Food Insecurity and Undernutrition in the Urban Slums of Bangladesh

A 2013 survey of slum households in Dhaka, Barisal and Sirajganj © World Food Programme, 2015

Cover photo: WFP/Maherin Ahmed Caption: Anwara Begum in Bashantek slum in Mirpur, Dhaka. Design by: Mohammad Inamul Shahriar Food Insecurity and Undernutrition in the Urban Slums of Bangladesh

A 2013 survey of slum households in Dhaka, Barisal and Sirajganj

World Food Programme © Shumon Acknowledgements

The Bangladesh Urban Slum Survey was conducted by WFP in Dhaka, Barisal, and Sirajganj in June 2013. The successful implementation of the survey and production of this report were made possible by the contributions of a skilled and dedicated group of actors.

The survey was organized by WFP’s Vulnerability Analysis and Mapping (VAM) unit, notably Nusha Choudhury, Kayenat Kabir, and Mahabub Alam. Data analysis, GIS mapping, and report finalization were also conducted by the VAM unit, with additional support from WFP’s Regional Bureau in Bangkok, namely Ellen Kiøsterud and Aaron Wise. Katy Elliott, a consultant, also contributed to the drafting of this report.

Training, data collection and management activities were coordinated by the local survey firm Data Analysis and Technical Assistance Limited (DATA). Md. Zahidul Hassan and his skilled team ensured that the survey progressed efficiently and according to the highest of standards. The enumerators who worked tirelessly, often in less than desirable conditions, to collect this data in a timely fashion are also gratefully acknowledged.

Thanks is also owed to several people who immeasurably improved this report by reading and providing feedback on various drafts: SS Arefeen, Anwarul Kabir, Rachel Fuli, Monira Parveen, and Sanne Bakker.

Finally, the Government of Spain, through the Millennium Development Goal Fund (MDG-F), provided funding for the survey. This contribution was fundamental to the implementation of this study and is recognized with much gratitude.

Christa Räder Country Representative World Food Programme Contents

iii 1 7 15 Acknowledgements 1. Introduction 2. Methodology 3. Food security

1.1 Background 2 2.1 Survey Design 8 and nutritional 1.2 Food Security, Nutrition 2.2 Defining Food Security status results and Poverty Context in and Nutrition 9 3.1 Dietary Quantity 16 Bangladesh 4 vi 2.3 Measuring Food Security 3.2 Dietary Quality 17 Figures and Tables 1.3 Survey Objectives 5 and Nutrition Status 10 3.3 Food Consumption 1.4 Summary 5 2.4 Survey Limitations 12 Score (FCS) 18 3.4 Household Food Insecurity Access Scale vii (HFIAS) 19 3.5 Nutritional Status of Abbreviations Women and Children 20 viii Executive Summary 23 33 41 49 57 4. Geography 5. Employment, 6. Dimensions 7. Risks and 8. Conclusions

4.1 Dhaka 24 income and of vulnerability coping Labour Market Dynamics 58 4.2 Barisal 27 economic among 7.1 Risks faced by Urban Slum Dwellers 50 Coping with Uncertainty 4.3 Sirajganj 29 status urban slum 59 7.2 Exposure to Shocks 51 5.1 Employment in the households Access to Public Services Urban Slums 35 7.3 Are the Urban Poor 6.1 Migration 42 60 Coping or Adapting to Risks 5.2 Hours and Wages 36 6.2 Gender 44 and Hazards? 52 5.3 Household-level Work 6.3 Education 44 7.4 Access to Social Safety and Income 37 Nets 54 6.4 Dependency 46 5.4 Household Income, 63 Expenses, Food Security, and Nutrition 38 Annexes

I. Additional Tables 64 II. Survey Questionnaire108 Figures and tables

Figures: Tables:

Figure 1. Households falling below Figure 14. Male and female Table 1. Demographic and Hardcore Food Poverty Line and participation rate by city 36 poverty data for Dhaka, Barisal, Food Poverty Line 16 and Sirajganj districts. 3 Figure 15. Median hourly wage by Figure 2. Mean food share of city and sex 37 Table 2. National and urban expenditures 18 poverty rates, 2000 and 2010. Figure 16. Median hourly wage by 5 Figure 3. Households by Food source of income and gender 37 Consumption Score category 18 Table 3. Sample size Figure 17. Number of income calculation for 2013 BUSS. 8 Figure 4. Households by HFIAS earners in household by city 38 category 19 Table 4. Frequency of food Figure 18. Monthly per capita consumption (days per week) Figure 5. Household responses to income by city (median) 38 17 individual HFIAS questions 19 Figure 19. Average monthly Table 5: Dietary composition Figure 6. Months during which expenditures per capita on food by city (percentage of per households did not have enough and non-food items 39 capita energy from 12 food food (share of all households) 19 groups) 17 Figure 20. Households falling Figure 7. Major Coping Strategies below HFPL and FPL 39 Table 6: Undernutrition figures (food related) 20 for women and adolescent Figure 21. Households by girls, 2013 BUSS, 2011 BDHS, Figure 8. Stunting (0-59 months) 20 HFIAS categories according to and FSNSP 2013 21 expenditure quintiles 39 Figure 9. Households falling below HFPL and FPL (Dhaka) 25 Figure 22. Migration status according to expenditure Figure 10. Average monthly food quintiles 43 and non-food expenditures per capita 26 Figure 23. Households by HFIAS category according to migration Figure 11. Stunting among children status 43 under 5 (Dhaka) 26 Figure 24. Households falling Figure 12. Major shocks below HFPL and FPL according experienced 27 to sex head of household 44

Figure 13. Working status of men Figure 25. Dependency ratio and women 35 categories according to expenditure quintiles 46

Figure 26. Household coping strategies employed in response to recent shocks 53

vi Abbreviations

ADB Asian Development Bank icddr,b International Centre for Diarrhoeal BBS Bangladesh Bureau of Statistics Disease Research, Bangladesh BDHS Bangladesh Demographic and Health IFPRI International Food Policy Research Survey Institute BMI Body Mass Index IRIN Integrated Regional Information BSS Barisal City Corporation Networks BUHS Bangladesh Urban Health Survey LGED Local Government Engineering BUSS Bangladesh Urban Slum Survey Department DATA Data Analysis and Technical Assistance MDG Millennium Development Goal Limited MDG-F MDG Achievement Fund DCI Direct Calorie Intake MICS Multiple Indicator Cluster Survey DFID Department for International Development MUAC Mid-Upper Arm Circumference (UK) NAR Net Attendance Ratio DMA Dhaka Metropolitan Area NGO Non-Governmental Organization FANTA Food and Nutrition Technical Assistance NIPORT National Institute of Population Research Project and Training FCS Food Consumption Score OMS Open Market Sales FPL Food Poverty Line RMG Ready-Made Garments FSNSP Food Security Nutritional Surveillance SCC Sirajganj City Corporation Project UPPR Urban Partnerships for Poverty Reduction GAR Gross Attendance Ratio UNDP United Nations Development GIS Geographic Information System Programme HAZ Height-for-Age Z-score UNICEF United Nations Children’s Fund HDDS Household Dietary Diversity Score UNFPA United Nations Population Fund HFIAS Household Food Insecurity Access Scale VAM Vulnerability Analysis and Mapping HFPL Hardcore Food Poverty Line WAZ Weight-for-Age Z-score HFSNA Household Food Security and Nutrition WFP World Food Programme Assessment WHO World Health Organization HIES Household Income and Expenditure Survey WHZ Weight-for-Height Z-score

vii EXECUTIVE SUMMARY

Bangladesh’s cities are growing at a rapid pace. According to the most recent Census, as of 2011, the urban population of Bangladesh was more than 35 million, or 23 percent of the total population; Dhaka alone was estimated to have more than 14 million residents. The UN estimates that Dhaka could number more than 27 million by 2030 and predicts that urban residents will outnumber rural residents by 2040.

While this transition is contributing to the rapid economic growth that is drawing millions out of poverty nationally, it is also creating pockets of deprivation within the slums of the country’s major cities. Although Government and development partners are beginning to turn their attention to this challenge, the nature and scale of support required is not currently well understood; however, the implications of not addressing it adequately are easy to imagine.

It was within this context that in 2006, WFP collaborated with IFPRI to conduct a survey in the urban slums of four cities to better describe the food security conditions that these households experience. The results from that survey found that nearly half of all urban slum households (48 percent) were consuming less than 2,122 kcal/capita/day. The 2006 study also highlighted that a household’s success in the urban labour market played an outsized role in determining its food security status – those with regular employment and good wages fared much better than those without.

In 2013, using the remaining funding provided by the Spanish Government (MDG-F), WFP conducted a follow-up survey in the slums of Dhaka, Barisal and Sirajganj to determine whether these conditions had changed and to update the profile of urban slum dwellers with undernutrition data on women and children.

Food Security Overall, the 2013 Bangladesh Urban Slum Survey findings suggest that according to several measures, the food security situation in the Dhaka slums has not materially improved over the intervening seven years. Half of slum households in Dhaka (50 percent) and nearly two- thirds of slum households in Barisal (63 percent) were consuming less than 2,122 kcal/ capita/day. Slum households in Barisal and Sirajganj were directing nearly 60 percent of their expenditures towards food, a comparatively higher proportion than the 2010 HIES found for all urban areas (48 percent). Moreover, while the dietary diversity patterns observed in the slums were similar to those found among urban households in previous surveys, the proportion of slum households reporting conditions consistent with food insecurity was very high, particularly in Sirajganj (68 percent). This finding, along with that suggesting food insecurity peaked during May and June in 2013, suggests that urban slum households

viii are particularly vulnerable to shocks, including the the complex makeup of Dhaka’s zones create pockets volatile economic and political climate which prevailed in of elevated vulnerability. The level of vulnerability was Bangladesh during the first half of 2013. especially acute among households located within the Kafrul-Badda and Gulshan areas of the city. More than half Nutrition of slum households in Gulshan and Kafrul-Badda were The undernutrition rates among women and children were consuming less than 2,122 kcal/capita/day compared to high and similar to those found in the 2013 Urban Health 42 percent in Old Dhaka. Nearly half of the children under Survey conducted by NIPORT/icddr,b. Nearly half of all five in Kafrul-Badda were stunted (48 percent), while only children under five in the urban slums (44 percent) were 37 percent of children in Ramna Tejgaon were too short for stunted (low height-for-age) and 16 percent were wasted their age. The factors underlying these spatial patterns are (low weight-for-height); these rates are markedly higher complex, but data are provided which may provide some than those found for all urban areas in the 2012 MICS (36 explanation (e.g., the livelihoods and migration patterns percent and 9 percent, respectively). The relative patterns within these zones of Dhaka are often very different). of undernutrition according to age were consistent with those found in other surveys, such that the prevalence of Income and Expenditures acute undernutrition was highest among 6- to 11-month- The labour market remains a fundamental factor in the olds (24 percent), while the proportion suffering from food security situation of slum households, and the chronic undernutrition accelerated throughout childhood, inequalities in earning power and support structures peaking at 56 percent among those aged 36-47 months. for women, who are increasingly driving the economic engine in cities, are directly contributing to the less-than- The 2013 BUSS findings also suggest that the proportion optimal food security conditions which were observed. of women considered undernourished in the urban slums Findings from the 2013 BUSS revealed that the median was high. Two in five adolescent girls (41 percent) were number of hours reportedly worked by men (224) and found to be thin for their age according to BMI, while women (220) earning an income in the month before the prevalence among all women in the survey was 20 the survey were comparable. The median hourly wages, percent.a Perhaps more telling, 27 percent of women however, were quite different: men earned twice per hour living in households in the lowest expenditure quintile compared to women (Tk 30 per hour vs. Tk 16 per hour). were too thin compared to just 16 percent of women The median hourly wages in Dhaka were also higher for living in households in the highest quintile. both men and women compared to the other two cities, with workers in Sirajganj and Barisal making 25 to 33 Food security and nutrition by city percent less each hour than their counterparts in Dhaka. As mentioned above, the food security and nutrition findings from the survey varied according to city. Slum A majority of households contained only one member households in Barisal and Sirajganj were more food earning an income (54 percent), while a third of households insecure according to a range of measures. Drilling (33 percent) contained two income earners. Households further into the Dhaka findings, the survey revealed that in Dhaka were earning substantially more income each

a These figures are for non-pregnant women in the survey; BMI was not measured for pregnant women.

ix month (Tk 2,633 per capita) than those in Barisal and Based on the findings from the 2013 Sirajganj (Tk 2,100 and 1,500 per capita, respectively). BUSS, a broad set of conclusions and forward guidance are summarised The average monthly per capita expenditures were to support Government and highest in Dhaka (Tk 3,722 per capita) compared development stakeholders’ efforts with Barisal (Tk 2,753 per capita) and Sirajganj (Tk to address the challenges of food 2,260 per capita). Driving these higher expenses for insecurity and undernutrition in the urban slum households in Dhaka were rent (Tk 813) urban slums. as well as elevated health, education and transport costs. Monthly income and expenditures appeared to be positively correlated.b Labour market dynamics Risks and Coping Low participation rates in the labour market among women, The 2013 BUSS found that households across all and highly inequitable pay for similar work compared to three cities were affected by one shock above all men, combine to reduce the ability of urban slum women others over the course of the preceding year—price to contribute to positive food security outcomes in their hikes—which affected a third of the households in households. Some key areas of focus for programme and Dhaka and Barisal and over 60 percent in Sirajganj. policy designers include: Moreover, natural disaster-related shocks such as water stagnation and flooding were reported at Extension of maternity safety net schemes to women in urban a much higher frequency by slum households in slums: Many poor women in rural areas benefit from the Barisal than in Dhaka or Sirajganj. A key source of Government’s Maternity Allowance which provides a adaptation to shocks in the urban slums is to send monthly stipend during this critical stage in life – yet coverage children to work. In times of hardship households in urban areas is low, particularly in the urban slums. Increased also reportedly used their savings to meet their access to this facility would support household incomes and needs or borrowed money from friends and relatives food expenditures when mothers are forced to leave the (24 percent), banks and NGOs or moneylenders. workforce.

Enforcement of maternity leave policies in private sector: Civil Most government social safety net programmes are servant workers in Bangladesh are entitled to six months of targeted at the rural poor with the urban poor in large paid maternity leave; the Labour Act (2006) stipulates that 16 cities (especially Dhaka) largely excluded from such weeks of paid maternity leave be provided to women working support. The percentage of Dhaka slum households in the private sector. Yet studies have shown that compliance participating in specific social safety net programmes with this law, particularly in the RMG sector, is quite low. was extremely low, though 16 percent said they had Receiving this benefit would help prevent against an increase benefitted from Open Market Sales. in household food insecurity that could occur as a result of women losing income during this time.

b The substantial discrepancy between income and expenditures may be partially explained due to 1) all figures reported for income here represent the median for a group or sub-group and 2) incomes are more likely to be under-reported in such surveys. Thus expenditures are the primary measure from which the report draws any conclusions. x Improvement in childcare options: Programmes and especially in Dhaka – existing social safety net policies that aim to improve access to safe and affordable programmes can be extended to urban slum childcare are needed to give urban slum women more households. The objectives, operations, and target choices and flexibility to work and earn an income to populations of these programmes should be re- support the household. evaluated to recognise the needs of the residents of urban slums, which vary from city to city and slum to Support efforts that promote responsible business slum. practices: Government and development partners have a role to play in ensuring the private sector (especially Livelihood and skills training: Support and capacity- RMG) recognises workers’ rights and meets minimum building around employment, entrepreneurship, and standards regarding employee compensation, other life skills (e.g. infant and young child feeding healthcare, training, and equality of opportunity and pay. practices) are needed as well. A lack of skills likely prevents many new migrants from finding more gainful Coping with uncertainty employment and better wages in urban areas. Price hikes and political-related disruptions to their income-earning activities were the most commonly Access to public services reported shocks by households for the year prior. Yet their The limited access to public services also has implications access to/participation in most of the formal social safety for programme and policy designers who are concerned nets, which are more widespread in rural areas, was very with the longer-term challenges to food insecurity and limited. Because these households also have very limited undernutrition in the urban slums. The 2013 BUSS found exposure to the formal financial sector, they reported lower levels of school attendance among older children, often having to take loans from friends or neighbours to confirming findings from previous surveys. This has the cope with such shocks. potential to accelerate the intergenerational transmission of poverty and keep families trapped in the urban slums. Financial literacy training: A large portion of the migrants from rural to urban areas are poor and have little to no Promotion of primary and secondary education: Primary formal education. Therefore, programmes which aim to and especially secondary education services need to build financial literacy – saving, interest, loans, etc. – be improved in the urban slums. In addition, young especially targeted to those workers employed in more people who work must continue to receive some form tenuous work (day labourers, rickshaw pullers), would of on-going schooling and/or skills training that will provide much-needed awareness about how to provide them with better life opportunities (and income- manage the unique economic challenges associated earning potential). with non-agricultural-based livelihoods. Provision of free/low-cost primary health and nutrition Building linkages to formal financial sector: Better access services: Urban slum areas need equitable, accessible among slum households to formal banking, savings, and sustainable primary health care, reproductive health and loans products is also needed. Innovative ideas are care, family planning and nutrition services. Innovative needed that would ensure protection to the interests of models are needed through which development both the banks and the slum households. partners can support the supply of such services with Extension of social safety nets to slums: Few urban slum Government and the private sector. households benefit from social safety net programmes,

xi

1 INTRODUCTION

02 1.1 Background 04 1.2 Food Security, Nutrition and Poverty Context in Bangladesh 05 1.3 Survey Objectives 05 1.4 Summary INTRODUCTION

1.1 Background

Over the past few decades, the urban population in Bangladesh has grown rapidly, primarily due to natural population growth and rural-urban migration.1 In 1974, the first census after Independence recorded the urban population at 6.2 million, representing roughly 9 percent of the country’s total population; in 2011, the most recent census reported 35.1 million people living in urban areas, nearly 23 percent of the total population.2 According to the United Nations Population Fund (UNFPA),3 if this trend continues Bangladesh’s urban population will exceed its rural population by 2040.

Urbanisation is a global trend. According to a global population analysis by the UN,4 there are now more people living in urban areas than in rural areas: as of 2014, 54 percent of the world’s population live in urban areas compared to just 30 percent in 1950, and this figure is projected to rise to 66 percent by 2050. Nearly 90 percent Definition: Slums of the increase will be concentrated in Asia and Africa. India, China and Nigeria alone are predicted to contribute The Bangladesh Bureau of Statistics to around one-third of the global urban population (BBS) defines a slum dwelling as one increase between 2014 and 2050; Bangladesh is one of made of the cheapest materials and seven countries projected to contribute 50 million or more. built on a temporary basis, mainly in Dhaka, the national capital which currently hosts around towns/cities near roads, mills, factories, 25 percent of the national urban population, is projected to small scale industries, railway stations, become the sixth largest mega-city in the world by 2050. market places, or on government- owned land/property. Slums are widely While urbanisation traditionally accompanies the transition accepted to have very poor housing; from an agrarian to industrialised economy, the unplanned high population density and room expansion of urban areas can also create many challenges crowding; poor environmental services, for the well-being of this growing urban population. In especially water and sanitation; low Bangladesh, rapid urbanisation has increased the number socio-economic status and lack of and population of urban slums, particularly in Dhaka. If security of tenure. properly managed, this shift of workers towards the cities can further drive the economic growth that has contributed to reducing the number of Bangladeshis living in poverty.

1 Islam, Nazrul, 1997, Addressing the Urban Poverty Agenda in Bangladesh, Asian Development Bank, pg 45-56. 2 BBS, 2011, National Series: Urban Area Report, 2011. Territorial expansion of existing urban areas and changes to how urban areas are officially defined have somewhat complicated exact measurements of this growth over time. See Population and Housing Census – 2011, BBS for further details. 3 UNFPA, 2011, Bangladesh Country Programme Document, 2012-2016 p2, http://profiles.unfpa.org/bangladesh 4 United Nations, Department of Economic and Social Affairs, Population Division, 2014. World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/ SER.A/352).

2 However, along with the economic promises of an Household Food Security in Urban Slum expanding urban workforce come many structural and Areas of Bangladesh (2006) policy implications that, if not addressed, could result in a In 2006, WFP commissioned a study which aimed to widening wealth gap that many economists now believe improve the collective knowledge and understanding of actually restricts economic growth.5 food insecurity in the urban slums of Bangladesh.6 That survey was conducted among households residing in Many factors are at play which work to increase the slum areas of Dhaka, Chittagong, Khulna, and Rajshahi. vulnerability of urban slum households—for example, The findings were analysed to develop a food security employment opportunities are often tenuous and staple profile of urban slum dwellers, model the determinants food prices can fluctuate widely, making it difficult for of food security for urban slum households and map the households to buy sufficient amounts and types of food intra-urban variation and distribution of these underlying required to enjoy healthy and active lives. Unhygienic, characteristics. One limitation of that study, however, crowded living environments with poor access to was that information was not collected on the nutritional health care and other public services exacerbate the status of urban slum household members. effects of this insecurity. Moreover, the urban poor often have fewer coping strategies to employ in the face of 2013 Bangladesh Urban Slum Survey food insecurity than their rural counterparts. In short, For the present study, WFP sought to update the food urban slum populations need to be healthy, nourished, security situation among urban slum households while educated and have access to sufficient housing, social also complementing this information with key nutrition services and protection if their contribution to the data on women and children. Instead of returning to the economy is to be optimised and sustained. original locations surveyed in 2006, the 2013 Bangladesh Urban Slum Survey (2013 BUSS) was conducted in three Given the scale and speed at which Bangladesh’s cities different types of cities found in Bangladesh: Dhaka, are growing, it is more important than ever to understand Barisal and Sirajganj. Dhaka, the capital, is a megacity; the unique dynamics and contexts with which these Barisal is a growing city in the southern coastal belt; and families must contend as they try to secure adequate Sirajganj is a growing town in the northwest located food amidst other needs and priorities. By collecting along the erosion-prone Jamuna/Brahmaputra River. and analysing data on the urban poor, WFP aims to promote, through positive and collaborative action Both of the studies overlap considerably according to among Government, development partners, and other the time of year during which information was collected stakeholders, a brighter, healthier, and more productive as well as the type of questions posed and thus a strong future for this often-overlooked population. basis exists for the comparison of these data.

Table 1. Demographic and poverty data for Dhaka, Barisal, and Sirajganj districts.

District Populationa Area (km2) Density Household Poverty Stunting Literacy (population/km2) Size rateb ratec rated

Dhaka 14,171,567 1,463 8,229 4.21 15.7 34.2 70.5 Barisal 2,324,310 2,784 835 4.49 54.8 38.3 61.2 Sirajganj 3,097,489 2,402 927 4.31 38.7 45.1 42.1 a Census 2011 (NB: this figure is for entire district) b Poverty Maps of Bangladesh 2010 c Undernutrition Maps of Bangladesh 2012 d MICS 2012-2013

5 Ostry, J D, A Berg, and C G Tsangarides (2014), “Redistribution, Inequality, and Growth”, IMF Staff Discussion Note 14/02. 6 IFPRI, BBS & WFP, 2008, Study of Household Food Security in Urban Slum Areas of Bangladesh, 2006.

3 preliminary results of Bangladesh Demographic and 1.2 Food Security, Nutrition Health Survey (BDHS) 2014 found that 78 percent of and Poverty Context in children had received all basic vaccinations by the age of 12 months.9 There has been an impressive reduction Bangladesh in infant and child mortality by more than two-thirds Food Insecurity since 1990. Moreover, Bangladesh is on track to meet The basic diet of households in Bangladesh consists the Millennium Development Goal (MDG) targets in child mainly of rice, edible oil and vegetables.7 The country is health and maternal health. self-sufficient in meeting its demand for rice and many other food commodities are also produced domestically. However, challenges remain for reducing the high rates Some essential items, such as wheat, pulses, and oil, are of stunting (chronic undernutrition) and wasting (acute mainly imported while the domestic production of fruits undernutrition) among children under the age of five. The and vegetables remain half of what is required.8 MICS 2012-2013 reported that 42 percent of children under the age of five were stunted and 9 percent were Despite impressive progress in domestic food wasted.10 It has been estimated that undernutrition costs production, a large segment of Bangladesh’s population Bangladesh more than Tk 7,000 crore ($1 billion) in lost faces challenges accessing a diet of sufficient quantity productivity every year, and even more in health care and diversity necessary for a healthy life. According costs.11 to the Food Security Nutritional Surveillance Project (FSNSP) 2013, Bangladesh has seen “real wage growth Moreover, the undernutrition rates are higher in the urban in the past five years but food price spikes [have placed] slums than in non-slum areas. The Bangladesh Urban balanced diets beyond the reach of many, particularly Health Survey (BUHS) 2013 reported that 50 percent of the urban poor and rural landless.” The Household slum children below five years were stunted compared Food Security and Nutrition Assessment (HFSNA) 2013 to 33 percent in non-slum areas.12 reported that one-quarter of households nationally were Poverty incidence food insecure (according to the Food Consumption Over the last 20 years, Bangladesh has made Score). significant progress according to key markers of human Very often the factors which underlie food insecurity development and the extreme poverty rate declined in urban areas are different from those in rural settings. by an impressive 17 percentage points between 2000 For example, the urban poor rarely have access to and 2010. However, like other developing countries, land for growing their own food; inter-generational Bangladesh has many citizens who struggle with the support networks tend to be weaker; and unhygienic costs of living or are unable to afford sufficient food to living conditions (and limited access to public services) meet their minimum nutritional requirements. translate into poorer health outcomes. These conditions For many, cities provide a springboard out of poverty. act to limit the range of coping strategies that are Yet while the prevalence of urban poverty and absolute available to households in poor urban areas. numbers of urban poor have declined considerably Undernutrition since 2001, that a large number of urban poor still remain The Government of Bangladesh, with the help and reflects that the promises of better economic outcomes support of its partners, is improving coverage and access have not been fully realised. to basic health, nutrition, and population services. The

7 BBS, 2010, Report of the Household and Expenditure Survey 2010, pg. 50. 8 HKI and James P. Grant School of Public Health (JPGSPH), 2014, State of Food Security and Nutrition in Bangladesh, 2013, Dhaka. 9 National Institute of Population Research and Training (NIPORT), Mitra and Associates, and ICF International. 2015. Bangladesh Demographic and Health Survey 2014: Key Indicators. Dhaka, Bangladesh, and Rockville, Maryland, USA: NIPORT, Mitra and Associates, and ICF International. 10 The World Health Organization’s (WHO) public health critical thresholds for stunting and wasting are 40 percent and 15 percent, respectively. 11 Howlader, Sushil Ranjan; Sethuraman, Kavita; Begum, Ferdousi; Paul, Dipika; Sommerfelt, A. Elisabeth; Kovach, Tara. 2012. Investing in Nutrition Now: A Smart Start for Our Children, Our Future. Estimates of Benefits and Costs of a Comprehensive Program for Nutrition in Bangladesh, 2011–2021. PRO-FILES and Nutrition Costing Technical Report. Washington, DC: Food and Nutrition Technical Assistance III Project (FANTA), FHI 360. 12 NIPORT and ICDDR,B, 2014, Bangladesh Urban Health Survey 2013.

4 Table 2. National and urban poverty rates, 2000 and 1.4 Summary 2010

In short, this report will explore the following questions in a a 2010/11 2000/01 order to better support programme and policy designers

National poverty rate 35.1 48.9 who are working to improve the food security, nutrition and survival of urban slum dwellers in Bangladesh: National extreme 17.6 34.3 poverty rate (%) What is the comparative state of wealth, food security Urban population 35,095,684 31,077,952 and nutrition of urban slum dwellers? Urban extreme poverty 7.7 20 rate (%) Who among the slum dwellers are most vulnerable to Number of extreme 2,702,368 6,215,590 poverty, food insecurity and undernutrition? urban poor What are the key drivers of their food insecurity? Urban poverty rate (%) 21.3 35.2 Number of urban poor 7,475,381 10,939,439 Are there geographic patterns in vulnerability (i.e. worse and better off slums areas) and what might a Population data: Census 2001 and 2011; Poverty data: Household Income and Expenditure Survey (HIES) 2010 and 2000 explain any differences in food insecurity and undernutrition? 1.3 Survey Objectives Have conditions in Dhaka slums improved/ deteriorated compared to 2006? The broad objective of the 2013 BUSS was to develop an updated food security and nutritional profile of urban What are the key shocks faced by the urban slum slum populations in Bangladesh. population and how do they cope with these shocks?

What is the impact of social safety nets, credit and The specific objectives of this study were threefold: saving facilities and community support on food i. To develop a gender disaggregated profile on security? livelihoods, income, dietary intake, water and sanitation, education, women’s labour, and child-care practices which affect food security, health, hygiene and nutritional status in the urban slums; ii. To identify and characterise the most food insecure and nutritionally vulnerable groups among the urban poor and identify common strategies that urban poor households use to cope with food insecurity and other shocks; iii. To identify geographical concentrations of food insecurity within the urban thanas.

