Social Design Engineering Series

SDES-2017-26

Time preferences of food producers between fishermen
and farmers: Do ”cultivate and grow” matter?

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

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

22nd December, 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.

Time preferences of food producers between fishermen
and farmers: Do “cultivate and grow” matter?
Yayan Hernuryadin*,† Koji Kotani*,‡,§,¶,|| Tatsuyoshi Saijo**,‡, *,§December 20, 2017

Abstract
Resource scarcity and food security are two important issues due to overexploitation ofnatural resources with increasing population, market demand and mass production, whereas
fishermen and farmers have been two main occupations that produce food, utilizing natural
resources. The production mode between fishermen and farmers is distinct in that fishermen(farmers) harvest (cultivate, grow and harvest), leading to different daily life style and culture. It is hypothesized that such differences in daily practices and production mode between
fishermen and farmers characterize their time preferences or discounting behaviors. We haveconducted a discounting elicitation experiment for fishermen and farmers in Indonesia. The
statistical analysis shows that the average (median) discount factors of farmers are 0.48 (0.50),
respectively, whereas those of fishermen are 0.30 (0.10). The betafit and median regressions
demonstrate that the discount factors of farmers are 9.8 % and 26.8 % higher than those of fishermen, respectively, implying that fishermen are much more shortsighted than farmers. This
result appears to reflect that farmers wait or “cultivate and grow” six months for their harvest
because of which they save some portion of their income, while fishermen catch or “harvest”
fish every day and typically use up their daily income. Although same policies have been uniformly implemented on these two occupations, the government may need some devices andeducation on fishermen to nurture a culture of “cultivate and grow” fish stock for promoting
long-term conservation behaviors as well as sustaining fishery and their lives.

Key Words: Time preferences; field experiments; food security; resource sustainability; fishermen; farmers* 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** Research Institute for Humanity and Nature‡

1

Contents

1

1

Introduction

2

2

Methods and materials
2.1 Study areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Discounting elicitation experiment . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Experimental procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Empirical method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4
4
4
6
7

3

Results

94

Conclusion

14

5

Bibliography

16

List of Figures

19

List of Tables

22

1

Introduction

2

Resource scarcity and food security are becoming two important issues due to overexploitation

3of natural resources with increasing population, high market demand and mass production. For ex-4

ample, Indonesia is known to be the second largest marine capture producer, contributing to food5

security in the world (FAO Fisheries and Aquaculture Departement, 2016). However, some impor-6tant fish stock in Indonesia is reported to have been severely depleted due to the overexploitation.

7

One of the main reasons for the overexploitation is that fishermen tend to choose environmentally8

unfriendly fishing gears or advanced technologies to catch more fish for immediate profits without

9

considering fish stock sustainability. For resource sustainability and food security, it is important10

to understand possible myopic behaviors or time preferences of food producers, and therefore, this11

paper seeks to address these issues by conducting field experiments.

12

Past literature empirically analyzes individual time preferences in relation to sociodemographic

13

factors by questionnaire surveys or experiments. Reimers et al. (2009) establish the correlation be-

14

tween individual time preferences and age, income, education, demonstrating that younger people215with less income and education prefer smaller immediate rewards to larger-later ones. Similarly,

16

Tanaka et al. (2010) demonstrate that household sociodemographic characteristics influence indi-

17

vidual time preferences in Vietnam and, particularly, people in poor villages tend to be less patient.

18

Harrison et al. (2002) also show that sociodemographic characteristics affects people’s discounting

19

behaviors in Denmark, reporting that age, income and education are highly correlated with indi-

20

vidual discount rates. Overall, these works suggest that sociodemographic factors are important

21

determinants for shaping individual time preferences and discounting behaviors, irrespective of the22

countries and locations.

23

Another group of works focuses on estimating time preferences of food producers by conduct-

24

ing field surveys or experiments. Nguyen (2011) estimates and compares time preferences between

25

fishermen and other occupations in Vietnam, and concludes that fishermen are more patient than26

other occupations especially because they participate in resource stock conservation programs.

27

Johnson and Saunders (2014) compare time preferences between fishermen and divers. They find28that divers are more farsighted than fishermen because divers are required to be patient for securing

29

healthy ecosystem service and environment for their jobs. Akpalu (2008) and Fehr and Leibbrandt

30(2011) examine time preferences of fishermen in relation to fishing activities, and find that short-31

sighted fishermen tend to use small mesh size and to violate fishing regulations for catching more

32

fish. Casse and Nielsen (2005) and Duquette et al. (2011) focus on time preferences of farmers,

33

illustrating that farmers who adopt best management practices in earlier stages or never do slash-34

and-burn agriculture are more farsighted. These studies establish that time preferences of food35

producers well correlate with daily production practices.

