Social Design Engineering Series

SDES-2019-8

Happiness, generativity and social preferences in a developing country

Mostafa Elsayedshahen
School of Economics and Management, Kochi University of Technology

Shibly Shahrier
BRAC University

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

26th July, 2019

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.

Happiness, generativity and social preferences in adeveloping country
Mostafa Elsayedshahen* Shibly Shahrier† Koji Kotani*,‡,§,¶,||
July 25, 2019

Abstract
Happiness, generativity and social preferences are pivotal factors for betterment and sustainability of societies. However, little is known about the relationship among happiness,
generativity and social preferences along with sociodemographic factors within a single analytical framework. We hypothesize that generativity and social preferences are the determinants
for happiness, and conduct a survey experiment collecting the data of five subjective happinessscales, generativity, social value orientation and sociodemographic variables in one urban area
(Dhaka) and two rural areas (Bogra and Gaibandha), Bangladesh. With the data, we empiricallycharacterize the determinants of subjective happiness with a focus on generativity and social
value orientation, controlling for sociodemographic factors. The statistical analysis consistently
shows positive association between subjective happiness and generativity, irrespective of thetypes of happiness scales, while social value orientation does not exhibit any significance. Rural
residents have lower peer relative happiness than urban ones, and household income has positive
relationship with general subjective happiness, leading each of these factors to be significant inoverall subjective happiness. In summary, our results suggest that generativity and income are
the main determinants, and economic growth with further urbanization, which is expected tooccur in future, can negatively affect people’s happiness if it brings a decrease in generativity.

Key Words: Happiness; generativity; social value orientation; sociodemographic factors;
developing countries* School of Economics and Management, Kochi University of Technology†

BRAC University
Research Institute for Future Design, Kochi University of Technology§
Urban Institute, Kyusyu University¶
College of Business, Rikkyo University||
Corresponding author, E-mail: kojikotani757@gmail.com‡

1

Contents
Nomenclature

2

1

Introduction

2

2

Methods and materials

5

3

Results

94

Conclusion

14

5

Bibliography

17

Nomenclature
AH

Absolute self-rated happiness

BDT Bangladeshi TakaGBC Generativity behavioral checklist
GSH General subjective happiness
GSU General subjective unhappiness
OLS Ordinary least squares
OSH Overall subjective happiness
PRH Peer relative happinessSD

Standard deviation

SH

Subjective happiness

SHS

Subjective happiness scale

SVO Social value orientation

1

1

Introduction2

Ancient philosophers and modern scholars, such as Aristotle and Bentham, claim that happiness3

is an ultimate goal of humans life (Lyubomirsky, 2001, Sato et al., 2015). Happiness is interpreted to4

be an outcome of “good life,” which might be driven by social status and/or some sociodemographic
2

5

factors in contemporary societies (Diener et al., 1985, Frey, 2008, John Knight, 2009, Asadullah

6

and Chaudhury, 2012). On the other hand, generativity and prosociality are claimed to be crucial

7

factors for the betterment and sustainability of societies, because they are conceptualized as a8

concern and commitment for people in current and future generations, and prosocial people with

9high generativity tend to help, mentor and educate general others (McAdams and de St. Aubin,

10

1992, Timilsina et al., 2019). Therefore, happiness, generativity and prosociality shall be considered11basic foundations of current and future societies, and this paper addresses the relationship among12

the three factors along with sociodemographic factors.

13

Social status and/or sociodemographic factors have been studied as possible determinants of

14“good life,” leading to an increase in individual happiness. Past literature has focused on examining

15

the association of particular economic factors and urbanization with happiness. Income is generally

16

reported to have positive relationship with happiness, while age, gender, education and religion have17

mixed outcomes (Tepperman and Laasen, 1990, Fujita et al., 1991, Lyubomirsky and Ross, 1997,

18

Kahneman et al., 1999, Easterlin, 2001, Helliwell, 2003, Easterlin, 2003, Blanchflower and Oswald,

19

2004, Biswas-Diener et al., 2004, Lyubomirsky et al., 2005, Kahneman et al., 2006, John Knight,

20

2009, Asadullah and Chaudhury, 2012, Jiang et al., 2012, Ngamaba and Soni, 2017, Asadullah

21

et al., 2018). Requena (2015) and Kim (2018) compare people’s happiness between rural and urban

22

areas in both developed and developing countries. They illustrate that rural residents have lower

23

happiness than urban ones in developing countries, while the opposite is true in developed countries.

24

They argue that the result may be due to living standards and availability of public goods depending

25

on the stages of economic development.