5

2 METHODOLOGY

08 2.1 Survey Design 09 2.2 Defining Food Security and Nutrition 10 2.3 Measuring Food Security and Nutrition Status 12 2.4 Survey Limitations METHODOLOGY

2.1 Survey Design The 2013 BUSS was a stratified, two-stage cluster survey designed to provide representative information on slum households in each of three distinct urban areas of Bangladesh – Dhaka Metropolitan Area (DMA), Barisal City Corporation (BCC), and Sirajganj City Corporation (SCC). WFP commissioned a survey firm, Data Analysis and Technical Assistance Limited (DATA), for the development of a sampling frame, strategy, and overall survey implementation. Sample Size The estimated sample size required to achieve the key objectives of the 2013 BUSS was developed by the Vulnerability Analysis and Mapping (VAM) unit of WFP and is presented in Table 3:

Table 3. Sample size calculation for 2013 BUSS

Prevalence Estimated Survey Sample 5% non- Prevalence 5% non- of food Precision Design b Area insecuritya Effect size response of stunting response

Dhaka 42% 0.04 2 1,173 1,235 43% 618 Metropolitan Area Barisal City 52% 0.05 2 767 807 45% 399 Corporation Sirajganj City 61% 0.05 2 724 754 34% 360 Corporation Total 2,796 1,378

a Bangladesh Urban Slum Study 2006 b Bangladesh Demographic and Health Survey 2011

Sampling Strategy To develop the sampling frame of clusters (or enumeration areas) from which the households would be sampled, DATA first compiled a listing of urban slum areas using data sources from the respective city corporations, World Bank, and UNDP’s Urban Partnerships for Poverty Reduction (UPPR) programme. After determining that each survey team could complete approximately 20 households per day of fieldwork, a set of clusters was sampled for Dhaka, Barisal, and Sirajganj from the master listing based on the sample size for each city. Once these clusters had been sampled, DATA staff conducted a complete census/household listing of the cluster in order to prepare for the sampling of households. Households were then sampled from the household listing in each cluster using a systematic random sampling approach. Development of Questionnaires Questionnaire modules for the 2013 BUSS were developed largely from those used in the 2006 study; DATA made some minor adjustments to improve user-friendliness. Following this, a round of formative research was conducted in Narayanganj slum to better understand the field conditions, after which an additional set of revisions and adaptations were made. After the English language

8 version was finalised, the tool was then translated into interviewing methods, reviewed questionnaires for Bangla. problems, and dealt with logistical issues on a daily basis. Each day, supervisors also sampled a set of The household survey questionnaire collected detailed questionnaires (5 percent) randomly from a pile of information on household demographics, education, completed questionnaires and rechecked with the health, employment, housing, food and non-food household to confirm interview quality. At the end of consumption expenditures, asset ownership, urban each day, supervisors and enumerators sat together to agriculture, loans and savings, participation in social discuss and correct any problems arising in the field. safety nets, self-assessment of food security and well- being, recent shocks to household welfare, community Data Entry and Cleaning participation, healthcare utilisation and nutritional status Collected data was then entered into the computer using of women of child-bearing age and children under the a customised MS Access data entry screen developed age of five (see Annex II). The household questionnaire by DATA. Once data entry was completed, two different was administered to each household selected for the techniques were employed to check for consistency survey. The head of household was interviewed to and validity of data as follows: complete the household questionnaire. However, for 1. Five percent of the completed questionnaires were certain modules, different respondents were queried: compared against entered data to measure the for example, the quantity of food consumed (in the food transmission of errors; consumption module) was typically answered by the main female while the price of the item was answered by 2. A logical consistency checking technique was the main male member of the household. employed to identify inconsistencies. If any inconsistencies or discrepancies were found, they Training for Fieldwork were flagged and reviewed. A six-day training was held for survey enumerators at IDB Bhaban (Rokeya Sarani, Agargaon) on best survey practices, including how to administer the questionnaire. 2.2 Defining Food Security WFP staff also participated in training sessions. A pre- test of the questionnaire was conducted, after which and Nutrition a two-day feedback session was arranged and the Food Security questionnaire was further revised for errors detected in Food security defines a situation in which “all people at all the field test before the questionnaire was finalised for times have physical and economic access to sufficient, printing. safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life.”13 Survey Methodology and Field Operation The field survey was carried out by ten teams, each Food security depends upon three main factors: consisting of six enumerators and a supervisor. Each 1. Availability of food: This is the extent to which of the ten teams was responsible for administering the sufficient quantity and quality of food is physically survey in thirteen to fifteen (13-15) clusters as assigned present in an area. It includes food found in markets, by the survey manager. In total, each team conducted produced on local farms or home gardens or provided the household listing in 13-15 communities and as food aid or gifts; administered the detailed household survey to 260-300 households. 2. Access to food: Even if food is available people cannot always access it. Food access is ensured Quality Control and Survey Supervision when communities, households and all individuals (Field, Head Office, Data Entry) have enough resources to obtain sufficient quantity Throughout data collection, quality control was and quality of food for a nutritious diet through a given special focus. One supervisor was responsible combination of home production, stocks, purchase, for overseeing each of the ten teams during the barter, gifts, borrowing or food aid; data collection process. Field supervisors checked

13 Food and Agriculture Organization (November 1996). “Rome Declaration on Food Security and World Food Summit Plan of Action.”

9 3. Utilisation of food: Even if food is available and can mother’s caring practices, and her access to adequate be accessed, inadequate utilisation of it will lead to food. undernutrition. Proper child care, providing a diet with Thus, there is no single measure which fully describes enough energy and nutrients, safe drinking water, food security and nutrition status; instead, a variety of adequate sanitation as well as knowledge of food storage, indicators are used provide insight into the varying processing, illness management and basic nutrition are aspects of consumption and nutrition. essential to achieving adequate food utilisation.

Food insecurity can be transitory or chronic in nature. Transitory food insecurity is of a temporary nature 2.3 Measuring Food caused by a negative event such as a natural disaster, Security and Nutrition Status illness of household member or loss of employment. Dietary Quantity Chronic food insecurity is the persistent inability of a household to meet its dietary needs over a long period. Daily Food Energy Consumption Per Capita14 Its main underlying cause is poverty and is characterised Individuals consuming less than a threshold amount of by seasonal shortages of food. Transitory food insecurity calories required to stay healthy and conduct physical can evolve to chronic food insecurity. activity can be classified as food insecure. Daily energy consumption needs vary according to the age, sex and Nutrition Status physical activity levels of household members. When the The nutritional status of a population is typically body lacks energy it compensates by slowing down its assessed through anthropometric measurements of physical and mental processes. A hungry mind cannot the most vulnerable—children under the age of five. concentrate, a hungry body does not take initiative, and Measurements of micronutrient levels in children and a hungry child loses the desire to play and study. The women (e.g., in blood and urine samples) are also Bangladesh Bureau of Statistics (BBS) uses two thresholds sometimes used to understand nutritional deficiencies in of food poverty (measured using the Direct Calorie Intake the population that are not readily observable to the eye. method15) in its Household Income and Expenditure Survey The first thousand days, between conception to two (HIES). The threshold of 2,122 kcal/capita/day represents years of age, represents the most critical window during the energy requirements in a basic food basket used to which good nutrition and health are established. When calculate the food poverty line. The lower threshold of 1,805 deprived of nutritious food, a child’s growth can be kcal/capita/day represents the ‘hardcore’ food poverty line. impaired, increasing the likelihood that she will become Dietary Quality ill, die, or otherwise not reach her physical or mental The state of being food secure includes households potential. As an adult, she will be less economically having access not only to sufficient quantities of food productive and more likely to be poor, thus perpetuating but also to sufficient quality so that they may pursue an the cycle of food insecurity and undernutrition. active and healthy life. It is this second perspective which Undernutrition is not a simple problem with a single cause the following indicators attempt to provide more insight and solution. Its immediate causes are inadequate dietary on. intake and illness, situations which frequently amplify Dietary Diversity one another to create a vicious circle: undernourished Households consuming an unhealthy diet which lacks children are less resistant to illness and often fall ill, while diversity can be classified as food insecure. Such sick children are unable to absorb nutrients from their households spend a large share, if not all, of their food diet as well as their healthy peers. Children entering this budget on staples (predominately rice in Bangladesh) undernutrition-infection cycle can fall into a potentially which provide a cheap source of calories. These fatal spiral as one condition amplifies the other. The households forfeit more nutritious items that provide underlying causes of undernutrition, however, are more proteins and micronutrients. Dietary diversity is measured complex and are linked to the child’s environment, her

14 Smith, Lisa C., and Ali Subandoro, 2007, Measuring Food Security Using Household Expenditure Surveys. Food Security in Practice. Technical guide series. Washington, D.C.: International Food Policy Research Institute. 15 The Direct Calorie Intake (DCI) method measures poverty incidences by taking into account the minimum level of food energy to maintain normal health as the threshold to measure poverty (BBS, HIES 2005). The other method of measuring poverty used by BBS is commonly known as Income Poverty which is based on the Cost of Basic Needs (CBN) method.

10 by assessing the number of food groups (out of seven) The FCS uses standardised thresholds that typically that a household consumes over a period of seven days. divide households into three groups; however, for this Households consuming four or fewer food groups are report, a fourth category was included to account for considered to have low diversity. The seven food groups the unique context of Bangladesh considering a daily are: cereals, tubers and root crops; pulses; vegetables; consumption of oil and an important consumption of fish.21 fruit; meat and fish; milk; and oil.16 The thresholds that define the four groups are: Staple Dependency ≤28: Poor Another method for measuring food insecurity is to assess >28 and ≤42: Borderline the degree to which the household’s diet is composed of >42 and ≤52: Acceptable (low) staples. Based on food supply data, around 70 percent >52: Acceptable (high) of food energy is derived from staples in low-income developing countries (i.e. cereals, roots, and tubers); Food-related coping strategies22 among developed countries, the figure is approximately When confronted with sudden negative events such 30 percent. Among developing countries, households as a natural disaster, food price spike, illness, or loss classified as having relatively good dietary quality receive of employment, households often cope by, among 55–70 percent of their energy from staples.17 Based on other strategies, buying cheaper foods, switching to this reference, the staple dependency indicator has the less preferred foods, and/or reducing the number of following thresholds:18 meals eaten during the day. These coping mechanisms may have severe nutritional impacts. For this report, 75+: Very high (very poor diet quality) groups have been created to classify the extremity of 60–75: High coping which households may have resorted to during 40–60: Medium the previous year. The scale runs from 1 (Neutral) to 4 <40: Low (Emergency). Households which did not experience Expenditures on Food any shocks were not asked about coping strategies and In addition, the proportion of total expenditures which assigned a zero in the scale. households direct towards food is another measure of Neutral strategies: food insecurity. Those that normally spend a high (>65 Workers in the household take on more work; non- percent) or very high (>75 percent) proportion of their workers in the household start working; members move total expenditures on food are considered food insecure elsewhere to find work; receive help from institution; eat as they have limited flexibility to cope with an increase in less preferred and inexpensive foods food prices, an unexpected household expense, or loss Stress strategies: of a job.19 Spend savings; sell assets; borrow money from a Food Consumption Score20 moneylender, institution, relatives/friends; send children The food consumption score (FCS) combines food to work; send dependents in the household to live with diversity, food frequency (the number of days each relatives elsewhere; purchase food on credit; restrict food group is consumed) and the relative nutritional adult consumption so that children can eat importance of each food group. For each food group the Crisis strategies: frequency represents the number of days an item was Reduce the quantity of consumption and/or pass an consumed, with a range from 0 (never) to 7 (every day). entire day without eating A weight is assigned to each food group, representing its Emergency strategies: Begging relative nutritional importance.

16 Sugar is included in the computation of the FCS but not in the dietary diversity. 17 Bouis, H. and J. Hunt. “Linking Food and Nutrition Security: Past Lessons and Future Opportunities.” Asian Development Review 17 (1999): 168-213. 18 Smith, 82. 19 Smith, 82. 20 Comprehensive Food Security & Vulnerability Analysis Guidelines. January 2009. 1st Edition. World Food Programme (WFP), Food Security Analysis Service. 21 WFP, IPHN and UNICEF, 2009. Household Food Security and Nutrition Assessment Report, pp.65-66. 22 WFP, 2014-2017 Strategic Results Framework, Indicator Compendium.

11 Household Food Insecurity Access Scale Wasting (low weight-for-height) is defined similarly and (HFIAS)23 represents a measure of acute undernutrition. The HFIAS, developed by the Food and Nutrition Technical Assistance (FANTA) Project, is a subjective measure of a household’s access to food and is derived 2.4 Survey Limitations from the responses given to a set of nine standard All exercises in household data collection are subject questions related to food consumption during the to known and unknown deficiencies—which cannot previous month. The questions assess the number of always be accounted for during the design, fieldwork, days in the previous month the respondents or household cleaning and analysis stages—that could potentially members experienced a range of vulnerabilities relating bias the findings. A primary limitation of the 2013 BUSS to lack of food and resources. was that there were likely some slum areas within Dhaka, Barisal, and Sirajganj that were not surveyed. The questions address the situation of all household No complete census and/or mapping of slum areas in members and do not distinguish adults from children Bangladesh existed when the sampling framework was or adolescents. By summing up the coded, weighted being developed; even if one did exist, the dynamic responses (Never – 0; Rarely – 1; Sometimes – 2; Often nature of slums would have surely resulted in new – 3), the following categories are subsequently derived: areas and larger populations than recorded. As such, 1 - Food Secure while every effort was made to create a comprehensive 2 - Mildly Food Insecure sampling frame, the findings contained in this report 3 - Moderately Food Insecure cannot be extrapolated to areas not included in the 4 - Severely Food Insecure survey sampling frame.

Nutritional Status of Women and Children It is also likely that the time-specific nature of some – Wasting, Stunting, BMI questions in the survey—several modules asked Indicators of nutritional status compare the respondents to recall events many months in the anthropometric measurements of children under the past—could have resulted in recall bias, whereby actual age of five (stunting, wasting and underweight) and conditions and events were not remembered correctly. BMI of non-pregnant women aged 14-49 years old The impact of this bias, however, is generally considered against a healthy reference population as defined by less problematic than the bias mentioned above, but the WHO.24 Stunting (low height-for-age) is defined as because some indicators were created using this having a height at least two standard deviations below recalled information, it nonetheless warrants mentioning. the median height for a reference population of children.

23 Coates, Jennifer, Anne Swindale and Paula Bilinsky. 2007. Household Food Insecurity Access Scale (HFIAS) for Measurement of Household Food Access: Indicator Guide (v. 3). Washington, D.C.: FHI 360/FANTA. 24 WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: Methods and development. Geneva: World Health Organization, 2006 (312 pages).

12

3 FOOD SECURITY AND NUTRITIONAL STATUS RESULTS

16 3.1 Dietary Quantity 17 3.2 Dietary Quality 18 3.3 Food Consumption Score (FCS) 19 3.4 Household Food Insecurity Access Scale (HFIAS) 20 3.5 Nutritional Status of Women and Children FOOD SECURITY AND NUTRITIONAL STATUS RESULTS

This chapter presents an overview of the key food security and nutrition findings from the urban slums of Dhaka, Barisal, and Sirajganj surveyed in the 2013 BUSS. A range of food security and nutrition outcomes (according to the indicators defined in Chapter 2) provides a comprehensive understanding of the situation in urban slums. Following this chapter, a more detailed series of sections explores the various underlying dynamics which are contributing to (or inhibiting) the achievement of food security and good nutrition by households in these areas. 3.1 Dietary Quantity Daily Food Energy Consumption per Capita The 2013 BUSS asked the main homemaker in each household the quantity (in grams) consumed of roughly 60 food items during the week before the survey. Her/his responses were used to calculate the total energy consumption of the household (in kilocalories) according to a methodology similar to the Direct Calorie Intake (DCI) method24 which is used by BBS for the HIES exercises. In Bangladesh, BBS have set the minimum calories threshold at 2,122 kcal/capita/day as the food poverty line (FPL); a lower threshold, the hardcore food poverty line (HFPL), is set at 1,805 kcal/capita/day.25

Results from the 2013 BUSS indicate that half of the households in Dhaka’s slums fell below the FPL. The proportion of households falling below the FPL in Sirajganj (48 percent) was slightly lower than that of Dhaka, while the slums in Barisal had the highest prevalence among the three cities surveyed, with 63 percent and 37 percent of households falling under the FPL and HFPL, respectively.

Households with per capita consumption below 1,805 kcal/day Households with per capita consumption below 2,122 kcal/day 70% 60% 50% 40% 30% 20% 10% 0% HIES 2005 (rural) HIES 2005 (all Dhaka 2006 (slum) Dhaka 2013 (slum) Barisal 2013 (slum) Sirajganj 2013 urban) (slum)

Figure 1. Households falling below Hardcore Food Poverty Line and Food Poverty Line

Compared to the findings from the 2006 study, the proportion of households falling below the FPL and HFPL in Dhaka in 2013 was slightly higher. These findings should be viewed in light of the fact that the survey took place in May/June 2013 following a sustained period of hartals and blockades, which shut down transport networks, thereby stripping transport workers in particular as well as street vendors, casual day labourers and rickshaw pullers of their earning

25 BBS, 2005, Household Income and Expenditure Survey, p.69.

16 power. In the first five months of 2013 there were more Table 4. Frequency of food consumption (days per week) than 30 nationwide strikes and blockades. Major food BUSS HFSNA HFSNA Nevertheless, it is important to note that slum households groups 2013 2009 Rural 2009 Urban may be more likely to fall below the FPL and HFPL than both urban households in general and rural households.26 Cereals and tubers 7.0 7.0 7.0 The 2006 study estimated that 48 percent of the urban Dairy products 1.4 1.5 2.1 slum households fell below the FPL and 29 percent Pulses 4.7 2.3 3.1 consumed less than HFPL, the latter of which was 11 percentage points higher than the prevalence found for Oil and fats 7.0 6.9 6.9 rural households and 5 percentage points higher than Vegetables 5.6 5.5 5.8 that found for urban households in the 2005 HIES. Sugar 2.0 2.9 3.9 3.2 Dietary Quality Meat and poultry 2.0 1.4 2.0 Diet Diversity Fish 3.0 3.8 4.2 Even though a wide range of food products is readily Fruits 4.0 0.6 1.4 available in the small shops and larger markets of urban areas, not all households are able to afford the cost of these goods, and those that can may only purchase in Staple Dependency such small quantities that all household members cannot The above figures mask, however, that households tend receive a healthy, nutritious diet. Moreover, households to meet their calorie requirements with large servings may not have the knowledge to ensure that all members of rice, flavoured with small quantities from other food receive a sufficiently diverse diet or that intra-household groups. Surveyed households were found to derive 64 apportioning of food is equitable. percent of their energy from cereals (overwhelmingly On average, households in the 2013 BUSS consumed rice).27 This finding is consistent with the 2010 HIES rice and oil every day; vegetables and pulses were also which found that urban households (slum and non- eaten regularly, while animal protein (fish, poultry or meat) slum) derived 63 percent of their energy from cereals, was consumed less frequently. Fruit was eaten about four chiefly rice. days per week while households consumed sugar and As Table 5 shows, households in Sirajganj were more dairy infrequently. This consumption pattern is consistent dependent on cereals than those in Dhaka and Barisal. with other food frequency findings in Bangladesh and In Dhaka households consumed more sugar and in aligns well with the results from the 2009 HFSNA. Barisal more pulses. The percentage of energy derived Overall, less than one percent of households in the 2013 from protein-rich food groups such as meat, eggs, BUSS had a low dietary diversity as defined by the HDDS. poultry and dairy is very low. The second main source of The majority of households (68 percent) consumed five energy is oils and fats: this again concurs with the 2010 or six food groups each day. HIES survey for all urban households.

Table 5: Dietary composition by city (percentage of per capita energy from 12 food groups)

Cereal Pulses Tubers Vegetable Fruits Meat Poultry Fish Egg Dairy Sugar Oil Other

Dhaka 60 3 3 4 3 1 1 3 1 1 5 12 3 Barisal 62 5 4 4 3 1 1 3 1 1 1 12 3 Sirajganj 71 2 3 3 3 1 0.5 2 0.5 1 1 9 2

26 The 2010 HIES did not publish complete estimates of the DCI method poverty rate which makes comparison with recent figures difficult. 27 According to the HIES 2010, urban households in Bangladesh consumed each day 403 grams of cereals, consisting of 344 grams of rice, 34 grams of wheat and 25 grams of other cereals. 17 It is likely that households which have a high dependency on staples are not consuming other more nutrient- 3.3 Food Consumption and micronutrient dense food groups in large enough Score (FCS) quantities to provide them a balanced and healthy diet. The Food Consumption Score (FCS) is a composite Thus, staple dependency is a useful indicator in the index of diet diversity (8 food groups) and frequency urban slum context where household members eat a of consumption of the respective food groups over a wider variety of foods than in rural areas though in smaller one week period. A very low percentage of urban slum (perhaps nutritionally insignificant) quantities. households in Dhaka and Barisal were found to have The 2013 BUSS found that households in Dhaka and poor or borderline food consumption scores (5 percent Barisal derived around 66 percent of their food energy and 3 percent, respectively); a slightly higher proportion of from staples (i.e. cereals and tubers); the proportion households in Sirajganj were identified as having a poor in Sirajganj was even higher at 75 percent. Overall, 84 or borderline FCS (15 percent). The low prevalence of percent of households derived a high (60-75 percent) households with poor or borderline food consumption in or very high (>75 percent) share of their energy from urban areas was also identified in 2013 FSNSP (8 percent staples. This staple dependency was particularly marked compared with 15 percent in rural areas) and 2009 HFSNA in Sirajganj where 54 and 42 percent derived a high and (17 percent compared with 27 percent in rural areas). very high proportion of calories from staples, respectively. Expenditure on food Poor Border line Acceptable (low) Acceptable (high) 100% Most of the food consumed by urban slum households was bought from markets. In addition, the income 80%

that these households generate must stretch to cover 60% several unavoidable monthly costs such as rent, 40% cooking fuel, water, electricity and transport payments that subsistence households in rural areas are less likely 20% to face. 0% Dhaka Barisal Sirajganj The 2013 BUSS found that households directed more Figure 3. Households by Food Consumption Score than half of their expenditures towards food alone category (Figure 2); this was considerably higher than the amount reported for all urban households in the 2010 HIES (48 percent). In Dhaka and Barisal, an additional 10 percent of households were found to have acceptable (low) FCS; meanwhile, one in five households in Sirajganj had 70% acceptable (low) FCS, which is more consistent with levels 60% found in rural settings. The 2013 FSNSP reported the 50% prevalence of sub-optimal consumption (poor, borderline and acceptable but low FCS) for households in Dhaka, 40% Barisal and Rajshahi divisions as 17 percent, 27 percent 30% and 33 percent respectively. 20% One possible explanation for the seemingly better access 10% to diverse foods among the urban slum households 0% compared to rural households might be the abundance of urban food markets which sell prepared and raw food

(urban) items. A significant proportion of the urban poor have HIES 2010 Barisal 2013 Dhaka 2006 Dhaka 2013 Sirajganj 2013 mobile livelihoods like rickshaw pullers, street vendors/ hawkers, who rely on low-priced street restaurants where Figure 2. Mean food share of expenditures animal products and cereals (mainly rice) are served.

18 Housemaids often eat at their places of work and thus explained in Section 2.3, the HFIAS asks households a have access to a better diet. Meanwhile, the incongruity series of questions regarding their experiences related found between good dietary diversity/frequency and the to food consumption. Sirajganj households revealed relatively high percentage of households that are energy greater vulnerability in their responses to all questions. deficient may be explained partially if urban households eat a relatively wide variety of food groups yet consume Sirajganj Barisal Dhaka small quantities of each food group. Worried that the household would not have enough food 3.4 Household Food Not able to eat the preferred Insecurity Access Scale foods Ate a limited variety of foods

(HFIAS) Ate food that they preferred not to eat The 2013 BUSS found that, according to the HFIAS Had to eat limited portions at measure, households in the Dhaka slums were much mealtimes more food secure than those in the 2006 study.28 The Ate fewer meals in a day percentage of severely food insecure decreased from Had no food to eat at all in the 66 percent in 2006 to 20 percent in 2013, while the household percentage of moderately food insecure remained Went to sleep at night hungry constant at around 21 percent. Meanwhile, the Went a whole day without eating anything prevalence of food security increased from 7 percent

to 45 percent between the two surveys. Households 0% 10% 20% 30% 40% 50% 60% 70% 80% reporting severe food-related stress also decreased Figure 5. Household responses to individual HFIAS dramatically: in 2013, only 17 percent of households questions in Dhaka slums reported having completely run out of food at any point in the previous month (compared to Seasonality of food insecurity 48 percent in 2006). The level of severe food insecurity Slum families often do not know whether they will be (according the HFIAS) found in 2013 among households able to afford to buy food from one day to the next. in the slums of Dhaka is comparable to that of the findings Just 48 percent of households surveyed in Dhaka in for urban areas in the 2013 FSNSP. 2013 reported that they experienced a situation over the last year when they did not have enough food to Severely Food Insecure Access meet their needs. This represents an improvement from Moderately Food Insecurity Access the 2006 study which found that 60 percent of Dhaka 50% slum dwellers experienced a situation in the previous 40% year when there was not always enough food to meet

30% household needs.

20% Dhaka Barisal Sirajganj 70% 10% 60% 0% 50% Dhaka FSNS Dhaka 2013 Barisal 2013 Sirajganj 2013 2013 (all urban) 40% Figure 4. Households by HFIAS category 30% 20% The responses reveal that households in the Sirajganj 10% slums held a far dimmer view of their food security 0% situation than those in the other two city slum areas. In Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Sirajganj 36 percent were classified as severely food Figure 6. Months during which households did not insecure and 32 percent moderately food insecure. As have enough food (share of all households)

28 See Chapter 2 for details on HFIAS and the method of calculation

19 Interestingly, in the 2006 study food shortages did not undermine their resilience to future shocks; households appear to be seasonal (responses peaked slightly for in Sirajganj were more likely to use such corrosive means. April and May). However, in the 2013 BUSS, households’ inability to meet their food needs peaked across all three 3.5 Nutritional Status of cities in May. Women and Children A change in wage rates may go some way towards This section presents the undernutrition findings for explaining this pattern: according to the 2013 FSNSP, children under the age of five as well as those of women during May – August non-agricultural daily urban wage aged 14-49 years old from the 2013 BUSS. The 2006 rates for men and women were below the annual average study did not measure child undernutrition rates; so at Taka 227 and 107 respectively compared with over where possible comparisons have been made with the Taka 250 and 115 for the rest of the year. The same report 2013 Bangladesh Urban Health Survey (2013 BUHS) also found that food insecurity as measured by HFIAS which disaggregated data by slum and non-slum areas.29 peaked from May – August. The 2013 BUSS results revealed that child undernutrition Food-related coping strategies rates (stunting, underweight and wasting) were above A number of factors impact a slum household’s ability to WHO critical threshold levels and were higher for boys buy food including price increases, lack or loss of work, than girls (see Tables A1-A3 in Annex I).30 ill health which prevents an earner from working, and political strikes that affect business and entrepreneurial Stunting Moderate Stunting Severe activity. When faced with such difficulties, households 40% are forced to rely upon various coping mechanisms, 30% some negative in nature, to bridge these difficult times. 20% Notably, just 2 percent of households in the 2013 BUSS reported receiving help from an institution such as the 10% Government or NGO when they experienced such 0% Dhaka Barisal Sirajganj BUHS 2013 MICS 2012- challenges. 2013 2013 2013 (slum) 13 (urban)

The most common coping strategies involve reducing Figure 8. Stunting (0-59 months) the quantity of the food households eat, which is considered a crisis coping strategy, and eating less Almost every other child (44 percent) under the age of preferred and inexpensive food, which is a neutral five was stunted and one-fifth (19 percent) were severely strategy. In Sirajganj 4 percent of households went a stunted. The prevalence of stunting was highest in whole day without eating, which is also a crisis strategy. Barisal at 53 percent with 31 percent of children severely stunted.31 Children were more likely to be stunted from Dhaka Barisal Sirajganj 45% around their first birthday until the age of four years when levels start dropping: some 54 percent of 12-47 month 30% olds were stunted.

15% The 2013 BUHS found a similar proportion of stunted children in the slums (50 percent), a decline from 57 0% percent in 2006, but still 8 percentage points above the national stunting prevalence of 42 percent (2012 MICS). Other anything Did not do Restrict adult without eating

Pass entire day Pass entire 29 of consumption children can eat children ICDDR,B, 2013, Preliminary Report: Bangladesh Urban Health Survey inexpensive food inexpensive Reduced quantity 30 The threshold levels are from a WHO publication: The management of consumption so that

Ate less preferred and less preferred Ate nutrition in major emergencies, Geneva 2000. http://www.who.int/topics/ nutrition/publications/emergencies/en/ Figure 7. Major Coping Strategies (food related) 31 Even while rigorous cleaning protocols were followed for the anthropometric data, some of the results are indicative of systematic biases having been introduced during fieldwork. Most notably, the stunting and wasting findings On average 29 percent of households employed a for Barisal are consistent with those which might be expected when the height ‘stress’ coping strategy and 29 percent a crisis strategy, of children was routinely measured short (more specifically, a clustering of children with height-for-age (HAZ) z-scores around -3 SD and absence of both of which compromise their food security status and such a cluster for weight-for-height z-scores).

