fsup baseline report final 23 june
DESCRIPTION
Baseline ReportTRANSCRIPT
FUNDED BY THE EUROPEAN UNION
IMPLEMENTED BY
BASELINE STUDY BY
Food Security for the Ultra Poor – Haor Baseline Study Report
FSUP-H Baseline Report, June 2010 ii
Principal Authors
Richard Caldwell
Executive Director
TANGO International, Inc.
Bruce Ravesloot
Asia Representative
TANGO International, Inc.
Md. Abdul Quddus
Team Leader, FSUP-H Baseline Study
Data Management Aid
Maqbul H. Bhuiyan
Executive Director
Data Management Aid
FSUP-H Baseline Report, June 2010 iii
Table of Contents
List of Tables, Figures and Pictures ................................................................................................... v
Acknowledgements ........................................................................................................................... viii
List of Abbreviations ........................................................................................................................... ix
Glossary of Bengali Terms................................................................................................................... x
Glossary of English Terms................................................................................................................... x
Executive Summary ............................................................................................................................. xi
1.0 INTRODUCTION .................................................................................................................... 1
1.1 Food security and poverty context in Bangladesh ................................................................ 1
1.2 Background of the FSUP-H project ....................................................................................... 2
1.3 Implementation framework of the FSUP-H project ............................................................... 3
1.4 FSUP-H site and impact group selection .............................................................................. 3
2.0 FSUP-H BASELINE STUDY .................................................................................................. 4
2.1 Rationale of the study ........................................................................................................... 4
2.2 Objectives of the study .......................................................................................................... 5
2.3 Scope of the study ................................................................................................................ 5
3.0 STUDY METHODS ................................................................................................................. 5
3.1 Study design.......................................................................................................................... 5
3.2 Quantitative study design ...................................................................................................... 5
3.2 Qualitative study design ...................................................................................................... 10
4.0 DEMOGRAPHIC CHARACTERISTICS ............................................................................... 12
5.0 LIVELIHOODS AND ECONOMIC SECURITY .................................................................... 15
5.1 Occupational patterns ......................................................................................................... 15
5.2 Household employment and income/expenditure ............................................................... 20
5.3 Income in peak and lean seasons....................................................................................... 24
5.4 Coping strategies for lean seasons ..................................................................................... 25
5.5 Migration .............................................................................................................................. 27
5.6 Loans ................................................................................................................................... 27
5.7 Assets .................................................................................................................................. 31
5.8 Housing characteristics ....................................................................................................... 36
6.0 FOOD SECURITY ................................................................................................................ 38
6.1 Food consumption score ..................................................................................................... 38
6.2 Food intake .......................................................................................................................... 40
6.3 Coping strategies ................................................................................................................ 43
6.4 Trend analysis ..................................................................................................................... 44
7.0 WATER AND SANITATION ................................................................................................. 49
7.1 Drinking, cooking and washing water sources .................................................................... 49
7.2 Arsenic testing ..................................................................................................................... 53
7.3 Sanitation ............................................................................................................................ 53
8.0 HEALTH PRACTICES AND ILLNESS ................................................................................ 57
8.1 Hand washing...................................................................................................................... 57
FSUP-H Baseline Report, June 2010 iv
8.2 Illness among adults and health-seeking behavior ............................................................. 58
9.0 PARTICIPATION AND ACCESS ......................................................................................... 60
9.1 Participation in development ............................................................................................... 60
9.2 Access to GoB services ...................................................................................................... 62
9.3 Access to other services ..................................................................................................... 66
9.4 Access to common property ................................................................................................ 68
10 DISASTERS AND CRISES .................................................................................................. 70
10.1 Natural disasters: effects and coping strategies ................................................................. 70
10.2 Household crises: effects and coping strategies................................................................. 72
10.3 Climate change ................................................................................................................... 74
11 FAMILY AUTHORITY AND DECISION MAKING ............................................................... 76
11.1 Household decision making ................................................................................................ 76
11.2 Family life attitudes ............................................................................................................. 80
11.3 Daily time patterns of men and women ............................................................................... 81
12 CHILD NUTRITION, ANTENATAL CARE AND FAMILY PLANNING ................................ 83
12.1 MCHN characteristics ......................................................................................................... 83
12.2 Anthropometric measurements ........................................................................................... 89
13 STATUS OF FEMALE-HEADED HOUSEHOLDS ............................................................... 91
14 CONCLUSION AND RECOMMENDATIONS ...................................................................... 92
FSUP-H Baseline Report, June 2010 v
List of Tables, Figures and Pictures
TABLES
Table 1: Four main pillars of food security 1
Table 2: Illustrative sample sizes for stratified random sampling using variants of deff and standard
error
8
Table 3: Sampling statistics of 1,892 households, by District and Haor type 8
Table 4: Qualitative techniques applied for the baseline survey 10
Table 5: Key demographic characteristics of the population, by District and Haor type 12
Table 6: Demography and dependency ratios, by District and Haor type 14
Table 7a: Primary and secondary occupations for individuals aged 8 years and older, by Haor type 16
Table 7b: Primary and secondary occupations for individuals aged 8 years and older, by District 17
Table 8: Income sources for previous 30 days, by Haor type 20
Table 9: Income sources for previous 30 days, by District 21
Table 10a: Key income and expenditure data for households, by District and Haor type 22
Table 10b: Detailed expenditure data, by District and Haor type 23
Table 11: Top ten ways of coping with lean periods (multiple response), by District 26
Table 12: Top ten ways of coping with lean periods (multiple response), by Haor type 26
Table 13: Type of work performed by those migrating out of the household within the last 12 months,
by District and Haor type
27
Table 14a: Key loan data for households, by District and Haor type 29
Table 14b: Detailed interest rate data for loans, by District and Haor type 29
Table 14c: Loan source for women, by District and Haor type 30
Table 15: Reasons for taking out a loan, by Haor type 30
Table 16: Reasons for taking out a loan, by District 31
Table 17: Average number of domestic assets owned, by District and Haor type 32
Table 18: Average number of productive assets owned, by District and Haor type 33
Table 19: Average number of land assets owned, by District and Haor type 33
Table 20: Average number of animal assets owned, by District and Haor type 34
Table 21: Average number of resource assets owned, by District and Haor type 34
Table 22: Average financial assets owned, in Taka, by District and Haor type 35
Table 23: Housing characteristics, by District and Haor type 36
Table 24: Food consumption score 38
Table 25: Proportion of sampled households by FCS threshold values 39
Table 26: Seasonal calendar 44
Table 27: Drinking water sources, by District and Haor type 49
Table 28: Cooking water sources, by District and Haor type 50
Table 29: Washing water sources, by District and Haor type 51
Table 30: Tube wells/tara pumps tested for arsenic, by District and Haor type 53
Table 31: Types of latrines used by adult men and women, by District and Haor type 54
Table 32: Types of latrines used by boys and girls 5-15 years of age, by District and Haor type 55
Table 33: Hand-washing behaviors among the FSUP baseline study households, by District and
Haor type (1)
57
Table 34: Hand-washing behaviors among the FSUP baseline study households, by District and
Haor type (2)
57
Table 35: Top ten illnesses experienced by adults in households during the previous 12 months, by
District and Haor type
58
Table 36: Usual treatment source for household members, by District and Haor type 58
Table 37: Household members involved in development processes 60
Table 38 Type of development institution/person that HH members were involved with 61
FSUP-H Baseline Report, June 2010 vi
Table 39: Type of collective action that households have participated in, by District and Haor type 61
Table 40: Proportion of households using various types of Government service providers, by District
and Haor type
63
Table 41: Types of services received by GoB service providers, by District and Haor type 65
Table 42: Proportion of households that have various property types available in their household
area, by District and Haor type
68
Table 43: Proportion of available property that is accessible by households, by District and Haor type 69
Table 44: Disasters experienced by households in the last 12 months, by District and Haor type 70
Table 45: Proportion of households experiencing various consequences of a natural disaster in the
last 12 months, by District and Haor type
71
Table 46: Household decision making, by Haor type (1) 76
Table 47: Household decision making, by Haor type (2) 77
Table 48 Household decision making, by District (1) 78
Table 49: Household decision making, by District (2) 79
Table 50: Attitudes about family life, by Haor type 80
Table 51: Attitudes about family life, by Haor type 81
Table 52: MCHN characteristics, by District and Haor type 85
Table 53: Weaning foods used, by District and Haor type 86
Table 54: Who attended last delivery, by District and Haor type 86
Table 55: Child health and immunization, by District and Haor type 87
Table 56: Health issues of mothers with children under 2, by District and Haor type 88
Table 57: Health issues of children under 2, by District and Haor type 89
Table 58 Anthropometric measurements 90
Table 59: Key variables for female-headed households, by district and Haor type 91
Table 60: Baseline values and recommendations for FSUP-H logframe indicators 92
FIGURES
Figure 1: Age distribution of study population, by sex 13
Figure 2a: Mean values of annual per capita income, by District 22
Figure 2b: Median values of monthly household cash income and expenditures per capita, by District
and Haor type
24
Figure 3: Average monthly incomes during peak and lean seasons, by District and Haor type 25
Figure 4: Mean FCS values, by District and Haor type 39
Figure 5a: Proportion of households reporting enough food, by month and Haor type (1) 40
Figure 5b: Proportion of households reporting enough food, by month and Haor type (2) 41
Figure 6 Mean number of lean months, by District and Haor type 41
Figure 7: Frequency of three 'square meals' taken a day in 12 months, by District 42
Figure 8: Frequency of three 'square meals' taken a day in 12 months, by Haor type 42
Figure 9: Coping Index‟ for households, by District and Haor type 43
Figure 10: Distances to sources of drinking water, by District and Haor type 51
Figure 11: Distances to sources of cooking water, by District and Haor type 52
Figure 12: Distances to sources of washing water, by District and Haor type 52
Figure 13: Types of service providers accessed, by District 63
Figure 14: Types of service providers accessed, by Haor type 64
Figure 15: Level of satisfaction with selected GOB services 65
Figure 16: Mean asset loss from households experiencing asset loss in a natural disaster in the last
12 months, by District and Haor type
71
Figure 17: Mean number of working days lost from households experiencing a natural disaster in the
last 12 months, by District and Haor type
72
FSUP-H Baseline Report, June 2010 vii
Figure 18: Loss of assets among households experiencing household crises in the last 12 months, by
District and Haor type
73
Figure 19: Loss of work days among households experiencing household crises in the last 12 months,
by District and Haor type
73
Figure 20: Average number of days lost due to illness for those households with an ill member
designated as a household crises in the last 12 months, by District and Haor type
74
Figure 21: Daily time use of men and women 82
Figure 22: Comparison between SHOUHARDO and FSUP-H malnutrition levels 90
PICTURES
Picture 1: Baseline enumerator using the PDA for a household interview 11
Picture 2: Agricultural day labor - males 15
Picture 3: Agricultural day labor - females 18
Picture 4: Cow rearing by ultra-poor households 19
Picture 5: Grameen Bank office 28
Picture 6: Jack fruit trees 35
Picture 7: Housing made of jute and straw 37
Picture 8: Housing made of corrugated iron 37
Picture 9: Women supporting household income through produce sales 45
Picture 10: Men fishing in the peak season 46
Picture 11: Non-agricultural day labor 47
Picture 12: Non-agricultural day labor - mat making 48
Picture 13: Woman uses hand tube well as the water source for washing 50
Picture 14: Ring slab latrine 54
Picture 15: Open defecation facilities 56
Picture 16: Village medicine shop 59
Picture 17: Union Health Center 59
Picture 18: Community collective action to improve road infrastructure 62
Picture 19: Women engaged in alternative livelihood activities 66
Picture 20: Government-owned Khas land 69
Picture 21: Damage to buildings as a result of natural disasters 70
Picture 22: Social mobilization around community issues 75
Picture 23: Balanced meal taken by a pregnant woman 84
FSUP-H Baseline Report, June 2010 viii
Acknowledgements
The baseline study was organized by TANGO International in partnership with Data Management Aid
(DMA). Special thanks go out to Mr. Md. Abdul Quddus, DMA Team Leader, Mr. Maqbul H. Bhuiyan,
Executive Director of DMA, and the DMA study team for their fantastic work in organizing the field
data collection, and their contributions to the study design and report.
The members of the baseline study team wish to thank the staff of CARE Bangladesh and their local
NGO partners for their time and effort during this study, in particular CARE‟s Social Development Unit
for their guidance to and facilitation of the qualitative data collection. We thank Mr. Zakir Khan, the
Team Leader of FSUP-H, and Mr. M. Zakaria, M&E Coordinator for FSUP-H, for all the support and
guidance they provided to this study.
We would also like to acknowledge Ms. Khaleda Khanom, Deputy Team Leader, and Ms. Salma
Akter, F&A Manager, the FSUP-H Technical Coordinators and the many CARE, ASD, POPI and SUS
field staff that did a great job in facilitating the baseline survey needs. This study would not have been
possible without their efforts.
Finally, we want to acknowledge the Government of Bangladesh professional staff, FSUP-H program
participants, the FSUP Program Coordinating Unit and EC Delegation officials who gave freely of their
time throughout the baseline process.
TANGO International
8 June 2010
TANGO International, Inc.
406 S. Fourth Ave.
Tucson, Arizona 85701
Tel: (1)-520-617-0977
Fax: (1)-520-617-0980
FSUP-H Baseline Report, June 2010 ix
List of Abbreviations
ASD Assistance for Slum Dwellers
BADC Bangladesh Agricultural Development Corporation
BBS Bangladesh Bureau of Statistics
BDHS Bangladesh Demographic and Health Survey
BMI Body Mass Index
CBO Community Based Organization
DAE Department of Agricultural Extension
DMA Data Management Aid
FoSHoL Food Security for Sustainable Household Livelihoods
FSUP-H Food Security for the Ultra-Poor in the Haor Region
FGD Focus Group Discussion
GoB Government of Bangladesh
KI Key Informant
MDG Millennium Development Goals
NCHS National Center for Health Statistics
NIPORT National Institute of Population Research and Training
NGO Non-Government Organization
POPI People‟s Oriented Program Implementation
PPS Probability Proportional to Size
SHOUHARDO Strengthening Household Ability to Respond to Development Opportunities
SUS Sabalamby Unnayan Samity
TBA Traditional Birth Attendant
UN United Nations
WB World Bank
WFP World Food Programme
VGD Vulnerable Group Development
VGF Vulnerable Group Feeding
FSUP-H Baseline Report, June 2010 x
Glossary of Bengali Terms
Ana Local unit of measuring gold/silver
Beel Open water body
Dadon Advance sale of crops/products
Khas Government-owned land or water bodies
Logni High interest loans
Madrasha Religious education center
Masjeed Mosque
Mohajan Informal moneylender
Salish Informal village court/arbitration
Upazila A geo-administrative unit under a district comprising several Unions
Union Parishad Lowest local government unit
Bengali Calendar:
Apr-may
May-Jun
Jun-Jul
Jul-Aug
Aug-Sep
Sep-Oct
Oct-Nov
Nov-Dec Dec-Jan
Jan-Feb
Feb-Mar
Mar-Apr
Baishak Jaisti Ashar Sravan Bhadra Ashin Kartik Agrahayan Payush Magh Falgun Chaitra
Glossary of English Terms
Decimal Decimal (100 decimals is equal to 1 acre)
Homestead The yard or compound of a household
Household A family unit, who share common resources for cooking and eating
FSUP-H Baseline Report, June 2010 xi
Executive Summary
Background
According to the 2005 Joint UN/GoB MDG report, Bangladesh was home to over 60 million food
insecure people (GoB-UN 2005). Income inequality and chronic poverty1 are the primary causes for
wide-spread food-insecurity, which is one of the most pressing crises facing Bangladesh today. To
respond to this challenge, CARE Bangladesh operates a longstanding and reputable program on food
security. The Food Security for the Ultra Poor - Haor (FSUP-H) Project, funded by the European
Union, was initiated in 2009. The design of the FSUP-H Project has taken into account the lessons
learnt from SHOUHARDO and other projects like FOSHoL, SETU etc. and is aligned with CARE-B‟s
long-term programming strategy for the Haor region.
The overall objective of FSUP-H Project is to reduce extreme poverty and food insecurity in the Haor
region of Northeast Bangladesh. The specific objective is to sustainably improve food access and
utilization and reduce vulnerability for women and their dependents in ultra poor households in
Sunamganj, Netrokona and Kishoreganj Districts
The project has four specific results:
a) Increase inclusion and capacity of 55,000 ultra poor HHs with focused attention to women
headed ultra poor HHs and their dependents, to actively engage with development processes
with greater support from their communities and local level institutions,
b) 55,000 ultra poor households (particularly women) have additional economic opportunities
and income, improving their access to food and household food security round the year,
c) 55,000 ultra poor households have reduced vulnerability to food insecurity and poverty and
improved resilience to quick and slow onset disasters, and
d) Improve and equitable utilization of food as well as reduced malnutrition among women and
their dependents in 55,000 ultra poor households.
Baseline study methodology
The objective of the baseline study is to better understand the current food insecurity, poverty and
vulnerability situation of the program impact group, and to establish baseline values of indicators for
intended outcomes against which future change can be measured in terms of: behavior, systemic
capacity and impact on the socio-economic conditions of target households such as number of food
insecure months, income and expenditure. In addition to tracking impact-level changes and livelihood
trends over time, the information and data generated by the survey will be useful in: designing future
similar projects and scaling up the current project.
The baseline survey was undertaken in January – February 2010, and utilized a combination of
quantitative and qualitative methods. The quantitative methods involved a detailed household-level
survey using Personal Digital Assistants (PDAs) for data collections instead of paper questionnaires;
and collection of anthropometric data from children aged 6-23 months, for which standard weight and
height scales were used. The survey utilized a multi-stage sample design stratified by district and haor
type. After data cleaning, the final sample size was 1892 respondents.
1 49.6% people live in poverty (below US$ 1 per day).
FSUP-H Baseline Report, June 2010 xii
The qualitative methods utilized mixed tools: male/female focus group discussions (24), key informant
interviews (30), in-depth interviews (12) and trend analysis (4 villages). Qualitative data collection
was organized by 3 teams made up of CARE and partner staff. Each team organized data collection
in 2 villages in the same district: one in the deep Haor, and one in the moderate Haor. Field research
was guided by CARE colleagues from the Social Development Unit (SDU) under overall coordination
by the FSUP-H M&E Coordinator and with inputs from the CARE Program Quality Unit in CBHQ
Demographic characteristics
The FSUP baseline survey included basic demographic information on 1,892 households and 8,957
individuals. The average age of the study population was 22.2 years old. On average, survey
households had about 2.3 adults of working age (15–60 years); 0.8 children under age 5; 1.4 children
between the ages of 5 and 14 years, and only 0.2 elderly persons above 60 years. For the FSUP-H
respondents, the total dependency ratio is 114.5%, which can be considered high. Adult members of
working age have more children to support than aged household members. Only about 2 percent of
individuals are reported to be disabled.
The gender ratio found in the study is 96, which means that there are 96 males for each 100 females.
For the overall population, the average age of Head of Household is 42.7 years. Almost 15 percent of
sampled households are female-headed, and female heads of households are significantly older
(p=.000) than their male counterparts averaging 48.2 and 41.7 years old, respectively. In terms of
household size the average for the study population is 4.8 people per household. Female-headed
households are significantly smaller than male-headed households (3.1 and 5.0, respectively. Eighty
five percent of household heads are married.
Livelihoods and economic security
The primary occupation of surveyed household members reflects the principle livelihood strategies of
households in the Haor region. Closely linked to occupations and livelihoods are economic indicators
of households, such as income, other cash sources, asset ownership, debt and savings. Together
these elements of economic security reveal how resilient households are to economic shocks and
natural disasters.
There are few distinct differences in occupational trends in the Haor region sampled. Agricultural and
non-agricultural labor are the two main livelihood opportunities, and together account for half of
primary occupations. Other important livelihood activities, in order of predominance, are fishing, petty
trade, and housemaid/servant. Few households engage in agriculture on their own fields; less than 1
percent overall. This is a direct result of the extensive degree of landlessness among the ultra poor in
Bangladesh. The majority of women are housewives. Non-agricultural laborer and housemaid/servant
are the most common primary occupations for women but account for less than 10% across all
Districts. The majority of respondents, including most women, reported having no secondary
occupation. Agricultural and non-agricultural day labor opportunities are the most common
occupational categories for those whose primary activities are in areas such as petty trade,
sharecropping, and fishing.
Each household had on average only 1.35 income earners. This can be considered quite low but
there could be seasonality factors due to the timing of the survey. The data by Haor type and District
show that the main household income sources align closely with the primary occupations, as was
FSUP-H Baseline Report, June 2010 xiii
expected based on number of income earners and main occupations. Casual agriculture labor is the
main income source across Haor areas and Districts, followed by casual non-agricultural labor.
Income from crop and animal sales is low overall. The mean monthly per capita income is 800 Taka
and the mean annual per capita income is 9,599 Taka, overall. The per capita income is significantly
lower in Sunamganj than in the other districts.
Mean monthly expenditure per capita is 1,419 Taka, and the median monthly expenditure per capita is
1,099 Taka. The majority of daily expenditure is on food purchases (72% of daily expenditure). The
purchase of tobacco products is the next highest daily expenditure (8%), followed by hiring manual
labor from others and purchasing fuel (including gasoline, kerosene, and fire wood). It is interesting to
note that the single highest monthly expenditure item is cell phone cards (48% of monthly
expenditure). The second highest monthly expenditure item is medical expenses (including fees,
medicine and travel) (36%). The single highest item annual expenditure is clothing for household
members (51% of annual expenditure), followed by social/religious events (14%) and household
goods (11%), on average. Expenditure on fishing or fish raising, agricultural equipment/input, and
livestock and poultry rearing accounted for less than 10% of annual expenditure each.
Median per capita monthly expenditures (including daily, monthly and annual expenditures) are
significantly higher than median per capita income. This is likely due to several factors. First of all,
there is the seasonality of the data collection; February falls in a lean period, which is characterized by
lower income and high lending. Secondly, respondents have the tendency to overestimate
expenditure and underestimate income.
There are significant differences between mean monthly income levels during peak and lean seasons.
The main coping strategies to deal with the lower income during lean seasons were adjusting meals
(60.1%), taking loans from friends/relatives (50.6%), and taking loans from money lenders (35.6%).
Selling labor in advance at reduced wage levels is another coping strategy used by households during
lean periods. Overall, 7% of households had at least one member who sold labor in advance.
Temporary migration was not a common coping strategy to deal with lean periods. However,
migration for employment purposes is relatively common in areas with a high degree of seasonal
work, such as the FSUP-H project area. In moderate Haor areas, 38.5% of households had
somebody migrate in the last 12 months for employment purposes; in deep Haor areas this was
32.3%. About 75% of those who migrated were heads of household, while about 20% were
sons/daughters. About 70% migrated to urban areas and 30% to other rural areas. While migration
went on throughout the year, there was more migration for employment purposes from August to
October. Agricultural contract labor and agricultural day labor are by far the most common types of
work performed by migrants.
78% of households overall held at least 1 current loan over the last 12 months. The average number
of loans per household overall was 1.4 and 35% of loans were taken by women. The average loan
amount was 6,652 Taka and overall interest rates were 51%. Overall, the outstanding loan amount at
the time of the interview was 5,393 Taka, which is about 81% of the average loan amount - indicating
a very high debt burden on households. The majority of loans (41%) were taken from money lenders,
NGOs (24%), and friends/relatives (23%). Only 6% of loans were taken from Grameen Bank and 4%
from clubs/CBOs. Informal money lenders give loans without collateral but instead charge higher
interest rates; the high level of lending from informal sources such as money lenders largely explains
the high interest rates found in this survey. The most common reasons for taking out a loan were
consumption purposes (food, clothing etc), followed by medical treatment and non-agricultural
purchases. Very few households reported taking out a loan for productive purposes such as the
FSUP-H Baseline Report, June 2010 xiv
purchase of agricultural tools/equipment, purchase of agricultural inputs, land leasing or mortgaging or
livestock purchases.
Assets are an integral component of livelihoods, and the accumulation and sale of assets reflect
important economic characteristics of households. Overall the ownership of productive assets in the
survey population was very low. Generally, far less than one out of ten households owned any of the
productive assets. Land ownership varied greatly among sampled households. Agricultural land
ownership was the highest and averaged 4.05 decimals per household, or less than 1/20th of one
acre. Ownership of homestead land averaged at 2.88 decimals per household. Chickens were the
most common animal asset owned, averaging 1.48 per household, followed by ducks and cows.
Ownership of resource assets such as timber and fruit trees, bamboo, and medicinal plants, was fairly
common in surveyed households. Cash with NGOs was the most common financial asset measured
and averaged 495 Taka per household.
The majority of all houses have floors made of mud (99.9% and 0.1% made of brick), walls made of
straw/jute or corrugated iron sheets/tin/wood, and roofs made of corrugated iron. Less than 1% of all
houses have brick walls and only 1 house in Kishoreganj had a concrete roof. Average total square
feet of living space is 175ft and the average number of rooms is 2 across all strata. About 10 percent
of households share their living space with their cattle, mostly for safety of the animals in absence of
more than one housing structure.
Food security
The survey used the Food Consumption Score (FCS), which is widely used by the World Food
Program and endorsed by FANTA, as a measure of diet diversity and diet quality. The FCS is derived
by weighting various food groups based on their protein value and assigning a score for each food
group consumed by the household during the recall period. For the FSUP survey population, 52.3% of
total households had an acceptable FCS, 31.5% had a borderline FCS and 16.2% had a poor FCS.
Overall, the mean number of lean months per year is 4.3 and the mean value for households that take
3 meals per day „most of the time‟ is 14%. The combined mean values for „most of the time‟ and
„often‟ is 56.3 %. There are two distinct annual lean periods in terms of insufficient food. The first lean
period is from April to June, with the leanest period in April-May (13%), the month of Baishak in the
Bengali calendar. The second lean period is from November to February with the leanest period in
Dec-Jan (12%), the month of Payush in the Bengali calendar. In both periods, almost 90% of
households in the sample reported insufficient food. The recovery from the insufficient food period in
April to June is notable longer than for the second lean period - with another smaller decrease in
August-September (31%) before reaching a peak at 63 percent in Oct-Nov. The highest number of
households report sufficient food in March-April (82%), with a very sharp decrease between the
Bengali months of Chaitra and Baishak.
The survey utilized a index to measure how households deal with food insecurity. A high score
indicates that households in specified areas avail themselves of a broad range of coping strategies to
deal with food insecurity; the higher the index value is - the higher the assumed stress on households.
Overall, the coping index score of almost 24 indicates a moderately-high level of stress on households
due to food insecurity. Bulk purchases of rice‟, „running out of food‟ and „reducing personal food
intake‟ were the top 3 coping strategies.
FSUP-H Baseline Report, June 2010 xv
Water and sanitation
Hand tube wells are the most common drinking water source followed by shallow tube wells and deep
tube wells. Overall, 97% of households depend on the various types of tube wells for drinking water.
Almost no households draw drinking water from open water sources such as ring wells, ponds and
rivers/canals. Hand tube wells are also the most common cooking water source followed by
rivers/canals and shallow tube wells. Most households reported open water sources for washing.
River/canals are the most common washing water source followed by hand tube wells and ponds. The
average distance to water sources for drinking, cooking and washing purposes is around 200 meters.
