Social Design Engineering Series

SDES-2020-4

Cooperation and cognition gaps for salinity: A field experiment of information provision

Khatun Mst Asma
School of Economics and Management, Kochi University of Technology

Shibly Shahrier
Research Institute for Humanity and Nature, Kyoto

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

1st June, 2020

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.

Cooperation and cognition gaps for salinity: A field
experiment of information provision
Khatun Mst Asma*,† Shibly Shahrier‡ Koji Kotani*,§,¶,||, **May 29, 2020

Abstract
Salinity along with climate change has devastating effects on people’s life, and thus, adaptation & mitigation strategies are needed to cope with its risks. Literature establishes an existence of cooperation & cognition gaps due to informational and residential differences thatmake the strategies’ implementation difficult. While little is known about how such gaps canbe reduced, we hypothesize that information provision about salinity through some lecture iseffective at reducing cooperation gaps among people by influencing their cognition in urban
and rural areas. We conduct a survey experiment, collecting data on donations, prosociality,
cognitive and sociodemographic factors of 900 subjects from one urban and two rural areasin Bangladesh. A climate donation game is instituted to measure cooperation among people where they are asked to donate to salinity risk reduction with or without the information
provision. The analysis shows that people who have prosocial orientation and perception ofhuman-induced climate change donate more than do those who do not, and urban people tend
to donate less than do rural people. However, urban people are identified to increase their donations by receiving the information provision much more than do rural people. These results
can be interpreted that urban people become more cooperative in response to the lecture than
do rural people, and cooperation gaps become smaller due to a change in cognition via information provision. Overall, the results demonstrate that informational and education programsfor salinity and climate change shall be effective and prioritized especially in urban areas toenhance cooperation for SDGs through affecting people’s cognition.

Key Words: Salinity; cooperation; cognition; information provision* School of Economics and Management, Kochi University of Technology†

Department of Agricultural Statistics, Bangladesh Agricultural University
Research Institute for Humanity and Nature, Kyoto§
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
1

Introduction

3

2

Methods
5
2.1 Survey areas and data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Experimental procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3

Results

12

4

Conclusion

20

5

Appendix21

Nomenclature
AD

Adaptation donation

BDT Taka, Bangladeshi currency
CD

Climate donation

MD

Mitigation donation

ME

Marginal effectMP

Marginal probability

NGOs Non-governmental organizations
SVO Social value orientationTD

Total donation

2

1

1

Introduction

2

Salinization of soil and water along with climate change in deltaic and coastal regions poses3

a significant threat for 600 million people in the world (Talukder et al., 2016, Jevrejeva et al.,4

2018, Rahman et al., 2019). Low-lying countries are suffering a lot from severe salinity problems,

5

which will be exacerbated in future according to climate change projections (Talukder et al., 2015,62016). In the light of continuously increasing salinity level in soil and water, there is an urgent

7

need to adopt adaptation & mitigation strategies for reduction of the associated risks. Numerous

8studies examine people’s cooperation or their cognition toward environmental problems such as

9climate change, finding an existence of the gaps in cooperation & cognition due to informational10

and residential differences that make the strategies’ implementation difficult (McCaffrey and Buhr,

11

2008, Shwom et al., 2008, Ortega-Egea et al., 2014, Islam et al., 2016). Therefore, it is crucial to12

understand cooperation & cognition gaps of environmental problems for designing the policies and13

achieving sustainable development goals. This paper addresses people’s cooperation, cognition

14

and the gaps by conducting field experiments.

15

Past research examines people’s cognition of, and cooperative behavior, toward environmental

16

problems. Several studies identify that knowledge and information about environmental issues

17

correlate significantly with proenvironmental activities (Kollmuss and Agyeman, 2002, Semenza

18

et al., 2008, Weber and Stern, 2011, Shoyama et al., 2013, Spence et al., 2014, Deryugina and19

Shurchkov, 2016, Goff et al., 2017). For example, Lorenzoni et al. (2007) state that the degree

20of people’s engagement with environmental activities relates to their cognition. Also, Fischer

21

and Charnley (2012) and Islam et al. (2016) establish that accurate perception or cognition about

22

climate change is positively related to people’s cooperative behaviors. Furthermore, Arbuckle et al.

23(2013) show that people who recognize the consequences of climate change are likely to support

24

climate change mitigation actions. Other researchers implement a survey on how information

25

provision about climate change affects people’s cooperation through eliciting the willingness to

26

pay for solving climatic problems (Shwom et al., 2008, Johnson et al., 2011, Yang et al., 2014,27

Abbas et al., 2016).
3

28

A group of previous studies examine the effect of residential differences in explaining envi-

29

ronmental concerns and people’s cooperation for solving environmental problems (Zahran et al.,30

2006, Shwom et al., 2008, Bel et al., 2014). For instance, Berk and Schulman (1995) and Berk and31

Fovell (1999) state that places and nature of climate in which people live influence their willing-32

ness to support various climate change strategies. In addition, Rajapaksa et al. (2018) show that33

rural, urban and slam people have different proenvironmental behaviors to protect the environment.

34

In another study, Huddart-Kennedy et al. (2009) show that rural and urban people have different

35

environmental concerns and rural people have a higher tendency to participate in environmentally36

supportive programs. Again, Shwom et al. (2008) examine the effect of residential differences37

on climate change policy support, finding that effectiveness of climate change policies largely

38

depends on people’s residential differences. Overall, residence difference is a prominent factor

39

in determining people’s environmental concerns and their cooperation to support environmental

40

protection measures.

41

A growing number of studies have been conducted on environmental and climate change prob-

42lems, analyzing the potential impact on people’s life and livelihood in relation to behaviors, cogni-

43tion and perceptions. However, the studies on how to resolve cooperation & cognition gaps among44

residential areas have been scarce and the issue remains unsolved. Given this gap in the literature,45

this study analyzes cooperation & cognition gaps by taking salinity problems along with climate

46

change in Bangladesh, seeking to provide a feasible method to reduce such gaps. Therefore, we

47design and institute a field experiment to examine the effect of information provision on people’s

48

cooperation for reducing salinity problems in urban and rural areas. The following research ques-49

tion is posed; “Does information provision about salinity through the lecture reduce cooperation50

gap for salinity problems by increasing people’s cognition in urban and rural areas?” Specifically,51

we conduct a survey experiment and collect data on donations to salinity problems, prosociality,

52

cognitive and sociodemographic factors from a total of 900 subjects in one urban area and two

53

rural areas at Bangladesh. The novel aspects of this study are (i) to consider cognitive & noncog-

54

nitive factors for analyzing people’s cooperation by conducting field experiments, (ii) to employ a455

climate donation game for measuring people’s cooperation where they are asked to actually donate

56

from their endowment for salinity risk reduction with or without the information provision and (iii)57

to empirically identify how people’s cooperation differs across areas and changes in response to58

the information provision for reducing salinity problems.

59

2

Methods60

2.1

Survey areas and data61

We conducted questionnaire surveys & field experiments in the districts of Dhaka, Jashore

62

and Satkhira in south-central and south-western Bangladesh (see figure 1). We consider Dhaka

63

as an urban area, while other two areas, Jashore and Satkhira are considered rural areas in this64

study. Dhaka is the capital of Bangladesh, which is one of the most populated cities in the world

65(Shahen et al., 2019). The rural areas, Jashore and Satkhira, are regarded as coastal areas of66

Bangladesh where land, ocean and atmosphere interact with each other. The villages in these areas67

are considered some of the least developed on the whole country and are highly vulnerable to68

cyclones, sea level rise, land erosion, storm surge and flooding hazards which have caused terrible

69

impacts on people’s living in these low lying coastal areas (Ahmad, 2019). In Jashore, the survey

70

was conducted in three upazilas (sub-districts) called Jashore sadar, Manirampur and Jhikargachha.