20 The 2013 BUHS found that non-slum children were far thin (BMI<18.5) compared with one-fifth of all women of less likely to be stunted (33 percent). childbearing age in the urban slums. Of these teenagers 16 percent were severely/moderately thin (BMI<17) Acute undernutrition among under-fives in the 2013 compared with 7 percent among all women. BUSS was 16 percent, which is slightly below that of the 2013 BUHS and 2006 BUHS (19 percent). The proportion One in four adolescent girls (25 percent) were short in of wasted children was highest in Sirajganj and among stature; the proportion for all women in the 2013 BUSS children aged 6-11 months old. was slightly lower at 20 percent. These undernutrition figures for teenagers living in urban slums were According to the 2013 BUHS more than one in five significantly worse than the national figures for 15- to women aged 15-19 years in slum areas reported having 19-year-olds published in the 2011 BDHS but similar to ever been pregnant, a percentage that had not changed those reported in the 2013 FSNSP (see Table 6). since the 2006 BUHS and which was considerably higher than the rate found in non-slum areas. Early pregnancy The 2013 FSNSP highlights that adolescent girls over the exacerbates an intergenerational cycle of undernutrition. age of 15 years are far more likely to be stunted than 10- to Children born to small and/or undernourished mothers 14-year-olds.32 It states this could be the result of an early are more likely to be undernourished themselves. If they cessation of growth associated with poor nutrition and survive they often have weakened immune systems and early childbearing. The FSNSP report found that adolescent lifelong physical and mental disabilities. women with children under five years old were more likely to have short stature than those without children (43 vs. 28 The results of the 2013 BUSS reveal a worrying nutritional percent), reflecting perhaps the effects of early marriage, status among adolescent girls (aged 14-19 years) in the poverty, and early pregnancy on linear growth. slums. Some 41 percent of 14- to 19-year-olds were too

Table 6: Undernutrition figures for women and adolescent girls, 2013 BUSS, 2011 BDHS, and FSNSP 2013

Short stature Underweight Short stature Underweight adolescent girls adolescent girls women age 15-49 women age 15-49 (<145cm) (BMI <18.5) (<145cm) (BMI <18.5)

2013 BUSS 25% 41% 20% 20% 2011 BDHS 13% 25% 13% 24% 2013 FSNSP 37% 42% 12% 17%

32 Helen Keller International and James P. Grant School of Public Health (JPGSPH). (2014). State of food security and nutrition in Bangladesh: 2013. Dhaka, BD: HKI and JPGSPH.

21

4 GEOGRAPHY

24 4.1 Dhaka 27 4.2 Barisal 29 4.3 Sirajganj GEOGRAPHY

4.1 Dhaka Dhaka, the capital of Bangladesh, is a megacity and according to the 2011 Census was home to around 14 million residents. UN Habitat, which in 2010 estimated Dhaka’s population closer to 15 million, projects that the city will grow to 19 million by 2020 and 21 million by 2025.33 By 2050, Dhaka is predicted to become the sixth largest mega-city in the world.34 Dhaka has an urban population almost four times larger than Chittagong, the port city with second largest urban population.

Dhaka is a dynamic city that has attracted significant industrial investment especially in the ready-made garment (RMG) sector. The results of the 2013 BUSS indicate that Dhaka’s slum dwellers are overwhelmingly economic migrants. Just 16 percent reported having always lived in the city. The majority had come to seek employment (76 percent) or to escape poverty elsewhere. Housing Dhaka slum dwellers have to endure overcrowded, cramped, unhygienic living conditions. More than three in four families reportedly shared a single room; the average size of a room was just 128 square feet. Sanitation is poor: 91 percent of families shared a toilet with other households. Around one in ten were using a temporary hanging latrine (katcha). Although some households reported having their rubbish collected (34 percent), another third were dumping their rubbish into a drain or ditch.

Slum dwellers in Dhaka on average pay more for rent than their counterparts in Barisal or Sirajganj (75 percent of Dhaka slum households were renting their dwelling) and 20 percent reported a major illness of a household member as a shock during the previous year. On top of these challenges, maintaining a steady stream of income was also hampered in 2013 by a series of hartals which were especially disruptive for transport workers and shopkeepers (major livelihoods in the slums).

Chapter 3 presented the food security situation for each of the three city slums surveyed in the 2013 BUSS and showed that the households in Dhaka were generally more food secure than those in Sirajganj and Barisal. Households in Dhaka were found to spend a lower share of their income on food; to consume more kilocalories per capita each day; to be more food secure according to the HFIAS indicator; and to be less likely to employ ‘stress’ coping strategies in the face of shocks. When faced with ‘shocks’ that were likely to affect their ability to meet food and non-food needs, Dhaka households were also less likely to employ negative

33 UN Habitat, State of the World’s Cities 2012/2013: Prosperity of Cities. 2013, pg. 152. 34 United Nations, Department of Economic and Social Affairs, Population Division, 2014, World Urbanization Prospects: The 2014 Revision, Highlights (ST/ESA/ SER.A/352).

24 food-related coping strategies such as restricting the quantity of food or eating less preferred or inexpensive food.

But despite these more favourable conditions, food insecurity and undernutrition were still present within the slums of Dhaka. Overall, though households in Dhaka tended to consume more calories on average than those in the other two cities, some zones within the capital were in fact consuming far less. Dhaka food security and nutrition status (by zone) To better understand the spatial distribution of food insecurity and undernutrition within Dhaka, wards known to have similar characteristics were grouped into zones and named after a dominant thana (or according to how the area is commonly referred by Dhaka city-dwellers).35

More than half of households in Gulshan, Dhanmondi-Mohammad- pur and Kafrul-Badda were consuming less than 2,122 kcal/ Zone capita/day; households in these Samples Wards zones were also more likely to fall below the HFPL (< 1,805 kcal/ Ward Boundary capita/day) than those in other Periphery Unions (Dhaka City) zones of Dhaka (see Figure 9).

Households with per capita consumption below 1,805 kcal/day Households with per capita consumption below 2,122 kcal/day 80%

60%

40%

20%

0% Kafrul-Badda Dhanmondi Gulshan Jatrabari Mirpur Pallabi Old Dhaka Ramna Thana Figure 9. Households falling below HFPL and FPL (Dhaka)

35 A full listing of slum areas surveyed within the three cities is available upon request from WFP Bangladesh.

25 Households in Kafrul-Badda had considerably lower Stunting Moderate Stunting Severe total monthly expenditures than those in other slum 50% zones (see Figure 10), and 15 percent of households in 40% 34% 29% 27%

the zone were directing a high or very high share of their 26% 25% 24%

30% 24% 24% 23% 22% expenditures towards food, the highest rate among all 21%

20% 14%

zones in Dhaka. Prevalence of high staple dependency 11% 9% was also highest in slums of Kafrul-Badda. While 18 10% percent of all slum households in Dhaka had a very high 0% share of staples in their diet, this figure rose to 32 percent Mirpur Pallabi Ramna Gulshan in Kafrul-Badda and 26 percent in Gulshan. Rejgaon Jatrabari Old Dhaka Sabujbagh Dhanmondi Monthly per capita food expenditure Kafrul-Badda Mohammadpur Monthly per capita non-food expenditure 4,500 Figure 11. Stunting among children under 5 (Dhaka)

4,000 Similarly among zones in Dhaka, stunting levels were highest in Old Dhaka (60 percent), followed by Kafrul- 3,500 Badda 48 percent).36

The shock most widely reported by households 3,000 throughout the zones was price hikes (highest in Kafrul- Badda, Old Dhaka, Gulshan and Mirpur-Pallabi). In Old 2,500 Dhaka, reporting of major illness or accident was notably high (36 percent). Experience of shocks appeared to be 2,000 less common among households in Ramna-Tejgaon and Dhanmondi-Mohammadpur zones.

1,500 In response to the encountered shocks, overall 18 percent of surveyed households reported employing 1,000 ‘crisis’ coping strategies – lower than that of the other cities – yet this masks dramatic variations observed

500 1,315 1,573 2,191 2,130 2,020 1,915 1,770 1,767 1,659 within the city. More than 39 percent of households in Kafrul-Badda zone reported using “stress” coping strategies. To cope with the greater number of shocks 0 that households in Kafrul-Badda reported experiencing, they were twice as likely to reduce the quantity of food Barisal Gulshan Sirajganj eaten and to eat less preferred and inexpensive foods. A Old Dhaka

Kafrul-Badda very small proportion (3 percent) of households in Kafrul- Mirpur Pallabi

Ramna Tejgaon Badda reported passing an entire day without eating and

Jatrabari Sabujbagh 9 percent restricted adult consumption so that children could eat (the figure for all slum zones within Dhaka was

Dhanmondi Mohammadpur 2 percent). Figure 10. Average monthly food and non-food expenditures per capita

36 The systematic bias noted in footnote 31 may also be suspected for anthropometric findings in Old Dhaka: the prevalence of wasting in this zone was extraordinarily low.

26 Price hikes Political unrest affecting employment Major illness or accident Loss of employment Household business failure Loss of property due to theft, flood, fire, etc. 70% 60% 50% 40% 30% 20% 10% 0% Barisal Mirpur Pallabi Ramna Tejgaon Gulshan Sirajganj Jatrabari Old Dhaka Sabujbagh Dhanmondi Kafrul-Badda Figure 12. Major shocks experienced

Possible contributors to food insecurity and undernutrition in Kafrul-Badda Political unrest and food price hikes As noted elsewhere, the main shocks experienced by Dhaka slum households were price hikes followed by political unrest, major illness, loss of employment and business failure. As Figure 12 shows, Kafrul-Badda and Old Dhaka households were more likely to experience these shocks than those in other zones. Households in Kafrul-Badda reported experiencing a higher than average share of price hikes (51 percent compared to 35 percent overall) and political unrest (37 percent versus 22 percent overall) as well as business failure (11 percent compared to 7 percent overall) and loss of employment (21 percent compared to 17 percent overall), while Old Dhaka households reported a higher incidence of major illness (36 percent compared to 19 percent overall). Education levels Some 46 percent of Kafrul-Badda’s adult population reportedly had no education at all compared to the overall Dhaka slum average of 35 percent. Just 72 percent of 6- to 10-year-olds were enrolled in primary school (89 percent for Dhaka slums overall) and only 43 percent of 11- to 15-year-olds were enrolled in secondary school (73 percent for Dhaka slums overall). Unsanitary, cramped conditions Nearly all (90 percent) of households in Kafrul-Badda were reportedly living in a single-roomed, corrugated iron dwelling. The majority used wood for cooking whereas in other Dhaka slums households were more likely to use gas. Kafrul-Badda households were also almost three times more likely to have a pit latrine (katcha) than other Dhaka slum households. They were also more likely to dump their rubbish in an open ditch or drain (61 percent).

4.2 Barisal and 40 percent living below the upper poverty line (21 Barisal is an old port on the Kirtankhola River, on the percent national urban poverty incidence). northern shore of the Bay of Bengal, 142 km south of Around 18 percent of the slum dwellers in Barisal had Dhaka. It is the capital of Barisal division. According to migrated to the town within the last 10 years; of these, the 2011 Census, Barisal’s urban population stands at most arrived in search of employment (75 percent) or to 339,308, making it the ninth-most populated city in the escape poverty (8 percent) or river erosion (7 percent). country. The UN projects that the population of Barisal Most were either self-employed (47 percent) or daily will nearly double to 653,000 by 2030.37 wage earners. Half of the female workers reportedly According to the 2010 HIES, urban extreme poverty earned income as unskilled day labourers or household incidence in Barisal division was highest among all helpers or in specialised trades (such as clerk or teacher) divisions, with 24 percent living below the lower poverty while 55 percent of the working men did so as unskilled line (versus 8 percent national urban poverty incidence) day labourers, in specialised trades (e.g. mechanic or

37 United Nations, Department of Economic and Social Affairs, Population Division (2015). World Urbanization Prospects: The 2014 Revision, (ST/ESA/SER.A/366).

27 electrician) or as a hawker/peddler. Compared to the six food groups a week (26 percent versus 35 percent in other two cities, a smaller proportion of working men in the other two areas). Barisal were doing so as rickshaw pullers or transport By comparison with Dhaka and Sirajganj, women in helpers. Barisal of childbearing age were less likely to be short Housing (< 145cm), severely/moderately thin (according to BMI) Most slum households in Barisal were renting (53 and have a MUAC of less than 23cm. Likewise, children percent), although a not insignificant proportion also under five were less likely to be wasted. However, these reported owning their dwelling (31 percent); on average, children appeared more likely to be stunted. Some 31 these households occupied two rooms. The average percent of under-fives were severely stunted (compared living space was nearly twice as large as that for to 19 percent in Dhaka) and 53 percent were stunted (44 households in Dhaka slums: 215 square feet compared percent Dhaka). with just 128 square feet in the capital. Contrary to slum However, while according to calorie intake slum households in Dhaka or Sirajganj, a high percentage of households in Barisal performed comparatively worse the households lived in dwellings which had several than those in Dhaka and Sirajganj, by several other separate structures (40 percent). The average number of measures of food insecurity households in Barisal were in people sharing a room (2.6) was also less than in Dhaka fact more similar to those of Dhaka and thus substantially (3.3) or Sirajganj (3.0). better off than those in Sirajganj. Households in Barisal Ninety-five percent of the slum households in Barisal were found to perform relatively well according to the reported using wood as cooking fuel. The main source FCS; devoted a lower proportion of their spending of drinking water was tubewell (91 percent). Though towards food compared to households in Sirajganj; sanitation was poor, a smaller proportion of households were much less likely to be identified as moderately or in Barisal slums reported sharing toilets (65 percent) with severely food insecure according to the HFIAS; and others households than the other two cities. much more likely to report they had experienced no shock in the previous year. This more comprehensive Barisal food security and nutrition status set of findings indicate that, generally, households in the As noted in Chapter 3, households in Barisal’s slums were slums of Barisal were more similar in their food security more likely to be energy deficient than their counterparts outcomes (and underlying patterns) to those in Dhaka in Dhaka and Sirajganj: 37 percent fell below the 1,805 and thus better off than households in Sirajganj. kcal/capita/day threshold (versus 25 percent in Sirajganj) and 63 percent fell below 2,122 kcal/capita/day (versus Low educational attainment among mothers is usually 48 percent in Sirajganj). strongly correlated with child undernutrition. Education levels in Barisal, however, were relatively good. By comparison with Dhaka, households in Barisal derived According to the 2011 Census, the literacy rate among less energy from cereals as well as all protein-rich groups women in Barisal was 75 percent, one of the best in the – meat, fish, dairy, poultry and egg. They were less likely country. The 2013 BUSS also found that children were to have high dietary diversity i.e., to consume more than

Possible contributors to food insecurity and undernutrition in Barisal Lack of income Households in the slums of Barisal still managed to earn less income per capita overall than those in Dhaka. A smaller proportion of women reportedly participated in the labour force in Barisal compared to Dhaka as well. Increasing the income earning opportunities for women, and the skill levels of the labour force in general, will be required in order to see further improvements. Migration The proportion of households which had always lived in the slums of Barisal was higher than that found in Dhaka (though lower than that of Sirajganj). Other findings in the survey suggest that there may be an association between length of time in the slums and poorer food security and nutritional outcomes. This tendency to be “trapped” may thus be more present in Barisal. However, the general migration findings suggest that households in Barisal were more likely to come in search of employment (i.e. pull factors) and not because they were forced from rural locations against their will.

28 more likely to be enrolled in primary and secondary was larger than that of Dhaka households and similar to school at the time of the survey than in the other two that of households in the slums of Barisal. cities; overall both men and women were less likely to A sizeable proportion of respondents noted that flooding have ‘no education’ and more likely to have completed of their rooms was common (45 percent). Many families secondary school or higher. Findings such as there are living in illegal settlements along the river bank are forced difficult to interpret and often mask a more complex to move during the monsoon, constructing temporary array of underlying factors influencing nutritional status. shelters in dangerous and unsanitary conditions along a More investigation is required to determine if the roadside for several months. According to the findings, nutrition findings for children under five are valid and, slum households in Sirajganj typically used wood (56 if so, to provide a framework for understanding such a percent) or straw/leaves/husks (16 percent) for cooking counterintuitive result. fuel; few households were using gas or electricity. 4.3 Sirajganj Sirajganj food security and nutrition status Sirajganj, the main town in the district of the same name, Findings from the 2013 BUSS show that slum households is about 110km northwest of Dhaka, beside the Jamuna in Sirajganj were more vulnerable to food insecurity than River. It used to be a major jute trade centre to rival those those in the capital. The most food insecure were poor of Kolkata and Naranyanganj. According to the 2011 households and those headed by women. Census Sirajganj has a population of 167,200, making it the twenty-fourth most populated city in Bangladesh. While the proportion of households with low calorie consumption was found to be similar to that of slum Unsurprisingly, given the size, position and lack of work inhabitants in Dhaka, staple dependency in Sirajganj was opportunities in the town, the slums of Sirajganj barely very high: some 54 percent of households were very resemble those of Dhaka. They tend to manifest as highly staple dependent compared with just 17 percent clusters of temporary settlements constructed primarily in the Dhaka slums. In Sirajganj, this prevalence rose from corrugated iron with mud or sand floors. Appearing to 60 percent among female-headed households and more like a collection of villages, they are situated on the 68 percent for households in the poorest expenditure edge of the town along a disused railway and along the quintile.38 To facilitate interpretation of these and other river bank. 2013 BUSS findings related to wealth quintiles, however, it According to 2013 BUSS findings, most of the households must be noted that most slum inhabitants (even those in in the slums of Sirajganj had always lived in the town (86 the ‘wealthiest’ quintile) are poor compared to non-slum percent). Of those households which had migrated into urban households. Sirajganj, most reported having done so because they Nearly one in six households in the slums of Sirajganj lost their rural homes to river erosion (42 percent) or (15 percent) were found to have a borderline FCS, were seeking work opportunities (26 percent). Half of the almost triple that found in Dhaka. This may suggest that female workers earning an income did so as unskilled households in Barisal do not have similar access to the day labourers, household helpers or simple traders (such wide array of food items that their counterparts in Barisal as potters, tailors), the majority of working men earned an and Dhaka find in urban markets. Moreover, 33 percent income as unskilled day labourers, rickshaw pullers, and of female-headed households Sirajganj were found transport helpers, while another 20 percent identified as to have a borderline FCS compared to just 13 percent specialised traders or hawkers/peddlers. among male-headed households. The proportion of Housing households with borderline FCS was also elevated in the Contrary to the situation in Dhaka, very few households lowest expenditure quintile (22 percent). in the slums of Sirajganj reported renting their rooms. The survey also found that, according to the HFIAS, some A high percentage were squatters (46 percent) and 36 percent of households in Sirajganj were classified as another quarter reportedly owned their dwellings. The severely food insecure and 32 percent as moderately average living space for a household (221 square feet)

38 Expenditure quintiles in the 2013 BUSS were computed by totalling the amount households reported spending on food and non-food items prior to the survey and ranking these amounts from highest to lowest; the bottom 20 percent were then categorised into Quintile 1, the next 20 percent into Quintile 2, and so forth.

29 food insecure; taken together, this was more than three level of vulnerability compared to the other two cities times the rate found in Dhaka (20 percent). In the 30 days surveyed. Nearly a quarter of all non-pregnant women before the survey, two out of three households reported aged 14-49 years (24 percent) were found to be too thin having to limit their portions at meal times because there according to BMI (i.e. less than 18.5), highest of the three was not enough food and one out of four households cities. Moreover, the proportion of pregnant women had at least one household member who went to sleep with a MUAC measurement less than 23 cm was 23 hungry. Similarly, 85 percent of households said there percent, double that found in Barisal (11 percent). The was a time in the last month when they did not have results for children under five were also discouraging. enough food to meet their family’s needs compared to One in five children (20 percent) were found to be 48 percent of households in Dhaka. acutely undernourished and 43 percent chronically undernourished. While further exploration and analysis is As Figure 5 showed, households in Sirajganj were required to more firmly establish the factors contributing considerably more likely to make food-related to the increased prevalence of undernutrition observed compromises than those in the other two cities where in Barisal, these general findings suggest that immediate a similar proportion of households use food-related attention is also required from Government and coping mechanisms. development partners to begin reducing such poor The nutritional status findings for women and children nutritional outcomes. in the urban slums of Sirajganj also suggest an elevated

Possible contributors to food insecurity and undernutrition in Sirajganj Political unrest and food price hikes The main shocks reportedly experienced by households in the slums of Sirajganj were price hikes (62 percent) followed by political unrest (43 percent), loss of employment (19 percent) and major illness (17 percent). Low education levels Almost half (47 percent) of Sirajganj’s slum dwellers had received no education and a further 23 percent had dropped out of primary school. While the percentage of children currently attending primary school was only slightly below that of the other two cities (86 percent), only about half of the 11- to 15-year-olds were currently enrolled in secondary school compared to nearly 75 percent in Dhaka and Barisal. Poverty More than 80 percent of households in Sirajganj were in the first and second wealth quintiles (64 percent and 18 percent, respectively). This suggests that households in Sirajganj were disproportionately poorer than the slum households surveyed in Dhaka and Barisal. Scarcity of work, low wages, especially for women Work appears scarce, especially for women, whose participation in the labour market was the lowest among the three cities: just 9 percent of women worked for an income compared with 27 percent in Dhaka. Women earning an income in Barisal were bringing home just Tk 1,500/month, or half of that earned by women working in the slums of Dhaka. The median hourly wage for working women in Baisal was Tk 11/hour (compared with Tk 16/hour for women in the slums of Dhaka). Corrosive coping mechanisms Coping mechanisms often further erode their capacity to face future shocks and render them vulnerable to food insecurity and undernutrition. Households in Sirajganj relied more frequently on food-related coping strategies in the face of shocks than those in Barisal or Dhaka.

30 31

5 EMPLOYMENT, INCOME AND ECONOMIC STATUS

35 5.1 Employment in the Urban Slums 36 5.2 Hours and Wages 37 5.3 Household-level work and income 38 5.4 Household income, expenses, food security, and nutrition EMPLOYMENT, INCOME AND ECONOMIC STATUS

Because very few households in the urban slums grow their own food,39 the food they consume is almost exclusively purchased from the local markets and street vendors. Urban households also have many non-food expenses (rent, transport, etc.) for which their rural counterparts are likely to pay less (if at all). In this regard, an urban slum household’s vulnerability to food insecurity and also child undernutrition is greatly affected by its ability to find work and generate income in the urban labour market.

The 2006 study found that paid employment was central to the food security status of urban slum households. In that survey, higher wages of the household head were found to be significantly correlated with the household consuming a sufficient amount of calories as well as having better dietary diversity and being less food insecure in terms of HFIAS. The stability of employment also proved important, such that households headed by individuals whose income source was tenuous (e.g. day labourer) were found to be more food insecure than those headed by permanent employees.

While the importance of employment and income to the food security of households and child nutrition status in urban Highlights slums is perhaps self-evident, the 2013 BUSS sought to better describe and understand the various dynamics within Male and female workers in Dhaka the urban slum labour market which determine whether work longer hours for a higher hourly households are able to thrive in this environment. wage than those in Barisal and In this chapter, a brief overview is provided of the key Sirajganj. livelihoods in the urban slums of Dhaka, Barisal, and Sirajganj The main occupations for male slum and the general participation levels of men, women and dwellers are rickshaw pulling, children in the labour market. A review of the hours worked specialised trades, unskilled day and wages earned is also presented in order to make concrete labour, hawking/peddling and serving the actual (and differential) economic benefits which men as helpers in shops. and women receive from this labour. This information is then aggregated and presented from the household-level Hourly wages for women are roughly perspective as households tend to operate as economic half of those of men across all units in their effort to secure enough food for their members. occupations and locations. Teenage Finally, the linkages between household economic status, girls and young women working for food insecurity and child undernutrition are explored, along income do so primarily in the garment with a brief discussion of the potential areas for investment industry. which may materially improve these conditions for the lowest-performing households.

39 Only one percent of urban slum households in the 2013 BUSS reported growing any crops, fruits, or vegetables, keeping livestock, or owning agricultural land of any type.

34 5.1 Employment in the of income-earning women working in salaried jobs was much higher (71 percent), which largely reflects the Urban Slums outsized role that the RMG industry plays as a source of Findings from the 2013 BUSS reveal that roughly 70 income for women living in urban slums. percent of all men in all the surveyed slums had worked for income in some capacity during the previous month (Figure 13).40 In contrast, less than a third of all women (30 percent) had worked for income during that time.41 The Ready Made Garments (RMG) industry employs around 4 million Men Women 80% workers in Bangladesh (80 percent 42 70% of whom are women ); only 60% agriculture provides more jobs in 50% the country.43 It has been estimated 40% that around 15 percent of all 16- to 30% 20% 30-year-old women work in the 44 10% garment industry. 0% No work/labour Work but no Work and earn- income ing income Figure 13. Working status of men and women There were some notable differences among the Among those men reportedly earning an income, the most livelihoods that men and women pursued according to common livelihoods were rickshaw pulling (16 percent), the city in which the slum was located. Nearly a third of specialised trades such as electrician/mechanic (14 women in Dhaka (31 percent) were reportedly working percent), hawking/peddling (12 percent) and unskilled for an income, while just half that proportion were doing day labour (12 percent). Among the females who reported the same in Barisal and Sirajganj (17 and 15 percent, earning an income, two out of five (39 percent) did so as respectively). This reflects that the RMG industry is a garment worker. The second most important source of primarily based in Dhaka, though there may be additional income for women in the urban slums was as a maid or characteristics of women in Dhaka that are associated household helper (21 percent). with an increased propensity to work.

It is notable that women living in urban slums appear more Due in part to the less dynamic labour market in Barisal and likely to work for income than those living in urban areas Sirajganj, a much higher proportion of men in the slums of more generally: the 2010 HIES found that 24 percent of these two cities reported earning income as unskilled day women living in urban areas worked. Similarly, the 2013 labourers (24 and 21 percent, respectively) than in Dhaka Bangladesh Urban Health Survey found that women in (11 percent). This pattern was even more dramatic among slum areas were more likely to work full time than women women: nearly one in five women earning an income in in non-slum and other urban domains: one in three Barisal and Sirajganj did so as unskilled labourers, while women in slums was working compared to just one in six just 5 percent of women in Dhaka worked in such activities. in non-slum areas. Linked to this dynamic in which garment factory jobs Regarding the stability of this work, the 2013 BUSS found dominate income opportunities in Dhaka was the finding that 42 percent of men were earning an income through that the largest source of earned income among men self-employment, while 33 percent were working in and women living in Dhaka slums was from a salary (the salaried jobs. About one in four men (24 percent) reported standard contract type in the RMG industry). In contrast, that they earned daily wages. Meanwhile, the proportion working men and women in Barisal and Sirajganj slums

40 The 2013 BUSS collected employment/work information for all household members aged 5 years and older; for children under 10 years, the mother/guardian was asked to provide this information. 41 Those women not earning an income were most often classified as homemakers (44 percent) or students (18 percent). 42 Khatun, F., M. Rahman, D. Bhattacharya, K. G. Moazzem, and A. Shahrin. 2007 “Gender and Trade Liberalization in Bangladesh: The Case of Ready-made Garments.” 43 Bangladesh Garment Manufacturers and Exporters Association (BGMEA). 2013 “Ready-made Garment Yearbook.” 44 Heath and Mobarak, Manufacturing Growth and the Lives of Bangladesh Women, January 2015.

35 were most likely to report that their income sources rate among this cohort of women stands at 45 percent came from less reliable forms—daily wages or via self- in Dhaka (compared to just 12 percent in Barisal and 8 employment. percent in Sirajganj). A more traditional pattern of labour market participation is observed over the remainder of Findings from the 2013 BUSS also reveal patterns the lifecycle, such that 20- to 39-year-old women are least regarding the participation in the labour market according likely to be working for an income, whereas this range to age and gender. Figure 14 shows the proportion of 15- coincides with the peak participation among men. The to 19-year-old women working for an income (44 percent) proportion working for an income drops off dramatically is nearly 1.5 times the rate for women overall (31 percent). late in life for both men and women. This pattern emerges more strongly when the results are displayed according to city, such that the participation

Dhaka (Male) Dhaka (Female) Barisal (Male) Barisal (Female) Sirajganj (Male) Sirajganj (Female) 100%

80%

60%

40%

20%

0% <15 years 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65+ years years years years years years years years years years years

Figure 14. Male and female participation rate by city

labourer (Tk 20 per hour) were among the best paid jobs. 5.2 Hours and Wages There were also interesting patterns in the number of hours Findings from the 2013 BUSS reveal that the median worked and wages according to whether an individual number of hours reportedly worked by men (224) and was living in the slums of Dhaka, Barisal, or Sirajganj. In women (220) earning an income in the month before part reflecting that garment factory jobs disproportionately the survey were comparable. The median hourly wages, account for the employment opportunities in Dhaka, the however, were quite different: men earned twice per hour median number of monthly hours worked by men and compared to women (Tk 30 per hour vs. Tk 16 per hour). women living in the slums of Dhaka were relatively high (225 hours/month and 224 hours/month, respectively). Among men, the most demanding type of employment In Barisal and Sirajganj, the median number of monthly appeared to be for those operating petty/retail businesses hours worked by men and women were notably lower. (300 hours/month) and working in the garments industry (260 hours/month); for women, garment factory jobs The median hourly wages in Dhaka were also higher for (260 hours/month) and working as a washerwoman (240 both men and women compared to the other two cities, hours/month) ranked highest. The median hourly wage with workers in Sirajganj making 25 to 33 percent less for men working in the garment factories was among the each hour than their counterparts in Dhaka. In Dhaka men lowest of all livelihoods (Tk 23 per hour); unskilled labour, have more opportunities to work in the garments factories street food vending, and rickshaw pulling all ranked and as motor transport drivers, which at an average of Tk better in terms of hourly wage. For women, working in 38 per hour commands the highest hourly wage of all the a specialised trade (Tk 18 per hour) and as an unskilled listed occupations.

