Characteristics of shiree beneficiary households

Contents

Executive summary

Part A: Introduction

1. Background

1.1 shiree

1.2 shiree Partners

2. Methodology

2.1 Purpose and objectives

2.2 Design and methods

2.3 Analytical framework

2.4 Limitations

Part B: Measuring the baseline

3. The sample

4. Household demography

4.1 Age structure

4.2 Marital status and family size

4.3 Education

5. Occupation

5.1 Overall

5.2 Female heads

6 Living condition

6.1 House and construction materials

6.2 Floor space

6.3 Water and sanitation (source and ownership)

7 Asset ownership

7.1 Landownership

7.2 Non land assets

8 Financial status

8.1 Loans and saving

8.2 Expenditure (NGOs, age groups, education, regions)

8.3 Income (NGOs, age groups, education, region ns)

9 Food security

10 Women’s empowerment

10.1 Asset ownership

10.2 Income

10.3 Mobility

11 Conclusion

Glossary

AAB ActionAid Bangladesh

AC Aid Comilla

BBS Bangladesh Bureau of Statistics

CNRS Centre for Natural Resources Studies

DSK Dustho Shashtho Kendra

GH Green Hill

IF Innovation fund (shiree)

IC- InterCoperation

MJSKS Mahideb Jubo Sonaj Kallyan Somity

NDP National Development Programme

PAB Practical Action Bangladesh

PUAMDO Panchbibi Upazila Adibashi Multipurpose Development Organization

SF Scale fund (shiree)

SCUK Save the Children UK

Executive summery

Background: The present baseline report (CMS1) is based on the BHHs of 17 shiree partners six of

whom have received support from the scale fund, and the innovation fund supports 11 NGOs,

all of whom were selected in different rounds of competitive processes. Overarching

consideration given to the interventions’ appropriateness for supporting the extreme poor to lift

themselves out of extreme poverty; and this is reflected in the geographic concentration of

shiree partners.

Scale fund partners include: DSK in Dhaka, Care Bangladesh and PAB in north, NETZ in northwest,

Save the Children UK and Uttaran in southwest. Innovation fund round one: Aid Comilla in Feni,

CNRS and InterCooperation in Sunamgonj, Green Hill in CHT, Shushilan in southwest, Innovation

round two: ActionAid Bangladesh in Nilphamary. InterCoperation in Rangpur, MJSKS in Kurigram,

NDP in Bogra, PUAMDO in Joypurhat, and SKS Foundation in Gaibandha.

Methodology: the Objective to provide a description of the pre-project (baseline) conditions of

BHHs for programme assessment at the end of the project for each NGO. Data is used from a

total of 73,492 BHHs: for scale NGOs the number stands at 64,378, for innovation round one 4,623

and for innovation round two 4,501. A structured format is used for collecting data from the BHHs

household demography, living condition including sanitation, landownership, loans and savings,

land and assets, income, expenditure, food security, women’s status. Data was collected and

computerized by NGOs with full time or contractual staff using shiree designed software. The

data is managed and computed by shiree MIS.

Limitations: the data – particularly the financial, might contain seasonal variations, differences

within the rural areas due to the difference in the time when the NGOs collected the data

between early 2010 and mid 2011, also due to various other factors such as geo-physical,

infrastructure and local economic factors, and different types and intensity of natural hazards

may also cause differences within the rural context.

Household Demography: Under-five year olds account for 11.4% of shiree BHHs compared with

10.3% nationally; the oldest group of 60+ years stand at 4.8%. Women in the 45+ age groups in

BHHs live longer at 19.8% compared with men at 18.0%. in urban context men live longer than

women. The average age of BHH heads stands at 43.3 years (SD= 14.4), the difference in the

mean age for the female heads at 47.4 years is significantly larger than the men’s at 41.4 years.

The oldest female heads are in their 50s and found primarily in the north while the youngest are

found in urban DSK at 41.2 years. 73.6% of all shiree heads is married with 17.7% reporting to be

widow or widower and the divorced or abandoned stands at 7.1% while 54.5% and 21.5% of

female heads are in the latter two categories. Overall, 31% of shiree BHHs are headed by

females with 30 % in rural and 40% in urban areas.

Family size: The overall mean size of shiree BHHs is 3.32 while those in urban area were slightly

larger at 3.80 compared with in rural areas at 3.27. The female headed households in the rural

areas are the smallest at 2.09. Female headed households constitute 31.3% of shiree households

with wide inter NGO variation.

Education: Almost half of family members aged 7 year or more have never attended school

while more than three-quarters of the household heads with more of the female heads (87.7%

compared with 74.1% of male heads) never received any schooling. The rural-urban differences

are minimal for the sexes.

Occupation: The major occupations reported after disaggregation by sex shows that for males’

laboring occupation is reported at 62% followed by rickshaw and boat pulling at 14.7%, petty

trade and business at 7.9% and fish catching or farming at 3.7%. For female heads major

occupations include laboring at 35.4%, domestic work 29.7% and begging 14.3%. The latter

occupations are more pronounced in the northern areas compared with southwestern. Petty

trading is highest among the women in GH (at 39.8%).

Living condition: Overall 58.1% of rural BHHs do not own the land on which they reside, just under

a quarter of the BHHs live on their own land and the rest live on other peoples’ land. In contrast,

overwhelming majority of shiree BHHs own their dwelling structures (79.3%). The materials that are

reported to be used for the walls are of the lowest quality for more than four-fifth of the

households with slight male female difference standing respectively at 81.2% and 78.6%. The

single most frequently used material is grass/jute stick/plastic etc, standing at 43.2% and 41.6%

respectively. In the rural areas, 70.8% and 60.2% BHHs have (males and females respectively)

used CI sheets in the roofs. Overall the mean size of houses where BHHs live is 129.5 sqft (SD=

65.9). The house size for female heads is smaller than their male counterparts respectively at

110.8 sqft (SDS = 60.3) and 137.0 sqft (SD=66.6). The smallest house sizes are found in the urban

location of DSK (73.1 sqft) and NETZ (80.8 sqft). The overall mean per capita floor space is 48.8

sqft (SD= 36.7) per person that is slightly larger in the rural areas at 51.5 sqft. The female headed

households in the rural context are better off with more floor space per person (66.7 sqft)

compared with the males (41.6 sqft). Use of safe drinking water source is universal for the shiree

BHHs (more than 80%) with only 21.2% and 20% respectively reported to self-own or share-own

hand tube wells. The ownership status appear to be somewhat influenced by the local

hydrological conditions.

Asset Ownership: Vast majority of BHHs do not own any cultivable land, standing at 94.6% and

89.6% respectively for male and female heads. Major non-land items owned by the BHHs include

‘other household items’ such as pots and pans, water container, crockery etc (97.1%), bed

including those made of bamboo (76.9%), agricultural implements including spade, shovel etc

(52.6%). Very few of the BHHs own any type or number of animals regardless of sex of family

head or geographic location while larger proportions of females do not own any work related

assets (56.6% and 99.3% respectively for rural and urban areas) and household items (17.1% and

7.2% respectively) compared with males (respectively for rural and urban 31.6% and 98.3% for

work equipment while for HH items 8.4% and 2.7%).

Loans: Up to nearly one-half of the female headed and 43% of their male counterparts reported

to have outstanding loans. For females the higher frequency sources include informal loans

without interest at 48.8% while 34.1% is indebted to with interest informal source such as money

lenders. Males more frequently borrow from the latter (42.7%) followed by with interest formal

sources such as MFIs and banks (26.1% compared with 6.9% female heads).

Expenditure: The overall mean monthly household expenditure stands at Tk. 1,377 (SD = 898) and

per capita per day at Tk. 15.0 (SD = 9.5), while for rural BHHs the monthly figure stand at Tk.1,178

and Tk. 3,278 for urban (the respective per capita levels are TK. 13.1 and TK. 32.5). There is highly

significant difference between the rural male heads at Tk. 1,337 and female heads Tk. 811. In the

urban context (DSK) the levels are much higher at Tk. 3,280 (SD= 1238) for the overall urban with

significant difference between the males (at Tk. 3,620) and female heads (Tk. 2,788). Highest

levels of expenditure are found in three Innovation round NGOs: Green Hill (at Tk. 1,576) and the

other two being in haor area (CNRS at Tk. 1,542 and InterCooperation at Tk. 1,429). The lowest

end of the spectrum includes Aid Comilla at TK. 782, IC-monga round at TK. 791, NDP at TK. 893,

and NETZ at Tk. 947. Age of the heads is a significant factor in determining the overall with the 30

to 49 age groups registering highest levels of expenditure (Tk. 1,539 and Tk. 1,507). Regional

disaggregation produce some surprising but significant results with highest expenditure observed

in the rural area is in haor (Tk. 1,478) followed by north (Tk.1,227). According to per capita

expenditure levels 96.8% of rural and 52.9% fall below the revised poverty threshold with 2009

prices (respectively Tk. 26 and Tk. 30).

Income: The overall mean monthly income is Tk.1281 (SD= 706) with rural-urban standing at Tk.

1151 and TK.2488 respectively and the male-female at TK. 1416 and TK. 961. NGO specific

highest income levels are observed for DSK (Tk. 2488) followed by four innovation round NGOs:

Shushilan (TK.1,560), GH (TK.1,480), CNRS (TK.1,469) and SKS (Tk. 1,255). The lowest income levels

also correspond with lower expenditures with Aid Comilla being the lowest (at TK.681) followed

by IC-monga (TK.767), NPD (TK.831), MJSKS (TK.923) and NETZ (TK.939). The per capita income

levels are for female heads across all NGOs with the overall standing at Tk. 16 and Tk. 13.2 for

males. Although the in-kind income as proportion of the total income is not very large it is

present across the rural NGOs in different degrees: the overall mean is 10.6% (SD= 23.8) with the

largest for the innovation round NGOs of SKS (34.8%) and Aid Comilla (34.5%) while it is smallest

among GH and Puamdo (respectively at 3.6%, and 1.8%). As observed for expenditure, income

is also comparatively higher for the age groups 30-39 years and 40-49 years respectively Tk. 1590

and Tk. 1557 with the more than 60 years group earning Tk. 1105. In the rural areas the income

levels are highest for the under-29 (Tk. 924) and Tk. 976 for the 30-39 year olds, falling to the

lowest of Tk. 579 for the over-60s. for rural males, income is highest for the age group of 40-49 at

Tk. 1361 and is lowest for the oldest at Tk. 1124 and the youngest at Tk.1204. In the urban context,

it is highest for the male 40-49 group at Tk. 2960 followed by the 50-59 group earning Tk.2900

while the females in the 30-39 group earn highest at Tk. 2109 followed by 40-49 group with Tk.

2021. As for education of household heads, the highest income is observed for those with 10

years or more schooling (Tk. 1548) and lowest (Tk. 1245) for those without any schooling. In the

rural areas and for both females and males, there is no clear pattern. In the urban area there is

no pattern for the female but for the males it is consistent with higher education levels. The

pattern of regional monthly income distribution is different from that observed for expenditure,

as the income in southwest (Tk.1,269) is found to be higher than in the north (Tk. 1,121). Other

differences follow the earlier pattern with it being highest in haor (Tk.1,309).

Income and expenditure difference: On average, the deficit of income over expenditure

balance stands at Tk.167 per month per households with the urban households are in larger

deficits (Tk. 820) compared with the rural (Tk. 89). The female headed households are more likely

to be in deficit (Tk. 174) than their male counterparts (Tk. 163).

Food security: Never (zero month in a year) able to take three meals a day without any difficulty

was reported by 78.7% while on the other end of the scale only 18.2% never taken one meal a

day in the previous year. Ability to take ‘mostly two meals/day’ was reported by 84.1% of the

BHHs. Food insecurity in terms of ability to take three meals a day, appears to be a major

characteristic of shiree BHHs.

Ownership of assets by women: More women from male headed families own jewelry (40.1%)

and poultry (20.5%) compared with those who head their own families respectively 20.1% and

16.3%. The ownership of land/house is reported by very few women from male headed

households (6.8%) while 35.6% of female heads report owning land/house.

Women’s earnings: vast majority of female heads (84.5%) report have own earnings while it is

only 38.2% for women in male headed families. As for control over their income 86.6% of the

former and 19.2% of latter report positively. Partial control is reported by 11 % of the former and

the latter’s 64%.

Women’s mobility: Female heads are more mobile for shopping (between 25.9% and 40.4% of

them respectively visit shops less than once a month or more frequently compared with

between 22.2% and 14.1% for the others). Visit to Union Parishad offices by the female heads is

more frequent (21.9% and 6.4%) compared with women from male led families (11.6% and 2.1%).

Visits to hospital is near identically distributed among both groups of women (around 30% from

both groups never visit, less than once a month, more than once a month and the non-

responses).

The concept of a Challenge Fund is relatively new to

Bangladesh. Harewelle International Ltd and PMTC

Bangladesh Ltd manage the fund in consultation with

EEP/shiree consortium partners including the Centre for

Development Studies (CDS) at Bath University, the British

Council and Unnayan Shamannay. The Management team is

responsible for monitoring and evaluating progress of all

funded projects. It gives priority to lesson-learning,

communications and experience-sharing across the

Government of Bangladesh portfolio for the extreme poor, and

with other development programmes. In addition, it will jointly

develop a framework to ensure that a multi-dimensional

understanding of extreme poverty is fully developed both in the

Challenge Fund itself and externally.

Part A: Introduction

1. Background

1.1 shiree

The problems of poverty in Bangladesh, though improved, are far from being solved.

Bangladesh is still one of the poorest countries in the world, and there is widespread poverty and

hunger at the national and regional levels. Achievement of the Millennium Development Goals

for Bangladesh needs specific initiatives to eradicate extreme poverty. The Economic

Empowerment of the Poorest Programme(EEP)/shiree is one of such initiatives. The name shiree

– the Bangla word for steps and an acronym for "Stimulating Household Improvements Resulting

in Economic Empowerment" – reflects the core approach of the programme which is to provide

households with the support needed to start and to continue climbing out of extreme poverty.

EEP/shiree programme is a partnership between the UK Department for International

Development (DFID) and the Government of Bangladesh (GoB). It is a £65 million challenge fund

designed to channel DFID funding to the NGO sector in Bangladesh, lift one million people out

of poverty, and help the government of Bangladesh achieve Millennium Development Goals 1

and 2 by 2015.

As a challenge fund shiree is responsible for

the disbursement of considerable amounts

of money through the allocation of

competitive grants. Currently it has 36

partner NGOs. The partnership

encompasses specific economic

empowerment sub projects under Scale

and Innovation Funds but also a growing

research and advocacy agenda.

The Scale Fund supports large projects

which apply tested and proven

approaches by generating assets,

improving incomes, decreasing

dependency and vulnerability, increasing

food security and providing sustainable

pathways out of extreme poverty. The Innovation Fund challenges NGOs to design and

implement innovative approaches to reducing extreme poverty in urban and rural areas of

Bangladesh.

Extreme poverty is a complex and dynamic phenomenon in which numerous social, cultural and

health factors influence a household’s ability to lift itself out of poverty or to sustain positive gains.

There are varying definitions of extreme poverty. EEP/shiree beneficiary households fall well

within the poorest 10% of the Bangladeshi population and identify people whose average per

capita expenditure for 2007 is below Tk. 22 per day, depending on the region as extreme poor.

EEP/shiree’s through its scale and innovation funds want to achieve four specific outputs. The

objectives are: i): Proven approaches to improving the livelihoods of the extreme poor taken to

scale; ii) Innovative approaches to improve the livelihoods of the extreme poor tested, evaluated

and successes ready for scaling up; iii) Increasing consistency in the understanding, sharing and

application of approaches to addressing extreme poverty and iv) Policy and practice at local

and national levels shows increasing recognition of the needs of the extreme poor.

Given the combination of delivery, innovation, learning and policy advocacy objectives

across the programme shiree has developed a comprehensive Change Monitoring System

(CMS). The change monitoring system includes five elements. CMS1: Household baseline

profile is the foundational element of the system. It provides a detailed assessment of the

status of all shiree households before significant project interventions have taken place and

provide the baseline from which to monitor change over time. The household profile is

completed by NGO field staff through a once‐only interview and acts as a baseline for

evaluation of project impact. A database of profiles for all shiree beneficiary households

(Scale and Innovation Fund) is maintained, each household has a unique identification

number allowing linkage to other CMS tools.

The present CMS1 report provides

baseline information of beneficiary

households of 17 shiree partners six

of whom have received support

from the scale fund, and the

innovation fund supports 11 NGOs,

all of whom were selected in

different rounds of competitive

processes. The partners of the first

round of innovation were selected

specifically for hard to reach

geographic areas such as haor,

Chittagong Hill Tract, and the

coastal belt. The second innovation round focused on interventions to mitigate the effect of

monga (or seasonal high unemployment in northern districts).