71

In Satkhira, one upazila namely Shyamnagar was selected for the survey (see figure 1).

72[Figure 1 about here.]73

Field surveys & experiments were conducted in both urban and rural areas and a total of 900

74

subjects from these areas was selected by using following random sampling procedures. We ap-

75

plied two different approaches for random sampling between urban and rural areas, because these76

areas have different geographic and sociodemographic characteristics. In urban area, we conducted

77

a randomization based on the proportion of each occupation to accurately represent the population.

78

At first, we calculated the percent of each occupation in the total population based on some previ5

79

ous surveys conducted by the government agencies (Bangladesh Bureau of Statistics, 2013, 2018).

80

Then we randomly selected the organizations according to occupational categories and the next81

step, we arbitrarily selected subjects from each of the organizations. We considered the subjects

82

from low-income to high-income occupation categories.

83

In rural areas, we obtained the list of all households in the selected areas with the help and84

support of local non-governmental organizations (NGOs). We randomly identified 600 households

85

by using the list and random number generator, among them 300 households from Jashore and 300

86

households from the Satkhira districts were selected. Randomization process was also followed

87

in selecting baseline and treatment group of information provision. In this process, households

88

were randomly divided into baseline group and treatment group within each urban and rural area.

89

We invited the household head (husband or wife in a household) from each of the households to90participate in our surveys & experiments. All subjects willingly participated in these surveys &91

experiments, providing written consent signed at the beginning. With the help of local people, the92

trained research staffs contacted each subject and conducted surveys & experiments with a pre-93

defined questionnaire. The first author was the chief administrator of these surveys & experiments.

94

Before administering the surveys & experiments, discussions were made with the local people and95

field observations were conducted.

96

2.2

Experimental setup

97

We implemented questionnaire surveys, climate donation (CD) game and social value orienta-

98

tion (SVO) game in each of urban and rural areas to collect necessary information about donations,99

prosociality, cognitive and sociodemographic factors.

100

Subjects’ sociodemographic information is collected, such as their age, gender, education,

101

household occupation and household income. Subjects are asked to provide their perception of102

the cause of climate change: human-induced, nature-induced climate change or others. During the103

survey, we introduce and explain human-induced and nature-induced climate change by using two

104

statements with colorful pictures/diagrams to know the subject’s perception of the cause of climate
6

105change. The first statement presents that human activities are responsible for climate change and106

another statement shows that climate change is nature-induced & it may occur even in the absence107

of human activities (see the appendix material for the perception of the cause of climate change).

108

After subjects understand the human-induced and nature-induced climate change, they are asked

109

to choose their answer among the four options: (1) “I agree with statement 1,” (2) “I agree with

110

statement 2,” (3) “Both statements are persuasive and I cannot choose,” (4) “I do not understand111

the statements and cannot choose.” We divide the answers into two categories: one category is

112

comprised of the subjects who answer that climate change is human-induced and the other cate-

113

gory is made up of the subjects who answer that climate change is nature-induced, cannot say and114

have no idea.

115

A climate donation (CD) game is instituted for measuring the degree of cooperation among116

people for reducing salinity problems. We design a new variant of a dictator game with two persons

117

where one person is considered a dictator and another one is a receiver. In this game, a dictator

118

decides how to divide a certain amount of money between herself and the receiver (see, e.g., Bolton

119

et al., 1998, Engel, 2011). For example, Hirose et al. (2020) apply a similar type of game to120

approximate people’s cooperation in climate change. However, the CD game is different from the121

typical dictator game in the following points: (i) Each subject is a dictator and she knows who is a122

receiver (i) A well-known organization is a receiver that works on different adaptation & mitigation

123

strategies of climate change.1

124

In the CD game, each subject is given 150 BDT as an initial endowment and asked to divide it

125

into two parts “for yourself” and “for the organization to reduce salinity problems.”2 The following

126

procedures are employed. First, three envelopes “original money,” “for yourself” and “for reducing

127

salinity problems” are prepared, and each subject is given the “original money” envelope that128

contains 150 BDT. Second, she is asked to split the 150 BDT into two envelopes, “for yourself”

129

or “for reducing salinity problems” as she wishes. Third, she is again asked to subdivide the1

Our donations along with the intentions are made to the organization “Adaptation Fund” that finances projects andprograms by aiming at supporting developing countries to fight against salinity and climate change problems.
2
The BDT is the Bangladeshi currency in taka (1 USD ≈ 85 BDT).

7

130

money in the “for reducing salinity problems” envelope into two parts “for adaptation” and “for131

mitigation” by writing and putting the memo into the envelope. Everything is recorded by an132

individual ID in the way that how each subject splits is considered privacy. At the end, each133

subject is allowed to take the “take it yourself” envelope to her home, while the “for reducing

134

salinity problems” envelopes are collected. We consider that how much one person is cooperative

135

for salinity problems is well proxied by the donation in the CD game.

136

In each of urban and rural areas, an experiment with the CD game is applied with and without

137

information provision to examine the effect of information provision on cooperation for reducing

138

salinity problems. With this experimental design, we seek to test the following hypothesis that139

information provision about salinity through the lecture is effective at reducing cooperation gaps

140

among people by influencing their cognition in rural and urban areas. In this experiment, there141

are the baseline group and treatment group that are randomly assigned for a session in each of

142

urban and rural areas. A subject in the treatment group receives a two-page summary & half an

143hour lecture about salinity, while a subject in the baseline group does not receive any summary and144

lecture regarding salinity. Salinity information is organized by referring to some books, reports

145

and articles (Mcleod et al., 2010, Habiba et al., 2013, Hasan et al., 2013, Mahmuduzzaman et al.,146

2014, Khanom, 2016, Alam et al., 2017). The summary sheet of salinity intrusion contains the147

definition, causes, impacts and measures (adaptation & mitigation strategies) of salinity (see the148

appendix material of summary lecture about salinity problems). All information is presented by149

the first author mainly to the treatment group. By comparing the baseline group with the treatment

150

group, we expect an increase in donations for salinity problems due to the effect of information

151

provision in urban and rural areas.

152

A concept of social value orientation (SVO) is applied to identify subjects’ social preferences

153

developed by Van Lange et al. (1997, 2007). This game comprises 9 choice tasks, each task has

154

three choice options for herself and the other. Each subject is asked to choose one option as155

the most preferred one among the three options, finally generating 9 choices of options in each156

orientation. In each task, three options are option (A): you get 500, and the other gets 100, option

8

157

(B): you get 500 and the other gets 500, and option (C): you get 560 and the other gets 330. A

158

choice of option (A) represents the competitive orientation, because the person who chooses option

159(A) maximizes the gap between the self point and the other’s one (500 − 100 = 400). The person

160

who chooses option (B) is the prosocial, because she maximizes the joint benefit (500 + 500 =

1611000). Finally, a choice of option (C) represents the individualistic person who maximizes her

162

own benefits without considering the other person (see, e.g., Van Lange et al., 2007, Shahen et al.,1632019). In the SVO game, four types of persons such as individualistic, prosocial, competitive and164

unidentified are classified based on 6 consistent choice of options or more in one orientation. If165

the subject does not make 6 consistent choices or more, then she is categorized as an unidentified

166

person. In this study, we make two groups from these four types of persons, group one is comprised

167

of only the prosocial person and another group is comprised of the other three types of SVO

168

persons.