Of the households that reported tube wells or tara pumps as a source for drinking, cooking or washing
water, 50.5% of households reported that the tube wells / tara pumps were tested for arsenic. Of the
tube wells/tara pumps that were tested, 14.1% were found to contain arsenic.
The most common type of latrines used by adult men and women are ring slab/offset latrines (with the
seal broken) and hanging/open latrines, followed by uncovered pit latrines and then open defecation.
Overall, the use of hygienic latrines such as ring slab/offset latrines (with the seal intact), septic
latrines, covered pit latrines and locally adapted hygienic latrines is very low in the project area.
Similar to adults, the most common types of latrines used by boys and girls 5-15 years of age are ring
slab/offset latrines (with the seal broken) and hanging/open latrines. For boys and girls, this is
followed by open defecation and then uncovered pit latrines – the opposite to adults. Overall, the use
of hygienic latrines such as ring slab/offset latrines (with the seal intact), septic latrines, covered pit
latrines and locally adapted hygienic latrines is very low.
Health and illness
The majority of respondents wash their hands before eating but less than half do so before preparing
food and only one-third wash their hands before feeding children. The majority of respondents wash
their hands after defecation but only one-third of respondents do so after cleaning a baby‟s bottom.
The use of ash or clay for hand washing is most common followed by use of only water. The use of
soap is least common.
The average number of illnesses cited per household was 2.4. The most common illness experienced
by adults during the previous 12 months was a cold attack, followed by gastric illness and diarrhea.
Only 3 percent of households reported no illnesses at all. Medicine shops and village doctors are the
most common treatment sources for household members.
Participation and access
Participation in community development processes is low at 4.5% of all households, which was too
low for meaningful analysis. Among the only 186 responses received, household head was mentioned
as the most common household member involved in development processes. Females (spouses plus
female heads of household) accounted for 15.1% the responses. The Masjeed or religious committee
was the most common type of institution that household members were engaged with for
development purposes in the last 12 months (24%), followed by engagement with NGOs (19%) as
village group members, which is often a prerequisite to receiving microcredit. Half (49.3%) of
respondents reported that their engagement with development institutions consisted of receiving
services, followed by being a volunteer (27.9%); committee member (19.3%); participant in activities
(19.3%); and recipient of training (1.9%). Only 5% of households had experience with collective action
FSUP-H Baseline Report, June 2010 xvi
in last 12 months, primarily consisting of road construction/repair and Mosque construction/repair.
Over two-thirds of households (68.7%) had accessed one or more GoB service providers in the
previous year. The most common service providers used were Union Parishad and Government
Immunization Services, followed by Government Family Planning, Upazilla Health Services and Union
Health Services. The most common services received from Union Parishad were categorized as
„other‟, which likely refers to government safety net programs. The most common service received
from Union and Upazila Health Services is medication followed by suggestions. The most common
service received from Government Family Planning are suggestions, medicines and vaccinations. For
Government Immunization Services, vaccinations are the main services received, as was to be
expected. Overall, training provided by GoB service providers is very low. At present, the main
sources of knowledge and skills for economic/livelihood activities are knowledge transfer from
previous generations, and from relatives and neighbors. The little external assistance that ultra-poor
households do receive comes primarily from NGOs. For all service providers, the majority of
respondents indicated they were satisfied or highly satisfied.
Only 33% of households reported receiving services from other non-government service providers.
The three most common non-government service providers were NGOs (76%), Grameen Bank
(16%), and Local Service Providers (18%). Less than 1% of households reported receiving services
from Commercial Banks, CBOs, input retailers/dealers and non-Government Vocational
Education/Training, respectively. The most common services received from NGOs were credit (68%),
suggestions (16%), and relief/aid (4%). The most common services received from Grameen Bank
were credit (99%) and suggestions (13%). The most common services received from Local Service
Providers were suggestions (75%), credit (65%), suggestions (16%), medicines (71%) and relief/aid
(12%).
Regarding access to common property, the highest proportion of households has access to
river/canals, followed by roadside sloping and beels/haors. Access to Khas land is lowest.
Disasters and crises
Overall, 78% of households reported that they did not experience a natural disaster in the previous
year. The highest proportion of disasters experienced in the last 12 months was wind damage, floods,
excessive rain and storms. Among those who experienced a natural disaster, the highest proportion of
households reported partial damage to their house, followed at a distance by loss of working days and
full damage to their house. For those affected, the mean asset loss per disaster was reported at Taka
3,017, and the mean number of working days lost reported by the 26.5% of affected households was
10. The most common coping strategies used by respondents to recover from a natural disaster were:
taking out a loan from friend/neighbor (41%), taking loan from a moneylender (31%), adjusting meals
(25%), using savings (25%), accepting help from others: (24%), purchasing on credit (21%) and
taking a loan from NGO (11%).
Respondents were also asked the same range of effect and coping strategy questions for a range of
household crises, not caused by natural disasters. Only 16.7% reported the occurrence of such crises
in the last 12 months. The most common types of household crises reported were illness of income
earners (57.2% of cases where a household crisis was reported) and illness of other household
members (32% of cases where a household crisis was reported). The main effects of the household
crises were asset/income loss and working days lost. The mean loss of assets for the survey
population was just under Taka 5,000. The mean number of working days lost for the survey
FSUP-H Baseline Report, June 2010 xvii
population was 36. The mean number of working days lost due to illness was also 36 (mode=15). The
most common coping strategies used by respondents to cope with household crises were: took out a
loan from friend/relative (42.7%), took out a loan from moneylender (36.0%), made adjustment to
meals (27.8%), accepted help from others (20.6%), purchased goods on credit (18.4%), used savings
(11.7%), took out a loan from an NGO (11.1%), took a grain loan (10.4%), ate famine foods (8.2%),
and accepted aid (5.4%)
Family authority and decision making
The highest proportion of decisions is made by the husband after discussion with the female
household member. It is also apparent that women have greater involvement in certain household
decisions such as minor household purchases, children‟s clothing and education, medical expenses
and in spending money that they have directly earned. Women have less involvement in expenditures
that relate to livelihoods, higher value assets, loans/savings and events such as weddings and
ceremonies and shelter in case of disasters. The proportion of decisions made without any
involvement by the female is low for almost all decision types, except salish decision making.
Overall, a higher proportion of women agree that the husband should help with household chores if
the female is working; and that they have the right to express their opinion, even when they disagree
with their husband. The proportion of women overall who disagree with the statement that it is better
to send a son to school than a daughter is also significantly higher. However, it is interesting to note
that despite the more liberal attitudes about family life expressed by women, the proportion of women
who agree that a wife should tolerate being beaten is significantly higher that the proportion who
disagrees.
Child nutrition, antenatal care and family planning
Of the total number of respondents, 70 percent did not have any children < 2 years of age. Of those
who did, 29 percent had one and 1 percent had two < 2 children. Virtually every mother breastfed her
child (99.5%) and 45% of overall mothers initiated breastfeeding with the first hour of birth. The
average age for introducing solid/semi-solid foods (weaning) was just over 5 months age. Overall,
35.5% of mother‟s took iron or folic acid supplements. The majority of mother‟s did not change the
amount of food that they consumed during their last pregnancy; 16% increased their food intake and
33% decreased their food intake. The majority of women also did not change the amount of rest they
took after the last birth. Only 23% took more rest than usual. Overall, mothers attended on average 1
ANC session. Qualitative data shows that there are very few periodic medical checkups during
pregnancy due to lack of knowledge, lack of money, and difficulties in communicating with the medical
centers. Overall, the majority of births were attended by Traditional Birth Assistants. Less than 1% of
births were attended by a doctor.
Only 7.4% of mothers reported suffering no illnesses in the last 12 months. The highest proportion of
women suffered from cold attacks, followed by gastric complications and anemia. The lowest
proportion of women suffered from Typhoid. Only 5.8% of children did not suffer any illnesses in the
last 12 months. The highest proportion of children suffered from cold attacks, diarrhea and
pneumonia. The lowest proportion of children suffered from skin diseases and other illnesses. For
households currently with a child 2 years of age or under, 81.8 % of the oldest child in this age group
has received at least one immunization. For those children who did receive immunizations, 72.9%
have immunization cards. For those children who needed antihelmintics, 47% received them. 57.7%
FSUP-H Baseline Report, June 2010 xviii
of < 2 children are underweight: 39.4% are moderately underweight and 18.3% are severely
underweight. Underweight is a composite index of height-for-age and weight-for-height. A child can be
underweight for his/her age because s/he is stunted, wasted or both. In general, an underweight
prevalence of > 30% is considered to be „very high‟.
Status of female-headed households
Almost 15% of the households sampled had female heads of household. When comparing female-
and male-headed households across Districts and Haor type, the following observations can be
made:
- female-headed households have significantly lower per capita monthly income levels than
male-headed households
- there are no significant differences in per capita expenditures between female- and male-
headed households, except in deep Haor areas where expenditures in female-headed
households are significantly lower
- female-headed households have significantly lower food consumption scores than male-
headed households
- female-headed households have a significantly higher coping strategy index score in
Kishoreganj and Sunamganj, and in deep Haor areas
*****
FSUP-H Baseline Report, June 2010 1
1.0 INTRODUCTION
1.1 Food security and poverty context in Bangladesh
“Food security exists when all people, at all times, have physical and economic access to sufficient,
safe and nutritious food that meets their dietary needs and food preferences for an active and healthy
life”2. Food security is said to have four main pillars: availability, access, utilization and stability with
regards to the availability and access dimensions of food security.
Table 1: Four main pillars of food security
Availability The availability of sufficient quantities of food of appropriate quality, supplied through domestic
production or imports (including food aid).
Access Access by individuals to adequate resources (entitlements) for acquiring appropriate foods for a
nutritious diet. Entitlements are defined as the set of all commodity bundles over which a person
can establish command given the legal, political, economic and social arrangements of the
community in which they live (including traditional rights such as access to common resources).
Utilization Utilization of food through adequate diet, clean water, sanitation and health care to reach a
state of nutritional well-being where all physiological needs are met. This brings out the
importance of non-food inputs in food security.
Stability To be food secure, a population, household or individual must have access to adequate food at
all times. They should not risk losing access to food as a consequence of sudden shocks (e.g.
an economic or climatic crisis) or cyclical events (e.g. seasonal food insecurity). The concept of
stability can therefore refer to both the availability and access dimensions of food security
In Bangladesh, there has been significant progress in improving the gross food availability, in
particular through cereal self sufficiency and improvements in land productivity. However, food access
and utilization continue to remain critically low, especially among the poorest and disaster affected.
According to the 2005 Joint UN/GoB MDG report, Bangladesh was home to over 60 million food
insecure people (GoB-UN 2005). Income inequality and chronic poverty3 are the primary causes for
wide-spread food-insecurity. This is compounded by the population growth of around 2 million
individuals annually combined with a reduction of around 82,900 hectares of tillable land annually due
to infrastructure and housing development, and industrialization. About a third of the population lives
below the lower poverty line with seriously imbalanced diets and extremely inadequate intake of fats,
protein and micronutrients. While poverty is one of the main underlying causes of food insecurity of
many people, it has manifested in wide scale malnutrition of various types.
In recent decades, malnutrition has become a major public health concern in Bangladesh, affecting
the well being of the majority of the population, particularly the children, adolescent girls and
pregnant/lactating women in the ultra-poor households. The 2005 Joint UN/GoB MDG report states
that nearly half the children are underweight or stunted, with 13 to 19 percent being severely
underweight or stunted in terms of being more than three standard deviations below the relevant
NCHS standards. Another 2005 report by Hellen Keller International4 states that almost 40 percent of
under-5 children in rural Bangladesh are reported as stunted and 46 percent are reported as
underweight, indicating that chronic under-nutrition is widespread. A 2009 study found that prevalent
macro malnutrition problems (NIPORT, 2009), particularly in under-5 children include underweight
(46%), stunting (36%) and wasting (16%) and maternal under nutrition measured by BMI (32%). This
2 World Food Summit, 1996
3 49.6% people live in poverty (below US$ 1 per day).
4 Bangladesh in Facts and Figures: 2005 Annual Report of the Nutritional Surveillance Project, Hellen Keller
FSUP-H Baseline Report, June 2010 2
suggests that children in Bangladesh suffer from short-term acute shortfall in food intake as well as
longer-term under-nutrition. It is important to note that there are also large differences in child
malnutrition rates across economic groups. Child malnutrition is pervasive among the poor. More than
60 percent of the children 6-71 months old suffering from stunting, belong to the bottom consumption
quintile.
In Bangladesh, there is an important spatial dimension to poverty and food insecurity creating
disproportionate affects on people in disaster risk prone areas, such as char lands, Haors and coastal
areas. In 2009, the GoB/WFP/WB undertook a joint vulnerability assessment to prioritize development
initiatives and resources in areas of highest food security needs (BBS, 2009), based on upazila-level
population estimates of individuals living below the lower poverty line, which is defined as food calorie
consumption of less than 1805 Kcal/person/day. The assessment identified six geographical
areas/clusters with 145 highly food-insecure and poverty-prone upazilas. The six identified clusters
were (i) The North-West disaster area (ii) The North-Central Chars (iii) The Drought Zone (iv) The
Haor Basin (v) The Coastal Zone and (vi) Chittagong Hill Tract.
1.2 Background of the FSUP-H project
CARE Bangladesh has a longstanding and reputable program on food security. After the successful
completion of the Integrated Food Security Program (IFSP) in 2003, CARE initiated the Strengthening
Household Ability to Respond to Development Opportunities (SHOUHARDO), which was
implemented during 2005-2009. The SHOUHARDO design was the largest development program in
Bangladesh at the time, and was designed to be consistent with CARE‟s Unifying Framework for
Poverty Eradication & Social Justice.5
FSUP-H is part of the program approach of CARE-B and the design of the project is based on the
analysis of underlying causes of poverty (UCP) and social injustice at multiple levels and, theories of
change (ToC) around three long term programming areas of CARE-B: marginalized women, extreme
poor people, and people living in environmental and geographical vulnerable areas. Design of FSUP-
H has taken into account the lessons learnt from SHOUHARDO and other projects like FOSHoL,
SETU etc. and is aligned with CARE-B‟s long-term programming strategy for the Haor region.
The FSUP-H project aims to make a sustainable impact on the lives of the four CARE Bangladesh
„impact groups‟: (a) most socially, economically and politically marginalized women, (b) lowest
category of the wellbeing ranking especially those people trapped in a set of unequal power relations,
(c) most marginalized groups in urban areas (the project will indirectly contribute to this by reducing
migration), and (d) most vulnerable people and communities prone to disasters and environmental
changes.
FSUP-H‟s overall objective aligns with the objectives outlined in the EC Country Strategy Paper for
Bangladesh (2007-2013) where poverty, gender inequality and access to food are prioritized. Project‟s
overall focus on the reduction of poverty and food insecurity fits well with the Millennium Development
Goals, especially Goal One: to eradicate extreme poverty and hunger, Goal Three: to promote
gender equality and empower women, and it also addresses Goal Five: to improve maternal health.
5 SHOUHARDO – a Title II program of USAID, Final Evaluation Report, December 2009, TANGO International
FSUP-H Baseline Report, June 2010 3
1.3 Implementation framework of the FSUP-H project
The FSUP-H project constitutes an intra-CARE partnership between CARE International UK and
CARE Bangladesh. CARE International UK is the formal project lead and holds overall contract
responsibility and accountability to the European Commission. CARE Bangladesh is responsible for
day-to-day management of project implementation. CARE Bangladesh implements the project with
three national partners: Assistance for Slum Dwellers (ASD); People‟s Oriented Program
Implementation (POPI); and Sabalamby Unnayan Samity (SUS).
The overall objective of the project is to reduce extreme poverty and food insecurity in the Haor region
of Northeast Bangladesh. The specific objective is to sustainably improve food access and utilization
and reduce vulnerability for women and their dependents in ultra poor households in Sunamganj,
Netrokona and Kishoreganj Districts
The project has four specific results:
e) Increase inclusion and capacity of 55,000 ultra poor HHs with focused attention to women
headed ultra poor HHs and their dependents, to actively engage with development processes
with greater support from their communities and local level institutions,
f) 55,000 ultra poor households (particularly women) have additional economic opportunities
and income, improving their access to food and household food security round the year,
g) 55,000 ultra poor households have reduced vulnerability to food insecurity and poverty and
improved resilience to quick and slow onset disasters, and
h) Improve and equitable utilization of food as well as reduced malnutrition among women and
their dependents in 55,000 ultra poor households.
1.4 FSUP-H site and impact group selection
Among the six clusters of highly food insecure and poverty prone areas stated in section 1.1, CARE
Bangladesh identified the Haor basin as the target area for its FSUP-H project. The Haor is a wetland
ecosystem in northeastern Bangladesh that is a saucer shaped shallow depression in the land that is
also known as a back swamp. The Haor is a remote and difficult area that is flooded every year during
the monsoon. It remains under water for 6-8 months of the year, turning Haor settlements mostly built
on earthen mounds into islands. Villages are regularly washed away, which plays a large role in
driving people to migrate to urban centers. Some of the most extensive seasonally flooded areas in
South Asia are located in the Bangladesh Haor region. During the dry season most of the water drains
out, leaving small shallow lakes or may completely dry out by the end of dry season. This exposes
rich alluvial soil, extensively cultivated for rice.
In selecting the Haor region, CARE Bangladesh took into account the seasonal dimensions and socio-
political factors that particularly increase the vulnerability of the Haor population. There are two related
seasonal dimensions to food insecurity in the Haor: the high exposure to cyclic climatic shocks such
as flooding, flash flooding and erosion; and the single-season food production and consequent
seasonal variation in food availability and pricing as there is only one annual rice harvest in the Haor
area. In the hoar area, there are traditionally two food-insecure lean seasons, January to mid-April
(between rice planting and harvest) and mid-July to September (during the monsoon). The first is
particularly severe for rural landless people because it coincides with the pre-harvest period of low
employment opportunities in agriculture.
In addition, socio-political factors such as gender, age, ethnicity and religion determine people‟s
FSUP-H Baseline Report, June 2010 4
position in society, their relationships to those in power, and access to resources and services, and
are equally important determinants of food insecurity in Bangladesh. In recent years, poor people
have increasingly lost their fishing rights in the Haors or rivers which had been their only source for
livelihoods and food-security for decades. These water bodies have now been taken over by powerful
people with political connections who control the majority of water bodies and only allow poor people
to fish for a payment of daily fees and a percentage of sales proceeds for a certain period. Moreover
the Haor region is considered socially conservative and imposes strict restrictions on women‟s
mobility. The harsh physical environment further impedes their movement. Poor women are
marginalized because of male-dominated systems and structures, unequal gender power relations,
and limited choices and opportunities. Wage discrimination is a significant contributor to food
insecurity for women in the Haors who earn approximately half the daily wage of their male
counterparts for the equal work, and even then are severely affected by the seasonality of work
availability. The absence of health services and transportation facilities affects especially pregnant
women severely
The Haor region covers Sunamganj, Habiganj and Moulvibazar districts and Sylhet Sadar Upazila, as
well as Kishoreganj and Netrokona districts outside the core Haor area. Based on a vulnerability
assessment of all the Union Parishads in the Haor region6, CARE found Kishoreganj, Netrokona and
Sunamganj had the largest number of communities in the highest vulnerability categories, and
selected these 3 districts for project implementation. Due to the remote location and difficult physical
conditions, government services are almost absent.
Within the three project districts, CARE Bangladesh undertook a rigorous selection process during the
start up phase of the FSUP-H project to identify 55,000 ultra poor households as the main project
target group, with an important focus on the women in these households. By October 2009, the
project team had completed 672 community WBAs and selected 645 communities in 94 unions of 17
upazilas in the three project districts. Based on this selection, the project team collected information
from 55,000 households and developed individual household profiles. These profiles captured key
information such as: sex of household heads, household size, primary and secondary occupation of
household heads, occupation of women in the households, homestead and cultivable land size,
number of livestock and poultry, types of latrine used, tube well ownership, NGO involvement and
loan status.
2.0 FSUP-H BASELINE STUDY
2.1 Rationale of the study
The main purpose of the survey was to generate baseline information and data on food security
status, poverty and vulnerabilities of the impact groups. By providing a benchmark the baseline survey
provided an opportunity to collect follow-up data and information over the life of the project to measure
effect and impact of project interventions/activities. This allowed FSUP-H staff to understand to what
extent the project contributes to improving food security and poverty level.
The information and data generated by the survey will be useful in: (1) designing future similar
projects; and (2) scaling up the current project; and (3) to track impact-level changes and livelihood
trends over time.
6 SHOUHARDO Haor Region: Union Selection Process, CARE 2005; Md, Raquibul Hasan, Village Selection
Survey; An Elaboration of the Process, CARE 2005.
FSUP-H Baseline Report, June 2010 5
2.2 Objectives of the study
The objective of the study is to better understand the current food insecurity, poverty and vulnerability
situation of the program impact group, and to establish baseline values of indicators for intended
outcomes against which future change can be measured in terms of: a) behavior, b) systemic capacity
and c) impact on the socio-economic conditions of target households such as number of food
insecure months, income and expenditure.
The specific objectives of the survey were to:
a) Assess socio-economic characteristics of the households;
b) Identify the level of food insecurity, diversity of food consumptions and prevalence of
malnutrition (including infant & child feeding practices) of the households;
c) Assess current ability of program HHs to participate in the development process and access
to different services;
d) Understand the natural crisis/shocks experienced by the households and coping mechanisms
(resilience);
e) Validate the needs and priorities of project participants, communities and institutions identified
in the project proposal.
f) Gather and analyze information for the purpose of in-depth learning and to assist the project
in modifying appropriate interventions, refining the Logframe and M&E plan.
2.3 Scope of the study
The scope of the survey is not limited to indicator measurement requirements of the project. The
survey will also seek to better understand livelihood issues of the ultra poor households of the hoar
regions. The study will also explore different aspects of household food security (availability, access
and utilization patterns), households‟ exposure to development processes and their ability to negotiate
for services and rights, vulnerability to climate changes etc. The study will produce household-level
analysis by district and Haor type.
3.0 STUDY METHODS
3.1 Study design
The baseline survey utilized a combination of quantitative and qualitative methods. These methods
were in part complementary, so that each type of information could contribute to an overall
understanding of households. The quantitative methods involved a detailed household-level survey,
while the qualitative methods utilized mixed tools.
3.2 Quantitative study design
The study collected data on a variety of subjects and issues by administering a structured
questionnaire at the household level with the key woman of the household and/or her spouse as
respondent. For collecting anthropometric data from children aged 6-23 months, standard weight and
height scales were used.
The household questionnaire was divided into ten sections, each covering a different aspect of
livelihoods or subjects relevant to CARE FSUP programming objectives. The following topics were
covered:
FSUP-H Baseline Report, June 2010 6
Section A: Identification – area identification, religion and ethnicity.
Section B: General Information on household members – includes elements of household
demographics, education, disabilities, marital status, primary and .secondary occupations
Section C: Economic Security – includes housing characteristics, ownership of assets,
household expenditures, income and employment, and loans.
Section D: Food Security – includes information on food consumption, months of food
sufficiency, and household food access.
Section E: Water and Sanitation – access to clean water and latrines.
Section F: Health Practices and Illness – data on hand-washing behaviors, illnesses.
Section G: Participation – Information on household participation in development processes
and access to services and common property
Section H: Natural Disasters – types of disasters that have impacted the household in the
previous year and their effect on the household.
Section I: Family Authority and Decision-making – decision-making at the household level
and attitudes about family life.
Section J: Child nutrition, Antenatal Care and Family Planning – information on breastfeeding
practices, food consumption during antenatal care, child food consumption, antenatal care
and family planning, immunizations, and anthropometrics of children 6-24 months.
The quantitative methods employed random selection criteria in order for the results to be generalized
at the household level to both District and Haor type (moderate and deep; discussed below). The
baseline study was not designed to be generalized by Haor type within Districts, as this would have
resulted in an unmanageable sample size for the household survey.
The questionnaire for the household survey was developed jointly by CARE Bangladesh, DMA and
TANGO staff, and was based in part on questions posed in similar food security baseline surveys in
Bangladesh and elsewhere. Technical input by DMA and CARE Bangladesh both before and during
training ensured that questions were relevant, culturally appropriate, well-translated, and the listed
response codes were correct. Draft instruments were pre-tested in approximately fifty households
during enumerator training, which took place in Mymensingh in January 2010. The final questionnaire
is attached as Annex 1.
A multi-stage sample design was used for the household survey. The first stage was a stratification
based on three districts – Kishoreganj, Netrokona, and Sunamganj - where FSUP is implementing its
program. The justification for using stratification at this level was to use the baseline to inform CARE
Bangladesh of the major differences among Districts in order that program adjustments could be
made and that future studies could disaggregate changes by location. The first sampling stage also
included a second stratification of the entire project are into two types of Haor – moderate and deep.
Each of the three districts has both types of Haor area, and they are found in different Upazilas (sub-
districts). The third stage of sampling was the selection of clusters (villages) within each Haor type in
each District using probability proportional to size selection methods. A limited number of clusters (20
per strata) were selected due to two factors – the relatively small geographical variation of Haor areas
within each district and the expected magnitude of intra-cluster variation being relatively large
compared to inter-cluster variation, which made it more reasonable to sample more households within
clusters to reduce error.
The fourth and final stage of the sampling process was the selection of households. In each cluster a
fixed number of households were randomly selected from a sampling frame of the households.
FSUP-H Baseline Report, June 2010 7
Systematic random sampling was used with a randomly selected starting point (household number on
the list) and a sampling interval when lists or maps were not available. The survey used no
replacement and instead sample sizes were upwardly adjusted to account for non-replacement
assuming a non-response rate of 5 percent.
The formula used to calculate the baseline sample size was the following:
n = deff(z/standard error)² (p) (1-p)
Where:
n = sample size
deff = design effect
z = standard score corresponding to a given confidence level (z = 1.645 for the 95%
confidence level)
Standard error = acceptable error level
p = expected proportion of the population expressing a particular characteristic
(1-p) = expected proportion without the characteristic
This formula is only appropriate for baseline measurements of multi-variable surveys. It establishes
variation and expected proportions of key variables which subsequent surveys can use to base
sample sizes required for estimating differences in means or proportions. In applying this formula p
was given a value of .5, as this maximized the influence the proportion of the population with any
given characteristic had on the size of the sample. For the baseline survey, z was fixed at 1.645 (95%
confidence limit) and p was set at 0.5. The two remaining variants were the design effect and the
standard error.
The term “deff” is the design effect. This provides a correction for the loss of sampling efficiency
resulting from the use of cluster sampling instead of simple random sampling, and the gain of
sampling efficiency resulting from stratification. It is the factor by which the sample size must be
multiplied by in order to produce survey estimates with the same precision as a simple random design
would. Ideally, an estimate of deff for the indicators of interest could be obtained from a prior survey in
a given setting, providing some insight on the similarity or homogeneity among households in the
clusters. Short of this, typical values from surveys conducted elsewhere are normally used. When
clustering is the only sampling stage prior to the random selection of households, a default value of
2.0 is commonly used. However, for this survey there was an additional stage of stratification.
Stratification actually increases the efficiency of sampling by accounting for variation in the sample
even before the sample is drawn. Thus, it usually has a design effect of less than 1.0. Combining the
two stages – stratification and clustering – usually results in a design effect between 1.0 and 2.0. In
the case of Bangladesh households, it is assumed a priori that inter-household variation is small
compared to that of population-based surveys that are district-wide. Thus a design effect (deff) of 1.6
was used, mainly due to the fact that two stages of stratification were employed.