26

Research on happiness along with social behaviors and psychological factors has gained wide

27

interest for the last few decades. The literature finds that charity or buying some gifts for friends28

has a positive association with happiness, demonstrating that such prosocial acts and individual29

happiness possess a positive feedback loop (Dunn et al., 2008, Konow and Earley, 2008, Konow,

30

2010, Dunn et al., 2010, Aknin et al., 2012, Dunn et al., 2014, Geenen et al., 2014, Bischoff and

31

Krauskopf, 2015, Koch, 2015, Nelson et al., 2016). There are several researches in psychology that3

32

examine the correlation between generativity and happiness. McAdams and de St. Aubin (1992),

33

de St. Aubin and McAdams (1995) and Keyes and Ryff (1998) establish the positive correlation

34

using student and adult subject pools in USA. Stewart et al. (2001) study a temporal change in35

happiness with middle-aged women in USA, finding that happiness does not necessarily decline

36

in age and has positive association with generativity. Hofer et al. (2007), Huta and Zuroff (2008),

37

Hofer et al. (2014, 2016) and Au et al. (2019) address possible mediators between happiness, life

38

satisfaction and generativity with student subjects in Canada and adult ones in Cameroon, Costa

39

Rica, Germany, Czech Republic and Hong-Kong, respectively. They find some mediators such as

40

symbolic immortality and altruism to explain the relation among the factors.

41

Previous studies establish that there is positive association between happiness and generativity

42

using two-variable correlation analysis possibly with structural modeling, and prosocial acts tend to43

increase happiness. However, it is claimed that prosocial acts are at most spontaneous or temporal,

44

and it is important to consider individual social preferences along with sociodemographic factors,

45

because the preference is established to be stable or not to change in the long run (Varian, 1992,

46

Aknin et al., 2012, Carlsson et al., 2014). Moreover, little is known about the relationship among47

happiness, generativity and social preferences along with sociodemographic factors within a single

48

analytical framework, despite the importance of the three factors in understanding the betterment

49

and sustainability of societies in future. We hypothesize that generativity and social preferences50

are the determinants for happiness, and conduct a survey experiment collecting the data of five51

subjective happiness scales, generativity, social value orientation and sociodemographic variables in52

one urban area (Dhaka) and two rural areas (Bogra and Gaibandha), Bangladesh. With the data, we

53

empirically characterize the determinants of subjective happiness with a focus on generativity and54

social value orientation, controlling for sociodemographic factors.

4

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2

Methods and materials

56

We conducted a questionnaire survey and experiment in three districts of Bangladesh: Dhaka,

57

Bogra and Gaibandha (figure 1). We consider them as one urban area of Dhaka and two rural areas of58

Bogra and Gaibandha. We choose the regions because they possess the same culture, language and59

religious variation except sociodemographic factors and economic development where Bangladesh

60

is culturally and ethnically a homogeneous country. Dhaka is the capital city representing an urban

61society and it is located between 23°550 and 24°810 north latitude, and between 90°180 and 90°570

62

east longitude (Dewan and Corner, 2014). The population, population density and total area are

63

14.51 million, 10 484 km−2 and 1371 km2 , respectively, which makes Dhaka the most populated

64

city in the world.

65

The rural areas consist of two districts: (i) Bogra and (ii) Gaibandha. Bogra consists of two

66

unions of the Shajahanpur subdistrict in the northern districts, Amrool and Chopinagar (figure 1).

67

The location of Shajhanpur subdistrict is between 24°410 and 24°500 north latitudes, and between

68

89°160 and 89°290 east longitudes. Shajhanpur’s land area is 54 783 acres; Amrool’s and Chopina-

69

gar’s land areas are 6106 acres and 4048 acres, respectively (Bangladesh Bureau of Statistics, 2011).

70

The population densities in Amrool and Chopinagar are 951 km−2 and 1357 km−2 , respectively,

71

whereas the country average is 1218 km−2 (Bangladesh Bureau of Statistics, 2011). Villages in72

these two unions are agrarian societies, while agro-based and small-scale businesses are available in73

very limited areas.

74

Gaibandha comprises three unions of the Palashbari subdistrict, which are Harinathpur, Hossain-

75

pur and Monoharpur. The location of the Palashbari is between 25°110 and 25°190 north latitude; and76

between 89°160 and 89°320 east longitude. Land area, population density and total population in the77

Palashbari are 45 774 acre, 1321 km−2 and 244 792, respectively (Bangladesh Bureau of Statistics,

782011). Villages in the unions are also agrarian societies and they are considered the least developed

79

in Bangladesh. All the dwellers engage in farming either as subsistence farmers or as cash-crop

80

farmers. In what follows, we refer to these study areas as Bogra and Gaibandha, interchangeably