36

Fishermen and farmers have been two main occupations that produce food through utilizing

37

natural resources, and it is crucial to understand their time preferences for food security and re-38

source sustainability. The production mode between fishermen and farmers is distinct in that fish-

39

ermen (farmers) harvest (cultivate, grow and harvest), and such differences in production mode

40

between fishermen and farmers may characterize their time preferences or discounting behaviors,

41

leading to different daily life style and culture. In Indonesia, farmers need to wait or “cultivate3

42

and grow” six months for their “harvest,” while fishermen catch or “harvest” fish every day and43

typically use up their daily income. This implies that farmers may require more patience than fish-

44

ermen for survival. Thus, it could be hypothesized that farmers are more farsighted than fishermen

45

in a case of Indonesia. To test this hypothesis, a discounting elicitation experiment is conducted46

for fishermen and farmers in the fields of Indonesia where the ethnicity, languages and sociodemo-

47graphic factors are relatively homogeneous except the production mode.

48

2

Methods and materials

49

2.1

Study areas

50

The questionnaire surveys and field experiments were conducted in 3 rural fishing villages and51

9 rural farming villages within Karawang district (figure 1). The district is located in the north part52

of Jawa Barat Province between 107°20 and 107°400 east longitude, and 5°560 and 6°340 south lati-

53

tude. The total land area is 1753.27 km2 . Karawang has fertile soil for agriculture. The population54

in 2015 is 2 273 579 with the density of 1094 km2 (BPS Statistics of Karawang Regency, 2016), and55

168 901 or 18.15 % of the inhabitants work in agriculture or fishery sectors (Karawang Regency

56

Government, 2015). Most fishermen in Karawang engage in day to day fishing in which they take57

3 or 4 hours to reach fishing grounds, whereas most farmers cultivate paddy and the production

58

needs 6 months from soil preparation to harvest. These villages are selected as fields for our study

59

because the ethnicity, languages and sociodemographic factors are relatively homogeneous except

60

the production mode between fishermen and farmers.
[Figure 1 about here.]

61

62

2.2

Discounting elicitation experiment

63

Previous studies on individual time preferences employ a multiple-price list procedure (Coller

64

and Williams, 1999, Harrison et al., 2002, Tanaka et al., 2010). This research employs a different465experimental procedure to elicit individual time preferences in the fields. The multiple-price list

66

procedure usually requires subjects to have a checking (bank) account or to come back to an exper-

67

imental site at a different date and time for receiving experimental rewards.1 However, the subjects

68

in our experiments are farmers and fishermen in Indonesia, and it is impossible for us to make such69

binding arrangements due to their living conditions, daily life styles and cultures. We also confirm

70that the multiple-price procedure in previous research cannot be well understood by subjects in71

the pilot experiments, because education of most fishermen and farmers in Karawang district is72highly limited. Therefore, we institute a new procedure of experiments to elicit individual time73

preferences from fishermen and farmers, called a discounting elicitation experiment.

74

A discounting elicitation experiment consists of an individual interview, asking farmers and75

fishermen whether they want to receive money today or wait for more money in the future. Since76

most subjects are not educated with limited literacy and cannot come back to experimental sites

77

in different time and dates, we design simple experiments in the way that our subjects can easily

78

follow and the monetary payments can be readily made on the spot. We start asking each subject a

79

simple question of whether he prefers options A or B consisting of

80

Option A: You receive 20000 Rp today.

81

Option B: You will receive 20000 + x Rp one month later.

82

Here, the value of x starts with x0 = 4000. When the subject prefers option A to B, the question

83

is updated by increasing the value of x by 4000 for option B, i.e., x = x1 = x0 + 1 · 4000 =

84

4000 + 4000 = 8000. Then, the updated question is again asked to the subject whether he prefers

85

option A to B. This process continues arbitrarily n times for x to be xn = 4000 + n · 4000 as far as

86

the subject prefers option A to B. When the subject prefers option B to A at an arbitrary nth trial,

87

we stop the procedure. Rather, we ask the subject a series of questions to identify the threshold88

value x between xn−1 and xn that makes him indifferent from receiving 20 000 Rp today. To this89

end, we keep asking the questions by gradually adjusting the value of x between xn−1 and xn until

90

each interviewer successfully elicits the threshold value x.
1

More than 70 % of our subjects do not have a bank account.