Overarching consideration given to the interventions’ appropriateness for supporting the

extreme poor to lift themselves out of extreme poverty; and this is reflected in the geographic

concentration of shiree partners. Nine out of the 17 partners are located in the northern part of

the country or the old Rajshahi division where the extent of poverty is second highest in the

country with 35.7% and 21.6% of the population respectively living below the upper and lower

poverty lines (with the national averages being 31.5% and 17.6%)1. Three NGOs operate in

Khulna division where poverty counts respectively stand at 31.2% and 15.2% (however, there are

pockets where poverty is highly concentrated).

In the comparatively least poverty prone division of Chittagong two NGOs operate while two

others are in the second least poverty stricken division of Sylhet. In the former one is in the highly

vulnerable coast of Feni district and the other is in Bandorban working exclusively with ethnic

minorities. In the latter division both the NGOs operate in haor areas. One partner works with

bottom 10% of the slum population in Dhaka city.

1 BBS (2011), Preliminary report of the 2010 household income and expenditure survey, Dhaka

Other CMS tools CMS2: Monthly Snapshot: enable an assessment of trends: what has changed at the household level? And what has happened (both project and non project events) that may have contributed to changes? CMS3: Socio‐economic and Anthropometric Surveys: provide in depth

socio-economic and nutritional data allowing an assessment of longer term change and the impact of project interventions. CMS4: Participatory review and project analysis: provide a forum for beneficiaries to explain changes in their lives and the reasons for these changes, as well as creating a platform for Innovation Fund NGOs to adapt and improve their innovations according to the needs of beneficiaries. CMS5: Tracking studies: provide quality longitudinal tracking studies documenting the dynamics of extreme poverty as it is experienced and changes in beneficiaries’ lives as a result of project interventions.

1.2 Shiree Partners

Scale fund partners

Care Bangladesh: nurtures skill for non-farm based income (such as honey, embroidered quilts)

and employment, improves access to common resources and promotes asset building, through

a community led approach, in Rangpur, Nilfamari, Lalmonirhaat and Gaibandha districts.

Dustho Shashtho Kendra (DSK): provides assets and cash stipends to start small businesses for

mainly women and some men in the slums of Dhaka City (Karail and Kamrangirchar).

NETZ: distributes primarily cattle for beef among ethnic minorities of northwest, creates market

linkages and improves access to safety nets, in Rajshahi, Naogaon, Dinajpur and

Chapainawabgonj districts.

Practical Action Bangladesh (PAB): uses sand-bars, under-utilised barren land and water

resources as well as non-farm micro-enterprises to develop diversified skills and creates market

linkages to generate income in riverine areas for river bank erosion victims in the north, in

Rangpur, Nilfamari, Lalmonirhaat and Gaibandha districts.

Save the Children UK (SCUK): diversifies livelihood options through asset transfers, skill

development, cash stipends and creates market linkages in disaster-prone areas and secures

safety nets in Khulna and Bagerhat districts.

Uttaran: transfers khas land to extreme poor households and provide support to develop

alternative livelihood options through assets and skills, in Satkhira and Khulna.

Innovation fund –round one (hard to reach locations)

Aid Comilla (AC): provides heifers to beneficiaries who transfer the first off-spring to second

batch of beneficiaries in cyclone prone Feni district.

Centre for Natural Resources Studies (CNRS): distributes khas Kanda land and provides inputs

and technical support for cultivating the virgin land, in haor areas of Sunamgonj.

Green Hill (GH): creates economic opportunities for hill people through conditional cash

transfers and skill training to cultivate cash crops, in Bandorban district in Chittagong Hill Tract.

InterCoperation (IC-1): provides technology transfers for production of early harvest rice variety,

cage culture of fish and floating vegetable gardens, in haor of Sunamgonj district.

Shushilan: introducing floating vegetable farming and crab fattening technologies in climate

vulnerable areas, in Jessore, Satkhira and Barguna districts.

Innovation round two (monga mitigation)

ActionAid Bangladesh (AAB): provides technical training and supervision for round the year

cultivation of vegetables and other crops on leased land by pairs of women to deleop bio-

diversity centres, in Nilfamary district.

InterCoperation (IC-2): combats monga through cow rearing and bio-gas technology, in

Rangpur district.

Mahideb Jubo Sonaj Kallyan Somity (MJSKS): reduces seasonal food insecurity through the

introduction of artificially inseminated heifers to produce milk during the lean employment

season or monga, reared by women in Kurigram district.

National development Programme (NDP): improves nutritional in-take of beneficiary households

through diversified vegetable farming on rented land, in Bogra district.

Panchbibi Upazila Adibashi Multipurpose Development Organization (PUAMDO):reduces impact

of monga by providing access to land (released from money lenders) and for Hog rearing to

female headed households from ethnic minority community, in Joypurhat district.

SKS Foundation: promotes strawberry and high value fruit cultivation on leased land for

generating alternative income sources, in Gaibandha.

2. Methodology

2.1 Purpose and objective of the report

Purpose: to conduct outcome assessment of the partner NGO programmes at the end of their

respective project period, which are vastly different from each other in a number of respects

such as locations, within the rural areas geographic differences, different interventions, number

of shiree Beneficiary Households (BHHs).

Objective: The data was collected for each of the shiree beneficiary households to provide a

description of the pre-project (baseline) socio economic conditions of BHHs to establish the

benchmarks for programme assessment for each NGO.

2.2 Design and methods

Design

For the present report data from a total of 73,722 BHHs who were recruited in the first two years

in the six scale fund NGOs and the 11 innovation fund NGOs recruited them in the first year.

Although the different NGOs have specific number of targets to recruit BHHs the present report is

based on those for whom data is available with shiree MIS. The NGO specific targets and the

CMS-1 database BHHs numbers are presented in annex table A1. The distribution of the BHHs

according to the funds is as follows: for scale NGOs the number stands at 64,378, for innovation

round one 4,623 and for innovation round two (monga mitigation) 4,501.

Methods

A structured format for collecting baseline data from the BHHs was initially developed by shiree

and then refined in consultation with partner NGOs. This format resembles the questionnaire

used for the CMS-3 survey in many respects, and the outcome of pre-testing of the CMS-3 tool

was used to refine the CMS-1 format. It contains sections on: targeting criteria, household

demography, living condition including sanitation, landownership, loans and savings, assets,

income, expenditure, food security, women’s status.

Shiree provided orientation to partner staff on the administration of the questionnaire, who

conducted data collection from each beneficiary before thay received any tangible benefit

from the NGOs. NGO staff –either regular or contractual, entered the data in database created

by shiree MIS and is managed at shiree MIS.

shiree MIS conducted data cleaning to weed out problematic data, Using standardised

methods such as consistency checks and other database procedures.

2.3 Limitations

It is to be noted that the data for the present baseline were collected by the NGOs at different

times: SCF collected 128 BHHs data in March 2008 and the remaining in 2010, Uttaran started in

December 2009 while the rest of the NGOs started in early 2010 and all completed in mid 2011.

Therefore, the data might contain seasonal variations particularly related with economic

activities in the rural context where agriculture is the single largest employment sector.

There is also likely to be some differences within the rural areas due to geo-physical,

infrastructure and local economic factors. Different types and intensity of natural hazards may

also cause differences within the rural context.

The 17 NGOs have different number of beneficiaries and have used different targeting indicators

such as income/expenditure levels, length of food insecurity, place of residence etc to reach

the bottom 10% of the local households.

Part B: Measuring the baseline

3. Characteristics of shiree beneficiary households

Table 3.1: Distribution of sample according to NGOs and selected characteristics

NGO

Sample

HHs

Household members Religion HHs with

disabled

member Male Female Total Muslim Minority Total

N % N % N % N % N % N % N % N %

Care 20219 27.4 31635 47.8 34613 52.2 66248 100 16203 80.1 4018 19.9 20219 100 1417 2.1

DSK 7000 9.5 12215 45.7 14539 54.3 26754 100 6971 99.6 29 0.4 7000 100 540 2.0

NETZ 3042 4.1 4412 46.0 5173 54.0 9585 100 948 31.2 2094 68.8 3042 100 174 1.8

PAB 14882 20.2 25824 49.2 26686 50.8 52510 100 1391 93.5 971 6.5 14882 100 720 1.4

SCF-UK 9874 13.4 12770 43.0 16894 57.0 29664 100 7945 80.5 1920 19.5 9874 100 1161 3.9

Uttaran 9581 13.0 14478 46.6 16617 53.4 31095 100 7300 76.2 2281 23.8 9581 100 580 1.9

Aid

Comilla 737 1.0 850 41.8 1183 58.2 2033 100 736 99.9 1 0.1 737 100 49 2.4

CNRS 755 1.0 1464 47.8 1596 52.2 3060 100 593 78.5 162 21.5 755 100 33 1.1

Green

Hill 1189 1.6 2307 50.0 2309 50.0 4616 100 25 2.1 1164 97.9 1189 100 73 1.6

IC-1 1000 1.4 2216 48.2 2317 51.8 4593 100 824 82.4 176 17.6 1000 100 85 1.9

Shushilan 942 1.3 977 45.7 1163 54.3 2140 100 726 77.1 216 22.9 942 100 37 1.7

Action

Aid 1200 1.6 1491 43.6 1929 56.4 3420 100 946 78.8 254 21.2 1200 100 69 2.0

IC-2 460 .6 624 45.1 759 54.9 1383 100 387 84.1 73 15.9 460 100 24 1.7

MJSK 636 .9 794 45.5 951 54.5 1745 100 552 86.8 84 13.2 636 100 86 4.9

NDP 885 1.2 1077 41.0 1551 59.0 2628 100 860 97.2 25 2.8 885 100 56 2.1

Puamdo 333 .5 442 45.1 538 54.9 980 100 0 0.0 333 100 333 100 24 2.4

SKS 987 1.3 1163 41.7 1629 58.3 2792 100 867 87.8 120 12.2 987 100 65 2.3

Total 73722 100.0 114739 46.8 130507 53.2 245246 100 89794 81.1 13928 19.9 73722 100 5193 2.1

In the total 73,722 households for which data is available for the present report the total number

of family members are 245,246 of which the number of females is 130,507 or 53.2% of the total

(table 3.1 above) or the male to female ratio is 87.92 males for every 100 females (conversely,

113.74 females for every 100 males). In terms of religion, the non-Muslims make up 19.9% of shiree

beneficiaries with highest concentrations in PUAMDO (100%), Green Hill (97.9%) and NETZ (68.8%),

all of which focus on respective ethnic minorities (respectively on Santals, hill people and Oraos).

Bangalee non-Muslims are present in Uttaran (23.8%), Shushilan (22.9%) and SCF (19.5%) all of

which are in the southaest of the country.

The presence of any disabled person has been reported by 2.1% households with the highest

4.9% in MJSKS and 3.9% in SCF.

4. Household Demography

4.1 Age structure

Age of household members

The age structure of all members of shiree BHHs does not appear to be of pyramid shape (chart

1.1) and there also appears some similarity with the national population distribution. The similarity

that is observable between the shiree households and the BBS data are in the youngest and

oldest age groups. Accounting for 11.4% (table 4.1) shiree population is the under five year age

group that is close to the national proportion of 10.3% (BBS, 2011) while at the oldest group these

are identical at 4.8%. Women in BHHs appear to live longer as the proportions of women in the

45+ age groups are larger at 19.8% compared with men at 18.0% (χ2 =1813.7; p<0.001 ).

Table 4.1: Distribution of household population according to age groups and sex

Age Groups

(years)

Sex

Both

N (%)

Male

N (%)

Female

N (%)

<5 14343 (12.5) 13572 (10.4) 27958 (11.4)

5-14 33505 (29.2) 31321 (24.0) 64745 (26.4)

15-24 12966 (11.3) 18009 (13.8) 30901 (12.6)

25-34 17556(15.3) 24404 (18.7) 41937 (17.1)

35-44 15949 (13.9) 17226 (13.2) 33108 (13.5)

45-54 9409 (8.2) 11484 (8.8) 20846 (8.5)

55-64 5737 (5.0) 8091 (6.2) 13734 (5.6)

65+ 5508 (4.8) 6164 (4.8) 11772 (4.8)

All 114743 (100) 130503 (100) 245246 (100)

F=1814; p<0.001

Chart 1.1: Age structure for shiree beneficiary household population

Rural-urban differences in age structure

Chart 1.2: Urban household age structure

There are more older males in urban shiree BHHs compared with females in most age groups

except for the productive age groups in the 15-44 range (33.4% and 46.5% respectively as shown

in chart 1.2). The gap in the productive age group is reduced in the rural areas but women

outnumber the men and they are older (20.6% for women and 18.1% for men in the 45+ age

group in chart 1.3).

According to the HIES data the proportion of women is lower than men in both the locations for

the age group of 50+ (15.5% and 14.2% for men respectively in rural and urban areas compared

12.5

29.2

11.3

15.3

13.9

8.2

5.0

4.8

10.4

24.0

13.8

18.7

13.2

8.8

6.2

4.8

40.0 30.0 20.0 10.0 0.0 10.0 20.0 30.0

Female

Male

15.6

35.2

8.4

10.4

14.6

7.8

4.3

3.7

13.0

28.3

10.7

20.1

13.7

7.0

4.3

2.8

40.0 30.0 20.0 10.0 0.0 10.0 20.0 30.0 40.0

FemaleMale

with 13.4% and 11.8% for women in urban). The average age of shiree BHH heads may explain

some of this.

Chart 1.3: Rural household age structure

Age of household heads

Overall, the average age of the heads of BHHs stands at 43.3 years (SD= 14.4) as shown in table

4.2 with significant variation among the 17 partner NGOs (F= 57.59; p<0.001). The older heads are

found in MISKS (50.4 years), NDP (48.5), SKS (47.7) and ActionAid Bangladesh -AAB (46.2) all in the

north of the country followed by SCUK (45.3), Aid Comilla -AC (44.5) both in the southern coastal

belt, Inter Cooperation –monga round or IC-2 (44.4), Care (43.3), and the rest are below the

overall average with the youngest being in Inter Cooperation – haor or IC-1 (40.2).

Multiple comparisons show that the mean age in these NGOs are significantly higher than most:

(i) MJSKS is significantly (p<0.001) different from all NGOs except for NDP and SKS; (ii) NDP is not

significant against AAB and SKS; (iii) SKS is not with AAB; (iv) AAB is not significant from SCF, AC,

IC-2, Puamdo; and (v) SCF is not from IC-2 and AC. On the other end of the spectrum, the

youngest household heads in IC-1 (40.2 years) are significantly so compared with all NGOs

except CNRS, GH and Puamdo, while the second youngest GH (40.9) is not significant from DSK,

NETZ, CNRS, Shushilan, and Puamdo.

The overall average age as well as the distribution by NGOs hides some significant differences

according to sex of HH head and the NGOs (table-3.2 below). The difference in the mean age

for the female heads at 47.4 years) is significantly larger than the men’s at 41.5 years (F= 242.2;

p<0.001). The differences in the mean age according to sex of HH head and the interaction

factor of sex and NGOs are found to be statistically significant2 (respectively, F= 9.49; p= 0.007

and F= 100.51; p<0.001).

The oldest female heads who are in their 50s are found in MJSKS (57.9 years), NDP (51.9), Care

(51.3) and PAB (50.7) followed by AAB (49.8), IC-monga (48.8 and SCUK (47.5). Except for Care

and PAB the other four NGOs are innovations for the monga mitigation in which selection of

2 Using GLM method (Type 1).

12.1

28.5

11.6

15.9

13.8

8.2

5.0

4.9

10.0

23.5

14.2

18.6

13.1

9.1

6.4

5.1

40.0 30.0 20.0 10.0 0.0 10.0 20.0 30.0

FemaleMale

BHHs might have been influenced by their vulnerability (due to their old age) during the lean

employment season. In the cases of Care and PAB the presence of relatively younger male

heads possibly suggest some form of balancing between the physically able and not for

undertaking intervention packages, or regional characteristic of large number of older female

heads who are extremely poor or both.

The youngest female heads are found in DSK or urban location (41.2), IC-haor (41.3) and Uttaran

(42.5) where the male heads are also relatively (to the overall mean age) younger (Table-2). In

the latter two younger heads are selected because their approach involves cultivation of crops

for which able body is likely to be an undeclared selection issue.