169

2.3

Experimental procedures170

We hired local supporting staffs and trained research assistants for conducting questionnaire

171

surveys & experiments. In rural areas, the experiments were implemented at district primary or172secondary schools. In urban area, experiments were mainly conducted at universities and colleges.

173

Local supporting staffs and trained research assistants contacted and requested people to participate

174

in our surveys & experiments following the random sampling procedures specified in each type of175

rural and urban areas. We conducted questionnaire surveys & experiments on a daily basis in rural

176

areas whenever we successfully collected a sufficient number of subjects. On the other hand, in177

urban area, surveys & experiments were conducted on a weekly basis, because urban people are178

busy, and it takes time to arrange subjects in a fixed day and place. Our surveys & experiments are

179

conducted with real monetary payment for motivating subjects to provide their actual information

180

and seriously participate in each game.

181

After arriving at the locations, subjects gathered in one room and were instructed not to com-

182

municate with each other during the experiments. First, they are asked to provide their sociodemo9

183graphic information in questionnaires. Next, they are asked to read the statements which describe184“human-induced” and “nature-induced” climate change, providing their answer after confirming

185

their understanding about these explanations. Then, subjects were given experimental instructions

186of the CD game in their native language (Bengali). We confirm that subjects fully understand187

the experimental rules. After finishing the CD game, subjects participate in the SVO game. We

188

provide the experimental instructions of SVO game to the subjects and again confirm subjects’189

understanding. At the end of the SVO game, we randomly matched two subjects as a pair and190

calculate their final payoff from the SVO. Research assistants helped to distribute the question-

191

naires & other documents to the subjects and to calculate the associated payoffs. On average total

192

payment 300 BDT was paid to each of the subject, where 50 BDT for the fixed participation fee, on

193

an average 130 BDT for the CD game and 120 BDT for the SVO game, respectively. One session

194

in our experiment included a questionnaire survey, CD and SVO games, taking 50 to 60 minutes

195

in the control group (80 to 90 minutes in the treatment group) where 15 to 30 subjects were partic-

196

ipated. After finishing the surveys & experiments, we sent the total amount of donated money to197

the organization called “Adaptation Fund.”[Table 1 about here.]

198

199

2.4

Statistical analysis

200

The donation through the CD game is a good proxy to estimate how much people care about or

201

are cooperative for reducing salinity problems. This paper uses three types of donations such as (1)202

total donation (sum of adaptation & mitigation donations) (TD); (2) adaptation donation (AD) and203(3) mitigation donation (MD) as the dependent variables that measure the degree of cooperation for

204

reducing salinity problems. The questionnaire surveys & experiments are used to collect necessary

205

information which is divided into three factors (i) Cognitive & noncognitive factors: information

206

provision about salinity through the lecture, perception of the cause of climate change and social

207

value orientation (SVO) (ii) Residential factor: area (urban and rural) (iii) sociodemographic fac-

208

tors: age, gender, education, household occupation and household income. These three factors are
10

209

used as independent variables in this study (table 1 provides the definitions of all variables).

210

The mean, median, standard deviation, minimum and maximum values of the key variables

211

are calculated and interpreted. Then, we implement some statistical analyses such as chi-square212

and Mann-Whitney tests to identify some qualitative differences of the key variables by urban and213

rural areas. To quantitatively characterize the relationship between the donations and independent

214

variables, we apply tobit regression due to the fact that the data include a certain number of 0

215

donations. In the tobit regression, the donation for reducing salinity problems by subject i is216

denoted by yi and it is defined to be equal to the latent variable yi∗ when yi∗ > 0. Otherwise, yi = 0

217when yi∗ ≤ 0. The tobit regression model is expressed asyi∗ = β0 + β1 Ii + β2 Pi + β3 Si + β4 Ai + β5 Ii × Ai + β6 Zi + εi

(1)

218

where yi∗ is a latent variable of the donation satisfying the relation yi = max{0, yi∗ }; Ii , Pi , Si and219

Ai are dummy variables associated with information provision, perception of the cause of climate

220

change, social value orientation and areas, respectively. Finally, Zi is a vector of sociodemographic

221

factors such as age, gender, education, occupation of the household head and household income

222(see table 1 for the definition of the variables) and εi is a normally distributed error term. The

223

βj s for j = 0, 1, 2, 3, 4, 5 are the parameters associated with the intercept, Ii , Pi , Si , Ai and an

224

interaction term of Ii × Ai , while β6 is a vector of the parameters associated with Zi , respectively.

225

These parameters are estimated via the maximum likelihood methods to characterize yi with a226

specification of equation (1) in the tobit regression framework, enabling to calculate the marginal227

effect of an independent variable on the donations (Wooldridge, 2010, 2019). A series of these228

tobit regression models are estimated by taking the total donation (TD), adaptation donation (AD)229

and mitigation donation (MD) as dependent variables for robustness check.

230

The conceptual framework in figure 2 visualizes the relationships among cognitive, noncog-

231

nitive factors and people’s cooperation for reducing salinity problems along with some sociode-232

mographic factors in urban and rural areas (see figure 2). With the framework in mind, our focus

233

is on estimating the coefficients β1 and β5 associated with information dummy and its interaction
11

234

with area dummy, controlling for other key variables as described in figure 2.3 Recall our research

235

question in this research: “Does information provision about salinity through the lecture reduce236

cooperation gaps among people in rural and urban areas?” In this regard, the estimated coeffi-

237

cients of β1 and β5 in equation (1) are key parameters enabling us to answer the research question.

238

Specifically, the hypotheses of our research question are posed as H0 : β1 = 0 & H00 : β5 = 0

239

and the alternatives are H1 : β1 > 0 & H10 : β5 6= 0. It is expected that subjects’ cognition is240

influenced by the lecture about salinity problems so that subjects become more cooperative for241salinity problems, however, the change in cooperation may differ across urban and rural areas in

242

response to the information provision.
[Figure 2 about here.]

243

244

3

Results

245

Table 2 presents the summary statistics of the dependent variables with and without information

246

provision for each of urban and rural areas with the total sample. Without receiving information

247

regarding salinity, urban subjects donate on an average 8.95 BDT, 5.18 BDT and 3.77 BDT, while248

these averages are 20.29 BDT, 10.99 BDT and 9.30 BDT with receiving information for each of to-249

tal, adaptation and mitigation strategies of salinity, respectively. On the other hand, without receiv-

250

ing information, rural subjects donate on an average 19.79 BDT, 10.46 BDT and 9.39 BDT, while251

these averages are 22.27 BDT, 11.71 BDT and 10.56 BDT with receiving information for each of252

total, adaptation and mitigation strategies of salinity, respectively. Regardless of information pro-253

vision, rural subjects donate more than urban subjects for each of total, adaptation and mitigation254

strategies of salinity. Table 2 demonstrates that information provision about salinity through the255

lecture increases donation for each of total, adaptation and mitigation strategies in both urban and256

rural areas. However, the donation gaps between with and without receiving information for each3

In this framework, the coefficients β1 and β5 are considered to represent the effects of information provision as
well as its interaction with areas on people’s cooperation for reducing salinity problems, respectively, after the effectsof all other key variables have been netted out (Wooldridge, 2010, 2019).

12

257

of total, adaptation and mitigation strategies of salinity are higher in the urban area than rural ar-258

eas, potentially suggesting that urban subjects react more in response to the information provision259

about salinity than rural subjects.

260[Table 2 about here.]