5

91

As of the payment for the discounting elicitation experiments, we paid the subject the reward on

92

20000
of successfully obtaining
the spot. For this, we prepare the lottery with the probability ρ = 20000+x

93

the value of 20000 + x Rp and the probability 1 − ρ of getting nothing. This lottery is made of94

yellow and red cards where the number of yellow cards is always 20 and the number of red cards is95

x. Since most subjects in our experiments are not familiar with the concept
adjusted to be 20 + 100096

of probabilities, we count the number of yellow and red cards in front of each subject, putting them97

into a bag. After preparing the lottery of a bag, we ask each subject whether he wants to pick the98

lottery for possibly getting 20000 + x Rp or receive 20 000 Rp for sure. Those who decide to pick99

the lottery get paid based on the outcome of the lottery on that spot, and those who decide not to100pick the lottery surely receive 20 000 Rp. In this way, we also implicitly elicit subjects’ attitudes

101

over uncertainty, risk averse, risk neutral or risk lover.

102

2.3

Experimental procedure103

We first contacted the village offices to get the consent for conducting our experiments. Among104

the 13 fishermen villages and 296 farming villages in Karawang, 3 fishing and 9 farming village

105

authorities gave us the permission to conduct experiments in their villages, respectively. We got

106

the list of households and residents from the village offices and based on the population of each107

village, we randomly selected the required number of households. Hereafter, we invited one earn-

108

ing member from each of the selected households to participate in our experiment by sending

109

them invitation letters. A total of 397 subjects participated in our experiment, among them 200

110

are fishermen and 197 are farmers. We conducted our experiments at village halls in each field.

111

To avoid possible biases from interactions among the participants, we confirmed that soon after112

participation, each respondent leaved the experimental hall.

113

In each experimental session, we provided written experimental instructions of our discounting

114

elicitation experiment to the respondents in Indonesia language, Bahasa. Additionally, an experi-

115

menter made oral presentation about the instructions, and confirmed participants’ understandings

116

about the experiment. Hereafter, we elicited respondents their individual discount factors and col6

117

lected their socioeconomic information through discounting elicitation experiments of individual

118

interviews. We implemented our experiments with monitory incentives to attract subjects to ex-

119

perimental sites and motivate them to attend the experiment seriously given the opportunity and120

transportation costs. The payoff calculation is announced to be based on the decisions and out-

121

comes of the discounting elicitation experiment, which is included in the experimental instructions

122and verbal presentations. After eliciting respondents’ discount factor in the discounting elicitation

123

experiments and socioeconomic information, subjects were paid based on the outcome of the ex-124

periments. The average experimental earning for the games including show-up fee was 65 Rp (≈125

5 USD), which is roughly equivalent to 1 day wage of a unskilled labor in Indonesia. Each session

126

in our experiment has 15 ∼ 20 participants, taking 2 ∼ 3 hours.

127

2.4

Empirical method

128

We apply betafit and median regressions to characterize the determinants of individual discount129

factors. The two models employ the same set of independent variables and are specified as follows:

di = β0 + β 1 xi + β2 zi + i

(1)

130

where subscript i represents each subject’s ID, di is an individual discount factor estimated in the131

discounting elicitation experiments, xi is a vector of independent variables of sociodemographic

132

information such as age, education, household income, children under 12, a number of household

133

members and family structure, zi is a dummy variable of occupations that takes 1 when subject i is134

a farmer, otherwise 0. The definitions of variables used in the regression analysis are summarized135

in table 1.

136

The betafit regression developed by Ferrari and Cribari-Neto (2004) is employed for our anal-137

ysis, since individual discount factors are bounded between 0 and 1. It assumes that individual

7

138

discount factors di s follow the beta distribution:f (di ; µ, φ) =Γ(φ)
dµφ−1 (1 − di )(1−µ)φ−1 ,ΓµφΓ((1 − µ)φ) idi ∈ (0, 1)

139

where E(di ) = µ, Var(di ) = µ(1−µ), φ is a precision parameter and φ − 1 is a dispersion param1+φ140eter. Different combinations of µ and φ can describe various types of beta densities including J

141

shapes, inverted J shapes and U shaped (Ferrari and Cribari-Neto, 2004). Since the distributions

142of individual discount factors estimated in our experiments are U shaped, we decide to use this143

betafit regression. The maximum likelihood methods are applied to identify unknown parameters144

β0 , β 1 , β2 with which the marginal effect of an independent variable on individual discount factors,

145

di , shall be derived and estimated.