36 Men Women occupations. The lowest paid men (garment workers and 35 30 helpers in transport and shops) were still paid a higher 25 hourly wage than the highest-paid female workers (day 20 labourers, shopkeepers and those working in specialised 15 trades). 10

Taka per hour Taka 5 0 Dhaka Barisal Sirajganj Comparing the working lives of male Figure 15. Median hourly wage by city and sex and female garment factory workers is revealing. Both work around 260 Women aged 25-44 years old were working the fewest hours a month with men earning Tk 23 number of hours each month, whereas men in this cohort per hour compared to Tk 15 per hour were working the most number of hours each month. for women. This is largely due to 1) Interestingly, teenage girls and young women reportedly female workers being placed into low- worked longer hours than any other age group (male or skill, and thus low-paying, jobs and female). Girls under the age of fifteen who had worked 2) differential wages for males and for an income in the previous month spent 260 hours females within the same job category. at their job; for this work they were being paid Tk 11 per A 2010 GTZ report45 noted that female hour (the rate increased to Tk 15 per hour among 15- to sewing machine operators earned just 19-year-old women). These findings are consistent with 71 Tk for every 100 Tk a male operator the 2006 study which found that the proportion of 15- to earned; likewise, female “helpers” 19-year-old women in urban slums working outside the earned 53 Tk for every 100 Tk a male home was 25 percent higher than that of the general helper earned. Women are also often population. Unfortunately, the implicit trade-off of children the first to lose their jobs in times of and adolescents in urban slums working to supplement reduced demand. household incomes is that the substantial costs are only likely to be fully realized over the long term (i.e. into adulthood).

Figure 16 reveals that the relative wage earned per hour of work was highest among those men and women who 5.3 Household-level work reported that the source of their income was from self- and income employment (Tk 33 and Tk 19 per hour, respectively). Households commonly operate as economic units and In fact, women’s monthly income and hourly wage rates thus it is difficult to explore household food insecurity were around half those of men’s across all the main without household-level measures of work and income. Thus, household-level employment and income is a Men Women necessary lens through which food security outcomes 35 30 must be examined—specifically, whether and how the 25 various sources and levels of income among households 20 affect these outcomes. 15 10 Among the households surveyed in the 2013 BUSS, 89 Taka per hour Taka 5 percent of household heads reported having worked for 0 Self-employed Salary Daily wage an income in the 30 days prior to the survey. There was little variation in this finding according to city. The proportion of Figure 16. Median hourly wage by source of women who were reportedly the head of their household income and gender and earning an income was just 58 percent, compared

45 2010, GTZ, Empowerment of Female Garment Workers

37 to 93 percent among male heads of households. Heads 3,000 of households with no education also appeared to be 2,500 somewhat less likely to have worked for an income during 2,000 1,500 the month prior to the survey. Taka 1,000 A majority of households contained only one member 500 earning an income (54 percent), while a third of 0 Dhaka Barisal Sirajganj households (33 percent) contained two income earners. The proportion of households in the slum areas of Dhaka Figure 18. Monthly per capita income by city (median) with two or more income earners (34 percent) was considerably higher than that of Barisal or Sirajganj (21 members had higher per capita incomes, though the and 22 percent, respectively). Among households in added benefit of each extra worker (in terms of income which the head reported having no education, 53 percent per capita) decreased due to dilution on account of the had two or more income earners; a similarly high finding larger household. Similar to a pattern in other findings, was found for households in the lowest economic quintile households which had spent between 3-5 years in their (50 percent). In addition to being on the lower end of the current location appeared to generate the most income socioeconomic spectrum, these households were also per capita (Tk 3,000) – this pattern is explored further in typically larger in size. Section 6.1 (Migration) but the mobility and networking

Dhaka Barisal Sirajganj capabilities of newer households, and the “trap” of poverty 80% that old tenants seem to experience, are quite strong predictors of success or failure. 70% 60% 5.4 Household income, 50% expenses, food security, and 40% nutrition 30% Identifying the factors which underlie the observed 20% income trends within urban slums is useful to the extent that it helps predict whether and how households achieve 10% food security and good nutritional outcomes. As noted 0% elsewhere, the 2006 study found household income to be No earners One earner Two earner Three or more earner strongly correlated with an urban slum household’s food Figure 17. Number of income earners in household security status. These households rely on food purchases by city from the markets and street vendors to provide the majority of their calories. Thus, individual households with Figure 18 shows the median monthly income per capita low monthly incomes per capita, ceteris paribus, will be earned in households surveyed in the 2013 BUSS. expected to dedicate a larger proportion of their income Households in Dhaka were earning substantially more towards food and rely upon (negative) coping strategies income each month (Tk 2,633 per capita) than those in more frequently when faced with shocks. Barisal and Sirajganj (Tk 2,100 and 1,500 respectively). Expenditure Quintiles According to the 2010 HIES, the average monthly income As Figure 19 shows, households falling in the lowest in households in all urban areas of Bangladesh was Tk expenditure quintile were spending about Tk 2,000 each 3,740 per capita.. This is notably higher than the average month on food and non-food items; those households in found in urban slum households in the 2013 BUSS (Tk the highest quintile were spending more than Tk 6,000 2,845 per capita). each month. Not surprisingly, household per capita Male-headed households in the 2013 BUSS were found income and monthly expenditures appear to be strongly to be earning slightly higher monthly incomes per capita correlated: the median monthly per capita income than female-headed households. Meanwhile, perhaps reported by households in the lowest expenditure quintile expectedly, households with more income earning was just Tk 1,862; households in the highest expenditure

38 Monthly per capita food expenditure Households with per capita consumption below 1,805 kcal/day Monthly per capita non-food expenditure Households with per capita consumption below 2,122 kcal/day 7,000 100% 88% 6,000 80% 5,000 70% 3,260 60% 4,000 49%

3,000 1,934 40% 1,512 30% 2,000 1,257 931 3,083 20% 13% 1,000 1,741 2,194 1,032 1,393 0 0% Lowest Second Third Fourth Highest Lowest Second Third Fourth Highest quintile quintile quintile quintile quintile quintile quintile quintile quintile quintile

Figure 19. Average monthly expenditures per Figure 20. Households falling below HFPL and FPL capita on food and non-food items quintile were reportedly earning more than twice this Severely food insecure access Moderately food insecure access amount each month (Tk 3,943 per capita). 100% The average monthly per capita expenditures were highest in Dhaka (Tk 3,722) compared with Barisal (Tk 80% 2,753) and Sirajganj (Tk 2,260). Driving these higher expenses for urban slum households in Dhaka were 60% rent (Tk 813) as well as elevated health, education and transport costs. 40%

Overall, slum households reported directing 56 percent of 20% their spending towards purchasing food. This represents a smaller proportion of spending on food compared to the 0% Lowest Second Third Fourth Highest 2006 study (62 percent), yet is slightly higher than the 2010 quintile quintile quintile quintile quintile HIES reported for all urban households in Bangladesh Figure 21. Households by HFIAS categories (48 percent). Given how the expenditure quintiles were according to expenditure quintiles computed, it would follow intuitively that households in the lowest quintiles were directing a larger proportion of their spending towards food purchases. Households in The relationship between income, expenditures, and the lowest expenditure quintile were directing nearly 53 nutritional status of women and children was less clear. percent of their spending each month to food; however, There appear to be small differences in the nutrition this was just 5 percentage points higher than households outcomes of children, most notably seen in the stunting in the highest expenditure quintile.46 Yet, households in the findings, where children living in households of the highest lowest expenditure quintile were found to be more food expenditure quintiles were found to be stunted at a lower insecure according to the HFIAS and staple dependency rate than children from households of lower economic (Figure 21): forty-one percent of these households were status. This finding accords well with other surveys in severely food insecure and some 46 percent had a diet for Bangladesh which have found that undernutrition is not which staples accounted for a very high proportion of total only a problem for poor households: the 2011 BDHS calories. For comparison, just 1 percent of households in reported that, 26 percent of children under five in the the highest expenditure quintile were deriving 75 percent wealthiest quintile were stunted and 21 percent of those or more of their calories from staple food groups. children were underweight.

46 This finding should be investigated further. One possible explanation is that the poorest households in urban slums have inelastic costs (e.g., rent) that act to drive down the overall proportion of expenditures that can go towards food; for the poorest households in rural areas, the proportion to food would be much higher overall and the inverse of the present trend would be observed.

39

6 DIMENSIONS OF VULNERABILITY AMONG URBAN SLUM HOUSEHOLDS

42 6.1 Migration 44 6.2 Gender 44 6.3 Education 46 6.4 Dependency DIMENSIONS OF VULNERABILITY AMONG URBAN SLUM HOUSEHOLDS

This chapter aims to create a profile of the urban slum population by analysing the data along a number of dimensions and relating these to food insecurity and undernutrition. 6.1 Migration Where do they come from? One of the main reasons of growth in the urban slum population has been rural-urban migration. The survey found that the majority of slum dwelling household heads in Sirajganj and Barisal had always lived in those cities whereas just 15 percent of Dhaka’s slum-dwelling household heads were born in the capital. This indicates that around 85 percent of the surveyed household heads living in Dhaka’s slums were migrants, albeit 49 percent had lived there for more than 10 years. Highlights Of this high proportion of migrants to Dhaka slums, 48 percent came from elsewhere in Households headed by women are more food the city, 34 percent from rural areas and 17 insecure by all indicators except they tend to percent from other cities. It is important to note provide their families with more kilocalories. that most of the new slums are now located in Education levels are particularly low in slums and Dhaka unions which were not surveyed in the school drop-out rates are high. It does appear 2013 BUSS. that households headed by the least educated In 1997 ADB reported that 60 percent of all are more food insecure, but it should be noted migrants in Dhaka city came from four of the that households headed by well-educated 19 greater districts: Faridpur, Dhaka, Comilla individuals who obtain better paid jobs are likely and Barisal, which are close to Dhaka.47 to move out of the slum. Why do they migrate? The 2013 BUSS confirms the findings of the 2006 The rural-urban migration is the result of a study - that households with larger proportions of combination of rural push factors such as dependents (non-working age members) poverty and family influence and urban pull consistently appear less able to attain higher factors such as better economic opportunity, levels of food security across all of the measures job availability, and presence of migrant of food security evaluated. relatives. The slum census conducted by BBS in 1999 reported economic factors as the main cause of rural to urban migration

47 Islam, Nazrul, 1997, Addressing the Urban Poverty Agenda in Bangladesh, Asian Development Bank.

42 (57 percent of responses) followed by local-level search of work are better able to improve their economic conflicts like land disputes, evictions (18 percent) and wellbeing, while people who are born in urban slums are natural disasters such as river erosion (15 percent).48 seemingly less able to dig themselves out of the cycle of Other studies during the 1990s came up with similar poverty. findings with economic shocks and natural disasters With regards to migration other studies have shown that identified as major reasons for rural to urban migration households who have migrated to urban areas 15 years The 2013 BUSS also found economic factors to be back are less likely to be poor.49 Analysing the nature the main reasons for migration among slum dwellers. of migration reveals that people who have come from Around 75 percent of household heads in Dhaka and rural areas are more likely to be in the lower quintiles Barisal slums who reported to have migrated to the compared with those who have come from other cities. city said the main reason for coming to the cities were According to the 2013 BUSS findings, households that in search of employment opportunities. By contrast, in had lived in the slum for 3-10 years were in a better state Sirajganj, which is prone to river erosion and floods, the according to the HFIAS indicator (Figure 23). These main reason was loss of property due to river erosion households were also less likely to have taken out a (42 percent), followed by employment opportunities loan to buy food in the month before the survey: just 9 (26 percent) and migration due to marriage (18 percent; primarily reported by female-headed households). Severely food insecure access Involvement in political conflict, land related dispute or Moderately food insecure access eviction were much less reported as main reasons for 60% moving to the city. Are the newly arrived more likely to be 40% food insecure? 20% 21% It is interesting to look into how slum dwellers fared 25% 12% 16% economically according to how long they had lived in the 20% slum. Figure 22 shows that non-migrant households were 22% 26% more likely to be in the lower expenditure quintiles while 16% 21% 16% those that had lived there for more than 10 years were 0% Two 3-5 years 6-10 10 years Always in Always in city <= 2 years 3-5 years 6-10 years 10+ years years or years or more city less 100% Figure 23. Households by HFIAS category according to migration status 80% percent of those that had lived in the city for 3-10 years 60% took out a loan compared with 19 percent of those who 40% had been there for less than two years or had been there 61% for more than 10 years. To establish a strong role of 20% 47% 34% 31% 32% migration in determining food security of the urban poor, 0% the relationship between poverty and food consumption Lowest Second Third Fourth Highest quintile quintile quintile quintile quintile pattern of urban slum dwellers will need to be explored further. Figure 22. Migration status according to expenditure quintiles The 2006 study found that migration history did not seem to be related to food security status. Households more likely to be in the higher expenditure quintiles. This whose heads were recent or earlier arrivals in a slum suggests that it may take migrant households a while to area were not more disadvantaged from a food security get established but that those who migrate most likely in perspective than those headed by permanent residents.

48 BBS, 1999, Census of Slum Areas and Floating Populations 1997, Vol I, Bangladesh Bureau of Statistics, Dhaka. 49 LGED & UNDP 2010, Urban Partnerships for Poverty Reduction Base Line Household Survey Report, Phase 1, Dhaka.

43 Consistent with the 2006 study however are the results 6.2 Gender which indicate that, according to more objective The 2006 study found that the food security status of measures, the food security status of female- and a household was often associated with the sex of the male-headed households are less distinguishable. For household head. For example, in that survey, households example, findings from the survey reveal that female- headed by females were found to have better dietary headed households were less likely to be energy diversity than male-headed households. However, deficient according to the calories consumed per capita female-headed households were less food secure in the household: while 43 percent of female-headed according to some of the calorie-based measures of households consumed less than 2,122 kcal/capita/day, food security used in that survey. more than half of male-headed households (51 percent) were categorised as such (Figure 24). Other food According to the results of the 2013 BUSS, approximately security indicators painted a more complicated picture, 12 percent of all households in the urban slums surveyed such as the results showing that, according to food share were headed by a woman. The proportion varied little of expenditures and share of calories from staples, there according to city: highest at 12 percent in Dhaka and was virtually no difference in the food security status of lowest (8 percent) in Barisal. these two types of households. The survey results indicate a (weak) relationship There are perhaps several angles through which these between female-headed households and food insecurity food security findings along the gender dimension might according to the Food Consumption Score: that is to be interpreted. More rigorous statistical analyses are say, female-headed households were marginally more needed to clarify the linkages between food insecurity likely to be classified as having an unacceptable diet (11 and gender in the urban slums. percent) than their male-headed peers (4 percent). More indicative, according to the HFIAS, female-headed 6.3 Education households were almost twice as likely to be severely The 2013 BUSS collected education-related information food insecure as male-headed households (34 vs. 18 for all household members aged five years and older. percent, respectively). Nearly 70 percent of female- Results from the survey reveal that the majority of the headed households reported that they worried during population living in urban slums of Dhaka, Barisal, and the previous month about not having enough food in Sirajganj had never attended school (32 percent) or had the household; only half of male-headed households failed to complete primary school (33 percent). There reported the same. And just 18 percent of female-headed was little difference within these overall results according households said their food consumption was adequate to gender. However, the proportion of household in the month before the survey whereas 31 percent of members who had attended and successfully households headed by men were comfortable with the progressed through some school did vary according amount consumed. to age, such that more than half of members aged 15- Households with per capita consumption below 1,805 kcal/day 29 years old had completed primary school or higher. Households with per capita consumption below 2,122 kcal/day A more apparent gender dynamic also emerged when 60% looking at educational attainment by age distribution: nearly half of men aged 40-44 years old (48 percent) had 51% completed primary school or beyond compared to just 40% 17 percent of women in the same age cohort. 42% In terms of city, Barisal had the highest proportion of 20% men and women who had completed primary school or beyond (44 percent); just one in three individuals in Dhaka (35 percent) and Sirajganj (33 percent) had at 0% Men Women least completed primary school. Economic status of the household was also associated with better educational Figure 24. Households falling below HFPL and FPL attainment: just 21 percent of those men and women according to sex head of household living in households in the bottom expenditure quintile

44 had completed primary school or beyond compared to secondary school in urban slums among boys (10 51 percent of those living in top quintile households. percent) and girls (16 percent) was 5 times higher than the national level. That survey also found that, within The 2013 BUSS education findings for households in slum households, just 48 percent of children beginning urban slums accord well with those from the 2013 BUHS Class 1 would reach Class 5 (the success rate was 79 which found that 26 percent of men and 32 percent of percent among households in other urban areas of the women living in urban slums had received no education. city corporation). Likewise, the 2010 HIES, which did not disaggregate urban data according to slum and non-slum areas, found The 2006 study found the relationship between the that 35 percent of women and 31 percent of men in literacy and educational attainment of the household urban Bangladesh had not progressed in school beyond head and the household food security status to be Class 1. The 2013 BUSS findings do however represent weaker than anticipated: a considerable improvement in educational attainment compared to the 2006 study which reported that nearly half of all men (45 percent) and women (51 percent) had The relationship between household never attended school. food security status and literacy and Given the challenging economic and social conditions educational attainment by the head or that households in urban slums experience, there is the senior woman in the household is also interest to understand whether and how these much less clear than anticipated. Overall, conditions are affecting the educational opportunities the food security status of urban slum for the current cohort of primary- and secondary-aged households is not closely related to the students. It is well-known that children are often forced to education levels of these individuals. Out- drop out of school early to perform domestic activities or migration may explain this. One would not generate additional income to support the general well- expect better educated individuals who being of their families. are able to use their education to qualify for more remunerative employment to Findings from the 2013 BUSS show that there is a remain in urban slums. Consequently, considerable drop off in school attendance among older one should not expect to find many food children (those aged 11-15 years) living in the urban secure households headed by well- slums of Dhaka, Barisal, and Sirajganj. Among all children educated individuals residing in urban aged 6-10 years, 83 percent were attending school slum study areas. at the time of the survey.50 However, just 54 percent of 11¬- to 15-year-olds were attending school at the time of the survey. The proportion of 11- to 15-year-olds attending school was highest in Barisal at 75 percent; The 2013 BUSS found a strong association between in Dhaka just 54 percent were attending at the time of the educational attainment of the household head and the survey. The proportion of girls aged 11-15 years the level of food insecurity in a household. The mean attending school was comparatively better in Barisal and FCS among households headed by individuals with no Sirajganj than that of boys; 49 percent of 11- to 15-year- education was 66.8; the mean for households headed old girls in Dhaka were attending. Also notable was the by those with secondary or higher schooling was 78.5. strong association between the economic status of the Likewise, the proportion of households consuming less household and the proportion of children attending than 2,122 kcal/capita/day was much greater among school: among households in the bottom quintile, just households headed by an individual with no education 33 percent of 11-15-year-olds were attending school (55 percent) than with secondary or higher schooling compared to 81 percent of those living in households of (35 percent). Households headed by someone with the highest quintile. no education were more than three times as likely to be The 2009 MICS reported the drop-out rate from categorised as severely food insecure according to the

50 The education-related questions in the 2013 BUSS did not capture, specifically, which grade level children aged 6-10 and 11-15 years were attending, thereby preventing the calculation of standard education indicators such as net/gross attendance ratio (NAR and GAR).

45 HFIAS (26 vs. 8 percent) and were found to spend just Tk average dependency ratios for households in the urban 1,710 per capita on food each month. slums of Dhaka, Barisal and Sirajganj were 1.05, 1.26 and 1.84 respectively (and thus had a higher number of non- Moreover, children under five whose mothers had no working than working age members). education were more likely to be stunted and wasted than those who had secondary or higher education. As Figure 25 shows, households in the lowest Similar results were found in the 2011 BDHS: the rate of expenditure quintile also recorded the highest average stunting among children under five whose mothers had dependency ratio. The survey findings also revealed that no education was 51 percent compared to 23 percent the dependency ratio is particularly high amongst the among those with secondary or higher schooling. new arrivals – those who reported living in the slum for two years or less. It is considerably lower for those who 6.4 Dependency had been in the slum for 3-10 years or those who had always lived in the city. According to the 2013 BUSS findings, 12 percent of household members in Dhaka were under the age of five. Households with additional, non-working members are In Barisal and Sirajganj, the proportion was 13 percent more likely to be food insecure. Among households and 8 percent, respectively. For the total surveyed with a dependency ratio greater than 1.0, nearly 2 in 5 population, one in three people were under the age of (39 percent) were reportedly consuming fewer than 15. This accords well with the 2013 BUHS which also 1,805 kcal/capita/day on a daily basis, more than double found that one in three city corporation slum dwellers the rate among households with a dependency ratio was under the age of 15. less than 0.5. Nearly one in four households with a dependency ratio greater than one were identified as The 2006 study found that households with larger severely food insecure according to the HFIAS and 24 proportions of dependents (non-working-age members) percent of these households derived a very high share were more food insecure according to several of the of their food energy from staples. These findings serve measures evaluated. A high ratio of dependents to to confirm those from the 2006 study and suggest that working-age adults creates additional pressure to programme and policy designers must strongly consider generate income sufficient to purchase enough food ways to mitigate the impact of having many non-working for all household members. The 2010 HIES found that age members in the urban slum households. the average dependency ratio for urban households in Bangladesh was 0.61. According to the 2013 survey, the

< 0.5 0.5 - 1.0 ≥1.0 1.2

1 17% 46% 26% 21% 0.8 35%

0.6

0.4 48% 0.2 39% 20% 26% 28% 0 Lowest quintile Second quintile Third quintile Fourth quintile Highest quintile

Figure 25. Dependency ratio categories according to expenditure quintiles

46 47

7 RISKS AND COPING

50 7.1 Risks faced by urban slum dwellers 51 7.2 Exposure to shocks 52 7.3 Are the urban poor coping or adapting to risks and hazards? 54 7.4 Access to Social Safety Nets RISKS AND COPING

This section explores how low-income urban slum dwellers cope with the multi- dimensional risks and shocks that they experience in the short and long term. 7.1 Risks faced by urban slum dwellers Urban slum households are increasingly exposed to risks and shocks of differing magnitude and nature compared to rural households. As noted in previous chapters, slum households generally lead a precarious existence, facing the constant threat of evictions (see box on next page), high competition in the informal labour market and high costs of food and rent. Some have to cope with natural disasters such as floods and water logging. Poverty, inadequate infrastructure, overcrowding and limited access to social services and health care seriously undermine their ability to overcome these challenges.

Highlights Eye witness – Akthara Khatun The main shocks reported by households are those that hit their ability to purchase “I have lived here for eight years. I came in food. Price hikes hit households hard across search of work and in the early days it was all three cities. After this the main shocks in better. But now I am sick and cannot work. Dhaka and Sirajganj are political unrest, I had to borrow 6000 Tk from relatives to household illness and loss of employment. pay for treatment. My husband is sick too. He is trying to work but he is always ill and Barisal households are more affected by we cannot afford treatment. natural events – floods and water stagnation. “We cannot ask our neighbours for help because we don’t know anyone. People When confronting price hikes households’ are always coming and going. first coping strategy is to cut their food consumption. But many are able to employ “Life here is so expensive. We have to pay less corrosive strategies such as eating less rent, fuel, health costs, food but we only preferred or inexpensive food, taking on get 300 Tk a day for day labour. So we more work or sending previous non-workers don’t have enough to meet our daily food out to work . needs.

These strategies may enable urban slum “In the village there are lots of relatives households to survive, but they will not and neighbours to help, but still I think life increase their overall welfare. is better here than in rural Bangladesh.”

50 A note on evictions

Urban slum dwellers remain under continuous threat of unplanned evictions from various sources including government, musclemen, politicians, developers and bureaucrats wanting to develop prime land. Anecdotal evidence suggests that the risk of eviction is higher in large cities such as Dhaka than in smaller towns and is highest among squatter slums rather than more structured ones. At least 60,000 people were displaced due to the evictions from 27 slums in Dhaka between 2006 and 2008.51 The Korail slum eviction in Dhaka in 2012 affected approximately 30,000 to 40,000 slum dwellers.52 The major reason for higher eviction rates in large cities is the dwelling ownership pattern. In Dhaka most slum households rent their rooms whereas in Sirajganj and Barisal a much higher proportion own their dwelling. This insecurity of land tenure leads to major barriers to effective service delivery. Government agencies, NGOs and donors are reluctant to invest capital in erecting permanent structures because of the constant threat of evictions.

—Adapted from Choudhury and Rader, WFP, 2013, Adaptation of the Poor to Food Insecurity and Shocks in the Urban Environment of Bangladesh

Lack of basic water, sanitation, drainage and solid-waste disposal services makes it impossible for the poor to 7.2 Exposure to shocks prevent contamination of water and food, maintain Over time the frequency and recurrence of shocks for adequate levels of hygiene or control insect-vectors of the slum dwellers have increased. In the 2013 BUSS, 60 diseases such as malaria. All of these factors contribute percent of the surveyed households reported that they to food insecurity and undernutrition. had faced at least one type of shock in the preceding year compared to 37 percent in the 2006 study. This indicates Slum inhabitants are also extremely vulnerable to social that either a higher proportion of slum households are repression. A World Bank study on the urban poor in experiencing various shocks or they have become more Dhaka reported a wide range of crime and violence aware of shocks. that occurs in slums.53 The most commonly reported are toll collection, musclemen induced violence, drug The type of shocks that slum households are and alcohol dealing, land grabbing-related violence, predominantly exposed to are economic shocks such as gambling and violence against women and children. price hikes, loss of employment and salary and business The study also reported that there are significant costs failure. In the 2006 study, households reported damage associated with violence such as medical treatment, loss to their house, major illness of household members and of productivity due to injuries and direct costs due to the loss of employment as major shocks which they had collection of tolls and that a majority (60 percent) of crime faced. and violence is not reported to police in anticipation that The 2013 BUSS found that among specific shocks no actions will be taken or due to fear of retaliation. households across all three cities were affected by

51 DFID, 2011. Eviction and the challenges of protecting the gains: A case study of slum dwellers in Dhaka city, Shiree Working Paper 3, Extreme Poverty Research Group (EPRG), Dhaka/Bangladesh, October 2011. 52 IRIN, 2012. A newspaper reporting by IRIN a Humanitarian News Analysis, a service of the UN office for the Coordination of Humanitarian Affairs, Dhaka, 22 May 2012. 51 World Bank, 2007, Dhaka: Improving Living Conditions for the Urban Poor, Report No. 35824 51 one shock above all others over the course of the in terms of employment and income security and have preceding year—price hikes—which affected a third limited access to social services.54 As discussed in the of the households in Dhaka and Barisal and over 60 previous chapter slum households are usually involved in percent in Sirajganj. The survey was conducted in a series of occupations with occasional unemployment June 2013 and in the pre-election year there were and underemployment, mainly in self-managed, lowly paid frequent political strikes and unrest of different forms and often hazardous informal sector jobs such as rickshaw which also led to increase in prices of food and non- pulling, petty trading, street vending, construction work, food items. Shocks due to price hikes were followed driving and transport and factory work. by political unrest (causing damage to employment A major adaptation mechanism among the urban poor is and livelihood), major illness of a household member to increase household income by sending young and loss of employment or non-payment of salary, all members to work. The share of male or female workers of which are economic shocks or shocks contributing aged 15- to 24-years-old was 23 percent in the urban to income-earning ability which compromise a slum-dwelling workforce, which is higher than the rate household’s ability to buy food. found in rural and urban areas more generally. Natural disaster-related shocks such as water The urban workforce has a larger share of female workers stagnation and flood were reported at a much higher than in the rural workforce. Teenage girls and young frequency by slum households in Barisal than in women also work longer hours than any other age group Dhaka or Siranjganj. It is worth mentioning that in the regardless of gender (a result due largely to their work in 2006 study, natural disaster was not considered a the RMG sector). critical shock by the slum households, though this could simply reflect a difference between the cities Urban slums have three times more child labour than the surveyed. national average which also leads to high rates of drop-outs of slum children from primary schools.55 The 2013 BUSS found that 22 percent of the urban slum-dwelling workforce 7.3 Are the urban poor was under 15 years old. Mothers working long hours outside the home rarely have a trusted network of older women to coping or adapting to risks care for their children, so they may end up keeping their and hazards? older children off school to look after their siblings. Sending children and adolescents out to work is likely to jeopardise This report considers a coping strategy as a specific their education, physical and emotional health and longer- response of the urban poor to a specific shock be it term personal and economic development. From a human long term or short term and adaptation as a positive or capital perspective it is a negative strategy. negative adjustment in their lifestyle to reduce the risks and negative impacts of seen or unforeseen shocks. Social networks in urban slums tend to be weaker than in rural areas because of the poor definition of community and Adaptation hence the lack of allegiance to it, lack of family members Poor urban households have devised strategies to living close by, particularly from different generations, help them survive in the tough environment in which and sometimes violence and crime, which weakens the they find themselves. Often they are unaware of the trust necessary for non-family collective action. However, negative implications that many of these strategies will limited access to social and financial services means urban have on their lives or that they often place the greatest slum households try to build a strong relationship with their burden on children and adolescents. neighbours and relatives who can serve as peer support Stability in income and employment contributes to the including as a financial lending source during times of crisis. resilience of the poor to withstand shocks. Studies on The 2006 study found the ability of a household to rely on urban poverty have clearly indicated that the urban neighbours or relatives for assistance constituted a sort of poor are at higher risk than their rural counterparts social safety net and was shown to be a determinant of the

54 WFP & IFPRI, 2006, Study of Household Food Security in Urban slums of Bangladesh, Dhaka; 2006, WFP, IFRPI, LGED & UNDP 2011, and Urban Partnerships of Poverty Reduction Base Line Household Survey Report, Phase 1, Dhaka; Hossain, Shahadat, 2005, Poverty, Household Strategies and Coping with Urban Life: Examining Livelihood Framework in Dhaka City, Bangladesh e-Journal of Sociology. Vol. 2. No. 1 55 UNICEF, 2010, Understanding Inequalities in Bangladesh: A prerequisite for achieving Vision 2021, Bangladesh

52 reliability of access to food. The 2013 BUSS found that Neutral strategies demonstrate the ‘better/improved’ social networks still often comprise an important coping ability of a household to cope with a shock or crisis. strategy for slum households. For instance more than half To cope with times of hardship, households reported of households reported that they can rely on neighbours using their savings to meet their needs (14 percent) or to help them through difficult periods peaking at 68 borrow money from friends and relatives (24 percent), percent in Sirajganj. As expected those who had been in banks and NGOs (11 percent) or moneylenders (8 the slum for less than two years were less likely to be able percent). These are considered ‘stress’ coping strategies. to rely on their neighbours (40 percent). A small percentage – and slightly higher in Dhaka than Coping strategies elsewhere – reported using neutral strategies that would Coping strategies practiced by households can be not compromise their present or future food security. defined by their severity as stress, crisis, emergency and These include finding more work or sending another neutral strategies. These strategies are: household member to work for the first time.