Table 2 provides estimated sample sizes using variants of deff and standard error. As can be seen,
for a given standard error the design effect raises the required sample size by modest amounts.
However, at a given deff, changes in the standard error have a profound effect on sample size
(because it is the denominator of a squared term). For the CARE Bangladesh FSUP survey it was
recommended that a sample size of 316 households per strata, or 1,896 total households be selected
(there are six strata – 3 districts by 2 Haor types). Data was collected from 16 randomly selected
households in each of twenty clusters within each stratum. Data was collected from 1,920
households. After data cleaning, 28 households were removed from the sample due to incomplete
FSUP-H Baseline Report, June 2010 8
interviews or other inadequacies, for a final tally of 1,892 households. Table 3 provides a breakdown
of the sample by District and Haor type.
Table 2: Illustrative sample sizes for stratified random sampling using
variants of deff and standard error.
Design
Effect Z
Standard
Error TERM P 1-P
Sample
Size
Sample
Size
*1.05
1 1.645 0.05 1082.41 0.5 0.5 271 284
1.2 1.645 0.05 1082.41 0.5 0.5 325 342
1.4 1.645 0.05 1082.41 0.5 0.5 380 400
1.6 1.645 0.05 1082.41 0.5 0.5 434 455
1.8 1.645 0.05 1082.41 0.5 0.5 488 512
2 1.645 0.05 1082.41 0.5 0.5 542 570
1.6 1.645 0.04 1691.26 0.5 0.5 676 710
1.6 1.645 0.05 1082.41 0.5 0.5 434 455
1.6 1.645 0.06 751.67 0.5 0.5 302 316
1.6 1.645 0.07 552.25 0.5 0.5 221 232
1.6 1.645 0.08 422.81 0.5 0.5 170 178
Subsequent surveys to estimate change from the baseline survey will utilize the following formula:
n = deff [(Z1 + Z2)2 * (sd1
2 + sd2
2) / (X2 - X1)
2]
This formula takes into account the magnitude of change that can be detected with 95 percent
confidence given the expected standard deviations for the indicators of interest.
Table 3: Sampling statistics of 1,892 households by District and Haor type
Sample Sizes
District
Kishorega
nj
Netrokona Sunamganj
# of Households Surveyed 628 634 630
% of Households Surveyed 33.2 33.5 33.3
Number of <2‟s Measured 146 124 132
By Haor Type Moderate Deep
# of Households Surveyed 947 945
% of Households Surveyed 51.1 49.9
Number of <2‟s Measured 227 175
The study collected data from 16 randomly selected households from each village/community. The
households included in the sampling frame, and thus eligible to be sampled, represented 55,000
extreme poor households in the Upazilas and who are the ultimate beneficiaries of FSUP (about 25%
of all households in the area). The Field Researchers collected data directly from the one of the
female participants from the household and from her spouse (if available). If the wife was not available
on the day of interview, the enumerator went on to the next randomly selected household. For
households with children between the ages of 6 and 23 months anthropometric data (height, weight
FSUP-H Baseline Report, June 2010 9
and age) was also collected on the same day using enumerators trained specifically for this function
and using appropriate scales and measuring boards.
Quantitative data collection took place from January 13 through
February 16, 2010 using Personal Digital Assistants (PDAs);
small hand-held computers that provide facilities for taking notes,
storing data and retrieving information, and running survey
software. PDA‟s are pen-based and use a stylus to tap selections
on menus and enter printed characters. TANGO International
used The Survey System (TSS) software package to build the
FSUP-H baseline questionnaire because it is one of the few
software packages that will accommodate non-Roman
characters. The Bangla TSS questionnaires were then
transferred to the PDAs using a series of .xml files.
When doing the interview, enumerators read each question off
the screen, just as if they were using paper questionnaires. Each
household was stored as an independent record. The Team
Leader or Supervisors downloaded data from enumerators each
day and stored that day‟s data on an SD memory card or laptop
using a unique file name. A copy of the data remained on the
PDA for the entire survey as a back-up.
The use of PDAs gives significant benefits over traditional paper-based surveys. Using PDA-based
questionnaires greatly reduces survey error, especially data entry error. In a PDA-based survey, as
data is being entered it is subject to validation, i.e. it is controlled on the spot for possible errors
(numbers out of range, percentages that do not add up to 100%, etc.) and some questions may be
enabled or disabled on the basis of replies to the previous questions. In addition, all logic rules, such
as skips to other questions or avoids, are controlled by the PDA and not by the enumerator. Such
rules are automatic, taking the enumerator to the next relevant question. This is particularly useful in
complex, multi-indicator surveys such as the FSUP-H baseline.
Using PDA-based survey instead of paper based questionnaire reduces the time needed for data
collection and processing. Once data is collected with paper-based surveys, each questionnaire then
has to be entered into a database by a data-entry clerk, and then cleaned of errors by a data analyst.
With a PDA-based survey there is no need for a subsequent data entry process since the data is
entered directly into a data file. This greatly reduces data entry errors (these are the largest single
source of error for a survey). Finally, PDA based surveys don‟t need to use paper and ink. This is
environmentally friendlier. In addition it reduces the logistics burden of carrying many questionnaires
around the field and then storing them in the office.
Data cleaning and analysis was undertaken as a 2-step process. The first round of data cleaning and
analysis was undertaken by DMA in March 2010, the second and final round of cleaning and analysis
was undertaken by TANGO International in April 2010. During the data cleaning process, 28
respondent files (1.5 percent) were discarded because of incompleteness/inadequacies. The final
sample size was 1892 respondents.
FSUP-H Baseline Report, June 2010 10
3.2 Qualitative study design
Qualitative data collection was organized by 3 teams made up of CARE and partner staff. Each team
included at least one female facilitator. Field research was guided by CARE colleagues from the
Social Development Unit (SDU) under overall coordination by the FSUP-H M&E Coordinator and with
inputs from the CARE Program Quality Unit in CBHQ.
The qualitative work focused on three main themes:
1. Maternal Child Health and Nutrition (MCHN)
2. Participation in Economic Activities
3. Participation in Development Activities
Data collection started on 10 February 2010 and lasted for 10 days. During this time, each team
undertook qualitative field work in 2 villages in the same district: one in the deep Haor, and one in the
moderate Haor. Villages were selected based on a degree of convenience in accessing the village,
and representativeness of the project objectives and main intervention areas.
The teams spent 5 days on data collection in each village; 4 days of qualitative work and 1 day to
finalize reporting. Data was recorded as hand-written notes and was transferred to structured
reporting templates on the same day it was collected.
Data cleaning and analysis was undertaken as a 2-step process. The first round of data cleaning and
analysis using top-line methodology was undertaken by CARE Bangladesh in February 2010,
additional analysis was undertaken by TANGO International in March 2010 for integration of findings
into this report. The data collection tools and guidelines, and qualitative findings are attached as
Annex 2.
Table 4: Qualitative techniques applied for the baseline survey
A. Focus group discussions (FGD) x 24
6 per village, different combinations possible
Time: 1-1.5hr
Semi-structured group discussions with 5-10
participants; male or female, no mixed; 1 facilitator with
same gender as the FGD participants; 2 note
takers/observers, ideally also same gender as FGD
participants; use of participatory mapping (Venn
diagram, social mapping) and ranking (problem,
preference and wealth ranking) techniques
B. Key informant interviews (KII) x 30
5 per village
Time: 1hr
In each village, 4 semi-structured interviews with
individuals in the village and minimum of 1 external KII
at union/upazilla level; 1 facilitator and 1 note taker
C. In-depth interview (IDI) x 12
2 per village
Time: 1-2hrs
Unstructured interviews. Individuals selected from
FGDs; 1 interviewer only. Focus was to collect “rich”
human interest stories to complement/add to FGD
findings.
D. Trend analysis (TA) x 4
Organized in 4 villages only (2 deep, 2
moderate)
Time: 2-3hrs
TA included two techniques: seasonal calendar (1-
1.5hrs) and daily pattern mapping (1-1.5hrs). Both
techniques were undertaken with females.
FSUP-H Baseline Report, June 2010 11
Picture 1: Baseline enumerator using the PDA for a household interview
FSUP-H Baseline Report, June 2010 12
4.0 DEMOGRAPHIC CHARACTERISTICS
The FSUP baseline survey included basic demographic information on 1,892 households and 8,957
individuals. The vast majority of respondent households in the survey are Muslim (85.7%), followed by
Hindu (14.1%) and „Other‟ (0.2%). Kishoreganj has a significantly higher proportion (p=.000) of
Muslims (91.2%) than Netrokona (80.6%) and Sunamganj (85.4%). There is also a significant
difference by Haor type (p=.000), with Moderate Haor having a higher proportion of Muslim
households (90.4%) than Deep Haor (81.1%). Virtually all households (99.9%) are Bengali.
The gender ratio found in the study is 96, which means that there are 96 males for each 100 females,
and the proportion of the sample that was 51.1 percent. This ratio portrays the opposite picture
regarding the male female ratio compare to the national level statistics for same category (i.e.
nationally male-female ratio is 104 males for each 100 females. Source: Statistical Pocketbook, BBS,
2004) but matches exactly the ratios found in the FoSHoL-CARE baseline study 2nd
cycle.
For the overall population, the average age of Head of Household is 42.7 years. Table 5 shows that in
Kishoreganj heads of household are significantly younger, but there is no difference by Haor type.
Almost 15 percent of sampled households are female-headed, but Netrokona has a significantly
higher proportion of female-headed households (17.4%). Again there is no difference by Haor type.
Female heads of households are significantly older (p=.000) than their male counterparts averaging
48.2 and 41.7 years old, respectively. In terms of household size the average for the study population
is 4.8 people per household, however Sunamganj has a significantly higher household average size at
5.3. Female-headed households are significantly smaller than male-headed households (3.1 and 5.0,
respectively).
The smaller household size for female headed households is a function of several factors. First, they
are older than their male counterparts, so the chance of having younger children is smaller, and the
chance that at least some of their children have married and moved out of the household is greater.
Also, they are less likely to have a counterpart male adult in the household. As a result, even though
many are still of reproductive age, their chances of having more children of their own are small.
Table 5: Key demographic characteristics of the population, by District and
Haor type
By District Kishoreganj Netrokona Sunamganj
Average age HHH
(yrs)
Overall 40.0*** 43.7 44.3
Moderate 40.5 44.1 44.3
Deep 39.5 43.3 44.2
Household Size
Overall 4.6 4.4 5.3***
Moderate 4.5 4.3 5.3
Deep 4.6 4.4 5.3
% female-headed
HHs
Overall 14.6 17.4** 12.1
Moderate 15.2 18.5 11.4
Deep 14.0 16.3 12.8
By Haor Type Moderate Deep
Average age HHH (years) 42.3 43.0
Household Size 4.8 4.7
% female-headed households 14.4 15.0
*** denotes p=.000; ** denotes p=.050
FSUP-H Baseline Report, June 2010 13
Only about 2 percent of individuals are reported to be disabled, and there are no significant
differences among the three Districts or by Haor type.
Eighty five percent of household heads are married; there is no significant difference between Haor
types. Only about 1 percent of the household heads was never married; 12 percent were widowed;
and 2 percent were divorced/ separated female household heads. The proportion of widowed
household heads is significantly higher in deep Haors in Netrokona.
Figure 1 shows the age distribution for the study population. The average age of the study population
is 22.2 years old. The median age is 16 (half of the population is below 16 years of age and half of the
population is above 16 years of age). There is a slight but obvious skewness in favor of females
between the ages of 18 and 30, and a slight bias of males in the ages between 35 and 45. Other than
these two anomalies ages are fairly equally distributed by gender. The modal (most common age) age
is 0 (infants between birth and one year old). Just over 21% of the population is under 5 years old,
and about 4 percent is over 60 years old.
Figure 1: Age distribution of study population, by sex
Ag
e, in
years
100
80
60
40
20
0
Frequency
400 300 200 100 0
Ag
e, in
years
100
80
60
40
20
0
Frequency
4003002001000
Sex
FemaleMale
Table 6 shows the demography and dependency ratios of FSUP-H households. On average, survey
households have about 2.3 adults of working age (15–60 years);7 0.8 children under age 5; 1.4
children between the ages of 5 and 14 years, and only 0.2 elderly persons above 60 years.
Three types of dependency ratios are presented in the table: child aged and total. The total
dependency ratio is defined as the ratio of the number of members in the age groups 0–14 years and
7 This is the notion of working age commonly used by demographers (see, for instance, Shryock et al. 1976). The
actual working age of individuals of course depends in part on their standard of living and can often be lower, especially for the poor.
FSUP-H Baseline Report, June 2010 14
above 60 years to the number of members of working age (15–60 years). The ratio is expressed in a
percentage. The total dependency ratio has strong and negative correlation with household income.
High dependency ratios mean a higher burden on household income. For the FSUP-H respondents,
the total dependency ratio is 114.5%, which can be considered high. Adult members of working age
have more children to support than aged household members.
Table 6: Demography and dependency ratios, by District and Haor type
Characteristic
District Haor Type Total
Kishoreganj Netrokona Sunam-
ganj Deep Moderate
Number of household members in the age group
0-4 years 0.8 0.7 0.9 0.8 0.8 0.8
5-14 years 1.3 1.3 1.6 1.4 1.4 1.4
15-60 years 2.2 2.2 2.5 2.4 2.3 2.3
Over 60 years 0.2 0.2 0.2 0.2 0.2 0.2
Demographic composition (percent)
0-4 years 18.2 16.4 17.0 17.1 17.3 17.2
5-14 years 28.9 28.7 30.8 29.7 29.4 29.5
15-60 years 49.4 49.9 48.2 49.4 48.8 49.1
Over 60 years 3.5 4.9 4.0 3.8 4.5 4.1
Total 100.0 100.0 100.0 100.0 100.0 100.0
Dependency ratio (percent)
Child (0-14) dependency ratio 104.2 100.9 112.4 104.8 105.2 105.0
Aged (>60) dependency ratio 8.7 8.0 9.3 8.1 11.0 9.5
Total dependency ratio 112.9 108.9 121.7 112.9 116.2 114.5
FSUP-H Baseline Report, June 2010 15
5.0 LIVELIHOODS AND ECONOMIC SECURITY
5.1 Occupational patterns
The primary occupation of surveyed household members reflects the principle livelihood strategies of
households in the Haor region. The survey collected data on the primary and secondary occupations
of all household members eight years of age and older, with a recognition that many have access to
multiple occupations in rural Bangladesh.
Tables 7a and 7b show the primary and secondary occupations for adults aged 8 years and older by
Haor type and District, respectively. What the data by Haor type and District show is that there are few
distinct differences in occupational trends in the Haor region sampled. As expected for the FSUP sub-
population, few households engage in agriculture on their own fields; less than 1 percent overall. This
is a direct result of the extensive degree of landlessness among the ultra poor in Bangladesh.
Sharecropping is more prevalent in the moderate Haor (3.8% versus 1.6%).
Agricultural and non-agricultural labor are the two main livelihood opportunities for FSUP households,
and together account for half of primary occupations, with no significant difference between Haor
types (table 7a). Other important livelihood activities, in order of predominance, are fishing, petty
trade, and housemaid/servant. Together, these five livelihood activities account for almost 65 percent
of primary occupations for men and women together. In terms of differences between moderate and
deep Haor areas, table 7a shows that sharecropping and fishing opportunities differ, with
sharecropping being more prevalent in the moderate Haor areas and fishing being more prevalent in
the deep Haor areas.
Picture 2: Agricultural day labor - males
The majority of respondents reported having no secondary occupation. This would be expected for
livelihoods such as salaried employees, business owners, many skilled laborers, etc., but not for
those who rely heavily on day labor opportunities. However, over 46 percent of agricultural and non-
agricultural day laborers have no secondary occupation/activity, suggesting that these individuals and
FSUP-H Baseline Report, June 2010 16
households have very low resiliency and could benefit greatly from diversifying their basic livelihood
skills.
Among those with secondary occupations, agricultural and non-agricultural day labor opportunities are
the most common occupational categories for those whose primary activities are in areas such as
petty trade, sharecropping, and fishing. Livestock husbandry is slightly more important as a secondary
occupation in the moderate Haor areas, but again there is very little difference in livelihood patterns
between the two Haor types.
Table 7a: Primary and secondary occupations for individuals aged 8 years and older, by Haor type.
Occupational Categories
Adults 8 years and older Deep Haor
Adults 8 years and older Moderate Haor
Primary occupation
Secondary occupation
Primary occupation
Secondary occupation
Men Women Men Women Men Women Men Women
No secondary occupation 64.8 89.2 66.0 85.7
Own agriculture (crop production) 1.1 0.1 0.3 0.0 1.5 0.4 0.6 0.1
Sharecropper 1.7 0.1 1.0 0.1 4.6 0.0 1.3 0.1
Own agriculture and sharecropper 0.6 0.0 0.3 0.0 0.9 0.0 0.1 0.0
Livestock husbandry 0.9 1.0 0.1 0.4 0.6 1.2 0.5 1.7
Agricultural laborer 36.5 1.3 12.6 0.2 37.9 0.3 10.4 0.1
Non-agricultural laborer 14.9 3.0 8.8 0.9 15.1 3.7 8.6 1.4
Housemaid/servant 3.0 6.0 0.1 1.1 2.5 5.0 0.1 1.3
Skilled labor8 2.5 0.5 0.3 0.2 2.6 0.2 0.5 0.1
Salaried employment (GOB-NGO) 3.6 1.0 0.1 0.0 4.2 1.0 0.2 0.0
Business 2.1 0.1 0.8 0.1 2.9 0.8 1.3 0.1
Petty business 7.8 1.2 1.3 0.2 7.0 1.5 0.8 0.3
Rickshaw/van pulling 3.6 0.0 0.6 0.0 6.5 0.1 1.5 0.1
Fishing (including fish culture) 10.5 0.2 7.5 0.1 3.4 0.1 6.8 0.0
Fishing laborer 0.9 0.1 0.4 0.0 0.2 0.0 0.4 0.0
Natural resource collection 0.5 0.6 0.1 0.1 0.2 0.4 0.2 0.2
Housewife 0.0 69.8 0.0 6.4 0.0 69.6 0.0 8.0
Beggar 0.5 0.7 0.0 0.1 0.5 0.8 0.1 0.1
Unemployed 3.8 7.1 0.2 0.1 4.2 7.6 0.1 0.1
Other 1.3 1.5 0.5 0.7 0.9 1.0 0.2 0.5
Unable to work 4.2 5.8 0.1 0.1 4.3 6.3 0.2 0.2
There are also many similarities in terms of primary and secondary opportunities by District, as Table
7b shows. Agricultural laborer is less important in Sunamganj compared to Kishoreganj and
Netrokona, but non-agricultural opportunities appear greater. Together, however, these two forms of
day laborer comprise approximately half of primary occupations for men and women combined in
FSUP-H Baseline Report, June 2010 17
each of the three districts. Sharecropping is significantly more common in Sunamganj and salaried
employment is more common in Netrokona. Petty business is a more common primary occupation in
Kishoreganj. In all three Districts about nine percent of respondents over 16 years of age are unable
to work, but not surprisingly the mean age of this category is 67 years.
As with the disaggregation by Haor type, agricultural and non-agricultural day labor opportunities
represent the most common secondary occupational categories for those whose primary activities are
in areas such as petty trade, sharecropping, and fishing. Petty business is slightly more important as a
secondary occupation in Kishoreganj, but again there are very few important differences in livelihood
patterns among the three Districts.
Table 7b: Primary and secondary occupations for individuals aged 8 years and older by District.
Occupational Categories
Adults 8 years and older Kishoreganj
Adults 8 years and older Netrokona
Adults 8 years and older Sunamganj
Primary occupation
Secondary occupation
Primary occupation
Secondary occupation
Primary occupation
Secondary occupation
Men Wom-
en Men
Wom-en
Men Wom-
en Men
Wom-en
Men Wom-
en Men
Wom-en
No secondary occupation 62.8 88.1 67.2 86.7 66.1 87.5
Own agriculture 1.2 0.2 0.2 0.1 1.0 0.4 0.5 0.0 1.4 0.1 0.6 0.0
Sharecropper 1.2 0.0 0.6 0.2 1.5 0.0 1.8 0.0 6.1 0.1 1.1 0.0
Own agr and sharecropper 0.9 0.0 0.1 0.0 1.0 0.0 0.2 0.0 0.4 0.0 0.3 0.0
Livestock husbandry 0.1 1.3 0.0 1.4 1.1 1.1 0.3 1.8 0.9 1.0 0.6 0.1
Agricultural laborer 38.5 1.6 12.3 0.2 46.9 0.5 8.7 0.2 27.9 0.4 13.2 0.2
Non-agricultural laborer 13.8 3.7 9.3 1.3 6.9 2.4 6.8 0.5 23.2 4.1 9.7 1.6
Housemaid/servant 1.4 4.4 0.0 1.3 3.3 7.9 0.2 1.4 3.4 4.3 0.2 0.8
Skilled labor 2.7 0.6 0.1 0.1 2.6 0.1 0.8 0.0 2.4 0.3 0.3 0.3
Salaried employment 3.3 0.9 0.1 0.0 5.5 1.9 0.2 0.0 3.0 0.3 0.1 0.0
Business 2.9 0.9 1.7 0.2 3.3 0.6 0.7 0.0 1.4 0.0 0.8 0.1
Petty business 12.0 1.6 2.2 0.4 4.3 1.9 0.8 0.1 6.4 0.6 0.3 0.3
Rickshaw/van pulling 7.6 0.1 2.4 0.1 4.3 0.0 0.6 0.0 3.7 0.0 0.2 0.0
Fishing (inc. fish culture) 5.5 0.1 7.2 0.1 6.8 0.1 9.8 0.0 8.5 0.2 4.9 0.0
Fishing laborer 0.3 0.0 0.5 0.0 0.5 0.0 0.5 0.0 0.9 0.1 0.2 0.0
Natural resource collection 0.0 0.6 0.0 0.0 0.4 0.9 0.0 0.4 0.6 0.0 0.4 0.1
Housewife 0.0 69.5 0.0 5.7 0.0 68.1 0.0 8.1 0.0 71.3 0.0 7.8
Beggar 0.4 0.9 0.0 0.1 0.9 1.2 0.1 0.0 0.2 0.3 0.0 0.3
Unemployed 3.8 6.1 0.1 0.1 4.1 6.8 0.3 0.1 4.0 8.9 0.1 0.0
Other 1.3 0.6 0.3 0.4 0.8 0.4 0.2 0.7 1.2 2.6 0.5 0.8
Unable to work 3.1 7.0 0.1 0.2 5.0 5.9 0.1 0.1 4.4 5.4 0.2 0.2
When comparing between men and women, table 7a and 7b show similarities by District and Haor.
The majority of women are housewives. Non-agricultural laborer and housemaid/servant are the most
8 Includes blacksmith, potter, porter, cobbler, carpenter, weaver etc.
FSUP-H Baseline Report, June 2010 18
common primary occupations for women but account for less than 10% across all Districts. Most
women reported having no secondary occupation. Qualitative data showed that while men primarily
earn income through agricultural and non-agricultural day labor, fishing and petty trade; the majority of
women do so through homestead activities such as fish processing / preparation of goods for market,
making handicrafts, and livestock and poultry rearing.
When asked about preferences for income generating activities, some important differences can be
noticed with the main occupations reported. Men indicated agriculture, cow rearing, fishing and
bamboo handicraft. Women indicated poultry and livestock rearing, shop keeping, agriculture and
small business. Furthermore, there were indications that ultra poor households have high rates of
child labor. Children were found to assist with fishing, livestock and poultry rearing, brick making,
paddy harvesting and vegetable cultivation.
In addition to the monetary benefits, community members identified other benefits of participation in
economic activities at the individual, household and community levels. At the individual level,
community members identified skills improvement, and improved social status as a key benefit.
Women stated increased mobility and communication as key benefits. Greater involvement of women
in economic activities was also seen as a way to better deal with lean periods when the males migrate
to sell labor. It was also mentioned that involvement of women in economic activities improved
education of children and reduce child labor, although others stated that it in fact contributed to child
labor. At the household level, reduced dependency on money lenders, improved household status,
increased food intake and diversity, better loan repayment, better home maintenance, reduced family
conflict over money and improved education for children were all seen as important benefits. At the
broader community level, increased employment opportunities for neighbors/friends and others,
increased access to essential goods due to newly established shops, improved women‟s decision-
Picture 3: Agricultural day labor - females
FSUP-H Baseline Report, June 2010 19
making in community issues, establishment of community schools, reduced dependency on
middle/rich class, reduced dependency on selling labor to other communities, and increased
community dignity all seen as important benefits
Community members also many identified costs of participation in economic activities including: travel
costs (in some cases as high as 150-200 Taka), wage days lost for skill training, and the need for
initial investments require high interest loans (perpetuating the debt cycle). There were also costs
identified that were specific to women, such as: wives are beaten if household chores are not
completed, children are not properly cared for and do not attend school regularly in absence of
mothers, and women are robbed of wages while traveling home from work. The time spent at
meetings/training and undertaking economic activities reduces time available for the traditional
household responsibilities of women. Although women participating in economic activities attempt to
distribute some of their household duties to other household members such as their husband or
relatives, this is rarely successful and often children end up doing the work.
Community members indicated that they would like to expand their economic activities, particularly in
the areas of cow rearing, other poultry and livestock rearing, fish culture, small shop keeping, small
business and homestead gardening. Males indicated additional preferences for cash-for-work,
rickshaw pulling and rice cultivation. Females indicated additional preferences for handicrafts (such as
bamboo products) and nursery development. In expanding their economic activities, community
members identified the following main barriers: lack of technical knowledge, support from GoB such
as livestock and fishery departments, capital, credit, production materials such as quality seeds,
access to water bodies; social kinship; and - for women - the prevailing social structures that prohibit
many women from participating in economic activities without the husband‟s consent, particularly with
respect to participation in agriculture and fishing.
Picture 4: Cow rearing by ultra-poor households
FSUP-H Baseline Report, June 2010 20
5.2 Household employment and income/expenditure
Closely linked to occupations and livelihoods are economic indicators of households, such as income,
other cash sources, asset ownership, debt and savings. Together these elements of economic
security reveal how resilient households are to economic shocks and natural disasters.
One of the first indicators of economic resiliency is the number of income earners per household. For
the survey population, each household had on average only 1.35 income earners. This can be
considered quite low but there could be seasonality factors due to the timing of the survey.
Kishoreganj and Netrokona had 1.31 and 1.29 income earners per household, respectively, while
Sunamganj had a small but statistically higher average (p=.000) of 1.45 income earners. Statistically,
the deep Haor areas had more income earners than the moderate Haor areas (1.39 versus 1.32,
respectively).
The data by Haor type and District show that the main household income sources align closely with
the primary occupations, as was expected based on number of income earners and main
occupations. Casual agriculture labor is the main income source across Haor areas and Districts,
followed by casual non-agricultural labor. Income from crop and animal sales is low overall. There are
few distinct differences in income sources between deep and moderate Haor regions sampled. The
main exception is the sale of fish/aquatic animal, which is significantly higher in the deep Haor. In turn,
sale of agricultural produce is higher in moderate than deep Haor areas. Income sources such as
salaried work, small business, petty trade and rickshaw/van pulling are more prevalent in deep Haor
areas.