261

Table 3 summarizes the basic statistics of the major independent variables for the urban, rural262

and overall subjects in the sample. Regarding the subject’s perception of the cause of climate

263

change, 77 % of urban subjects and 72 % of rural subjects perceive that climate change is caused

264

by human-induced factors. The major difference between the urban and rural areas is observed

265

in the SVO dummy variable. It indicates that 13 % of urban subjects are prosocial, while 44 % of266

rural subjects are prosocials. Specifically, 40 out of 300 urban subjects are prosocial, while 265

267

out of 600 rural subjects are prosocial. This implies that prosociality among people is significantly

268

higher in the rural areas than the urban area. In fact, a similar result is confirmed by several other269

studies, such as Shahrier et al. (2016, 2017), Timilsina et al. (2019), demonstrating that prosocial

270

people are more dominant in rural areas than in urban areas.

271

The mean age of the subjects does not vary between urban and rural areas, and the overall

272

average age is 40 years old (see table 3). With respect to gender, 25 % of urban subjects are273

female, while 56 % of rural subjects are female. Regarding education, urban subjects possess 12274

years of schooling as a median, while rural subjects usually receive 8 years of schooling as a275

median. According to occupation, all of urban subjects are engaged in non-agricultural activities

276

such as business, government & private job, or working as a day laborer. On the other hand, 40 % of277

rural subjects are engaged in agricultural activities such as rice cultivation, shrimp and crab culture.

278

The average monthly household income of urban subjects is almost three times (37 460.28 BDT)

279

higher than rural subjects (13 674.21 BDT). The standard deviation (SD) of urban households’

280monthly income is almost seven times as much as the SD of rural households’ monthly income.

281

This indicates that urban subjects’ earnings are significantly higher than rural ones, whereas rural282

subjects experience less income disparity and standard of living than urban ones. Overall, the283summary statistics of cognitive & noncognitive and sociodemographic factors indicate that urban
13

284

subjects have higher income, education and knowledge about climate change but they are less285

prosocial than rural subjects.

286[Table 3 about here.]287

We apply a Mann-Whitney test to check the distributional differences for each of total, adapta-

288tion and mitigation donations of salinity between urban and rural areas. A null hypothesis is that289

the distributions of the donations between urban and rural areas are the same. The results reject the290

null hypotheses, showing that there are distributional differences of TD (Z = −7.02, p < 0.01),

291

AD (Z = −5.17, p < 0.01) and MD (Z = −8.24, p < 0.01) between urban and rural areas. This292

can also be interpreted that cooperation for salinity problems differs between urban and rural sub-293

jects, being consistent with table 2. We also run a Mann-Whitney test to examine the relationship294

between income and areas with the null hypothesis that the income distributions between urban

295

and rural areas are the same. The result confirms that there is a difference in the income distribu-

296

tions between urban and rural areas (Z = 16.53, p < 0.01). Finally, the chi-square tests are applied297

to qualitatively examine whether the frequencies of the key explanatory variables are independent298

of areas. The following pairs of the variables are considered: (1) information provision vs areas,299(2) perception of the cause of climate change vs areas, (3) SVO vs areas, (4) gender vs areas, (5)300

education vs areas and (6) occupation vs areas. We find that cases (1), (3), (4), (5) and (6) reject301

the null hypotheses at 1 % significance level. Case (2) also rejects the null hypothesis at 10 %302

significance level. Overall, it appears that the key variables are qualitatively correlated with areas,303

and thus are controlled as independent variables in the regression analyses that follow.

304

Tobit models 1, 2 and 3 in table 4 present the regression results for total, adaptation and mit-

305

igation donations, respectively. Models 1-1, 2-1 & 3-1 report the estimated coefficients for inde-

306

pendent variables in the tobit regression. Models 1-2, 2-2 & 3-2 present the estimated marginal307

probability of each independent variable based on the estimated coefficients in each model, in-308

dicating a change in the likelihood for a subject to donate a strictly positive amount of money

309

when the independent variable increases by one unit, holding other factors fixed. Models 1-3, 2-3

310& 3-3 present the estimated marginal effect of each independent variable, indicating a change in
14

311

the donation when the independent variable increases by one unit, holding other factors fixed. In

312

model 1-1, it is identified that information provision, perception of climate change, SVO and area

313

dummies have positive effects at 1 % significance levels, while an the interaction term between in-

314

formation provision and area dummies as well as occupation of the household head have negative315

effects on total donation at 1 % and 5 % significance levels, respectively.

316

In model 2-1, information provision, perception of climate change, SVO and area dummies

317

exhibit positive effects at 1 %, 10 %, 1 % and 1 % significance levels, while an the interaction term

318

between information provision and area dummies and the gender dummy show negative effects on

319

adaptation donation at 1 % and 10 % significance level, respectively. In model 3-1, we also find320

that information provision, perception of climate change, SVO, area dummies as well as household

321

income have positive effects at 1 %, 10 %, 1 %, 1 % and 5 % significance levels, while an the322interaction term between information provision and area dummies as well as occupation of the323household head have negative effects on mitigation donation at the 1 % and 10 % significance324

level, respectively.

325

Some consistent tendencies are observed in models 1-1, 2-1 and 3-1 regarding information

326

provision, perception of climate change, prosociality and area dummy.4 On the basis of such esti-

327

mated coefficients in models 1-1, 2-1 and 3-1, we next report the associated marginal probability

328

in models 1-2, 2-2 and 3-2 as well as the associated marginal effect in models 1-3, 2-3 and 3-3

329

together. In what follows, we mainly focus on reporting the marginal probabilities and effects of

330

information provision, perception of climate change, prosociality, area dummy and the interac-

331tion term between information provision and area dummy, because they are identified to remain332

significant in all models.
4
The estimations also reveal some other significant independent variables. Regarding gender dummy, female isinterpreted to be significant with negative sign in the donation for adaptation, but not significant for total and mitigation(see table 4). This may be due to the fact that Bangladeshi females usually play a main role in managing household
issues. When they get extra money from some sources as they did in our experiment, they are more likely to think about
their household needs as the first priority than males. Regarding occupation dummy, agricultural households are foundto be significant with negative sign in the donations for total and mitigation. We conjecture that agricultural households
in Bangladesh have already taken various measures against salinity in their daily life, discouraging themselves todonate. Finally, household income is identified to be statistically significant at positive sign for mitigation in table 4.
However, the practical magnitudes are judged to be small.

15

333

Subjects who receive the treatment of information provision about salinity through the lec-

334

ture (in the treatment group) are more likely to donate by 7 %, 9 % & 7 % and donate 4.39 BDT,

335

2.67 BDT & 1.90 BDT more for total, adaptation and mitigation than subjects who do not (in the336baseline group), respectively (see the results associated with “information provision” row in MP

337

and ME of table 4). The results demonstrate that the treatment of information provision is quite ef-

338

fective at increasing the donations for salinity problems, being in line with some literature. Botzen

339

et al. (2009), Acquah and Onumah (2011) and Yang et al. (2014) report that information disclosure340

can increase people’s willingness to pay for climate change, especially when there exists the lack341

of knowledge. Borghans et al. (2008) and Chen et al. (2020) argue that processing new information342

is part of cognitive factors in human-decision processes. In this sense, we interpret that subjects’343

cognition or understanding is influenced by receiving the information, inducing subjects to be more344

cooperative for salinity problems through the channel of cognitive factors as described in figure 2.