Stress strategies, such as borrowing money or The major coping strategies practised by urban spending savings, are those which indicate a reduced slum households were to reduce both the quantity ability to deal with future shocks due to a current and quality of food consumed, borrowing money reduction in resources or increase in debts. from friends/relatives, eating less preferred food and spending savings. Borrowing money from friends and Crisis strategies, such as selling productive assets, relatives, which is considered a stress strategy, was the directly reduce future productivity, including human most widely practised coping strategy in Dhaka slum capital formation. households while in Barisal and Sirajganj households Emergency strategies, such as selling one’s land, were more likely to reduce their food consumption. affect future productivity, but are more difficult to Borrowing money either from friends/relatives, a bank/ reverse or more dramatic in nature. institution or money lender was also reported as the main coping strategy in Dhaka in the 2006 study (40

Emergency percent), followed by reduction in food consumption Crisis Neutral (15 percent). In 2006 Dhaka slum households were less Stress No Shock experienced likely to spend savings to cope with shocks (6 percent vs. 17 percent in 2013) but were more likely to take on 100% extra work (9 percent vs. 5 percent in 2013).

Figure 26 shows that 32 percent of the surveyed 80% households reported using stress strategies to chope with shocks. These coping strategies are not specific to urban slum households but are common among the poor population in Bangladesh. Households in the urban 60% slums borrowed from friends and relatives because of the limited institutional set-ups for loans, while in rural areas money lenders and microcredit institutions are the 40% major sources of borrowing. Surveyed households in the towns of Barisal (11 percent) and Sirajganj (16 percent) reported higher frequency of institutional borrowing than 20% in Dhaka (7 percent). Borrowing from formal institutions to cope with shocks has both positive and negative implications: the urban poor may find it hard to access 0% such funds during their time of need; however, they are Dhaka Barisal Sirajganj likely to avoid the excessively high interest rates which Figure 26. Household coping strategies employed are common in more informal lending arrangements. in response to recent shocks

53 Loans and savings registered with municipal authorities and have no legal As mentioned above the main non-food-related strategy status. Most government social safety net programmes that households reported when confronted with a shock are targeted at the rural poor with the urban poor in large was to take a loan. Half of the surveyed households cities (especially Dhaka) largely excluded from such in Dhaka slums and around 60 percent in Barisal and support. Sirajganj reported having outstanding loans with the The 2013 BUSS illustrates the point well. It asked average amount being highest in Sirajganj (Tk 15,457 households whether they were members of a range per household). In Dhaka the amount outstanding was of programmes and networks including Public Works higher at Tk 26,915 and one in three households in Programmes (food or cash for work), the Gratuitous Dhaka and Sirajganj and two in five in Barisal were failing Relief Programme, trade associations, labour unions or to repay mainly because they did not have enough slum dwellers associations (basti bashi). The percentage income to do so. of slum dwellers joining such groups was extremely low. Many households did manage to put aside some Sirajganj households were more able to access safety money. In Barisal and Sirajganj more than 60 percent nets. Around 22 and 27 percent of households in Barisal reported having some savings but on average these and Sirajganj reported participating in the multi-agency only amounted to Tk 14,846 and Tk 7,429 respectively. UPPR programme, few of the surveyed households in Fewer reported to save in Dhaka (45 percent) but the Dhaka reported participating. This may simply reflect that value of their savings was greater (Tk 21,226). However, Dhaka’s large slum population limits the coverage that the majority cannot access or can only partially access organisations are able to reach. these savings when needed because they are invested Overall around one in five households across all three for a fixed period of time. Few households reported cities reported buying rice and/or wheat at cheaper receiving any interest on their savings. prices from the Government Open Market Sales (OMS) programme. The percentage of Dhaka slum households 7.4 Access to Social Safety participating in specific social safety net programmes Nets was extremely low though 16 percent said they had benefitted from Open Market Sales. Informal settlements have little support or services from the formal public sector. The central government and Asked which of the enlisted community leaders local urban authorities cannot respond adequately to and organisations were most effective to assist their the growing demand for electricity, gas, water, sanitation, households to overcome difficulties, one in five in Dhaka sewerage, waste management, transport, health and around half in Barisal said ‘none’. Households services and education. On top of that, Government in Sirajganj felt slightly more supported, singling out agencies, NGOs, and donors are reluctant to invest in community organisations, ward commissioners, more effective service delivery and infrastructure in Pourshava chairmen and local NGO staff. In times of slum areas because of the lack of land tenure inherent need, very few households in the 2013 BUSS reported in such locations. Food transfers, public works and other receiving any help from an institution such as an NGO, safety net schemes are rarely available to slum dwellers government body or religious organisation. because they often have no official address, are not

54 55

8 CONCLUSIONS

58 Labour market dynamics 59 Coping with uncertainty 60 Access to public services CONCLUSIONS

The findings from the 2013 Bangladesh Urban Slum Survey serve to highlight the unique and challenging conditions which impact the food security of a slum household and the nutritional status of its members therein. The various measures used to assess the current food security situation in the slums of Dhaka, Barisal, and Sirajganj suggest that while these households have access to a broad range of food items sold in the large urban markets and by street vendors, the amount of calories consumed and the nutritional quality of that diet is comparatively worse than related findings in urban areas as a whole and, in some cases, in rural areas as well. Indeed, though some of the 2013 BUSS food security-related findings for Dhaka showed marginal improvements compared to those from the 2006 study, overall these findings do not suggest that the situation for urban slum dwellers had improved in a meaningful way over the previous seven years. The reasons underlying this finding are varied and complex, but the survey results provide some indication as to where programme and policy makers might focus their efforts if they are to improve these outcomes for slum dwellers in the coming years.

The following represents a set of general conclusions about the nature of food insecurity and undernutrition in the urban slums and a summary of ideas about how these issues might be addressed in the near and medium term.

Labour market dynamics Perhaps the most salient findings from the 2013 BUSS when considering food insecurity within the urban slums were related to the linkages between household incomes and labour market dynamics. More specifically, while a majority of working-age men in the slums appeared to work for income in some capacity, the participation rate among working-age women was low in Dhaka and very low in Barisal and Sirajganj. Moreover, even for those women who were working, the hourly/daily wage gap as compared with men was striking – less than half in some cases. The likely contribution of these two factors to food insecurity in the urban slums, where food is almost exclusively purchased in shops and markets, cannot be overestimated. Households with more income earners had higher overall per capita incomes and, by extension, more resources to spend on both food and non-food goods and services. Likewise, better paying sources of work also generate greater levels of income and expenditures for the household.56

56 It is reasonable to assume that jobs with better wages are associated with additional household income and expenditures; however, further investigation and analysis of the 2013 BUSS data is needed to better understand these patterns.

58 Thus, improving the labour market dynamics for and meets minimum standards regarding employee urban slum women represents an important area for compensation, healthcare, training, and equality of programme and policy designers concerned with food opportunity and pay. Responsible practices would insecurity to target. The survey found that participation benefit these employers in the form of a healthier, in the labour market decreased rapidly following more loyal and productive workforce. adolescence and ticked back up in early middle age. A short summary of potential opportunities that could be Coping with uncertainty approached along several fronts (i.e. public, private, and development sectors) includes: Another theme which emerged from the 2013 BUSS findings was the unique difficulties that slum households Extension of maternity safety net schemes to experience as they tried to manage the realities of urban women in urban slums: Many poor women in rural living. Price hikes and political-related disruptions to their areas benefit from the Government’s Maternity income-earning activities were the most commonly Allowance which provides a monthly stipend during reported shocks by households for the year prior. Yet this critical stage in life – yet coverage in urban areas is their access to/participation in most of the formal social low, particularly in the urban slums. Increased access safety nets, which are more widespread in rural areas, to this facility would support household incomes and was very limited. Instead, urban slum households food expenditures when mothers are forced to leave reported often having to take loans from friends or the workforce. neighbours to cope with such shocks. Their responses Enforcement of maternity leave policies in highlight that, in addition to the absence of traditional private sector: Civil servant workers in Bangladesh safety nets, urban slums households have very limited are entitled to six months of paid maternity leave; the access to and/or familiarity with the formal financial Labour Act (2006) stipulates that 16 weeks of paid sector as well. This represents a significant limitation for a maternity leave be provided to women working in the household which earns income and is dependent upon private sector. Yet studies have shown that compliance cash expenditures to purchase food. A series of possible with this law, particularly in the RMG sector, is quite low. areas to target emerges: Receiving this benefit would help prevent against any Financial literacy training: A large portion of the increase in household food insecurity that could occur migrants from rural to urban areas are poor and have as a result of women losing income during this time. little formal education. They are then usually Improvement in childcare options: Programmes confronted with managing part-time employment (or and policies that aim to improve access to safe and self-employment) and the infrequent/ad hoc nature of affordable childcare are needed to give urban slum income which is associated with this type of work, all women more choices and flexibility to work and earn without real access to formal banking. Smoothing an income to support the household. consumption in this context would be challenging for even the most educated. Therefore, programmes Support efforts that promote responsible which aim to build financial literacy – saving, interest, business practices: Government and development loans, etc. – especially targeted to those workers partners have a role to play in ensuring the private employed in more tenuous work (day labourers, sector (especially RMG) recognises workers’ rights

59 rickshaw pullers), would provide much-needed that exists in the urban slums. Perhaps no finding in the awareness about how to manage the unique 2013 BUSS was more indicative of this possibility than economic challenges associated with non- that which showed that households which had always agricultural-based livelihoods. lived in the urban slum (i.e. had not immigrated) were most likely to find themselves in the lowest expenditure Building linkages to formal financial sector: quintile. The intergenerational transmission may only Linked with more financial literacy is the need for accelerate. Thus, limited access to public services has better access among slum households to formal implications for programme and policy designers who banking, savings, and loan products. Innovative ideas are concerned with the longer-term challenges to food are needed that would ensure protection to the insecurity and undernutrition in the urban slums.57 interests of both the banks and the slum households. Promotion of primary and secondary education: Extension of social safety nets to slums: Few Primary and especially secondary education services urban slum households benefit from social safety net need to be improved in the urban slums. In addition, programmes, especially in Dhaka – existing social young people who work must continue to receive safety net programmes can be extended to urban some form of on-going schooling and/or skills training slum households. The objectives, operations, and that will provide them with better life opportunities target populations of these programmes should be re- (and income-earning potential). evaluated to recognise the needs of the residents of urban slums, which vary from city to city and slum to Provision of free/low-cost primary health and slum. nutrition services: Urban slum areas need equitable, accessible and sustainable primary health care, Livelihood and skills training: Support and reproductive health care, family planning and nutrition capacity-building around employment, services. Innovative models are needed through entrepreneurship, and other life skills (e.g. infant and which development partners can support the supply young child feeding practices) are needed as well. A of such services with Government and the private lack of skills likely prevents many new migrants from sector. finding more gainful employment and better wages in urban areas. Further complicating the interpretation of the 2013 BUSS findings is our understanding that the urban slums Access to public services are not static – the population is hyper dynamic, with households moving into and out of slums constantly. The survey also reinforced previous findings and They are moving into the slums in search of better perceptions about the gap in public services available economic opportunities and also because they are to households in the urban slums and the negative losing their rural homes to flooding. The underlying externalities this enables and promotes. The 2009 MICS challenges are complex. noted that children living in slum areas were less likely Though progress has been made, more effort is required to progress in their schooling than children living in non- from Government and development partners if there slum urban areas. Although there are certainly many is to be further improvements in the food security and push factors which drive children in the urban slums nutrition situation in the urban slums of Bangladesh. WFP into the workforce early, that access to formal education Bangladesh is already using these findings to reshape facilities is limited, especially after primary school, no how it will engage in urban programming. It is hoped that doubt contributes to these low rates of attendance. the findings presented in this report will further contribute Moreover, children who do not get proper educations to the development of sound food security and nutrition are probably also less likely to escape the trap of poverty policies and programmes which target the urban poor.

57 While the 2013 BUSS collected data related to health care, infrastructure (e.g. electricity connection), and public sanitation, these findings go beyond the scope of the summary presented here in Chapter 8. Organisations specifically interested in the analysis and interpretation of these data are encouraged to access the microdata available from WFP.

60 61

ANNEX

64 I. Additional tables 108 II. Survey questionnaire ADDITIONAL TABLES

Table A1: Nutrition status of children aged 0-59 months by background variables (STUNTING)

Severe Stunting Stunting (HAZ Mean z-score (HAZ < -3SD) -- WHO < -2SD) -- WHO

Yes Total Yes Total Mean Row N% Unweighted Row N% Unweighted Count Count

Sex of child Male 21.1 737 45.6 737 -1.64 Female 16.9 778 42.8 778 -1.70 Total 18.9 1515 44.1 1515 -1.67 Child age categories 0-5 mos 11.5 180 19.8 180 -.63 6-11 mos 12.4 202 26.5 202 -.90 12-17 mos 23.8 207 50.2 207 -1.85 18-23 mos 25.7 173 55.2 173 -2.06 24-35 mos 21.2 291 56.0 291 -2.04 36-47 mos 19.6 247 56.1 247 -2.08 48-59 mos 18.0 215 39.5 215 -1.98 Total 18.9 1515 44.1 1515 -1.67 City Dhaka 18.6 717 43.9 717 -1.66 Barisal 30.7 392 52.7 392 -2.10 Sirajganj 17.4 406 42.6 406 -1.47 Total 18.9 1515 44.1 1515 -1.67 Mother’s education None 25.0 375 53.3 375 -2.04 Primary incomplete 20.5 358 45.2 358 -1.56 Completed primary 18.1 283 38.6 283 -1.63 Secondary incomplete 10.7 342 36.5 342 -1.37 Secondary complete or higher 12.2 87 35.1 87 -1.42 Total 18.9 1445 44.0 1445 -1.67 Livelihood Self-employed 17.2 670 46.3 670 -1.72 Groupings Salary 20.7 297 43.9 297 -1.67 Daily wage 20.2 484 42.2 484 -1.67 Work, no pay 0.0 0 0.0 0 No work, income 18.3 64 36.2 64 -1.30 Total 18.9 1515 44.1 1515 -1.67 Duration in current Always in city 21.4 689 40.6 689 -1.95 city <= 2 years 21.5 93 50.8 93 -1.83 3-5 years 28.4 104 43.9 104 -1.94 6-10 years 16.5 194 45.2 194 -1.65 10+ years 16.9 435 43.7 435 -1.50 Total 18.9 1515 44.1 1515 -1.67 Wealth group Lowest quintile 22.1 727 45.8 727 -1.79 Second quintile 22.4 325 47.7 325 -1.81 Third quintile 18.4 218 42.4 218 -1.55 Fourth quintile 13.6 157 42.1 157 -1.48 Highest quintile 11.7 88 38.3 88 -1.58 Total 18.9 1515 44.1 1515 -1.67 Zones within Dhaka Kafrul-Badda 24.1 149 47.9 149 -1.74 Dhanmondi Mohammadpur 13.7 79 36.6 79 -1.55 Gulshan 21.0 79 46.3 79 -1.83 Jatrabari Sabujbagh 11.4 116 38.0 116 -1.43 Mirpur Pallabi 21.8 182 45.4 182 -1.63 Old Dhaka 25.8 55 59.9 55 -2.39 Ramna Tejgaon 8.8 57 36.3 57 -1.36 Total 18.6 717 43.9 717 -1.66

64 Table A2: Nutrition status of children aged 0-59 months by background variables (UNDERWEIGHT)

Severe Underweight Underweight (WAZ Mean z-score (WAZ < -3SD) -- WHO < -2SD) -- WHO

Yes Total Yes Total Mean Row N% Unweighted Row N% Unweighted Count Count

Sex of child Male 12.5 785 37.5 785 -1.65 Female 12.4 829 34.2 829 -1.58 Total 12.5 1614 35.8 1614 -1.61 Child age categories 0-5 mos 5.7 191 19.0 191 -.99 6-11 mos 11.4 225 28.0 225 -1.44 12-17 mos 12.0 226 32.9 226 -1.57 18-23 mos 17.4 186 46.3 186 -1.83 24-35 mos 15.0 305 42.9 305 -1.85 36-47 mos 13.4 256 40.8 256 -1.76 48-59 mos 11.1 225 37.2 225 -1.75 Total 12.5 1614 35.8 1614 -1.61 City Dhaka 12.6 740 36.0 740 -1.63 Barisal 10.3 442 30.9 442 -1.30 Sirajganj 11.9 432 34.8 432 -1.51 Total 12.5 1614 35.8 1614 -1.61 Mother’s education None 17.7 402 40.0 402 -1.87 Primary incomplete 11.4 388 37.1 388 -1.56 Completed primary 13.2 299 36.0 299 -1.58 Secondary incomplete 6.6 359 27.2 359 -1.37 Secondary complete or higher 11.4 92 30.9 92 -1.38 Total 12.6 1540 35.4 1540 -1.61 Livelihood Self-employed 11.5 716 37.1 716 -1.59 Groupings Salary 15.8 310 35.7 310 -1.70 Daily wage 10.3 519 35.0 519 -1.59 Work, no pay 0.0 0 0.0 0 No work, income 13.5 69 29.0 69 -1.50 Total 12.5 1614 35.8 1614 -1.61 Duration in current Always in city 15.7 752 41.1 752 -1.72 city <= 2 years 16.7 93 43.0 93 -1.93 3-5 years 7.9 107 42.4 107 -1.71 6-10 years 11.7 199 33.1 199 -1.69 10+ years 11.5 463 32.4 463 -1.47 Total 12.5 1614 35.8 1614 -1.61 Wealth group Lowest quintile 11.7 788 38.0 788 -1.71 Second quintile 14.1 338 38.3 338 -1.76 Third quintile 11.3 230 33.7 230 -1.53 Fourth quintile 14.5 164 34.8 164 -1.53 Highest quintile 10.4 94 29.6 94 -1.30 Total 12.5 1614 35.8 1614 -1.61 Zones within Dhaka Kafrul-Badda 13.5 155 35.8 155 -1.56 Dhanmondi Mohammadpur 16.5 82 33.5 82 -1.80 Gulshan 11.5 80 30.3 80 -1.62 Jatrabari Sabujbagh 8.0 118 34.5 118 -1.54 Mirpur Pallabi 13.2 188 41.3 188 -1.66 Old Dhaka 12.7 57 39.5 57 -1.80 Ramna Tejgaon 12.8 60 33.9 60 -1.52 Total 12.6 740 36.0 740 -1.63

65 Table A3: Nutrition status of children aged 0-59 months by background variables (WASTING)

Severe Wasting Wasting (WHZ < Mean z-score (WHZ < -3SD) -- WHO -2SD) -- WHO

Yes Total Yes Total Mean Row N% Unweighted Row N% Unweighted Count Count

Sex of child Male 7.0 739 18.1 739 -.96 Female 3.7 766 14.9 766 -.74 Total 5.2 1505 16.4 1505 -.84 Child age 0-5 mos 6.8 168 18.9 168 -.42 categories 6-11 mos 8.3 208 23.8 208 -1.06 12-17 mos 6.7 210 17.4 210 -.75 18-23 mos 6.0 179 14.7 179 -.96 24-35 mos 3.5 288 11.8 288 -.87 36-47 mos 4.0 245 15.0 245 -.80 48-59 mos .4 205 13.1 205 -.94 Total 5.1 1503 16.3 1503 -.84 City Dhaka 5.3 716 16.5 716 -.86 Barisal 4.6 382 13.7 382 -.07 Sirajganj 5.7 407 20.1 407 -.86 Total 5.2 1505 16.4 1505 -.84 Mother’s education None 5.4 380 17.5 380 -.85 Primary incomplete 6.0 356 17.2 356 -.86 Completed primary 3.4 280 15.6 280 -.86 Secondary incomplete 4.8 335 13.4 335 -.78 Secondary complete or higher 12.1 86 18.7 86 -.64 Livelihood Total 5.3 1437 16.2 1437 -.83 Groupings Self-employed 4.3 670 16.1 670 -.77 Salary 5.0 293 17.3 293 -.96 Daily wage 5.4 477 16.2 477 -.80 Work, no pay 0.0 0 0.0 0 No work, income 13.5 65 15.7 65 -1.03 Total 5.2 1505 16.4 1505 -.84 Duration in current Always in city 7.2 688 17.2 688 -.71 city <= 2 years 8.5 88 20.1 88 -1.18 3-5 years 2.2 98 19.3 98 -.78 6-10 years 3.6 192 13.4 192 -.91 10+ years 5.0 439 16.2 439 -.81 Total 5.2 1505 16.4 1505 -.84 Wealth group Lowest quintile 2.7 723 12.6 723 -.76 Second quintile 6.6 321 18.7 321 -.93 Third quintile 6.5 215 15.9 215 -.87 Fourth quintile 5.9 157 22.9 157 -.93 Highest quintile 6.5 89 13.6 89 -.70 Total 5.2 1505 16.4 1505 -.84 Zones within Dhaka Kafrul-Badda 4.8 150 14.5 150 -.54 Dhanmondi Mohammadpur 7.3 80 20.4 80 -1.10 Gulshan 4.2 78 14.6 78 -.84 Jatrabari Sabujbagh 4.5 116 17.0 116 -1.05 Mirpur Pallabi 6.4 181 18.5 181 -1.04 Old Dhaka 1.9 56 6.7 56 -.47 Ramna Tejgaon 6.2 55 19.8 55 -.90 Total 5.3 716 16.5 716 -.86

66 Table A4: Nutrition status of women aged 14-49 by background variables (BMI).

Woman height less than Woman Body Woman BMI Categories 145cm Mass Index (BMI) Yes Total Severely thin/ Mildly thin Normal Over weight Obese Total moderately thin RowN% Unweighted Count Mean RowN% RowN% RowN% RowN% RowN% RowN%

Women Nutrition - Age 14-19 years 25.0 425 19.25 15.5 25.2 56.6 2.3 .4 100.0 Categories 20-29 years 19.1 1288 21.75 6.4 11.2 63.6 16.6 2.1 100.0 30-39 years 19.1 544 23.25 3.3 6.9 61.0 20.3 8.4 100.0 40-49 years 20.0 249 22.25 5.9 10.7 62.8 19.0 1.6 100.0 Total 20.3 2506 21.71 7.3 12.7 61.6 15.2 3.3 100.0 City Dhaka 20.3 1073 21.71 7.3 12.7 61.5 15.2 3.3 100.0 Barisal 17.9 688 22.03 5.9 10.3 65.1 15.6 3.2 100.0 Sirajganj 20.7 745 21.44 8.7 14.9 60.5 13.7 2.2 100.0 Total 20.3 2506 21.71 7.3 12.7 61.6 15.2 3.3 100.0 Education completion None 23.4 730 21.99 7.2 10.6 61.3 17.9 2.9 100.0 categories Primary incomplete 21.0 569 21.66 10.9 12.7 56.7 16.5 3.3 100.0 Completed primary 20.3 404 21.11 5.9 16.2 65.9 10.6 1.3 100.0 Secondary incomplete 14.1 608 21.76 3.9 14.3 63.6 13.6 4.7 100.0 Secondary complete or higher 21.0 195 21.75 8.4 10.2 64.5 12.3 4.7 100.0 Total 20.3 2506 21.71 7.3 12.7 61.6 15.2 3.3 100.0 Livelihood Groupings Self-employed 20.2 1087 21.86 6.1 14.3 60.4 15.4 3.7 100.0 Salary 19.6 502 21.74 8.1 11.4 60.7 17.2 2.6 100.0 Daily wage 19.1 766 21.40 8.7 13.4 63.7 11.1 3.2 100.0 Work, no pay 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 No work, income 25.4 151 21.69 6.4 7.9 65.1 17.1 3.5 100.0 Total 20.3 2506 21.71 7.3 12.7 61.6 15.2 3.3 100.0 Duration in current city Always in city 20.9 1216 22.42 4.9 9.7 61.8 18.8 4.7 100.0 <= 2 years 22.8 133 20.06 11.0 17.6 63.0 8.3 .0 100.0 3-5 years 17.6 137 21.19 10.3 14.3 62.5 9.4 3.7 100.0 6-10 years 19.6 256 21.33 6.1 16.9 63.3 11.3 2.4 100.0 10+ years 20.2 764 21.93 7.5 11.3 60.6 17.1 3.5 100.0 Total 20.3 2506 21.71 7.3 12.7 61.6 15.2 3.3 100.0 Wealth group Lowest quintile 23.3 1086 20.87 12.3 15.0 61.9 9.6 1.1 100.0 Second quintile 18.5 528 21.88 6.3 10.3 62.5 14.9 6.0 100.0 Third quintile 22.5 377 21.39 5.6 14.1 64.1 14.3 1.9 100.0 Fourth quintile 16.4 286 21.74 6.3 14.1 61.7 15.6 2.3 100.0 Highest quintile 20.5 229 22.67 5.7 9.9 58.0 21.4 5.0 100.0 Total 20.3 2506 21.71 7.3 12.7 61.6 15.2 3.3 100.0 Zones within Dhaka Kafrul-Badda 21.8 184 21.33 6.5 9.8 73.8 9.6 .3 100.0 Dhanmondi Mohammadpur 24.6 110 20.69 9.5 22.1 56.4 9.0 3.0 100.0 Gulshan 23.7 117 21.80 8.0 7.8 65.4 13.7 5.1 100.0 Jatrabari Sabujbagh 20.5 175 21.44 7.1 19.7 55.1 17.2 .8 100.0 Mirpur Pallabi 18.7 300 21.77 7.2 11.7 63.6 12.8 4.7 100.0 Old Dhaka 24.2 98 23.67 3.6 5.3 52.3 31.6 7.2 100.0 Ramna Tejgaon 8.5 89 21.79 9.7 10.4 56.1 21.4 2.4 100.0 Total 20.3 1073 21.71 7.3 12.7 61.5 15.2 3.3 100.0 Table A5: Distribution of household members according to highest level of education completed by background variables (Males)

Education completion categories (per DHS)

Primary Completed Secondary Secondary com- None Total incomplete primary incomplete plete or higher

Row N% Row N% Row N% Row N% Row N% Row Unweighted N% Count

Household 5-9 years 20.3% 78.9% .0% .8% 0.0% 100.0% 524

age categories 10-14 years 12.4% 57.3% 11.9% 18.4% 0.0% 100.0% 432

15-19 years 10.6% 29.6% 25.9% 26.8% 7.0% 100.0% 287

20-24 years 17.7% 26.3% 14.6% 28.0% 13.4% 100.0% 372

25-29 years 27.9% 19.2% 18.4% 28.1% 6.3% 100.0% 757

30-34 years 31.9% 20.9% 14.0% 23.0% 10.1% 100.0% 607

35-39 years 42.0% 20.6% 13.0% 17.7% 6.6% 100.0% 432

40-44 years 36.0% 15.7% 17.3% 19.0% 12.1% 100.0% 242

45-49 years 45.6% 14.8% 20.8% 11.6% 7.2% 100.0% 161

50-54 years 52.8% 30.6% 6.7% 8.9% 1.0% 100.0% 121

55-59 years 50.2% 10.6% 4.4% 25.8% 9.0% 100.0% 111

60-64 years 69.0% 8.9% 10.2% 10.8% 1.1% 100.0% 105

65+ years 61.9% 17.0% 8.2% 5.3% 7.6% 100.0% 130

Total 29.4% 32.0% 13.4% 18.8% 6.3% 100.0% 4281

City Dhaka 29.5% 31.9% 13.6% 18.8% 6.2% 100.0% 1818

Barisal 23.4% 33.1% 11.2% 20.9% 11.4% 100.0% 1217

Sirajganj 30.8% 35.6% 8.9% 16.7% 8.0% 100.0% 1246

Total 29.4% 32.0% 13.4% 18.8% 6.3% 100.0% 4281

Wealth group Lowest quintile 41.6% 34.4% 8.7% 13.8% 1.5% 100.0% 1875

Second quintile 30.1% 33.6% 15.3% 17.7% 3.3% 100.0% 909

Third quintile 27.2% 32.0% 15.7% 18.3% 6.8% 100.0% 623

Fourth quintile 25.5% 29.7% 14.3% 21.4% 9.2% 100.0% 467

Highest quintile 19.6% 29.6% 13.6% 24.4% 12.8% 100.0% 407

Total 29.4% 32.0% 13.4% 18.8% 6.3% 100.0% 4281

Zones within Kafrul-Badda 39.3% 31.7% 9.5% 16.6% 2.8% 100.0% 337

Dhaka Dhanmondi Mohammadpur 26.4% 37.3% 13.8% 16.8% 5.7% 100.0% 202

Gulshan 34.2% 26.9% 13.2% 16.3% 9.4% 100.0% 198

Jatrabari Sabujbagh 30.1% 31.7% 16.0% 17.5% 4.8% 100.0% 310

Mirpur Pallabi 27.6% 30.9% 12.0% 19.8% 9.7% 100.0% 450

Old Dhaka 19.1% 34.6% 19.5% 23.6% 3.2% 100.0% 167

Ramna Tejgaon 23.4% 31.8% 15.0% 24.1% 5.7% 100.0% 154

Total 29.5% 31.9% 13.6% 18.8% 6.2% 100.0% 1818

68 Table A6: Distribution of household members according to highest level of education completed by background variables (Females)