Table 8: Income sources for previous 30 days, by Haor type
Income Sources
(multiple response)
Deep Haor Moderate Haor
N % of Responses N % of Responses
Selling vegetables 2 0.2 0 0.0
Selling livestock/poultry/birds 5 0.5 5 0.5
Selling agricultural produce 13 1.4 28 3.0
Selling fish/aquatic animals 106 11.4 26 2.8
Self-employed (carpenter, barber, etc.) 35 3.8 37 4.0
Salaried 63 6.8 83 9.0
Casual labor (agriculture) 544 58.3 501 54.3
Casual labor (non-agriculture) 288 30.9 276 29.9
Rickshaw/van pulling 45 4.8 82 8.9
Small business 129 13.8 140 15.2
Petty trade 6 0.6 7 0.8
Remittances/Pensions/Savings 5 0.5 10 1.1
Renting/leasing out property 1 0.1 2 0.2
Relief assistance from GoB or NGO 5 0.5 0 0.0
Selling HH assets 13 1.4 15 1.6
Begging 56 6.0 38 4.1
Total 1316 141.1% 1250 135.6%
FSUP-H Baseline Report, June 2010 21
When comparing income sources among districts, sale of fish/aquatic animals is higher in Sunamganj
than in the other districts. Casual non-agriculture labor and begging are also significantly higher in
Sunamganj, while casual agriculture labor is higher in Netrokona. Small business and rickshaw/van
pulling are highest in Kishoreganj.
Table 9: Income sources for previous 30 days, by District
Income Sources
(multiple response)
Kishoreganj Netrokona Sunamganj
N % of
Responses N
% of
Responses N
% of
Responses
Selling vegetables 0 0.0 1 0.2 1 0.2
Selling livestock/poultry/birds 6 1.0 3 0.5 1 0.2
Selling agricultural produce 15 2.4 12 2.0 14 2.2
Selling fish/aquatic animals 39 6.3 39 6.4 54 8.6
Self-employed (carpenter, barber, etc.) 22 3.5 28 4.6 22 3.5
Salaried 41 6.6 55 9.0 50 8.0
Casual labor (agriculture) 318 51.3 399 65.4 328 52.5
Casual labor (non-agriculture) 157 25.3 132 21.6 275 44.0
Rickshaw/van pulling 60 9.7 31 5.1 36 5.8
Small business 134 21.6 65 10.7 70 11.2
Petty trade 3 0.5 6 1.0 4 0.6
Remittances/Pensions/Savings 2 0.3 5 0.9 8 1.3
Renting/leasing out property 0 0.0 2 0.3 1 0.2
Relief assistance from GOB or NGO 2 0.4 2 0.3 1 0.2
Selling HH assets 6 1.0 16 2.6 6 1.0
Begging 24 3.9 22 3.6 48 7.7
Total 829 133.7 818 134.1 919 147.0
Table 10a shows the mean and median monthly income and expenditure per capita. The mean
monthly per capita income is 800 Taka overall. The per capita income is significantly lower in
Sunamganj than in the other districts. When comparing between Haor types, the mean per capita
income in the deep Haor is significantly higher than in the moderate Haor, but the median per capita
income in the deep Haor is significantly lower than in the moderate Haor.
Mean monthly expenditure per capita is 1,419 Taka, and the median monthly expenditure per capita is
1,099 Taka. When comparing across haor type, the median monthly per capita expenditure is
significantly lower in the moderate Haor.
FSUP-H Baseline Report, June 2010 22
Table 10a: Key income and expenditure data for households, by District and Haor type
Income/expenditure
Variable
District Haor Type Total
Kishoreganj Netrokona Sunam-
ganj Deep Moderate
Mean monthly per capita
income (Taka) 845 880 674c 821
a 779 800
Median monthly per capita
income (Taka) 750 750 614
b 700
b 750 717
Mean monthly household
expenditures per capita
(Taka)
1,255b 1,583 1,478 1,487 1,353 1,419
Monthly median household
expenditures per capita
(Taka)
1,115 1,097 1,071 1,136 1,059b 1,099
Letters denote significant differences among Districts or between Haor types for a given variable.
Significance levels for comparisons: a = .10; b = .05; c = .00
Figure 2a shows the mean values of annual per capita income. For the survey population overall, the
mean annual per capita income is 9,599 Taka. Annual per capita income in Kishoreganj and
Netrokona are 10,136 Taka and 10,567 Taka, respectively – with no significant differences. However,
nnual per capita income in Sunamganj is significantly lower at 8,090 Taka.
Figure 2a: Mean values of annual per capita income, by District
Table 10b shows detailed expenditure data, by District and Haor type. Data is presented in three
categories, namely: daily, monthly and annual expenditure. The majority of daily expenditure is on
food purchases (72% of daily expenditure). The purchase of tobacco products is the next highest daily
expenditure (8%), followed by hiring manual labor from others and purchasing fuel (including gasoline,
kerosene, and fire wood). The remainder of the daily expenses goes to daily allowance for children,
transportation and beverages.
FSUP-H Baseline Report, June 2010 23
It is interesting to note that the single highest monthly expenditure item is cell phone cards (48% of
monthly expenditure). The second highest monthly expenditure item is medical expenses (including
fees, medicine and travel) (36%). The single highest item annual expenditure is clothing for household
members (51% of annual expenditure), followed by social/religious events (14%) and household
goods (11%), on average. Expenditure on fishing or fish raising, agricultural equipment/input, and
livestock and poultry rearing accounted for less than 10% of annual expenditure each.
Table 10b: Detailed expenditure data, by District and Haor type
Expenditures in Taka
District Haor Type Total
Kishoreganj Netrokona Sunam-
ganj Deep Moderate
N 628 634 630 947 945 1892
Daily Expenditures
Food purchases 99.8 107.5 131.7c 117.8
c 108.2 113.0
Daily allowance for children 5.3 b
3.8 4.4 4.7 4.4 4.5
Transportation 4.2 3.8 6.4 4.8 4.7 4.8
Cigarettes 7.3 7.8 19.4 8.4 14.6 11.5
Beverages 0.2 0.5 1.0 c 0.6 0.6 0.6
Fuel (livelihood) 0.5 0.1 2.2 1.3 0.3 1.0
Manual labor 4.7 b
11.3 16.6 11.5 10.2 10.9
Kerosene oil 4.2 15.6 c
4.4 4.5 11.7 c 8.1
Wood fuel 6.0 c 1.7 3.4 4.6 2.8 3.7
Total: 132.2 152.1 189.5 158.2 157.5 158.1
Monthly Expenditures
Shelter rental 18.1 a 2.9 0.4 9.5 4.8 7.1
Insurance 4.1 3.2 4.5 2.8 5.1 a 3.9
Utilities 17.2 a 12.6 6.1 7.4
b 16.6 12.0
Education 45.5 52.8 55.7 62.2 a 40.4 51.3
Child care 17.2 18.5 38.7 c 21.0 28.6
b 24.8
Medical/dental care 100.5 c 45.4 41.8 61.8 63.1 62.5
Medicine 333.5 c 228.6 171.8 276.1
c
212.8 b
244.5
Medical travel 27.0 c 15.4 16.3 18.4 20.7 19.5
Cell phone cards 433.6 372.3 a
486.4 500.2 363.0 c 430.6
Transportation 14.2 20.7 20.4 16.8 20.1 18.4
Loan payment 28.7 c 4.2 32.2 22.5 20.9 21.7
Total 1,039.6 776.8 874.3 998.7 796.1 896.3
Yearly Expenditures
Clothing 1,765.0 1845.1 c 1577.7 1716.2 1742.8 1729.5
Livelihood equipment 244.2 162.8 b
266.6 214.4 234.3 224.4
Agr equipment/inputs 127.0 166.8 273.1 b
155.7 222.3 189.0
Fishing/fish-raising 265.3 a 340.1 407.1 470.8 204.1
c 337.6
Household goods 250.1 583.8 c
234.1 a 330.7 382.6 356.6
Livestock/poultry 94.3 c 83.9 23.0
b 55.8 78.4 67.1
Social/religious events 487.2 456.4 428.6 508.4 b
406.2 457.3
Dowry payment 57.8 93.3 49.4 90.4 43.3 66.9
Total: 3290.9 3734.2 3259.6 3542.4 3314.0 3428.4
Letters denote significant differences among Districts or between Haor types for a given variable. Significance levels for comparisons: a = .10; b = .05; c = .00
FSUP-H Baseline Report, June 2010 24
Figure 2 shows that median per capita monthly expenditures (including daily, monthly and annual
expenditures) are significantly higher than median per capita income. This is likely due to several
factors. First of all, there is the seasonality of the data collection; February falls in a lean period, which
is characterized by lower income and high lending. More information on lending and other coping
strategies is provided in section 5.4. Secondly, respondents have the tendency to overestimate
expenditure and underestimate income. It is important to note here that accurate income and
expenditure data collection requires very detailed questioning. The income and expenditure data
presented is this report, while indicative of income and expenditure levels, is most useful for
assessing trends over time and relative change between baseline and endline measurements.
Figure 2b: Median values of monthly household cash income and expenditures per capita, by District
and Haor type
5.3 Income in peak and lean seasons
Figure 3 clearly shows that there are significant differences between mean monthly income during
peak and lean seasons. When comparing peak season income levels across districts and between
Haor types, there are no significant differences in mean monthly income levels among districts, and
between Haor types. When comparing lean season income levels across districts and between Haor
types, the mean monthly income level in Sunamganj is significantly lower than in the other two
districts.
When asked for the main reasons that cause a lean period of income for a household, many
respondents found it difficult to clearly express the reasons for this. After probing, 82 percent of those
mentioned no opportunity for other/alternative work as a reason, 81 percent mentioned poor health of
the main income earner, 72 percent identified seasonal work, and 18 percent identified inability to
work due to bad weather/disaster. There were very few differences among districts and between Haor
types.
FSUP-H Baseline Report, June 2010 25
Figure 3: Average monthly incomes during peak and lean seasons, by District and
Haor type
5.4 Coping strategies for lean seasons
Table 11 and table 12 show the top ten ways of coping with lean periods by District and Haor type, as
per respondents‟ answers from a multiple choice list of 31 possible responses. Overall, adjusting
meals is the main coping mechanism (60.1%), followed by taking loans from friends/relatives (50.6%),
and taking loans from money lenders (35.6%).
When comparing across districts (table 11), a higher number of households take loans from
friends/relatives in Kishoreganj than in the other Districts. Percentage of households taking loans from
a moneylender is highest in Sunamganj. Adjusting meals is lower in Kishoreganj than in the other two
Districts, while eating famine foods is higher in Sunamganj. Accessing savings is higher in Netrokona
than in the other two districts.
FSUP-H Baseline Report, June 2010 26
Table 11: Top ten ways of coping with lean periods, by District
Coping Mechanisms
(multiple response)
Kishoreganj Netrokona Sunamganj
N % of
Responses N
% of
Responses N
% of
Responses
Adjusting meals 342 54.5 404 63.7 391 62.1
Taking loans from friends/relatives 407 64.8 275 43.4 275 43.7
Taking loans from a money lender 188 29.9 203 32.0 283 44.9
Purchasing goods on credit 187 29.8 197 31.1 217 34.4
Accessing savings 146 23.2 257 40.5 129 20.5
Taking loans from an NGO 86 13.7 120 18.9 141 22.4
Relying on relief/aid 62 9.9 128 20.2 105 16.7
Eating famine foods 37 5.9 19 3.0 137 21.7
Reducing treatment costs 55 8.8 66 10.4 17 2.7
Temporarily migrating 52 8.3 40 6.3 44 7.0
Total 1721 274.0 1819 286.9 1888 299.7
When comparing between deep and moderate Haor types, table 12 shows few distinct differences.
Adjusting meals and informal lending are the main coping mechanisms in both Haor areas.
Table 12: Top ten ways of coping with lean periods, by Haor type
Coping Mechanisms
(multiple response)
Deep Haor Moderate Haor Overall
N % of
Responses N
% of
Responses N
% of
Responses
Adjusting meals 594 62.7 543 57.5 1137 60.1
Taking loans from friends/relatives 516 54.5 441 46.7 957 50.6
Taking loans from a money lender 313 33.1 361 38.2 674 35.6
Purchasing goods on credit 286 30.2 315 33.3 601 31.8
Accessing savings 267 28.2 265 28.0 532 28.1
Taking loans from an NGO 220 23.2 127 13.4 347 18.3
Relying on relief/aid 155 16.4 140 14.8 295 15.6
Eating famine foods 94 9.9 99 10.5 193 10.2
Reducing treatment costs 78 8.2 60 6.3 138 7.3
Temporarily migrating 62 6.5 74 7.8 136 7.2
Total 2794 295.0 2634 278.7 5428 286.9
Respondents were also asked about selling advance labor, separately from the multiple choice
question described in tables 11 and 12 above. Selling labor in advance is another coping strategy
used by households during lean periods. Qualitative data shows that advance labor is usually sold at
reduced wage levels. Overall, 7% of households had at least one member who sold labor in advance.
There was no significant difference between Haor types but there were significant differences among
Districts: Kishoreganj 5.4%, Netrokona 4.9%, Sunamganj 10.8% (p=.000).
FSUP-H Baseline Report, June 2010 27
5.5 Migration
Tables 11 and 12 show that temporary migration was not a common coping strategy to deal with lean
periods. However, migration for employment purposes is relatively common in areas with a high
degree of seasonal work, such as the FSUP-H project area. In moderate Haor areas, 38.5% of
households had somebody migrate in the last 12 months for employment purposes; in deep Haor
areas this was 32.3%. Moderate Haor areas also had a significantly higher average number of
household members migrating out of the village in the previous 3 months: 0.43 persons versus 0.36
persons for Deep; (p=.018). There were no differences when comparing among districts.
About 75% of those who migrated were heads of household, while about 20% were sons/daughters.
About 70% migrated to urban areas and 30% to other rural areas. There were no differences when
comparing these values among district or Haor type. While migration went on throughout the year,
there was more migration for employment purposes from August to October.
Table 13 shows the types of work performed by those migrating out of the household. Agricultural
contract labor and agricultural day labor are by far the most common types of work. When comparing
across districts, agricultural contract labor is higher for migrant workers from Sunamganj than in the
other two districts. In Netrokona, agricultural day labor is higher. In Kishoreganj, salaried employment
is higher than in Netrokona and Sunamganj.
While it is still mainly men who migrate for agricultural contract and day labor, qualitative data showed
that an increasing number of women also migrate for economic purposes, with many young females
migrating to work in garment factories.
Table 13: Type of work performed by those migrating out of the household within the last 12 months,
by District and Haor type
(N=672, multiple response) District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
Agricultural contract labor 37.1 39.5 57.8 42.3 46.0 44.3
Agric. day labor 31.9 53.3 26.1 32.6 40.3 36.8
Non-agric. day labor 1.6 3.8 2.4 3.6 2.7 3.1
Salaried empl - fixed business 17.1 2.4 4.7 11.1 6.6 8.6
Salaried employee 9.2 1.0 0.5 5.2 2.7 3.9
Maid/servant 4.8 8.6 2.8 3.9 6.6 5.4
Other 4.3 9.5 7.1 7.5 5.2 6.3
5.6 Loans
Table 14a shows that 78% of households overall held at least 1 current loan over the last 12 months.
When comparing across Haor types, a significantly higher number of households in moderate Haor
areas (80%) held loans than in deep Haor areas (75%). There were no significant differences when
comparing among districts.
The average number of loans per household overall was 1.4. When comparing across Haor types, the
average number of loans per household was significantly lower in deep Haor areas (1.2) than in
moderate Haor areas (1.5). There were no significant differences when comparing among districts.
FSUP-H Baseline Report, June 2010 28
The average loan amount was 6,652 Taka. When comparing across Haor types, the average loan
amount per household was significantly lower in deep Haor areas (6,346 Taka) than in deep Haor
areas (6,938 Taka). When comparing across districts, the average loan amount in Sunamganj was
significantly lower than in the other districts. Overall, the outstanding loan amount at the time of the
interview was 5,393 Taka, which is about 81% of the average loan amount - indicating a very high
debt burden on households. In Sunamganj, the outstanding loan amount was significantly lower than
in the other districts. There were no significant differences between Haor types.
Picture 5: Grameen Bank office
There was no significant difference in loan source among moderate and deep Haor areas, with the
majority of loans (41%) taken from money lenders, NGOs (24%), and friends/relatives (23%). Only 6%
of loans were taken from Grameen Bank and 4% from clubs/CBOs. Informal money lenders give
loans without collateral but instead charge higher interest rates. The high level of lending from
informal sources such as money lenders largely explains the high interest rates found in this survey.
FSUP-H Baseline Report, June 2010 29
Table 14a: Key loan data for households, by District and Haor type
Loan Variable
District Haor Type Total
Kishoreganj Netrokona Sunam-
ganj Deep Moderate
N
Households with a loan (%) 80 74 79 75 80 b 78
Average number of loans per HH 1.5 1.1 b
1.4 1.2 c 1.5 1.4
Average loan amount (Taka) 7,148 6,944 5,880
b 6,346
a 6,938 6,652
Outstanding loan amount (Taka) 5,732 6,103 4,448 c 5,284 5,482 5,393
Outstanding as a % of average
loan amount 80.2 87.9 75.6 83.3 79.0 81.1
Letters denote significant differences among Districts or between Haor types for a given variable.
Significance levels for comparisons: a = .10; b = .05; c = .00
Table 14b shows that overall interest rates were 51%. The overall interest rates in Sunamganj were
significantly higher than in the other districts, and the rates in deep Haor areas were significantly
higher than in moderate Haor areas. Interest rates of money lenders were the highest, followed by
friends/family, NGOs and the Grameen bank. It is interesting to note that while Grameen bank
maintains a unified interest rate of 20% throughout the country, the survey data shows a range of 14-
20%.
Table 14b: Detailed interest rate data for loans, by District and Haor type
Interest Rate
District Haor Type Total
Kishoreganj Netrokona Sunam-
ganj Deep Moderate
N 628 634 630 947 945 1892
Overall Interest rate (%) 46 45 62 c 59
a 44 51
Moneylenders 79 55 112 75 94 84
NGOs 13 15 25 17 23 19
Friends/relatives 63 55 37 65 43 56
Banks 11 8 13 11 8 10
Grameen Bank 14 20 19 14 21 18
GOB 12 9 13 10 14 11
Clubs/CBOs 39 57 55 43 50 16
Table 14c shows that, overall, 35% of loans over the 12-month recall period were taken by women.
Almost all women (98%) had taken a loan from the Grameen Bank, which reflects the Grameen
Bank‟s policy of lending to women. The proportion of women who took a loan from NGOs is also high
(88%), for similar reasons. The proportion of women taking loans from moneylenders is the lowest
among all loan sources.
FSUP-H Baseline Report, June 2010 30
Table 14c: Loan source for women, by District and Haor type
Loan source
District Haor Type
Kishoreganj Netrokona Sunam-
ganj Deep Moderate
Loans to Women (%) 29 32 43 39 30 35
Moneylenders 11 7 12 12 9 10
NGOs 84 86 92 90 82 88
Friends/relatives 20 24 21 21 21 21
Banks 33 29 50 33 33 33
Grameen Bank 96 100 98 98 97 98
GOB 0 0 50 50 50 50
Clubs/CBOs 12 10 33 13 12 12
Letters denote significant differences among Districts or between Haor types for a given variable.
Significance levels for comparisons: a = .10; b = .05; c = .00
Tables 15 and 16 show that the most common reasons for taking out a loan were consumption
purposes (food, clothing etc), followed by medical treatment and non-agricultural purchases. Lending
for consumption purposes was higher in deep Haor areas than in moderate Haor areas. Very few
households reported taking out a loan for productive purposes such as the purchase of agricultural
tools/equipment, purchase of agricultural inputs, land leasing or mortgaging or livestock purchases.
Table 15: Reasons for taking out a loan, by Haor type
Reason for Loan
(multiple response)
Deep Haor Moderate Haor Overall
N % of
Responses N
% of
Responses N
% of
Responses
Purchase agricultural tools/equipment 18 2.4 15 2.2 33 2.3
Purchase agricultural inputs 60 8.1 76 11.0 136 9.5
Land leasing or mortgaging 27 3.6 8 1.2 35 2.4
Livestock purchases 11 1.5 10 1.5 21 1.5
Non-agricultural purchases 249 33.6 158 23.0 407 28.5
Medical treatment/medicine 436 58.8 358 52.0 794 55.5
Consumption (food, clothes, etc.) 975 131.4 819 119.0 1794 125.5
Education 31 4.2 16 2.3 47 3.3
House repair/construction 115 15.5 96 14.0 211 14.8
Marriage/social 43 5.8 22 3.2 65 4.5
Total 1965 264.8 1578 229.4 3543 247.8
FSUP-H Baseline Report, June 2010 31
Table 16: Reasons for taking out a loan, by District
Reason for Loan
(multiple response)
Kishoreganj Netrokona Sunamganj
N % of
Responses N
% of
Responses N
% of
Responses
Purchase agricultural tools/equipment 16 3.3 13 2.8 4 0.8
Purchase agricultural inputs 56 11.6 26 5.7 54 11.0
Land leasing or mortgaging 17 3.5 11 2.4 7 1.4
Livestock purchases 9 1.9 9 2.0 3 0.6
Non-agricultural purchases 138 28.6 125 27.3 144 29.4
Medical treatment/medicine 330 68.5 252 55.0 212 43.3
Consumption (food, clothes, etc.) 604 125.3 541 118.1 649 132.4
Education 14 2.9 21 4.6 12 2.4
House repair/construction 59 12.2 73 15.9 79 16.1
Marriage/social 22 4.6 20 4.4 23 4.7
Total 1265 262.4 1091 238.2 1187 242.2
Qualitative data clearly showed the impact that high interest rates are having on households. These
high rates perpetuate the household debt cycle, which leads to use of loans for day-to-day
consumption purposes and prevents productive investments – as can be seen from the very high debt
burden in Table 14 and loan uses described in Tables 15 and 16.
Many community members specifically mentioned the high interest rates of NGOs and even called it
exploitative. Households that have no choice but to take loans at these high interest rates, often end
up taking additional loans and selling land to pay their weekly installments. It was stated that the credit
provided by NGOs is not suitable for the needs of ultra poor, who instead require soft or even interest-
free loans. Soft loans are preferred over current NGO credit arrangements that require weekly
installments, which are hard to maintain.
It was also mentioned that there is an important gender dynamic to NGO credit. Although loans are
given to women, decisions regarding loan use and repayment are frequently made by men who are
not properly trained to optimize business opportunities or manage household income/expenditures.
5.7 Assets
Assets are an integral component of livelihoods, and the accumulation and sale of assets reflect
important economic characteristics of households. Each respondent was questioned about ownership
of fifty-four different assets, divided into six asset classes – domestic, productive, land, animal,
resource and financial. Asset ownership is a powerful economic indicator to monitor over time as it
reflects household-level decision-making regarding where to invest additional resources.
Table 17 shows results for 16 domestic assets. Relatively few assets differed significantly by Haor
type, but there was greater ownership of cupboards, lanterns and mobile phones in deep Haor, and
FSUP-H Baseline Report, June 2010 32
greater ownership of showcases in moderate Haor. Kishoreganj had significantly greater ownership of
six assets, including both gold and silver jewelry, suggesting that household domestic asset
ownership is greater in this District. In contrast, domestic asset ownership is least in Netrokona.
Table 17: Average number of domestic assets owned, by District and Haor type
Domestic assets District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Chairs 0.27 0.35 0.48b 0.38 0.35 0.37
Beds 1.07 1.01 0.91b 0.98 1.01 1.00
Cupboards 0.16c 0.03 0.07 0.10
b 0.07 0.09
Tables 0.13b 0.15
b 0.19
b 0.16 0.15 0.16
Showcases 0.17c 0.07 0.09 0.10 0.13
b 0.11
Dressing tables 0.01 0.00 0.00 0.00 0.01 0.00
Watches 0.08 0.06 0.07 0.08 0.06 0.07
Clocks 0.04 0.02 0.03 0.03 0.03 0.03
Lanterns 0.80c 0.38 0.48 0.64
c 0.47 0.55
Radios 0.01 0.01 0.03 0.02 0.01 0.02
TVs 0.02 0.00 0.01 0.02 0.00 0.01
Cassette players 0.01 0.00 0.01 0.01 0.01 0.01
Electric fans 0.04b 0.02 0.00 0.03 0.02 0.02
Mobile phones 0.13 0.10 0.13 0.14b 0.10 0.12
Gold jewelry (ana) 1.05b 0.77 0.82 0.88 0.88 0.88
Silver jewelry (ana) 7.50c 4.00 4.34 5.47 5.08 5.27
Letters denote significant differences among Districts or between Haor types for a given asset. Significance levels for comparisons: a = .10; b = .05; c = .00
Productive assets include various types of transportation and livelihood equipment, and are an
important indicator of a household‟s investment in livelihood opportunities. Overall the ownership of
productive assets in the survey population was very low. Generally, far less than one out of ten
households owned any of the productive assets (Table 18). Productive assets related to fishing (boats
and nets) were significantly more common in deep Haor, while boats and bicycles were more
commonly owned in Kishoreganj. Aside from these differences there was little differentiation among
Districts.
FSUP-H Baseline Report, June 2010 33
Table 18: Average number of productive assets owned, by District and Haor type
Productive assets District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Boat 0.04c 0.08 0.10 0.09
c 0.05 0.07
Motorcycle 0.00 0.00 0.00 0.00 0.00 0.00
Rickshaw/van 0.02 0.01 0.01 0.01 0.02 0.02
Bicycle 0.04b 0.01 0.00 0.00 0.04
b 0.02
Sewing machine 0.01 0.00 0.00 0.01 0.01 0.01
Shallow/hand-tube well 0.07 0.01 0.00 0.02 0.04a 0.03
Power tiller 0.00 0.00 0.00 0.00 0.00 0.00
Paddle thresher 0.01 0.00 0.00 0.00 0.00 0.00
Spray machine 0.00 0.00 0.00 0.00 0.00 0.00
Plough 0.01 0.02 0.02 0.01 0.02 0.02
Fishing nets 0.21 0.32 0.45 0.48c 0.18 0.33
Other 0.03 0.25 0.07 0.20 0.03 0.12
Letters denote significant differences among Districts or between Haor types for a given asset. Significance levels for comparisons: a = .10; b = .05; c = .00
Land assets, measured in decimals, are provided in Table 19. Land ownership varies greatly among
sampled households, so differences between Haor types or among Districts have to also be large to
be significantly different. Between Haor types, only homestead land differs significantly and ownership
is greater in moderate Haor than in deep Haor (3.44 and 2.32 decimals per household, respectively).
Significantly less homestead land is owned in Kishoreganj compared to Netrokona and Sunamganj.
Netrokona has more land leased in, while Kishoreganj has more land leased out. Netrokona
households also averaged 1.48 decimals of „other‟ land thought to be different from the six categories
of land pre-coded in the survey. Overall ownership of agricultural land is highest and averaged 4.05
decimals per household, or less than 1/20th of one acre.
Table 19: Average number of land assets owned, by District and Haor type
Land assets (in decimals*)
District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Homestead land 1.98c 3.49 3.16 2.32 3.44
c 2.88
Agricultural land 3.49 4.94 3.73 3.53 4.58 4.05
Land lease - IN 3.21 1.72b 4.85 3.07 3.45 3.26
Land lease - OUT 2.23b 0.26 0.92 1.51 0.76 2.80
Haor land 0.14 0.33 0.44 0.36 0.25 0.30
Pond/ditch 0.02 0.06 0.15 0.03 0.13 0.08
Other land 0.04 1.48c 0.11 0.54 0.55 0.55
Letters denote significant differences among Districts or between Haor types for a given asset. Significance levels for comparisons: a = .10; b = .05; c = .00
*100 decimals is equal to 1 acre
FSUP-H Baseline Report, June 2010 34
Chickens were the most common animal asset owned, averaging 1.48 per household (Table 20).
