Social Design Engineering Series

SDES-2020-15

Who perceive seasonality change? A case of the Meghnabasin, Bangladesh

Moinul Islam
Research Institute for Future Design, Kochi University of Technology

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

25th December, 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.

Who perceive seasonality change? A case of theMeghna basin, Bangladesh
Moinul Islam* Koji Kotani†,‡,§,¶,||
December 24, 2020

Abstract
Global climate change is a scientifically demonstrated phenomenon, but there are great discrepancies in different societies about how people perceive it. It has been claimed that people’s
correct perceptions to climate change are necessary for mitigation and adaptation; however,most research in this regard has focused on knowledge about temporal trends of climate: noreports have examined people’s perceptions of climatic regularity and patterns, i.e., seasonalitychange. In this study, we investigate people’s perception to seasonality change in the Meghnabasin, Bangladesh where catastrophic flooding occurs and the number of seasons in an annual
calendar year is reported to have decreased from six to four. We conduct interviews with 7
experts and surveys with 1011 respondents. With the data, we empirically characterize participants’ perceptions to seasonality change in relation to sociodemographic factors and life
experiences. The results show that dependence on natural resources in profession, experiencesof natural disasters and life history in the residential area shape people’s accurate perception
to climate seasonality.

Key Words: Perception to seasonality change; natural resource dependence; experiences ofnatural disasters; life history; Bangladesh* Research Institute for Future Design, Kochi University of Technology†

Research Institute for Future Design, Kochi University of Technology
School of Economics and Management, 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
2.1 Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4
5
6
7

3

Results

8

4Conclusion

115

References

13

List of Figures

15

List of Tables

18

2

1

1

Introduction

2

Climate change impacts on the seasonal distribution of the weather anomalies worldwide. Such3

changes of weather will impact more in the areas where water availability and timing are important4

factors to control ecosystem (Weltzin et al., 2003). Climate change altered the seasonal distribu-

5

tions of rainfall in tropical areas (Easterling et al., 2000, Feng et al., 2013). Bangladesh is located6in the tropical region and climatically endangered (Mechler and Bouwer, 2015). Islam and Kotani

7(2016) identify that, in Bangladesh, seasonality of temperature and precipitation patterns have

8changed over the last 25 years. Formerly, there were six seasons in Bangladesh and now there9

are just four. One of the main concerns of this seasonal change in Bangladesh is that many poor

10

inhabitants will fall in the poverty trap due to their dependance on nature in subsistence life. To

11ensure the seasonal change adaptation and sustainable development, it is important to understand12

the possible important factors that shape individuals seasonal change perception. Therefore, this13

paper address the issue by conducting field survey in rural and urban areas of Bangladesh.

14

A group of research focus on the importance of personal experience of natural disaster on cli-

15

mate change perception based on the primary information by using questionnaire survey in various

16

disaster-prone countries in the world. Frondel et al. (2017) identify that, in Germany, personal

17

experiences of adverse events are strong drivers of climate risk perception. They establish that dis-

18

aster experience guides individuls to shape their climate change perceptions. Dai et al. (2015) show

19

that, in China, physical or financial damages due to extreme weather events are positively corre-20

alted with global climate change beliefs. Zaalberg et al. (2009) conduct survey on flood victims

21

and non-victims in the Netherland and identify that victims perceive themselves as more vulnera-22

ble than non-victims to future flooding. Siegrist and Gutscher (2006) select the respondents from23high and low flood risk areas of Switzerland and identify that respondents experiences of flooding24

is positively related with their flood risk perceptions. Overall, these studies identify that experience25

of disaster shaping individual’s climate risk perception in different countries.