146

For robustness check, we employ the quantile regression approach developed by Koenker and147

Basset (1978) and Koenker and Hallock (2001), following the specification of equation (1). The

148

quantile regression estimate one parameter vector for each quantile under a weak assumption that

149

each quantile of error terms is zero, i.e., Quantθ (i ) = 0 for θ ∈ (0, 1) where θ represents a150

quantile level. The quantile regression is based on the least absolute distance, and therefore, it

151

can efficiently estimate a set of the unknown parameters β0 , β 1 , β2 even under non-normal and152

skewed distributions with outliers. Since the distributions of individual discount factors in our

153

experiments are identified to be non-normal and bi-modal around 0 and 1 (see figure 2), the quantile

154

regression can be considered appropriate. We decide to run the median regression of θ = 0.5 for155

the comparison with the result in the betafit regression.

156[Table 1 about here.]157

All of the variables in table 1 are expected to be determinants of individual discount factors.

158

In particular, our main focus is on occupation dummy to capture how individual discount factors

159

are different between fishermen and farmers. Our hypothesis is proven to be true when the dummy

160

variable consistently exhibits statistical and economic significance to affect individual discount161

factors. We have tried many different specifications of the models in addition to the models we8162

present in this paper by including the interaction terms of two independent variables and different

163

sets of independent variables. However, our results do not differ significantly, and the results we164will present in what follows remain consistent.

165

3

Results

166

Table 2 presents the summary statistics of major independent variables and individual discount167

factors for fishermen, farmers and the total sample. The overall average age is 45 years old (see168

the “overall” column in table 2), however, once we look at the ages of fishermen and farmers,169

we find a clear difference between them such that farmers are 8 years older than fishermen on

170

an average. Definitely, fishing is more labor intensive than farming, and thus fishermen tend to171

retire earlier than farmers. Regarding education, farmers are more educated than fishermen since

172

farmers usually get the junior high school degree, whereas fishermen usually have only the primary173

education. One the other hand, when we look at the median of education, we do not find any

174

difference between fishermen and farmers. The difference in mean and median education can be

175

explained by the standard deviation (hereafter SD) of education. The SD of farmers’ education is176

1.13 which is twice as much as the SD of fishermen’s education. This finding implies that some177

farmers are highly educated, for instance, the university degree (see the “max” row under education178

in table 2). The gap in education is relatively low in fishing societies and the fishermen who are179

the most educated are senior high school graduates.

180[Table 2 about here.]

181

Farmers earn more than the fishermen in that farmers’ average monthly income is approxi-

182

mately 1 million rupiahs higher than fishermen. The income gap among the farmers is higher than

183

that of the fishermen since the SD of farmers’ monthly income is almost twice as much as the SD184

of fishermen’s income. This finding indicates that some farmers’ earning is significantly higher

185

than that of the majority, whereas fishermen experience less disparity in their income and standard

186

of living. In case of the number of children under 12 years of age per household, the number9

187of household members and family structure, farming society and fishing society are not different

188

from one another. In summary, farmers are relatively old and highly educated compared with the189

fishermen, and in addition, farmers’ income is higher and comes with a relatively larger income

190

gap than fishermen’s one.
[Figure 2 about here.]

191

192

The median (mean) discount factors of farmers and fishermen are 0.500 (0.482) and 0.100

193(0.302), respectively. This states a significant difference of estimated individual time preferences194

between farmers and fishermen, implying that fishermen discount their future more heavily than

195

farmers. Figure 2 shows the frequency distributions of individual discount factors between fisher-196

men and farmers. The vertical axis denotes the percentage of frequencies and the horizontal axis

197

denotes the discount factors. The highest spike in the frequency distribution for farmers occurs at

198

discount factors close to 1, while it does at 0 for fishermen. The finding in figure 2 is in line with the199summary statistics. On the basis of the summary statistics and figure 2, we run a Mann-Whitney200

test to examine whether the distribution of discount factors are the same between fishermen and201

farmers or not. The null hypothesis is that the distributions of discount factors are independent

202

of individual occupations between farming and fishing. Our test rejects the null hypothesis at 1 %203significance level. This result suggests that the distributions of farmers’ and fishermen’s discount204

factors differ from one another.

205[Table 3 about here.]206

The summary statistics, frequency distributions and the Mann-Whitney test suggest that in-207

dividual discount factors are different between farmers and fishermen. To further characterize

208

the relationship, we run betafit and median regressions. Columns 2 and 3 of table 3 present the209

marginal effects of the independent variables on individual discount factors in betafit and median210

regressions, respectively. The results show that age, education, household income and occupation

211

dummy are the significant predictors of discount factors in betafit regression, while occupation
10

212

dummy is the only significant determinant in the median regression. Although we have tried many213

different specifications of betafit and median regressions, we have obtained a consistent result that214

the occupation dummy of fishermen or farmers is the statistically and economically significant

215

predictor for individual discount factors.