81

mentioning them as rural areas.
5

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We collected 105 subjects in Dhaka by an individual survey, basing our randomization on the83

proportion of each occupation in the population (Bangladesh Bureau of Statistics, 2015). The number

84

of subjects needed in each occupation was determined, and we selected a number of organizations

85

for each occupation. After that, we contacted these organizations and we randomly invited subjects

86

from these organizations based on their responses. The response rates for organizations and for87

subjects were 50 % and 60 %, respectively. We collected 142 and 150 subjects in two rural areas of88

Bogra and Gaibandha, respectively, by an individual survey with the following random sampling89

procedures. First, we obtained a list of the households that resides in Bogra and Gaibandha

90

from local city offices, and we randomly chose households that approximate the representatives

91

for each area. Second, we sent a letter to invite one member (who earns income) from each92

household to participate in our survey and experiment, and the response rate was 75 %. As of93

the measurement for the main variables in our analysis, the subjective happiness scale (SHS),

94

generative behavioral checklist (GBC) and social value orientation (SVO) are employed to represent95

the subjects’ happiness, generativity and social preferences, respectively (McAdams and de St.

96

Aubin, 1992, Van Lange et al., 1997, McAdams et al., 1998, Lyubomirsky and Lepper, 1999, Van

97

Lange et al., 2007).

98[Figure 1 about here.]99

We use the subjective happiness scale of a four-item measurement developed by Lyubomirsky

100

and Lepper (1999) where each item is a 7-point Likert scale. The first question in the scale reports

101

individual absolute self-rated happiness (AH) by stating “in general, I consider myself” and its

102

anchors are “not a very happy person” and “a very happy person.” The second item reports individual

103

relative happiness as compared with peers by stating “compared to my peers, I consider myself”

104

and its anchors are “less happy” or “more happy” and it is called peer relative happiness (PRH.)

105

Third and fourth items correspond to a general description of a happy and/or unhappy person where106

subjects make a choice to describe them most. In the items, “some people are generally very happy.

107

They enjoy life no matter what is going on, getting the most of everything. How much does this108

sentence describe you?” and “some people are generally not very happy. Although they are not6109

depressed, they never seem as happy as they might be. How much does this sentence describe you?”110

The anchors are “not at all” and “a great deal.”, which are called general subjective happiness and111

unhappiness (i.e., GSH and GSU), respectively. To calculate the overall subjective happiness (OSH),112

the average of the four items is calculated, while the fourth item is reversely coded.

113

The generative behavior checklist (GBC) developed by McAdams and de St. Aubin (1992) is114

employed to measure the frequency of people’s generative behaviors in the last two months. This115

measure contains a list of 50 activities, out of them, only 40 activities are considered indicators

116

of “generativity.” “Taught somebody a skill,” “Gave a money to a charity,” “Made a decision that117

influence many people” and “Served as a role model for a young person” are some examples of the118

generative activities. Subjects need to tick one of the three options for each activity. The “zero,”

119“one” or “two” are the scores to indicate that subjects have not participated in each generative activity,

120

participated in it once or participated in it more than once over the last one year, respectively. The

121

score of the generativity for each subject is calculated as the sum of the scores in all the 40 items.

122

The social value orientation (SVO) developed by Van Lange et al. (1997) is employed to identify

123

the social preference for each subject. This game consists of 9 items, each of which contains

124

three choices. Subjects are asked to make one choice for each item for dividing an amount of125

money between himself and a stranger, for example, (A) you get 500 and the other gets 100,126(B) you get 500 and the other gets 500 and (C) you get 560 and the other gets 330. Option (A)127represents the competitive person as it maximizes the gap between self point and the other’s point

128(500 − 100 = 400). Option (B) represents the prosocial person as it maximizes the joint benefit

129(500 + 500 = 1000), while option (C) is the individualistic person as it maximizes own benefits

130

without considering the other (Van Lange et al., 2007). Four types of individual SVO are identified

131

by this game, i.e., individualistic, competitive, prosocial and unidentified, based on their choices

132

in the game. When the subject makes a consistent choice in 6 items for one orientation (i.e.,

133

individualistic, competitive or prosocial), then she is considered to be that orientation, otherwise

134“unidentified.” We randomly matched two subjects as a pair to compute their final payoff based on

135

their performances in the game. Subjects are paid on average 100 BDT for the SVO, while a fixed

7

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participation fee of 150 BDT is paid to all the subject and the total payment on average is 250 BDT