26

Several studies prioritize the importance of geographic location on correct perception of cli-

27

mate changes. Leiserowitz (2006), Gromet et al. (2013), Kahan et al. (2011), McCright and Dunlap
328(2011) notice that climate change perceptions very geographically as a function of cultural and ide-29

ological factors. Generally, people live in same region for longer period of time cluster together

30

based on these factors. Akerlof et al. (2013), Egan and Mullin (2012), Goebbert et al. (2012),

31

Hamilton and Keim (2009), Howe et al. (2013), Capstick and Pidgeon (2014) conclude that ge-

32

ographic location is correalted with personal experience of extreme weather events and climate

33

variability which shape individuals climate change perceptions. Overall, these studies identify that34

geographic location of individuals can be an important factor to shape individual’s climate change

35

perception.

36

Climate seasonality is likely to become more pronounced in Bangladesh, where daily life is37

dependent on the weather anomalies. Citizens may be able to reduce the impact of seasonal vari-38

ability by adapting or adjusting their daily lives in response to weather stimuli. However, not all

39

individuals may respond similarly to seasonal changes. To estimate the impact of future climate

40

change, it is necessary to develop a predictive framework towards understanding who should adapt

41

to seasonal changes and how and why they should do so. It could be hypothesized that there are42

several important determinants of individuals correct perception of the climate seasonality. To

43

test this hypothesis, a field survey was conducted on the local residents of Bangladesh, where the44dependance on natural resources in profession, natural disaster experience, and life history in the45

residential area are identified as important determinants of the climate seasonality perception.

46

2

Methods47

We consider people’s perception to climate seasonality as a main variable of our interest. In

48

the study region, it has been hypothesized that the temperature and rainfall trends over the last 25

49

years have become less variant. The changes in the seasonal patterns are documented to be more

50

clearly visible through visual observations and experiences to climate in the region, such as shorter

51

winter, longer summer, less rainfall and so on (Alam and Rabbani, 2007). Islam and Kotani (2016)

52

empirically establish that the annual calendar in the study region transitions from six seasons to4

53four seasons by applying nonparametric statistical analyses to the meteorological data over the54

last 50 years. Building upon the evidences in the study region demonstrated by past literature, we

55

address who perceive such climate seasonality by statistical analyses that will follow.

56

2.1

Study area

57

We choose the Meghna Basin area of Bangladesh as our study area because of frequent flood-58

ing and climate seasonality. Within the Meghna Basin, we select the administrative upazilas of59

Narsingdi Sadar and Roypura, because they are characterized by different production potentials.

60

Narsingdi Sadar includes villages 1 – 14 (figure 1). Narsingdi Sadar is located between 23°460 and61

23°580 north and between 90°360 and 92°500 east. According to the census of 2011, the total area

62

of Narsingdi Sadar was 52 742 acres where 149 820 households lived, the population was 707 525

63

and the population density was 3315 km−2 . Roypura includes villages 15 – 37 (figure 1). Roypura64

is located between 23°550 and 24°40 north and between 90°440 and 90°590 east, and the area is

65

77 287 acres. There were 110 520 households, and the population was 535 796. The population

66

density of Roypura was 1713 km−2 . Narsingdi Sadar had a higher industrial potential and Roypura

67

a relatively high agricultural potential.1[Figure 1 about here.]

68

69

The study areas had relatively uniform climate conditions with respect to temperature, high70

humidity, and heavy rainfall. They are located at a low altitude, and the average annual temperature

71

ranges from 13 °C to 35 °C. Heavy rainfall usually occurs from June to October, which is the72

monsoon season. Three major rivers — the Meghna, Old Brahmaputra and Arial Khan — are the73main water source for the area. Local agriculture is completely dependent on the natural water74

supply. During the monsoons, flooding is a normal part of the local ecology. The 1998 flood was

75

the most serious in that region, destroying the economy of the whole area. It inundated over 70 %76of the total land area and caused severe damage to lives and properties. In all, over 30 million
1

Census 2011 information is available at www.bbs.gov.bd.

5

77

people were affected as were about 1 million homesteads. Riverbanks in the Meghna Basin are not78

protected by embankments. Locals have to resort to adaptation strategies to reduce the impacts of79

floods.