345

Subjects with the perception of human-induced climate change are more likely to donate by346

7 %, 5 % & 6 % and donate 3.76 BDT, 1.56 BDT & 1.63 BDT more for total, adaptation and mitiga-

347

tion than subjects with other perceptions, respectively (see the results associated with “perception348

of climate change” row in MP and ME of table 4). This result is considered another confirmation349

of how cognitive factors are important for cooperative behaviors toward salinity problems. Kragt

350

et al. (2016) find a similar result that people who do not believe that climate change is caused351

by human actions have a lower willingness to pay for greenhouse gas emissions than those who

352

believe. Regarding the SVO dummy, prosocial subjects are more likely donate by 9 %, 9 % & 8 %353

and donate 5.56 BDT, 2.83 BDT & 2.27 BDT more for total, adaptation and mitigation than those354

with other SVOs, respectively (see the results associated with “prosocial” row in MP and ME of355

table 4). The result demonstrates that the noncognitive factor such as prosociality is also an impor-356

tant factor to determine people’s cooperation for reducing salinity problems as argued in Borghans

357

et al. (2008) and Chen et al. (2020). It is also considered consistent with some previous studies

358

that establish positive association between prosociality and cooperation to other issues (see, e.g.,359

Van Lange et al., 2007, Shahrier et al., 2017).

16

360

Rural subjects are more likely donate by 14 %, 13 % & 26 % and donate 7.68 BDT, 3.95 BDT

361& 6.66 BDT more for total, adaptation and mitigation than urban subjects, respectively (see the re-362

sults associated with “area” row in MP and ME of table 4). This result suggests that rural subjects

363

generally donate more than urban subjects towards salinity problems, reflecting that rural subjects

364

have experiences or observe the consequences of salinity problems. Therefore, they possess a

365

high motivation to improve these problems. Botzen et al. (2009) find that the probability of rural

366

inhabitants to undertake flood mitigation action is almost one third larger than urban inhabitants.

367

Huddart-Kennedy et al. (2009) also argue that urban and rural subjects have different types of con-368

cerns about environmental problems to influence their proenvironmental behaviors. Therefore, we369

believe that experiences and observations by rural subjects in salinity problems induce themselves

370

to become more cooperative than urban subjects.

371

The coefficients of the interactions term between information provision and area dummy are372

estimated to be consistently significant with negative sign for total, adaptation and mitigation (see373table 4). The results imply that rural subjects are less likely to increase the donations in response374

to the treatment of information provision than urban subjects. In other words, urban subjects are375

more likely to increase the donations when the information is provided through the lecture. To

376

quantitatively characterize the impact of information provision between urban and rural areas, the377

marginal probabilities and marginal effects of the interaction terms between information provision378

and area dummy for total, adaptation and mitigation are reported (see the “information × urban379(rural)” row in MP and ME of table 4).5

380

The MP and ME results of the interaction terms demonstrate that urban subjects who receive

381

the treatment of information provision about salinity through the lecture are more likely to donate382

by 21 %, 18 % & 17 % and donate 9.08 BDT, 4.60 BDT & 3.41 BDT more for total, adaptation and383

mitigation than urban subjects who do not, respectively, at 1 % significance levels. Regarding rural5

The marginal probability (MP) of the interaction term, “information × urban (rural),” can be interpreted as achange in the likelihood for a urban (or rural) subject to donate in the treatment group of information provision ascompared with the baseline group of no information provision. Likewise, the marginal effect (ME) of the interaction
term, “information × urban (rural),” can be interpreted as a change in the donations by a urban (or rural) subject in thetreatment group of information provision as compared with the baseline group of no information provision.

17

384

subjects, such significant results are not exhibited in any model. These results suggest that urban

385(rural) subjects highly (do not) react to the treatment in the way that urban subjects become more386

cooperative or increase the donations to salinity problems in response to the lecture, while rural

387

subjects do not change. In fact, the MP and ME results in table 4 are confirmed to be quite con-388

sistent with the observed tendency in the donations by rural and urban subjects between treatment389

and baseline groups in table 2.

390

Now, we are ready to provide the answers to our research question “Does information provision391

about salinity through the lecture reduce cooperation gaps among people in rural and urban areas?”392

The research question is expressed as the following hypotheses; the null hypotheses are H0 : β1 =393

0 & H00 : β5 = 0, while the alternatives are H1 : β1 > 0 & H10 : β5 6= 0 in the regression of394

equation (1). Overall, the regression results consistently reject the null hypotheses, supporting the395

alternatives with β̂1 > 0 and β̂5 < 0 with 1 % statistical significances. The estimation results396

can be interpreted that urban people donate less than rural people on average, while the treatment397

of information provision is generally effective irrespective of areas. However, urban people are398

identified to increase their donations by receiving the information provision much more than rural

399

people. It means that our answer to the research question is “yes,” i.e., cooperation gaps between

400

urban and rural areas are reduced by the treatment of information provision.

401[Table 4 about here.]402

Berenguer et al. (2005), Huddart-Kennedy et al. (2009), Shahrier et al. (2016, 2017) and Ra-

403

japaksa et al. (2018) report some clear differences between urban and rural people in many aspects,

404such as cognition, experiences, motivations and attitudes that influence their daily cooperative be-405

haviors to various social issues. In general, rural people are established to take more cooperative406

behaviors to environmental and public goods provision problems than urban people, even after con-407

trolling for prosocial value orientations (Shahrier and Kotani, 2016, Shahrier et al., 2016, Timilsina

408

et al., 2017, 2019). They argue that the differences in daily life experience and practice between

409

urban and rural people shape their culture to characterize such cooperative behaviors. It is also

410

reported that rural people have experienced and observed salinity problems as impacts of their life,
18

411

livelihoods, health and wellbeing (Vineis et al., 2011, Talukder et al., 2016, Paul and Jabed, 2018,

412

Asma and Kotani, 2019). Based on these findings in literature, we argue that rural subjects are

413

highly motivated to donate due to their experiences, practices and observations regarding salinity

414

problems as compared with urban subjects.

415

A key question is now “why do urban subjects increase their donations in response to the treat-416

ment of information provision as compared with rural subjects?” As described in our conceptual

417

framework of figure 2, it is well known that human behaviors are mainly characterized by the418

three factors, economic factors, noncognitive factors and cognitive factors (Borghans et al., 2008,

419

Chen et al., 2020). In particular, they claim that noncognitive factors are something impossible to420change in the short run by some interventions such as education or policies. In our experiment,421

social value orientation of prosociality is considered a noncognitive factor, while the perception422

of climate change and some sociodemographic variables are cognitive and economic factors, re-423

spectively. With these ideas in mind, the treatment of information provision is interpreted to affect424

cognitive factors in human-decision processes for salinity problems (figure 2).

425

Urban people in Bangladesh are usually considered to have few experience & observations

426

about salinity problems. That is, they are generally unfamiliar with salinity problems. However,427

they have more chances and better amenities to acquire cognitive abilities than rural people. For

428

instance, it is well known that education of schooling and availability of various opportunities in429

living environment are positively correlated with people’s cognitive abilities (see, e.g., Rinder-

430

mann, 2008, Ritchie and Tucker-Drob, 2018, Rogers et al., 2019). If this is the case, it is our431

conjecture that urban people have better cognitive abilities than rural people. Because understand-432

ing and processing new information is part of cognitive abilities, urban subjects in our experiment433

are considered to properly understand and react to the information provision, increasing their do-

434

nation when they are unfamiliar with salinity problems. On the other hand, we conjecture that rural

435

people do not react to the information provision, because they are familiar with salinity problems

436

which are all described in the lecture.