Ownership of chickens was also significantly higher in Kishoreganj where it averaged 1.70 per
household. Ducks were the second most common animal asset and averaged 0.68 per household,
but were significantly more common in deep Haor, and significantly less common in Sunamganj than
in Kishoreganj or Netrokona. Cows were the third most commonly owned animal asset but were least
common in Kishoreganj.
Table 20: Average number of animal assets owned, by District and Haor type
Animal assets District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Cows 0.20b 0.27 0.30 0.25 0.27 0.26
Buffalo 0.00 0.00 0.00 0.00 0.00 0.00
Goats 0.12 0.08 0.09 0.09 0.10 0.10
Sheep 0.00 0.00 0.02 0.01 0.01 0.01
Chickens 1.70 b
1.43 1.30 1.53 1.42 1.48
Ducks 0.81 0.73 0.49 b
0.80b 0.56 0.68
Pigs 0.00 0.00 0.00 0.00 0.00 0.00
Other 0.02 0.00 0.00 0.01 0.01 0.01
Letters denote significant differences among Districts or between Haor types for a given asset. Significance levels for comparisons: a = .10; b = .05; c = .00
Ownership of some resource assets, which included timber and fruit trees, bamboo, and medicinal
plants (mostly for use against cough and fever, used in lieu of adequate health care service), was
fairly common in surveyed households. Bamboo trees were the most commonly owned resource
asset and averaged just over three trees per household, but were significantly more common in
moderate Haor areas, and significantly less common in Kishoreganj, where ownership of timber and
fruit trees was also significantly less compared to the two other Districts.
Table 21: Average number of resource assets owned, by District and Haor type
Resource Assets District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Timber trees 0.42 0.63 b
0.99 c 0.65 0.71 0.68
Fruit trees 0.87 c 1.40 1.30 1.16 1.23 1.20
Bamboo trees 1.65 b
3.55 3.87 1.74 4.34 c 3.04
Medicinal plants 0.15 0.02 0.03 0.01 0.12 0.07
Others 0.01 0.06 0.15 b
0.10 0.15 0.08
Letters denote significant differences among Districts or between Haor types for a given asset. Significance levels for comparisons: a = .10; b = .05; c = .00
The last asset category was financial assets and results are shown in Table 22. Cash with NGOs
averaged 495 Taka per household and was the most common financial asset measured. Households
in deep Haor had significantly more cash with NGOs (587 Taka compared to 403 Taka in moderate
Haor), but significantly less loans or credits given to others. Very few households had any cash at
banks but there was a slightly higher amount in Kishoreganj, where cash on hand was also
significantly higher. Cash with NGOs was highest in Sunamganj.
FSUP-H Baseline Report, June 2010 35
Table 22: Average financial assets owned, in Taka, by District and Haor type
Financial Assets District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Cash at bank 2.39 b
0.00 7.32 5.91 a 0.54 3.23
Cash w/ NGO 416.86 456.53 612.67 a 587.14 403.38
a 495.36
Insurance 62.49 57.82 45.64 38.80 71.86 55.32
Cash on hand 203.40 c 90.89 130.85 121.79 161.33 141.54
Loan/credit to others 187.42 c 75.21 2.96 56.90 119.96
a 88.40
Other 24.20 59.76 14.23 20.85 44.78 32.80
Letters denote significant differences among Districts or between Haor types for a given asset. Significance levels for comparisons: a = .10; b = .05; c = .00
Picture 6: Jack fruit trees
FSUP-H Baseline Report, June 2010 36
5.8 Housing characteristics
Table 23 shows the housing characteristics of households. The majority of all houses have floors
made of mud (99.9% and 0.1% made of brick), walls made of straw/jute or corrugated iron
sheets/tin/wood, and roofs made of corrugated iron. Less than 1% of all houses have brick walls and
only 1 house in Kishoreganj had a concrete roof. Total square feet of living space is 175ft and the
average number of rooms is 2 across all strata. About 10 percent of households share their living
space with their cattle, mostly for safety of the animals in absence of more than one housing structure.
When comparing across districts, the proportion of houses using corrugated iron/tin/wood building
materials is significantly higher than in the other districts. The proportion of houses with mud walls is
significantly higher in Sunamganj. Total living area was significantly higher in Sunamganj and sharing
of living space with cattle was significantly higher in Netrokona than in the other districts. When
comparing across Haor region, the proportion of houses with mud or straw/jute walls was significantly
higher in moderate Haor than in deep Haor.
Table 23: Housing characteristics, by District and Haor type
House characteristics District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 196 166 203 220 242 565
Wall
Material
Brick 0.3 0.5 0.6 0.4 0.6 0.5
CI sheet/tin/wood 56.4c 24.4 19.8 34.1 32.9 33.5
Mud 0.5 5.5 16.3 c 4.2 10.7
c 7.5
Bamboo 5.9 7.9 11.7 8.7 8.4 8.5
Straw/jute/etc. 36.9 b
61.7 51.4 52.7 47.4 a 50.1
Roof
Material
CI sheet/tin 91.4 b
83.1 83.7 87.1 85.0 86.0
Straw/jute/etc. 7.8 b
16.2 15.7 12.4 14.1 13.3
Other 0.7 0.6 0.7 0.5 0.8 0.7
Total area (square feet) 173.6 170.2 179.7 a 172.6 176.3 174.5
Average number of rooms 2.0 2.1 2.0 2.0 2.0 2.0
Share with cattle (%) 9.1 11.8 b
7.6 8.7 10.4 9.5
Letters denote significant differences among Districts or between Haor types for a given variable.
Significance levels for comparisons: a = .10; b = .05; c = .00
FSUP-H Baseline Report, June 2010 37
Picture 7: Housing made of jute and straw
Picture 8: Housing made with corrugated iron
FSUP-H Baseline Report, June 2010 38
6.0 FOOD SECURITY
6.1 Food consumption score
The Food Consumption Score (FCS) is widely used now by the World Food Program and endorsed
by FANTA9 as a measure of diet diversity and quality, and is derived by weighting various food groups
based on their protein value and assigning a score for each food group consumed by the household
during the recall period. Points for the FSUP baseline study are assigned as follows:
Table 24: Food consumption score
Food Group Score
Cereals: 2 points
Pumpkin, squash carrots, sweet potatoes : 2 points
White potatoes, white yams: 2 points
Dark green leafy vegetables: 3 points
Other vegetables: 1 point
Papayas, mangoes: 3 points
Other fruits: 1 point
Meat: 4 points
Eggs: 4 points
Fresh or dried fish/shellfish: 4 points
Legumes/pulses: 3 points
Milk/Dairy: 4 points
Oil/fats: 0.5 points
Sugar/honey: 0.5 points
Total Possible: 34.0 points
The thresholds used for the FSUP study are: 0-4 is poor, 4-8 is borderline food security and 9+ is
acceptable food security. These are modified from the World Food Program‟s Comprehensive Food
Security and Vulnerability Assessment Guidelines and are specific for the FSUP study. Future
measurements of the FCS within FSUP should use the same food group weights and the same
thresholds.
Table 25 shows the responses organized by thresholds. The highest proportion of sampled
households with acceptable FCS values is located in Kishoreganj, and the highest proportion of
sampled households with poor FCS values is located in Sunamganj. When comparing among deep
and moderate Haor areas, the highest proportion of sampled households with acceptable FCS is
located in deep Haor areas, and the highest proportion of households with poor and borderline FCS
values is located in moderate Haor areas. One of the reasons for the higher score in deep Haor areas
is the higher consumption of fish (which scores 4 points) in those areas. However, it is also important
to note that the scores provided in Table 25 relate to the timing of the data collection, particularly the
difference in food consumption in peak and lean seasons, as will be elaborated on below.
9 Food Aid and Nutritional Technical Assistance Project of USAID.
FSUP-H Baseline Report, June 2010 39
Table 25: Proportion of sampled households by FCS threshold values
FCS Thresholds District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
Poor (0-5) 8.3 16.9 23.5 14.1 18.3 16.2%
Borderline (6-8) 23.4 36.1 34.8 30.0 32.9 31.5%
Acceptable (9+) 68.3 47.0 41.7 55.9 48.8 52.3%
The mean FCS values are shown in Figure 4. Overall, FCS values are higher than 8 and can be
considered acceptable. When comparing among Haor areas, the deep Haor area has a significantly
higher FCS value than the moderate Haor area. When comparing among districts, Kishoreganj has a
significantly higher FCS value than the other two districts; with Sunamganj having the lowest FCS
value overall.
Figure 4: Mean FCS values, by District and Haor type
When analyzing by food group, the responses show that almost all household members (99%)
consumed „cereals‟; mostly rice and in few cases wheat flour/puffed rice. The second most frequent
(71%) food group consumed was „fresh and dried fish‟. This high level of fish consumption can be
partly attributed to the timing of data collection, which was undertaken at a time when the water levels
were dropping, and fish catch and drying was high. The third most frequent food group was „other
vegetables‟ (55%). This can be explained by the fact that data collection was undertaken in the
harvesting season of various types of indigenous vegetables, which were then available at relatively
lower prices. The fourth most frequent food group (54%) was „white potatoes and white yams‟,
followed by „oil/fats‟ (42%), „dark green leafy vegetables‟ (30%) and „pumpkin, carrots, squash, or
sweet potatoes‟ (20%). The remaining food groups were all < 5%.
FSUP-H Baseline Report, June 2010 40
6.2 Food intake
Figure 5a shows the households reporting enough food by month. It is important to focus on the
overall shape of the curve here, as there will be some respondent error in terms of their recall relative
to the mid-points between the months, as shown in the figure below.
The figure shows two distinct lean periods in terms of insufficient food. The first lean period is from
April to June, with the leanest period in April-May (13%), the month of Baishak in the Bengali
calendar. The second lean period is from November to February with the leanest period in Dec-Jan
(12%), the month of Payush in the Bengali calendar.
In both periods, almost 90% of households in the sample report insufficient food. The recovery from
the insufficient food period in April to June is notable longer than for the second lean period - with
another smaller decrease in August-September (31%) before reaching a peak at 63 percent in Oct-
Nov. The highest number of households report sufficient food in March-April (82%), with a very sharp
decrease between the Bengali months of Chaitra and Baishak.
It is important to note that the lean period shown here slightly differs from lean seasons in other food
insecure areas in Bangladesh, because the harvesting season of the boro rice in Haor areas takes
place slightly earlier than in other areas.
Figure 5a: Proportion of households reporting enough food, by month and Haor type (1)
Figure 5b below shows that in the period June-July to Oct-Nov, the proportion of households reporting
enough food is lower in deep Haor areas than in moderate Haor areas. The figure also shows a
higher number of households in deep Haor areas reporting sufficient food in the period January to
May. This matches the FCS value findings, which show that households in the deep Haor areas have
a significantly higher FCS value for the period in which the data was collected: January to February.
FSUP-H Baseline Report, June 2010 41
Figure 5b: Proportion of households reporting enough food, by month and Haor type (2)
Figure 6 shows the mean number of lean months, by District and Haor type. Overall, the mean
number of lean months is 4.3. When comparing across Districts, there are significant differences
among all Districts, whereby Sunamganj has the highest mean number of lean months and
Kishoreganj has the lowest number. When comparing across Haor types, the number of lean months
in moderate Haor areas is significantly higher than in deep Haor areas.
Figure 6: Mean number of lean months, by District and Haor type
FSUP-H Baseline Report, June 2010 42
Figure 7 compares frequency of three square meals taken among Districts. Overall, the mean value for
households that take 3 meals per day „most of the time‟ is 14%. The mean values for „most of the time‟ and often
combined is 56.3 %. Households with the highest frequency of taking three square meals per day are
located in Kishoreganj. Households with the lowest frequency of taking three square meals per day
are located in Sunamganj.
Figure 7: Frequency of three 'square meals' taken a day in 12 months, by District
Figure 8 compares frequency of three square meals taken among deep and moderate Haor areas.
Households with the highest frequency of taking three square meals per day are located in deep Haor
areas. Households with the lowest frequency of taking three square meals per day are located in
moderate Haor areas.
Figure 8: Frequency of three 'square meals' taken a day in 12 months, by Haor type
FSUP-H Baseline Report, June 2010 43
6.3 Coping strategies
Households were asked to indicate how they dealt with food insecurity. Questions D4 - D11 with a 12
month recall asked respondents whether households had to replace rice with grains, skip meals,
reduce food intake, run out of food, worry about where food would come from, purchase rice in bulk to
use it sparingly, purchase food on credit and/or borrow food/take donated food. Response categories
for these questions ranged from „Most of the time‟ to „Never‟.
Presenting all the data in a table would make meaningful interpretation difficult so a coping index was
created for questions. The index was computed by giving „Most of the time‟ a value of 5, „Often‟ a
value of 4, etc. The highest possible score would be 40. A high score indicates that households in
specified areas avail themselves of a broad range of coping strategies to deal with food insecurity; the
higher the index value is - the higher the assumed stress on households. This Index is suggested as a
useful monitoring tool for FSUP-H.
Overall, the coping index score of almost 24 indicates a moderately-high level of stress on households
due to food insecurity. Comparison across Haor types shows that the coping index score is
significantly higher in deep Haor areas than in moderate Haor areas. Coping index scores in
Kishoreganj and Sunamganj are statistically the same but Netrokona shows a significantly lower score
than the other two Districts. „Bulk purchases of rice‟, „running out of food‟ and „reducing personal food
intake‟ were the top 3 coping strategies, both overall and when disaggregated by district and Haor
type10
.
Figure 9: ‘Coping Index’ for households, by District and Haor type
.
10
Excluding question D8; although indicative of household stress and, therefore, included in the Index, worrying is not a meaningful coping strategy
FSUP-H Baseline Report, June 2010 44
6.4 Trend analysis Table 26 shows the results of a trend analysis/seasonal calendar undertaken in 4 villages:
1) Chorpara village, Itna Upazilla, Kishoreganj (deep Haor),
2) Sutarpara village, Sutarpara Union, Karimganj Upazilla, Kishoreganj (moderate Haor),
3) Boali village, Khaliajhury Upazilla, Netrokona (deep hoar),
4) Khurshimul village, Mohanganj Upazilla, Netrokona (moderate Hoar).
As part of this qualitative exercise, community members were asked to describe selected occurrences
and activities during the 12 months of the year, and to score the intensity of occurrences/activities. A
higher number indicates higher intensity as perceived by community members. In Kishoreganj, scores
were assigned on a scale from 0-10; in Netrokona on a scale from 0-5, which were subsequently
multiplied by 2 for the purpose of this analysis. As a result, lower intensity in the 0-1 range on the 10-
scale may not be properly reflected for the Netrokona villages.
Table 26 : Seasonal calendar Months Apr-may May-
Jun Jun-Jul
Jul-Aug
Aug-Sep
Sep-Oct
Oct-Nov
Nov-Dec
Dec-Jan Jan-Feb
Feb-Mar
Mar-Apr
Bangla month
Baishak Jaisti Ashar Sravon Bhadra Ashin Kartic Agrahayan
Payush Magh Falgun Chaitra
Rainfall
Village 1 ♦♦ ♦♦♦ ♦♦♦♦ ♦♦♦♦ ♦♦ ♦
Village 2 ♦♦♦ ♦♦♦ ♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦ ♦♦♦♦ ♦♦ ♦♦ ♦♦ ♦♦♦
Village 3 ♦♦
♦♦
♦♦♦♦♦♦♦♦♦♦
♦♦♦♦♦♦♦♦♦♦
♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦
♦♦
♦♦
Village 4 ♦♦ ♦♦ ♦♦♦♦♦♦♦♦♦♦
♦♦♦♦♦♦♦♦♦♦
♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦ ♦♦ ♦♦
Food crisis
Village 1 ♦♦ ♦♦♦♦♦ ♦♦♦
♦♦♦♦♦♦
Village 2 ♦♦ ♦♦♦ ♦♦♦ ♦♦♦♦♦ ♦♦♦♦♦♦ ♦♦♦♦♦♦♦
Village 3 ♦♦♦♦♦♦♦♦♦♦
♦♦♦♦♦♦♦♦♦♦
♦♦♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦♦♦
♦♦♦♦
Village 4 ♦♦♦♦♦♦ ♦♦♦♦ ♦♦♦♦♦♦♦♦♦♦
♦♦♦♦♦♦♦♦♦♦
Disease
Village 1 ♦ ♦♦ ♦♦ ♦♦♦♦♦♦ ♦♦ ♦♦
Village 2 ♦♦♦♦♦♦ ♦♦ ♦♦ ♦♦ ♦♦♦ ♦♦ ♦♦♦♦♦ ♦♦♦♦ ♦♦♦♦♦ ♦♦♦♦♦
Village 3 ♦♦♦♦♦♦♦♦♦♦
♦♦♦♦
♦♦♦♦
♦♦♦♦♦♦
Village 4 ♦♦♦♦ ♦♦♦♦ ♦♦♦♦♦♦♦♦ ♦♦♦♦ ♦♦♦♦
Migration
Village 1 ♦ ♦ ♦ ♦♦♦♦ ♦♦
Village 2 ♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦
Village 3 ♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦♦♦
♦♦♦♦♦♦♦♦
♦♦♦♦
♦♦♦♦♦♦♦♦
Village 4 ♦♦ ♦♦♦♦♦♦ ♦♦♦♦♦♦ ♦♦♦♦ ♦♦♦♦♦♦
Male level of work
Village 1 ♦♦♦♦♦ ♦♦♦♦♦
♦♦ ♦ ♦ ♦♦ ♦♦♦♦ ♦♦♦♦♦ ♦♦♦♦♦ ♦♦
♦♦♦♦♦ ♦
Village 2 ♦♦♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦
♦♦♦ ♦♦♦ ♦♦♦ ♦♦♦♦♦ ♦♦♦♦♦ ♦♦♦♦ ♦♦♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦ ♦♦ ♦♦
Village 3 ♦♦♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦
♦♦
♦♦
♦♦
♦♦ ♦♦
Village 4 ♦♦♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦
♦♦♦♦ ♦♦♦♦ ♦♦♦♦♦♦ ♦♦♦♦♦♦ ♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦ ♦♦♦♦
Female level of work
Village 1 ♦♦♦♦♦♦ ♦♦ ♦ ♦
Village 2 ♦♦♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦
♦♦ ♦ ♦ ♦♦
Village 3 ♦♦♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦
♦♦
♦♦
♦♦
♦♦ ♦♦
Village 4 ♦♦♦♦♦♦♦♦♦♦ ♦♦♦♦♦♦♦♦
♦♦ ♦♦ ♦♦ ♦♦♦♦♦♦ ♦♦♦♦ ♦♦ ♦♦
The descriptions provided by community members for the selected activities/occurrences over a 12-
month period match quite closely across the 4 villages, and are reported below based on qualitative
data collected.
FSUP-H Baseline Report, June 2010 45
The lean season ended in the first half or April and April-May is a busy period for households; during
this time they earn most of the income to pay back informal loans taken during the lean season, so-
called „logni‟. Men undertake agricultural day labor and are very busy completing the rice harvest
before the monsoon begins. Other work for men includes reaping, thrashing, and straw drying.
Women undertake the collection of rice from the fields, rice winnowing, boiling, drying and storing
(including making pulp rice – muri); and cow paddy/straw drying and storing. Children often help with
the collection of rice. At the same time, women are also harvesting ground nuts, sweet potatoes and
are making cow dung coils as fuel for cooking – in addition to their regular household chores. In Boali
village, community members stated that men get paid 10-12 mounds of rice for harvesting per season
and women get 3-4 mounds of rice, one saree and 2 meals of food at end of the season. During this
period, storms start increasing in intensity and frequency.
Picture 9: Women supporting household income through produce sales
During the period May- June, men continue the reaping and harvesting of rice and women continue
collection of rice from the fields, rice winnowing, boiling, drying and storing, and drying/storing of cow
dung and straw. At this time, men also do earth work and homestead raising to protect their homes,
and repair their houses, boats and nets. In this month, fishermen in Chorpara village take dadon,
conditional informal loans. This is a period of heavy rainfall. Many people suffer from colds, fevers,
coughs and influenza.
In the period June-July, men are mainly involved with fishing and the ongoing reparation of their
fishing nets and boats. In Sutarpara, women also help with these reparations. Fish catches are not
good and because of high waves in this period they cannot go out onto the water every day; fishing
FSUP-H Baseline Report, June 2010 46
only earns the men about 150-200 Taka per day. To meet the income shortfall, many men take „logni‟
and „dadon‟ from local Mohajonee for 5-6 months at average interest rates for 50%. Some men also
undertake short migration to Sylhet and Dhaka for contract labor on earth work and rickshaw pulling.
Men spend a lot of time playing cards and gossiping while women sew Kantha and make bamboo
handicrafts. In this period, community members report heavy rainfall and many suffer fevers,
headaches and influenza.
In July-August, there is sufficient fish to catch and men intensify their fishing in rivers and Haors.
However, Chorpara village reports that there is less fish than before. Fishing earns the men on
average 250-300 Taka per day. Women primarily sew Kantha. There is heavy rainfall in this period
and community members suffer from influenza, fevers and coughs.
In the period August-September, most of the men continue fishing the rivers and Haors. In Khurshimul
village, men also work on separating jute fibres. If men observe that there are sufficient fish to be
caught, they take „dadon‟ and try their fortune – with the potential of earning 250-300 Taka per day. If
they observe limited number of fish, men migrate to Sylhet or Dhaka for contract labor. During the
period, women are not involved in income-generating work. The rains are decreasing – community
members report diarrhea and dysentery.
September-October marks the start of the peak fishing season and men are very busy. Income from
fishing is reported to be same as previous months: 250-300 Taka per day. Women do no income-
related work. Diarrhea and dysentery cases are increasing. There are also some cases of jaundice
reported.
Picture 10: Men fishing in the peak season
FSUP-H Baseline Report, June 2010 47
In October-November, the fishing season is winding down. Men remain busy fishing but fish
availability decreases; women do not do income-related work. Average income from fishing is
reported as lower than previous months: 200-250 Taka per day. The ultra poor people in the villages
migrate to Dhaka, Chittagong, Sylhet, Bhairab, Ashuganj, Aliganj, and Volaganj to do contract labor.
To meet transportation and other expenses, villagers from Boali village report having to take 6-month
loans from money lenders at 200% annual interest rates. In some cases, the entire household
migrates to do work such as brick making – earning 60-70 Taka per day / household member. This
period marks the beginning of a food crisis and the ultra poor reduce start reducing food intake –
eating two rice meals/day and one roti meal. There is increased incidence of water-borne diseases
such as diarrhea, dysentery and jaundice. Fever, coughs and malaria are also reported. As a result of
water levels in the Haor dropping in this period, open latrines are becoming separated from water
bodies.
Picture 11: Non-agricultural day labor
In the period November-December, there are very limited opportunities for income-generating work in
the villages. Many households migrate to do contract labor such as brick making. Men and women
that remain behind start preparing paddy seed beds and planting seeds as agricultural day. Many
ultra poor are forced to take „logni‟. The villages in Netrokona report no income-related activities at all.
Overall, the food crisis continues and the ultra poor reduce continue to reduce food intake – eating
two rice meals/day and one roti meal.
In the period December-January, the main source of income is agricultural day labor, if available. Men
are becoming increasingly busy transplanting rice, sowing sweet potato and groundnuts. The women
work on uprooting rice seedlings and preparing them for transplantation. Community members in
FSUP-H Baseline Report, June 2010 48
Chorpara village also report that some ultra –poor women catch fish. The villages in Netrokona report
no income-related activities at all. The food crisis continues and food intake is reduced to one or two
meals per day. Many people suffer from fevers and colds, and community members in Sutarpara
report pneumonia among children.
In the period January-February, the main source of income is still agricultural day labor, if available.
Men continue rice transplantation, work on developing paddy irrigation, and weeding; earning them
about 150 Taka/day. Women harvest potatoes and receive 5kg out of every 40kg that they harvest as
payment. In Chorpara village some women continue to catch fish. Some households migrate to areas
where they can sell labor. The food crisis continues and most of the ultra poor households do not take
more than 2 meals per day.
In the period February-March, there are no opportunities for men to earn income in the villages. Most
of the households migrate to areas where they can sell their labor. Women in Netrokona are reported
to harvest groundnuts, chili, and sweet potato, and to do earth work. Food rationing to a maximum of
two meals per day continues and some households have to go without any meals on some days.
Community members report that there are an increasing number of storms in this period.
In the first half of March-April, there are still no income-generating opportunities with the exception of
some earth work that men are involved in. Migration to sell day labor remains common. The food
crisis continues for another 2-3 weeks and reduced food intake during this time is still very common.
There are heavy rainfall and storms. April marks the end of the lean season.
Picture 12: Non-agricultural day labor - mat making
The patterns shown in table 26 and the accompanying qualitative descriptions of the different periods
match the peak and lean periods shown in figures 5 and 6. The most severe food crisis occurs in the
period October to February, after which there is a relatively quick recovery in April when the rice
harvest starts. Adjusting meals and informal lending are the most common coping strategies in lean
periods, which correspond with tables 11 and 12 in Section 5, and the top 3 coping strategies in
described in Section 6.3.
FSUP-H Baseline Report, June 2010 49
7.0 WATER AND SANITATION
7.1 Drinking, cooking and washing water sources
Table 27 shows the drinking water sources by District and Haor region. Hand tube wells are the most
common water source followed by shallow tube wells and deep tube wells. Overall, 97% of
households depend on the various types of tube wells for drinking water. Almost no households draw
drinking water from open water sources such as ring wells, ponds and rivers/canals.
When comparing across Haor types, significantly more households in deep Haor areas use hand tube
wells than in moderate Haor areas. In turn, in moderate Haor areas, significantly more households
use shallow tube wells that in deep Haor areas.
When comparing across Districts, the proportion of households using hand tube wells in Kishoreganj
is significantly higher than in the other two districts. The proportion of households using shallow tube
wells is significantly higher in Netrokona; the proportion of households using deep tube wells is
significantly higher in Sunamganj.
Table 27: Drinking water sources, by District and Haor type
Drinking Water
(% of HHs)
District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Hand tube well 72.8c 59.9 55.7 64.7
c 60.8 62.8%
Tara pump 0.5 0.2 0.0 0.1 0.3 0.2%
Deep tube well 7.2 8.5 19.5c 12.7 10.8 11.7%
Shallow tube well 19.1 29.5c 20.2 20.4 25.5
b 22.9%
Ring well/ indara 0.3 0.0 1.6 0.1 1.2 0.6%
Pond 0.2 0.0 1.0 0.1 0.6 0.4%
River/canal 0.0 1.9 2.1 1.9 0.7 1.3%
Letters denote significant differences among Districts or between Haor types for a given water
source. Significance levels for comparisons: a = .10; b = .05; c = .00
Table 28 shows the cooking water sources by District and Haor region. Hand tube wells are the most
common water source followed by rivers/canals and shallow tube wells. Deep tube wells and ponds
are the next most common water sources for cooking. Almost no households draw cooking water from
tara pumps and ring wells.
When comparing across Haor types, significantly more households in moderate Haor areas use hand
and shallow tube wells than in deep Haor areas. In turn, in deep Haor areas, significantly more
households use river/canal water for cooking that in moderate Haor areas.