Education completion categories (per DHS)

Primary Completed Secondary Secondary com- None Total incomplete primary incomplete plete or higher

Row N% Row N% Row N% Row N% Row N% Row Unweighted N% Count

Household < 5 years 100.0% 0.0% 0.0% 0.0% 0.0% 100.0% 1

age categories 5-9 years 18.7% 79.8% .8% .8% 0.0% 100.0% 539

10-14 years 8.0% 60.1% 14.0% 17.0% .8% 100.0% 434

15-19 years 11.8% 28.5% 16.5% 29.6% 13.6% 100.0% 565

20-24 years 23.2% 22.7% 19.6% 27.9% 6.6% 100.0% 888

25-29 years 30.8% 25.0% 16.8% 23.4% 3.9% 100.0% 724

30-34 years 45.8% 21.8% 15.7% 11.3% 5.4% 100.0% 350

35-39 years 57.4% 24.3% 5.8% 8.6% 3.8% 100.0% 262

40-44 years 63.4% 19.9% 13.9% .9% 2.0% 100.0% 161

45-49 years 66.8% 20.0% 3.9% 9.1% .2% 100.0% 129

50-54 years 82.8% 10.9% 1.8% 4.4% .1% 100.0% 237

55-59 years 75.5% 13.8% 4.7% 5.9% .1% 100.0% 102

60-64 years 87.5% 6.4% 5.8% .3% 0.0% 100.0% 69

65+ years 88.7% 7.0% .1% 4.2% 0.0% 100.0% 116

Total 34.7% 33.0% 12.0% 15.9% 4.3% 100.0% 4577

City Dhaka 34.9% 33.2% 12.0% 15.6% 4.2% 100.0% 1973

Barisal 27.5% 27.8% 13.7% 24.0% 7.0% 100.0% 1287

Sirajganj 35.4% 31.4% 9.9% 17.6% 5.5% 100.0% 1317

Total 34.7% 33.0% 12.0% 15.9% 4.3% 100.0% 4577

Wealth group Lowest quintile 44.4% 37.5% 9.6% 8.0% .5% 100.0% 2036

Second quintile 35.0% 38.0% 11.0% 13.7% 2.4% 100.0% 948

Third quintile 36.5% 31.1% 13.3% 15.3% 3.8% 100.0% 663

Fourth quintile 30.9% 30.1% 14.7% 18.3% 6.0% 100.0% 499

Highest quintile 22.7% 26.0% 12.6% 27.7% 10.9% 100.0% 431

Total 34.7% 33.0% 12.0% 15.9% 4.3% 100.0% 4577

Zones within Kafrul-Badda 45.7% 33.7% 10.5% 8.1% 2.0% 100.0% 390

Dhaka Dhanmondi Mohammadpur 31.2% 35.8% 17.1% 12.3% 3.6% 100.0% 213

Gulshan 38.6% 24.5% 7.8% 20.0% 9.2% 100.0% 208

Jatrabari Sabujbagh 32.1% 34.6% 15.5% 13.9% 3.9% 100.0% 318

Mirpur Pallabi 33.9% 33.5% 10.4% 18.1% 4.1% 100.0% 507

Old Dhaka 29.0% 36.1% 9.6% 20.0% 5.4% 100.0% 174

Ramna Tejgaon 27.1% 32.3% 16.4% 21.0% 3.1% 100.0% 163

Total 34.9% 33.2% 12.0% 15.6% 4.2% 100.0% 1973

69 Table A7: Enrolment status of children aged 6-10 and 11-15 by background variables (Males)

Education age (Enrolment): Education age (Enrolment): 6-10 years old 11-15 years old 6-10 years old: YES 11-15 years old: YES Current enrolment among all HH members

No Yes Total No Yes Total Row N% Row N% Unweighted Row N% Row N% Unweighted Count Count

City Dhaka 18.9 81.1 219 44.8 55.2 147

Barisal 13.2 86.8 148 33.0 67.0 102

Sirajganj 15.8 84.2 177 40.9 59.1 131

Education completion None 10.4 89.6 58 67.1 32.9 42

categories Primary incomplete 9.9 90.1 34 25.1 74.9 24

Completed primary 0.0 100.0 22 27.7 72.3 14

Secondary incomplete 26.7 73.3 41 40.5 59.5 18

Secondary complete or higher 0.0 100.0 14 6.9 93.1 14

Duration in current Always in city 11.3 88.7 82 47.8 52.2 51

city <= 2 years 0.0 100.0 6 17.4 82.6 9

3-5 years 52.8 47.2 15 37.3 62.7 5

6-10 years 12.0 88.0 24 49.2 50.8 9

10+ years 8.3 91.7 42 12.5 87.5 38

Livelihood Groupings Self-employed 19.2 80.8 77 9.9 90.1 53

Salary 10.2 89.8 36 78.7 21.3 23

Daily wage 3.9 96.1 41 39.4 60.6 32

Work, no pay 0.0 0.0 0 0.0 0.0 0

No work, income 10.7 89.3 15 38.5 61.5 4

Wealth group Lowest quintile 37.2 62.8 295 62.1 37.9 195

Second quintile 16.6 83.4 105 52.4 47.6 84

Third quintile 13.7 86.3 64 40.9 59.1 47

Fourth quintile 4.2 95.8 43 32.2 67.8 25

Highest quintile .3 99.7 37 9.4 90.6 29

Zones within Dhaka Kafrul-Badda 35.9 64.1 41 41.3 58.7 25

Dhanmondi Mohammadpur 21.7 78.3 25 54.0 46.0 16

Gulshan 12.0 88.0 31 50.3 49.7 12

Jatrabari Sabujbagh 8.9 91.1 39 54.2 45.8 28

Mirpur Pallabi 16.6 83.4 56 42.1 57.9 33

Old Dhaka 21.4 78.6 17 43.2 56.8 18

Ramna Tejgaon 5.4 94.6 10 31.4 68.6 15

Total 18.9 81.1 219 44.8 55.2 147

70 Table A8: Enrolment status of children aged 6-10 and 11-15 by background variables (Females)

Education age (Enrolment): Education age (Enrolment): 6-10 years old 11-15 years old 6-10 years old: YES 11-15 years old: YES Current enrolment among all HH members

No Yes Total No Yes Total Row N% Row N% Unweighted Row N% Row N% Unweighted Count Count

City Dhaka 15.9 84.1 234 46.4 53.6 174

Barisal 6.7 93.3 154 17.7 82.3 98

Sirajganj 10.3 89.7 164 18.7 81.3 133

Education completion None 15.7 84.3 60 50.8 49.2 40

categories Primary incomplete 4.3 95.7 35 40.3 59.7 32

Completed primary 36.1 63.9 21 70.7 29.3 11

Secondary incomplete 1.1 98.9 41 12.3 87.7 25

Secondary complete or higher 5.6 94.4 19 54.7 45.3 4

Duration in current Always in city 11.0 89.0 89 42.5 57.5 54

city <= 2 years 39.2 60.8 9 0.0 100.0 5

3-5 years 91.2 8.8 6 16.7 83.3 5

6-10 years 0.0 100.0 22 83.6 16.4 16

10+ years 1.1 98.9 50 1.0 99.0 32

Livelihood Groupings Self-employed 12.6 87.4 74 19.8 80.2 48

Salary 1.2 98.8 44 48.5 51.5 17

Daily wage 27.4 72.6 52 59.1 40.9 40

Work, no pay 0.0 0.0 0 0.0 0.0 0

No work, income 0.0 100.0 6 0.0 100.0 7

Wealth group Lowest quintile 33.0 67.0 283 71.2 28.8 210

Second quintile 11.5 88.5 117 40.3 59.7 80

Third quintile 3.0 97.0 76 29.1 70.9 45

Fourth quintile 2.4 97.6 36 29.0 71.0 41

Highest quintile 11.2 88.8 40 28.7 71.3 29

Zones within Dhaka Kafrul-Badda 19.4 80.6 43 70.3 29.7 45

Dhanmondi Mohammadpur 14.8 85.2 29 28.8 71.2 17

Gulshan 13.3 86.7 15 33.1 66.9 20

Jatrabari Sabujbagh 11.5 88.5 48 59.1 40.9 26

Mirpur Pallabi 21.9 78.1 56 37.5 62.5 39

Old Dhaka 7.9 92.1 29 45.8 54.2 14

Ramna Tejgaon 19.6 80.4 14 33.2 66.8 13

Total 15.9 84.1 234 46.4 53.6 174

71 Table A9: Enrolment status of children aged 6-10 and 11-15 by background variables (Total)

Education age (Enrolment): Education age (Enrolment): 6-10 years old 11-15 years old 6-10 years old: YES 11-15 years old: YES Current enrolment among all HH members

No Yes Total No Yes Total Row N% Row N% Unweighted Row N% Row N% Unweighted Count Count

City Dhaka 17.4 82.6 453 45.6 54.4 321

Barisal 9.9 90.1 302 25.3 74.7 200

Sirajganj 13.2 86.8 341 30.0 70.0 264

Education completion None 13.7 86.3 118 59.4 40.6 82

categories Primary incomplete 7.2 92.8 69 37.0 63.0 56

Completed primary 26.3 73.7 43 48.0 52.0 25

Secondary incomplete 10.2 89.8 82 33.8 66.2 43

Secondary complete or higher 4.3 95.7 33 10.7 89.3 18

Duration in current Always in city 11.1 88.9 171 45.4 54.6 105

city <= 2 years 19.8 80.2 15 14.1 85.9 14

3-5 years 76.7 23.3 21 20.4 79.6 10

6-10 years 3.6 96.4 46 79.4 20.6 25

10+ years 3.1 96.9 92 9.4 90.6 70

Livelihood Groupings Self-employed 15.0 85.0 151 14.7 85.3 101

Salary 4.8 95.2 80 70.0 30.0 40

Daily wage 20.8 79.2 93 49.2 50.8 72

Work, no pay 0.0 0.0 0 0.0 0.0 0

No work, income 8.0 92.0 21 12.6 87.4 11

Wealth group Lowest quintile 35.1 64.9 578 67.0 33.0 405

Second quintile 13.9 86.1 222 47.5 52.5 164

Third quintile 7.9 92.1 140 35.5 64.5 92

Fourth quintile 3.4 96.6 79 30.2 69.8 66

Highest quintile 5.9 94.1 77 19.5 80.5 58

Zones within Dhaka Kafrul-Badda 28.3 71.7 84 57.4 42.6 70

Dhanmondi Mohammadpur 18.3 81.7 54 40.3 59.7 33

Gulshan 12.4 87.6 46 39.6 60.4 32

Jatrabari Sabujbagh 10.2 89.8 87 56.5 43.5 54

Mirpur Pallabi 19.7 80.3 112 39.8 60.2 72

Old Dhaka 11.5 88.5 46 44.4 55.6 32

Ramna Tejgaon 12.2 87.8 24 32.2 67.8 28

Total 17.4 82.6 453 45.6 54.4 321

72 117

9

6

8 157 300 4 776 4

525 2 37 47.8% 38 11 249 1.3% 21 9 779 .9% 4 2924 28.7% 10 22 1.5% 0 104 .5% 308 34 1316 40 .7% 959 17.4% 15 .5% 22.7% 22 766 10.7% 166 77 73 3723 62 .7% 778 17.9% 35 3.1% 0 1050 44.1% 128 13 .6% 1216 20 25 1.9% 863 1288 15.3% 19 .8% 3.3% 59 16 1.7% 124 Table A10: Employment status of household members (weighted) 15 176 1.3% .3% 273 25.3% 883 1.8% 97 2 33 78 8 1019 .3% 54.0% 1.4% 1635 29.5% .3% 12 3 71 77 1295 1973 1195 City 18 .3%City City 1.0%City 2534 .5% 100.0% 42 2.9% 19 47 559 32 27 225 20.6% .1% .7%Dhaka Barisal 22 2.9%Sirajganj 27 13 Total 256 54.8% 35 1020 0 What was your work 19 1263 55.8% .5% 3 28 1.8% 67.3% 100.0% 185 situation over the past 45.2% 573 4282 7 days? 40 Gender Code1822 Gender Code1 19 Gender Code7 Gender Code 47.4% 1.9% 138 17.7% 24 7 .4% 788 2.2% 3076 33.1% 100.0% 12 18 1.3% 280 2.1% 35 Male 43.5%Female Total Male 25 Female Total 58.4%Male 3 Female Total 1.0%Male 66.9%Female Total 89 20 0.0% 33.8% 79 867 32 13 .8% 5 20 1.5% 0 4.8% 2.6% 15 3.3% 1820 1247 100.0% 60 100.0% ColumnUnweight - ColumnUnweight - Column9.8% Unweight - ColumnUnweight - ColumnUnweight - ColumnUnweight 474 - ColumnUnweight - ColumnUnweight 4 - Column2.7% Unweight - Column Unweight3943 - Column Unweight- ColumnUnweight - 58 2 68.2% 16 1.9% 8 1.5% N % ed Count N1.8% % ed Count N % ed Count38 N % ed Count N % ed Count N % ed Count N.8% % ed855 Count N % ed Count N % ed Count49 17.4%N % ed Count N % ed Count N % ed8859 Count 10.7% 782 16.1% 0 .7% 31 122 1.4% 0.0% 13 41.1% 17.6% 1.4% 28 0 1.6% 291 11 .4% 6.8% 3977 46 83.9% 1 561 799 67.4% 29 7.1% .6% 100.0% 19 .9% 1.2% 22.9% Work for payment (wage, salary, self employed work) 1.0% 1 50 1217 3 2.9% 4199 59 23.3% 1146 100.0% 9 508 .2% 24 .3% .4% 24.3% 24 Work with pay (apprentice/family business) 2.1% 12 1.5% 18.0% 8 3.1% 8176 .1% 588 884 3 2563 2.0% 21.4% 10 35 1.2% 15 19 Was employed but did not work 1.0% 8.3% 82.0% 0 .8% 28.3% 1.9% 37 964 19.6% 247 1021 2.1% 100.0% 38 4.2% 186 21.4% 5 .1% 1818 8 20 1.7% Looked for work b ut did not get any 2.6% 22 4.8% 131 100.0% 2 20.1% 3.5% 1.6% 661 33.9% .3% 1131 2.1% 183 0 1341 Studies (student) 1337 0 1.3% 2.0% 857 1.3% 17.2% 10.7% 19 28.7% 2.5% 304 514 211 66 2.8% 0 2362 .5% 66.1% 2.8% 3 6.2% Housework/homemaker 329 2505 19.7% 2.3% 143 2514 .3% Have you worked for .4% 18.5% 54 19.0% 9 2.3% 204 0 2.4% 222 .8% 3 1 100.0% salary/wage or for Under aged (not student) 100.0% 1060 408 4.6% 1666 40 5 2.3% 0.0% .6% 132 100.0% 9 profit or in the house 2.9% 1 1.9% 101 .8% .2% 0 219 Elderly/retired 1266 100 77.4% 141 39.0% 12 1 1.7% 3.3% .1% 107 2.0% 23 1.3% 20.0% 0 186 54.3% 331 Physcially/mentally handicap 6.5% 8 2326 100.0% 46 22.6% 182 0 1.6% 9.3% 100.0% 0.0% 61 2.1% 153 12 3791 0.0% 35 45.7% 211 Did not need to work 2 0.0% 72.3% 86 100.0% .2% 0 0.0% 8.2% 15 .1% 1.7% 224 1896 43.1% 42 100.0% 462 Others 4 100.0% 73 5.1% 257 27.7% 12 3 21.5% 0 100.0% 2.8% 377 .5% 4 1592 56.9% 4.7% 2 364 Total 3 76.6% 44 7.2% 102 34 107 0 100.0% 0.0% 11.9% 144 3 .1% 3488 46.4% 2.5% 182 Yes 100.0% 0 100.0% 4577 10.3% 23.4% 142 11.9% 43 4 64 15.8% 1 53.6% .1% 8 1.0% 131 1.9% 10.8% 19 No 77.5% 0 901 100.0% 4 2.6% 0 2 21.3% 85 99 .3% 6.2% 100.0% 4.7% 2 .6% Total 53 0 185 21.3% 7.0% 428 22.5% 0.0% 0 21 16 .3% 3332 4.5% 24 81 21.1% 1.9% 1 0 226 17.2% 0.0% 168 180 1.6% 34 4233 .3% - 100.0% 24.0% 20 4 2.8% 11.2% 5 39.9% 126 5 57 .3% hold farm or in any Day labor (unskilled) 5 23.0% 4.1% .0% 22 125 12.4% 10.3% 5 8.3% business firm over the 176 .3% 0 0.0% 4.1% 35 Rickshaw puller 0.0% 0 4 138 10.1% 161 past one month? 124 .3% 0.0% 100 6 11.4% 1.6% 4.7% 1 0.0% 94 4.8% House help/maid (salaried) 2 12 12.4% 13.6% 3 What major employ .1% 143 64 5.2% 0.0% 1 - 15.8% 6 3.0% 4 10.6% 24 6 10.7% Washerwoman/laundryman .1% 24 5.1% .8% 2 ment did you have 70 3 .1% 7.9% 6 154 4.6% 90 .9% 17.8% over the past month? Helper (transport, shop, other activities) .3% 7.9% .3% 23 0.0% 5.9% 9 6 189 559 1 7 10.6% 8.8% 2.4% 202 7.4% 0.0% 35 Simple trades (potter, smith, tailor, barber, construction).3% 59 4.5% 0.0% 2 0.0% 10.1% 0 2 1 6.0% .4% 1.0% 336 0 5.1% 157 1.4% 1 .1% 3 Specialized trades (clerk, teacher, electrician, mechanic)11.3% 1 14.7% 0.0% 1 3.4% 1.6% 206 27 .3% 124 1 73 3.6% 12.0% .6% 14.2% 5 12 Garments worker 4.1% 1 0.0% 1 0.0% 2.9% 9.6% 68 9.7% .4% 9 .8% 3076 0.0% 7 .9% 8 4.8% Motor transport driver 0 0.0% 1 6.6% 23 100.0% 54 4.0% 6 13.6% 2.9% 0 .7% 0.0% 2 10.0% .2% Street food vendor 0 177 .3% 2.3% 55 13.7% 8.3% 1 8.9% 2 223 .4% 15 0.0% .4% 0 .1% Hawker/peddler 3 71 2.1% 9.1% 2 0.0% .6% 855 100.0% 2.0% 5 0 .3% 318 0.0% 1 .0% Petty retail business / shop owner 1 16.2% .2% 5 0 0.0% 15.0% 1 4 .0% .4% 9 .7% 3977 Medical, healer 0 .9% 6.2% 7.7% .0% 4 0.0% .0% 40 13.3% 7.1% 12 51 Religious leader 11.9% 3 0 0.0% .3% 0.0% .3% 0 .5% 77 .2% 884 100.0% 1.0% .1% 24 Farmer 4.7% 1 2 .0% .1% .0% 0 .2% .0% 9.6% .5% 1021 14 Agricultural laborer .2% 2 .6% .0% .1% 111 3 .3% 9.5% 4.0% 0.0% 2.6% Fisherman/Fish farmer 0.0% 0 .4% 4 .1% 1 2.1% 1337 .4% .1% .3% Apprentice 100.0% 60 .3% .0% 0 0.0% 5 .1% .2% .0% .3% 1.3% .2% 1 Beggar 1060 7.7% 0.0% 3 1.0% .8% 8.3% 0.0% 0.0% .2% 0 Employed abroad 100.0% .3% 3 0.0% .2% Pensioner 0.0% .0% .2% 0 100.0% .1% 181 .1% 1.5% Others .2% .3% 6.9% 10 0.0% 1896 .2% 8.2% Total .0% 0.0% 100.0% 0 0.0% .4% .3% 100.0% 4.9% 13 6.6% .0% .3% 4 100.0% 112 7.8% .0% 100.0% 0 901 100.0% 8.4% 11 22 100.0% 166 14 80 17 190 29 324 33 737 129 34 594 317 3000 33 419 470 4000 38 236 897 49 5000 37 684 127 6000 30 504 146 7000 33 295 3000 160 6200 29 80 198 4000 90 6000 25 190 186 4000 85 6000 30 324 121 3000 59 4900 31 737 88 129 3000 43 5200 31 594 88 317 2500 419 3600 78 5000 23 3977 470 4000 236 3000 27 4000 30 6000 3600 5000 1896 31 49 897 155 15 Table A11: Median hours worked, monthly income, and wages of working household684 members7500 according3000 to background22 5000 characteristics.1060 26 127 108 504 8000 2000 6000 1021 33 146 94 901 3977 31 260 295 7500 1500 559 4200 80 160 73 1390 Hours member worked previous month 198 7500 Monthly income3000 (computed)176 5000 38 Hourly wage for all workers (computed) 90 250 66 190 186 7500 3000 166 5000 969 30 (computed)85 240 3076 324 121 6000 3200 4000 523 33 216 1337 901 737 59 768 25 225 88 7000 2000 389 5000 594 Gender Code43 884 Gender Code 325 33 Gender Code 216 88 6000 1500 240 6000 129 Employment age 419 260 78 4000 855 3000 7000 3975 12 317 categories 216 3977 106 0 Male236 260Female 27 Total Male3076 Female Total 1508 Male Female 16 Total 220 1896 6750 3000 118 5000 470 155 240 15 1001 6750 1262 21 Median Valid N Median Valid N Median208 Valid1060 N Median7000 Valid N Median3200 Valid47 N Median Valid N Median30 Valid N Median Valid49 N Median Valid897 N 108 168 22 6250 729 3500 4500 1168 29 684 208 1021 900 31 127 182 901 417 31 94 4848 4000 5600 39 31 504 196 208 3977 187 146 < 15 years 250 73 559 6750 650 3000 0 12 29 295 160 1390 539 0 30 160 15-19 years 210 66 192 176 6000 278 3000 15 30 198 224 969 161 3977 33 220 166 17 90 33 20-24 years 240 3076 6000 3075 3000 5000 1245 36 186 208 224 523 14 85 25-29 years 230 1337 901 7000 1321 3600 16 24 121 200 768 0 6000 155 33 59 30-34 years 234 200 389 7000 2600 18 28 884 208 325 723 7000 166 24 88 208 240 901 18 43 25 35-39 years 224 8000 1007 0 88 855 224 3975 7500 296 30 78 40-44 years 220 140 106 6750 57 14 25 3076 208 1508 25 7000 780 32 27 3977 45-49 years 220 220 118 7500 5 13 25 1001 224 1262 0 3000 7500 2642 30 15 1896 City 224 47 6 155 17 25 50-54 years 200 240 6000 1540 7000 38 1060 729 150 1168 3076 1642 108 14 22 28 55-59 years 240 900 240 6250 3000 14 4000 30 1021 417 156 39 1188 836 19 901 20 60-64 years 240 187 0 64 94 3977 650 224 150 1500 5000 599 28 65+ years 160 220 539 0 73 17 559 25 278 224 2500 146 6000 34 1390 Education completion 208 3977 160 499 66 16 176 25 Total 224 161 208 6750 3000 375 5000 33 969 categories (per DHS) 3075 240 1245 282 401 16 166 20 Dhaka 225 14 260 6000 3000 177 7500 36 3076 523 1321 234 716 3977 14 901 27 Barisal 208 0 180 155 7000 3000 134 5000 29 1337 768 723 240 2496 349 11 389 27 Sirajganj 216 901 120 166 7500 3000 129 5000 884 325 Total 224 1007 140 296 1267 202 16 240 32 57 7000 3500 86 5000 855 3975 None 210 25 220 780 659 189 15 106 25 5 224 8000 3600 901 6000 3076 1508 Livelihood Groupings Primary incomplete 220 0 176 465 315 15 118 33 224 2642 7500 3500 104 5000 1001 1262 242 6 370 514 16 47 19 Completed primary 240 3076 1642 5000 156 14 220 5000 3000 58 729 1168 Secondary incomplete 240 1188 315 169 17 900 30 230 836 6000 4000 50 6000 417 39 Secondary complete or higher 240 120 64 3076 158 17 187 0 150 234 599 7000 3000 96 6000 650 146 1896 16 539 0 Total 224 160 216 499 7000 245 3000 5000 278 220 375 170 19 161 3977 Self-employed 216 282 401 144 2000 25 Duration in current city 176 220 9000 3075 Salary 260 177 139 41 16 14 1245 716 225 3977 6750 4000 1321 30 Daily wage 192 150 134 40 17 155 2496 208 349 6000 219 3600 0 30 130 129 559 723 0 166 Work, no pay 200 344 4500 901 30 1267 150 234 202 7000 1007 No work and/or income 86 128 33 296 659 210 220 189 7000 3200 57 Total 224 901 118 25 16 33 780 465 180 240 315 7500 5 Always in city 224 104 1337 0 12 33 2642 Wealth group 370 208 224 514 6000 6 <= 2 years 220 58 3076 23 20 1642 315 210 240 169 7000 14 3-5 years 230 50 1188 21 25 836 3076 240 210 158 7500 64 6-10 years 240 96 150 21 26 599 245 224 225 1896 7000 146 10+ years 216 170 160 15 25 499 144 200 200 375 Total 224 282 17 33 401 139 220 41 224 177 Lowest quintile 240 716 15 25 3977 Zones within Dhaka 219 260 40 242 134 Second quintile 208 2496 16 22 344 200 559 225 129 349 Third quintile 224 1267 16 30 128 210 224 202 Fourth quintile 220 17 86 27 118 160 659 189 Highest quintile 240 17 901 27 240 465 315 Total 224 1337 16 104 23 225 370 514 Kafrul-Badda 240 16 58 25 240 315 169 Dhanmondi Mohammadpur 210 15 50 30 224 3076 158 Gulshan 240 14 96 25 245 1896 Jatrabari Sabujbagh 208 14 170 144 Mirpur Pallabi 220 15 139 41 Old Dhaka 260 17 219 40 Ramna Tejgaon 208 18 344 559 Total 225 16 128 118 1337 29 38 31 973 565 888 90.5% 724 956 56.3% 350 288 71.9% 262 371 63.6% 757 62.4% .2% 89.2% 607 60.2% 2.9% 28.4% 432 50.7% 13.3% 242 0.0% .1% 56.2% 4.1% 161 .1% 2.1% .4% 59.5% .4% .5% 3.1% 121 .0% 6.1% 62.7% 4.3% 111 .9% 9.9% 1.6% 7.7% 70.4% 2.2% 105 .1% .1% 1.1% 1.1% 89.1% 13.9% 4.9% 130 1.4% .0% 1.2% 3.7% 69.4% 14.5% 9.8% 4281 1.2% 0.0% 3.9% .1% 68.7% 7.0% 16.2% 10.4% 1818 10.6% 0.0% 0.0% 3.6% 3.3% 83.4% 40.3% 41.5% 1216 19.4% 0.0% Table A12: Distribution of working household members according.0% to sources of income according to4.4% background1.6% 85.1%variables 25.3% 3.2% 49.7% 1247 14.2% 1.5% 0.0% 5.2% 0.0% 69.4% 28.3% 14.1% 29.7% 4281 21.5% .8% 8.3% 0.0% 0.0% 20.3% .2% 7.5% 30.3% 29.6% 4281 0.0% 9.2% .8% 0.0% 40.7% 10.2% 69.4% 22.0% 24.2% 29.9% 0 Gender Code 9.0% .5% 8.7% .7% 69.4% 29.8% 21.7% 0.0% 34.9% 19.3% .2% Male 4281 6.0%Female.7% .8% 23.2% 10.0% Total 24.9% 2.4% 29.7% 23.7% .8% 1231 6.1% .2% 4.9% 23.5% 9.8% 6.5% 12.5% .9% 29.8% 29.7% 1407 .9% Self-em- Salary Daily Work, No work Total Self-em- Salary Daily.6% Work,0.0% No work Total Self-em14.3% - Salary18.9% 14.0%Daily Work, No work Total 39.6% 26.3% .9% 0.0% 25.1% .7% ployed wage no pay and/or 500 ployed wage no pay and/or ployed wage15.9% no pay and/or 25.1% .8% 34.2% 3.6% .7% 23.6% 11.4% Employment age 35.7% 29.7% .8% income 797 .9% income 26.9% 5.6% 10.0% income categories 32.8% 36.2% .5% 24.3% 3.6% 0.0% 22.6% 345 .4% 1.7% 22.7% 3.9% 31.1% 22.5% 16.8% Row N % Row30.7% N % Row11.5% N % Row.9% N % Row49.7% N % Unweight- Row N % Row N % Row N % Row.7% N % Row N % Unweight- Row N % Row N % Row N % Row1.0% N % Row N % Unweight- 4280 35.4% 3.6% 19.6% 14.0% .9% ed Count 4.9% 24.8% 5.0% .7% ed Count 23.2% 89.9% ed1929 Count 15.9% 1242 .8% 16.8% 8.3% 1.2% 3.6% .7% 29.4% 9.5% 10.0% 45.8% 853 26.1% 0.0% 20.2% .8% 16.4% 156 2.4% 5.9% 0.0% .7% 18.2% 7.7% 12.7% 50.4% 1259 < 15 years .7% 22.4% .9% 19.6% .9% 163 5.8% 3.6% 23.7% 33.8% 25.2% 24.5% .0% 29.7% 21.9% 1.5% 22.5% 7.8% 1481 15-19 years 10.3% 288 25.8% .8% 27.4% 6.7% 22.6% 3.6% 23.1% 11.3% 27.8% 20-24 years 30.6% 13.2% 1.5% 6.6% .0% .0% 957 761 6.1% 4.4% 19.4% 29.0% 18.6% 16.8% 7.2% 0.0% 21.9% 9.1% 694 25-29 years 38.8% .0% 7.3% 16.5% 0.0% 16.8% 2610 7.4% 2.8% 22.5% 24.1% 403 30-34 years 44.5% 5.3% 4.8% 5.6% .7% 7.2% 0.0% 1.6% 1875 6.4% 4.0% 16.5% 26.6% 0.0% 21.5% 10.0% 290 35-39 years 51.2% 23.1% 1.5% 3.7% 21.9% 0.0% 18.7% 0.0% 909 22.3% 4.3% 17.1% 43.0% 358 40-44 years 59.1% 23.7% 16.8% 7.9% 0.0% 0.0% 19.8% .9% 623 15.5% .4% 35.9% 23.2% 4.4% .1% 161 30.1% 32.7% 213 45-49 years 46.4% 12.0% .0% 6.7% 21.9% 0.0% 16.5% .2% 467 4.4% 3.6% 129 26.0% 53.6% 173 City 50-54 years 39.7% 9.8% 13.2% 37.7% 21.9% 0.0% 27.3% 0.0% 407 29.4% .5% 64.5% 55-59 years 18.2% 4.4% 11.3% 237 31.8% 23.4% 247 40.1% 23.1% 0.0% 32.7% 25.3% .6% 4.4% .9% 50.2% 60-64 years 13.3% 4281 5.3% 29.2% 102 22.5% 18.4% 8857 28.4% 23.1% 0.0% 28.1% 20.1% .4% .9% 49.8% 12.3% 337 4.3% 68 16.5% 26.6% 3791 65+ years 28.5% 21.0% 22.9% 1.0% .4% 21.4% .4% 0.0% .4% 4.4% 19.3% 57.3% Gender Code Total 16.8% 202 12.9% 117 22.2% 10.2% 2504 29.4% 23.1% 26.5% 17.0% .9% 1.1% 60.7% .2% 198 0.0% 12.4% 26.0% Dhaka 29.4% 20.8% 29.7% 9.3% 4576 10.5% 2562 16.7% .7% 22.7% .8% 16.3% .8% 50.2% Barisal 32.9% 310 4.4% 1973 26.2% 10.8% 8857 32.8% 13.9% 31.1% 21.9% 10.4% .3% 29.7% 1.0% 450 4.4% 18.3% 27.6% 1.8% Education completion Sirajganj 27.4% 29.4% 32.3% 25.8% 1288 9.8% 4281 37.0% 1.0% 4.9% 3.4% 1.1% 19.8% 69.4% categories Total 24.3% 167 1315 26.6% 8.5% .8% 4576 29.4% 34.0% 37.1% 31.4% 3.4% 50.2% .6% 154 3.9% .7% 16.5% 20.2% .7% Male 29.4% 20.1% 32.2% 71.5% 4576 10.0% 8857 38.5% 3.2% 4.0% 1.3% 42.1% 23.8% .3% 46.1% Female 22.9% 1.1% 25.0% 1818 0 2647 0.0% 23.1% 56.8% 4.8% 36.0% 8.3% 61.7% Total 18.2% .9% 4.3% .8% 20.9% .4% 2845 29.4% 26.6% 23.8% 29.6% 2.8% 4576 6.4% 41.5% 17.6% 8.7% 1.3% 42.2% 27.8% .9% 1100 None 37.5% 2.3% 28.1% 33.7% 4576 5.4% 21.7% 4.4% 3.6% 45.6% 20.3% .6% 45.8% Duration in current city Primary incomplete 17.9% .7% 29.6% 0.0% 1665 21.7% 22.4% 23.8% 2.1% 1416 39.6% 16.9% 40.3% Completed primary 15.2% 11.3% .5% 22.5% .8% 598 28.8% 23.7% 0.0% 24.6% 1.9% 64.7% 1438 40.9% 11.2% 50.2% 14.3% 1.1% .9% 22.9% Secondary incomplete 30.9% 19.6% 72.5% 600 8855 27.2% .6% .1% 14.5% 23.1% 5.7% 11.6% Secondary complete or higher 16.8% 25.9% 0.0% 1322 28.0% 25.6% .4% 26.1% 68.3% 868 9.8% 15.1% 10.8% 14.5% 23.2% 9.0% Total 29.4% 13.1% 14.0% .7% 74.3% 253 163 15.9% 1.0% 2.6% 18.2% 20.2% 9.8% 4.5% Always in city 11.0% 13.9% 21.9% 45.9% 23.1% 1.3% 68.5% 4575 18.2% 4.7% 170 Wealth group <= 2 years 10.9% 13.4% 3.5% 22.8% 38.1% 22.3% 24.6% 69.4% 306 22.9% .9% 3.4% 80 18.3% 25.1% 14.0% 3-5 years 43.5% 3.6% 23.1% 51.6% 859 29.7% 3.6% 7 16.5% 29.0% 10.9% 6-10 years 21.1% 3.3% 48.3% 26.6% 20.6% 38.4% 15.9% 2820 8.3% 7 23.2% 54.6% 10+ years 44.4% 6.3% 19.5% 1.6% 3909 23.1% 18 17.8% 50.2% Total 44.7% 16.1% 3.9% 26.0% 1857 21.0% 17.1% 98 15.9% 49.3% Lowest quintile 16.4% 5.5% 15.9% 27.4% 23.1% 46.6% 14.0% 43.8% 1287 Zones within Dhaka Second quintile 4.4% 24.2% 210 25.6% 25.9% 42.0% 19.7% 51.9% 966 Third quintile 2.6% 22.6% 2034 30.6% 20.9% 68.8% 16.6% 50.2% 838 Fourth quintile 3.1% 948 33.5% 19.1% 67.9% 10.9% 51.1% 8857 Highest quintile 5.8% 664 31.4% 35.6% 69.3% 16.5% Total 52.0% 727 29.4% 33.8% 4.3% 65.0% 499 Kafrul-Badda 54.8% 415 30.5% 23.7% 7.0% 76.6% 431 Dhanmondi Mohammadpur 48.6% 406 33.0% 3.8% 69.4% 4576 Gulshan 45.8% 628 26.2% 1.1% 69.4% 390 Jatrabari Sabujbagh 51.6% 957 23.4% 4.4% 71.1% 213 Mirpur Pallabi 49.7% 341 34.4% 72.2% 208 Old Dhaka 49.8% 317 31.4% 65.4% 318 Ramna Tejgaon 3791 20.6% 64.0% 507 Total 29.4% 75.8% 174 71.4% 163 68.7% 1973 661

