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This work was carried out under the
Collaborative Adaptation Research
Initiative in Africa and Asia
(CARIAA), with financial support
from the UK Government’s
Department for International
Development (DFID) and the
International Development Research
Centre (IDRC), Canada. The views
expressed in this work are those of
the creators and do no necessarily
represent those of DFID and IDRC
or its Board of Governors.
Website: www.deccma.com Twitter: @deccma
Introduction
Household Listing (HL) is a diminutive census of all
individual households within an enumeration area (EA).
The objective of the HL was to finalize those households
which would be surveyed to collect further in depth
information in DECCMA Household Social Survey.
A Scenario of Migration: Findings of Household Listing in Origin Area
Survey of the GBM Delta, Bangladesh Prodip K. Das* & Mohammad Rashed Alam Bhuiyan**
Refugee and Migratory Movements Research Unit (RMMRU)
DECCMA 5th Consortium Workshop, 30 Aug - 2 Sept 2016, India
19 Coastal districts
153 Upazilas
14771 Mauzas
Selection For DECCMA Study
14 Coastal districts
41 Upazilas
50 Mauzas (EA)
Each Mauza → 200 HHs
listed
Unit of Analysis: Mouza
Hazard Type Weightage
Storm surge 0.323
Flood 0.098
Salinization 0.278
Erosion 0.308
Max 200 dwelling unit per cluster
Study Findings
Female Male
337
5400
189
2764
Gender of Household Head
Non-Migrant Migrant
Methodology Development of Multi-hazard Map
WS + WE + Wsa + WF ≤ 1 Weightage computed (W) for Storm surge (S),
Erosion (E), Salinity (Sa), Flood (F).
WSmax + WEmax + Wsamax + WFmax=1 Maximum Weightage (W) for four hazards storm
surge (S), Erosion (E), Salinity (Sa), Flood (F).
Hmulti-hazard = Hstorm + Herosion + Hsalinity + Hflood
Weightage Calculation
Multi-hazard Category
1. Very low (12 EAs)
2. Low (11 EAs)
3. Medium (10 EAs)
4. High (9 EAs)
5. Very High (8 EAs)
i. Social damage
ii. Economics damage
Study Area
Conclusion
8713 HHs were listed and it was found that 34% of HHs (2962) are
migrant HHs. Of these, almost 11% (949) are international migrant HHs,
and 23% (2013) are internal migrant HHs, which is almost two or three
times greater than existing estimates from most prior studies (HIES,
2010; Sharma and Zaman, 2009; Hossain, Kazal and Ahmed, 2013).
Noakhali district had the highest rate of HHs with at least one migrant
(56%), while Cox’s Bazar had the lowest percentage of migrant HHs (22%).
However, when only counting international migration, Cox’s Bazar ranked
first (271 HHs). A female was head of household in 12% of cases.
Before migration 9% of HHs were involved in regular salaried employment
(i.e. informal sector/Driving/Factory/Tailoring), and after migration 24%
of migrants were involved in regular salaried employment(i.e.
Construction/Factory/Transport worker).
These finding reveal important policy requirements for managing internal
migration, namely the implementation of a resilient and inclusive urban
development strategy involving the relocation of industries.
Migrant Non-Migrant
542
1663
378 554
344
764
1703
2766
Dwelling-structure types of households
Mud/Thatched/Jhupri Pacca Semi-Pacca Tin Shed
0
500
1000
1500
2000
2500
Occupation types of HH head
Non-Migrant Migrant
0
200
400
600
800
1000
1200
1400
1600
1800
2000
HHs distribution by district Non-Migrant Migrant
0 500 1000 1500
Unpaied Homecare
Transport Worker
Small Business
Regular Salaried Employee
Other(Labour, Doctor, Monk etc.)