When comparing across Districts, the proportion of households using hand tube wells in Kishoreganj
is significantly higher than in the other two districts. The proportion of households using shallow tube
wells is significantly higher in Netrokona; the proportion of households using deep tube wells is
significantly higher in Sunamganj.
FSUP-H Baseline Report, June 2010 50
Table 28: Cooking water sources, by District and Haor type
Cooking Water
(% of HHs)
District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Hand tube well 56.7 c 35.5 32.1 34.7 48.0
c 41.4%
Tara pump 0.5 0.2 0.0 0.0 0.3 0.2%
Deep tube well 4.3 5.8 12.9 c 7.3 8.0 7.7%
Shallow tube well 16.9 20.7 a 14.4 12.2 22.4
c 17.3%
Ring well/ indara 0.3 0.0 2.1 0.4 1.2 0.8%
Pond 1.6 11.7 8.1 7.2 7.1 7.1%
River/canal 19.7 26.2 30.5 38.0 b
12.9 25.5%
Letters denote significant differences among Districts or between Haor types for a given water source.
Significance levels for comparisons: a = .10; b = .05; c = .00
Table 29 shows the washing water sources by District and Haor region. Most households reported
open water sources for washing. River/canals are the most common water source followed by hand
tube wells and ponds. Almost no households use water from tara pumps, ring wells and deep tube
wells for washing.
Picture 13: Woman uses hand tube well as the water source for washing
FSUP-H Baseline Report, June 2010 51
When comparing across Haor types, significantly more households in moderate Haor areas use hand,
deep and shallow tube wells, and ponds than in deep Haor areas. In turn, in deep Haor areas,
significantly more households use river/canal water for washing that in moderate Haor areas –
presumably, due the almost year round access to this water source. When comparing across Districts,
the proportion of households using hand tube wells in Kishoreganj is significantly higher than in the
other two districts. The proportion of households using ponds is significantly higher in Netrokona; the
proportion of households using river/canals is significantly higher in Sunamganj.
Table 29: Washing water sources, by District and Haor type
Washing Water
(% of HHs)
District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Hand tube well 45.1 c 17.5 17.1 22.1 31.0
c 26.5%
Tara pump 0.3 0.0 0.0 0.0 0.2 0.1%
Deep tube well 2.9 1.6 4.0 2.1 3.5 b
2.8%
Shallow tube well 12.6 14.5 7.3 7.8 15.1 c 11.5%
Ring well/ indara 0.3 0.2 1.7 0.0 1.1 0.6%
Pond 12.7 29.2 b
18.7 17.6 22.9 a 20.2%
River/canal 26.0 37.1 51.0 c 49.8
c 26.1 38.0%
Other 0.2 0.0 0.2 0.1 0.1 0.1%
Letters denote significant differences among Districts or between Haor types for a given water source.
Significance levels for comparisons: a = .10; b = .05; c = .00
Figure 10 shows the distances to various drinking water sources by District and Haor type. Overall,
the mean distance to water sources is slightly higher than 200 meters (205m). There is no significant
difference between the distance to drinking water in deep and moderate Haor areas. When comparing
across districts, the distance to drinking water is significantly higher in Sunamganj than in the other
districts.
Figure 10: Distances to sources of drinking water, by District and Haor type
(N=1892)
FSUP-H Baseline Report, June 2010 52
Figure 11 shows the distances to various cooking water sources by District and Haor type. Overall,
the mean distance to water sources is higher than 200 meters (216m), and slightly higher that the
distance to drinking water sources. There is no significant difference in the distance to cooking water
among districts. When comparing across Haor types, the distance to cooking water sources is
significantly higher in deep Haor areas.
Figure 11: Distances to sources of cooking water, by District and Haor type
(N=1892)
Figure 12 shows the distances to various washing water sources by District and Haor type. Overall,
the mean distance to water sources is less than 200 meters (185m). When comparing across districts,
there is no significant difference between distance to washing water sources in Kishoreganj and
Netrokona. However, the distance to washing water is significantly higher in Sunamganj than in the
other two districts. (dry season) When comparing across Haor types, the distance to washing water
sources is significantly higher in deep Haor areas.
Figure 12: Distances to sources of washing water, by District and Haor type
(N=1892)
FSUP-H Baseline Report, June 2010 53
7.2 Arsenic testing
Of the households that reported tube wells or tara pumps as a source for drinking, cooking or washing
water, 45% of households in Kishoreganj reported that the tube wells / tara pumps were tested for
arsenic, which is significantly lower than in Netrokona and Sunamganj where 53% of households
reported that the tube wells / tara pumps were tested for arsenic. When comparing across deep and
moderate Haor type, 55% of households in moderate Haor areas reported that the tube well/tara
pumps were tested for arsenic, versus a significantly lower 45% in moderate Haor areas.
Of the tube wells/tara pumps that were tested, 12%, 18% and 11% were found to contain arsenic in
Kishoreganj, Netrokona and Sunamganj, respectively. The 18% in Netrokona is significantly higher
than the percentages in the other two districts. When comparing across Haor types, the percentage of
tube wells/tara pumps that contained arsenic was significantly higher in deep Haor areas at 17% than
the 11% in moderate Haor areas.
Table 30: Tube wells/tara pumps tested for arsenic, by District and Haor type
District Haor Type Total N
Kishoreganj Netrokona Sunamganj Deep Moderate
Tested (%) Yes 45.2c 53.4 52.9 45.8
c 55.1 50.5 933
No 36.4 34.2 30.3 35.0 32.4 33.7 623
Do not know 18.4 12.4 16.8 19.2 12.5 15.8 293
Has arsenic (%) 12.7 18.7c 10.7 17.6
c 11.2 14.1 132
No arsenic (%) 87.3 81.3 89.3 82.4 88.8 85.9 814
Significance levels for comparisons among Districts/across Haor type: a = .10; b = .05; c = .00
7.3 Sanitation
The most common type of latrines used by adult men and women are ring slab/offset latrines (with the
seal broken) and hanging/open latrines, followed by uncovered pit latrines and then open defecation.
Overall, the use of hygienic latrines such as ring slab/offset latrines (with the seal intact), septic
latrines, covered pit latrines and locally adapted hygienic latrines is very low in the project area.
Qualitative data confirms that few households have a sanitary latrine. Shoes are seldom worn when
visiting the latrine.
When comparing across districts, the use of hanging/open latrines and ring slab/offset latrines (with
the seal broken) is significantly higher in Kishoreganj than in the other districts. There are no
significant differences across Haor types, and there are no significant differences in latrine use by
adult men and women.
FSUP-H Baseline Report, June 2010 54
Table 31: Types of latrines used by adult men and women, by District and Haor type
Latrine Type District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N Men 562 504 459 782 743 1892
Women 581 529 467 810 767 1577
Ring-slab/offset
latrine (water seal)
Men 2.7 1.6 0.4 1.0 2.3 1.6
Women 2.8 1.5 0.4 0.9 2.5 1.6
Ring-slab/offset
Latrine (seal broken)
Men 49.3 c 30.6 29.8 34.9 39.7 37.2
Women 49.1 c 31.4 29.8 35.6 39.4 37.4
Pit latrine (covered)
Men 1.2 0.2 0.2 0.4 0.8 0.6
Women 1.5 0.2 0.2 0.4 1.0 0.7
Pit latrine
(uncovered)
Men 7.3 12.5 14.4 11.3 11.0 11.1
Women 7.2 13.0 14.6 11.0 11.7 11.4
Septic latrine
Men 0.4 0.0 0.2 0.0 0.4 0.2
Women 0.3 0.0 0.0 0.0 0.3 0.1
Hanging/open latrine
Men 34.5 b
52.0 48.8 47.3 41.7 44.6
Women 34.8 b
52.2 49.0 47.3 42.2 44.8
Locally adapted
hygienic latrine
Men 0.0 0.4 0.0 0.0 0.3 0.1
Women 0.2 0.4 0.0 0.1 0.3 0.2
Open defecation
Men 4.6 2.8 6.1 5.1 3.8 4.5
Women 4.1 1.3 6.0 4.8 2.6 3.7
Significance levels for comparisons: a = .10; b = .05; c = .00
Similar to adults, the most common types of latrines used by boys and girls 5-15 years of age are ring
slab/offset latrines (with the seal broken) and hanging/open latrines. For boys and girls, this is
followed by open defecation and then uncovered pit latrines – the opposite to adults. Overall, the use
of hygienic latrines such as ring slab/offset latrines (with the seal intact), septic latrines, covered pit
latrines and locally adapted hygienic latrines is very low.
Picture 14: Ring slab latrine
FSUP-H Baseline Report, June 2010 55
When comparing across districts, the use of ring slab/offset latrines (with the seal broken) by boys
and girls is significantly higher in Kishoreganj than in the other districts. For hanging/open latrines, the
use by girls is significantly lower in Kishoreganj than in the other districts, and significantly higher for
boys in Sunamganj than in the other districts. Open defecation by boys is significantly higher in
Netrokona than in the other districts. There are no significant differences across Haor types.
Table 32: Types of latrines used by boys and girls 5-15 years of age, by District and Haor type
Latrine Type District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N Boys 348 282 325 482 473 955
Girls 366 280 305 476 475 951
Ring-slab/offset
latrine (water seal)
Boys 2.9 1.1 0.3 1.0 1.9 1.5
Girls 2.7 1.8 0.3 0.8 2.5 1.7
Ring-slab/offset
Latrine (seal broken)
Boys 45.1 c 24.5 27.4 30.7 35.3 33.0
Girls 46.7 c 24.6 29.8 33.4 36.2 34.8
Pit latrine (covered)
Boys 1.4 0.0 0.3 0.2 1.1 2.1
Girls 1.4 0.0 0.3 0.4 0.8 0.6
Pit latrine
(uncovered)
Boys 6.9 15.2 14.8 13.7 10.4 12.0
Girls 6.6 12.9 15.7 11.3 11.4 11.4
Septic latrine
Boys 0.6 0.0 0.3 0.0 0.6 0.3
Girls 0.5 0.0 0.0 0.0 0.4 0.2
Hanging/open latrine
Boys 33.0 35.8 46.2 a 39.4 37.2 38.3
Girls 32.8 a 43.6 44.9 41.0 38.7 39.9
Locally adapted
hygienic latrine
Boys 0.0 0.4 0.0 0.0 0.2 0.1
Girls 0.3 0.0 0.0 0.0 0.2 0.1
Open defecation
Boys 10.1 23.0 b
10.8 14.9 13.3 14.1
Girls 9.0 17.1 8.9 13.0 9.7 11.4
Significance levels for comparisons: a = .10; b = .05; c = .00
Enumerators were also asked to personally verify that the latrines used by respondents were
functioning and to describe their condition and cleanliness. Ninety percent of latrines observed were
found to be functional, all showed signs of use, 63 percent of latrines were considered relatively clean,
and for 55 percent latrines the surrounding area was considered clean. However, for these questions
there were only 40 responses/observations each, which is not in any way representative for the study
population. Reasons for this could be the distance from the interview location to the latrine area,
which may have been inconvenient for the enumerator to cover in the allotted interview time.
FSUP-H Baseline Report, June 2010 56
Picture 15: Open defecation facilities
FSUP-H Baseline Report, June 2010 57
8.0 HEALTH PRACTICES AND ILLNESS
8.1 Hand washing
Overall, the majority of respondents wash their hands before eating but less than half do so before
preparing food and only one-third wash their hands before feeding children. The majority of
respondents wash their hands after defecation but only one-third of respondents do so after cleaning
a baby‟s bottom. Qualitative data shows that hand washing with soap and ash after defecation is
uncommon.
When comparing across districts, hand-washing behavior before food preparation is significantly
higher in Netrokona than in the other districts. Hand washing after cleaning baby‟s bottoms and before
feeding children is significantly lower in Netrokona. There are no significant differences across Haor
types.
Table 33: Hand-washing behaviors among the FSUP baseline study households, by District and Haor
type (1)
When are hands washed… District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Before food preparation 49.7 59.0c 40.6 49.0 50.6 49.8
Before eating 91.9 87.4 94.4 91.0 91.4 91.2
Before feeding children 38.2 25.1 c 40.6 35.7 33.5 34.6
After defecation 96.5 88.0 98.4 94.6 94.0 94.3
After cleaning babies bottoms 43.3 22.1 c 38.9 35.1 34.4 34.7
Other 13.7 c 5.7 7.9 9.7 8.5 9.1
Do not wash hands 0.0 0.0 0.0 0.0 0.0 0.0
Significance levels for comparisons: a = .10; b = .05; c = .00
The use of ash or clay for hand washing is most common followed by use of only water. The use of
soap is least common, which is confirmed by qualitative data. When comparing across districts, the
use of water only is significantly higher in Sunamganj, the use of ash or clay is significantly higher in
Netrokona, and the use of soap is significantly higher in Kishoreganj. When comparing across Haor
types, the use of water only is significantly higher in moderate Haor, the use of ash or clay is
significantly higher in deep Haor. There are no significant differences in use of soap between deep
and moderate Haor types.
Table 34: Hand-washing behaviors among the FSUP baseline study households, by District and Haor
type (2)
Hands normally washed
with…
District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Water only 33.9
32.6 42.9 c 34.0 38.9
a 36.5
Ashes or clay 48.1 58.7 c 44.0 53.7
c 46.8 50.3
Soap 18.0 c 8.7
b 13.2
b 12.2 14.3 13.3
Significance levels for comparisons: a = .10; b = .05; c = .00
FSUP-H Baseline Report, June 2010 58
8.2 Illness among adults and health-seeking behavior
The average number of illnesses cited per household was 2.4: 2.4 for Kishoreganj, 2.5 for Netrokona
and 2.2 for Sunamganj. The average number of illnesses reported by households in deep and
moderate Haors was 2.4 for both Haor types. Only 2.6% (49 households) experienced no illnesses at
all in the last 12 months. The most common illness experienced by adults during the previous 12
months is a cold attack, followed by gastric illness and diarrhea. There are no significant differences
among districts and between Haor types.
Table 35: Top ten illnesses experienced by adults in households during the previous 12 months, by
District and Haor type
Illness
(multiple response)
District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
Number of Responses 1521 1611 1364 2265 2231 4496
Cold attack 77.9 90.4 61.7 76.9 76.5 76.7
Gastric illness 46.2 44.0 41.6 43.4 44.4 43.9
Diarrhea 21.3 30.4 17.0 23.8 22.1 22.9
Dysentery 20.1 14.2 16.5 19.4 14.4 16.9
Anemia 13.9 19.1 17.8 17.6 16.2 16.9
Rheumatic fever 8.1 9.5 15.6 11.4 10.7 11.0
High/low blood pressure 4.6 9.6 6.3 5.4 8.4 6.9
Typhoid fever 9.9 8.7 2.2 6.8
7.1 6.9
Skin diseases 8.9 6.2 4.4 7.1 5.9 6.5
Asthma 4.9 6.3 4.3 5.0 5.4 5.2
Other 11.6 5.7 10.2 8.4 9.8 9.1
„Other‟ illnesses, cannot be disaggregated into individual illnesses, and therefore are not included in the top ten
illnesses.
Table 36 shows that medicine shops and village doctors are the most common treatment sources for
household members. Other treatment sources not reflected in table 36 include private paramedics/
LMA, Union Health Center, District Hospital, Homeopath, Kabiraj, untrained doctor, and Ojha/Jhar
Fuk; each accounted for less than 2%.
Table 36: Usual treatment source for household members, by District and Haor type
Treatment sources
District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
628 634 630 947 945 1892
Medicine shop 23.1 c 40.7 41.7 31.4 39.0
c 35.2
Village doctor 32.3 33.0 35.1 35.9 b
31.0 33.5
Upazila Health Center 14.2 14.0 5.4 c 11.7 10.7 11.2
Private MBBS 14.2 c 4.4 6.2 9.6
b 6.9 8.2
Private clinic 3.5 2.1 3.2 2.9 3.0 2.9
Significance levels for comparisons: a = .10; b = .05; c = .00
FSUP-H Baseline Report, June 2010 59
Picture 16: Village medicine shop
Picture 17: Union Health Center
FSUP-H Baseline Report, June 2010 60
9.0 PARTICIPATION AND ACCESS
9.1 Participation in development
Participation in the development process is overall low at 4.5% of all households, which was too low
for meaningful analysis. In Kishoreganj it is 4.9 percent, in Netrokona 7.3 percent, and in Sunamganj it
is 1.1 percent. Participation in Sunamganj is significantly lower than the other two Districts (p=.000),
and in Kishoreganj it is significantly lower than in Netrokona (p=.044). Participation averages at 4.4
percent in both Deep and Moderate Haor.
Respondents were also asked who in the household participated but only 186 responses were
received from 174 households (a few households identified more than one person participating) -
9.2% of all respondents. Among the 186 responses, household head was mentioned as the most
common household member involved in development processes. Females (spouses plus female
heads of household) accounted for 15.1% of the 9.2%, or about 1.6% of the overall population.
Overall, the responses are too low for meaningful analysis.
More than 30% of responses were for the category other, which may have been used to record the
option “all household members‟, instead of checking all multiple response boxes.
Table 37: Household members involved in development processes
Household member involved
(multiple response)
Responses
N Percent
Household head
91 48.9%
Spouse 20 10.8%
Son/daughter 12 6.5%
Father/mother 4 2.2%
Daughter/son-in-law 1 0.5%
Grandson/granddaughter 1 0.5%
Other 57 30.6%
TOTAL 186 100.0%
Note: Female participation included 8 female heads of household plus spouses. There could
also be other females participating (e.g., mothers) but the data does not allow for
disaggregation at this level.
When asked about the type of development institution that the household member was
involved/engaged in, while participating in local development processes in the last 12 months, only
162 responses were collected. Again, this cannot be meaningfully disaggregated by district and Haor
type. Nonetheless, statistical testing among Haor types showed no significant difference.
Among the 162 responses, the Masjeed or religious committee was the most common response
(24%) followed by participation in NGOs (19%) as village group members, which is often a
prerequisite to receiving microcredit. Almost 22 percent of respondents who answered this question
stated that they did not know the type of development institution the household was involved with.
Again this may indicate that there were multiple institutions involved and respondents found it difficult
to recollect which ones exactly.
FSUP-H Baseline Report, June 2010 61
Table 38: Type of development institution/person that HH members were involved with
Type of development institution involved
(multiple response) Responses
N Percent
Union Parishad Chairman/Counselor
3 1.9%
Union Parishad Standing Committee
4 2.5%
Bazar Committee 12 7.4%
Masjeed or Religious Committee
39 24.1%
School/Madrasa Management Committee
12 7.4%
PTA 2 1.2%
Village Court/Salish 5 3.1%
NGO 31 19.1%
CBO 2 1.2%
Other 17 10.5%
DNK 35 21.6%
TOTAL 162 100.0%
When asked about the nature of household member‟s involvement/engagement with the development
institutions/persons stated in table 38, half (49.3%) of respondents reported that they had received
services, which may support the idea that the 30% „Other‟ in table 37 was used to indicate
participation by the entire household. Other types of participation were: volunteer (27.9%); committee
member (19.3%); participant in activities (19.3%); and recipient of training (1.9%). Only 2 households
received training: 1 in awareness on social issues, the other in awareness on H/N issues.
Only 5% of households had experience with collective action in last 12 months. In Kishoreganj it was
6.5 percent, in Netrokona 6.6 percent, and in Sunamganj it was significantly lower at 3.5 percent
(p=.019). Participation averaged 4.6 percent in Deep and 6.4 in Moderate Haor, but these values are
not significantly different. Among the 5% of households that had experience with collective action, the
main types of action were road construction/repair and Mosque construction/repair. Again, this cannot
be meaningfully disaggregated by district and Haor type.
Table 39: Type of collective action that households have participated in, by District and Haor type
Collective Actions
(multiple response)
District Haor Type Total
Kishoreganj Netrokona Sunam-
ganj Deep Moderate
N 42 42 22 44 62 106
Road construction/repair 59.5 35.7 54.5 27.3 64.5 49.1
Canal/pond digging 0.0 4.8 0.0 0.0 3.2 1.9
Bamboo bridge construction 2.4 9.5 0.0 11.4 0.0 4.7
Embankment construction/repair 7.1 9.5 4.5 13.6 3.2 7.5
Graveyard construction/repair 4.8 9.5 0.0 6.8 4.8 5.7
Mosque construction/repair 31.0 61.9 31.8 59.1 32.3 43.4
Homestead raising/protection 4.8 7.1 9.1 4.5 4.8 4.7
School construction/repair 4.8 4.8 4.5 6.8 4.8 5.7
FSUP-H Baseline Report, June 2010 62
Picture 18: Community collective action to improve road infrastructure
9.2 Access to GoB services
Over two-thirds of households (68.7%) had accessed one or more GoB service providers in the
previous year. Table 40 shows the types of GoB service providers used by households in the last 12
months. The most common service providers used were Union Parishad and Government
Immunization Services, followed by Government Family Planning, Upazilla Health Services and Union
Health Services. All other service providers listed in table 40 were 0.5% or less. Department of
Fisheries, Department of livestock, Department of Cooperatives, and Government Vocational/
Educational Training all recorded zero responses.
FSUP-H Baseline Report, June 2010 63
Table 40: Proportion of households using various types of Government service providers, by District
and Haor type
Service Provider District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 446 433 421 681 619 1300
Dept. of Agr. Extension (DAE) 0.2 0.2 0.7 0.4 0.3 0.4
Government Land Office 0.0 0.2 0.0 0.1 0.0 0.1
Dept. of Youth Development 0.0 0.5 0.0 0.3 0.1 0.2
Dept. of Women‟s‟ Affairs 0.0 0.9 0.5 0.6 0.4 0.5
Government Family Planning 23.5 b
15.2 15.2 18.2 17.9 18.1
Govt. Immunization Services 41.3 40.6 55.1 c 39.6 52.0
c 45.5
Union Parishad 47.8 67.4 c 57.5
b 61.5 53.0
a 57.5
BADC Seed Department 0.4 0.0 0.0 0.0 0.2 0.2
Union Health Services 16.2 c 1.4 5.0 10.6
c 3.7 7.3
Upazila Health Services 24.2 20.6 7.1 c 18.5 16.3 17.5
Significance levels for comparisons: a = .10; b = .05; c = .00
Figures 13 and 14 show the types of GoB service providers accessed by District and Haor type. When
comparing across districts, Government Family Planning and Union Health Services was significantly
higher in Kishoreganj than in the other two districts. Union Parishad was significantly higher in
Netrokona. In Sunamganj, Government Immunization Services was significantly higher and Upazilla
Health Services was significantly lower than in other districts.
Figure 13: Types of service providers accessed, by District
(N=1300)
When comparing across Haor types, Government Immunization Services was significantly higher in
the moderate Haor, and Union Parishad and Union Health Services was significantly higher in the
deep Haor.
FSUP-H Baseline Report, June 2010 64
Figure 14: Types of service providers accessed, by Haor type
(N=1300)
Table 41 shows the types of services received by GoB service provider, by District and Haor type. For
Union and Upazila Health Services, the most common service received is medication followed by
suggestions. For Family Planning, suggestions, medicines and vaccinations are the main services
received. For Government Immunization Services, vaccinations are the main services received, as
was to be expected.
Overall, training provided by GoB service providers is very low. Union Parishad was not included in
the table because 95% of services provided were reported as „Other‟. „Other‟ could refer to safety nets
such as the government programs for supporting vulnerable populations: the Vulnerable Group
Feeding (VGF) program, which provides food to low income and other vulnerable groups who cannot
meet basic needs for survival due to natural disasters or socio-economic circumstances, such as age,
illness or disease; and the Vulnerable Group Development (VGD) program, which aims to enable the
poorest rural women and their family members to overcome food insecurity and their low social and
economic status. These kinds of safety net programs were indicated in qualitative data collection as
the main service that Union Parishads are known for. It is important to note that although these
programs were highly valued, community members had concerns about the transparency and equity
in recipient targeting.
It is apparent that the majority of services provided are health related. Qualitative data confirmed the
lack of services on economic activities. Community members particularly mentioned the need for
more and better technical assistance in the areas of livestock rearing, fishery and agriculture.
FSUP-H Baseline Report, June 2010 65
Table 41: Types of services received by GoB service providers, by District and Haor type
Service Provider District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
Union Health Services N 95 7 38 100 40 280
Suggestions 26.5 33.3 54.5 26.4 54.2 33.3
Medicines 77.9 83.3 77.3 79.2 75.0 78.1
Vaccinations 32.4 0.0 31.8 30.6 29.2 30.2
Other 2.9 0.0 9.1 2.8 8.3 4.2
Upazila Health Services N 167 141 51 191 168 718
Suggestions 52.3 43.5 48.4 44.9 52.4 48.3
Medicines 86.2 77.2 58.8 90.6 76.2 84.1
Vaccinations 13.8 22.8 19.4 11.8 25.7 18.1
Other 0.9 9.8 0.0 3.1 5.7 4.3
GoB Family Planning
N
188 120 167 240 235 475
Formal training 0.0 0.0 1.6 0.0 0.9 0.4
Suggestions 73.1 67.2 89.1 75.2 76.6 75.9
Medicines 54.8 75.0 95.3 72.7 70.3 71.6
Vaccinations 51.0
1.9
43.8 75.0 49.6 62.2 55.6
Other 1.9 1.6 1.6 0.8 1.8 1.3
GoB Immunization Services
N
262 259 393 421 493 914
Informal training 0.5 0.0 0.0 0.0 0.3 0.2
Suggestions 29.6 19.7 22.6 26.1 22.1 23.9
Medicines 15.6 25.8 47.9 27.6 34.4 31.3
Vaccinations 93.0 97.8 93.2 96.0 93.3 94.5
Other 2.2 2.2 4.3 5.1 1.2 3.0
Figure 15 shows the level of satisfaction with the services received through the various GoB service
providers. For all service providers, the majority of respondents indicated they were satisfied or highly
satisfied.
Figure 15: Level of satisfaction with selected GOB services
FSUP-H Baseline Report, June 2010 66
9.3 Access to other services
Respondent were also asked about services that they had received from non-government service
providers. Overall 67% of households reported not receiving any services from other non-government
service providers. When disaggregated by District, the number of households receiving no services
from non-government service providers was significantly higher (p=.020) in Netrokona (78%) than in
Kishoreganj (65%) and Sunamganj (58%). When disaggregated by Haor type, the number of
households receiving no services from non-government service providers was also significantly higher
(p=.000) in moderate Haor areas (74%) than in Deep Haor (60%).
Overall, the three most common non-government service providers (for individuals who reported
receiving services) were NGOs (76%), Grameen Bank (16%), and Local Service Providers (18%).
Less than 1% of households reported receiving services from Commercial Banks, CBOs, input
retailers/dealers and non-Government Vocational Education/Training, respectively. The most common
services received from NGOs were credit (68%), suggestions (16%), and relief/aid (4%). The most
common services received from Grameen Bank were credit (99%) and suggestions (13%). The most
common services received from Local Service Providers were suggestions (75%), credit (65%),
suggestions (16%), medicines (71%) and relief/aid (12%).
Qualitative data collection showed that community expectations for economic and development
activities primarily revolve around facilitating access to Khas water bodies, access to credit, and
capacity development. Men and women share expectations around external support for increased
participation in community decision making, improved flood protection and improved children‟s
education. While men‟s expectations focus mainly on economic opportunities and strengthened links
to livestock and agriculture services; women expressed expectations around increased opportunities
to have a voice in community affairs, improved health services and access to life skills training.