661 514

514 204 100

204 100 9 6

9 100 6 331 189

331 211 6 189 1 561 211 462 189 1 508 27 561 462 364 68 1 27 15 508 561 364 182 68 54 27 8 15 508 182 19 68 54 223 304 8 15 19 428 30.0 54 223 0 143 304 35.0 8 428 168 4 223 0 408 168 143 5000 15.3 304 22 0 4 141 51 408 7000 16.0 143 22 6 4 51 182 24 141 2500 20.0 408 176 6 1 14 51 24 15 182 4000 25.0 141 198 1 6 14 4 24 377 20.0 15 5000 28.8 182 168 6 76 14 4 0 3500 144 10.0 16.7 260 377 15 35 2880 0 1 4 6000 8 15.0 38.5 250 144 377 3 2000 1 0 Table A13: Median monthly hours worked, monthly income, and0 hourly wage of working household members according4600 to livelihood types. 2 6.3 28.6 175 8 144 12 2100 0 150 1 0 9000 1 11.1 33.3 208 2 8 1500 200 0 6 0 10 4000 5 16.7 30.0 260 1 Gender Code 2 2600 168 0 15 10 33.3 0 7000 35 18.2 25.0 Hours member worked previous month232 (computed 5Monthly income (computed) Hourly wage for all workers (computed) 1 240 1700 35.0 10 318 0 8800 13 15.0 Male Female 140 Total Male Female Total Male 3 15.4 Female Total 35 5 234 6000 3000 30.6 5000 27.3 Median Valid N Median Valid0 N Median225 Valid3977 N Median Valid N Median Valid13 N Median Valid N Median Valid2 N Median Valid112 N Median Valid N 3 35 120 7000 4200 16.0 1500 901 15.0 13 250 112 6 20 2 What major Day labor (unskilled) 180 130 8000 21.4 661 3 4500 30.4 employment 112 182 901 2 10 did you have 6 Rickshaw puller 198 2 260 4000 600 33.3 514 over the past 2550 0.0 901 150 206 19 month? 2 House help/maid (salaried) 240 6000 30.8 204 6 2400 7200 165 20.0 3076 22 Washerwoman/laundryman 286 6000 206 22.6 2 30 3000 9 180 0 25.0 Helper (transport, shop, other activities) 13 250 7000 3076 38.5 206 5000 331 240 232 3000 Simple trades (potter, smith, tailor, barber, construction 0.0 208 6000 28.6 1500 211 3076 160 240 5000 Specialized trades (clerk, teacher, electrician, mechanic, 15 19.4 208 9000 34.7 462 224 180 Garments worker 0 25.0 260 4000 33.3 2250 364 150 200 Motor transport driver 4500 232 8000 25.0 Street food vendor 182 90 5000 20 140 9000 15.0 Hawker/peddler 150 19 216 225 2000 27.3 3000 Petty retail business / shop owner 428 224 0 300 1500 18.2 Medical, healer 168 14 80 4500 30.4 Religious leader 240 0 22 4400 16 Farmer 100 0.0 3000 6 Agricultural laborer 7200 165 120 13.3 3000 1 Fisherman/Fish farmer 0 180 180 25.0 6 Apprentice 232 220 2000 0.0 Beggar 35 240 5000 26.0 Employed abroad 35 3 150 0 30.2 Pensioner 3 12 200 Others 6000 12 6 30 Total 6750 6 15 240 15 224 318

318 3977

3977 Table A14: Distribution of household population according to age and sex

Gender Code Male Female Total

Column Unweighted Column Unweighted Column Unweighted N% Count N% Count N% Count Dhaka

Household age < 5 years 11.7% 390 11.3% 379 11.5% 769 categories 5-9 years 10.8% 219 10.6% 232 10.7% 451 10-14 years 9.7% 165 9.6% 187 9.7% 352 15-19 years 7.6% 120 11.9% 273 9.8% 393 20-24 years 8.9% 180 14.3% 409 11.7% 589 25-29 years 13.1% 355 12.2% 315 12.7% 670 30-34 years 11.2% 264 7.5% 152 9.3% 416 35-39 years 7.9% 181 5.9% 106 6.9% 287 40-44 years 5.3% 105 4.1% 73 4.7% 178 45-49 years 3.5% 58 2.4% 37 2.9% 95 50-54 years 2.5% 44 4.8% 92 3.7% 136 55-59 years 2.1% 33 1.9% 34 2.0% 67 60-64 years 2.1% 37 1.4% 28 1.8% 65 65+ years 3.6% 57 1.9% 35 2.7% 92 Total 100.0% 2208 100.0% 2352 100.0% 4560

Barisal

Household age < 5 years 13.0% 236 13.8% 268 13.4% 504 categories 5-9 years 10.5% 139 9.7% 143 10.0% 282 10-14 years 9.3% 119 8.7% 112 9.0% 231 15-19 years 6.3% 70 8.6% 132 7.5% 202 20-24 years 5.6% 78 14.5% 263 10.2% 341 25-29 years 12.9% 214 13.1% 224 13.0% 438 30-34 years 11.8% 200 7.2% 106 9.4% 306 35-39 years 9.7% 142 5.8% 65 7.7% 207 40-44 years 5.2% 69 3.0% 38 4.1% 107 45-49 years 4.1% 47 3.3% 39 3.7% 86 50-54 years 3.0% 33 5.3% 74 4.2% 107 55-59 years 2.3% 28 2.3% 32 2.3% 60 60-64 years 2.4% 27 1.5% 22 1.9% 49 65+ years 4.0% 51 3.2% 38 3.6% 89 Total 100.0% 1453 100.0% 1556 100.0% 3009

Sirajganj

Household age < 5 years 8.5% 232 8.0% 220 8.2% 452 categories 5-9 years 12.5% 166 11.6% 164 12.0% 330 10-14 years 12.7% 148 10.5% 135 11.6% 283 15-19 years 9.1% 98 11.7% 160 10.4% 258 20-24 years 8.2% 114 10.7% 216 9.5% 330 25-29 years 9.7% 188 10.0% 185 9.8% 373 30-34 years 7.8% 143 6.4% 92 7.1% 235 35-39 years 7.5% 109 7.8% 91 7.7% 200 40-44 years 5.3% 68 4.5% 50 4.9% 118 45-49 years 5.0% 56 4.7% 53 4.8% 109 50-54 years 4.2% 44 6.3% 71 5.3% 115 55-59 years 4.5% 50 2.7% 36 3.6% 86 60-64 years 3.7% 41 1.4% 19 2.5% 60 65+ years 1.3% 22 3.6% 44 2.5% 66 Total 100.0% 1479 100.0% 1536 100.0% 3015

77 1240 820 102 760 47 2820 21 613 170 191 117 163 55 37 7 859 216 9 170 761 163 72 192 51 98 156 18 473 33 859 7 306 657 26 353 163 288 1322 41 185 63 19 18 111 100.0% 1242 100.0% 33 170 306 145 80 100.0% 22 69 94 65 1322 7.9% 100.0% 29 39 61 859 506 4.6% 100.0% 16 58 44 369 297 2.2% 100.0% Table A15: Distribution of household heads according to duration spent in current163 city according to background variables75 209 151 7.7% 4 50.5% 100.0% 70 32 202 246 8.1% 170 25.3% 100.0% 31 306 79 122 9.2% 4.7% Duration15 in current city 8.4% 100.0% 54 7.7% 136 859 1322 3.6% 41 49.0% 100.0% Row N % Unweight Row N % 8 Row N % Row N % 80 Row N % Row N % Always in city 1.1%<= 2 years 7.7%3-5 years30 6-10 years 10+ years113 100.0%Total 585 80 46.9% 163 6.7% 38 143 100.0% 218 - 8.9% Unweight- Unweight- 16.5% Unweight- 64.3% Unweight- Unweight- 29 7.9% 46 10 174 ed442 Count 9.6% ed Count ed Count 8.6% ed Count 49.0% ed Count 100.0% ed Count 21 8.9% 306 195 77 4.2% 170 1.9% 48.6% 100.0% 37 6.6% 39 234 100.0% City Dhaka 15.9% 1322 8.9% 20 16.0% 56.7% 39 7.7% 46 859 Barisal 57.9% 11.4% 13 16.9% 47.0% 100.0% 368 37 7.9% 55 109 Sirajganj 86.3% 6.9% 13 10.0% 44.5% 100.0% 313 7.4% 89 Total 163 12 65 100.0% 18.3% 268 8.4% 16.0% 46.2% Male 24 10.1% 27 77 75 100.0% Gender Code 18.5% 203 5.4% 13.8% 49.0% Female 18 3.2% 306 101 100.0% 2610 16.8% 170 10.3% 6 14.4% 48.0% Total 10 7.7% 40 157 100.0% 210 18.3% 8.9% 11 17.9% 50.4% None 1322 2.9% 36 100.0% 2820 Education completion categories 18.4% 14 102 18.7% 42.0% 50 Primary incomplete 27 7.8% 15.4% 40 10.6% 22.9% 32 63.0% 56 100.0% 1067 Completed primary 8 7.3% 18.8% 5 6.4% 16.0% 38 49.0% 613 100.0% 609 Secondary incomplete 14.0% 10 6 7.5% 37 36.7% 100.0% 354 Secondary complete or higher 22.5% 17.7% 35 7.4% 15 100.0% 536 Total 14.0% 117 8.5% 15.6% 50.0% 79 8.9% 18 100.0% 254 Self-employed 18.3% 7.7% 18.0% 51.9% 24 15.2% 100.0% Livelihood Groupings Salary 18.6% 8.8% 6.9% 216 47.8% 2820 9 7.4% 100.0% Daily wage 19.3% 9.2% 16.0% 50.6% 1229 9.0% 100.0% No work, income 15.9% 192 8.0% 19.8% 49.0% 577 9.8% 100.0% Total 19.5% 5.5% 13.6% 49.6% 836 Lowest quintile 7.5% 100.0% 18.3% 7.6% 14.8% 43.5% 178 Second quintile 8.9% Expenditure quintiles 25.4% 6.9% 20.4% 59.5% 2820 Third quintile 13.3% 18.4% 10.9% 14.0% 52.8% 564 Fourth quintile 17.9% 11.0% 7.9% 44.9% Highest quintile 16.0% 564 14.6% 6.4% 54.9% Total 16.5% 564 19.4% 5.5% 61.1% Kafrul-Badda 28.5% 564 50.5% Dhanmondi Mohammadpur 18.3% 12.9% 20.4% 564 Zones within Dhaka Gulshan 11.7% 2.0% 20.5% 2820 Jatrabari Sabujbagh 7.9% 4.2% 11.1% 220 Mirpur Pallabi 5.7% 9.2% 10.4% 140 Old Dhaka 15.7% 12.5% 140 Ramna Tejgaon 23.5% 16.5% 200 Total 25.8% 340 11.3% 100 15.9% 100 1240 Row N % Unweight Row N % Row N % Row N % Row N %

360 137 45 1048 542 347 181 498 103 89 44 1498 11 542 3 1368 281 227 6 130 504 263 100 2 1498 115 18 80 11 561 45 281 96 9 312 664 100 35.5% 39 2 203 598 54 38.2% 542 11 290 Table A16: Among HoH who have not always been in current city, percent66 distribution according to nature of migration to current location (slum) by back- 41 50.0% 229 6 132 ground characteristics 664 35.6% 50 114 0 100.0% 1498 228 36.4% 36 163 4 100.0% 644 158 29.6% 281 Type of migration to36 current slum 1 100.0% 359 35.6% 78 17.7% 119 542 0 100.0% 394 Intra-city Urban-urban 41.9%Rural-urban International Total 143 25.2% 73 97 11 100.0% 101 27.5% .3% 57 - 7.5% Unweight74 - Unweight95 - Unweight5 - 100.0% Unweight1498 - 33.6% 1.4% ed664 Count 17.7% ed 15Count ed 95Count ed Count2 100.0% ed 196Count 34.1% 2.1% 291 18.3% 281 132 4 100.0% 251 28.6% .3% City Dhaka 46.6% 170 13.7% 37 123 0 100.0% 296 Barisal 35.6% .3% 35.2% 153 17.7% 45 542 11 100.0% 361 Sirajganj 40.4% 34.6% .1% 50 17.5% 47 77 4 100.0% 394 Total 34.1% .3% 46.4% 664 14.9% 72 43 100.0% 1498 Male 39.9% .5% 5 Gender Code 45.0% 23.4% 80 29 100.0% 193 Female 58 34.4% 1 56.6% 13.8% 0.0% 100.0% Total 106 281 35.6% 62 0 132 46.4% 25.9% .7% 100.0% None 153 17 49.2% 92 1 130 Education completion categories 40.0% 17.7% .1% 100.0% Primary incomplete 157 17 41.4% 28 11 165 57.6% 16.8% 0.0% 100.0% Completed primary 190 52 29.5% 29 1 261 42.3% 21.3% .3% 100.0% Secondary incomplete 664 21 38.7% 360 0 76 Secondary complete or higher 52.0% 16.9% .5% 100.0% 98 21 31.5% 0 91 Total 45.5% 14.1% .0% 100.0% 72 17 35.6% 0 1048 Self-employed 46.4% 17.7% .4% 100.0% 49 36 42.3% 1 Livelihood Groupings Salary 48.2% 16.4% 100.0% 82 181 35.0% 0.0% 0 Daily wage 44.5% 17.0% 100.0% 19.9% .3% 1 No work, income 42.9% 147 14.8% 100.0% 42.0% .2% 3 Total 51.5% 31 17.4% 100.0% 35.0% 1.3% Lowest quintile 46.4% 25 20.4% 100.0% Second quintile 36.3% .1% Expenditure quintiles 34.2% 504 17.7% 100.0% Third quintile 32.5% 0.0% 40.2% 8.1% 100.0% Fourth quintile 35.5% 55.7% .2% 100.0% Highest quintile 15.4% 43.9% .3% 100.0% Total 43.0% 47.8% 1.0% Kafrul-Badda 13.3% 100.0% 46.4% 0.0% Dhanmondi Mohammadpur 8.2% 100.0% Zones within Dhaka 48.5% 0.0% Gulshan 24.8% 49.6% 0.0% Jatrabari Sabujbagh 35.9% Mirpur Pallabi 37.1% .0% 17.7% Old Dhaka 44.7% 0.0% Ramna Tejgaon 56.8% 1.0% Total 38.9% .3% 30.6% 46.6% 79 5.0% 2.9% 2.6% 5.0% 2.8% 4.8% 2.2% 6.1% 17.9% 5.0% 2.8% 5.1% 0.2% 1.0% 5.0% 1.2% 15.6% 5.1% 2.4% 2.8% 5.2% 0.3% 3.5% 2.9% 0.4% 0.1% 2.2% 5.0% 0.0% 1.5% 0.5% 0.0% 0.0% 0.3% 3.8% 0.1% 0.4% 0.6% 2.7% 2.8% 1.3% 0.4% 0.1% 2.8% 7.3% 1.5% 0.0% 0.0% 1.8% 5.0% 2.7% 0.4% 0.0% 12.8% 2.7% 5.0% 1.3% 0.9% 0.0% 7.8% 1.9% 3.9% 1.5% 0.0% 0.3% 3.3% 3.4% 7.2% 0.0% 0.0% 0.0% 12.7% 2.8% 2.7% 1.3% 0.0% 0.6% 74.1% 11.7% 1.3% 5.0% 0.9% 0.0% 0.0% 74.9% 20.4% 2.9% 1.2% 0.4% 0.3% 25.7% 12.7% 2.0% 2.4% 0.0% 0.3% 74.0% 12.5% 10.1% 0.3% 0.0% 0.0% 0.4% 77.0% 16.7% 2.8% 3.6% 0.0% 0.2% 0.0% 52.6% 7.4% 9.7% 1.3% 0.6% 0.1% 0.0% 74.0% 1.9% 12.6% 1.3% 0.4% 1.7% 0.4% 72.9% 3.7% 2.4% 8.9% 0.1% 0.0% 0.3% 0.4% 72.1% 2.3% 0.8% Table A17: Among household heads who have migrated7.3% to current city, percent distribution12.7% of households0.0% according0.0% to reason for migration to current loca- 0.0% 75.2% 1.1% 3.2% 42.1% 14.0% 0.0% 0.5% tion (slum) by background characteristics. 0.4% 75.6% 1.3% 2.8% 14.0% 3.3% 0.9% 2.6% 0.9% 79.3% 1.1% 0.0% 0.6% 13.1% 0.4% 0.0% 2.5% 0.0% 74.0% What was the reason to migrate to this1.2% city from village or source of origin? 1.5% 2.1% 12.2% 0.0% 0.0% 3.2% 0.0% 79.5% 0.7% 0.5% 3.4% 12.7% 0.0% 0.3% 1048 Loss of property Loss 2.6%of property Loss 0.0%of property In 80.1%search of To get out of Land related Involvement in Eviction Marriage migration Others Total 3.3% 2.3% due0.6% to flood due to river erosion due to cyclone employment poverty13.5% dispute political0.9% conflict 0.0% 347 City 2.3% 0.0% 76.7% 1.3% 5.0% 0.6% 11.3% 0.0% 0.0% 103 2.0% 0.4% 71.2% Row Row Row Row Row 1.7%Row Row Row 9.1%Row Row Row Unweight- 0.5% 10.5% 0.5% 2.0% N % 6.0%N % 0.0%N % 74.0%N % N % N % N % N % N % N % N % ed1498 Count 0.4% 2.3% 0.6% 18.0% 0.4% 0.0% 100.0% 1.5% 0.0% 75.3% 1368 2.7% 2.8% 0.4% 12.7% 1.0% 0.0% 100.0% Gender Code Dhaka 2.6% 0.9% 76.7% 130 1.1% 0.7% 12.7% 0.0% 0.0% 100.0% Barisal 2.6% 0.3% 75.5% 1498 0.8% 1.0% 18.3% 0.0% 0.0% 100.0% Sirajganj 2.2% 0.4% 59.4% 1.3% 561 1.0% 14.5% 1.1% 0.2% 100.0% Education completion categories Total 2.3% 0.4% 74.0% 1.0% 312 Male 0.0% 1.5% 13.1% 0.0% 100.0% 0.7% 73.1% 203 0.6% 0.0% 0.0% 100.0% Female 3.1% 0.0% 68.3% 8.9% 0.6% 290 Total 1.3% 2.6% 0.0% 100.0% 0.0% 70.4% 12.7% 132 0.1% 0.4% 0.4% 100.0% None 3.2% 18.8% 0.4% 76.7% 1498 0.8% 3.1% 100.0% Primary incomplete 2.1% 0.0% 77.0% 5.8% 0.5% .7% 100.0% 163 Duration in current city Completed primary 2.9% 0.0% 74.0% 14.8% 170 Secondary incomplete 0.6% 1.0% 100.0% .9% 0.0% 73.5% 14.8% 0.2% 1.3% 100.0% 306 Secondary complete or higher 2.6% 0.7% 85.4% 13.6% 859 Total 1.0% 100.0% 2.8% 0.5% 78.5% 10.3% 1498 <= 2 years 1.1% 100.0% 6.1% 0.4% 75.4% 3.6% 0.5% 100.0% 644 Livelihood Groupings 3-5 years 1.7% 1.0% 63.2% 0.6% 12.8% 100.0% 359 6-10 years 3.1% 0.0% 72.4% 0.1% 100.0% 394 10+ years 1.2% 0.0% 86.1% 0.5% 100.0% 101 Total 2.6% 0.0% 74.1% 0.4% 100.0% 1498 Self-employed 3.5% 0.7% 1.2% 100.0% Expenditure quintiles Salary 2.2% 0.0% 196 0.6% 100.0% Daily wage 1.1% 0.0% 251 0.6% 100.0% No work, income 2.0% 0.4% 296 0.8% 100.0% Total 2.4% 361 0.7% 100.0% Lowest quintile 5.3% 394 0.0% 100.0% Second quintile 0.0% 1498 0.5% 100.0% Zones within Dhaka Third quintile 2.4% 193 0.4% 2.2% 100.0% Fourth quintile 132 2.3% 4.0% 100.0% Highest quintile 130 0.0% 4.8% 100.0% Total 165 0.6% 3.2% 100.0% Kafrul-Badda 261 7.3% Dhanmondi Mohammadpur 100.0% 5.0% 76 Gulshan 100.0% 0.3% 91 Jatrabari Sabujbagh 100.0% 4.5% 1048 Mirpur Pallabi 100.0% 2.5% Old Dhaka 100.0% 3.3% Ramna Tejgaon 100.0% 11.6% Total 0.0% 7.0% 5.0% Table A18. Distribution of households

Column N % Unweighted Count

City Dhaka 95.4% 1240 Barisal 2.6% 820 Sirajganj 1.9% 760 Gender Code Male 88.1% 2610 Female 11.9% 209 Education completion categories None 41.5% 1067 Primary incomplete 19.4% 609 Completed primary 13.2% 354 Secondary incomplete 19.0% 536 Secondary complete or higher 6.9% 254 Duration in current city Always in city 18.3% 1322 <= 2 years 8.9% 163 3-5 years 7.7% 170 6-10 years 16.0% 306 10+ years 49.0% 859 Livelihood Groupings Self-employed 41.1% 1229 Salary 27.1% 577 Daily wage 20.8% 836 No work, income 10.9% 178 Expenditure quintiles Lowest quintile 20.0% 1139 Second quintile 20.0% 575 Third quintile 20.0% 444 Fourth quintile 20.0% 356 Highest quintile 20.0% 306 Dhaka zones Kafrul-Badda 17.0% 220 Dhanmondi Mohammadpur 11.2% 140 Gulshan 10.9% 140 Jatrabari Sabujbagh 15.8% 200 Mirpur Pallabi 28.4% 340 Old Dhaka 9.1% 100 Ramna Tejgaon 7.6% 100

81 Table A19. Food consumption score and groups by selected background variables.

Food Consumption Food Consumption Groups Score

Mean Poor Borderline Acceptable low Acceptable high

City Dhaka 70.16 0.7% 4.3% 9.6% 85.4% Barisal 69.98 0.4% 2.6% 10.9% 86.1% Sirajganj 61.23 0.7% 14.9% 20.4% 64.0% Gender Code Male 70.53 0.6% 3.9% 9.6% 86.0% Female 66.08 1.8% 8.8% 12.1% 77.3% Education None 66.80 0.9% 5.9% 9.4% 83.8% completion Primary incomplete 69.82 0.8% 4.1% 11.6% 83.6% categories Completed primary 70.90 1.1% 4.1% 11.6% 83.3% Secondary incomplete 73.42 0.2% 3.1% 9.0% 87.7% Secondary complete or higher 78.49 0.1% 1.1% 7.0% 91.7% Livelihood Self-employed 70.58 0.5% 3.5% 9.1% 87.0% Groupings Salary 71.93 0.4% 4.7% 10.5% 84.4% Daily wage 68.28 0.8% 4.9% 9.4% 85.0% No work, income 66.14 1.9% 6.8% 12.4% 78.9% Wealth group Lowest quintile 56.86 2.7% 15.2% 18.3% 63.7% Second quintile 64.74 0.8% 5.0% 14.0% 80.2% Third quintile 71.36 0.0% 1.7% 7.3% 91.0% Fourth quintile 75.56 0.0% 0.4% 5.5% 94.2% Highest quintile 81.39 0.0% 0.0% 4.3% 95.7% Duration in Always in city 71.71 0.1% 3.2% 11.4% 85.3% current city <= 2 years 65.85 1.8% 6.4% 12.2% 79.6% 3-5 years 70.17 0.0% 1.7% 7.4% 91.0% 6-10 years 67.18 0.7% 5.6% 10.0% 83.7% 10+ years 70.98 0.8% 4.6% 9.2% 85.3% Dhaka zones Kafrul-Badda 67.75 0.0% 6.9% 7.4% 85.7% Dhanmondi Mohammadpur 66.32 0.8% 5.4% 10.4% 83.4% Gulshan 68.72 0.0% 7.5% 10.4% 82.0% Jatrabari Sabujbagh 72.93 0.0% 2.4% 10.6% 87.0% Mirpur Pallabi 70.92 0.5% 2.5% 11.6% 85.3% Old Dhaka 75.00 1.7% 2.6% 7.3% 88.4% Ramna Tejgaon 68.92 4.2% 4.7% 5.7% 85.4%