Table 4.2: Distribution of mean age (years) of household heads according to NGOs and sex

NGO Male Female Both

Mean SD Mean SD Mean SD

Care 40.6 14.0 51.4 14.1 43.3 14.8

DSK 42.1 12.5 41.2 13.1 41.7 12. 8

NETZ 39.6 13.4 47.1 12.7 41.9 13.6

PAB 40.7 13.5 50.7 14.2 42.8 14.2

SCF-UK 43.9 15.5 47.5 15.7 45.4 15.7

Uttaran 42.5 14.1 42.5 13.5 42.5 13.9

Aid Comilla 40.9 14.6 46.6 17.2 44.5 16.5

CNRS 39.6 12.7 46.7 14.4 41.1 13.4

Green Hill 40.1 12.1 45.5 12.6 41.1 12.4

IC-1 39.9 11.4 41.3 11.6 40.2 11.4

Shushilan 41.7 12.8 45.9 12.6 42.5 12.9

Action Aid 44.6 15.4 49.8 14.4 46.8 15.2

IC-2 41.9 14.4 48.8 12.3 44.4 14.1

MJSK 44.9 15.0 57.9 16.0 50.4 16.7

NDP 45.3 14.4 51.9 12.8 48.5 14.1

Puamdo 40.1 11.1 46.6 9.9 42.2 11.1

SKS 44.3 14.1 51.3 11.8 47.7 13.5

Total 41.5 13.9 47.4 14.6 43.3 14.4

4.2 Marital status and family size

This section reports on the data on the marital status of the BHH heads, and the family sizes.

Marital status

Overall 73.6% of all shiree heads is married with 17.7% reporting to be widow or widower and the

proportion of the heads whose marriages have been dissolved through divorce or

abandonment stands at 7.1% (table 4.3 below, statistically highly significant). Both of the latter

figures increase sharply when sex of the heads are disaggregated respectively to 54.5% and

21.5% for female headed households. The proportion of male heads in these two categories is

negligible. The female heads who were married at the time of data collection are likely to be in

this situation (the de-facto heads) due to old age/infirmity, illness or disability of their husbands

living with the women. The Life Histories that were conducted on some BHHs at all six of the first

Scale Fund NGOs by the Research Officers of shiree partners, suggest that the inability of the

husbands to earn an income due to these factors is a major cause, among others, of the

households’ ‘descent’ in to extreme poverty.

Table 4.3: Percentage distribution of household heads according to sex and marital status

Household Head Single Married

Widow/

widower

Divorced/

abandoned Total

Male heads 1.7 96.7 1.1 0.5 100

Female heads 1.4 22.6 54.5 21.5 100

Both 1.6 73.6 17.7 7.1 100

Χ2 =47022; p<0.001

Female heads

Overall, 31% of shiree beneficiary households are headed by females with no discernible

regional/geographic pattern/concentration although there large differences among the NGOs

(table 4.4 below). There are differences within geographic locations such as: in the north there

are fewer female heads in Care (25.1%) and PAB (21.5%) compared with MJSKS (42.1%) and SKS

(48.8%); while in the southwest more female heads in SCF (41.1%) and Uttaran (37.3%) than

Shushilon (17.8%).

Table 4.4: Distribution of female headed households according to NGOs

NGO Number %

CARE 5079 25.1

DSK 2875 41.1

NETZ 912 30.0

PAB 3209 21.6

SCF 4154 42.1

UTTARAN 3615 37.7

Aid Comilla 459 62.3

CNRS 167 22.1

Green Hill 226 19.0

IC (Round-1) 207 20.7

SHUSHILAN 168 17.8

ActionAid 518 43.1

IC (Round-2) 168 36.5

MJSKS 268 42.1

NDP 426 48.1

PUAMDO 108 32.4

SKS 484 49.0

Total 23043 31.3

However, the vastly different approaches3 to reduction of extreme poverty that have been

adopted by the shiree partners may explain the variations from the mean. The targeting criteria

and indicators and the methods used to identify and select the beneficiaries may also have

influenced the presence of female headed households in each of the partners. The sex of the

beneficiaries – where the women have been targeted to receive the benefits, the proportion of

females heads are larger than overall mean.

There are some pronounced regional difference in the distribution of sex of household heads

(table 4.5 below) with the highest proportion in the urban (41.1% in Dhaka) location followed by

southwest (38.9%) and northwest (30.2%) while the ‘other’ category representing the second

highest concentration (39.5%).

Table 4.5: Regional difference in sex of household head

Region Male head Female head Both

N % N % N %

North 28659 74.7 9725 25.3 38384 100

Northwest 2355 69.8 1020 30.2 3375 100

Southwest 12460 61.1 7937 38.9 20397 100

Haor 1381 78.7 374 21.3 1755 100

Urban 4125 58.9 2875 41.1 7000 100

Others 1700 60.5 1111 39.5 2811 100

Total 50680 68.7 23042 31.3 73722 100

(χ2 =1668.57; p<0.000 )

Family size

The family size of shiree beneficiary households (BHHs) which was expected to be smaller than

the because of their poverty condition is in fact much smaller than the national averages that

have declined compared with 20054 (overall and disaggregated by locations) as shown in table

4.6 below. The overall mean size of shiree BHHs is 3.32 (SD= 1.55) while those in urban area were

slightly larger at 3.80 (SD= 1.60) compared with in rural areas at 3.27 (SD= 1.53)5. When the effect

of location is tested after controlling for the sex of household heads it produces insignificant

results.

On the other hand, the male-female comparison of mean family sizes produces highly significant

differences independently within the rural (F= 221; p<0.001) and urban (F=20.16; p<0.001) areas

with the female headed households being much smaller than their male counterparts. The

female headed households in the rural areas are the smallest of the four disaggregated means,

at 2.09.

Table 4.6: Average household size

Source All Urban Rural

Male Female Both Male Female Both

National (BBS/HIES) 4.50 - - 4.41 - - 4.53

shiree baseline (CMS-1) 3.32 4.28 3.11 3.80 3.77 2.09 3.27

3 As well as the coverage number. 4 BBS (2011) Preliminary report on Household Income and Expenditure Survey- 2010, Dhaka 5 The test result is as follows: t=26.68 and p<0.001

4.3 Education

Samples were collected on the educational status of all BHH members in terms of the

completion of the last year of schooling (‘last class passed’) and the results are presented

according to the education status used in the HIES 2010 by BBS. Almost half of family members

aged 7 year or more have never attended school while more than three-quarters of the

household heads with more of the female heads never received any schooling in table 6 below

(the results are highly significant).

Female disadvantage is evident as more of them have no schooling and fewer have gained

some education compared with their male counterparts. However, compared with the heads

other members (aged 7 years or more) of the households are better off as more of them

received schooling up to various levels.

Table 4.7: Percentage distribution of household members and heads according to schooling and

sex

School years

completed

Household members (7+ Years)

N (%)

Household Heads

N (%)

Males Females Total Males Females Total

No schooling

16195

(37.62)

49168

(55.98)

65362

(49.94)

37528

(74.05)

20203

(87.68)

64639

(78.30)

Passed Class I-IV

18103

(42.05)

22950

(26.13)

41057

(31.37)

6548

(12.92)

1758

(7.63)

5625

(11.27)

Passed Class V-IX

7525

(17.48)

14633

(16.66)

22158

(16.93)

5919

(11.68)

947

(4.11)

3030

(9.32)

Passed SSC and above

865

(2.01) 747 (0.85)

1610

(1.23)

593

(1.17) 44 (0.19)

140

(0.87) Χ2 = 4769; p<0.001 Χ2 = 1965; p<0.001

It may be expected that the education status of the children of shiree BHHs may improve with

graduation out of extreme poverty6.

Rural-urban comparison: household heads

There appear very little differences rural-urban among the males according to level of schooling

while some differences are discernible for the females (table 4.8). Illiteracy is very high among

the heads more so for females in rural context (87.9%) than urban but still higher (84.4%) than

males (respectively 74.2 and 73.5). Females in the urban context have had more schooling

compared with rural female heads. Χ2=3424.614; p<0.001

6 CMS-1 data may be further analysed to inquire about the educational status of those aged between 7 to 15 years.

Table 4.8: Distribution of heads according to schooling, sex and location

NGO Name

School years completed (male and Female) Total

No schooling Class I-IV Class V-IX SSC and above

M F M F M F M F M F

Care 11583

(76.5)

4792

(94.3)

1313

(8.7)

123

(2.4)

2092

(13.8)

155

(3.1)

149

(1.0)

7

(0.1)

15140

(100)

5079

(100)

DSK 3033

(73.5)

2427

(84.4)

497

(12.0)

275

(9.6)

485

(11.8)

144

(5.0)

80

(1.9)

8

(0.3)

4125

(100)

2875

(100)

NETZ 1744

(81.9)

848

(93.0)

195

(9.2)

38

(4.2)

181

(8.5)

26

(2.9)

10

(0.5)

0

(0)

2130

(100)

912

(100)

PAB 9457

(81.0)

3060

(95.4)

1013

(8.7)

83

(2.6)

1068

(9.1)

61

(1.9)

132

(1.1)

3

(0.1)

11673

(100)

3209

(100)

SCF UK 3331

(58.2)

3284

(79.1)

1630

(28.5)

627

(15.1)

672

(11.7)

206

(5.0)

71

(1.2)

8

(0.2)

5720

(100)

4153

(100)

Uttaran 3910

(65.5)

2931

(81.1)

1206

(20.2)

405

(11.2)

728

(12.2)

227

(6.3)

87

(1.5)

17

(0.5)

5965

(100)

3616

(100)

Aid Comilla 159

(57.2)

339

(73.9)

43

(15.5)

53

(11.5)

67

(24.1)

65

(14.2)

8

(2.9)

2

(0.4)

278

(100)

459

(100)

CNRS 470

(79.9)

150

(89.8)

52

(8.8)

11

(6.6)

56

(9.5)

5

(3.0)

10

(1.7)

1

(0.6)

588

(100)

167

(100)

GreenHill 702

(72.9)

211

(93.4)

94

(9.8)

7

(3.1)

154

(16.0)

7

(3.1)

12

(1.2)

1

(0.4)

963

(100)

226

(100)

IC-1 698

(88.0)

188

(90.8)

45

(5.7)

15

(7.2)

43

(5.4)

4

(1.9)

7

(0.9)

0

(0)

793

(100)

207

(100)

Shushilan 530

(68.5)

131

(78.0)

130

(16.8)

23

(13.7)

104

(13.4)

14

(8.3)

10

(1.3)

0

(0)

774

(100)

168

100

ActionAiD 513

(75.1)

466

(90.1)

100

(14.6)

30

(5.8)

70

(10.2)

20

(3.9)

0

(0)

1

(0.2)

683

(100)

517

(100)

IC-2 207

(70.9)

153

(91.1)

42

(14.4)

12

(7.1)

40

(13.7)

3

(1.8)

3

(1.0)

0

(0)

292

(100)

168

(100)

MJSK 288

(78.3)

244

(91.0)

36

(9.8)

16

(6.0)

38

(10.3)

7

(2.6)

6

(1.6)

0

(0)

368

(100)

268

(100)

NDP 402

(87.6)

400

(93.9)

27

(5.9)

18

(4.2)

25

(5.4)

7

(1.6)

4

(0.9)

0

(0)

459

(100)

426

(100)

Puamdo 174

(77.3)

104

(96.3)

10

(4.4)

1

(.9)

40

(17.8)

3

(2.8)

1

(0.4)

0

(0)

225

(100)

108

(100)

SKS 361

(71.8)

430

(88.8)

81

(16.1)

44

(9.1)

58

(11.5)

9

(1.9)

3

(0.6)

1

(0.2)

503

(100)

484

(100)

Total 37562

(74.1)

20158

(87.5)

6514

(12.9)

1781

(7.7)

5921

(11.7)

963

(4.2)

593

(1.2)

49

(0.2)

50679

(100)

23042

(100)

5. Occupation

Data was collected for each household member about their main occupation with a pre-

coded list of 22 headings. These are presented for those who are aged between 15 and 65

years according to sex, in annex table –B1(statistically highly significant).

Overall

Among all household members aged between 15 -65 years, for just over one in ten members no

occupation was reported (likely due to unemployment or inability to work). The major

occupations reported regardless of gender include agricultural and other casual labour

(37.96%), domestic help (9.26% -primarily women) and rickshaw/boat (6.02% - overwhelmingly

men). Disaggregation by sex shows that for males laboring occupation is reported at 57%

followed by rickshaw and boat pulling at 13.65%, petty trade (hawking etc) and business (small

shop, tea stall etc) at 4.75% and fish catching or farming at 3.51%.

For females as a whole home making is most frequently reported occupation at 37.17% followed

by agricultural and casual labour at 20.53%, domestic worker at 16.04% and begging at 3.40%.

That nearly 40% of the female members of shiree BHHs are reported to be engaged in ‘menial’

type of work is suggestive of the BHHs’ extreme poverty status7. This situation is more pronounced

when the occupations of the household heads are considered.

‘Menial’ work

When the household heads are considered separately and sex is controlled for female heads

the situation changes drastically as the ‘menial’ type of work as main occupation is reported for

nearly four-fifth of the women (table -5.1 below). Two of the most ‘menial’ or least preferred

particularly in the rural context, domestic work and begging are reported for respectively 29.7%

and 14.3% of the female heads.

For the male heads hard, physical type of work is predominant (begging is reported for 2.4%).

Agricultural and casual labour, and rickshaw pulling or boat plying account for 76.7% of the

male heads. Petty trade or other business as primary occupation is a distant third at 5.5%.

These findings are indicative of the careful targeting to select the economic bottom 10% of the

local population, undertaken by the partners and supported by shiree. The process of changes

taking place with regard to the occupational pattern for the household heads may need to be

monitored and studied. It is likely that the occupational pattern will change as a result of

graduation out of extreme poverty.

7 May be a topic for further research

Table 5.1: Distribution of household heads according to major primary occupations and sex

Occupation

Sex Both

N (%) Male

N (%)

Female

N (%)

Agricultural day labour 20424

(40.3 )

3595

(15.6)

24033

(32.6)

Other Day labour 10997

(21.7)

4562

(19.8)

15555

(21.1)

Domestic maid 203

(0.4)

6843

(29.7)

7003

(9.5)

Rickshaw/van/boat/bullock/push cart 7450

(14.7 )

46

(0.2)

7520

(10.2)

skilled labor (manual) 1166

(2.3 )

138

(0.6)

1253

(1.7)

Fishing in open water 1875

(3.7 )

392

(1.7)

2285

(3.1)

Petty trade and other business 2787

(5.5 )

622

(2.7)

3465

(4.7)

Begging 1216

(2.4)

3295

(14.3)

4497

(6.1)

Others 2585

(5.1 )

714

(3.1)

4128

(5.6)

Do not work 2129

(4.2 )

1406

(6.1)

3539

(4.8)

Total 50679

(100)

23042

(100)

73721

(100) Χ2 =26966.803; p<0.001

‘Menial’ work and NGOs

The annex table -B2 presents the distribution of female heads according to NGOs and five

specific occupational headings, viz. begging, domestic work, labourer, petty trade and

housewife while the rest of occupations are placed in ‘others’(the distribution is highly significant,

χ2 = 5912; p<0.001). The highest proportions of beggars are found in the greater northern districts:

MJSKS (at 27.6%), SKS (22.9%), Puamdo (19.4%), NETZ (19.1%), and Care (17%). The first three

NGOs are those implementing monga-mitigation innovations. In the southwest the pattern is as

followed: SCUK(16.3%), Uttaran (11.6%) and Shushilan (10.1%). Begging is not an option in GH (0%

in CHT), or for very few in CNRS and IC-1 (both in Haor respectively at 3.6% and 5.3%),

Domestic work as occupation is the single highest for the female heads as a whole with

comparatively higher concentrations in some NGOs. The highest frequencies are observed in

SKS (at 68%), NPD (56.8%), AC (53.2%), and MJSKS (45.1%), all being monga-mitigation

interventions except for AC who operate in Feni district but an innovations intervention. The

lowest frequencies for domestic work are observed in GH (0.9%) and Puamdo(2.8%). For more

than a third in Dhaka-based DSK domestic work is the main occupation.

The wage labour (agricultural labour and casual/day labour) category accounts for more than

one-third of the female heads with high NGO specific concentrations. The highest frequencies

are observed in Puamdo (70.4% in Panchbibi), Shushilan (63.1% in Bagerhaat), Action Aid (62.2%

in Nilphamary), Uttaran(55.8% in southwest),and IC-1 (54.1% in haor). On the other hand, the

smallest frequencies are found in SKS (1.9% in Lalmonirhaat) and NPD (7.7% in Bogra).

Petty trading is highest among the women in GH (at 39.8%), which is likely to include head-load

hawking and street/way side vending or very small shop keeping.

Regional effect on ‘menial’ work

When the geographic locations are controlled a clearer regional pattern emerges for the

primary occupations of the female household heads (table 5.2) with wage laboring being more

pronounced in the rural (excluding ‘other’) at between 37.7% in the north and in haor 47.1%

compared with 10.6% in the urban where domestic work is single largest (at 35.3). large

proportions of the females are engaged in domestic work in north (32.7%) and haor (28.6%). In

northwest and north begging is third most frequently reported primary occupation for the

females (respectively at 19.1% and 16.3%).