437

In the globalization process, urban areas will expand and grow, and near about 65-75 % of the

19

438

world population are predicted to concentrate on urban areas in Asia and Africa over the next 50

439

years (American Association for the Advancement of Science, 2016, Shahen et al., 2019). There-440

fore, urban areas will play a more important role in addressing environmental and climate change

441

problems through urban planning and policies than ever (Fujii et al., 2017). Environmental poli-442

cies are promoted by political leaders to protect natural environment and climate (Rosenzweig443

et al., 2010). To cope with such issues, it shall be very important to increase urban people’s en-

444

vironmental cognition by providing programs and educations, especially when urban people do445

not have enough experience and observations about environmental and climate change problems

446

in their daily life. Our results suggest that the priority of such programs and education to increase447

environmental cognition should be given to urban people whose life is more likely to be detached448from natural environment and climate. Systematically organizing such programs and education449

at national and global levels shall help reduce cognition and cooperation gaps between urban and450

rural areas, contributing to sustainable development goals (SDGs).

451

4

Conclusion

452

This paper has examined the effect of information provision on people’s cognition and coop-453

eration for reducing salinity problems in urban and rural areas. We hypothesize that information

454

provision about salinity through the lecture is effective at reducing cooperation gaps among peo-455

ple by influencing their cognition in rural and urban areas. To this end, we have implemented456

the climate donation game, social value orientation game and questionnaire surveys for collecting

457

data on donations to salinity problems, cognitive & noncognitive and sociodemographic factors

458

of 900 subjects from one urban area and two rural areas in Bangladesh. The results show that459

people who have prosocial orientation and perception of human-induced climate change donate

460

more than those who do not, and urban people tend to donate less than rural people. However, it

461

is identified that urban people increase their donations by receiving information provision much462

more than rural people. We argue that urban people become cooperative in response to the lecture,

20

463

because they are unfamiliar with salinity problems, but have cognitive abilities to understand the464

new information in the lecture.

465

We note some limitations to our research and possible directions of future research. This re-466

search focuses on salinity to examine cognition and cooperation gaps, because it is one of the most467

important problems in Bangladesh for achieving SDGs. To generalize the findings in our research

468

regarding the effectiveness of information provision, future research should apply to other types of469

environmental and poverty problems where there exists gaps between urban and rural areas. This

470

study neither addresses the long-run effect of information provision on people’s cooperative behav-

471

iors nor directly quantifies cognitive abilities due to several constraints such as time, subjects and472

budgets. Future research can be implemented to track the long-run effect of information provision473

by tracking a temporal change in people’s behaviors over time as well as to collect some cogni-

474

tive factors for further characterization. However, it is known that directly measuring cognitive475

abilities is a difficult tasks. Therefore, some caution shall be necessary to conduct such research.

476

These caveats notwithstanding, it is our belief that this research becomes an important first step in477

understanding cooperation and cognition between urban and rural areas, contributing to SDGs.

478

5

Appendix479

We provide (i) an instruction summary used for collecting the data associated with the per-480

ception of climate change and (ii) a brief summary of the lecture about salinity problems to

481

Bangladeshi people in the field as materials of the appendices.

21

References
Abbas, A., Amjath-Babu, T., Kächele, H., and Müller, K. (2016). Participatory adaptation toclimate extremes: An assessment of households’ willingness to contribute labor for flood risk
mitigation in Pakistan. Journal of water and climate change, 7:621–636.
Acquah, H. and Onumah, E. E. (2011). Farmers perception and adaptation to climate change: An
estimation of willingness to pay. Agris on-line papers in economics and informatics, 3:31–39.
Ahmad, H. (2019). Bangladesh coastal zone management status and future trends. Journal ofcoastal zone management, 22:1–6.
Alam, M. Z., Carpenter-Boggs, L., Mitra, S., Haque, M., Halsey, J., Rokonuzzaman, M., Saha, B.,
and Moniruzzaman, M. (2017). Effect of salinity intrusion on food crops, livestock, and fish
species at Kalapara coastal belt in Bangladesh. Journal of food quality, 2017:1–23.
American Association for the Advancement of Science (2016). Rise of the city. Science, 352:906–
907.
Arbuckle, J. G., Prokopy, L. S., Haigh, T., Hobbs, J., Knoot, T., Knutson, C., Loy, A., Mase, A. S.,
McGuire, J., and Morton, L. W. (2013). Climate change beliefs, concerns, and attitudes toward
adaptation and mitigation among farmers in the midwestern United States. Climatic change,
117:943–950.
Asma, K. M. and Kotani, K. (2019). Salinity and water-related disease risk in coastal Bangladesh.
Research Institute for Future Design, Kochi University of Technology, Working paper SDES2019-9.
Bangladesh Bureau of Statistics (2013). District statistics. Technical report, Bangladesh Bureauof Statistics.
Bangladesh Bureau of Statistics (2018). District statistics. Technical report, Bangladesh Bureauof Statistics.
Bel, G., Fageda, X., and Mur, M. (2014). Does cooperation reduce service delivery costs? Evidence from residential solid waste services. Journal of public administration research andtheory, 24:85–107.
Berenguer, J., Corraliza, J. A., and Martin, R. (2005). Rural-urban differences in environmental
concern, attitudes, and actions. European journal of psychological assessment, 21:128–138.
Berk, R. A. and Fovell, R. G. (1999). Public perceptions of climate change: A ‘willingness to pay’assessment. Climatic change, 41:413–446.
Berk, R. A. and Schulman, D. (1995). Public perceptions of global warming. Climatic change,
29:1–33.
Bolton, G. E., Katok, E., and Zwick, R. (1998). Dictator game giving: Rules of fairness versus
acts of kindness. International journal of game theory, 27:269–299.
22

Borghans, L., Duckworth, A. L., Heckman, J. J., and Ter Weel, B. (2008). The economics andpsychology of personality traits. Journal of human resources, 43:972–1059.
Botzen, W. J., Aerts, J. C., and van den Bergh, J. C. (2009). Willingness of homeowners to mitigate
climate risk through insurance. Ecological economics, 68:2265–2277.
Chen, Y., Feng, S., Heckman, J., and Kautz, T. (2020). Sensitivity of self-reported noncognitive
skills to survey administration conditions. Proceedings of the National Academy of Sciences of
the United States of America, 117:931–935.
Deryugina, T. and Shurchkov, O. (2016). The effect of information provision on public consensus
about climate change. PLoS ONE, 11:1–14.
Engel, C. (2011). Dictator games: A meta study. Experimental economics, 14:583–610.
Fischer, A. P. and Charnley, S. (2012). Risk and cooperation: Managing hazardous fuel in mixed
ownership landscapes. Environmental management, 49:1192–1207.
Fujii, H., Iwata, K., and Managi, S. (2017). How do urban characteristics affect climate change
mitigation policies? Journal of cleaner production, 168:271–278.
Goff, S. H., Waring, T. M., and Noblet, C. L. (2017). Does pricing nature reduce monetary support for conservation?: Evidence from donation behavior in an online experiment. Ecologicaleconomics, 141:119–126.
Habiba, U., Abedin, M. A., Shaw, R., and Hassan, A. W. R. (2013). Salinity-induced livelihood
stress in coastal region of Bangladesh. In Abedin, M. A., Habiba, U., and Shaw, R., editors,
Water insecurity: A social dilemma (Community, environment and disaster risk management),
volume 13, chapter 7, pages 139–165. Emerald Group Publishing Limited.
Hasan, M., Shamsuddin, M., and Hossain, A. (2013). Salinity status in groundwater: A study of
selected upazilas of southwestern coastal region in Bangladesh. Global science and technology
journal, 1:112–122.
Hirose, J., Kotani, K., and Nakagawa, Y. (2020). Is climate change induced by human? The
impact of the gap in perceptions on the cooperation. Research Institute for Future Design,
Kochi University of Technology, Working paper SDES-2020-2.
Huddart-Kennedy, E., Beckley, T. M., McFarlane, B. L., and Nadeau, S. (2009). Rural-urban
differences in environmental concern in Canada. Rural sociology, 74:309–329.
Islam, M., Kotani, K., and Managi, S. (2016). Climate perception and flood mitigation cooperation:
A Bangladesh case study. Economic analysis and policy, 49:117–133.
Jevrejeva, S., Jackson, L., Grinsted, A., Lincke, D., and Marzeion, B. (2018). Flood damage
costs under the sea level rise with warming of 1.5° C and 2° C. Environmental research letters,
13:1–11.
Johnson, D. M., Halvorsen, K. E., and Solomen, B. D. (2011). Upper midwestern US consumers
and ethanol: Knowledge, beliefs and consumption. Biomass and bioenergy, 35:1454–1464.
23

