Factors affecting habitat preference forenvironmental migrants in coastal Bangladesh

Pankaj Kumar
Research Manager (Adaptation and Water); Head (IPBES TSU-SCM)
Lead Author (IPBES NEXUS assessment; GEO 7 Report)
Institute for Global Environmental Strategies (IGES), Japan
kumar@iges.or.jp

Contents of the Presentation• Background• Methodology• Results
Operational definition
We refer to ‘environmental migrant’ as the households who were displaced from theirrural dwelling place due to environmental hazards and later settled in other rural areas orurban slums.
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Damage of river embankments and temporary solutions to connect the villages

3Flooded and disconnected houses

Submerged agricultural fields

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5Flooded and disconnected houses

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6

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Temporary slum dwellers

7This study specifically addresses two research questions:(i) What factors drive environmental migrants to choose relocation
destinations following climatic events?
(ii) How do environmental migrants' habitat preferences impact theirpost-migration well-being?

Methodology
Study Design
Quantitative approach used in this study.
The research is based on primary data collected from the respondents.

Sampling technique and data collection
408 household respondents data was collected by applying a random and systematic
random sampling method, respectively. We interviewed 200 respondents from urban
slums in Khulna and Satkhira and 208 respondents from rural areas of Koyra of Khulnaand Shyamnagar of Satkhira.

Methodology (cont.)
Analytical Tools
Linear normalization
This study used linear normalization or commonly known as min-max normalization tocalculate dependent variable that is, ‘Household satisfaction score of the environmentalmigrants’. The formula of linear normalization:
𝑿𝒊 − 𝑿𝒎𝒊𝒏
𝑵𝒐𝒓𝒎𝒂𝒍𝒊𝒛𝒆𝒅 𝒗𝒂𝒍𝒖𝒆 𝒐𝒇 𝑿 =… … . . (𝟏)𝑿𝒎𝒂𝒙 − 𝑿𝒎𝒊𝒏
Here, Xi= This denotes the single data point from the dataset for variable X.
Xmin= This is the lowest value of the variable X from the dataset.
Xmax= This is the highest value of the variable X from the dataset

Methodology (cont.)
The minimum value of that feature transformed into a 0, the maximum value transformed into a 1,
and every other value transformed into decimals between 0 and 1.
Finding the mean of the normalized numbers was the next step after normalization was finished.
∑𝒏𝑵𝒐𝒓𝒎𝒂𝒍𝒊𝒛𝒆𝒅 𝒗𝒂𝒍𝒖𝒆 𝒐𝒇 𝑿𝒊
Average normalized value = 𝒊=𝟏………. (2)𝒏Here, n= Total twelve variables that was normalized by the formula applied in equation (1).
Analytical tool
- Regression models (OLS and logistic)

Table 1: Variables used to construct respondents’ well-being score
Variables Name
I have better income prospect here
I felt much secured here
My neighbors supported me at time of a hazard and otheremergencies
Community level help
Help from elite people
Living expenditure has been very affordable for me here
I thought that I would have a better social capital here
I would receive more financial advantage here in any postdisaster situation
I receive help from political leader at time of a hazard
emergency
I have an advantage of accessing to natural resources here
Government help post disasterI would receive more assistance from political figures here inany post-disaster situation

Unit of Measurement Literature Support
Likert scale
Benítez et al.,2019
Likert scale
Blanco and Díaz, 2005
Likert scale
Blanco and Díaz, 2005
Likert scale
Likert scale
Likert scale
Likert scale
Likert scale

Blanco and Díaz, 2005
Blanco and Díaz, 2005
Xiong et al., 2023
Xiong et al., 2023
WaterAid, 2021

Likert scale

WaterAid, 2021

Likert scale
Likert scale
Likert scale

WaterAid, 2021
Joarder et al. (2017)
Joarder et al. (2017)12

Fig. 1: Map of the study locations
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Results- Determinants affecting the well-being of theenvironmental migrant households- What other factors affect the habitat choice of theenvironmental migrant households

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Table 2: Regression results of determinants of household well-being
Variables
Environmental migrant type (Ref: Rural dwellers)Slum dwellers
Gender (Ref: Women)
MenAge (years)
Living duration in the current area (in years)
Household size (in numbers)
Monthly income (log transformed)
Training on income-generating activities (in numbers)
Total preparation actions taken (numbers)
Alternative income source (1=Yes; 0=Otherwise)
Access to educational support (1=Yes; 0=Otherwise)
Access to healthcare support (1=Yes; 0=Otherwise)
Access to training support (1=Yes; 0=Otherwise)
Access to financial support (1=Yes; 0=Otherwise)
Access to agricultural support (1=Yes; 0=Otherwise)
Access to social safety net support (1=Yes; 0=Otherwise)
Access to sanitation support (1=Yes; 0=Otherwise)
Access to safe drinking water (1=Yes; 0=Otherwise)
Source of receiving early warning (in numbers)
Duration of suffering from disasters (in months)
Interaction term (alternative income source* number of benefits received)
Intercept
Number of observations
R-squared