Table 5.2 Regional distribution of selected occupations for female heads

Region Labour

Domestic

help Begging

Pretty

trade

House

wife Others Total

N % N % N % N % N % N % N %

North 3669 37.7 3176 32.7 1584 16.3 228 2.3 285 2.9 783 8.1 9725 100

Northwest 464 45.5 258 25.3 195 19.1 7 0.7 7 0.7 89 8.7 1020 100

Southwest 3298 41.6 1763 22.2 1113 14.0 101 1.3 328 4.1 1334 16.8 7937 10

Haor 176 47.1 107 28.6 17 4.5 6 1.6 16 4.3 52 13.9 374 100

Urban 304 10.6 1015 35.3 307 10.7 187 6.5 112 3.9 950 32.9 2875 100

Others 188 16.9 493 44.4 65 5.9 93 8.4 93 8.4 179 16.1 1111 100

Total 8099 35.1 6812 29.6 3281 14.2 622 2.7 841 3.6 3387 14.7 23042 100

6. Living Condition

6.1 Housing and construction materials

Ownership

Overall 58.1% (annex table B-3) of rural BHHs do not own the land on which they reside, of which

34.2% live on khas land (appears to include agricultural land as well as sides of embankments

and roads, the latter two are not technically khas land to which people have right of

possession). Very large proportions of BHHs live on state owned land in NETZ (100%), GH (80.6%),

PAB (71.4%), and SCUK (42.4%) for some specific reasons. The ethnic minorities in NETZ have been

settled on khas land most likely for political reasons after they had lost their own land. The land in

the hills of Chittagong are traditionally community owned but individual families do not posses

legal documentation as per the law. In case of PAB the outcome is due primarily to its targeting

of the river erosion affected people who have taken refuge on road and flood protection

embankment sides (some may live on agricultural land adjacent to the structures). In SCUK it

also likely to be a combination of those infrastructures and agricultural land, as many of their

BHHs had to move out of their original homes that became waterlogged following the cyclones

of Sidr and Aila.

On the other hand, just under a quarter of the BHHs live on their own homestead land with the

largest proportion being in SKS (90%), followed by AAB (67.3%), Shushilan (50.7%), Puamdo

(46.6%) and CNRS (43.8%).

In contrast to homestead land ownership, overwhelming majority of shiree BHHs own their

dwelling structures or in other words they live in their own houses, followed by renting (due

overwhelmingly to DSK BHHs) at 9.3% and 3.7% living rent-free in the houses owned by others

(table 6.1 below). The rent-free living in houses owned by others is frequently reported in

PUAMDO where the BHHs are ethnic minorities. This is also reported albeit at much lower rates in

NETZ and Green Hill (both 8.9%), Aid Comilla (7.8%) and CNRS (6.8%).

Table 6.1: Distribution of households according to NGO and dwelling house ownership

NGO

House ownership

Total Owned Rented Parent Parent in law

Other no-family

rent free Others

Care 18020

(89.1)

59

(0.3)

526

(2.6)

129

(0.6)

1017

(5.0)

468

(2.3)

20219

(100)

DSK 137

(2.0)

6641

(94.9)

164

(2.3)

2

(0.0)

36

(0.5)

20

(0.3)

7000

(100)

NETZ 2425

(79.7)

2

(0.10)

134

(4.4)

73

(2.4)

272

(8.9)

136

(4.5)

3042

(100)

PAB 14282

(96.0)

32

(0.2)

145

(1.0)

26

(0.2)

228

(1.5)

169

(1.1)

14882

(100)

SCF-UK 8188

(82.9)

10

(0.1)

283

(2.9)

63

(0.6)

216

(2.2)

1114

(11.3)

9874

(100)

Uttaran 8022

(83.7)

63

(0.7)

597

(6.2)

123

(1.3)

415

(4.3)

361

(3.8)

9581

(100)

Aid Comilla 561

(76.1)

15

(2.0)

70

(9.5)

17

(2.3)

58

(7.9)

16

(2.2)

737

(100)

CNRS 502

(66.5)

1

(0.1)

78

(10.3)

25

(3.3)

51

(6.8)

98

(13.0)

755

(100)

Green Hill 1038

(87.3)

8

(0.7)

20

(1.7)

10

(0.8)

106

(8.9)

7

(0.6)

1189

(100)

IC-1 770

(77.0)

21

(2.1)

61

(6.1)

34

(3.4)

52

(5.2)

62

(6.2)

1000

(100)

Shushilan 854

(90.7)

0

(0.0)

44

(4.7)

14

(1.5)

27

(2.9)

3

(0.3)

942

(100)

Action Aid 1045

(87.1)

3

(0.3)

25

(2.1)

16

(1.3)

37

(3.1)

74

(6.2)

1200

(100)

IC-2 396

(86.1)

2

(0.4)

25

(5.4)

10

(2.2)

20

(4.3)

7

(1.5)

460

(100)

MJSK 545

(85.7)

1

(0.2)

17

(2.7)

8

(1.3)

7

(1.1)

58

(9.1)

636

(100)

NDP 723

(81.7)

1

(0.1)

35

(4.0)

9

(1.0)

47

(5.3)

70

(7.9)

885

(100)

Puamdo 205

(61.6)

0

(0.0)

16

(4.8)

11

(3.3)

84

(25.2)

17

(5.1)

333

(100)

SKS 738

(74.8)

5

(0.5)

47

(4.8)

12

(1.2)

35

(3.5)

150

(15.2)

987

(100)

Total 58451

(79.3)

6864

(9.3)

2287

(3.1)

582

(0.8)

2708

(3.7)

2830

(3.8)

73722

(100)

Figures in the parentheses indicate row percentages. Χ2 =72450; p<0.001

House construction materials

Condition of the dwelling houses of the BHHs – indicated by the construction materials used for

walls and roofs, has been used as an objective indicator of poverty particularly for the bottom

10% of the local population. This is also an area which undergoes changes as peoples’ life

conditions improves. A list of seven materials was used to assess the condition of the housing

super structure, as listed in the following two tables. The poorest materials are the

grass/leaves/polythene etc, mud and bamboo (splinters) while corrugated iron (CI) sheets or tins

and wood are an improvement on this. Brick/cement/concrete are common in urban areas but

is considered highest quality (and costly) materials in rural areas.

The materials that are reported to be used for the walls are of the lowest quality for nearly four-

fifth of the households with the male-female difference is slight standing respectively at 81.2%

and 78.6% (table 6.2). The single most frequently used material is also of the cheapest of quality –

grass/jute stick/plastic etc, where the frequencies are very close at 43.2% and 41.6%

respectively. The male-female difference is closer for those using the more costly CI sheets

respectively at 16.0% and 17.6%

In the urban context around three-quarters have reported to use CI sheets in walls, and the

difference in frequency is very slight between males and females. Some use of brick and

cement are reported respectively for males and females at 9.1% and 8.3%, while between 13%

and 16.4% report using the cheapest materials.

In the rural context overwhelming majority of shiree BHHs reported to use the cheap and poor

quality construction materials with differences between the sexes is near non-existent at 87.3%

and 87.5 respectively. Just under half of the BHHs report to use the cheapest and poorest quality

materials (46.6% and 46.5% respectively).

It may be expected that some changes will take place in the condition of the housing materials

in terms of repair and improvements starting in the short term8.

Table 6.2: Distribution of households according to wall construction materials, location and sex of

household heads

Materials (walls) Rural Urban Total

Male Female Male Female Male Female

Grass/jute stick/

leaves/plastic

21697

(46.6)

9376

(46.5)

167

(4.3)

200

(7.0)

21785

(43.2)

9576

(41.6)

Bamboo 10407

(22.4)

3661

(18.2)

341

(8.3)

266

(9.3)

10748

(21.2)

3927

(17.0)

Mud 8521

(18.3)

4600

(22.8)

11

(0.3)

7

(0.2)

8532

(16.8)

4607

(20.0)

CI sheets/Tins 4929

(10.6)

1939

(9.6)

3176

(76.7)

2111

(73.4)

8105

(16.0)

4050

(17.6)

Wood 625

(1.3)

295

(1.5)

17

(0.4)

20

(0.7)

642

(1.3)

315

(1.4)

Brick/cement 165

(0.4)

121

(0.6)

370

(9.1)

239

(8.3)

535

(1.1)

360

(1.6)

Others 210

(0.5)

175

(0.9)

32

(0.8)

32

(1.1)

242

(0.5)

207

(0.9)

Total 46554

(100)

20167

(100)

4125

(100)

2875

(100)

50679

(100)

23042

(100)

8 CMS-2 may include question(s) about repair and small improvements.

Figures in the parentheses indicate column percentages. Rural: Χ2=339; p<0,001. Urban: Χ2=32; p<0.001.

overall: Χ2=341; p<0.001.

The roofs of the houses show some improvement on the

walls as 70.8 % and 61.2% of the rural BHHs (males and

females respectively) have used CI sheets (table 6.3

below). This should be seen in the context of the

different quality of CI sheets available in the market as

well the age and rust-condition of the materials. During

the period between the 1990s and the recent price hike

low quality (very thin – almost paper-like) and low

priced CI sheets were widely available in the local

markets which might have made them affordable even

for the extreme poor.

Table 6.3: Percentage distribution of households according to roofing materials, location and sex

of household heads

Materials Rural Urban Total

Male Female Male Females Male Female

Grass/jute stick/

palm leaf/plastic

11528

(24.8)

6716

(33.3)

91

(2.2)

95

(3.3)

11619

(22.9)

6811

(29.6)

Bamboo 199

(0.4)

145

(0.7)

251

(6.1)

130

(4.5)

450

(0,9)

275

(1.2)

Clay tiles 1570

(3.4)

980

(4.9)

9

(0.2)

9

(0.3)

1579

(3.1)

989

(4.3)

CI sheets/Tin 32946

(70.8)

12140

(60.2)

3646

(88.4)

2533

(88.1)

36592

(72.2)

14673

(63.7)

Cement/brick/rod 44

(0.1)

27

(0.1)

75

(1.8)

62

(2.2)

119

(0.2)

89

(0,4)

Others 267

(0.6)

159

(0.8)

53

(1.33)

46

(1.6)

320

(0.6)

205

(0.9)

Total 46554

(100)

20167

(100)

4125

(100)

2875

(100)

50679

(100)

23042

(100) Figures in the parentheses indicate column percentages. Rural: χ2 = 724; p<0,001. Urban: χ2 =18.1; p=0.003. Overall: χ2

=550; p=0.001.

The female heads use the cheapest quality materials (grass/polythene etc) more frequently

(33.3%) than the males (24.8%) in the rural areas.

In the urban location the overwhelmingly high frequencies of CI sheets as roofing material for

both males and females (88.4% and 88.1%) is not surprising.

6.2 Floor space

Mean floor space

Data was collected on the length and width of the main living areas or houses in feet. The sizes

of the houses were computed in square feet (sqft) at the analysis stage and the results are

presented in annex table-B4 for the 17 NGOs disaggregated by the sex of household heads.

Overall the mean size of houses where BHHs live is 129.5 sqft (SD= 65.9). For the female heads it is

Box 1: Electricity

Shiree BHHs in the rural context

have no access to electric supply

(at 98.3%) while 71% in the urban

context is connected to the main

lines provided by the house owners

(and 15% no connection). Illegal

connection is used by 14%. A total

of 53 BHHs are using solar power

with 13 being in Uttaran.

smaller than their male counterparts respectively at 110.8 sqft (SDS = 60.3) and 137.0 sqft

(SD=66.6). The results are highly significant for NGO (F= 31.2; p<0.001), sex of head (F=39.0;

p<0.001) and the interaction between the sex and NGO (F= 16.0; p<0.001).

The smallest house sizes are found in the urban location of DSK (73.1 sqft) and NETZ (80.8 sqft). In

the former 94% of BHHs live in rented houses, and a ceiling on the rent was used as targeting

criteria which likely to have resulted in the selection of BHHs who live in small houses. In the latter

the majority of the BHHs are from ethnic minority community; 20% of whom live rent-free in other

peoples’ houses that are unlikely to enable them to live in large houses!

Local geo-physical and land ownership conditions in haor and southwest coast may explain the

smaller than average house sizes for rural BHHs, in CNRS (105.2 sqft) and SCUK (122.7 sqft). The

BHHs in the former are likely to live on land (higher than the paddy fields but lower than the

kandas where the main villages are located) that are submerged during the monsoon when the

houses have to be physically moved to higher grounds.

In SCUK areas in southwest coast, there is a scarcity of high grounds on which to build houses

due to naturally low lying land, expansion of shrimp farms and water logging of vast areas of

land following the cyclones of Sidr and Aila. Many of the BHHs live on the sides of flood

protection embankments and roads.

Floor space per person

Table 6.4: Distribution of per capita housing space according to NGOs and sex of household

head

Location Mean per capita floor space (sqft)

Male heads Female heads Both

Care 43.56 82.42 53.16

DSK/urban 20.28 29.24 23.60

NETZ 25.09 51.33 32.94

PAB 45.29 82.51 52.68

SCF-UK 42.43 61.44 50.27

Uttaran 41.63 60.96 47.90

Aid Comilla 47.54 66.73 58.71

CNRS 26.75 60.42 34.11

Green Hill 54.59 39.20 54.50

IC-1 32.43 49.79 36.03

Shushilan 78.78

78.78

Action Aid 53.53 83.72 60.78

IC-2 39.99 71.40 51.60

MJSK 44.73 92.07 64.90

NDP 48.16 86.12 65.88

Puamdo 43.33 74.06 53.67

SKS 42.83 82.95 62.42

Rural 43.58 71.89 51.47

All 41.63 66.67 48.82

In order to take in to account the different family sizes the floor area of houses were converted

into square feet per capita; an indicator that might improve with graduation out of extreme

poverty. The overall mean availability of dwelling space is 48.8 sqft (SD= 36.7) per person that is

increased slightly in the rural areas to 51.5 sqft (table 6.4 below). The differences among the

NGOs are significant for NGO (F= 5.76; p=0.001), sex of household head (F= 70.43; p<0.001) and

the interaction between the NGO and sex (F= 109.9; p<0.001).

The female headed households in the rural context are better off with more floor space per

person (66.7 sqft) compared with males (41.6 sqft) and this is true across all NGOs except for

Shushilan and Green Hill where these are much higher for males (respectively 78.8 and 54.6

sqft). The larger floor space available in most of the female headed households is likely to be

strongly associated with their much smaller family sizes in both rural and urban areas (table 6.4

below).

6.3 Water and sanitation

Drinking water

The source of drinking water and the ownership of hand tube wells, used by the BHHs appear to

be influenced by local conditions: firstly when they are categorized by rural and urban locations,

and secondly for the rural by geo-hydrological conditions. The latter conditions include salinity

intrusion, which determine the depth of the water table from which to extract drinking water.

Table 6.5: Percentage distribution of BHHs according to NGOs and sources of drinking water

NGO

Sources used

Piped Hand

tube well

Open

well

Pond-

river

Shallow/deep

tube well Others Total

CARE 446

(2.2)

19610

(97.0)

133

(0.7)

22

(0.1)

3

(0)

3

(0)

20219

(100)

DSK 4479

(64.0)

2105

(30.1)

6

(0.1)

5

(0.1)

310

(4.4)

95

(1.3)

7000

(100)

NETZ 115

(3.8)

2135

(70.2)

571

(18.7)

2

(0.1)

214

(7.0)

3

(0.1)

3042

(100)

PAB 399

(2.7)

14423

(96.9)

26

(0.2)

16

(0.1)

1

(0) 3 (0.1)

14882

(100)

SCF 64 (0.6) 2296

(23.2)

118

(1.2)

4390

(44.5) 1718 (17.4)

1292

(13.0)

9874

(100)

UTTARAN 142

(1.5)

4494

(46.9) 40 (0.4)

1597

(16.6) 3028 (31.6) 280 (2.9)

9581

(100)

Aid

Comilla 7 (0.9) 718 (97.4) 1 (0.1) 2 (0.3) 8 (1.1) 3 (0.3)

737

(100)

CNRS 5 (0.7) 731 (96.8) 0 (0) 17 (2.3) 0 (0) 2 (0.2) 755

(100)

Green Hill 101

(8.5) 548 (46.1)

351

(29.5)

182

(15.2) 3 (0.3) 4 (0.4)

1189

(100)

IC -1 0 (0) 982 (98.2) 2 (0.2) 16 (1.6) 0 (00 0 (0) 1000

(100)

Shushilan 1 (0.1) 378 (55.0) 1 (0.1) 62 (6.6) 360 (38.2) 0 (0) 942

(100)

ActionAid 22 (1.8) 1147

(95.5) 31 (2.7) 0 (0) 0 (0) 0 (0)

1200

(100)

IC – 2 8

(1.7)

452

(98.3)

0

(0)

0

(0)

0

(0)

0

(0)

460

(100)

MJSKS 5 (0.8) 628 (98.7) 1 (0.2) 0 (0) 0 (0) 2 (0.3) 636

(100)

NDP 0 (0) 883 (99.8) 0 (0) 0 (0) 0 (0) 2 (0.2) 885

(100)

PUAMDO 1 (0.3) 332 (99.7) 0 (0) 0 (0) 0 (0) 0 (0) 333

(100)

SKS 7 (0.7) 973 (98.6) 5 (0.5) 1 (0.10 0 (0) 1 (0.1) 987

(100)

All 8502

(7.9)

52977

(71.9)

1286

(1.7)

6312

(8.6) 5645 (7.7)

1600

(2.3)

73 722

(100) Figures in the parentheses indicate row percentages

Use of safe drinking water source is ‘universal’ – coverage is 80% or more, for the shiree BHHs with

some location differences. The first is the urban-rural difference as piped supply water is the

source for 64% of urban households (table 6.5 above) while hand tube well is the source for 76%

of all rural households. The latter may be misleading as there is large variation among rural

locations as well as that the geo-hydrological condition in rural Bangladesh is not uniform.