80

2.2

Data

81

This study uses primary data collected from the field survey. We design the survey to examine82

public perceptions to seasonality changes and the related issues: the aim is to elicit individual per-83

ceptions and obtain insight into the reasons for the correct perceptions. We use a general household

84

as the unit of analysis and regard the earning male as the decision maker. A structured question-

85

naire is employed to collect the data. Care is given with the items in the survey questionnaire

86

to ensure that respondents do not require expert knowledge to answer. The data were collected

87

through interviews by research assistants using stratified random sampling. We conducted the sur-88

vey with 1020 households. However, nine households did not complete the questionnaire, and89

we omitted them from the analysis. Thus, the final sample comprises 1011 respondents. We also

90

survey seven experts that specialize in flood control; they answer the questions related to climate

91

changes and perceptions of flood risk in the study area. We design a separate questionnaire to92

elicit the expert responses about various issues concerning climate change and flooding there. The

93

results of the experts’ interviews are not included in the statistical analysis; however, we reference

94

their responses to ensure qualitative judgement throughout the study.

95

The questionnaire for general respondents comprises the following range of items: perceptions

96

to climate change, in particular, seasonality changes; socioeconomic conditions and variables;

97

damage from and experiences of major floods; participation in disaster risk management and adap-

98

tation measures; and life history in the residential area. We use the variables listed in table 1 for99

statistical analysis. Note that we have collected the climate data concerning seasonal changes in

100

Bangladesh, demonstrating that the distributions of seasonal mean temperatures and mean rainfall

101

anomalies have become homogeneous among the seasons. More specifically, climate variation has

102

declined in the way that the annual calendar in the study area transitions from six to four seasons6103(Islam and Kotani, 2016). Our survey is designed to check whether the respondents perceive such104

seasonality changes.
[Table 1 about here.]

105

106

Households in the study area are subject to the risks of seasonal flooding, and those who meet107

the criteria and participate in the survey were compensated for their time with a gift of an equivalent

108

value. All the items in the questionnaire had to be answered. We categorize the flood experiences

109of respondents based on their financial losses from the 1998 flood. Three questions concerning cli-

110mate change on local flood, sea level rise and climate change are posed as variables of information

111

access. Bangladeshis have a moderate level of awareness of climate change. Figure 2 presents the112

respondents’ overall climate change awareness and attitudes. Surprisingly, 78 % of respondents

113

have knowledge about climate change. However, only 50 % consider climate change as an im-

114

portant issue. To obtain the respondents’ perceptions of seasonality changes, we ask “Have you

115

noticed seasonal changes in your area over the last 25 years?” Most respondents (65.28 %) think

116

that such changes have not been evident, while the impact of climate change on the Meghna Basin117

should be considerable (Mirza, 2003, Masood and Takeuchi, 2015).2[Figure 2 about here.]

118

119

2.3

Statistical analysis

We apply logit regression to model the respondents’ perception to climate seasonality in relation to sociodemographic variables, experiences and information access. Whether or not a respondent perceives climate seasonality is expressed as a binary-choice model of logiti regression,
assuming that the perception depends on observable factors. Let Yi be a dummy variable taking Yi = 1 when respondent i perceives seasonality change, otherwise Yi = 0. The probability
for respondent i to perceive climate seasonality, denoted by Prob(Yi = 1), is assumed to follow
2

That region will have played a major role in the occurrences of flooding in Bangladesh over the next several
decades (Mirza, 2003). The peak flow of water in the basin is predicted to increase by 39.10 % during the monsoons
and decrease up to 26.90 % in the dry season (Kamal et al., 2013).

7

the distribution function F evaluated at Xi β where Xi is a 1 × (k + 1) vector of explanatory
variables for respondent i (Xi = (1, xi1 , . . . , xik )) and β is a (k + 1) × 1 vector of parameters(β = (β0 , β1 , . . . , βk )0 ). The logit regression takes the following form of a distribution function:

Prob(Yi = 1) =exp (Xi β)
1 + exp (Xi β)

(1)

120

A specification of equation (1) enables us to estimate parameters β via maximum likelihood and121

the probability for a respondent to perceive climate seasonality in relation to explanatory variables

122(Wooldridge, 2016, 2010).