DV: Well-being score
Coefficient (R. Std. Err.)-0.107* (0.029)-0.033** (0.016)-0.001 (0.001)
0.000 (0.001)
0.001 (0.004)-0.013 (0.016)-0.005 (0.006)-0.006 (0.015)
0.011 (0.018)
0.051* (0.017)
0.047** (0.020)
0.042* (0.016)
0.017 (0.016)-0.000 (0.016)-0.004 (0.015)-0.013 (0.015)
0.029*** (0.015)
0.027* (0.005)-0.003 (0.009)-0.007*** (0.004)
0.602* (0.152)
408
0.24

It explores the determinants ofwell-beingscoresamong
environmental migrants residing inrural and slum areas using
multivariate regression analysis.
The regression results suggest thatenvironmental migrants in slumareas have a well-being score of0.107 units lower compared tothose in rural areas (p < 0.10).
Additionally, men's well-beingscores are 0.033 units lower thantheir counterparts (p < 0.05). Thesefindings underscore a nuanced
relationship where accumulating
advantages may not necessarily
linearly enhance overall wellbeing.

*** p<0.01, ** p<0.05, * p<0.10
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Table 3: Determinants of habitat preference among environmental migrants
Migrant type
Variables
Well-being score
Gender (Ref: Women)
MenAge (in years)
Living duration at the current area (in years)
Household size (in numbers)
Monthly income (log transformed)
Training on income generating activities (in number)
Disaster preparedness actions adopted (in number)
Duration of sufferings from disasters (in months)
Access to educational support (1= Yes; 0= Otherwise)
Access to healthcare support (1= Yes; 0= Otherwise)
Access to financial support (1= Yes; 0= Otherwise)
Access to agricultural support (1= Yes; 0= Otherwise)
Access to social safety net support (1= Yes; 0= Otherwise)
Access to sanitation support (1= Yes; 0= Otherwise)
Access to safe drinking water (1= Yes; 0= Otherwise)
Received early warning (1= Yes; 0= Otherwise)Intercept
Number of observations
Log likelihood
AIC(1=Slum dweller; 0=Rural dweller)
Odd Ratio (Std. Err.)
0.003** (0.007)
3.313*** (2.217)
1.066*** (0.037)
0.899* (0.025)
0.834 (0.157)
3.337 (2.982)
0.916 (0.231)
0.288** (0.164)
4.441* (1.466)
6.557* (4.238)
25.843* (17.730)
0.983 (0.641)
0.604 (0.357)
0.454 (0.260)
57.005* (40.062)
3.672** (2.199)
0.943 (0.218)
0.000*** (0.000)
408-50.46136.92This table presents thefactors influencing income
levels among environmental
migrants in rural and slumareas. The regression results
indicate that the explanatory
variables explained 33% of
the variation in income
levels. Factors such as time
constraints during the survey
process, cultural differences,and individual heterogeneity
accounted for the remaining
unexplained variation.

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Figure 2 : Monthly income vs well-being among rural and slum residents

Although not any significant difference in monthly income between the rural and slum residents (BDT 5000 and BDT 12500), we
found a slight positive association between income and well-being for both groups. Also, the trend lines show that as well-being
levels increase, the monthly income of rural dwellers surpasses that of slum dwellers. This illustrates that higher well-being is
associated with higher income, specifically for rural dwellers. In contrast, urban dwellers do not show a notable increase in wellbeing corresponding to their income levels.
17Figure 3: Well-being pattern of the respondents with respect to duration

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This figure illustrates that environmental migrants living in rural areas generally remain in their current location longer than slum
residents. This figure depicts that rural residents with longer living durations exhibit higher well-being scores than theircounterparts in slum areas. Additionally, migrants who migrate to rural areas are typically less likely to migrate to urban areas
because they hold a strong attachment to the rural context, including social connections, psychological attachment, and a sense ofsecurity in rural environments.
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Figure 4: societal, political, and financial differences among the environmental migrants

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Slum dwellers have agreater number of sources
for receiving early warnings
about disasters compared torural dwellers. Both groups
receive post-disasterassistance from local elites,
political leaders,communities, peer groups,
and the government tovarying extents. Visualizing
the disparities, migrating tourban areas emerges as alogical and sensible
decision for individuals
seeking improved living
standards and to mitigate
environmental risks, thusenhancing resilience toenvironmental hazards.
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Concluding remarks- In rural areas respondents appeared to enjoy better social capital than slum areas- Respondents in rural areas appeared to enjoy higher level of income- Despite lower degrees of access to different services (e.g., sanitation, education etc.),
respondents in rural areas exhibited higher level of well-being score20