The hand tube well is the source of drinking water for over 95% of shiree BHHs living in the ‘plains’,

regardless of the number of respondents as shown by the frequencies for Care’s 97%, IC(round-

1)’s 98.2% and PUAMDO’s 99.7%. Distribution of the Ownership of the hand wells is not similar

among the shiree BHHs.

Ownership of hand tube wells

The universal access rate to safe drinking water belies one of the characteristics of the extreme

poor: they are unable to invest in the source of the drinking water thus being dependent on

others for their lives. Only 21.2% and 20.0% respectively reported to self-own or share-own (jointly

with others) hand tube wells (table 6.6 below). However their lack of investment-ability is

mitigated by their access to tube wells owned by others (overall 36.4%) and those supplied

through government/public sources (13.5% overall) albeit with regional variation.

Table 6.6: Distribution of households according to NGOs and ownership of hand tube wells

NGO Ownership

Own Shared Owned by

others Public NGO Others Total

Care 5450

(27.8)

6506

(33.2)

6038

(30.8)

1047

(5.3)

90

(0.5)

479

(2.4)

19610

(100)

DSK 24

(1.1)

64

(3.0)

523

(24.8)

39

(1.9)

137

(6.5)

1318

(62.6)

2105

(100)

NETZ 42

(2.0)

114

(5.3)

591

(27.7)

1219

(57.0)

140

(6.6)

31

(1.5)

2137

(100)

PAB 4498

(31.2)

3051

(21.2)

4672

(32.4)

1541

(10.7)

245

(1.7)

416

(2.9)

14423

(100)

SCF-UK 62

(2.7)

97

(4.2)

1565

(68.2)

330

(14.4)

48

(2.1)

194

(8.4)

2296

(100)

Uttaran 190

(4.2)

271

(6.0)

2594

(57.7)

1059

(23.6)

82

(1.8)

298

(6.6)

4494

(100)

Aid

Comilla

85

(11.8)

32

(4.5)

500

(69.6)

95

(13.2)

1

(0.1)

5

(0.7)

718

(100)

CNRS 4

(0.5)

16

(2.2)

112

(15.3)

487

(66.6)

110

(15.0)

2

(0.3)

731

(100)

Green

Hill

2

(0.4)

8

(1.5)

66

(12.0)

174

(31.8)

286

(52.2)

12

(2.2)

548

(100)

IC-1 0

(0)

30

(3.1)

178

(18.1)

637

(64.9)

86

(8.8)

51

(5.2)

982

(100)

Shushilan 80

(15.4)

60

(11.6)

180

(34.7)

163

(31.5)

35

(6.8)

0

(0)

518

(100)

Action

Aid

260

(22.7)

74

(6.5)

665

(58.0)

119

(10.4)

12

(1.0)

17

(1.5)

1147

(100)

IC-2 74

(16.4)

44

(9.7)

295

(65.3)

34

(7.5)

5

(1.1)

0

(0)

452

(100)

MJSK 91 50 352 42 49 44 628

NGO Ownership

Own Shared Owned by

others Public NGO Others Total

(14.5) (8.0) (56.1) (6.7) (7.8) (7.0) (100)

NDP 162

(18.3)

60

(6.8)

553

(62.6)

57

(6.5)

27

(3.1)

24

(2.7)

883

(100)

Puamdo 40

(12.0)

47

(14.2)

102

(30.7)

58

(17.5)

44

(13.3)

41

(12.3)

332

(100)

SKS 150

(15.4)

52

(5.3)

278

(28.6)

34

(3.5)

18

(1.8)

441

(45.3)

973

(100)

Total 11214

(21.2)

10576

(20.0)

19264

(36.4)

7135

(13.5)

1415

(2.7)

3373

(6.4)

52977

(100) Figures in the parentheses indicate row percentages. Χ2 = 38595; p<0.001

Regional effect

The ownership status appear to be somewhat influenced by hydrological conditions. The

frequencies of ownership in difficult hydrological areas are far lower compared with other rural

areas: in the northwest, respectively for owned and share-owned at 3.3 and 6.5%), in southwest

coast9 these are 4.5% and 5.9% respectively, in haor10 standing at 0.2% and 2.7%, and in the

three NGOs categorized as ‘other’ 11.6% and 4.7%. The high costs of sinking wells in these

conditions are likely to prevent the extreme poor from making the required investment.

Table 6.7: Regional distribution of households according to ownership of hand tube wells

Region

Ownership

Own Shared

ownership

Owned by

others Public NGO Other Total

North 10523

(28.3)

9777

(26.3)

12300

(33.0)

2817

(7.6)

419

(1.1)

1397

(3.8)

37233

(100)

Northwest 82

(3.3)

161

(6.5)

693

(28.1)

1277

(51.7)

184

(7.5)

72

(2.9)

2469

(100)

Southwest 332

(4.5)

428

(5.9)

4339

(59.4)

1552

(21.2)

165

(2.3)

492

(6.7)

7308

(100)

Haor 4

(0.2)

46

(2.7)

290

(16.9)

1124

(65.6)

196

(11.4)

53

(3.1)

1713

(100)

Urban 24

(1.1)

64

(3.0)

523

(24.8)

39

(1.9)

137

(6.5)

1318

(62.6)

2105

(100)

Other 249

(11.6)

100

(4.7)

1119

(52.1)

326

(15.2)

314

(14.6)

41

(1.9)

2149

(100)

Total 11214

(21.2)

10576

(20.0)

19264

(36.4)

7135

(13.5)

1415

(2.7)

3373

(6.4)

52977

(100)

Figures in the parentheses indicate row percentages. Χ2 = 28139; p<0.001

9 Uttaran, SCUK and Shushilan 10 CNRS and IC-round 1

The government’s role in ensuring availability of safe drinking water in in the difficult geo-physical

conditions is noticeable as large proportions of the extreme poor use government installed tube

wells. In the haor areas 65.6% reported to access the source followed by northwest (dominated

by the arid Barind Tract) it stands at 51.7% while in southwest more than one in five uses public

sources.

The role of NGOs in supplying the hardware in these or other locations is minimal (overall 3.5%)

except for the hills (part of the ‘other’ category) where 32.3% of the households reported to

access NGO supplied tube wells. For the extreme poor in the hills (24.1%) and the urban areas

(44.4%) the presence of ‘other’ category of ownership may suggest community management of

water sources is important. In the urban the house owners play a role in ensuring access to safe

water for the extreme poor tenants.

Sanitation

Poverty has often been associated with unhygienic practices and behavior, and present

database includes the type of latrines used by BHHs (but not behaviour). A hygienic latrine will

have a superstructure for privacy, water-sealed pan so that bad odor does not escape or

insects cannot pass in and out of the septic tank, and the tank is completely enclosed or lined

without any connection to open spaces.

The current sanitation condition for BHHs is somewhat unhygienic with just over a third and 10%

are using unhygienic latrines (table 6.8 below): use of open space/bush and ‘hanging’ latrines

(the latter has privacy but not the other features). Use of open spaces for latrines is particularly

high in certain locations where more than 50% has reported to practice this. The practice is

universal (94.8%) among NETZ BHHs in the northwest. This is followed by three NGOs that operate

in difficult terrains for installation of sanitary latrines such as in haor (CNRS at 79.2% and for IC-1 at

70%) and in the hills of CHT where 75.8% of GH BHHs use open spaces in the forests. These are

also high in IC-monga (59.6%) and PAB (59.4%).

Although nearly one-half of shiree BHHs use ‘ring and slab’ type of latrines CMS-1 does not

include any data on the presence or not of the other features of hygienic latrines – water sealing

of the pans. After installation the water seals can be retained or broken; there is little data

available elsewhere on this practice. Χ2 = 30280 ; P<0.000

Table 6.8: Distribution of BHHs according to NGO and place of defecation

NGO Open/bush/hanging Pit latrine Ring/slab complete sanitary Others Total

Care 6430 (31.8)

1828 (9.0)

10928 (54.0)

1029 (5.1)

4 (.0)

20219 (100)

DSK 787

(11.2) 181 (2.6)

3511 (50.2)

1995 (28.5)

526 (7.5)

7000 (100)

NETZ 2887 (94.9)

51 (1.7)

93 (3.1)

2 (.1)

9 (.3)

3042 (100)

PAB 8845 (59.4)

1819 (12.2)

3971 (26.7)

79 (.5)

168 (1.1)

14882 (100)

SCF-UK 1716 (17.4)

1154 (11.7)

6668 (67.5)

172 (1.7)

164 (1.7)

9874 (100)

Uttaran 1241 (13.0)

1633 (17.0)

6397 (66.8)

199 (2.1)

111 (1.2)

9581 (100)

Aid Comilla 39

(5.3) 24

(3.3) 636

(86.3) 33

(4.5) 5

(.7) 737

(100)

CNRS 598

(79.2) 10

(1.3) 143

(18.9) 2

(.3) 2

(.3) 755

(100)

Green Hill 903

(75.9) 111 (9.3)

106 (8.9)

12 (1.0)

57 (4.8)

1189 (100)

IC-1 700

(70.0) 176

(17.6) 121

(12.1) 3

(.3) 0

(.0) 1000 (100)

Shushilan 67

(7.1) 353

(37.5) 517

(54.9) 4

(.4) 1

(.1) 942

100.0

Action Aid 537

(44.8) 70

(5.8) 587

(48.9) 3

(.3) 3

(.3) 1200 (100)

IC-2 274

(59.6) 63

(13.7) 122

(26.5) 0

(.0) 1

(.2) 460

(100)

MJSK 144

(22.6) 115

(18.1) 368

(57.9) 5

(.8) 4

(.6) 636

(100)

NDP 354

(40.0) 24

(2.7) 506

(57.2) 1

(.1) 0

(.0) 885

(100)

Puamdo 156

(46.8) 1

(.3) 154

(46.2) 21

(6.3) 1

(.3) 333

(100)

SKS 267

(27.1) 68

(6.9) 643

(65.1) 2

(.2) 7

(.7) 987

(100)

Total 25945 (35.2)

7681 (10.4)

35471 (48.1)

3562 (4.8)

1063 (1.4)

73722 (100)

Χ2 = 30280 ; P<0.000

Regional effect

The frequency of use of unhygienic latrines is highest in the northwest with around nine in ten of

both males and females reporting followed by in the haor respectively at 75% and 71% and the

other NGOs between 55% and 32.5% reporting it (table 6.9 below). The use of semi hygienic ring-

slab latrines is highest in the southwest (respectively by 62.7% and 65.6%), followed in the urban

location by 52.3% and 47.1%, in the ‘other’ three NGOs by 33.5% males and 61.0% females and

by 44.6% and 39.5% in the north. .

Table 6.9: Regional distribution of households according to sex of heads and place of defecation

Region

Sex

Place of defecation Total

Open/bush/hanging Pit latrine Ring/slab complete sanitary Other

North

Male

Female

11745

(41.0)

3083

(10.8)

12776

(44.6)

915

(3.2)

140

(0.5)

28659

(100)

4752

(48.9)

880

(9.0)

3843

(39.5)

203

(2.1)

47

(0.5)

9725

(100)

Northwest

Male

Female

2116

(89.9)

35

(1.5)

186

(7.9)

13

(0.6 )

5

(0.2)

2355

(100)

927

(90.9)

17

(1.7)

61

(6.0)

10

(1.0)

5

(0.5)

1020

(100)

Southwest

Male

Female

1692

(13.6)

2056

(16.5)

8373

(67.2)

215

(1.7)

124

(1.0)

12460

(100)

1332

(16.8)

1084

(13.7 )

5209

(65.6)

160

(2.0)

152

(1.9)

7937

(100)

Haor

Male

Female

1033

(74.8)

152

(11.0)

191

(13.8)

5

(0.4)

0

(0)

1381

(100)

265

(70.9)

34

(9.1)

73

(19.5)

0

(0)

2

(0.5)

374

(100)

Urban

Male

Female

405

(9.8)

85

(2.1)

2158

(52.3)

1112

(27.0)

365

(8.8)

4125

(100)

382

(13.3)

96

(3.3)

1353

(47.1)

883

(30.7)

161

(5.6)

2875

(100)

Others

Male

Female

935

(55.0)

123

(7.2)

570

(33.5)

24

(1.4)

48

(2.8)

1700

(100)

361

(32.5)

36

(3.2)

678

(61.0)

22

(2.0)

14

(1.3)

1111

(100)

All

Male

Female

17926

(35.4)

5534

(10.9)

24254

(47.9)

2284

(4.5)

682

(1.3)

50680

(100)

8019

(34.8)

2147

(9.3)

11217

(48.7)

1278

(5.5)

381

(1.7)

23042

(100) Figures in the parentheses indicate row percentages Χ2 = 23515 ; P<0.001

7. Asset Ownership

The assets that the BHHs own is an important indicator of poverty status as it is considered a

strong proxy indicator for income; lack of ownership of assets is a major characteristic of extreme

poverty, and lack of ownership has been used very frequently by shiree partners as a selection

criteria for the beneficiaries. Therefore, it is expected that at baseline situation asset ownership

by BHHs are most likely to be at very low levels, a situation which is assumed to change for better

over the course of participation by the BHHs in shiree supported interventions.

The assets can be roughly categorized as landholding, animal, work equipment, and household

items. By definition the extreme poor will not own outright or have access11 to any cultivable

land there might be some exceptions particularly in cases of quality of land such as water

logging or high salinity in the southwest. Little change is expected in ownership but there might

be positive changes in access to productive land over the course of shiree12.

However, changes in other asset categories are likely to emerge in the short term as with repair

and improvements in housing condition.

7.1 Land ownership

As expected the vast majority of BHHs do not own any cultivable land, standing at 94.6% and

89.6% respectively for male and female heads (table 7.1 below). That the partners of shiree have

made strong efforts at identifying the extreme poor is strongly evident in table -13. However, the

presence of BHHs in the more than 1.5 acre category is surprising, and disaggregation of NGOs

reveal that between 40.1% (males) and 48.1% (females) in SCUK fall in this category. Shiree had

agreed to relax the land ceiling upwards in case of poor quality land (mainly waterlogged

following Sidr and Aila cyclones) as a selection criterion for recruitment of second year

beneficiaries for SCUK but not to this extent.

Table7.1: Percentage distribution of households according to cultivable land ownership and sex

of household heads

Size category (acres) Ownership of cultivable land

Male heads Female heads

0.0 49699

(94.6)

18970

(89.6)

0.01 – 0.04 66

(0.1)

29

(0.1)

0.05 - 0.19 61

(0.1)

9

(0.0)

0.20 – 0.49 43

(0.1)

4

(0.0)

0.50 – 1.49 1

(0.0)

0

(0.0)

1.50+ 2681

(5.1)

2162

(10.2)

All 52551

(100)

21174

(100) Χ2= 658; p<0.001

11 Different types of tenancy and free use of land defines access. 12 As has been observed at certain CLP locations

7.2 Non-land assets

A pre-coded list of 27 items were used in CMS-1 questionnaire to record the non-land assets

owned by the BHHs. These are presented in the annex table-B5. Major items owned by the BHHs

include ‘other household items’ such as pot and pans, water container, crockery etc (97.1%),

bed including those made of bamboo (76.9%), agricultural implements including spade, shovel

etc (52.6%). These are followed by two items that are needed in the northern part of the country

because of the severity of winter compared with other parts; blanket (42.8%) and wooden box

for storage purpose (38.9%).

As monetary values for the assets have not been collected it is useful to use the non-land asset

categories and distribute the households according to the number of items owned by them

disaggregated by sex of household head (table 7.2 below).