216

The betafit regression shows that an additional year increase in age is associated with a 0.3 %217

rise in discount factors. Although this effect might be considered rather small, the discount factor

218

is estimated to increase by 4 % for one standard deviation rise (≈ 12 years) in age. The result219

reveals that young people tend to discount future more heavily than old people, which is consistent220

with the previous studies (Green et al., 1994, Harrison et al., 2002, Reimers et al., 2009). It is

221

claimed that as people become old, the level of self-control is known to increase and also, people

222

become more generative and think more about the future generations up until the age of 60 years

223(Green et al., 1994, McAdams et al., 1993).

224

The effect of education demonstrates that getting more education induces people to care more

225

about future since betafit regression estimates a 3 % rise in discount factor in relation to an increase226

in education category. Harrison et al. (2002) and Reimers et al. (2009) show that more educated227

people tend to be future-oriented or farsighted, and our result is in line with this finding. Similar to228

the effect of age, education should enhance self-control mechanisms and cognitive abilities (Bauer

229

and Chytilova, 2010). Moreover, education is known to train people to logically think, simulate

230

and organize the future, helping make a balance between present and future events.

231

Finally, we look at the effect of occupations on discount factors, more precisely, how farmers

232

or fishermen have different time preferences, controlling for other socioeconomic factors fixed.

233

The occupation dummy is identified to be the important predictor of individual discount factors in234

both betafit and median regressions with 1 % statistical and economic significance. Betafit regres-235

sion estimation tells that farmers’ discount factors are 9.5 % higher than fishermen, while median236

regression estimates a 26.8 % higher discount factors for farmers than those of the fishermen. Con-237

sistent with our summary statistics, both the regression estimations confirm that fishermen tend to

11

238

discount their future more heavily than farmers.2

239

Several past studies demonstrate that the occupations and the associated life practices are im-

240

portant determinants to characterize individual behaviors and preferences such as social prefer-241

ences and competitiveness (Hoekstra, 1985, Casse and Nielsen, 2005, Henrich et al., 2005, Ak-242

palu, 2008, Duquette et al., 2011, Fehr and Leibbrandt, 2011, Nguyen, 2011, Leibbrandt et al.,243

2013, Johnson and Saunders, 2014). Given the literature, our study could be considered an ad-

244

ditional evidence with respect to the effect of occupations on “time preferences” for the specific245

case of the food producers, fishermen and farmers, in Indonesia. Now, a key question is why occu-

246

pations of farmers and fishermen significantly affect their time preferences to be different? Here,247

we argue that the difference comes from their daily life practices and production mode, that are

248

distinct between farmers and fishermen. In other words, farmers “cultivate, grow and harvest” the249

crops, while fishermen only “harvest” fish in their food production. The differences characterize

250

their discounting behaviors.

251

Fishermen in Karawang typically fish every day and can receive daily income by selling the252

fishes in their local auction markets. They easily expect that they can go for fishing tomorrow or

253

the day after tomorrow, earning money for their living whenever their money is short. Because of254such income generating practices of daily fishing, they are neither accustomed to, nor motivated255

for saving. In our experiments, we have confirmed that fishermen almost use up their total daily

256

earning on the same day. Therefore, their saving is identified to be very low from our data and indi-257

vidual questionnaires (the median of saving in fishermen sample is zero). In addition, in our survey,258

we asked the fishermen whether they believe that fish stocks are exhaustible or not. Surprisingly,

259

80.5 % of the fishermen respond that the “God” always provides fish in the sea, and therefore, fish260

stock is inexhaustible.3 In summary, the daily fishing of “earn and burn practices” and the belief
2This finding looks inconsistent with Nguyen (2011) since they show that, in Vietnam, fishermen are more patientthan people with other occupations. However, one key difference between our study and Nguyen (2011) is that thefishermen in Vietnam participated in resource conservation programs, such as permanent area closures to recover
fishery, while Indonesian fishermen in our study do not experience any conservation or training program for resource
sustainability. We argue that the conservation program induces Vietnamese fishermen to become more patient thanothers.
3
Fishermen in Karawang have a festival called “Nadran Laut.” This festival is an acculturation tradition of Islamicand Hindu that has been inherited over hundreds years generations by generations. The objective of this festival is to

12

261

of “inexhaustible” fish stock remain the part of their culture in fishing villages, inducing fishermen262

to be shortsighted.