123

The explanatory variables used in this study include (1) education of household heads, (2)124

income, (3) natural resource dependence in profession, (4) losses in the 1998 flood, (5) flood

125

information from neighbors, (6) living in an inherited house, (7) 20 years of residence in the study

126

area, (8) distance of a house from nearest river and (9) information access about local flood risk, sea

127level rise and climate change risks. The definitions of the variables are summarized in table 1. We

128

are also interested in identifying the marginal effect of each explanatory variable on the response

129

probability of Prob(Yi = 1), holding all other variables fixed. More specifically, we estimate

130

the marginal impact on the probability for a respondent to perceive climate seasonality when one

131

explanatory (dummy) variable increases by one unit (or changes from zero to one) via equation (1).

132

3Results

133

In our analysis, the perception of seasonal changes in Bangladesh is the outcome variable which134

is guided by different socioeconomic, geographic, demographic and regional climate knowledge

135

variables. The estimated coefficient of the variables and the marginal effect is reported in table 2.

136

The binary logit model results identify the relationship between the seasonal change perception and137

the control variables we use in the model. We notice that education of the household head is an

138

important variable to improve the probability of correct seasonal change perception. The marginal

139

effect of the logit model shows that educated respondent have 6 % higher probability of correct per8

140

ception than those without any formal education. These findings reflect the result of Kvaløy et al.

141(2012), where they identify positive correlation between education and the perception of global

142

warming. Education improve the concern of seasonal changes and shape individual’s perception

143

of weather anomalies inconsistent patterns compared to previously experienced conditions.
[Table 2 about here.]

144

145

Income is one of the key indicators of individual’s socioeconomic condition. This income level146

impacts on seasonal change perception in Bangladesh. In our analysis education is considered147

as a categorical variable and constructed 9 different categories after carefully observing the range

148

of national level income group classifications. The average gap between each income group is149

approximately 5000 BDT (USD 65). Our logit regression result shows that change of income cat-150

egory from one group to the next increase the probability of correct seasonal change perception by

151

5 %. This change can be related with the fact that the low income group, who are more vulnerable152

to changing climate are less concerned about the seasonal changes in Bangladesh.

153

Livelihood and occupation in Bangladesh are not free from the dependence of nature. Tradi-154

tional agriculture, fishery and agricultural related business are highly dependent on the changes

155of weather anomalies (e.g; rainfall, temperature). Having an occupation dependent on natural re-156

sources exerted a clear influence on perceiving climate seasonality. The regression results showed157that the coefficient for such occupations was positive and significant. According to the estimated158

marginal effect, respondents with such occupations were 21 % more likely to perceive climate sea-

159

sonality than other participants. Farming has a close interaction with the natural anomalies. As of160

our knowledge, no literature empirically analyze the individual’s perception on seasonality change

161

in relation to the natural resource dependence in occupation. Our result clearly shows that natural

162

resource dependence in occupations have important impact on shaping the perception of weather

163

anomalies. Climate change and natural disasters adversely impact production in many agriculture-164

dependent households. Thus, farming households are aware of climate variability and can identify

165

the most common climate factors.

9

166

We observe that natural disaster victims have significant positive relationship to seasonal change

167

perception. We found a clear difference between flood victims’ and non-victims’ knowledge and168

perceptions of seasonal change. Recently flood losses have sharply increased in Bangladesh. For

169

instance, 1998 flood is one of the most devastating disasters Bangladesh has ever experienced and170

affected almost the whole country. We clearly notice that the probability of making a correct re-171

sponse to changes in seasonal trends by a 1998 flood victim as significantly positive. An household

172

head who experienced losses of 1 lach BDT (1300 USD) had a 13 % higher probability of perceiv-

173

ing climate seasonality than one who did not suffer any loss. In addition, residents who received

174

flood information from neighboring households had an 11 % higher chance of perceiving climate

175

seasonality. These findings suggest that flood loss and information shape perceptions of climate

176

change in Bangladesh. From our empirical findings, it is evident that local flooding experience

177

provides opportunities to engage with the seasonality change perception. People may not take178

necessary action to mitigate climate change if they lack first-hand experience of its consequences.

