economic behavior and organization, 138:111–124.
Huang, L. and Smith, M. D. (2014). The dynamic efficiency costs of common-pool resource
exploitation. American economic review, 104:4071–4103.
Ito, M., Saeki, D., and Green, L. (2011). Sharing, discounting, and selfishness: An JapaneseAmerican comparison. Psychological record, 61:59–75.
20

Johnson, A. E. and Saunders, D. K. (2014). Time preperences and the management of coral reef
fisheries. Ecological economics, 100:130–139.
Karawang Regency Government (2015). Information on report performance of Karawang District
Government 2015 (in Indonesia). Technical report, Karawang Regency Government.
Ma, Q., Pei, G., and Jin, J. (2015). What makes you generous? The influence of rural and urban
rearing on social discounting in China. PLoS ONE, 10:e0133078.
Massey, D. S. (2002). A brief history of human society: The origin and role of emotion in social
life. American sociological review, 67:1–29.
Murphy, R. O., Ackermann, K. A., and Handgraaf, M. J. J. (2011). Measuring social value. Judgment and decision making, 6:771–781.
Nguyen, Q. (2011). Does nurture matter: Theory and experimental investigation on the effect ofworking environment on risk and time preferences. Journal of risk and uncertainty, 43:245–270.
Osinski, J. and Karbowski, A. (2017). Delaying rewards has greater effect on altruism when thebeneficiary is socially distant. PLoS ONE, 12:e0170387.
Reimers, S., Maylor, E. A., Stewart, N., and Chater, N. (2009). Associations between a oneshot delay discounting measure and age, income, education and real-world impulsive behavior.
Personality and individual differences, 47:973–978.
Shahrier, S., Kotani, K., and Kakinaka, M. (2016). Social value orientation and capitalism insocieties. PLoS ONE, 11:e0165067.
Shahrier, S., Kotani, K., and Saijo, T. (2017). Intergenerational sustainabilty dillemma and thedegree of capitalism in societies: A field experiment. Sustainability science, 12:957–967.
Sutter, M. (2007). Are teams prone to myopic loss aversion? An experimental study on individual
versus team investment behavior. Economics letters, 97:128–132.
Tanaka, T., Camerer, C. F., and Nguyen, Q. (2010). Risk and time preferences: Linking experimental and household survey data from vietnam. American economic review, 100:557–571.
Timilsina, R., Kotani, K., and Kamijo, Y. (2017). Sustainability of common pool resources. PLoS
ONE, 12:e0170981.
Uskul, A. K., Kitayama, S., and Nisbett, R. E. (2008). Ecocultural basis of cognition: Farmers andfishermen are more holistic than herders. Proceedings of the National Academy of Sciences of
the United States of America, 105:8553–8556.
Yang, X. and Carlsson, F. (2016). Influence and choice shifts in households: An experimental
investigation. Journal of economic psychology, 53:54–66.

21

List of Figures1
2
3

The study area: Karawang and Jakarta . . . . . . . . . . . . . . . . . . . . . . . . 23
A social value orientation (SVO) game developed by Murphy et al. (2011) . . . . . 24
Frequency distributions of individual and group discount factors across three societies 25

22

Figure 1: The study area: Karawang and Jakarta

23

You Receive

85

85

85

85

85

85

85

85

85
You

1
Other Receive

85

76

68

59

50

41

33

24

15

You Receive

85

87

89

91

93

94

96

98

100

Other

You

2
Other Receive

15

19

24

28

33

37

41

46

50

You Receive

50

54

59

63

68

72

76

81

85

Other

You

3
Other Receive

100

98

96

94

93

91

89

87

85

You Receive

50

54

59

63

68

72

76

81

85

4

Other

You
Other Receive

100

89

79

68

58

47

36

26

15

You Receive

100

94

88

81

75

69

63

56

50

Other

You

5
Other Receive

50

56

63

69

75

81

88

94

100

You Receive

100

98

96

94

93

91

89

87

85

6

Other

You
Other Receive

50

54

59

63

68

72

76

81

85

Other

Figure 2: A social value orientation (SVO) game developed by Murphy et al. (2011)24

(a) Frequency distributions of individual discount factors across the three societies of the fisheries, the agrarian and the urban
Agrarian

0

0

.5

1

30

Urban

0

10

20

Percent

10

20

30

Fisheries

0

.5

1

(b) Frequency distributions of group discount factors across the three societiesof the fisheries, the agrarian and the urban 2
Agrarian

0

0.00

0.50

1.00

60

Urban

0

20

40

Percent

20

40

60

Fisheries

0.00

0.50

1.00

Figure 3: Frequency distributions of individual and group discount factors across three societies

25

List of Tables
1
2
3

Definitions of the variables used in the analysis . . . . . . . . . . . . . . . . . . . 27
Summary statistics of field experiments and socioeconomic characteristics: 159
groups with 477 observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Marginal effects of individual discount factors on group discount factors in betafit
regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

26

Table 1: Definitions of the variables used in the analysis
Variables

Description

Individual discount factor

Percentage rate of discounting the future monetary value that will surely be received one month
later in such a way that the discounted future value equals the value of receiving 20 000 Rp today.
The individual discount factor that is the lowest among the three members in a group.
The individual discount factor that is the middle among the three members in a group.
The individual discount factor that is the highest among the three members in a group.