263

Farmers in Karawang need to cultivate and grow paddy or wait six months for harvesting,264

because of which they are motivated to save, invest and accumulate for their survival. In our study

265

areas, farmers cultivate arable land for their livelihood which is basically their own property, and266

they maintain or even accumulate their capital and wealth through saving gold as a preparation for

267

uncertain future. Natural disasters and calamities are main concerns of uncertainty for farmers,

268

because they destroy whole agricultural productions in the regions. However, fishermen can still269

go fishing within a few days even after natural disasters. This is one example that come from270

differences in the production mode between fishermen and farmers that affect the motivations of271

how to prepare for future. The data also shows that farmers’ saving is much higher than fishermen’s

272

one, which is consistent with the result of individual time preferences. We conjecture that farmers’

273

daily practices and the mode of production for cultivating, growing and harvesting crops require274

farmers to wait, save and invest for future, nurturing them to be farsighted as part of cultures.

275

The motivations of maintaining and transferring capital and wealth to the next generations

276

could be different between farmers and fishermen due to their production mode. In farming vil-277

lages, maintaining and transferring capital and wealth, such as land and irrigation, from one gen-278

eration to the subsequent generations is very important as a common practice for their survival,279

because these two factors are crucial in cultivating and growing crops. On the other hand, in280

fishing villages, fishermen do not have specific capital and wealth to maintain and transfer to the281

subsequent generations except their hands-on experiences and skills for harvesting as part of hu-

282

man capital, because fishing is mainly labor intensive.4 Therefore, the differences in motivations283

to maintain and transfer their capital and wealth to the future generations may be attributed to the284

production mode, explaining why farmers are more farsighted than fishermen.

285

Past studies demonstrates that daily practices, history and environment as part of culture affectexpress gratitude to God for providing fish in the last year, praying to catch more fish in the current and future withsafety in the sea. In this festival, fishermen slaughter a buffalo and they go to sea together with bringing the buffalo’s
head, and then throw it to the sea as gratitude to God.
4
Fishing vessels and gears belong to the owners, not to individual fishermen in Indonesia.

13

286

human behaviors and preferences (Boyd and Richerson, 1985, Schultz et al., 1997, Henrich et al.,287

2005, Tomasello et al., 2005, Dawkins, 2006, Gilbert and Wilson, 2007, Richerson and Boyd, 2008,

288

Wilson et al., 2009, O’Brien et al., 2010, Moya et al., 2015, Gerlach et al., 2014, Szpunara et al.,289

2014, Shahrier et al., 2016, Timilsina et al., 2017, Shahrier et al., 2017). We demonstrate that daily

290

life practices and production modes related to the occupation of farmers and fishermen characterize

291

their individual time preferences. At the same time, this research brings some new questions and292

hopes for people’s behaviors over the future. Some effective policies or new education systems

293

such as training/conservation programs or institutions to change people’s cultures to be patient

294

shall be essential. For this, “cultivate and grow” shall matter based on our findings. In fishery, it295

is necessary for fishermen to be farsighted toward sustainable fishery. To this end, nurturing a new

296

culture of fishermen in Indonesia for “cultivating and growing fish” through some public education297

or programs shall be an important first step.

298

4Conclusion

299

The past studies show how daily practices, history and environment as part of culture influence300

human behaviors and preferences. Fishermen and farmers as main food producers play important

301

roles for food security and resource sustainability. The production mode between fishermen and302

farmers is distinct in that fishermen (farmers) harvest (cultivate, grow and harvest), leading to303

different daily life style and culture. It is hypothesized that such differences in daily practices and304

production mode between fishermen and farmers characterize their time preferences or discounting

305

behaviors. To examine this hypothesis, we have conducted a discounting elicitation experiment for

306

fishermen and farmers in the fields of Indonesia.

307

The result of this study reveals that fishermen are much more shortsighted than farmers. The

308

results of betafit and median regressions illustrate that the discount factors of farmers are 9.8 %309(26.8 %) higher than that of the fishermen, controlling for other sociodemographic factors. We

310

argue that this difference comes from daily life practices and production mode associated with

14

311

their occupations of farmers and fishermen. The results well reflect the fact that farmers cultivate

312

and grow paddy or wait six months for their harvest and they tend to save some portion of the313

income for the future, while fishermen catch fish every day and use up their daily income. Although314

same policies have been uniformly implemented on these two occupations, the government may

315

need some new devices and education on fishermen to nurture a culture of “cultivate and grow”

316

fish stock and to be more farsighted for promoting long-term conservation behaviors in sustaining

317

fishery and their lives.