Khanom, T. (2016). Effect of salinity on food security in the context of interior coast of
Bangladesh. Ocean & coastal management, 130:205–212.
Kollmuss, A. and Agyeman, J. (2002). Mind the gap: Why do people act environmentally and what
are the barriers to pro-environmental behavior? Environmental education research, 8:239–260.
Kragt, M. E., Gibson, F., Maseyk, F., and Wilson, K. A. (2016). Public willingness to pay forcarbon farming and its co-benefits. Ecological economics, 126:125–131.
Lorenzoni, I., Nicholson-Cole, S., and Whitmarsh, L. (2007). Barriers perceived to engaging
with climate change among the UK public and their policy implications. Global environmemtal
change, 17:445–459.
Mahmuduzzaman, M., Ahmed, Z. U., Nuruzzaman, A., and Ahmed, F. R. S. (2014). Causes ofsalinity intrusion in coastal belt of Bangladesh. International journal of plant research, 4:8–13.
McCaffrey, M. S. and Buhr, S. M. (2008). Clarifying climate confusion: Addressing systemic holes, cognitive gaps, and misconceptions through climate literacy. Physical geography,
29(6):512–528.
Mcleod, E., Poulter, B., Hinkel, J., Reyes, E., and Salm, R. (2010). Sea-level rise impact models
and environmental conservation: A review of models and their applications. Ocean & coastalmanagement, 53:507–517.
Ortega-Egea, J. M., Garcı́a-de Frutos, N., and Antolı́n-López, R. (2014). Why do some people
do “more” to mitigate climate change than others? Exploring heterogeneity in psycho-social
associations. PLoS ONE, 9:1–17.
Paul, A. and Jabed, M. A. (2018). Salinity has made our life terrible: A qualitative investigationof human sufferings in the Chittaging coast. Oriental geographer, 59:1–18.
Rahman, M., Penny, G., Mondal, M., Zaman, M., Kryston, A., Salehin, M., Nahar, Q., Islam,M., Bolster, D., and Tank, J. (2019). Salinization in large river deltas: Drivers, impacts andsocio-hydrological feedbacks. Water security, 6:1–8.
Rajapaksa, D., Islam, M., and Managi, S. (2018). Pro-environmental behavior: The role of public
perception in infrastructure and the social factors for sustainable development. Sustainability,
10:1–14.
Rindermann, H. (2008). Relevance of education and intelligence at the national level for the
economic welfare of people. Intelligence, 36:127–142.
Ritchie, S. and Tucker-Drob, E. (2018). How much does education improve intelligence? A metaanalysis. Psychological science, 29:1358–1369.
Rogers, R., Ma, D. H., Nguyen, T., and Nguyen, N. A. (2019). Early childhood education andcognitive outcomes in adolescence: A longitudinal study from Vietnam. Education economics,
27:658–669.

24

Rosenzweig, C., Solecki, W., Hammer, S. A., and Mehrotra, S. (2010). Cities lead the way inclimate change action. Nature, 467:909–911.
Semenza, J. C., Hall, D. E., Wilson, D. J., Bontempo, B. D., Sailor, D. J., and George, L. A.
(2008). Public perception of climate change: Voluntary mitigation and barriers to behavior
change. American journal of preventive medicine, 35:479–487.
Shahen, M. E., Shahrier, S., and Kotani, K. (2019). Happiness, generativity and social preferences
in a developing country: A possibility of future design. Sustainability, 11:1–17.
Shahrier, S. and Kotani, K. (2016). Labor donation or money donation? Pro-sociality on prevention of natural disasters in a case of cyclone Aila, Bangladesh. Singapore economic review,
61:1640007–26.
Shahrier, S., Kotani, K., and Kakinaka, M. (2016). Social value orientation and capitalism insocieties. PLoS ONE, 11:1–19.
Shahrier, S., Kotani, K., and Saijo, T. (2017). Intergenerational sustainability dilemma and thedegree of capitalism in societies: A field experiment. Sustainability science, 12:957–967.
Shoyama, K., Managi, S., and Yamagata, Y. (2013). Public preferences for biodiversity conservation and climate change mitigation: A choice experiment using ecosystem services indicators.
Land use policy, 34:282–293.
Shwom, R., Dan, A., and Dietz, T. (2008). The effects of information and state of residence on
climate change policy preferences. Climatic change, 90:343–358.
Spence, A., Leygue, C., Bedwell, B., and O’Malley, C. (2014). Engaging with energy reduction:
Does a climate change frame have the potential for achieving broader sustainable behaviour?
Journal of environmental psychology, 38:17–28.
Talukder, M. R. R., Rutherford, S., and Chu, C. (2015). Salinization of drinking water in thecontext of climate change and sea level rise: A public health priority for coastal Bangladesh.
International journal of climate change: Impacts & responses, 8:1–32.
Talukder, M. R. R., Rutherford, S., Phung, D., Islam, M. Z., and Chu, C. (2016). The effect ofdrinking water salinity on blood pressure in young adults of coastal Bangladesh. Environmental
pollution, 214:248–254.
Timilsina, R., Kotani, K., and Kamijo, Y. (2017). Sustainability of common pool resources. PLoS
ONE, 12:e0170981.
Timilsina, R. R., Kotani, K., and Kamijo, Y. (2019). Generativity and social value orientation
between rural and urban societies in a developing country. Futures, 105:124–132.
Van Lange, P. A., Bekkers, R., Schuyt, T. N., and Vugt, M. V. (2007). From games to giving:Social value orientation predicts donations to noble causes. Basic and applied social psychology,
29:375–384.

25

Van Lange, P. A., De Bruin, E., Otten, W., and Joireman, J. A. (1997). Development of prosocial, individualistic, and competitive orientations: Theory and preliminary evidence. Journal of
personality ans social psychology, 73:733–746.
Vineis, P., Chan, Q., and Khan, A. (2011). Climate change impacts on water salinity and health.
Journal of empidomiology and global health, 1:5–10.
Weber, E. U. and Stern, P. C. (2011). Public understanding of climate change in the United States.
American psychologist, 66:315–328.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.
Wooldridge, J. M. (2019). Introductory econometrics. South-Western College Publishing.
Yang, J., Zou, L., Lin, T., Wu, Y., and Wang, H. (2014). Public willingness to pay for CO2
mitigation and the determinants under climate change: A case study of Suzhou, China. Journalof environmental management, 146:1–8.
Zahran, S., Brody, S. D., Grover, H., and Vedlitz, A. (2006). Climate change vulnerability andpolicy support. Society and natural resources, 19:771–789.