137

per subject.
This study hypothesizes that generativity and social preferences are the determinants for happiness. To test this hypothesis, parametric and nonparametric statistical analyses are employed byutilizing the data of SHS, GBC, SVO and sociodemographic factors collected in a questionnaire
survey and experiment. The nonparametric Mann-Whitney tests are applied to check the differencein the distributions of subjective happiness (SH) between the urban and rural areas or between
prosocial and proself orientations in SVO, while the Pearson correlation is applied to discover thenature of the relationship between SH and generativity and the key sociodemographic factors. We
also employ regression analyses to quantitatively identify the determinants of SHS. The Poisson
regression is applied for the four components of SHS as the scales are count variables (i.e., absoluteself-rated happiness (AH), peer relative happiness (PRH), general subjective happiness (GSH)
and general subjective unhappiness (GSU), while we apply ordinary least squares (OLS) for theaggregate SHS (i.e., overall subjective happiness) as it is a continuous variable. The following
equation is estimated for Poisson regression and OLS (k ∈ {AH, PRH, GSH, GSU, OSH}):Y k = β0k + β1k X + β2k S + β3k Z + k

(1)

138

where Y k is the explanatory variable (AH, PRH, GSH, GSU and OSH), X is the generativity

139

score of subjects, S is a dummy variable representing SVO that takes unity for individualistic and140

competitive subjects (i.e., proself) and 0 otherwise, Z is a vector of sociodemographic variables

141

that may affect SH and k is the error term (See table 1 where the definitions of every variable used

142

in the statistical analysis). The parameters βi s for i = 0, 1, 2 are the coefficients for intercept, X

143

and S to be estimated, while β3 is a vector of coefficients for independent variables Z related to144sociodemographic factors.

145

With the regression analyses of equation (1), we intend to examine a conceptual framework for146

the relationship among subjective happiness (SH), generativity and SVO along with sociodemo-

8

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graphic factors in figure 2. In this framework, a coefficient on each key variable, βi , i = 0, 1, 2, is148

considered to represent the marginal effect of that variable on SH, after the effects of other variables

149

are netted out. For example, β1 is considered to represent the effect of generativity on SH, after the150

effects of SVO and sociodemographic factors have been netted out, while some possible mediators

151

may play roles in characterizing subjective happiness through several independent variables. In this152

research, our focus is on estimating β1 , β2 and β3 in figure 2. The interpretation of these coefficients153in Poisson regression is derived as follows (Wooldridge, 2013); the marginal effect of a continuous

154

independent variable such as generativity should be calculated by the formula of 100 × βj to be a155

percentage change in Y when the continuous variable increases by one unit. In the case of a dummy

156

independent variable such as SVO (proself = 1, otherwise 0), it is calculated by exp(βj ) − 1 to be

157

interpreted as a percentage change in Y when the dummy variable increases from 0 to 1.
[Figure 2 about here.]

158

159

3

Results

160

Table 1 summarizes basic statistics of generativity, social value orientation (SVO), sociodemo-

161

graphic variables and subjective happiness (SH) for the urban subjects (Dhaka), the rural subjects

162(Bogra and Gaibandha) and overall subjects in the sample. The mean score of subjects’ generativity

163

in the urban area, the rural areas and overall sample are fairly similar at 28.57 points (SD = 12.3),

164

29.25 points (SD = 11.88) and 29.09 points (SD = 11.98), respectively. By contrast, SVO is165

different among the urban area, the rural areas and the overall sample as the percentages of pro-166

self subjects are 84 %, 64 % and 70 %, respectively. The result is consistent with past literature167

in that sense that the percentage of proself people becomes higher in urban areas than in rural

168

areas (Shahrier et al., 2016, Timilsina et al., 2017, 2019). Another difference is on the average

169

year of education as urban subjects have 12.68 years (SD = 4.91), rural subjects have 8.97 years

170(SD = 3.86) and overall subjects in the sample have 9.95 years (SD = 4.47) on an average.

9

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The largest variation in the sample is found in average household income as it is 47.7 thousand

172

BDT (SD = 49.02), 16.6 thousand BDT (SD = 21.41) and 24.81 thousand BDT (SD = 34.02) in173

the urban area, the rural areas and the overall sample, respectively. Regarding happiness, urban

174

subjects have a higher SH in the four scales of subjective happiness scale than rural subjects and175

overall subjects in the sample, leading the overall subjective happiness (OSH) to be higher for urban

176

subjects with an average of 5.12 points (SD = 0.99) than rural subjects with an average of 4.72

177

points (SD = 1.05) and overall subjects in the sample with an average of 4.83 points (SD = 1.69).

178

These statistics suggest that urban subjects may generally have a higher level of SH than rural

179

subjects, regardless of happiness scales, being in line with Requena (2015) and Kim (2018).

180[Table 1 about here.]