318

We note some limitations of our research and the direction of future research. This research319

was conducted in small-scale fisheries under open access and top-down management system in

320

Indonesia. Fishermen in the experiments have never participated or gotten involved in any type of321

resource conservation programs or management so that they only catch fish every day with their322

belief that the stocks are inexhaustible. However, in reality, fishermen in other countries experi-

323

ence participating in such programs or engage in different types of fishing such as more large-scale

324

fisheries or industrialized one, potentially having different ways of thinking, behaviors and prefer-

325

ences. Therefore, our results should be understood with some caution. Future research should be

326

able to focus more on other types of fishery societies or the effects of resource conservation and327

training programs of “cultivate and grow” on individual time preferences of fishermen as well as

328

people with other occupations, considering various production modes.

15

5

Bibliography

Akpalu, W. (2008). Fishing regulations, individual discount rate, and fishermen bahaviour in adeveloping country fishery. Environment and development economics, 13:591–606.
Bauer, M. and Chytilova, J. (2010). The impact of education on subjective discount rate in gandanvillages. Economic development and cultural change, 58:643–669.
Boyd, R. and Richerson, P. J. (1985). Culture and the evolutionary process. University of Chicago
press.
BPS Statistics of Karawang Regency (2016). Karawang regency in figures 2016. Technical report,
BPS Statistics of Karawang Regency.
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.
Coller, M. and Williams, M. B. (1999). Elicting individual discount rates. Experimental economics,
2:107–127.
Dawkins, R. (2006). The selfish gene. Oxford university press.
Duquette, E., Higgins, N., and Horowitz, J. (2011). Farmer discount rate: Experimental evidence.
American journal of agricultural economics, 94:451–456.
FAO Fisheries and Aquaculture Departement (2016). The state of world fisheries and aquaculture
2016. Technical report, Food Agriculture Organization of the United Nation (FAO).
Fehr, E. and Leibbrandt, A. (2011). A field study on cooperativeness and impatience in the tragedyof the commons. Journal of public economics, 95:1144–1155.
Ferrari, S. L. P. and Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions.
Journal of applied statistics, 31:799–815.
Gerlach, K. D., Spreng, R. N., Madore, K. P., and Schacter, D. L. (2014). Future planning: Default
network activity couples with frontoparietal control network and reward-processing regions during process and outcome simulations. Social cognitive and affective neuroscience, 9:1942–1951.
Gilbert, D. T. and Wilson, T. D. (2007). Prospection: Experiencing the future. Science, 317:1351–
1354.
Green, L., Fry, A. F., and Myerson, J. (1994). Discounting of delayed rewards: A life-span comparison. Psychological science, 5:33–36.
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.
Henrich, J., R., Bowles, S., Camerer, C. F., Fehr, E., Gintis, H., McElreath, R., Alvard, M., Barr, A.,
Ensminger, J., Heinrich, N. S., Hill, K., Gil-White, F., Gurven, M., Marlowe, F. W., Patton, J. Q.,
and Tracer, D. (2005). “Economic man” in cross-cultural perspective: Behavioral experiments
in 15 small-scale soceities. Behavioral and brain sciences, 28:795–855.
16

Hoekstra, D. (1985). Choosing the discount rate for analysing agroforestry systems/technologies
from a private economic viewpoint. Forest ecology and management, 10:177–183.
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.
Koenker, R. and Basset, G. (1978). Regression quantiles. Econometrica, 46:33–50.
Koenker, R. and Hallock, K. F. (2001). Quantile regression. Journal of economic perspectives,
15(4):143–156.
Leibbrandt, A., Gneezy, U., and List, J. A. (2013). Rise and fall of competitiveness in individualistic and collectivistic societies. Proceedings of the National Academy of Sciences of the UnitedStates of America, 110:9305–9308.
McAdams, D. P., St. Aubin, E., and Logan, R. L. (1993). Generativitiy among young, midle, andolder adults. Psychology and aging, 8:221–230.
Moya, C., Boyd, R., and Henrich, J. (2015). Reasoning about cultural and genetic transmission:Developmental and cross-cultural evidence from Peru, Fiji, and the United States on how people
make inferences about trait transmission. Topics in cognitive science, 7:595–610.
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.
O’Brien, M. J., Lyman, R. L., Mesoudi, A., and VanPool, T. L. (2010). Cultural traits as units ofanalysis. Philosophical transactions of the royal society B, 365:3797–3806.
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.
Richerson, P. J. and Boyd, R. (2008). Not by genes alone: How culture transformed human
evolution. University of Chicago press.
Schultz, W., Dayan, P., and Montague, P. R. (1997). A neural substrate of prediction and reward.
Science, 275:1593–1599.
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.
Szpunara, K. K., Spreng, R. N., and Schactera, D. L. (2014). A taxonomy of prospection: Introducing an organizational framework for futureoriented cognition. Proceedings of the National
Academy of Sciences of the United States of America, 111:18414–18421.
17

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.
Tomasello, M., Carpenter, M., Call, J., Behne, T., and Moll, H. (2005). Understanding and sharing
intentions: The origins of cultural cognition. Behavioral and brain sciences, 28:691–735.
Wilson, D. S., O’Brien, D. T., and Sesma, A. (2009). Human prosociality from an evolutionary
perspective: Variation and correlations at a city-wide scale. Evolution and human behavior,
30:190–200.