Table 7.2: Percentage distribution of households according to asset types, number owned and

sex of family heads

Assets

type

Number

of items

Rural Urban

Male Female Both Male Female Both

Animals

0 42621

(91.5)

18986

(94.1)

61607

(92.3)

4101

(99.4)

2868

(99.8)

6969

(99.6)

1 2272

(4.9)

680

(3.4)

2952

(4.4)

14

(0.3)

3

(0.1)

17

(0.2)

2 1068

(2.3)

318

(1.6)

1386

(2.1)

2

(0.05)

2

(0.1)

4

(0.1)

3+ 601

(1.3)

183

(0.9)

784

(1.2)

8

(0.2)

2

(0.1)

10

(0.1)

Statistical test result χ2=136; p<0.001 ns

Work

equipment

0 14720

(31.6)

11409

(56.6)

26129

(39.2)

4056

(98.3)

2852

(99.2)

6908

(98.7)

1 6940

(14.9)

3687

(18.3)

10627

(15.9)

66

(1.6)

22

(0.8)

88

(1.3)

2 12100

(26.0)

3142

(15.6)

15242

(22.8)

2

(0.05)

1

(0.03)

3

(0.04)

3 6911

(14.8)

1154

(5.7)

8065

(12.1) 0 0 0

4+ 5891

(12.7)

775

(3.8)

6666

(10.0)

1

(0.02) 0

1

(0.01)

Statistical test result χ2=5068; p<0.001 ns

Household

effects 0

3901

(8.4) 3446 (17.1) 7347 (11.0) 110 (2.7) 206 (7.2) 316 (4.5)

1

6706

(14.4) 5011 (24.8) 11717 (17.6) 555 (13.5) 637 (22.2) 1192 (17.0)

2

8392

(18.0) 4126 (20.5) 12518 (18.8) 1249 (30.3) 869 (30.2) 21 18 (30.3)

3

6629

(14.2) 2443 (12.1) 9072 (13.6) 1033 (25.0) 578 (20.1) 1611 (23.0)

4

5111

(11.0) 1684 (8.4) 6795 (10.2) 642 (15.6) 344 (12.0) 986 (14.1)

Assets

type

Number

of items

Rural Urban

Male Female Both Male Female Both

5+

15823

(34.0)

3457

(17.1)

19280

(28.9)

536

(13.0)

241

(8.4)

777

(11.1)

Statistical test result χ2=3412; p<0.001 χ2=217; p<0.001

Very few of the BHHs own any type or number of animals regardless of sex of family head or

locations while larger proportions of females do not own any work related assets (56.6% and

99.2% respectively for rural and urban areas), and household items (17.1% and 7.2% respectively)

compared with males (respectively for rural and urban 31% and 98.3% for work equipment while

for HH items 8.4% and 2.7%).

In urban context negligible proportion of males or females won any work related items while

large proportions of males and females in the rural areas own such items which might largely

include agricultural implements and other handy tools.

More frequent ownership of household items reported by the BHHs compared with animals and

work equipment, is not surprising as some of the items are owned by large proportions of BHHs

(annex table B5). Although a near identical proportion of males (34.1%) and females (34.8%)

own between two to three items nearly twice as many males (43.7%) own four or more items

compared with females (24.8%) indicating that the female heads are relatively worse-off.

Shiree partners have planned largely to transfer animals and to lesser extent work related assets

to the BHHs in order to generate income, which appears to be justified by the above pattern (or

lack) of asset ownership. However, in the urban context trading has been planned to be a major

intervention area and thus capital in cash or kind are likely to be transferred to the BHHs but the

value of business capital is not captured by traditional list of assets that has a rural orientation.

Over the course of shiree the animal category is very likely to register positive change at an

early stage of intervention as most shiree partners have planned/proposed to transfer different

types of animal to the BHHs. Except for DSK in urban and AAB and SKS in rural location all

proposed rural interventions include transfer of animals of different types, sizes and number

regardless whether they are scale up or innovation interventions.

Business capital (in cash or kind) is another area where changes may be observed in the

immediate period particularly in Care, DSK, PAB, SCUK. Relatively quicker return from trading

activities may also increase investment in other assets including house repair and improvement,

purchase of animals.

8. Financial Status

Financial status of BHHs is ascertained with reference to indebtedness, savings, cash household

expenditure and income of all household members (cash and in-kind). Positive changes are

expected in these indicators at the end of the project period compared with baseline, as part of

graduation out of extreme poverty.

8.1 Loans and saving

Data on households having outstanding loans at the time of recruitment in to shiree activities

were collected against four pre-identified sources in order to assess the type and severity of

indebtedness. Up to nearly one-half of the female heads and 43% of their male counterparts

reported to have outstanding loans (table 8.1 below). For females the higher frequency sources

include informal loans without interest (such as from relatives and friends at 48.8% while 34% is

indebted to with interest informal source such as money lenders (which carry very interest rates).

Males more frequently borrow from the latter (42.7%) followed by with interest formal sources

such as MFIs and banks) and without interest informal sources.

The mean amount of outstanding loan is difficult to evaluate without any reference or yardstick

but the females are less indebted compared with males.

Table 8.1: Households reporting to have outstanding loans

Source of loan

With loans

(%) Mean amount outstanding (Taka)

Male

heads

Female

heads

Male

heads

Female

heads Total P

Informal without interest 25.0 48.8 2955 2365 2760 =0.04

With interest informal loan 42.7 34.1 5956 3928 5613 Ns

Formal loan with interest 26.1 6.9 3055 2673 3030 Ns

Loan from shomity or

CBO With interest 1.3 1.5 3545 2664 3347 Ns

Others 7.8 11.4 3319 1815 2910 <0.001

Includes multiple response χ2 = 477; p<0.001

The presence of formal source in cases of over a quarter of males is surprising as this has been an

exclusion criterion for BHH selection. The very small presence of females indicates both the

exclusion criteria and the reluctance on the part of formal sector including NGOs to lend to the

extreme poor.

It remains to be seen if participation in shiree activities leads to any major change in the pattern

of indebtedness. It may increase their credit worthiness with the MFIs and/or that BHHs

themselves opt for more loans from the informal sources with or without interest, in order to

augment the resources transferred from shiree partners.

Savings

Some problems have been observed with data on savings of BHHs. A total of 6,280 reported

‘yes’ to question whether or not they have any savings but the data on the place and the

amount of savings is available for 1,650 households in the CMS-1 database, of whom 210 are

female headed and the male headed number 1,440. The following table 8.2 is based on those

households for whom the amount of saving is available.

Savings is an area that is likely to change very quickly as most partners plan to encourage their

BHHs to take part in saving activities. CMS-3 reports show steady increase in the proportion of

BHH sample where the saving balance is increasing. However, participation in saving schemes is

supposed to be voluntary and not compulsory as per shiree advice to the partners.

Table 8.2: Households reporting to have savings

Place of

saving

With saving (%) Mean amount of saving (Taka)

Male

heads

Female

heads

Male

heads

Female

heads Total

P

(male-female)

NGO 63.8 34.6 990 883 982 ns

At home 14.6 34.1 306 384 327 0.03

Others 21.6 31.3 843 955 862 ns Χ2 =420; p<0.001

8.2 Expenditure

Expenditure data was collected with a list of 45 pre-coded items recording only cash

expenditure in the frequency13 that the responded felt comfortable recollecting. The present

analyses are carried out using those items that the BBS used up to 2005 in their HIES surveys. The

results show that the overall mean monthly expenditure stands at Tk 1377 (SD = 898) and the per

capita per day at Tk. 15.0 (SD = 9.5). these however hide large difference between rural and

urban households: respectively the monthly expenditures are Tk. 1178 and Tk. 3278 while per

capita per day stand at Tk. 13.1 and 32.5 (table 8.3, last two columns).

Sex and NGO pattern

The overall expenditure levels hide significant difference between the rural and urban contexts

(F= 8854; p<0.001), the mean value respectively standing at Tk. 1178 (SD = 561) and Tk. 3280 (SD

= 1239). Among the rural NGOs the mean expenditure for the male and female heads

respectively stand at Tk. 1,522 and Tk. 1057 (F= 137.6; p<0.001), the latter’s expenditure being

69.5% of the former. In the urban context (DSK) there is significant (F=8.213; p=0.004) difference

between the males (at Tk. 3,620) and female heads (Tk. 2,788), and AAB (Tk. 18.2).

Compared with the ceilings for income proposed by DSK for selecting their participants the

overall average expenditure is higher than the first year of implementation (ceiling is Tk. 3,000)

but lower than the second year ceiling (of Tk. 4,500)14.

13 Weekly (such as for regular food items), monthly (such as for education, rent, toiletries) and annually (such as for house

repair) 14 The other difference allowed for in the second year was the ceiling on house rent that was increased from Tk.800 to Tk.

1,000 per month

Table 8.3: Distribution of mean monthly expenditure according to NGOs and sex of household

heads

NGO

Male Female Both

Month Per capita/

day Month

Per capita/

day Month

Per capita/

day

Care 1311 12.5 724 15.0 1164 13.1

DSK 3620 30.8 2788 34.9 3280 32.5

NETZ 1071 10.4 658 12.9 947 11.1

PAB 1435 13.2 917 17.1 1323 14.0

SCF 1270 12. 5 702 11.1 1031 11.9

Uttaran 1439 13.3 989 15.4 1269 14.1

Aid

Comilla 934 10.3 689 11.7 782 11.2

CNRS 1702 13.1 981 15.0 1542 13.5

Green

Hill 1626 14.3 1363 19.0 1576 15.3

IC-1 1549 11.4 970 11.1 1429 11.3

Shushilan 126 1.8 78 1.9 117 1.8

Action

Aid 1471 15.6 974 18.2 1257 16.7

IC-2 928 9.1 554 11.0 791 9.8

MJSKS 1288 12.8 751 17.5 1062 14.8

NDP 1075 10.2 698 13.1 893 11.6

Puamdo 1271 13.4 866 18.9 1140 15.2

SKS 1706 16.2 960 18.9 1340 17.5

Rural 1337 12.5 811 14.5 1178 13.1

Total 1522 14.0 1057 17.0 1377 15.0

Per month mean: sex: F=14.36, p=0.001; NGO: 85.53, p<0.001, interaction: F=50.97, p<0.001. Per capita/day mean: sex: F=

8.7, p=0.022; NGO 37.38, p<0.001; interaction factor: F=56.26, p<0.001

When the rural NGOs and sex of heads are controlled the lower level of monthly expenditure for

females continues across all 16 NGOs (table 8.3 above). The difference among the NGOs and

sexes produces some unexpected but highly significant results (for NGOs F=86.0, sex F= 22.5 and

for sex-NGO interaction F= 51.0; for all p<0.001). The highest levels of monthly expenditure are

found in four Innovation round NGOs: SKS (at Tk 1746), CNRS (Tk 1702), Green Hill (Tk. 1,626) and

InterCooperation or IC-1 in haor (Tk. 1,549).

Other than the urban DSK (Tk. 3620), high expenditure levels are observed in PAB (Tk.1439) and

Uttaran (Tk.1435). The lowest expenditures are found in Aid Comilla and IC-2 (or monga round)

respectively Tk. 934 and Tk. 928.

The expenditure levels for female headed households are reported to be highest among the

NGOs where these are also highest overall. These are: DSK (at Tk. 2788), GH (Tk. 1,363), Uttaran

(Tk.989), CNRS (Tk. 981) AAB (Tk. 974), IC-1 (Tk. 970) and SKS (Tk. 960). The lowest levels of

expenditure for female heads are found in IC-2 or monga (Tk. 554), NETZ (Tk. 658), Aid Comilla

(Tk.689), NDP (Tk.698) and SCUK (Tk.702).

The per capita income per day is consistently higher for the female headed households

compared with their male counterparts for all 17 NGOs with the highest reported in DSK (Tk. 34.9)

and GH (Tk. 19.0); followed by SKS and PUAMDO (for both Tk.18.9) and AAB (Tk. 18.2). the results

are significant with following test figures: for sex, F=5.87, p=0.02; NGO F= 37.40, p< 0.001, inter

F56.26, p< 0.001.

Multiple comparisons reveal that the differences in the mean monthly expenditure are

significant in most cases few exceptions. The mean expenditures in DSK, NETZ, IC-1 and NDP are

significantly different from all other NGOs with p<0.001. SCF, Aid Comilla and CNRS are similarly

different except in comparison with respectively AAB and MJSKS, IC-2 and Green Hill. There is no

significant difference between Care and PUAMDO, and the former is also not different from

Green Hill and latter MJSKS. PAB, SKS and AAB are not significantly different from each other.

Uttaran and SKS are not different from AAB. The remaining mean differences are all highly

significant.

Age effect

Age of the heads appears to be a significant factor in determining the rural expenditure levels

(F= 1256; p<0.001) with the 30 to 39 and 40 to 49 age groups (table 8.4 below) registering highest

levels of expenditure (respectively at Tk. 1,308 and Tk. 1,272). When controlling for sex to see the

effect of age the results are mixed with significant outcomes for sex (F =63.8; p<0.001) and the

interaction factor of sex and age but insignificant for age groups.

Table 8.4: Distribution of mean monthly expenditure according to age groups and sex of

household head

Age group

(years) N

Rural Urban Total

(SD) Male Female Both Male Female Both

Under 29 12014 1244 948 1191 3411 2691 3053 1321 (699)

30-39 21277 1385 1015 1308 3574 2086 3344 1539 (892)

40-49 16668 1450 882 1272 3775 2946 3465 1507 (973)

50-59 10637 1388 758 1124 3810 2448 3340 1328 (945)

60+ 12977 1137 891 891 3395 2251 2923 1038 (829)

Total 73573* 1337 811 1178 3620 2788 3280 1377 (898)

*Does not add up to 73722 due to, non-reporting (livingoncharity), missing data.

Multiple comparisons In the rural context. show that the expenditures for these two groups are

significantly higher than the other groups (p<0.001) except for 40-49 group that have significantly

lower expenditure compared with its younger counterparts. All differences are significant

(p<0.001).

In the urban context, monthly expenditure is highest for the 40-49 years age group (Tk. 3465)

followed closely by the 30-39 group (Tk. 3344) and the 50-59 (Tk. 3340). The urban distribution is

significant for sex of heads (F =56.1; p =0.002) and the ineraction factor (F =12.5; p<0.001) but not

so for the agr groups.

Multiple comparison among the urban age groups reveals that the difference in mean for the

40-49 group is highly significant compared with both the youngest and oldest groups (for both

p<0.001) while it is less so against the 30-39 group (p=0.011) and insignificantly higher than the 50-

59 group.

Education of household heads

The very low levels of education for the household heads that has been observed earlier is

extremely unlikely to change over the lifetime of shiree interventions but it may be useful to

describe the baseline expenditure levels in terms of schooling in the urban and rural locations as

well as by their sex. The monthly per household expenditure appears to increase in both areas

along the education ladder (table-7.5 below) that is significant for overall distribution (F =142;

p<0.001), the rural location (234; p<0.001) and the urban context (F= 24.0; p< 0.001).

Table 8.5: Distribution of mean monthly expenditure according to education and sex of

household head

Education status N Rural Urban Total

(SD) Male Female Both Male Female Both

No schooling 57608 1332 791 1149 3578 2757 3213 1344

(887)

Passed Class I-IV 8277 1344 942 1263 3798 2955 3497 1470

(935)

Passed Class V-IX 6871 1357 977 1307 3651 2982 3499 1507

(882)

Passed SSC and above 816 1397 956 1326 3832 2878 3634 1708

(1164)

All 73572 1337 811 1178 3620 2788 3278 1377

(898)

When multiple comparisons are run to test the differences among the age groups highly

significant results are produced for the overall and the rural context (all with p<0.001 except for

the illiterate category’s mean against the high school category with p= 0.036). in the urban area

the tests are significant for the illiterate group with p<0.001, and for others there is no significant

difference in the mean values among the education categories.

Regional effect

That there is a great urban-rural divide in expenditure levels is evident from the above (table 7.3)

as per expectation but there may also be intra-rural differences among shiree BHHs who are

expected to be from the bottom economic 10% of the local population.

That regional differences contribute to extreme poverty outcomes is a shiree theme for

qualitative research, and is borne out by the broad differences between the north and the

southwest in terms of the two measures of expenditure (table-7.6 below). It also clearly suggests

that the ethnic minorities of the northwest (Tk. 986 per month and Tk. 11.6 per head/day) are the

worst off among shiree BHHs, followed by the victims of cyclones Sidr and Aila in the southwest

(Tk.1101 and Tk. 12.4 respectively). The second highest expenditure is reported in the haor region

(Tk. 1478 and Tk. 12.3 respectively). The differences among the regions are highly significant for

both the monthly average (F= 13470; p<0.001) and per capita per day expenditure (F= 8117;

p<0.001).

Table 8.6: Distribution of expenditure according to regions

Regions Per household/Month

(Tk,)

Per capita/day

(Tk.)

North 1227 13.7

Northwest 968 11.6

Urban 3280 32.5

Southwest 1101 12.4

Haor 1478 12.3

Other 1152 13.0

All 1177 13.2

Multiple comparisons of differences in the mean values among the regions, show that the

differences in both the measures of expenditure among the regions is statistically significant with

p<0.001 except between the southwest and the other category with p=0.001.