Livestock Farmer
Fishing
Fish/Shrimp Farmer
Factory Worker
Dressmaker/tailor
Domestic Employee
Crop Farmer
Construction Worker
Changing occupation due to migration
After Migration
Before Migration
126 88 90
324 321
0
100
200
300
400
500
600
High Low Medium Very High Very Low
Comparing Migration Type by Hazard Level
Internal International
0
20
40
60
80
100
120
140
Re
han
ia
Utt
ar B
he
juga
nia
Bh
uiy
ar C
har
Gen
dam
ara
Bar
abak
ia
Fari
dab
ad
Ch
ota
Bal
iata
li
Ch
ar L
aksh
mi
Ho
glap
ash
a
Haj
ikh
ali
Bh
adu
lia
Seka
nd
erp
ur
Be
ni B
abu
Ro
ad
Ph
ult
ala
Bad
arkh
ali
Be
loka
ti
Sard
arn
agar
Saga
rkat
hi
Dig
anga
Nak
na
Ch
ar E
lah
i
Shya
mp
ur
Am
urk
ata
Ch
eri
nga
(P
art)
Sara
l (p
art)
Am
arga
chh
ia
Mo
mre
jpu
r
Dak
shin
Kat
-Tal
i
Shah
Mir
pu
r
Bau
ria
Go
rakg
hat
a P
asch
im P
ara
Safl
apu
r
De
mu
sia
Len
gurb
il
Go
l Dig
hir
Dak
shin
Ro
ad
Gh
oti
ban
ga
Mak
ard
hw
aj
Bab
up
ur
Sree
pu
r
Bai
dya
pas
ha
Kal
cham
a
Ban
ajo
ra
Utt
ar B
asu
nia
Bh
uia
ra
Rad
han
agar
Kri
shn
anag
ar
Tura
bga
nj
Bah
adu
rpu
r
Ram
garh
Sit
aku
nd
a R
.F (
par
t)
High Low Medium Very High Very Low
Migration type in EAs by hazard level Internal International
0
100
200
300
400
Bar
abak
ia
Bh
uiy
ar C
har
Ch
ar L
aksh
mi
Ch
ota
Bal
iata
li
Fari
dab
ad
Gen
dam
ara
Ho
glap
ash
a
Re
han
ia
Utt
ar B
he
juga
nia
Nan
de
rkan
di
Bad
arkh
ali
Be
loka
ti
Be
ni B
abu
Ro
ad
Bh
adu
lia
Dig
anga
Haj
ikh
ali
Ph
ult
ala
Saga
rkat
hi
Sard
arn
agar
Seka
nd
erp
ur
Am
arga
chh
ia
Am
urk
ata
Ch
ar E
lah
i
Ch
eri
nga
(P
art)
Dak
shin
Kat
-Tal
i
Mo
mre
jpu
r
Nak
na
Sara
l (p
art)
Shah
Mir
pu
r
Shya
mp
ur
Mai
tbh
anga
Bau
ria
De
mu
sia
Gh
oti
ban
ga
Go
l Dig
hir
Dak
shin
Ro
ad
Go
rakg
hat
a P
asch
im P
ara
Len
gurb
il
Safl
apu
r
Bab
up
ur
Sree
pu
r
Bah
adu
rpu
r
Bai
dya
pas
ha
Ban
ajo
ra
Bh
uia
ra
Kal
cham
a
Kri
shn
anag
ar
Mak
ard
hw
aj
Rad
han
agar
Ram
garh
Sit
aku
nd
a R
.F (
par
t)
Tura
bga
nj
Utt
ar B
asu
nia
High Low Medium Very High Very Low
Distribution of EA by hazard level and HH type Migrant Non-Migrant
*Prodip K. Das, Research Assistant,
DECCMA-BD, E-mail ID:
**Mohammad Rashed Alam Bhuiyan,
Co investigator, DECCMA-BD, E-mail ID:
Method and Strategy for subsequent
in-depth household survey
Trained a survey team with Tablet/iPad (Equal male and female ratio)
Gender-sensitive survey techniques
Individualized questionnaire structures
Multi-layer data checking
Data uploaded to main server
0
50
100
150
200
250
300
350
400
Bagerhat Barguna Bhola Chadpur Chittagong Cox's Bazar Gopalganj Jessore Khulna Lakhmipur Noakhali Pirojpur Potuakhali Satkhira
Migration Types by Districts Internal International