26

List of Figures
1
2

Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
A conceptual framework describing the relationships among cognitive, noncognitive, sociodemographic factors and people’s cooperation . . . . . . . . . . . . . . 29

27

Figure 1: Study area28

Figure 2: A conceptual framework describing the relationships among cognitive, noncognitive,
sociodemographic factors and people’s cooperation

29

List of Tables
1
2
3
4

Definitions of variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary statistics of the dependent variables . . . . . . . . . . . . . . . . . . . .
Summary statistics of the independent variables . . . . . . . . . . . . . . . . . . .
Regression coefficients and marginal effects of the independent variables in the
tobit regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

30

31
32
33
34

31

Years.
Male 0 and Female 1.
Years of schooling 0 to 14 (0 = No schooling & refused group2, 1 = Class one, 2 = Class two, 3 = Class three, 4 = Class four, 5 = Class five,
6 = Class six, 7 = Class seven, 8 = Class eight, 9 = Class nine, 10 = SSC/equivalent, 11 = Eleven class/equivalent, 12 = HSC/equivalent,
13 = Graduate/equivalent, 14 = Post graduate/equivalent).
Non-agriculture 0 and Agriculture 1.
Monthly income in BDT.

Urban 0 and Rural 1.

Takes the value 1 when the subject receives information about salinity through the lecture, otherwise 0.
Takes the value 1 when the subject chooses human-induced climate change,otherwise (Nature-induced, can not say and no idea) 0
Takes the value 1 when the subject is prosocial, otherwise (individualistic, competitive and unidentified) 0.

Donation for reducing salinity problem (sum of adaptation and mitigation donations in BDT).
Donation for salinity adaptation strategies in BDT.
Donation for salinity mitigation strategies in BDT.

Description

their educational level.

2 The subjects who do not provide their educational qualification is refused group. We merge refused group with no schooling because most of the uneducated subjects refused to provide

1 BDT stands for Bangladeshi currency in taka.

Occupation of the household head
Household income

Prosocial
Residential factor
Area
Sociodemographic factors
Age
Gender
Education

Total donation
Adaptation donation
Mitigation donation
Independent variables
Cognitive & noncognitive factors
Information provision
Perception of the cause of climate change

Dependent variables (BDT)1

Variables

Table 1: Definitions of variables

Table 2: Summary statistics of the dependent variables
Area
Urban
Without information With information
provision
provision

Rural
Without information With information
provision
provision

Overall

Total donation
Average (Median) 1
SD 2
Min
Max

8.95 (10.00)
10.23
0.00
50.00

20.29 (10.00)
22.73
0.00
150.0019.79(10.00)
24.08
0.00
150.0022.27 (10.00)
23.97
0.00
150.0018.89 (10.00)
22.54
0.00
150.005.18 (0.00)
7.63
0.00
50.00

10.99 (5.00)
14.31
0.00
50.00

10.46 (5.00)
17.18
0.00
150.0011.71 (10.00)
14.45
0.00
100.0010.08 (5.00)
14.71
0.00
150.003.77 (0.00)
7.13
0.00
50.00

9.30 (0.00)
18.21
0.00
150.009.39 (5.00)
11.56
0.00
100

10.56 (5.00)
16.43
0.00
140

8.82 (5.00)
14.25
0.00
150.00

150

150

300

300

900

Adaptation donation
Average (Median)
SD
Min
Max
Mitigation donation
Average (Median)
SD
Min
Max
Sample size
1 Median in parentheses.
2 SD stands for standard deviation.

32

Table 3: Summary statistics of the independent variables
Area

Overall

Urban

Rural

0.77 (1.00)
0.42
0.00
1.00

0.72 (1.00)
0.45
0.00
1.00

0.73 (1.00)
0.44
0.00
1.00

0.13 (0.00)
0.34
0.00
1.00

0.44 (0.00)
0.50
0.00
1.00

0.34 (0.00)
0.47
0.00
1.00

41.56 (40.00)
12.36
19.00
78.00

40.19 (40.00)
11.61
16.00
80.00

40.64 (40.00)
11.87
16.00
80.00

0.25 (0.00)
0.43
0.00
1.00

0.56 (1.00)
0.50
0.00
1.00

0.46 (0.00)
0.50
0.00
1.00

10.37 (12.00)
4.28
0.00
14.00

6.49 (8.00)
4.18
0.00
14.00

7.78 (9.00)
4.60
0.00
14.00

0.00 (0.00)
0.00
0.00
0.00

0.40 (0.00)
0.49
0.00
1.00

0.27 (0.00)
0.44
0.00
1.00

37460.28 (30000)
49638.01
3333.33
80000013674.21 (11966.67)
7837.26
1333.33
76166.67

21602.90 (14741.67)
31405.05
1333.33
80000

300

600

900

Perception of the cause of climate change
Average (Median) 1
SD 2
Min
Max
Prosocial
Average (Median)
SD
Min
Max
Age
Average (Median)
SD
Min
Max
GenderAverage (Median)
SD
Min
Max
Education
Average (Median)
SD
Min
Max
Occupation of the household head
Average (Median)
SD
Min
Max
Household income
Average (Median)
SD
Min
Max
Sample size
1 Median in parentheses.
2 SD stands for standard deviation.

33

34
−0.01
−1.87
−0.20
−4.57**
1.04

0.14***

16.33***−11.91***−0.0002−0.02−0.003−0.06**
0.01

0.21***
0.02

0.07***
0.07***
0.09***

13.68***
5.01***
7.14***

−0.01
−1.43
−0.15
−3.43**
0.80

9.08***
1.42

7.68***

4.39***
3.76***
5.56***−0.01−2.74*
−0.16
−2.05
−0.94

9.60***−6.36**

8.42***
2.49*
4.33***−0.0003−0.06*−0.003−0.04−0.02

0.18***
0.04

0.13***

0.09***
0.05*
0.09***−0.01−1.74*
−0.10
−1.29
−0.60

4.60***
1.40

3.95***

2.67***
1.56*
2.83***

Tobit regression 2 (AD)CoefficientMP
ME
Model 2-1
Model 2-2
Model 2-3

0.03
1.11
−0.18
−3.05*
2.67**

16.90***−8.11***

8.78***
2.98*
3.94***

0.001
0.02−0.004−0.06∗
0.05**

0.17***
0.10

0.26***

0.07**
0.06*
0.08***

0.02
0.62
−0.10
−1.67*
1.50**

3.41***
0.43

6.66***

1.90**
1.63*
2.27**

Tobit regression 3 (MD)
CoefficientMP
ME
Model 3-1
Model 3-2
Model 3-3***significant at the 1 percent level, **at the 5 percent level and *at the 10 percent level.
TD, AD and MD stand for total, adaptation and mitigation donations, respectively.
1 MP stands for a marginal probability to indicate a change in likelihood for a subject to donate (above zero) when an independent variable increases by one unit, holding other factors fixed.
2 ME stands for a marginal effect to indicate a change in the donation when one independent variable increases by one unit, holding other factors fixed.
3 r stands for base group.
4 The tobit regressions are computed with the natural logarithm of household monthly income.

Cognitive & noncognitive factors
Information provision (r3= Did not receive information)
Perception of climate change (r = Others)
Prosocial (r = Others)
Residential factor
Area (r = Urban)
Information provision × Area
Information provision × urban
Information provision × rural
Sociodemographic factors
Age
Gender (r = Male)
Education
Occupation of household head (r = Non-agriculture)
Household income4

Tobit regression 1 (TD)
Coefficient
MP1
ME2
Model 1-1
Model 1-2
Model 1-3

Table 4: Regression coefficients and marginal effects of the independent variables in the tobit regressions