Lowest individual discount factor
Middle individual discount factor
Highest individual discount factor
Group discount factor

AgeHousehold income
Household members
Number of proself members

Percentage rate of discounting the future monetary value as a group of three people that will surely
be received one month later in such a way that the discounted future value equals the value of
receiving 20 000 Rp today.
Average age of members in a group.
Average household income of group members per month in 1 million rupiahs.
Average number of household members in a group.
Number of proself members in a group.

Society dummy variables (The reference = the fisheries)
Agrarian dummy
It takes one when the group of three people is in the agrarian soceity, otherwise zero.
Urban dummy
It takes one when the group of three people is in the urban society, otherwise zero.

27

Table 2: Summary statistics of field experiments and socioeconomic characteristics: 159 groups
with 477 observations
Fisheries

Agrarian

Urban

Overall

0.045 (0.233)
0.335
0.001
0.952

0.417 (0.452)
0.383
0.002
0.952

0.278 (0.371)
0.347
0.007
0.952

0.111 (0.353)
0.366
0.001
0.952

0.100 (0.337)
0.360
0.001
0.870

0.500 (0.499)
0.373
0.003
0.952

0.333 (0.399)
0.331
0.000
0.833

0.317 (0.414)
0.362
0.001
0.952

0.134 (0.040)
0.233
0.001
0.952

0.184 (0.091)
0.237
0.003
0.952

0.154 (0.067)
0.188
0.000
0.952

0.158 (0.067)
0.222
0.000
0.952

0.322 (0.100)
0.339
0.007
0.952

0.505 (0.500)
0.344
0.010
0.952

0.397 (0.352)
0.289
0.013
0.952

0.410 (0.333)
0.333
0.007
0.952

0.556 (0.598)
0.365
0.013
0.952

0.809 (0.909)
0.221
0.100
0.952

0.646 (0.727)
0.301
0.067
0.952

0.674 (0.833)
0.317
0.013
0.952

40.839 (39.000)
7.405
30.00059.00048.877 (48.333)
8.071
29.66768.00040.250 (40.500)
9.043
23.00056.66743.543 (43.667)
9.048
23.00068.0002.777 (2.500)
1.351
1.167
12.6673.771 (3.100)
2.357
1.733
7.967

4.253 (3.300)
2.773
1.100
18.333

3.579 (3.000)
2.289
1.100
18.3334.485 (4.333)
1.233
2.000
8.000

4.222 (4.000)
1.365
2.333
11.6674.875 (4.667)
0.942
3.333
7.667

4.508 (4.333)
1.226
2.000
11.667

1.945 (2.000)
0.897
0.000
3.000

1.719 (2.000)
0.940
0.000
3.000

1.500 (1.000)
0.923
0.000
3.000

1.730 (2.000)
0.932
0.000
3.000

Group discount factor
Median (Average)1
SD2
Min
Max
Individual discount factor
Median (Average)
SD
Min
Max
Lowest individual discount factor
Average (Median)3
SD
Min
Max
Middle individual discount factor
Average (Median)
SD
Min
Max
Highest individual discount factor
Average (Median)
SD
Min
Max
Age
Average (Median)
SD
Min
Max
Household income
Average (Median)
SD
Min
Max
Number of household members
Average (Median)
SD
Min
Max
Number of proself members
Average (Median)
SD
Min
Max

1 Average in parentheses for group and individual discount factors.
2 SD stands for standard deviation.
3 Median in parentheses for the variables except group and individual discount factors.

28

Table 3: Marginal effects of individual discount factors on group discount factors in betafit regressions
Model 3 (Fisheries)

Types of societies
Model 4 (Agrarian)

Model 5 (Urban)

0.208***(0.075)0.539***(0.086)0.052(0.068)0.277***(0.095)0.562***(0.131)−0.098(0.073)0.193(0.150)0.476***(0.137)0.249(0.153)0.208(0.183)0.382**(0.180)0.253(0.174)−0.001−0.002(0.002)(0.002)
Household income
0.004
0.000
(0.006)(0.006)
Number of household members−0.024*−0.025*(0.014)(0.014)
Number of proself members−0.025*−0.029(0.020)(0.019)Society dummy variables (The reference = the fishery)
Agrarian
0.096**(0.049)Urban
0.086*(0.046)0.000(0.003)0.014(0.013)−0.010(0.015)−0.038(0.026)−0.006(0.005)0.009(0.014)−0.055**(0.023)−0.057(0.036)−0.001(0.004)0.004(0.009)−0.011(0.025)0.032(0.034)

54

57

48

Variables
Individual discount factors
Lowest individual discount factor
Middle individual discount factor
Highest individual discount factor

Model 1Model 20.185**(0.074)0.542***(0.086)0.075(0.068)

Age

Observations

159

159

***significant at the 1 percent level, **significant at the 5 percent level and *significant at the 10 percent level29