179

Better understanding of the mechanism that influences such perception may be relevant to commu-

180

nication strategies for risk management. Individuals having direct experience of incidents directly

181

linked with seasonal change may undertake sustainable behavior.

182

We consider the residence time as a geographic factor that indicates individuals experience with183

the local weather. Long residence in an area allows people to observe climate changes and patterns.

184

They can accurately compare previous and present conditions of climate variables. Long residence

185

also allows the sharing of experience with others. We notice that inherit householders have in-186

teraction with local residents as well as with local environment. In addition, the experience from187

the ancestors also help to shape their personal perception in local Bangladesh. Our respondents

188

share their experience beyond the structural questionnaire and most of them noticed their ancestor

189

face less environmental variations than the present time. Our analysis suggest that individuals who

190

have an inherit house in the area have 7 % higher possibility to perceive the changing seasonality

191

in local Bangladesh compared to the individuals who don’t receive any house inherently.

192

We also identify the distance of households from rivers as a geographic factor and it is an im-

10

193

portant variable regarding flood vulnerability. People living closer to rivers are concerned about

194

water-related disasters. Riverbanks in the Meghna Basin lack embankments. Therefore, local

195

communities are completely dependent on self-adaptation strategies. In this analysis, 53 % of re-196

spondents had been living in the study area for over 20 years. We found that longer residence could

197

increase the probability of correct perception of climate change by 6 %. Long-term residence in198

an area promotes accurate understanding of local weather trends. Longer residence helps increase

199

communication, which builds a stronger sense of community; it helps establish networks to share

200

information and inspire people to adapt.

201

We find that information about flood risk among residents guided them in developing accurate

202

perceptions of seasonal changes at the local scale. According to our estimated marginal effect,203

knowledge of local flood risk increased the likelihood of perceiving local climate change season-

204

ality by 9 %. We observed no relationship between information about global climate change and205

perceptions of local seasonal changes. Our findings suggest that efforts to enhance public infor-

206

mation about global climate change are not the crucial factor in improving perceptions of local

207

seasonality change issues. Rather, information about local climate change risk is more appropriate

208

for increasing local seasonality perceptions.

209

4Conclusion

210

The present study examine the influence of profession, disaster experience, and socio-economic

211

factors on perceiving climate seasonality. The development of mitigation and adaptation policies

212

targeting local communities is guided by perceptions of climate change. This paper offers insights

213

into adaptation to climate change by improving socioeconomic characteristics: it addresses the214

existing lack in the literature—especially in developing regions—and it presents an approach to215

recognizing climate seasonality. This empirical study finds that without personal experience, it is216

difficult for individuals to accurately perceive climate change. Having an occupation that is de-217

pendent on natural resources also increases an understanding of climate seasonality. Experience of

11

218

flood damage raised the likelihood of correctly perceiving seasonal changes. Education also played219

an important role. Our results offer potentially important guidance for public risk perception. Sim-220

ply considering people’s risk perception may be insufficient, but it is important for resilience. To

221develop disaster-resilient communities towards finding better ways to manage risks for extreme

222

and catastrophic events; perception of climate change is a key determinant in this regard.

223

Understanding and perceiving changed seasonality by natural-resource users is potentially im-

224

portant: it has an influence on their adaptation practices. We may hypothesize that people with225

flood experience are more likely to engage with climate change issues. Perceiving changes in local

226

weather patterns is an important precursor to proactive adaptation to climate change. Individual

227

perceptions of changes in local climate play an important role in shaping vulnerability to global

228

climate change, adaptive behavior, and support for adaptation and mitigation policies. There is a229

wide discrepancy between climate change and accurate perception. Better education guide citi-

230

zens to give greater weight to the local climate changes; that in turn shapes risk perceptions of the231

general public.