Poverty thresholds

Shiree has identified thresholds for extreme poverty separately for the rural and urban locations

with the 2007 prices15 and then adjusted these upwards to account for inflation in 200916. There

are two sets of thresholds each for the rural and urban locations. These are presented in table

7.7 below.

Table 8.7: shiree households below poverty thresholds in rural and urban locations

Location Frequency (%)

2007 prices 2009 prices

Rural

Urban

(<Tk. 22) 98.2

(<Tk. 26) 39.0

(<Tk. 26) 96.8

(<Tk. 30) 52.9

In the rural areas 98.2% and 96.8% of shiree households are below the thresholds for extreme

poverty (respectively below Tk.22 per person per day and Tk. 26) while in the urban areas where

a different set of thresholds are used because of higher cost of living compared with the rural,

39% and 52.9% fall below the urban thresholds (respectively below Tk. 26 and Tk. 30).

15 Based on 2005 HIES survey by BBS, Overseas Development Institute, UK, commissioned by DFID. 16 Mallorie, Edward (2010), commissioned by shiree.

8.3 Income

Income data was collected with a pre-coded list of 34 sources/items on yearly basis for cash

and in-kind earnings for the households. The data was recomputed to arrive at ‘regular’ income

using the BBS definition of income used up to 2005 HIES data collection, in monthly household

and per capita/day measures. These include both the cash and in-kind income and presented

below according to NGOs, age groups and regional differences all being disaggregated by sex

of the household heads.

NGO pattern

The overall mean monthly income is Tk.1,281 (SD= 706) which however hides significant location

and sex differences: the rural-urban income standing at respectively Tk. 1141 and Tk. 2487 (F

=7930; p<0.001) and the male-female at Tk.1416 and Tk. 961 (F = 143; p<0,001). The income for

females as a proportion for the males’ is 67.9%. The distribution of mean monthly income among

the NGOs presented in table 8.8 below are significant (for sex F= 19.2, NGO F=28.0, and the sex-

NGO interaction factor F=85.0, with p<0.001 for all three factors).

Table 8.8: Distribution of income according to NGOs and sex of household heads

NGO

Male Female Both

Month Per capita/

day Month

Per

capita/

day

Month Per capita/

day

Care 1211 11.5 639 13.1 1067 11.9

DSK 2836 24.2 1978 24.5 2488 24.3

NETZ 1064 10.3 647 12.6 939 11.0

PAB 1308 12.1 816 15.2 1202 12.8

Uttaran 1376 13.1 1013 16.8 1240 14.5

Aid Comilla 832 9.2 590 9.9 681 9.6

CNRS 1643 12.6 858 12.6 1469 12.6

Green Hill 1528 13.6 1274 18.4 1480 14.5

IC-1 1285 9.4 817 9.5 1188 9.4

Shushilan 1659 24.0 1136 26.8 1566 24.5

Action Aid 1360 14.3 903 16.6 1163 15.3

IC-2 909 8.9 519 10.2 767 9.4

MJSKS 1158 11.5 602 13.9 924 12.5

NDP 1041 9.8 605 11.0 831 10.4

Puamdo 1071 11.4 718 15.8 956 12.8

SKS 1650 15.6 845 16.7 1255 16.1

Rural 1279 12.2 788 14.6 1151 12.8

All 1416 13.2 961 16.0 1281 14.0

Highest income levels are observed for DSK (Tk.2485/month), Shushilan (TK.1,566), Green Hill

(TK1480), CNRS (TK.1469) and SKS (TK.1255); except DSK all are innovation interventions and at

four different geographic locations. Access to natural resources (except for SKS) such as sea,

rivers and the Sundarbon for the second, for the second hill resources and water of haor for the

third may to some extent explain their high levels of income. The expenditure levels for the rural

four NGOs were also observed to be on the higher side.

The lowest income levels also correspond with lower expenditures with Aid Comilla being the

lowest (at TK.681/month) followed by IC-monga (TK.767), NPD (TK.831), MJSKS (TK.924) and NETZ

(TK.939). Except the first other three NGOs are located in the north testing monga mitigation

activities (except NETZ).

The per capita income levels are higher for the females across all 17 NGOs, in contrast to the

monthly income levels. The on average smaller family sizes of the female heads and the number

of such families compared with male headed may be a reason for this.

Multiple comparisons of differences in mean monthly income among the 17 NGOs produces

mixed results. The incomes in Care, DSK and Aid Comilla are all significantly different from other

14 NGOs with p<0.001. Incomes in Uttaran, Shushilan and IC-2 are insignificant compared with

respectively SKS, CNRS and NDP. Incomes in NETZ, MJSKS and Puamdo are insignificant against

each other. CNRS, Green Hill and Shushilan are insignificant against each other. The mean

incomes in PAB, IC-1, AAB and SKS are insignificantly different from each other. All other

differences in income are – positively or negatively, significant.

In-kind income

It was assumed that the total income of the extreme poor would include earnings in kind such as

food, gleaning of residual crops, catching of fish, gathering of food from the wild and other

natural resources for home consumption as well as for sale. Although the in-kind income as

proportion of the total income is not very large it is present across the rural NGOs in different

degrees. The annexed table-B6 presents some evidence to this effect. Although the overall

mean is 10.6% (SD= 23.8) there is wide variation and significant differences among the NGOs (F=

238; p<0.001).

It is largest for the innovation round NGOs: SKS (34.8%), Aid Comilla (34.5%), MJSKS (27.0%), CNRS

(15.6%) and IC-haor (15.4%%) while it is smallest among PAB, GH and Puamdo (respectively at

5.9%, 3.6%, and 1.8%).

In-kind income constitutes less than five percent (4.3%) of regular income for urban BHHs which

are likely to be from begging, domestic work, child labour etc.

Age effect

As observed for expenditure overall income is also comparatively higher for the age groups

within 30 to 49 years’ range (table 8.9 below) respectively at Tk.1590 and Tk. 1557 and the oldest

group earning the least at Tk. 1105 (F= 595; p<0.001). the differences that are observed among

the age groups are all highly significant with p<0.001 except between those in the under-92

years and 50-59 groups.

In the rural areas when sex is controlled for age effect, the distribution is significant for sex (F= 64;

p=0.001) and sex and age group interaction (F= 167; p<0.001). Monthly income for the younger

women in the under-29 and 30-39 years groups are highest (respectively at Tk.924 and Tk.976)

and then fall steadily with increased age (to as low as Tk.579 for the over 60s) while for the males

income peaks at the age group of 40-49 (TK.1361) and are lower at younger age groups

(between Tk.1204 and Tk.1316) and at older age (between Tk.1329 and Tk.1124).

Table 8.9: Distribution of mean monthly regular income according to age groups and sex of

household heads.

Age group

(years)

Rural Urban Total

Male Female Both Male Female Both N Tk. (SD)

Under 29 1204 924 1161 2751 1905 2292 10516 1361

(712)

30-39 1316 976 1253 2865 2109 2583 18532 1590

(915)

40-49 1361 878 1218 2960 2021 2609 14496 1557

(998)

50-59 1329 739 1088 2900 2000 2503 9316 1375

(963)

60+ 1124 579 883 2504 1663 2158 10839 1105

(851)

All 1279 788 1141 2836 1978 2485 63699 1431

(918)

In the urban context the pattern is slightly different with differences being significant for all three

effects: for sex (F= 428; p<0.001), age group (F14.5; p= 0.01), and for sex and age interaction (F=

2.60; p= 0.034). For the females income is highest in the age group of 30-39 years at Tk. 2109

while for the males it is highest in the age groups of 40-59 between Tk. 2960 and 2900. For both

sexes the lowest income is in unsurprisingly the oldest age group at Tk.1663 and Tk. 2504

respectively for females and males.

Education effect

In general education has a positive impact on income levels as the latest HIES data shows that

among those under the lower poverty line 27.1% in rural and 15.6% in urban have no education

and those completed secondary or higher education accounting for respectively 6.1% and

0.8%. The heads of shiree households may not experience any change in their educational

status during programme participation but some education may enable them to convert the

support from shiree partners in to better outcomes over the project life time.

Overall, income increases with higher level of education to Tk. 1548 for those who passed SSC or

above and the lowest income is observed for the illiterate heads at Tk. 1245, as expected (table

8.10, last column). This distribution is highly significant (F = 238; p<0.001).

On the other hand, in rural areas, although the effect of education, after controlling for sex, is

not statistically significant there appears to be slight increase in monthly income with increasing

education for females and males combined (table 7.10 above). Except for the sex difference

(F= 30.3; p= 0.012) and interaction factor (F= 45.8; p<0.001) any expected pattern of increasing

income with higher education is not discernible either for the females or the males at the present

stage.

Table 8.10: Distribution of mean monthly income according to education and sex of household

head

Education status Rural Urban Total

Male Female Both Male Female Both N* Tk. (SD)

No schooling 1269 769 1110 2813 1952 2433 50531 1245

(698)

Passed Class I-IV 1338 980 1279 2872 2176 2623 5960 1446

(723)

Passed Class V-IX 1288 951 1250 2922 2019 2716 5915 1394

(695)

Passed SSC and above 1303 1004 1264 2932 1961 2719 692 1548

(818) * Total number do not add up to the number of BHHs due to: (i) non collection of any income data by SCUK, and (ii) non-

response, and (iii) missing data.

In the urban context the overall income levels apparently increase with higher educational

status at the baseline, a situation which may change with participation in shiree intervention.

Female disadvantage in terms of lower income compared with their male counterparts

continue. For the females income levels do not follow any pattern but for the males there is small

but consistent increase along the education ladder with the highest income levels are observed

for those who passed class V-X and SSC and beyond respectively at Tk. 2922 and Tk. 2932. The

distribution of income is significant for sex (F = 428; p<0.001), education (F = 14.5; p=0.014) and

the interaction factor (F = 2.6; p=0.034).

Regional difference

The pattern of regional monthly income distribution is different (table 8.11) from that observed for

expenditure, as the income in southwest (Tk. Tk. 1269) is found to be higher than the north (Tk.

1121). The lowest level of income is observed among the primarily ethnic minority BHHs in the

northwest where it is Tk. 941followed by the other category where it is Tk. 1065 (F= 6896, p<0.001).

Multiple comparison of differences in the mean monthly income shows that the differences

among the regions are all highly significant with p<0.001 except in case of the difference

between the southwest and haor.

Table 8.11: Distribution of regular income according to regions

Region Per household/

Month (Tk,)

Per capita/day

(Tk.)

North 1121 12.5

Northwest 941 11.2

Urban 2487 24.4

Southwest 1269 15.4

Haor 1309 10.8

Other 1065 11.9

In terms of per capita income per day the proportion of female headed households and their

smaller family size appear to have influenced the distribution pattern. The smallest per capita

income in the hoar (Tk. 10.8) is likely to be the result of fewer female headed and more male

headed households with relatively large families. The second highest per capita income in

southwest is likely to be due the reverse of the situation in haor. The highest income in the urban

location where 41.1% of the BHHs is female headed, does not follow the family size explanation

for higher per capita income as the urban incomes albeit for extreme poor households are

much greater than in the rural areas.

Poverty thresholds: income

The poverty thresholds as measured by per capita regular income shows (table 7.11 below)

fewer rural households below the two thresholds (86.6% and 92.8% according to measures with

2007 prices and 2009 prices respectively) compared with the thresholds as measured by

expenditure (see table 7.7 above). However the proportions increase in the case of urban

households with 61.9% and 70.9% respectively.

Table 8.12: Distribution of households according to poverty thresholds

Location Frequency (%)

2007 prices 2009 prices

Rural

Urban

(<Tk. 22) 86.8

(<Tk. 26) 61.9

(<Tk. 26) 92.8

(<Tk. 30) 70.9

8.4 Income-expenditure balance

The extreme poor shiree beneficiaries appear to be living beyond their means! Their regular

incomes – in cash and kind, are lower than their expenditures with an overall deficit of Tk.16717

(SD= 538) per month (table 8.13 below). The level of deficit is much higher in the urban location

(Tk. 820, SD = 1077) compared with the rural (Tk.89, SD = 361). However, the male-female

differences in the deficits in the urban is negligible while in rural it is visible (respectively, Tk. 100

and Tk. 63) but statistically insignificant. The effects of NGO and the interaction between NGO

and sex are significant (respectively, F= 135 and F= 5.3 both has p<0.001).

Table 8.13: Distribution difference between income and expenditure (Taka) according to NGO

and sex of household head

NGO Male Female Both

Care -102.0 -84.6 -97.6

DSK -815.7 -827.0 -820.3

NETZ -7.5 -11.0 -8.6

PAB -126.6 -101.6 -121.2

Uttaran -62.9 24.4 -30.1

Aid

Comilla -102.5 -99.7 -100.7

CNRS -58.6 -122.6 -72.7

Green -97.5 -89.7 -96.0

17 Excludes SCF (income data not available) and Shushilan (expenditure data unreliable)

NGO Male Female Both

Hill

IC-1 -264.5 -152.8 -241.4

Action

Aid -111.5 -70.3 -93.7

IC-2 -19.1 -34.9 -24.9

MJSKS -129.9 -148.7 -137.9

NDP -33.4 -92.7 -61.9

Puamdo -200. 5 -147.9 -183.4

SKS -56. 2 -115.5 -85.3

Rural -100.0 -63.0 -89.7

All -163.6 -174.0 -166.7

Table 8.14: Regional distribution of difference between income and expenditure

Region Deficit (Tk,)

North -106.1265

Northwest -26.4168

Urban -820.9797

Southwest -30.2620

Haor -168.8365

Other -86.4718

The regional distribution of the income-expenditure deficits are highly significant (F = 2705;

p<0.001), and multiple difference show that except for the difference between northwest and

southwest other differences are highly significant with p<0.001.

9. Food security

The respondents were asked about the number of months they experienced five food in-take

situations in terms of the number of days they are able to take food. Each household reported

the number of months they were able to take the pre-coded number of meals a day. Never

(zero month in a year) able to take three meals a day without any difficulty was reported by

78.7% (table 9.1 below) while on the other end of the scale only 18.2% never taken one meal a

day in the previous year. Ability to take ‘mostly two meals/day’ was reported by 84.1% of the

BHHs (16.7% for 4-5 months and 67.4% for more than six months ). Food insecurity in terms of

ability to take three meals a day, appears to be a major characteristic of shiree BHHs.

There are very little differences between the male and female heads of households with

exception that 9.7% of males and 16.0% of females reported to take ‘mostly one meal a day’ for

more than six months in the last one year.

Table 9.1: Percentage distribution of households according to food taking status and frequency

Food in-take status

(No of meals/day)

Frequency in last one year Male-

female test 0

month 1 month 2-3month

4-5

month

6+

months Total

Mostly one meal /day

Male

Female

18.2

17.2

20.1

9.9

11.3

7.0

43.1

45.7

37.6

17.2

16.2

19.4

11.7

9.7

16.0

100

Χ2=1228

P<0.001

Mostly two meals/day

Male

Female

2.8

2.5

3.5

0.9

0.9

1.0

12.1

11.6

13.0

16.7

16.5

17.2

67.4

68.4

65.2

100

Χ2 =117

P<0.001

Three meal/day some

difficulties

Male

Female

26

21.9

34.9

11.9

12.2

11.3

40.4

42.9

34.9

12.1

12.7

10.5

9.7

10.2

8.5

100

Χ2=1415

P<0.001

No difficulty, three

meals/day

Male

Female

78.7

78.2

80.1

6.0

6.4

5.3

12.1

12.3

11.4

1.8

1.6

1.7

1.4

1.3

1.4

100

Χ2 =48.5

P<0.001

10. Women’s Empowerment

In order to ascertain women’s empowerment status at the baseline stage three proxy indicators

have been used, namely, asset ownership, income and control over it, and mobility. Data was

collected from one adult ‘responsible’ (wife of male heads and the female heads) from all BHHs.

The questions were asked to women in private (without any male presence). The first two

indicators are likely to suggest women’s ‘fall back’ position.

10.1 Ownership of assets by women

A pre-coded list of items was read out to the women to collect samples on the assets that they

owned themselves. The data on the women’s responses is presented for according to the sex of

household heads. There appear to be some difference in the types of assets owned by women

from the two categories of BHHs. More women from male headed families own jewelry (40.1%)

and poultry (20.5%) compared with those who head their own families respectively at 20.1% and

16.3% (table 10.1). The jewelry is likely to be small pieces of earrings and nose pins usually

provided by parents at marriage. The ownership of land/house is reported by very few women

from male headed households (6.8%) as these are likely to be owned by husbands, unless in

such cases where the women inherited the land from or house paid for by, their parents or

brothers. This is more likely to be true for the female heads, 35.8% of whom report to own

land/house. In many cases women may continue to live on land or in house owned by departed

husbands, sons or brothers.

That only just over a third of the female heads report to own household items is surprising and

can be further inquired in to.