18

List of Figures
1
2

The study area: Karawang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Frequency distributions of discount factors between fishermen and farmers . . . . . 21

19

Figure 1: The study area: Karawang

20

Fishermen

Farmers

30

33.5

20

Percent

21
19.5
18

13

10

11.5

9

5.5
4

6

6

3.5 3.5
2.5

2.5
1.5

1

2.5
1

1.5

1

1.5 1.5

2

1.5

3

2.5

2
1

1

2.5 2.5 2.5
0.5

2.5
1.5

0

1

3.5

0

.5

1

0

.5

1

Discount Factor

Figure 2: Frequency distributions of discount factors between fishermen and farmers

21

List of Tables
1
2

3

Description of socioeconomic characteristics . . . . . . . . . . . . . . . . . . . . 23
Summary statistics of field experiment and socioeconomic characteristic between
fishermen and farmer, 397 observations (fishermen 200 observations, farmers 197
observations) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Marginal effects of betafit regression and median regression for Discount Factor
between fishermen and farmers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

22

Table 1: Description of socioeconomic characteristics
Variables

Description

Discount Factor

Discounting value, the amount of money today divided by the amount of money 1 month later(20,000 rupiahs)
Farmers (1) and Fishermen (0)Years
Categorical variable (Illiterate (0), Elementary Level (1), Junior High School Level (2), SeniorHigh School Level (3) College Degree (4), University Degree (5).
Household income per month in 1 million rupiahs
Number of children under 12 years of age in the household
Number of household member
Single family structure are coded as 1, otherwise (joint family) as 0

Occupation Dummy
Age
Education
Household Income
Children under 12
Member of Household
Family structure23

Table 2: Summary statistics of field experiment and socioeconomic characteristic between fishermen and farmer, 397 observations (fishermen 200 observations, farmers 197 observations)
Work setting
Fishermen
Farmers

OverallAge
Average (Median) 1
SD 2
Min
Max

40.955 (40.000)
12.09918.00072.00048.741 (47.000)
10.87123.00080.00044.818 (45.000)
12.13518.00080.0001.025 (1.000)
0.535
0.000
3.000

1.584 (1.000)
1.133
0.000
5.000

1.302 (1.000)
0.926
0.000
5.000

2.852 (2.000)
2.434
0.500
20.0003.953 (3.000)
4.164
0.700
30.0003.398 (2.600)
3.444
0.500
30.0001.055 (1.00)
1.152
0.000
7.000

0.659 (1.00)
0.827
0.000
5.000

0.873 (1.00)
1.062
0.000
7.000

4.535 (4.00)
1.954
1.000
12.0004.264 (4.00)
1.933
1.000
18.0004.418 (4.00)
2.060
1.000
18.0000.609 (1.00)
0.489
0.000
1.000

0.698 (1.00)
0.459
0.000
1.000

0.654 (1.00)
0.475
0.000
1.000

0.302 (0.100)
0.344
0.001
0.952

0.482 (0.500)
0.374
0.003
0.952

0.391 (0.206)
0.370
0.001
0.952

Education
Average (Median)
SD
Min
Max
Household income
Average (Median)
SD
Min
Max
Children under 12
Average (Median)
SD
Min
Max
Number of household members
Average (Median)
SD
Min
Max
Family structure
Average (Median)
SD
Min
Max
Discount factor
Average (Median)
SD
Min
Max

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

24

Table 3: Marginal effects of betafit regression and median regression for Discount Factor between
fishermen and farmers
Variable
Age
Education
Household income
Children under 12
Number of household members
Family structure (base group=joint family)
Occupation dummy (base group=fishermen)

Betafit regression

Median regression

0.003**(0.001)0.032*(0.019)−0.007*(0.004)−0.010(0.015)−0.009(0.008)−0.017(0.031)
0.098***(0.034)0.004(0.004)0.022(0.047)−0.006(0.011)−0.008(0.038)−0.011(0.019)−0.029(0.078)0.268***(0.085)***significant at the 1 percent level, **significant at the 5 percent level and *significant
at the 10 percent level25