232

Awareness of climate change is particularly important in regions where much of the population

233

is highly exposed and sensitive to the impacts of such changes. Understanding climate risks is234

important: it can influence adaptation practices and disaster management. The World population

235

is going to be concentrated in urban cities within next 50 years and so on. Urbanization is taking

236

place in Asia and Africa, and 65–75% of the world population will be concentrated in urban cities

237

in Asia and Africa (Wigginton et al., 2016, McDonnell and MacGregor-Fors, 2016, Henderson

238

et al., 2016). However, the perception about changing seasonality and climate change may become239

less likely to be predicted by the urban people who are less dependent on nature for their daily

240

life. Therefore, it will be very difficult for the majority of population to become concerned about241

ongoing climate changes. This is alarming in the way that without clear climate change perception,242

societies may become reluctant to reduce the activities which can directly and indirectly affect the243

success of climate policies.

125References

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personally experience global warming, and if so how, and does it matter? Global Environmental
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Capstick, S. B. and Pidgeon, N. F. (2014). Public perception of cold weather events as evidence
for and against climate change. Climatic Change, 122(4):695–708.
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(2000). Climate extremes: observations, modeling, and impacts. Science, 289(5487):2068–
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13

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Kamal, R., Matin, M., and Nasreen, S. (2013). Response of river flow regime to various climate change scenarios in Ganges-Brahmaputra-Meghna basin. Journal of Water Resources andOcean Sciences, 2:15–24.
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14

List of Figures
1
2

The Meghna basin area of Bangladesh . . . . . . . . . . . . . . . . . . . . . . . . 16
Respondents’ overall idea about climate change in the study area . . . . . . . . . . 17

15

Figure 1: The Meghna basin area of Bangladesh

16

No
22%

Very
important very
8% important
8%
Importantenoughimportant
17% enough
17%

No
22%

Yes
78%

I don’tknow I don’t
48% know
48%

Important
23%important23%
Not important at
Somewhat
Not important at
Somewhatallimportantallimportant
1%
3%
1%
3%
(b) How important is the climate change issue?

Yes
78%

(a) Do you know about climate change?
Figure 2: Study area respondents’ overall idea of climate change
Figure 2:Figure
Study area
respondents’ overall
ideaideaof climate
2: Respondents’
overall
about change
climate change in the study area

To obtain the respondents’ perceptions of seasonal changes, we asked, “Have you noticed seasonal changes inTo overobtainrespondents’
perceptionsof seasonalchanges,
we thatasked,“Have
youhadnoticedseasonalchanges inyour area
thethelast
25 years?” Most
respondents(65.28%)thoughtsuchchangenot been
evident.
your
area
over
the
last
25
years?”
Most
respondents(65.28%)thoughtthatsuchchangehad
notbeen
evident.
The impact of climate change on the Meghna Basin should be considerable (Masood & Takeuchi, 2015; Mirza,The
impactofclimatechangeontheMeghnaBasinshouldbeconsiderable(Masood&
Takeuchi,2015;
Mirza,
2003). That region will play a major role in future flooding in Bangladesh (Mirza, 2003). The peak flow of
2003).
That
region
will
play
a
major
role
in
futureflooding
inBangladesh(Mirza,2003).
The
peak
flow
of
water in the basin may increase by 39.1% during the monsoons and decrease up to 26.9% in the dry season
water
in
the
basin
may
increaseby
39.1%
duringthemonsoons
anddecrease
up
to
26.9%
in
the
dry
season(Kamal, Matin, & Nasreen, 2013).
(Kamal, Matin, & Nasreen, 2013).
Table 1. Overview of variables used in the statistical analysis
Table 1. Overview of variables used in the statistical analysis
VARIABLESVARIABLES
Dependent variable
Dependent variable
Seasonal change perception
Seasonal change perception
Independent variables
Independent variables
Education
EducationType