Picture 19: Women engaged in alternative livelihood activities
FSUP-H Baseline Report, June 2010 67
At present, the main sources of knowledge and skills for economic/livelihood activities are knowledge
transfer from previous generations, and from relatives and neighbors. The little external assistance
that ultra-poor households do receive comes primarily from NGOs. Examples mentioned by
respondents included livestock and poultry rearing, credit groups, increased crop production, market
development, homestead gardening. Women appear to be mainly involved in micro credit and men
are also involved in earthwork, flood protection and infrastructure projects. Among the limited number
of women who participate in income-related activities in deep haor regions, it was mentioned that a
large number are widows and that access by married women with families is more difficult. NGOs also
facilitate community participation in development/economic activities. This is valued by community
members as the limited participation of the ultra poor in local committees is an apparent concern.
Knowledge of economic and development opportunities appears to be low. The main sources of
information about external assistance for economic and development activities are NGO workers. This
information is often channeled to community members by village leaders and prominent community
members such as school teachers, health workers and local elites; and does not reach everyone
equally. Union Parishad officials are the main source of information for GoB development activities. It
was noted that Union Parishad officials do not pass information to everyone; they prefer to share
information with their supporters only.
Similar to participation in economic activities, decision making around participation in NGO and GoB
development activities is heavily influenced by the rich and politically powerful, as well as by kinship
ties. Although the poor and ultra poor do participate in the development process, they have little voice
regarding types and recipients of benefits, resource allocation and arbitration. Community members
recognize the purposive targeting of women by NGOs and are very supportive of this. However, it is
important to note that within the family the nature of participation by women is often still determined by
male household members.
Community members stated that the community benefits generated from participation in development
activities are more important than monetary benefits. These benefits include reduced cost of travel,
market and health care connectivity, improved drinking water, flood protection, improved access to
other service providers, and improved school attendance/ reduced dropout rates. Non-monetary
benefits generated at the individual and household level include improved social dignity for the ultra
poor, increased women‟s participation and more joint decision-making between men and women.
There are serious concerns about how benefits are distributed; with benefits going more to those with
kinship relations and the economic means to bribe officials or invest in development activities. An
example of the latter is the installation of village tube wells; the more well-off community members
usually pay the security deposits for the wells and then end up controlling irrigation to the benefit of
their own crops and those of their kin. Community members also mentioned the high fees provided to
local experts hired for training, which in some cases come to 25% of the total available funds for the
local project.
There appear to be significant costs involved with participation in development activities. Similar to
participation in economic activities, there are non-monetary costs associated with time away from
home by women – such as reduced care for children, and inability to do household chores, which
causes stresses between husband and wife. At the same time there are monetary costs (primarily for
males) as participation in development activities takes time away from work. There are also reports of
food being stolen when males are away.
FSUP-H Baseline Report, June 2010 68
There are also direct monetary costs involved in participating in development activities. To get
VGD/VGF cards, community members commonly must take high interest loans to bribe Union
Parishad members. Convincing Union Parishad members to allow their participation also requires a
significant time investment and in some cases community members must also provide physical labor
to help convince them.
Power relations due to kinship and politics are considered the main barriers to access to development
opportunities by the ultra poor. In addition, women face additional barriers due to their lack of access
to information, their lack of confidence in speaking publicly and the ongoing discouragement by men
(and some women as well) that prevents them from participating in community dialogues and
meetings. To overcome these barriers, community members stated that there is an urgent need for
capacity development on rights issues.
9.4 Access to common property
Table 42 shows the proportion of households that have various types of property available in their
area, disaggregated by District and Haor type. Overall, Beel/Haor and canal/river are the most
common land property types, followed by Khas land, roadside sloping and Khas ponds. Khas pond
and Khas land are most common in Kishoreganj, and most common in Deep Haor. Roadside sloping
and grazing land are both more common in Netrokona.
Table 42: Proportion of households that have various property types available in their household
area, by District and Haor type
Types of property District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 628 634 630 947 945 1892
Khas pond 11.5 2.4 4.1 7.4 4.6 6.0
Khas land 32.8 18.8 9.4 32.5 8.0 20.3
Roadside sloping 3.3 18.3 0.2 6.2 8.4 7.3
Embankments 7.3 0.6 1.7 3.6 2.9 3.2
Railway grounds 0.5 0.2 0.2 0.4 0.1 0.3
Beel/Haor 74.7 92.7 74.4 84.3 77.0 80.7
River/Canal 78.0 88.8 88.4 91.7 78.5 85.1
CBO water body 5.7 1.3 1.1 6.3 5.7 6.0
Grazing land 2.9 10.9 4.1 6.0 5.9 6.0
Forest 1.3 0.2 0.2 0.1 1.0 0.5
Hills 0.3 1.1 0.2 0.1 1.0 0.5
Significance levels for comparisons: a = .10; b = .05; c = .00
Table 43 shows the proportion of available property that is accessible by households, which means
they can use the resources for household or livelihood purposes. Railway, forest and hill land were
excluded from the table due to an inadequate number of responses for meaningful analysis. Overall,
the highest proportion of households has access to river/canals, followed by roadside sloping and
beels/haors. Access to Khas land is lowest. When comparing across districts, access to Khas pond,
road side sloping and river/canals is highest in Sunamganj – with access to roadside sloping reported
FSUP-H Baseline Report, June 2010 69
as 100%. Access to embankments and CBO water bodies is highest in Kishoreganj. Access to Khas
land in Kishoreganj, and embankments in Netrokona and Sunamganj was reported as 0%.
Table 43: Proportion of available property that is accessible by households, by District and Haor type
Types of property District Haor Type Total
N Kishoreganj Netrokona Sunamganj Deep Moderate
Khas pond 116 12.5 18.8 64.3 7.0 55.6 25.9
Khas land 385 0.0 15.1 23.3 8.1 9.2 8.3
Roadside sloping 138 23.8 46.6 100.0 50.8 30.8 43.5
Embankments 64 40.8 0.0 0.0 11.8 53.3 31.3
Beel/Haor 1526 31.8 50.0 46.3 45.4 40.9 43.3
River/Canal 1613 68.2 66.6 79.2 76.5 65.6 71.5
CBO water body 115 52.8 37.5 15.5 39.3 16.7 28.7
Grazing land 113 33.3 21.7 19.2 17.5 28.6 23.0
Significance levels for comparisons: a = .10; b = .05; c = .00
Respondents were also asked what kind of activities household members were allowed to do on the
properties they had access to. However, answers to common property uses allowed were quite
varied, suggesting a need for clarification and awareness-building in this area.
Qualitative data showed that community members specifically stated the restricted access to open
Khas water for fishing purposes and restricted land access for rice cultivation as main barriers to
economic development. Community members emphasized the need for increased advocacy by NGOs
and other stakeholders for increased access to Khas land and water to increase participation of the
ultra poor in economic activities.
Picture 20: Government-owned Khas land
FSUP-H Baseline Report, June 2010 70
10 DISASTERS AND CRISES
10.1 Natural disasters: effects and coping strategies
Overall, 78% of households reported that they did not experience a natural disaster in the previous
year. In both Netrokona and Sunamganj, 81% experienced no disaster, and in Kishoreganj
significantly fewer (71%) experienced no disaster. In deep Haor, 75% of households did not
experience a disaster, while in moderate Haor the proportion was significantly higher (p=.022) at 80%.
Table 44 provides data for those households that did experience a natural disaster. The highest
proportion of disasters experienced in the last 12 months were wind damage, floods, excessive rain
and storms. Wind damage is locally called „Aphal‟; strong winds that damage standing crops, cause
soil erosion and uproot trees.
Table 44: Disasters experienced by households in the last 12 months, by District and Haor type
Type of natural disaster District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 181 122 121 233 191 424
Flood (flash/monsoon) 37.6 29.5 28.1 38.6 25.1 32.5
Drought 2.2 0.8 3.3 1.7 2.6 2.1
Storm 18.1 18.0 26.4 17.6 24.1 20.5
River erosion 0.0 1.6 5.0 3.4 0.0 1.9
Excessive rain 44.2 3.3 22.3 25.3 27.2 26.2
Water logging 12.7 15.6 8.3 14.6 9.4 12.3
Land slide 0.6 0.0 0.0 0.4 0.0 0.2
Wind damage 50.8 45.1 17.4 46.8 40.9 39.6
Soil erosion 1.1 5.7 0.8 3.4 1.0 2.4
Picture 21: Damage to buildings as a result of natural disasters
FSUP-H Baseline Report, June 2010 71
Respondents, who reported experiencing a natural disaster in the last 12 months, were subsequently
asked what the effect of that particular disaster was on their household. The highest proportion of
households experienced partial damage to their house, followed at a distance by loss of working days
and full damage to their house.
Table 45: Proportion of households experiencing various consequences of a natural disaster in the last
12 months, by District and Haor type
Effect of natural disaster District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 184 124 123 238 193 431
Loss of working days 54.3 c 7.3 4.1 31.1
c 20.7 26.5
Damaged house fully 6.5 c 16.1 14.6 10.9 12.4 11.6
Damaged house partially 66.8 a 72.8 74.0 77.7
b 61.1 70.3
Damaged poultry and livestock 8.2 7.3 0.0 c 8.0
c 2.6 5.6
Loss of productive assets 1.1 0.0 0.0 0.8 0.0 0.5
Crop loss 5.4 1.6 c 8.1 2.9 7.8
b 5.1
Loss of HH goods 3.3 8.1 0.0 c 4.2 3.1 3.7
Loss of trees 13.6 b 8.1 1.6 6.7 10.9 8.6
Tube well damage 0.5 0.0 0.0 0.0 0.5 0.2
Latrine damage 9.8 5.6 0.8 c 8.0
a 3.6 6.0
Other 1.1 5.6 0.0 2.5 1.6 2.1
Figure 16 shows the mean asset/income loss reported by households who experienced a disaster in
the last 12 months, disaggregated by district and Haor type. The mean asset loss per disaster was
reported at around Taka 3,017. There are no significant differences among districts but the asset loss
in moderate Haor is significantly higher than in deep Haor.
Figure 16: Mean asset loss from households experiencing asset loss in a natural disaster
in the last 12 months, by District and Haor type
FSUP-H Baseline Report, June 2010 72
Figure 17 shows the mean number of working days lost reported by the 26.5% of households who
indicated this effect in table 45, disaggregated by district and Haor type. The mean number of working
days lost is 10. When comparing across districts, the mean number of working days lost is
significantly higher in Kishoreganj than the other two districts. There is no significant difference across
Haor types.
Figure 17: Mean number of working days lost from households experiencing a natural
disaster in the last 12 months, by District and Haor type
The most common coping strategies used by respondents to recover from a natural disaster were:
taking out a loan from friend/neighbor (41%), taking loan from a moneylender (31%), adjusting meals
(25%), using savings (25%), accepting help from others: (24%), purchasing on credit (21%) and
taking a loan from NGO (11%).
10.2 Household crises: effects and coping strategies
Respondents were also asked the same range of effect and coping strategy questions for a range of
household crises, not caused by natural disasters. Only 16.7% reported the occurrence of such crises
in the last 12 months – ranging from 16-19% among Districts and across Haor type, with no significant
differences. The most common types of household crises reported were illness of income earners
(57.2% of cases where a household crisis was reported) and illness of other household members
(32% of cases where a household crisis was reported). All other responses were less than 5%.
The main effects of the household crises were asset/income loss and work days lost. Figure 18 shows
the mean loss of assets, disaggregated by district and Haor type. The mean loss of assets was just
under Taka 5,000. Comparison among districts shows that asset loss was significantly higher in
Netrokona than in Sunamganj. There is no significant difference across Haor types.
FSUP-H Baseline Report, June 2010 73
Figure 18: Loss of assets among households experiencing household crises
in the last 12 months, by District and Haor type
(N = 306)
Figure 19 shows the mean number of working days lost, disaggregated by district and Haor type. The
mean number of working days lost was 36. Comparison among districts and across Haor types
showed no significant differences.
Figure 19: Loss of work days among households experiencing household
crises in the last 12 months, by District and Haor type
(N = 306)
Figure 20 shows the mean number of working days lost as a result of illness of either income earner
or other household members, disaggregated by district and Haor type. The mean number of working
days lost due to illness was 36 (mode=15). Comparison among districts shows that the number of
working days lost due to illness is significantly higher in Netrokona than in Sunamganj. There are no
significant differences across Haor types.
FSUP-H Baseline Report, June 2010 74
Figure 20: Average number of days lost due to illness for those households with an ill member designated as a household crises in the last 12 months, by District and Haor type
(N=305)
The most common coping strategies used by respondents to cope with household crises were
(n=316): took out a loan from friend/relative (42.7%), took out a loan from moneylender: (36.0%),
made adjustment to meals (27.8%), accepted help from others (20.6%), purchased goods on credit
(18.4%), used savings (11.7%), took out a loan from an NGO (11.1%), took a grain loan (10.4%), ate
famine foods (8.2%), and accepted aid (5.4%)
10.3 Climate change
Qualitative data collection included some exploratory questions around climate change in qualitative
data collection, the findings of which are by no means robust. Community members reported
increased temperatures, more extreme storms, irregular flash floods and irregular/infrequent rainfall.
They inferred multiple linkages between these changing weather-related characteristics and livelihood
impacts such as reduced crop and fishing yields, less migratory birds, increased insect infestation and
crop disease, and reduced soil fertility. On e common example mentioned was that climate variability
has reduced ability to predict flash flood; previously crops could be harvested prior to flash flood
It was noted that in the last ten years the water levels of the Haor have been reduced significantly and
sedimentation has increased; beels and marshlands were filling up, which negatively affected fishery
and agricultural practices due to lack of water in the dry season. In addition, the increased irrigation
required as a result of the reduced rainfall and lower water levels make agricultural practices more
costly; reducing profits derived from agriculture.
As a result of these changes, many poor and ultra poor households can no longer rely on daily fishing
labor as the main source of income but must now do agricultural day labor and poultry rearing to
make a living. Overall, community members noted limited capacity to adapt to these changes,
particularly for the ultra poor.
FSUP-H Baseline Report, June 2010 75
In addition, community members highlighted man-made problems that compounded the problems
considered to be caused by climate variability. For example, the use of insecticides on crop land
reduced fishing yields and also decreased day labor opportunities for pulling weeds.
To address the impacts of climate change, community members stated the need to organize
development activities that focus on river dredging, promotion of more resilient crops and agricultural
practices, and provision of training on climate change and how to adapt.
Picture 22: Social mobilization around community issues
FSUP-H Baseline Report, June 2010 76
11 FAMILY AUTHORITY AND DECISION MAKING
11.1 Household decision making
Tables 46 to 49 show the types of decision making for 12 different types of household decisions,
disaggregated by Haor type and district. All questions were answered by a female household
member. The highest proportion of decisions is made by the husband after discussion with the female
household member. It is also apparent that women have greater involvement in household decisions
such as minor household purchases, children‟s clothing and education, medical expenses and in
spending money that they have directly earned. Women have less involvement in expenditures that
relate to livelihoods, higher value assets, loans/savings and events such as weddings and ceremonies
and shelter in case of disasters. The proportion of decisions made without any involvement by the
female is low for almost all decision types, except salish decision making.
Tables 46 and 47 show that when the data is disaggregated by Haor type, the proportion of decisions
made by the husband after discussion with the female household member is significantly higher in
deep Haor than moderate Haor areas for many of the decisions. For several decisions, the proportion
of women not involved in decision making is significantly higher in moderate than in deep Haor. It is
important to note that a relatively high number of women answered not applicable (not listed in tables
below) to the various decision types, which could be interpreted that they were uncomfortable
responding.
Table 46: Household decision making, by Haor type (1)
Decision
Haor Type
Overall Deep Moderate
Buying small food items, groceries, toiletries
Can decide alone 20.3 20.2 20.3
Decide w/ husband or other adult male 14.2 14.7 14.4
Husband decides after discussion 58.1 a 53.4 55.8
Not involved in decision 7.4 11.7 c 9.5
Buying clothing for yourself and your children
Can decide alone 13.2 15.1 14.2
Decide w/ husband or other adult male 14.4 13.1 13.8
Husband decides after discussion 65.8 b 59.3 62.5
Not involved in decision 6.6 12.5 c 9.6
Spending money that you yourself have earned
Can decide alone 24.6 25.7 25.1
Decide w/ husband or other adult male 6.9 9.3 8.1
Husband decides after discussion 63.5 a 58.6 61.1
Not involved in decision 5.0 6.5 5.7
Buying or selling major household assets (land, livestock, crops)
Can decide alone 11.6 10.9 11.3
Decide w/ husband or other adult male 16.5 19.2 17.8
Husband decides after discussion 65.2 b 60.7 63.0
Not involved in decision 6.8 9.1 7.9
Buying or selling jewelry
Can decide alone 5.4 8.0 8.6
Decide w/ husband or other adult male 13.0 12.0 12.5
Husband decides after discussion 72.1 71.6 71.8
Not involved in decision 5.8 8.4 7.1
FSUP-H Baseline Report, June 2010 77
Use of loans or savings
Can decide alone 10.3 10.4 10.3
Decide w/ husband or other adult male 14.4 12.8 13.6
Husband decides after discussion 70.5 69.3 69.9
Not involved in decision 4.8 7.5 b 6.2
Table 47: Household decision making, by Haor type (2)
Decision
Haor Type
Overall Deep Moderate
Expenses for your children’s education
Can decide alone 12.5 14.2 13.3
Decide w/ husband or other adult male 10.8 10.3 10.5
Husband decides after discussion 73.3 70.1 71.7
Not involved in decision 3.4 5.5 4.5
Expenses for your children’s marriage
Can decide alone 9.8 9.7 9.7
Decide w/ husband or other adult male 17.9 26.7 a 22.1
Husband decides after discussion 69.7 c 58.1 64.2
Not involved in decision 2.6 5.6 a 4.0
Medical expenses for yourself or your children
Can decide alone 13.6 16.9 b 15.3
Decide w/ husband or other adult male 14.2 11.5 12.8
Husband decides after discussion 70.0 68.9 69.5
Not involved in decision 2.1 2.8 2.4
Expenses for family planning (contraceptives)
Can decide alone 6.2 5.9 6.1
Decide w/ husband or other adult male 6.2 6.4 6.3
Husband decides after discussion 83.6 81.8 82.7
Not involved in decision 3.9 5.9 4.9
To move to shelter during time of disaster
Can decide alone 11.2 11.2 11.2
Decide w/ husband or other adult male 24.8 24.8 24.8
Husband decides after discussion 54.5 c 48.4 51.5
Not involved in decision 9.4 15.5 b 12.4
Actively participate and involved in salish decision making
Can decide alone 4.9 8.3 c 6.8
Decide w/ husband or other adult male 7.4 11.8 c 9.8
Husband decides after discussion 19.0 23.3 b 21.4
Not involved in decision 68.8 c 56.
6 62.0
Tables 48 and 49 show that when the data is disaggregated by district, the proportion of decisions
made by the husband after discussion with the female is significantly lower in Netrokona than in the
other two districts. Correspondingly, the proportion of decisions wherein the female is not involved at
all is significantly higher in Netrokona for many decision types. However, it is interesting to note that
the proportion of decisions that females can make on their own is also significantly higher in
Netrokona than in the other two districts for several of the decision types.
FSUP-H Baseline Report, June 2010 78
Table 48: Household decision making, by District (1)
Decision
District
Kishoreganj Netrokona Sunamganj
Buying small food items, groceries, toiletries
Can decide alone 16.1b 23.1 21.5
Decide w/ husband or other adult male 12.9 21.5b 8.5
Husband decides after discussion 68.2 39.5c 60.4
Not involved in decision 2.8 15.9c 9.6
Buying clothing for yourself and your children
Can decide alone 12.7 19.0c 10.8
Decide w/ husband or other adult male 11.4 21.9c 7.8
Husband decides after discussion 72.3 44.0c 71.6
Not involved in decision 3.6 15.2c 9.9
Spending money that you yourself have earned
Can decide alone 20.7 41.6c 15.4
Decide w/ husband or other adult male 7.2 11.8a 5.7
Husband decides after discussion 68.0 44.7c 67.4
Not involved in decision 4.1 1.9b 11.4
Buying or selling major household assets (land, livestock, crops)
Can decide alone 12.1 15.6 6.7b
Decide w/ husband or other adult male 12.8a 22.4 19.0
Husband decides after discussion 69.1 51.5b 66.7
Not involved in decision 6.0 10.5 7.7
Buying or selling jewelry
Can decide alone 9.3 11.0 6.5
Decide w/ husband or other adult male 10.5 25.6b 6.7
Husband decides after discussion 75.0 52.4b 80.2
Not involved in decision 5.2 11.0 6.5
Use of loans or savings
Can decide alone 9.9 13.1a 8.0
Decide w/ husband or other adult male 9.9 21.5c 9.4
Husband decides after discussion 75.6 58.0c 75.9
Not involved in decision 4.6 7.3 6.6
FSUP-H Baseline Report, June 2010 79
Table 49: Household decision making, by District (2)
Decision
District
Kishoreganj Netrokona Sunamganj
Expenses for your children’s education
Can decide alone 11.8 15.9 12.7
Decide w/ husband or other adult male 9.4 18.5b 5.7
Husband decides after discussion 76.9 60.8c 75.0
Not involved in decision 1.8c 4.8 6.6
Expenses for your children’s marriage
Can decide alone 10.2 17.5 5.2b
Decide w/ husband or other adult male 17.1b 22.1 26.8
Husband decides after discussion 70.2 54.5c 63.6
Not involved in decision 2.5 5.8 4.5
Medical expenses for yourself or your children
Can decide alone 14.3 19.9c 11.5
Decide w/ husband or other adult male 8.4 22.3c 7.5
Husband decides after discussion 75.4 55.3c 78.1
Not involved in decision 1.8 2.6 3.0
Expenses for family planning (contraceptives)
Can decide alone 6.1 3.0c 8.3
Decide w/ husband or other adult male 5.9 13.0b 1.8
Husband decides after discussion 87.0 78.5c 81.1
Not involved in decision 1.0b 5.5 8.8
To move to shelter during time of disaster
Can decide alone 10.6 16.8c 6.3
Decide w/ husband or other adult male 20.0b 28.0 26.1
Husband decides after discussion 62.0 39.5c 53.6
Not involved in decision 7.3 15.7 13.9
Actively participate and involved in salish decision making
Can decide alone 10.8 7.9 1.9c
Decide w/ husband or other adult male 16.5 5.7b 9.1
Husband decides after discussion 20.1 14.9 30.7c
Not involved in decision 52.5 71.5c 58.3
Qualitative data supports the quantitative findings around household decision making presented in the
tables above. Qualitative data also shows a trend of an increasing role of women in household
decision making. For example, women‟s participation in economic activities is increasingly jointly
discussed, although it is important to note that men still make the final decision and many women
believe that the men often know best. Profit and loss decisions vary by household. There is also
indication of increased independence in household processes. For example, women‟s freedom of
movement appears to be expanding. Previously women shopkeepers relied on their husbands to
acquire shop stocks, now more women are able to acquire the goods themselves. External contact
(like with NGOs) appears to have been an important factor in these trends.
FSUP-H Baseline Report, June 2010 80
11.2 Family life attitudes
Tables 50 to 51 show the attitude about family life, disaggregated by Haor type and district. All
questions were answered by a female household member. Overall, a higher proportion of women
agree that the husband should help with household chores if the female is working; and that they have
the right to express their opinion, even when they disagree with their husband. The proportion of
women overall who disagree with the statement that it is better to send a son to school than a
daughter is also significantly higher. However, it is interesting to note that despite the more liberal
attitudes about family life expressed by women, the proportion of women who agree that a wife should
tolerate being beaten is significantly higher that the proportion who disagrees.
When disaggregated by Haor type, Table 50 shows that a significantly higher proportion of women in
deep Haor agree that the husband should help with chores if the wife is working and married women
should be allowed to work outside the home. However, a significantly higher proportion of women in
deep Haor also agree that a wife should tolerate being beaten and that it is better to send boys to
school instead of girls.
Table 50: Attitudes about family life, by Haor type
Attitudes about family life
Haor Type
Total Deep Moderate
N= 947 945 1892
The important decisions in the
family should be made only by men
Agree 49.2 45.8 47.5
Disagree 47.7 52.2 49.9
DNK 3.1 2.0 2.5
If the wife is working outside the
home, then the husband should help
her with household chores
Agree 66.1b 60.5 63.3
Disagree 27.8 34.5 31.1
DNK 6.1 5.0 5.5
Married women should be allowed
to work outside the home
Agree 53.1b 46.3 49.7
Disagree
42.2 49.0
45.6
DNK 4.6 4.7 4.7
The wife has a right to express her
opinion even when she disagrees
with her husband
Agree 66.8 66.0 66.4
Disagree 28.7 29.1 28.9
DNK 4.4 4.9 4.7
A wife should tolerate being beaten
by her husband in order to keep the
family together
Agree 83.5c 74.3 78.9
Disagree 14.6 23.3 18.9
DNK 1.9 2.4 2.2
It is better to send a son to school
than a daughter
Agree 27.1b 21.1 24.1
Disagree 66.8 72.4 69.6
DNK 6.0 6.6 6.3
When disaggregated by district, Table 51 shows mixed results. A significantly higher proportion of
women in Netrokona agree that the important decisions in the family should only be made by men.
There are significant differences among the three districts in the proportion of women who agree with
the statements that the husband should help with chores if the wife is working and married women
should be allowed to work outside the home; whereby the highest proportion of women who agrees is
in Kishoreganj and the lowest proportion is in Sunamganj. The proportion of women who agree that
the wife has a right to express her opinion even when she disagrees with her husband is also
significantly lower in Sunamganj.
FSUP-H Baseline Report, June 2010 81
Although findings indicate that Sunamganj is the most conservative of the three districts, it is
important to note that the proportion of women who agrees that it is better to send a son to school
than a daughter is significantly lower in Sunamganj than in the other two districts; whereby
Kishoreganj shows the highest proportion of women who agrees. In contrast, Kishoreganj shows a
significantly lower proportion of women who agree that a wife should tolerate being beaten by her
husband in order to keep the family together
Table 51: Attitudes about family life, by District
Attitudes about family life
District
Kishoreganj Netrokona Sunamganj
N=628 N=634 N=630
The important decisions in the
family should be made only by
men
Agree 41.6 58.2 c 42.7
Disagree 55.3 40.4 54.3
DNK 3.2 3.2 3.0
If the wife is working outside the
home, then the husband should
help her with household chores
Agree 76.3 c 66.6
c 47.1
c
Disagree 18.6 30.6 44.1
DNK 5.1 2.8 8.7
Married women should be allowed
to work outside the home
Agree 62.1 c 47.5
c 39.7
c
Disagree 31.8 50.3 54.6
DNK 6.1 2.2 5.7
The wife has a right to express her
opinion even when she disagrees
with her husband
Agree 68.9 70.3 60.0 c
Disagree 24.7 26.7 35.4
DNK 6.4 3.0 4.6
A wife should tolerate being
beaten by her husband in order to
keep the family together
Agree 75.6 b 80.3 80.8
Disagree 21.5 17.8 17.5
DNK 2.9 1.9 1.7
It is better to send a son to school
than a daughter
Agree 33.3 22.9b 16.2
Disagree 62.9 68.8 77.1
DNK 3.8 8.4 6.7
11.3 Daily time patterns of men and women Qualitative data collection was organized to gain a better understanding of daily time spending of
men, women involved in work and women who stay at home. Graphic representations are added in
annex 3. Figure 20 below provides one example from a moderate Haor village in Kishoreganj. It is
important to note that the data collected is specific to the month of February around the time when
agricultural day labor, primarily in rice fields, is coming to an end. Patterns will likely be different in
other seasons.