181

Figure 2(a) is a histogram to present the distribution of OSH for overall subjects in the sample

182

where the vertical axis denotes the frequencies and the horizontal axis denotes OSH. The highest

183

spike is found between 4.5 and 5 points and the distribution appear to follow a normal distribution,184

but be slightly skewed at one side (almost a bell-shaped distribution). We run a Shapiro-Francia

185

normality test with the null hypothesis that the OSH distribution is normal. The result shows that the186

null hypothesis is not rejected even with a 10 % significance level, meaning that the OSH distribution

187

follows a normal distribution. Therefore, we run a ordinary least squared (OLS) regression for OSH,

188

while other happiness scales are analyzed by applying Poisson regressions. Figure 2(b) presents a

189

scatter plot between OSH (vertical axis) and generativity (horizontal axis) where one dot represents

190

an observation for each subject in our sample. This scatter plot appears to suggest that there is a191

positive association between the two, and we confirm that there is a positive association between

192

OSH and generativity by Pearson correlation (r = 0.11, p < 0.03).

193

Figure 2(c) is a box plot to demonstrate a difference in OSH distributions between prosocial

194

and proself subjects. It presents that the OSH distribution in proself subjects is located slightly

195

higher than that in prosocial subjects with respect to the medians and the supports. To statistically196

check the distributional difference, we run a Mann-Whitney test with the null hypothesis that197

the OSH distributions between proself and prosocial subjects are the same. The result finds
10

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that there is no significant difference in the OSH distributions between prosocial and proself

199

subjects (Z = −0.426, p = 0.67). We next look at the relation between OSH and some key

200

sociodemographic factors such as household income and residence areas. The Pearson correlations

201

demonstrate that OSH and household income (residence area) have positive (negative) association

202

with r = 0.18, p < 0.01 (r = −0.15, p < 0.01). Figure 2(d) is a boxplot to visualize a difference

203in the OSH distributions between rural and urban subjects, showing that urban subjects tend to204

have a higher OSH than rural ones. The Mann-Whitney test confirms that there is a significant

205

difference in OSH distributions (Z = 3.38, p < 0.01) between urban and rural subjects. Overall,206

these statistical analyses suggest that generativity, income and residence area could be considered207

possible candidates as determinants for subjective happiness, while SVO may not.
[Figure 3 about here.]

208

209

We conduct regression analyses to quantitatively understand how SH is characterized by gen-210

erativity, SVO, household income and residence area, controlling for other sociodemographic

211

factors (See table 2).1 The first four columns in table 2 present the marginal effects of independent

212

variables on the four components of subjective happiness scale, such as absolute self-rated happiness

213(AH), peer relative happiness (PRH), general subjective happiness (GSH) and general subjective214

unhappiness (GSU), using Poisson regressions, and the last column shows the marginal effect on

215

overall subjective happiness (OSH) using the OLS. The results reveal that generativity is statistically216

significant (i.e., p < 0.05 except in AH regression p < 0.10 and in GSH regression p < 0.01)

217

across the four different scales of SH and OSH. More specifically, the expected AH, PRH and218

GSH increase by 13.17 %, 15.57 % and 31.15 %, respectively, and GSU declines by 19.17 % with a219

rise in subjects’ generativity score by one standard deviation (SD). The relationship between OSH

220

and subjects’ generativity score is consistent with the four scales of SH as OSH increases by 0.11

221

points when subjects’ generativity score goes up by one-SD. These results show that generativity

222

is a consistent and robust determinant across all scales of SH and OSH, indicating that subjects’
1

We have tried different specifications of regression models to check the robustness of our results with possible
interaction terms, confirming that the main results in table 2 remain the same.

11

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generativity could be one of the most important factors on their happiness.

224

Table 2 shows that residence area and household income are only sociodemographic factors that

225

come with statistical significance and associated with different subjective happiness scales. The

226

PRH and OSH for rural subjects are 41 % and 0.25 points lower than these for urban subjects.2 An

227

increase in the household income by 10 BDT is associated with a rise in GSH and OSH by 5 %228

and 0.04 points, respectively. The results demonstrate that generativity, urban-rural difference and229

household income are main determinants of SH, which can be considered to be line with previous

230

literature. de St. Aubin and McAdams (1995) find that generativity is positively correlated with

231

SH, and Ball and Chernova (2007) demonstrate that there is a strong positive relationship between

232

income and SH. In developing countries, Requena (2015) confirms that subjects in urban areas have233

a higher level of SH than subjects in rural area.
[Table 2 about here.]