Description
Description

Mean

SD
Mean

SD

1 = correct perception,Dummy
1 = correct perception,
Dummy0 = otherwise
0 = otherwise

0.65

0.47
0.65

0.47

1.19
0.11

1.19

1.75
3.37

1.75

0.22
0.95

0.22

Type

Natural resource dependent
Natural resource dependent
profession
profession

Educationno education (0) to post graduate (5)0.11
Education2categoryno education (0) to post graduate (5)2category
Incomeno income (0) to over 40,000 BDT (8)3.37
Income3categoryno income (0) to over 40,000 BDT (8)category3
1 =17
natural res. dependent profession,Dummy
1 = natural res. dependent profession,0.95
0=Dummy otherwise
0 = otherwise

Loss in 1998 flood (lakh)

Continuous

IncomeIncomeamount in lakh BDT4

4

0.56

0.9

List of Tables
1
2

The variables in statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Estimation of the logit regression for respondents’ perception to climate seasonality 20

18

19

0.46
0.46
0.45

1.19
1.75
0.22
0.9
0.34
0.50
0.50
0.38

0.47

SD

2

0: illiterate, 1: primary school, 2: secondary school, 3: college, 4: bachelor’s degree and 5: postgraduate degree
0: < 5000, 1: 5000 - 9999, 2: 10000 - 14999, 3: 15000 - 19999, 4: 20000 - 24999, 5: 25000 - 29999, 6: 30000 - 34999, 7:35000 - 39999 and 8: ≥ 40000
3
It is whether a subject’s profession is dependent on natural resources or not.
4
1 lakh BDT = 100 000 BDT = 1300 USD

1

0.32
0.30
0.27

Dummy
Dummy
Dummy

Information access to local flood = 1, otherwise 0Information access to sea level rise = 1, otherwise 0Information access to climate change = 1, otherwise 0

0.65

Mean

0.11
3.37
0.95
0.56
0.13
0.51
0.53
0.82

Correct perception = 1, otherwise 0

Description

Ordered categories No eduction (0) to postgraduate (5)Ordered categories No income (0) to over 40 000 BDT (8)DummyNatural resource dependence = 1, otherwise 0
Continuous
Amount in lakh BDT4
Dummy
Flood info from neighbors = 1, otherwise 0
Dummy
Living in an inherited house = 1, otherwise 0
Dummy
Residing in the area over 20 years = 1, otherwise 0
DummyDistance to the nearest river ≤ 1 km = 1, otherwise 0

Dummy

Dependent variable
Seasonality change perception

Independent variables
Education1
Income2
Natural resource dependence3
Loss in 1998 flood
Flood info from neighbors
Living in an inherited house
Residing over 20 years
Living near a river
Information accessto local flood
to sea level rise
to climate change

Type

Variable

Table 1: The variables in statistical analysis

Table 2: Estimation of the logit regression for respondents’ perception to climate seasonality
Explanatory variable
Socio-economic
Education
IncomeNatural resource dependence in profession
Natural disaster experience
Loss in flood 1998
Geographic location
Living in an inherited house
Residing over 20 years
Living near a river
Information
Flood info from neighbors
Information access tolocal floodsea level rise
climate change

Model

Marginal effect0.308***(0.081)0.253***(0.055)1.102***(0.309)0.059***(0.015)0.048***(0.010)0.210***(0.058)0.649***(0.164)0.124***(0.031)0.360**(0.157)0.322**(0.150)0.359*(0.187)0.069**(0.030)0.062**(0.028)0.069*(0.036)

0.578**(0.241)0.110**(0.046)0.479*(0.249)−0.260(0.251)−0.188(0.169)0.091*(0.047)−0.050(0.048)−0.036(0.032)

Constant−2.438***(0.411)

Sample sizeLR χ2 statistic
Log-likelihood
Pseudo-R2

1011
174.680−565.448
0.134*** significant at 1 % percent level** significant at 5 % percent level* significant at 10 % percent level
Standard errors are in parentheses.

20