Men typically start their day between 5-6am. The first thing they do is pray, put the cow to field and
clean the shed. Men then usually put in 1-2 hours of work in the rice fields such as transplantation,
weeding, fertilizing and irrigation before taking breakfast between 8-9am; followed by more work in the
rice fields until taking a 1-2 hour lunch break around 1pm. They return from the fields between 5-6pm,
feed the cow and put it to shed. The time between 6-8pm is commonly spent resting or gossiping,
wandering around or going to the market. Dinner is taken around 8-9pm after which men go to sleep.
Income derived from a typical day describe above ranges between 150-200 Taka per day. In some
cases men get 200 Taka, excluding meals or 150 Taka including 3 meals, although 200 Taka
including meals is also reported.
FSUP-H Baseline Report, June 2010 82
Women who stay at home get up at the same time as their husband, between 5-6am. They start the
day by fetching water, sweeping the house and the courtyard, cleaning the cooking pot, helping the
husband put the cow out, and cooking breakfast. If there are school-going children in the household,
they are sent to school after breakfast. After breakfast, the women start a range of household chores,
including: coiling dung for fuel, collecting vegetables and fish, collecting firewood, washing clothes,
cleaning pots and cooking meals. Women usually bath before preparing lunch and take two rest
periods. One in late morning and one after serving lunch, during which time they gossip and stitch
kantha. Children are washed between 5-6pm and preparations are made for dinner. Before dinner,
the women spend about one hour with the children to help them learn. After dinner, the women stay
up longer to clean the cooking pot and house, and to pray before going to bed around 10pm.
Women who are involved in income-generating activities get up around 4am to give them enough time
to complete the first chores of the day. If breakfast and dinner are provided by the employer, they then
work from 5am to around 7pm. If not, they work from 8am to 5pm. Lunch is almost always included.
After work they must fetch water, clean the cooking pot, feed the children and cook dinner for their
husband. After dinner they prepare the bed for their husband and do some small household chores
before going to sleep at around 10pm. Women commonly spend around 12-13 hours working and
about 2-3 hours doing household chores. Payment for women varies and can range from 50-100 Taka
for uprooting rice seedlings without meals to proportions of the harvest.
Figure 21: Daily time use of men and women
FSUP-H Baseline Report, June 2010 83
12 CHILD NUTRITION, ANTENATAL CARE AND FAMILY PLANNING
12.1 MCHN characteristics
All questions in this section relate to < 2 children. The respondent is always the child‟s mother. Of the
total number of respondents, 70 percent did not have any children < 2 years of age. Of those that did,
29 percent had one and 1 percent had two < 2 children. Table 52 provides an overview of Maternal
Child Health and Nutrition characteristics, disaggregated by District and Haor type. Note that there
were no significant differences between Haor types.
Virtually every mother has breastfed her child (99.5%) and 45% of overall mothers initiated
breastfeeding with the first hour of birth. The percentage of women who initiated breastfeeding after
the first hour was significantly lower in Netrokona than in the other districts.
The average age for introducing solid/semi-solid foods (weaning) was just over 5 months age,
whereby the average age in Sunamganj was significantly lower at 4.6 months. Table 52 shows the
proportion of women introducing solid/semi-solid foods for 0-3, 4-6, 7-9 and 10+ months. This data
can be used to track change over time in the introduction of solid/semi-solid foods, based on project
recommendations on proper weaning practices.
Qualitative data showed that most women know the value of colostrums to newborn health. However,
in some villages traditional practices and superstitions prevent mothers from providing colostrums to
newborns. Nutritional information for newborns was usually obtained from the village doctor or health
worker. Exposure to media is an additional source of nutritional info.
Overall, 35.5% of mother‟s took iron or folic acid supplements. The proportion of mother‟s who took
these supplements was significantly higher in Kishoreganj. Taking iron and folic acid during
pregnancy varied by village; where health workers were present, use was common. Lack of
knowledge and mother-in-laws who discourage use of both supplements presented barriers in other
villages.
The majority of mother‟s did not change the amount of food that they consumed during their last
pregnancy; 16% increased their food intake and 33% decreased their food intake. Qualitative data
showed that some mothers reduce food intake to two meals a day during pregnancy to keep the size
of the baby smaller. A large baby during pregnancy makes it hard to work and the want to avoid the
complicated delivery of a large baby as the hospital is far away and costly to access. Mothers do have
some knowledge on food supplements during pregnancy – primarily from health workers, media, and
village elders. However, limited financial resources often prohibit taking more nutritious foods. Dietary
diversity while breast feeding is also poor.
The majority of women also did not change the amount of rest they took after the last birth. Only 23%
took more rest than usual. Qualitative data indicated that the gender of the baby determines the
amount of rest (this means a period of light work) for new mothers: 9 days rest for a male child; 7 days
for female. After the 7-9 day period, women resume regular work and only rest while breast feeding.
Qualitative data also shows that most women do not take rest during their pregnancy either. They
continue to complete their daily activities, taking only a little rest after chores are done. Household and
community members generally help take on some of the household chores if the pregnant mother
becomes sick.
FSUP-H Baseline Report, June 2010 84
Picture 23: Balanced meal taken by a pregnant woman
Overall, mothers attended on average 1 ANC session. Qualitative data shows that this is usually after
3 months of pregnancy. The proportion of mothers attending ANC was significantly higher in
Kishoreganj than in the other Districts. Qualitative data shows that there are very few periodic medical
checkups during pregnancy due to lack of knowledge, lack of money, and difficulties in
communicating with the medical centers. Medical check-ups are only used for serious complications.
Additional barriers to obtaining pre-natal care are: mother in law and some elders do not approve; and
when the husband is unable to accompany wife to doctor, social norms prevent a woman from
traveling alone.
FSUP-H Baseline Report, June 2010 85
Table 52: MCHN characteristics by District and Haor type
Hand-washing Behaviors District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N= 196 166 202 271 293 564
Average age solid/semi-solid food introduced (months)
5.4 5.3 4.6 b 5.1 5.1 5.1
When breastfeeding
initiated
Within 1st
hour (%) 45.4 55.4 37.1 46.5 44.4 45.4
After 1st
hour (%) 54.6
b 44.6
c 62.9 53.5 55.6 54.6
Age of introducing solid foods
0-3 months 15.8 21.4 43.9 c 23.8 29.4 26.9
4-6 months 63.7 57.2 25.9 c 51.9
b 45.1 48.1
7-9 months 18.4 17.9 25.8 b
22.1 20.7 21.4
10+ months 2.1 3.5 4.3 2.2 4.8 3.6
Took iron or folic acid supplements (%)
47.4 c 28.9 29.2 34.3 36.5 35.5
Changes in amount of food
consumed
More (%) 15.2 16.9 15.8 17.2 14.7 15.9
Same (%) 47.7 50.6 55.6 50.2 52.6 51.4
Less (%) 37.1 32.5 28.6 32.6 32.8 32.7
Number of ANC sessions attended
1.2 c 0.7 0.8 0.9 0.9 0.9
Amount of rest
after last birth
More than usual 25.4 18.7 23.2 22.3 22.9 22.6
About the same 62.9 72.3 65.0 67.8 65.2 66.4
Less than usual 11.7 9.0 11.8 9.9 11.9 11.0
Letters denote significant differences among Districts or between Haor types for a given variable.
Significance levels for comparisons: a = .10; b = .05; c = .00
Table 53 shows the different types of weaning foods used by mothers for their most recently born
child, disaggregated by District and Haor type. On average, mothers in Kishoreganj used 2.2 different
weaning foods, mothers in Netrokona used 1.8 weaning foods, and mothers in Sunamganj used 1.6
weaning foods.
When comparing across Districts, the use of kichori, Soji/Sagu/Barli, and cow/goat milk is significantly
lower in Sunamganj. The use of rice powder/soup is significantly lower in Kishoreganj; and the use of
potato and egg is significantly lower in Netrokona.
When comparing across Haor types, the use of Soji/Sagu/Barli is significantly higher in deep Haor;
and the use of cow/goat milk, rice powder/soup and fruits/juices is significantly higher in moderate
Haor. Qualitative data shows that nutrition-related information for newborns is obtained from the
village doctor and health assistant.
FSUP-H Baseline Report, June 2010 86
Table 53: Weaning foods used, by District and Haor type
Weaning Foods District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 196 166 202 271 293 564
Baby formula/Cerelac 3.4 3.1 2.1 4.3 1.7 2.9
Khichori 35.6 27.5 16.4 b
28.6 25.0 26.6
Soji/Sagu/Barli 37.7 39.7 25.7 b
44.9 b
25.9 34.3
Cow/goat milk 21.2 17.6 2.9 c 9.8 17.2
b 13.9
Rice powder/soup 58.9 a 69.5 77.1 64.3 71.6
c 68.3
Potato 21.9 4.6 c 17.1 15.7 14.2 14.9
Egg 8.9 0.8 c 3.6
b 3.2 5.6 4.6
Banana/other fruits and juices 28.8 22.1 17.9 19.5 25.9 b
23.0
Letters denote significant differences among Districts or between Haor types for a given weaning food. Significance levels
for comparisons: a = .10; b = .05; c = .00
Table 54 shows birth attendance during the last delivery, disaggregated by District and Haor type.
Overall, the majority of births were attended by Traditional Birth Assistants. Less than 1% of births
were attended by a doctor. It is important to note that in the qualitative data collection most women
reported that they had experienced a newborn die, many before the newborn was six months old.
Qualitative data further shows that local doctors and health workers are used for small complications;
hospital for serious complications. Most women have home births with traditional birth attendants or
family assisting, even though they recognize these individuals are often untrained. Few can save
money for emergency delivery. In the case of emergencies, community members will help to hire
transport and cover costs; neighbors will help with childcare. Some women reported being reluctant
to go to the hospital because doctors frequently will not attend to the ultra-poor. Many birth attendants
are untrained. Hygienic practices during and after delivery are uncommon in most areas.
Table 54: Who attended last delivery, by District and Haor type
Birth Attendee District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 197 166 203 273 293 564
Friend/relative 11.7 9.0 2.5 8.4 6.8 7.6
TBA 67.0 47.0 76.8 62.3 66.9 64.7
TTB 19.3 40.4 16.7 25.6 23.5 24.6
Doctor 0.5 0.6 1.5 0.7 1.0 0.9
FWV (nurse/paramedic/FWV) 1.5 3.0 1.0 2.2 1.4 1.8
Other 0.0 0.0 1.5 0.7 0.3 0.5
For households currently with a child 2 years of age or under, 81.8 % of the oldest child in this age
group has received at least one immunization. The proportions by District do not vary statistically
(p=.136) nor do they vary by Haor type (p=.484). For those children who did receive immunizations,
72.9% have immunization cards. These cards are significantly more common in moderate Haor but do
not vary significantly by District (p=.257). Of children 9 months and older, just over 50% were fully
immunized, as verified through their immunization cards.
FSUP-H Baseline Report, June 2010 87
Qualitative data shows that when health care workers come to villages, virtually all babies are
immunized and receive their full dose of vaccines, although most mothers do not know about the
different types of immunization or the benefits/risks of immunization.
For those children who needed antihelmintics, 47% received them. The proportion of children
receiving antihelmintics was significantly higher in Kishoreganj. Just over 47% percent of children
received vitamin A supplements in the last 6 months, whereby the highest proportion of children
received vitamin A supplements in Kishoreganj.
Table 55: Child health and immunization, by District and Haor type
Child health and
immunization
District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 196 166 203 220 242 565
Oldest under 2 receiving at least
1 vaccination (%) 86.2 77.7 80.8 80.6 82.9 81.8
Proportion of those immunized
with immunization cards 72.8 68.2 76.8 79.8 c 65.5 72.8
Proportion of children 9 months
and older fully immunized 49.5 51.1 51.5 47.9 53.3 50.7
Proportion receiving
antihelmintics, if needed 39.0
c 23.4
b 31.5
b 30.9 32.4 31.7
Proportion receiving vitamin A
supplements 63.4
b 47.5 41.3 46.4 48.1 47.3
Letters denote significant differences among Districts or between Haor types for a given variable. Significance levels for
comparisons: a = .10; b = .05; c = .00
Table 56 describes the health issues of mothers with children under 2, disaggregated by District and
Haor type. Overall, only 7.4% of mothers reported suffering no illnesses in the last 12 months. The
highest proportion of women suffered from cold attacks, followed by gastric complications and
anemia. The lowest proportion of women suffered from Typhoid. When comparing across districts, the
proportion of women suffering from cold attacks, gastric complications and diarrhea is significantly
higher in Kishoreganj than in the other districts. The proportion of women suffering from anemia is
significantly higher in Netrokona; and the proportion of women suffering from cold attacks and
dysentery is significantly lower in Sunamganj than in the other districts.
When comparing across Haor types, the proportion of women suffering from four out of the seven
listed illnesses is significantly higher in deep Haor than in moderate Haor. In turn, the proportion of
women suffering no illnesses in the last 12 months is significantly lower in the moderate Haor.
Qualitative data showed that physical weakness, anemia, abdominal pain, back pain, fever, bleeding
and uterus complications are common for lactating mothers. Health care for complications are
commonly addressed locally. Few have the resources to seek treatment at the hospital.
Relatives/community members assist with household chores and childcare when mothers suffer
complications. Information about complications is commonly obtained from NGO and GoB health
workers, village doctor, TBA and village elders. Post-natal check-ups are rare due to limited
knowledge, resources, and communication challenges. In some cases, mothers report that their
FSUP-H Baseline Report, June 2010 88
mother-in-laws do not permit them to seek medical care. Use of Vitamin A supplements after giving
birth varies. In some villages it is not taken even when distributed by health workers. Ultra poor and
poor women do not take vitamin A supplements. There appears to be limited knowledge of the
benefits of these supplements.
Table 56: Health issues of mothers with children under 2, by District and Haor type
Illnesses District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 197 166 203 273 293 566
Proportion of mothers suffering
no illnesses 4.1 1.2 15.7
c 5.1 9.6
b 7.4
Cold attack 80.7 b
90.4 60.1 c 79.9
b 72.7 76.1
Gastric complications 32.5 b
21.7 24.6 26.4 26.6 26.5
Anemia 22.8 35.5 b
21.7 26.4 25.9 26.1
Diarrhea 24.4 b
16.3 13.8 22.0 b
14.7 18.2
Dysentery 20.3 17.5 11.8 b
19.8 b
13.3 16.4
Rheumatic fever 3.0 3.6 10.8 c 3.7 8.2
b 6.0
Typhoid 6.6 b
4.2 1.0 5.1 a 2.7 3.9
Letters denote significant differences among Districts or between Haor types for a given variable. Significance levels for
comparisons: a = .10; b = .05; c = .00
Table 57 describes the health issues of children under 2, disaggregated by District and Haor type.
Overall, only 5.8% of children did not suffer any illnesses in the last 12 months. The highest proportion
of children suffered from cold attacks, diarrhea and pneumonia. The lowest proportion of children
suffered from skin diseases and other illnesses. There are no significant differences between Haor
types. When comparing across districts, the proportion of children suffering from cold attacks is
significantly lower in Sunamganj than in the other districts; and the proportion of children suffering
from diarrhea and skin diseases is significantly higher in Netrokona.
Qualitative data shows that Children of two years commonly experience pneumonia, influenza,
typhoid, hepatitis, pneumonia, cold, fever, chicken pox, measles and diarrhea. TBA, village elders and
health workers provide advice to mothers about childhood disease and will lend money for children‟s
emergency treatment. In most cases there is no gender disparity with respect to health care for
children, although in some villages it is common for male children to receive foods of higher nutritional
quality and more of them.
FSUP-H Baseline Report, June 2010 89
Table 57: Health issues of children under 2, by District and Haor type
Illnesses District Haor Type Total
Kishoreganj Netrokona Sunamganj Deep Moderate
N 197 166 203 273 293 566
Proportion of children suffering
no illnesses 5.6 3.0 8.4
b 7.0 4.8 5.8
Cold attack 86.2 92.8 80.8 b
86.4 86.0 86.2
Diarrhea 34.2 48.2 b
32.0 36.3 38.7 37.5
Pneumonia 25.0 22.9 23.2 23.1 24.3 23.7
Dysentery 13.8 16.9 12.3 15.8 12.7 14.2
Skin diseases 7.7 13.3 a 4.4 8.1 8.2 8.1
Other 0.5 2.4 3.4 1.8 2.4 2.1
Letters denote significant differences among Districts or between Haor types for a given illness. Significance levels for
comparisons: a = .10; b = .05; c = .00
12.2 Anthropometric measurements
Table 58 shows the result of anthropometric measurements carried out with 398 children aged 6-23
months: 54% boys and 46% girls. The average age was 14.7 months, with no significant differences
among districts, Haor types or gender.
Weight-for-age (underweight) is a composite index of height-for-age and weight-for-height. A child can
be underweight for his/her age because s/he is stunted, wasted or both. Weight-for-age is a useful
tool in clinical settings for continuous assessment of nutritional progress and growth. Children whose
weight-for-age is below minus two standard deviations from the median of the reference population
are classified as underweight. Table 58 shows that 57.7% of < 2 children are underweight: 39.4% are
moderately underweight and 18.3% are severely underweight. A prevalence of > 30% is considered to
be „very high‟.
There is limited national data available for < 2 children; the majority of published data available for
comparison reflects anthropometric scores for < 5. However, the 2004 BDHS sample for < 2 children
in rural Bangladesh (< -2 SD) estimated stunting at 42.5%, wasting at 19.4% and underweight at
50.2%. Compared to such national data sets, the anthropometric scores for stunting and underweight
in the Haor region appear high.
When disaggregated by district, the proportion of children with moderate stunting is significantly lower
in Netrokona than in Kishoreganj. The proportion of children with moderate wasting is significantly
higher in Netrokona than in Kishoreganj, and significantly lower in Sunamganj than in Netrokona. The
proportion of children with moderate underweight is significantly higher in Sunamganj than in
Netrokona. When comparing across sex, the proportion of girls with severe stunting is significantly
lower than boys. There are no significant differences across Haor type.
FSUP-H Baseline Report, June 2010 90
Table 58: Anthropometric measurements
HAZ
stunting
WHZ
wasting
WAZ
underweight
Moderate Severe Moderate Severe Moderate Severe N
District
Kishoreganj 41.3 20.3 8.4 0.7 40.6 18.9 143
Netrokona 26.81 22.0 14.6
4 0.0 31.7 19.5 123
Sunamganj 34.8 25.0 6.83 0.0 45.5
3 16.7 132
Haor Type
Deep 34.7 22.5 9.2 0.0 36.4 19.7 173
Moderate 34.7 22.2 10.2 0.4 41.8 17.3 225
Sex of child
Male 32.1 26.5 9.8 0.5 38.6 19.5 215
Female 37.7 17.57 9.8 0.0 40.4 16.9 183
Total Sample 34.7 22.4 9.8 0.3 39.4 18.3 398 Notes: Moderate (-2.01 to -3.00 SD) Severe (< -3.00 SD) 1Netrokona different from Kishoreganj at 0.05 significance level
3Sunamganj different from Netrokona at 0.05 significance level
4Netrokona different from Kishoreganj at 0.10 significance level
7Female different from Male at 0.05 significance level
HAZ=Height-for-age z-score WHZ=Weight-for-height z-score WAZ=Weight-for-age z-score
The estimates in FSUP-H appeared reliable when compared with corresponding baseline estimates
from the SHOUHARDO anthropometric surveys for < 2 in the Haor area, assuming no overlap in
beneficiaries. Figure 21 shows that the 2006 pre-intervention values for the Haor region are very
similar to the FSUP-H pre-intervention findings for the same area. A comparison between
SHOUHARDO baseline and endline findings also shows the impact that effective interventions can
have on child malnutrition.
Figure 21: Comparison between SHOUHARDO and FSUP-H malnutrition levels
FSUP-H Baseline Report, June 2010 91
13 STATUS OF FEMALE-HEADED HOUSEHOLDS Table 60 provides an overview of key variables for female-headed households that provide a good
overview of their food security and livelihood status, as compared to male-headed households. Almost
15% of the households sampled had female heads of household. When comparing female- and male-
headed households across Districts and Haor type, the following observations can be made:
- female-headed households have significantly lower per capita monthly income levels than
male-headed households
- there are no significant differences in per capita expenditures between female- and male-
headed households, except in deep Haor areas where expenditures in female-headed
households are significantly lower
- female-headed households have significantly lower food consumption scores than male-
headed households
- female-headed households have a significantly higher coping strategy index score in
Kishoreganj and Sunamganj, and in deep Haor areas
Table 59: Key variables for female-headed households, by district and Haor type
Collective Actions
District Haor Type Total
Kisho-reganj
Netro-kona
Sunam-ganj
Deep Moderate
N 628 634 630 947 945 1892
Female-headed HHs (%) 14.5 17.4 12.1 14.4 14.9 14.6
Monthly PC Income (Taka)– Male HHH 866 911 c 676 843
c 788 816
Monthly PC Income (Taka) – Female HHH 718 b
732 660 692 723 707 c
Monthly PC Expenditures (Taka) – Male HHH 1242 1426 1488 1361 a
1411 1327
Monthly PC Expenditures (Taka) – Female HHH 1158 1344 1096 1123 1303 1395
Food consumption score – Male HHH 9.2 c 8.3
b 7.6 8.6
c 8.1 8.3
c
Food consumption score – Female HHH 8.4 7.7 7.2 7.7 7.9 7.8
Coping strategy index – Male HHH 24.2 22.6 24.3 23.9 c 23.5 24.2
Coping strategy index – Female HHH 26.3 c 22.4 25.8
c 25.7 23.6 23.5
Letters denote significant differences between gender of head of Household. Significance levels for comparisons: a = .10; b = .05; c = .00
FSUP-H Baseline Report, June 2010 92
14 CONCLUSION AND RECOMMENDATIONS The baseline study findings provide important information, which can be used by FSUP-H partners to
measure impact-level changes in food security and livelihood trends over time. An overview of the
relevant findings for the FSUP-H baseline logframe indicators that state baseline and endline surveys
as means of verification is provided in table 60 below, including recommendations to improve the
indicators.
Table 60: Baseline values and recommendations for FSUP-H logframe indicators
Objective indicators Relevant baseline finding Recommendation
Overall Objective: At least 40%
of the targeted 55,000 ultra-
poor women have graduated
out of extreme poverty
/ Index to be developed by CARE
Bangladesh using the livelihoods
and economic security baseline
values
Specific Objective: Prevalence
of chronic malnutrition among
women has decreased by <to
be determined by baseline> %
by 2013
Anthropometric findings for < 2
Stunting: moderate 34.7 / severe 22.4
Wasting: moderate 9.8 / severe 0.3
Underweight: moderate 39.4 / severe 18.3
There was no malnutrition
measurement for adult women.
Revise this indicator to < 2.
Specific Objective: At least 70%
of households reported at least
3 meals/day, including during
lean periods
The mean value for households that take 3
meals/day „most of the time‟ in the last 12
months is 14% („most of the time‟ and often
combined is 56.3 %)
If CARE takes the 14% as the
baseline value, then the 70%
target is likely too high.
Specific Objective: Reduced
asset loss due to improved
resilience to natural disasters
and shocks
The mean asset loss per disaster, among
households who experienced a disaster in the
last 12 months, was reported at around Taka
3,017.
CARE needs to determine a
realistic target based on intensity
of coverage by project activities
Indicator Result 1 Relevant baseline finding Recommendation
At least 70% of individuals are
able to negotiate access to
services with local government,
service providers and local
leaders (in the areas of health,
livestock, agriculture, fisheries,
social protection)
68.7% of households had accessed one or
more GoB service providers in the previous
year
This indicator should be
reformulated around actual
levels of access, as ability to
negotiate is hard to measure and
is a lower-level indicator. To
make the indicator more
meaningful, CARE could
consider reformulating the
indicator around particular
services (see table 40), which
are expected to be the focus of
project interventions.
At least 30% of women
participate in any of the
following: UP standing
committees, SMC, PTA and
local arbitration in project areas
Participation in the development process was
low at 4.5% of all households. The response
rate for this variable was too low for meaningful
analysis. Regarding participation of women: a
total of 186 responses were given from 174
households – 9.2% of respondents. Females
(spouses plus female heads of household)
accounted for 15.1% of 9.2%, which is about
1.6% of the overall population.
If CARE wants to keep this
indicator, we would suggest
stating the baseline value as 1%.
FSUP-H Baseline Report, June 2010 93
Indicators Result 2 Relevant baseline finding Recommendation
% of women have increased
income, particularly through
rural sales networks and
assemble markets
Income data was collected at the household
level. Per capita monthly income of female-
headed households is 707 Taka.
Reformulate this indicator to
capture per capita monthly
income of female-headed
households
At least 40% of women from
ultra-poor households reduced
debt from unsustainable
sources (particularly
moneylenders)
Almost all women (98%) had taken a loan from
the Grameen Bank, which reflects the
Grameen Bank‟s policy of lending to women.
The proportion of women who took a loan from
NGOs is also high (88%), for similar reasons.
The proportion of women taking loans from
moneylenders is the lowest among all loan
sources.
Perhaps this indicator could
better be formulated around
(female-headed) household debt
burden. If the indicator is not
changed, then CARE should
consider which loan sources
qualify as unsustainable.
% in productive utilization of
income (% of expenditure on
assets, % of expenditure on
food)
Overall, daily expenditure on food is 113 Taka.
Less than 1 in 10 households own productive
assets: an overview of household productive
asset ownership is provided in table 18.
It is recommended to split this
indicator into two separate ones:
productive asset ownership and
daily food expenditure.
Indicators Result 3 Relevant baseline finding Recommendation
No indicators measured by
baseline, as per FSUP-H
logframe
Based on the baseline findings, the following indicators are suggested for tracking
as proxies for household resilience to natural disasters and household crises:
Mean value of asset loss (baseline = 3,017 Taka)
Mean number of working days lost (baseline = 10 days)
Combination of coping strategies applied by households (baseline values =
see sections 10.1 and 10.2)
Indicators Result 4 Relevant baseline finding Recommendation
% of households reporting
increased food consumption
and improved dietary diversity
This survey utilized the Food Consumption
Score (FCS) to measure food consumption and
dietary diversity: 16.2% of households had
poor FCS, 31.5% had borderline FCS and
52.3% had acceptable FCS.
CARE needs to determine a
realistic target based on intensity
of coverage by project activities
Improved infant and young child
feeding practices in 80% of
VDCs (including exclusive
breastfeeding, early initiation of
breastfeeding, weaning)
Overall, 45.4% of mothers initiated
breastfeeding with the 1st hour.
26.9% of mothers started weaning between 0-3
months, 48.1% between 4-6 months, 21.4%
between 7-9 months and 3.6% after 10
months.
It is recommended to
reformulate this indicator as two
separate indicators around
breastfeeding and weaning.
% of household reporting
reduced prevalence of diarrhea
22.9% of household reported diarrhea in the
last 12 months
/
At least 80% of pregnant
women from ultra-poor
households received
appropriate supplements (i.e.
folic acid, iron and vitamin A)
from government health
services
Overall, 35.5% of mother‟s took iron or folic
acid supplements. No data was collected on
vitamin A intake of pregnant women, only for
children.
It is recommended to
reformulate the indicator around
iron and folic acid supplements.
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