234

235

Generativity is demonstrated to be a robust and consistent determinant for SH, while SVO is236not. Past literature demonstrates that prosocial actions (NOT preferences) lead to an increase in237

happiness (Dunn et al., 2008, Konow, 2010, Dunn et al., 2014, Koch, 2015), while there is little

238

research to examine people’s happiness with different social value orientations or preferences. It is239

generally considered that proself and prosocial people may have their own way to be happy and tend240

to consistently make proself and prosocial choices, respectively, because the proselves (prosocials)

241

become happier by being more proself (prosocial) than by being more prosocial (proself). However,242

we conjecture that the magnitude of a change in individual SH may not be characterized by SVO

243

or be different between prosocial and proself people, based on the fact that the SVO variable is244

insignificant in our analyses. Rather, the magnitude of a change in individual SH may be more245

attributed to something more personal or person-specific factors such as self-esteem, self-positivity

246

or self-efficacy along with SVO.
2

As mentioned in section 2, the marginal effect of a dummy variable on PRH (OSH) is calculated by the following
formula: exp(βj ) − 1 where βj is an estimated coefficient for the dummy variable. For instance, exp(0.347) − 1 ≈0.41 = 41 %.

12

247

Generativity is calculated by the scores of how many activities from the generativity behavioral

248

checklist (GBC) people have taken in the last two months. The GBC contains certain activities

249

both prosocial and proself people are likely to take in their daily life. Some activities in GBC such250

as “learned a new skill,” “produced a plan for an organization or group outside my family” and251“was elected or promoted to a leadership position” may be likely to be taken by proself people.

252

On the other hand, the activities in GBC such as “gave money to a charity,” “taught somebody a253skill” and “made something for somebody and then gave it to them” may be likely to be taken

254

by prosocial people. Simply, it appears that there are mainly two channels of motivations to be

255

more generative for future generations: (i) proself and (ii) prosocial channels, implying that the256basic motives behind the generative actions could be different. Proself people may be driven by257“legacy motives,” whereas prosocial people may be driven by “motives of helping hands” for future258

generations (Bang et al., 2017, Wade-Benzoni, 2019, Timilsina et al., 2019). Therefore, it is our259

conjecture that generative actions may be able to uniformly contribute to individual SH, irrespective

260

of prosocial and proself motives behind the actions, and therefore, generativity is a consistent and261

robust determinant for SH in the analysis.

262

Our results also show that PRH and OSH vary by residence areas, and urban people have higher263

PRH and OSH than rural people. Life in a rural society is known to be homogeneous in terms of264

choice sets, social status and sources of happiness.3 For instance, a main and common entertainment

265

among rural people in Bangladesh is attending a social gathering, “mela,” in which all people in266

the village can come and gather at the same time and place. In that gathering, people enjoy all the267

social and communal activities together by sharing a feeling of “commonality.” On the other hand,

268

urban areas are heterogeneous in terms of choice sets, social status and sources of happiness as

269

there are more possibilities in many aspects. For instance, entertainment in urban areas includes

270

wider varieties with more accessibility, giving people more freedom of choices. In this type of urban

271

environment, people can express different preferences and value judgments over what to do and3

A PRH question in GBC uses 7-point Likert scales, and its anchors are “less happy” and “more happy.” It meansthat the middle point of the scale (e.g., 4 points) describes the subject who is “as happy as her peers.” An average PRH
for rural subjects is closer to 4 points than that for urban subjects, implying that rural ones might feel closer to be “ashappy as their peers” than urban ones.

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how to spend, inducing themselves to see a “difference” of how each of people is distinct from one

273

another. Therefore, urban people may be able to consider themselves as happy in their own way,274

because they are considered to choose being so, leading urban people to feel happier as compared275

with their peers than rural people.

276

Cities are predicted to expand and grow with further urbanization over the next 50 years,277

and 65 % - 75 % of the earth population will reside at the cities in Asia and Africa (American

278

Association for the Advancement of Science, 2016, Wigginton et al., 2016). Economic theory279

establishes that urbanization brings about an increase in people’s income, potentially implying that280

economic growth with urbanization is positive on happiness along with our result as well as the past

281

literature (Wheaton and Lewis, 2002, Bloom et al., 2008, Zhang, 2011, Requena, 2015). However,282

behavioral sciences and social psychology report that urbanization is changing human societies in283

the way that people’s generativity declines, so-called, “generativity crisis” (see, e.g., Sasaki, 2004,

284

Timilsina et al., 2019). Sasaki (2004) claims that the rapid economic growth, urbanization and social

285

changes in Japan have led to a decline in people’s generativity. Some other scholars also argue that286

degrowth of economies is inevitable to ensure wellbeing for future generations (Schneider et al.,287

2010, Alexander, 2012, Andreoni and Galmarini, 2014, Buchs and Koch, 2019). Given the two288

possible paths of growth and degrowth, a natural question arises “which is better, economic growth289with urbanization or degrowth for the current and future generations’ wellbeing?” Our results in

290

this research clearly suggest that the answer depends on whether and how economic growth with291

urbanization (and/or degrowth) affects generativity, which should be addressed and established in292

further researches.