Table 10.1: Percentage distribution of women according to assets and sex of household head

Type of Asset Male Headed Female Headed Both Test results

Land/house 3457

( 6.8)

8239

(35.8)

11696

(15.9)

Χ2 =10586

P <0.001

Productive asset 837

(1.7)

903

(3.9)

1740

(2.4) Χ2 =1046

P <0.001

Livestock 1449

(2.9)

816

(3.5)

2265

(3.1)

Χ2 =666

P <0.001

Poultry 10400

(20.5)

3747

(16.3)

14147

(19.2) Χ2 =866

P <0.001

Sewing machine 161

(0.3)

154

(0.7)

315

(0.4)

Χ2 =280

P <0.001

Other HH items 7390

(14.6)

8375

(36.3)

15765

(21.4) Χ2 =4880

P <0.001

Jewelry 20323

(40.1)

4629

(20.1)

24952

(33.9)

Χ2 =2986

P <0.001

Cash savings 735

(1.5)

588

(2.6)

1323

(1.8) Χ2 =294

P<0.001

Other 233

(0.5)

417

(1.8)

650

(0.9)

Χ2 =512

P<0.001

10.2 Women’s earnings

Women respondents were asked if they had any income of their own and the extent of control

over their income (how it is spent). The frequencies of those women who have income are not

surprisingly different for the women from the two types of households report to have their own

income (table 10.2 below). That around15% women from female headed families report to have

no earnings is likely due to their old age or other physical infirmity that make them dependent on

others such as children, relatives or neighbours. The challenge for shiree and its partners is find

ways to support them because advancing age might not enable them to avail income earning

opportunities.

Table 10.2: Percentage distribution of women according to income and control over incomes

status and sex of household heads

Status

Household Head

Both Male Female

Have income

19364

(38.2)

19480

(84.5)

38844

(52.7)

χ2 = 13640 p<.001

Control over income

None

3051

(15.8)

379

(1.9)

3430

(8.8)

Partial 12382

(63.9)

3133

(10.9)

14515

(37.4)

Full 3720

(19.2)

16867

(86.6)

20587

(53.0)

No response 208

(1.1)

101

(0.5)

309

(0.8)

χ2 = 17753 p<.001

That some women from the female headed households report to have only partial control over

their income – albeit only 10.9%, may suggest the presence of other family members. It is not

surprising that 86.6% of these women have full control over their income (table 10.2 above).

On the other hand having partial control (or joint household decision making) –reported by 64%

from male headed families, may suggest that the women from extreme poor households are

relatively more empowered than common wisdom has it. That around one in five has full

control, on the other hand, may be viewed as indicative of a lack of empowerment at the

baseline! What changes take place – in use of income from female beneficiaries, may need to

be monitored to ascertain if the status of women and household well being.

10.3 Women’s mobility

As a component indicator for women’s empowerment data was collected on their mobility in

terms of the frequency of them visiting different places (as listed in the table 9.3 below). It was

expected that women from extreme poor families would be more mobile compared with other

women with the female heads likely to be more so. The extent of their mobility appears to be

shaped by their own or others’ needs. Female heads are more mobile for meeting their regular

needs such as shopping (between 25.9% and 40.4% of them respectively visiting shops less than

once a month or more frequently compared with between 22.2% and 14.1% for the other

women.

Visit to Union Parishad offices by the female heads is more frequent (21.9% and 6.4%) compared

with women from male headed families (11.6% and 2.1%). This is because the UP offices are

physically close by, and provide different benefits such as safety nets and allowances from the

government. On the other hand more than half of the women from both categories reported

never to visit upazila headquarters probably because of the distance and availability of any

direct benefits.

Visits to hospital is near identically distributed among both groups of women (around 30% from

both groups never visit, less than once a month, more than once a month and the non-

responses).

Table 10.3: Distribution of women according to places of visit, sex of household heads and

frequency

Place of

Visit

Not at all <once

in a month

>once

in a month

No Response Test

Male Female Male Female Male Female Male Female

Shopping 18377

(36.3)

3913

(17.0)

11263

(22.2)

5966

(25.9)

7155

(14.1)

9309

(40.4)

13884

(27.4)

3857

(16.7) Χ2 =7687

P<0.001

Relatives 4779

(9.4)

2767

(12.0)

29391(

58.0)

10794

(46.8)

7975

(15.1)

4411

(19.1)

8834

(17.4)

5070

(22.0) Χ2 =794

P<0.001

Hospital 16267

(32.1)

7023

(30.5)

15192

(30.0)

6786

(29.5)

3195

(6.3)

2308

(10.0)

16025

(31.6)

6925

(30.1) Χ2 =320

P<0.001

Union

Parishad

23073

(45.5)

8292

(36.0)

5864

(11.6)

5040

(21.9)

1078

(2.1)

1474

(6.4)

20664

(40.8)

8236

(35.7) Χ2 =2412

P<0.001

Upazila

HQ

26742

(52.8)

11742

(51.0)

1245

(2.5)

1325

(5.8)

215

(0.4)

327

(1.4)

22477

(44.4)

9648

(41.9) Χ2 =739

P<0.001

Near By

village 7667

(15.1)

2611

(11.3)

21509

(42.4)

8070

(35.0)

10064

(19.9)

7184

(31.2)

5177

(22.6)

16616

(22.5)

Χ2 =1249

P<0.001

Social

Function 16900

(33.3)

7256

(31.5)

12106

(23.9)

5087

(22.1)

3194

(6.3)

2525

(11.0)

18479

(36.5)

26653

(35.5)

Χ2 =486

P<0.001

For Work 17111

(33.8)

4826

(20.9)

3523

(7.0)

2285

(9.9)

8329

(16.4)

7963

(34.6)

21716

(42.9)

7968

(34.6)

Χ2 =3674

P<0.001

11 Conclusion

The present report describes the baseline condition of shiree BHHs within data limitations, as the

situation is before the start of the interventions implemented by the partner NGOs who are

scaling up ‘proven’ experiences or testing out new ideas. Targeting by the partners to reach the

economic bottom 10% of the local population appears to have been robust; very poor

segments of population have been reached with perhaps tolerable degrees of slippages. This is

the class of the poor –some chronic and others transient, who have been left behind by

conventional development efforts including the government and NGOs.

The primary thrusts of the partners’ approaches – transferring material and financial benefits,

appear to be consistent with the near complete absence of resource ownership among the

BHHs. The difference in the types of benefits provided to BHHs between the rural and urban

NGOs – mainly farm-based with some non-farm rural sub sectors in the former and trading and

productive sub sectors in the latter is context specific. Given the high dynamism of urban

economy compared with the rural faster improvements can be expected in the former while the

differences within the latter- in local economy and type of intervention, are likely to determine

rural outcome.

Given the extent of resourcelessness – material, financial and human, achievement of

sustainable graduation out of poverty may require closer than conventional monitoring going

beyond activities and outputs focusing on processes.

Annex A

Annex table A1: CMS1 Coverage up to 16 August 2011

Scale fund NGO Total HH CMS1

Target Data Set

CARE 20,000 20,219

DSK 10,000 7,000

NETZ 9,000 3,042

PAB 16,850 14,882

SCF-UK 15,000 9,873

UTTARAN 12,000 9,581

Total 82,850 64,378

Innovation NGO (round-1)

Total HH

Target CMS1 Data Set

Aid Comilla 1,500 737

CNRS 1,500 755

Green Hill 1,200 1,189

IC (Round-1) 1,000 1,000

SHUSHILAN 1,000 942

Total 6,200 4,623

Innovation NGO (round-2

or monga)

Total HH CMS1

Target Data Set

ActionAid 1,200 1,200

IC (Round-2) 800 460

MJSKS 635 636

NDP 1,000 885

PUAMDO 775 333

SKS 1,000 987

Total 5,410 4,501

Grand Total 94,460 73,492

Annex B

Annex table B1: Percentage distribution of primary occupations of household members (15-65

years) by sex

Occupation Male Female Total

Does not work 8.33 13.71 11.37

Agricultural day labour 35.98 11.07 21.92

Other day/casual labour 21.67 9.46 14.78

Domestic maid 0.47 16.04 9.26

Rickshaw/van/boat/bullock/push cart 13.65 0.12 6.02

Skilled labour 2.15 0.71 1.34

Own agriculture 0.06 0.02 0.04

Fishing in open water 3.34 0.67 1.84

Aquaculture/fish farming 0.17 0.02 0.09

Livestock/poultry 0.04 0.25 0.16

Industrial labour / garment labour 0.97 1.23 1.12

Petty trade/business 3.06 0.78 1.78

Other business 1.69 0.34 0.93

Cottage industry/handicraft 0.60 1.07 0.87

Service 0.34 0.18 0.25

Transport worker 0.18 0.005 0.08

Begging 1.28 3.40 2.47

Scavenging 0.04 0.17 0.11

Rag picker 0.14 0.28 0.22

Housewife 0.32 37.17 21.12

Student 3.63 2.32 2.89

Migrant worker 0.38 0.08 0.21

Others 1.51 0.88 1.16

Total 100.00 100.00 100.00

Χ2 = 62302; p<0.001

Annex table B2: Percentage distribution of female heads according to selected primary

occupations and location

Location Wage labour

Location

Domestic help

Location Housewife Location Begging Location

Pretty trade

Location Others

Puamdo 70.40 SKS 68.00 NDP 12.90 MJSK 27.60 Green Hill 39.80 DSK 32.90

Shushilan 63.10 NDP 56.80 Green Hill 6.60 SKS 22.90 DSK 6.50 Aid

Comilla 22.20

Action Aid

62.20 Aid

Comilla 53.20 Uttaran 5.30 Puamdo 19.40 Care 3.60 SCF-UK 21.80

Uttaran 55.80 MJSK 45.10 PAB 5.00 NETZ 19.10 Puamdo 1.90 NDP 14.80

IC-1 54.10 CNRS 37.50 Aid

Comilla 4.90 Care 17.00 CNRS 1.80 CNRS 14.30

IC-2 51.20 Care 36.20 CNRS 4.80 SCF-UK 16.30 Shushilan 1.80 IC-1 14.00

PAB 48.20 DSK 35.40 DSK 4.00 IC-2 14.90 SCF-UK 1.40 Action

Aid 13.10

NETZ 42.40 SCF-UK 29.00 IC-1 3.90 PAB 14.30 IC-1 1.40 Green Hill 11.90

Green Hill 40.70 NETZ 28.00 SCF-UK 3.30 Uttaran 11.50 PAB 1.10 Uttaran 11.70

CNRS 38.10 IC-2 28.00 Action

Aid 2.30 DSK 10.70 Uttaran 1.10 NETZ 9.20

Care 32.50 PAB 24.30 Care 2.10 Shushilan 10.10 Action

Aid 1.10 Care 8.50

SCF-UK 28.10 IC-1 21.30 IC-2 1.20 Action

Aid 9.70 MJSK 0.70 Shushilan 7.70

MJSK 19.40 Shushilan 16.70 NETZ 0.80 NDP 7.30 NETZ 0.50 PAB 7.10

Aid Comilla

12.30 Uttaran 14.60 Shushilan 0.60 Aid

Comilla 7.20 NDP 0.50 MJSK 7.10

DSK 10.60 Action

Aid 11.60 SKS 0.40 IC-1 5.30 SKS 0.40 SKS 6.40

NDP 7.70 Puamdo 2.80 MJSK 0.00 CNRS 3.60 Aid

Comilla 0.20 Puamdo 5.60

SKS 1.90 Green Hill 0.90 Puamdo 0.00 Green Hill 0.00 IC-2 0.00 IC-2 4.80

Χ2 = 5911; p<0.001

Annex table B3: percentage distribution of rural households according to location and ownership of homestead land

Location Ownership status

Self Khas Not own Missing data Care 38.95 3.75 47.94 9.36 NETZ - 100.00 - - PAB 5.82 71.44 17.06 5.68 SCUK 32.70 42.44 3.30 21.57 Uttaran 6.02 23.55 11.96 58.47 Aid Comilla 37.72 6.38 30.26 25.64 CNRS 43.78 1.06 2.12 53.04 Green Hill - 80.57 0.08 19.34 IC-1 15.10 27.80 52.70 4.40 Shushilan 50.74 12.31 36.94 - Action Aid 67.25 1.33 - 31.42 IC-2 31.09 25.00 40.22 3.70 MJSK 27.67 11.16 57.39 3.77 NDP 20.45 29.04 48.02 2.49 Puamdo 46.55 7.81 39.64 6.01 SKS 89.97 5.57 0.10 4.36 Total 24.18 34.21 23.87 17.74

Annex table B4 Distribution of house size (square feet) according to location and

sex of HH heads

L NGO Sex Mean Std. Deviation N

Care Male 146.0541 56.51722 15227

Female 118.3141 53.10440 4992

Both 139.2052 56.96290 20219

DSK Male 75.5110 32.61702 4407

Female 69.0247 28.61513 2593

Both 73.1083 31.34935 7000

NETZ Male 81.8339 35.28527 2131

Female 78.3754 36.71113 911

Both 80.7982 35.74731 3042

PAB Male 156.2719 56.99292 11925

Female 130.9357 54.50726 2957

Both 151.2377 57.40324 14882

SCF-UK Male 132.1110 76.93252 5801

Female 108.2461 70.20499 4072

Both 122.2682 75.15213 9873

Uttaran Male 134.2417 77.11646 6476

Female 117.0622 68.68765 3105

Both 128.6741 74.91857 9581

Aid Comilla Male 138.2955 81.86396 308

Female 125.8415 72.64398 429

Total 131.0461 76.82428 737

CNRS Male 106.4153 49.23905 590

Female 100.7818 54.17767 165

Both 105.1841 50.37643 755

Green Hill Male 180.2318 104.25354 1182

Female 169.1429 81.34582 7

Both 180.1665 104.11004 1189

IC-1 Male 143.5511 85.43759 793

Female 125.3913 80.15614 207

Both 139.7920 84.65293 1000

Shushilan Female 160.8376 63.66320 942

Total 160.8376 63.66320 942

Action Aid Male 134.3531 47.90468 912

Female 118.7118 42.14382 288

Both 130.5992 47.04714 1200

IC-2 Male 130.4069 47.27773 290

Female 113.6412 49.24526 170

Both 124.2109 48.64021 460

MJSK Male 145.3178 52.86238 365

Female 121.6494 45.43109 271

Both 135.2327 51.15321 636

NDP Male 154.0784 52.85822 472

Female 140.8886 50.96489 413

Both 147.9232 52.36950 885

Puamdo Male 136.9050 53.67685 221

Female 106.3929 58.85857 112

Both 126.6426 57.23568 333

SKS Male 138.7149 52.21949 505

Female 120.7199 49.16380 482

Both 129.9271 51.51674 987

Total Male 137.0312 66.59870 52547

Female 110.7470 60.25871 21174

Both 129.4819 65.92245 73721

Annex Table B5: Percentage distribution of households according to assets and sex of HH head.

Items Male Female Both

% % %

1. Cattle 1.4 0.6 1.1

2. Calf 1.1 0.5 0.9

3. Goat 4.8 3.5 4.4

4. Poultry 1.1 0.5 0.9

5. Pigs 0.5 0.4 0.5

6. Other Livestock 0.8 0.5 0.7

7. Rickshaw 3.0 0.4 2.2

8. Boat 0.7 0.1 0.5

9. Sewing machine 0.3 0.3 0.3

10. Cottage 2.2 1.5 2.0

11. Agricultural implements 60.2 36.0 52.6

12. Fishing Net 5.5 2.0 4.4

13. TV 0.6 0.4 0.5

14. Radio 0.5 0.2 0.4

15. Mobile Phone 1.9 0.8 1.5

16. Bicycle 4.2 0.7 3.1

17. Fan 6.4 7.6 6.8

18. Wooden Box 44.0 27.9 38.9

19. Blanket 44.5 39.0 42.8

20. Furniture 25.4 8.1 20.0

21. Wardrobe 3.9 2.2 3.4

22. Chairs 11.8 4.3 9.4

23. Mattress 8.1 7.6 7.9

24. Bed 80.8 68.4 76.9

25. Other HH Item 97.4 96.6 97.1

26. Gold Jewelry 23.3 17.5 21.5

27. Silver Jewelry 2.2 1.9 2.1

28. Other 17.1 17.0 17.1

Annex table B6: Distribution of percentages of in-kind income in total regular income

NGO Share of in-kind income SD

Care 12.57 25.01

Netz 11.60 30.09

PAB 5.89 16.85

Uttaran 8.51 22.43

AC 34.50 38.20

CNRS 15.64 26.76

GH 3.40 9.86

IC-1 15.36 26.81

Shushilan 5.35 14.91

AAB 12.81 23.38

IC-2 7.81 20.35

MJSKS 26.95 29.60

NPD 7.94 24.95

Puamdo 1.76 11.14

SKS 34.79 34.53

Total 10.5457 23.78278

 

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