293

4

Conclusion

294

This paper has analyzed the relationship among subjective happiness (SH), generativity and295

social value orientation (SVO) within a single analytical framework, hypothesizing that generativity

296

and SVO are the determinants for happiness. We conduct a survey experiment collecting the data

14

297

of five subjective happiness scales, generativity, SVO and sociodemographic variables in an urban

298city (Dhaka) and rural areas (Bogra and Gaibandha), Bangladesh. With the data, we empirically299

characterize the determinants of SH with a specific focus on generativity and SVO, controlling for300

other factors. The statistical analysis shows a positive association between SH and generativity,

301

irrespective of the types of happiness scales, while SVO does not exhibit any significant effect.

302

Rural people have lower peer relative happiness than urban ones, and household income has a

303

positive relationship with general subjective happiness, leading each of these factors to be significant

304

in overall subjective happiness.

305

The results demonstrate that generativity and income are the main determinants, and further

306

urbanization, which is expected to occur in future, will positively affect people’s happiness if it

307

can bring an increase in generativity and income. However, some authors claim that the economic308

growth along with urbanization and social changes is likely to decrease people’s generativity (Sasaki,

309

2004, Timilsina et al., 2019). If this is the case, we may need to think about the way how we

310

can develop our societies to not only achieve economic growth but also raise or maintain people’s

311

generativity for wellbeing. If it is considered impossible, we may need to pursue the degrowth312

scenario as suggested by some past literature (Schneider et al., 2010, Alexander, 2012, Andreoni

313

and Galmarini, 2014, Buchs and Koch, 2019). Which way, economic growth with urbanization

314

or degrowth, is better for wellbeing of the current and future generations? This is an important

315

question, and our answer is “it depends on whether and how economic growth with urbanization

316(and/or degrowth) would affects people’s generativity.”

317

We note some limitations to our study and future avenues of research. This paper studies

318

happiness, generativity and SVO in only one country (i.e., Bangladesh) which is considered a319

culturally and ethnically homogeneous society. We believe that further studies with the same

320

analytical framework in other countries will widen our understanding on generativity and some321

missing factors in relation to SH. In addition, we do not study the detailed mechanism of how and322

why generativity and happiness are positively associated. Therefore, future studies should be able

323

to focus on addressing how each of generative behaviors more directly influences happiness than

15

324

the others by collecting finer individual behavioral data. Along with it, the most importantly, future

325

researches should focus on clarifying whether and how economic growth affect the generativity and326

happiness within a single framework. To this end, some lab & field experiments and/or neuroscience327

research can be conducted to see how people perceive or the brains react when people take &328

observe generative actions. These caveats notwithstanding, it is our belief that this research is329

an important 1st step in understanding people’s happiness, generativity and social preferences in330

developing countries for the betterment and sustainability of societies.

16

5

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List of Figures1
2
3

Map of Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
A conceptual framework for the relationship among subjective happiness, generativity, social value orientation and sociodemographic factors . . . . . . . . . . . . 24
An overview of happiness with respect to generativity, social value orientation andurban-rural differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

22

Figure 1: Map of Bangladesh

23

Figure 2: A conceptual framework for the relationship among subjective happiness, generativity,
social value orientation and sociodemographic factors
Subjective happiness

𝛽2
Sociodemographic
factors

Generativity

Social value
orientation24

(b) A scatter plot between overall subjective happiness (OSH) and generativity

4

5

30
0

2

10

3

20

Frequency

6

40

7

Overall subjective happiness (OSH)(a) Frequency distribution of overall subjective
happiness (OSH)

2

3

4

5

6

7

0

10

20

Overall subjective happiness(c) A box plot between overall subjective happiness (OSH) and social value orientation (SVO)

40

50

60

70

(d) A box plot between overall subjective happiness (OSH) and residence areas
Urban

4

6

8

Rural

2

4

6

8

Overall subjective happiness (OSH)

Proself2

Overall Subjective happiness (OSH)

Prosocial

30

Generativity

Figure 3: An overview of happiness with respect to generativity, social value orientation andurban-rural differences

25

List of Tables
1
2

Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Regressions results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

26

27
4.91
5.08
5.22
2.72
5.12
105

Subjective happiness scales (SHS)
Absolute self-rated happiness(AH)
Peer relative happiness(PRH)
General subjective happiness(GSH)
General subjective unhappiness (GSU)
Overall subjective happiness(OSH)

Observations

5
5
5
2
5.25

28
0
16
30
4
0
-

Median

1.36
1.42
1.47
1.68
0.99

12.30
0.37
0.69
4.91
49.02
0.37
0.26
2.14
0.46
-

SD

1
1
1
1
2.50

6
0
0
0
7
0
0
1
0
-

Min

7
7
7
7
7

66
1
3
20
300
1
1
11
1
-

Max

292

4.79
4.66
4.82
3.38
4.72

29.25
0.64
0.97
8.97
16.56
0.04
0.01
4.54
0.38
-

Mean

5
5
5
3
4.75

29
1
10
12
4
0
-

Median

1.44
1.51
1.38
1.66
1.05

11.88
0.48
1.25
3.86
21.41
0.21
0.09
2.34
0.49
-

SD

1
1
1
1
1.50

2
0
0
0
0.50
0
0
1
0
-

Min

Rural
(Bogra and Gaibandha)

7
7
7
7
7

70
1
5
17
250
1
1
14
1
-

Max

337

4.82
4.77
4.93
3.21
4.83

29.07
0.70
0.82
9.95
24.81
0.07
0.02
4.41
0.36
0.74

Mean

5
5
5
3
4.75

29
0
10
15
4
0
1

Median

1.42
1.50
1.42
1.69
1.05

11.98
0.46
1.16
4.47
34.02
0.26
0.15
2.30
0.48
0.44

SD

Overall

1
1
1
1
1.50

2
0
0
0
0.50
0
0
1
0
0

Min

7
7
7
7
7

70
1
5
20
300
1
1
14
1
1

Max

8 Rural is a dummy variable that takes 1 when a subject is living in Bogra and Gaibandha and 0 otherwise.

7 Family structure is a dummy variable that takes 1 when it is a joint family and 0 otherwise.

6 No. of siblings is a count variable for the number of siblings.

5 Religion is a dummy variable that takes 1 when the subject is non-Muslim and 0 otherwise.

4 Gender variable is a dummy variable that takes 1 when the subject is male and 0 otherwise.

3 Age variable is defined as an ordered categorical variable of 0, 1, 2, 3, 4 and 5 where 0 is under 20 and 5 is above 60 and the rest is with an interval of 10 years.

2 Proself is a dummy variable for SVO and it takes the value of 1 if subject is characterized to be individualistic or competitive and 0 otherwise.

1 Generativity is defined as a count variable that takes values from 0 to 70 based on how many activities subjects have done in the last year from a generativity behavior checklist.

28.57
0.84
0.39
12.68
47.73
0.16
0.07
4.05
0.30
-

Mean

Proself2
Age3
Years of education
Household income in thousand
Gender4
Religion5
No. of siblings6
Family structure7
Rural (Bogra and Gaibandha)8

Generativity1

Urban(Dhaka)Table 1: Summary statistics

28
397
11.73
0.00360.011*(0.006)0.233(0.164)-0.054(0.203)0.146(0.179)-0.019(0.047)-0.012(0.019)0.003(0.004)0.436(0.306)-0.016(0.425)0.001(0.035)0.168(0.153)(1)
Absolute
self-rated
happiness(AH)

397
19.25*
0.00530.013**(0.006)-0.008(0.169)-0.347*(0.184)0.001(0.183)0.036(0.044)0.0135(0.020)0.002(0.003)0.152(0.354)-0.102(0.563)-0.029(0.036)0.024(0.150)(2)
Peerrelative
happiness(PRH)*** P < 0.01, ** P < 0.05, * P < 0.10
Numbers in parentheses are standard errors.

Observations
Wald χ2
Pseudo R2

Family structure

No. of siblings

Religion

GenderHousehold income in thousand

Years of education

Age Square

Age

Rural (Bogra and Gaibandha)

Proself

Generativity

Variables

397
44.55***
0.01020.026***(0.006)-0.086(0.164)-0.251(0.190)0.0863(0.161)-0.013(0.042)-0.001(0.020)0.005***(0.002)0.223(0.359)-0.342(0.526)-0.061(0.037)0.049(0.147)(3)
Generalsubjective
happiness
(GSH)

397
26.07***
0.0087-0.016**(0.008)0.109(0.192)-0.367(0.244)0.093(0.207)-0.009(0.052)0.021(0.022)0.004(0.003)0.07(0.339)0.521(0.494)-0.021(0.038)-0.259(0.185)(4)
Generalsubjective
unhappiness(GSU)

Marginal effects of Poisson regression

Table 2: Regressions results

397
29.30***
0.065

0.009**
0.004)
0.062(0.126)-0.257**(0.126)0.08(0.125)-0.001(0.031)
0.005(0.014)0.004**(0.002)0.229(0.250)0.031(0.403)-0.027(0.024)-0.006(0.107)(5)Overallsubjective
happiness(OSH)

OLS