82 Table A20: Mean food share of expenditure and food share of expenditure groups by selected back- ground variables.

Food share of expenditure Share of total expenditure used on food

Mean Very high High Medium share Low share of share of share of of expenditure expenditure expenditure expenditure on food on food on food on food

City Dhaka .5200409 0.9% 9.3% 49.2% 40.7%

Barisal .5784793 4.0% 22.0% 55.3% 18.7%

Sirajganj .5924714 5.5% 28.3% 48.9% 17.3%

Gender Code Male .5253703 0.9% 10.3% 50.2% 38.6%

Female .5056234 1.6% 7.3% 43.7% 47.4%

Education None .5269600 1.2% 9.5% 51.0% 38.2% completion categories Primary incomplete .5183014 0.8% 9.2% 48.3% 41.7% Completed primary .5234700 1.3% 9.4% 48.4% 40.9%

Secondary incomplete .5197131 0.2% 11.0% 47.6% 41.1%

Secondary complete or higher .5201457 2.3% 13.1% 48.1% 36.5%

Livelihood Self-employed .5282542 0.8% 9.6% 52.9% 36.8% Groupings Salary .5174721 0.9% 11.4% 45.4% 42.3%

Daily wage .5298218 1.3% 12.0% 50.5% 36.2%

No work, income .5036750 1.9% 4.0% 43.3% 50.8%

Wealth group Lowest quintile .5263619 1.0% 12.3% 48.7% 38.0%

Second quintile .5255340 1.3% 8.9% 49.5% 40.3%

Third quintile .5353847 1.9% 8.6% 54.4% 35.0%

Fourth quintile .5317023 0.9% 12.4% 50.4% 36.3%

Highest quintile .4959334 0.1% 7.6% 43.5% 48.8%

Duration in current Always in city .5369380 1.6% 12.0% 52.5% 33.8% city <= 2 years .5367508 1.8% 11.8% 49.6% 36.8%

3-5 years .5377557 0.2% 12.7% 55.3% 31.8%

6-10 years .5264673 0.5% 10.5% 52.1% 36.9%

10+ years .5117856 1.0% 8.2% 46.2% 44.5%

Dhaka zones Kafrul-Badda .5405608 2.1% 13.0% 47.9% 37.0%

Dhanmondi Mohammadpur .4954409 0.1% 6.6% 50.6% 42.6%

Gulshan .4805109 0.0% 4.2% 43.7% 52.1%

Jatrabari Sabujbagh .5222755 0.0% 8.8% 54.2% 37.0%

Mirpur Pallabi .5342231 1.0% 9.9% 48.8% 40.3%

Old Dhaka .5224129 1.7% 9.4% 50.6% 38.3%

Ramna Tejgaon .5066120 0.6% 10.5% 46.9% 41.9%

83 Table A21: Households consuming less than 1805 and 2122 kcal/per capita by selected background variables.

Daily per capita Distribution over and Distribution over and kcal (using hh under 1805 kcal per under 2122 kcal per size) capita per day capita per day

Per capita Per capita Per capita Per capita consumption consumption consumption consumption Mean below 1805 of 1805 kcal/ below 2122 of 2122 kcal/ kcal/day day or more kcal/day day or more

City Dhaka 2182.61 26.5% 73.5% 49.8% 50.2% Barisal 2030.85 37.0% 63.0% 63.3% 36.7% Sirajganj 2135.61 24.6% 75.4% 47.6% 52.4% Gender Code Male 2165.13 27.3% 72.7% 51.0% 49.0% Female 2274.40 22.9% 77.1% 42.5% 57.5% Education None 2110.73 27.1% 72.9% 54.5% 45.5% completion Primary incomplete 2157.88 29.7% 70.3% 51.6% 48.4% categories Completed primary 2141.34 29.6% 70.4% 50.5% 49.5% Secondary incomplete 2267.85 23.7% 76.3% 44.2% 55.8% Secondary complete or higher 2459.33 19.2% 80.8% 34.7% 65.3% Livelihood Self-employed 2184.94 27.4% 72.6% 49.6% 50.4% Groupings Salary 2182.92 26.0% 74.0% 50.2% 49.8%

Daily wage 2141.06 27.3% 72.7% 50.6% 49.4% No work, income 2207.47 25.1% 74.9% 50.7% 49.3% Wealth group Lowest quintile 1720.93 64.8% 35.2% 87.8% 12.2% Second quintile 1957.89 31.9% 68.1% 69.7% 30.3% Third quintile 2115.83 19.6% 80.4% 49.4% 50.6% Fourth quintile 2357.08 11.2% 88.8% 30.2% 69.8% Highest quintile 2735.63 6.2% 93.8% 13.5% 86.5% Duration in current Always in city 2229.72 25.7% 74.3% 49.8% 50.2% city <= 2 years 2179.65 24.8% 75.2% 49.6% 50.4% 3-5 years 2267.98 23.0% 77.0% 42.2% 57.8% 6-10 years 2126.76 29.2% 70.8% 57.4% 42.6% 10+ years 2160.46 27.3% 72.7% 49.1% 50.9% Dhaka zones Kafrul-Badda 2084.42 28.7% 71.3% 53.4% 46.6% Dhanmondi Mohammadpur 2094.17 32.1% 67.9% 58.7% 41.3% Gulshan 2033.25 35.0% 65.0% 58.2% 41.8% Jatrabari Sabujbagh 2150.82 26.7% 73.3% 48.1% 51.9% Mirpur Pallabi 2288.32 21.8% 78.2% 45.9% 54.1% Old Dhaka 2278.82 18.1% 81.9% 42.0% 58.0% Ramna Tejgaon 2303.83 28.5% 71.5% 43.8% 56.2%

84 Table A22: Dietary composition by selected background variables.

Per Per Per Per capita Per Per Per capita Per ‘Per Per Per capita Per Per capita capita capita capita energy capita capita energy capita capita capita energy capita energy energy energy energy from energy energy from energy energy energy from energy from other from from from vegetables from from poultry’ from fish’ from egg’ from sugar’ from oil’ and out of cereals pulses’ tubers fruits’ meat dairy’ household

Mean Mean Mean Mean Mean Mean Mean Mean Mean Mean Mean Mean Mean

City Dhaka 1354.93 73.28 74.27 88.35 66.61 30.06 19.01 67.59 27.04 18.38 32.73 261.06 69.29 Barisal 1281.19 72.26 81.34 79.50 56.51 21.04 13.09 58.30 19.70 13.13 30.48 236.78 67.51 Sirajganj 1519.48 54.45 69.80 73.16 51.88 22.86 9.14 38.42 14.36 15.74 21.19 197.23 47.90 Gender Code Male 1347.96 72.67 73.69 87.61 66.28 29.17 18.64 67.68 26.46 18.20 31.74 256.90 68.15 Female 1416.64 74.63 79.15 89.09 65.12 33.86 19.08 60.02 27.55 18.40 38.07 278.28 74.51 Education completion None 1353.39 68.95 74.84 82.56 63.25 22.74 16.33 61.38 24.99 14.26 20.10 245.81 62.13 categories Primary incomplete 1343.27 76.25 72.80 82.67 62.05 31.73 17.36 64.01 23.70 18.15 37.27 258.25 70.36 Completed primary 1325.75 68.47 68.44 90.25 61.88 35.63 16.26 68.74 27.57 21.06 35.20 256.07 66.02 Secondary incomplete 1359.36 72.79 76.23 95.37 76.89 37.34 23.59 76.41 29.77 22.56 41.28 276.83 79.43 Secondary complete or higher 1459.26 95.96 82.30 108.66 72.58 33.20 27.44 76.94 33.93 24.48 63.54 299.97 81.08 Livelihood Groupings Self-employed 1356.69 74.78 72.71 90.06 67.07 31.94 17.86 65.57 27.60 18.24 31.27 261.54 69.61 Salary 1340.14 73.11 76.04 86.59 67.45 29.26 23.83 69.94 28.86 21.58 37.42 263.25 65.45 Daily wage 1344.58 71.27 74.90 87.01 60.59 25.11 15.18 70.06 22.66 13.79 30.27 252.95 72.67 No work, income 1416.20 68.31 75.46 84.02 69.24 30.97 15.50 57.28 24.73 18.00 28.64 252.17 66.95 Wealth group Lowest quintile 1226.02 52.30 62.59 62.14 30.85 6.52 5.18 34.35 13.61 5.33 10.30 180.93 30.81 Second quintile 1312.55 63.61 72.15 76.90 47.29 17.79 9.79 51.17 19.61 10.75 15.95 214.12 46.22 Third quintile 1339.04 75.33 70.86 84.77 63.44 20.81 16.11 66.56 24.26 18.05 27.29 241.42 67.89 Fourth quintile 1405.98 80.30 76.51 98.52 77.19 37.54 23.45 78.99 34.25 22.49 41.16 302.65 78.06 Highest quintile 1497.01 92.86 89.73 116.73 111.44 65.70 38.73 102.77 41.25 34.30 67.42 356.66 121.02 Duration in current city Always in city 1337.42 74.39 76.19 85.42 74.78 42.04 18.84 57.13 30.10 19.37 42.54 287.33 84.17 <= 2 years 1408.55 67.29 80.06 84.78 50.13 31.10 13.92 70.13 31.21 9.59 15.61 251.25 66.03 3-5 years 1430.96 71.84 76.73 95.92 72.17 33.46 23.28 77.25 23.69 16.97 28.77 260.97 55.96 6-10 years 1319.70 74.39 77.92 93.16 61.48 23.80 18.89 68.85 24.34 16.27 32.24 261.66 54.08 10+ years 1353.85 73.02 71.13 86.26 66.25 26.14 18.66 67.47 25.65 20.14 32.38 249.04 70.44 Dhaka zones Kafrul-Badda 1418.79 62.30 67.19 69.01 60.55 27.89 14.85 73.82 21.53 14.47 13.36 201.93 38.74 Dhanmondi Mohammadpur 1331.40 64.45 73.64 94.21 63.01 27.50 11.93 58.30 24.86 10.01 31.15 241.84 61.85 Gulshan 1322.56 69.14 72.17 72.02 53.65 24.36 17.31 65.23 27.06 25.96 13.59 229.44 40.74 Jatrabari Sabujbagh 1284.22 74.77 76.72 95.23 77.48 20.23 20.65 72.32 30.35 19.05 27.99 288.62 63.20 Mirpur Pallabi 1376.18 78.19 79.87 91.00 64.34 37.41 21.75 64.34 29.15 19.93 45.96 283.01 97.18 Old Dhaka 1347.41 77.28 67.65 98.00 82.31 35.55 21.28 66.13 27.45 25.32 44.37 301.18 84.91 Ramna Tejgaon 1370.18 90.69 76.01 110.62 71.12 33.34 24.86 74.82 27.29 13.21 52.38 279.86 79.45 Table A23: Household Food Insecurity Access Scale (HFIAS) by selected background variables.

HFIAS score HFIA category

Mean Food Mildly Food Moderately Severely Food Secure Insecure Food Insecure Insecure Access Access Access

City Dhaka 4.26 45.3% 14.4% 20.7% 19.5% Barisal 3.94 39.2% 21.9% 23.3% 15.7% Sirajganj 7.30 20.2% 11.8% 32.1% 35.9% Gender Code Male 3.98 46.8% 14.9% 20.5% 17.7% Female 6.69 29.2% 12.3% 25.0% 33.5% Education None 5.27 36.1% 14.9% 23.2% 25.8% completion Primary incomplete 4.51 41.9% 14.0% 24.6% 19.4% categories Completed primary 3.88 50.9% 13.8% 16.5% 18.7% Secondary incomplete 3.13 54.8% 14.1% 19.3% 11.9% Secondary complete or higher 2.12 64.0% 17.0% 11.0% 8.0% Livelihood Self-employed 4.12 47.2% 13.8% 19.6% 19.4% Groupings Salary 3.56 52.9% 13.3% 17.9% 15.9%

Daily wage 4.75 35.7% 17.5% 25.7% 21.2% No work, income 6.08 31.8% 15.1% 25.2% 27.9% Wealth group Lowest quintile 7.91 18.0% 13.3% 28.0% 40.8% Second quintile 5.08 32.4% 17.1% 25.5% 25.1% Third quintile 3.42 48.5% 17.3% 19.5% 14.7% Fourth quintile 2.99 59.4% 12.8% 15.9% 11.9% Highest quintile 2.17 65.0% 12.4% 16.2% 6.4% Duration in current Always in city 4.67 42.1% 12.7% 25.6% 19.6% city <= 2 years 4.64 43.5% 14.9% 16.5% 25.2% 3-5 years 2.98 51.6% 14.5% 21.5% 12.4% 6-10 years 3.72 55.3% 12.2% 16.1% 16.4% 10+ years 4.53 41.3% 16.0% 21.7% 21.1% Dhaka zones Kafrul-Badda 4.81 37.9% 17.3% 22.7% 22.0% Dhanmondi Mohammadpur 4.54 39.5% 21.9% 17.4% 21.2% Gulshan 3.99 55.2% 11.3% 14.2% 19.3% Jatrabari Sabujbagh 5.19 40.3% 10.8% 22.9% 26.1% Mirpur Pallabi 3.52 52.8% 12.0% 20.2% 15.0% Old Dhaka 4.25 40.9% 17.5% 28.7% 12.8% Ramna Tejgaon 3.89 44.4% 14.1% 18.3% 23.2%

86 Table A24: Coping strategy categories by selected background variables.

No shock Coping category experienced

Mean Neutral Stress Crisis Emergency

City Dhaka 39.4% 9.9% 32.3% 18.0% .3% Barisal 33.3% 13.9% 24.7% 27.9% .2% Sirajganj 16.4% 11.0% 31.4% 39.7% 1.5% Gender Code Male 39.1% 9.4% 32.4% 18.8% .2% Female 37.0% 14.8% 28.9% 18.5% .7% Education None 38.3% 11.5% 30.1% 19.9% .2% completion Primary incomplete 32.9% 9.6% 37.6% 19.6% .3% categories Completed primary 43.0% 7.0% 32.5% 17.5% .0% Secondary incomplete 42.3% 8.5% 32.1% 16.5% .6% Secondary complete or higher 41.2% 12.8% 28.1% 17.9% 0.0% Livelihood Self-employed 36.9% 10.8% 34.3% 17.6% .5% Groupings Salary 45.0% 9.6% 28.3% 17.1% .0%

Daily wage 39.6% 9.1% 29.9% 21.1% .4% No work, income 29.5% 10.1% 37.8% 22.5% 0.0% Wealth group Lowest quintile 34.5% 7.9% 28.5% 28.2% 1.0% Second quintile 36.0% 8.7% 33.6% 21.4% .3% Third quintile 42.3% 10.3% 31.7% 15.7% .0% Fourth quintile 42.9% 10.6% 28.7% 17.7% .0% Highest quintile 38.5% 12.8% 38.1% 10.6% 0.0% Duration in current Always in city 30.6% 13.6% 33.8% 21.9% .2% city <= 2 years 45.2% 11.7% 27.6% 14.9% .7% 3-5 years 41.9% 10.0% 27.9% 20.2% 0.0% 6-10 years 47.5% 6.5% 30.3% 15.4% .3% 10+ years 37.5% 9.6% 33.6% 19.1% .3% Dhaka zones Kafrul-Badda 35.5% .1% 24.4% 39.2% .8% Dhanmondi Mohammadpur 42.9% 3.5% 40.7% 12.9% 0.0% Gulshan 50.8% 1.3% 29.1% 18.9% 0.0% Jatrabari Sabujbagh 37.1% 7.6% 46.5% 8.5% .3% Mirpur Pallabi 39.2% 22.8% 20.6% 17.2% .2% Old Dhaka 22.7% 12.5% 51.9% 12.9% 0.0% Ramna Tejgaon 52.7% 7.8% 33.0% 6.5% 0.0%

87 Table A25: Per capita food and non-food expenditure by selected background variables.

Monthly per capita Monthly per capita Monthly per capita food expenditure non-food expendi- expenditure (Taka) (Taka) ture (Taka)

Mean Mean Mean

City Dhaka 1909.37 1812.66 3722.03 Barisal 1573.30 1179.77 2753.07 Sirajganj 1314.87 945.34 2260.21 Gender Code Male 1887.28 1741.26 3628.54 Female 1910.56 2066.90 3977.47 Education None 1718.83 1562.14 3280.97 completion Primary incomplete 1832.62 1765.20 3597.82 categories Completed primary 1900.88 1733.17 3634.05 Secondary incomplete 2138.80 2054.56 4193.36 Secondary complete or higher 2364.13 2458.89 4823.03 Livelihood Self-employed 1899.06 1747.98 3647.04 Groupings Salary 1958.53 1887.95 3846.48

Daily wage 1810.31 1614.48 3424.79 No work, income 1829.10 1941.71 3770.81 Wealth group Lowest quintile 1032.13 931.03 1963.16 Second quintile 1392.57 1257.12 2649.69 Third quintile 1740.79 1511.66 3252.45 Fourth quintile 2193.99 1933.57 4127.56 Highest quintile 3083.14 3259.68 6342.82 Duration in current Always in city 2020.09 1812.23 3832.32 city <= 2 years 1726.16 1519.67 3245.83 3-5 years 2000.12 1849.57 3849.69 6-10 years 1835.28 1651.19 3486.47 10+ years 1870.00 1845.19 3715.19 Dhaka zones Kafrul-Badda 1659.50 1464.63 3124.13 Dhanmondi Mohammadpur 1766.61 1862.52 3629.13 Gulshan 1769.69 1990.65 3760.33 Jatrabari Sabujbagh 1914.99 1775.99 3690.98 Mirpur Pallabi 2020.05 1839.55 3859.60 Old Dhaka 2130.31 1961.98 4092.29 Ramna Tejgaon 2191.36 2061.30 4252.66

88 Table A26: Type of coping strategy by selected background variables.

Spent Sold Borrowed Borrowed Borrowed Workers Previous Sent Sent de- Moved Received Started Reduced Ate less Purchase Pass Restrict Increased Did not Other savings Assets money money money in HH non-work- children pendents elsewhere help from begging quantity preferred food on entire adult con- purchas- do any- (specify) from a from an from rel- took on ers in HH to work in HH to to find institution of con- and inex- credit day sumption ing food thing money- institution atives or more began live with work (NGO, sumption pensive without so that in credit lender (bank, friends work working relatives religious, food eating children NGO) Govt., etc.) can eat

City Dhaka 17.2% 3.2% 7.9% 7.2% 24.2% 4.6% 5.4% .5% 1.2% .6% 1.9% .3% 18.2% 12.6% 4.0% .5% 2.2% .2% 8.5% 1.7%

Barisal 10.4% .5% 4.3% 11.0% 15.9% 1.5% .8% 0.0% .6% 1.7% 1.0% .2% 27.8% 20.2% 3.4% .5% 2.3% .4% 16.3% 4.9%

Sirajganj 15.8% 4.7% 11.4% 16.4% 32.4% 2.3% 3.7% 2.1% 3.7% 4.5% 4.1% 1.5% 39.8% 33.5% 12.0% 4.3% 11.5% 4.0% 17.0% .9%

Gender Code Male 18.0% 3.3% 8.5% 7.8% 24.3% 4.4% 5.1% .5% 1.4% .8% 2.0% .2% 18.9% 13.1% 4.0% .3% 2.4% .3% 8.8% 1.1% Female 9.9% 1.3% 2.3% 5.8% 21.9% 3.7% 7.0% .4% .6% .4% 1.4% .7% 18.6% 14.0% 5.3% 2.6% 2.7% .1% 9.8% 6.6% None Education 15.0% 2.6% 8.4% 7.6% 22.6% 4.3% 5.7% 1.2% 2.0% 1.0% 1.3% .2% 20.0% 14.3% 4.3% .9% 2.2% .3% 8.9% 1.9% completion Primary incomplete categories 14.6% 4.0% 7.9% 7.9% 27.5% 6.2% 7.0% .0% .8% .9% 2.7% .3% 19.6% 13.2% 3.3% .7% 2.6% .3% 7.1% 1.3% Completed primary 21.2% 2.2% 8.4% 7.9% 28.0% 4.8% 6.0% 0.0% .4% .7% 2.0% .0% 17.5% 12.7% 4.8% .1% 2.8% .4% 12.5% 2.3% Secondary incomplete 21.3% 4.8% 7.4% 6.8% 22.5% 3.1% 3.3% .0% 1.2% .1% 2.4% .6% 16.9% 11.2% 3.7% .1% 2.5% .1% 9.6% 1.0% Secondary complete or higher 16.1% 1.4% 5.0% 7.6% 20.6% 3.1% 2.1% .1% .1% .1% 2.0% 0.0% 17.8% 12.8% 5.4% .2% 1.7% .0% 5.1% 3.1% Always in city

Duration in current <= 2 years 18.3% 3.3% 8.5% 10.2% 25.5% 6.4% 5.7% .7% 1.6% .9% 3.7% .5% 17.9% 12.3% 4.2% .2% 3.0% .1% 9.0% 2.5% city 3-5 years 16.4% 3.8% 5.3% 6.1% 20.6% 2.6% 3.6% .0% .9% .6% 1.3% .0% 17.2% 8.9% 3.2% .0% 1.5% .2% 9.8% .9%

6-10 years 16.7% 2.0% 9.9% 3.9% 22.9% 3.0% 7.1% .0% 1.5% .8% .1% .4% 21.1% 16.8% 4.3% 1.3% 2.8% .4% 7.0% 1.4%

10+ years 14.4% 3.2% 8.1% 8.1% 30.0% 4.6% 4.4% 1.8% .5% .5% .0% 0.0% 22.5% 20.2% 5.6% 1.6% 1.5% .6% 9.9% 1.6%

Self-employed 17.2% 3.0% 6.5% 9.0% 30.5% 6.5% 8.1% 1.7% 1.9% 1.2% 3.7% 1.0% 28.6% 20.5% 4.1% 1.8% 5.5% .6% 9.2% 2.3%

Livelihood Salary 15.6% 6.4% 10.1% 8.2% 23.3% 4.6% 8.2% .8% 1.4% 2.0% 2.6% .3% 21.7% 16.2% 7.3% .9% 3.4% .1% 5.8% 1.3% Groupings Daily wage 14.6% 2.8% 7.6% 6.5% 23.9% 2.7% 2.2% .0% 1.3% .5% 3.0% .0% 15.7% 10.0% 1.6% 0.0% 1.2% .3% 11.5% 2.3% No work, income 17.8% 2.0% 10.9% 5.9% 16.8% 4.9% 4.6% 0.0% 1.7% .0% .2% .0% 17.7% 11.7% 3.9% .0% .8% .3% 9.4% 1.3% Lowest quintile 19.9% 1.7% 4.3% 8.0% 26.1% 3.5% 3.2% 0.0% .0% 0.0% .0% 0.0% 10.6% 7.5% 3.7% .0% 1.1% .0% 8.6% 1.4% Second quintile Wealth group 17.6% 4.1% 6.5% 10.7% 24.5% 3.4% 3.6% .2% 1.9% 2.3% 1.9% .2% 21.9% 14.8% 4.6% .5% 3.6% .4% 14.4% 1.7% Third quintile 11.1% .5% 8.9% 8.7% 16.3% 4.6% 2.7% 2.2% .0% 1.9% 1.2% .7% 15.5% 20.0% 6.6% 4.2% 1.8% 0.0% 6.3% .1% Fourth quintile 25.2% 2.3% 1.2% 9.4% 18.9% 4.2% 8.8% .0% 0.0% .0% 2.5% 0.0% 20.2% 10.6% 3.0% 0.0% 2.8% 0.0% 7.2% 3.9% Highest quintile 18.7% 2.4% 6.0% 7.0% 21.0% 2.4% 3.3% 1.7% 2.0% .1% 1.5% .3% 15.4% 10.1% 2.5% 0.0% 1.2% .3% 4.6% .9% Kafrul-Badda 16.0% 3.7% 9.9% 6.0% 27.2% 5.5% 6.5% 0.0% 1.2% .3% 2.1% .3% 19.2% 12.8% 4.2% .2% 2.4% .2% Dhanmondi Mohammadpur 9.0% 2.0%

Dhaka zones Gulshan 20.9% 8.5% 12.9% 12.7% 26.3% 6.5% 12.0% 1.3% 1.9% 1.5% 5.3% .8% 39.6% 26.2% 2.7% 2.6% 8.5% 0.0% .1% 0.0%

Jatrabari Sabujbagh 19.0% 1.4% 7.7% 7.4% 34.5% 3.0% 1.8% .6% .9% .6% 6.5% 0.0% 12.9% 2.4% 2.6% .2% 2.8% 0.0% 8.1% 2.6%

Mirpur Pallabi 13.7% 11.0% 14.4% 4.0% 23.5% 4.9% 2.6% 0.0% 2.0% .1% .6% 0.0% 18.9% 10.2% 7.2% 0.0% 0.0% 0.0% .7% 1.5%

Old Dhaka 19.6% .5% 6.6% 10.4% 32.1% 2.4% 3.3% 1.0% .9% .1% .3% .3% 8.5% .9% 2.3% 0.0% 0.0% 0.0% 10.0% 1.2%

Ramna Tejgaon 12.0% .1% 4.8% 2.6% 9.3% 5.7% 5.5% .2% 1.5% 1.1% .5% .2% 17.4% 20.1% 4.5% 0.0% 0.0% .2% 13.4% 2.8%

31.6% 1.7% 2.0% 12.3% 42.4% 6.6% 6.8% 0.0% 0.0% 0.0% 0.0% 0.0% 12.9% 4.0% 1.9% 0.0% 4.1% 0.0% 18.1% 0.0%

8.4% 2.1% 9.3% 4.2% 22.4% 0.0% 2.9% 0.0% .6% 0.0% 0.0% 0.0% 6.5% 7.1% 8.5% 0.0% .9% 1.5% 6.9% 3.0% Table A27: Staple share of energy consumed by selected background variables.

Share of Groups by percentage of calories from staples food energy from staples

Very high High share Medium share Low share of share of sta- of staples in of staples in staples in diet Mean ples in diet diet diet Row N Row N Row N Row N

City Dhaka .6638966 17.5% 59.5% 22.6% .4% Barisal .6764049 20.2% 60.6% 19.0% .2% Sirajganj .7491227 53.8% 41.6% 4.4% .2% Gender Code Male .6651698 18.4% 58.7% 22.5% .4% Female .6695990 16.5% 63.7% 19.8% .0% Education None .6832927 23.8% 59.7% 16.5% .0% completion Primary incomplete .6651836 15.6% 63.4% 20.8% .2% categories Completed primary .6596012 16.6% 56.8% 26.5% 0.0% Secondary incomplete .6458109 14.9% 55.4% 28.2% 1.5% Secondary complete or higher .6301225 5.0% 59.8% 34.5% .7% Livelihood Self-employed .6629061 15.0% 62.6% 22.0% .5% Groupings Salary .6563503 15.7% 58.6% 25.0% .7%

Daily wage .6715546 21.7% 55.1% 23.2% .0% No work, income .6898530 30.6% 55.9% 13.6% 0.0% Wealth group Lowest quintile .7470922 45.5% 52.3% 2.2% .0% Second quintile .7043537 25.1% 69.0% 5.9% .0% Third quintile .6650924 13.3% 68.1% 18.6% 0.0% Fourth quintile .6290591 6.5% 59.5% 34.0% .0% Highest quintile .5839361 1.0% 47.2% 49.9% 1.9% Duration in current Always in city .6444929 14.1% 51.4% 34.4% .1% city <= 2 years .6880466 18.8% 68.3% 12.9% 0.0% 3-5 years .6748862 20.9% 62.3% 16.7% 0.0% 6-10 years .6719774 22.0% 58.7% 17.4% 1.8% 10+ years .6664084 18.1% 60.2% 21.6% .2% Dhaka zones Kafrul-Badda .7155988 31.5% 62.5% 6.0% 0.0% Dhanmondi Mohammadpur .6779104 18.2% 66.0% 15.3% .4% Gulshan .6927079 25.7% 61.3% 13.0% 0.0% Jatrabari Sabujbagh .6407172 11.6% 59.3% 29.1% 0.0% Mirpur Pallabi .6475628 11.1% 61.0% 27.7% .2% Old Dhaka .6236148 7.6% 54.2% 38.2% 0.0% Ramna Tejgaon .6435926 21.2% 41.9% 32.8% 4.0%

90 Table A28: Per capita expenditure by selected background variables

Monthly Monthly Monthly Monthly Monthly Monthly Monthly Monthly per Monthly Monthly per capita per capita per capita per capita per capita per capita per capita capita food per capita per capita expenditure onexpenditure on expenditure expenditure expenditure expenditure expenditure expenditure non-food expenditure clothing and housing on fuel and on household on medical on education on transport expenditure footwear lighting effects

Mean Mean Mean Mean Mean Mean Mean Mean Mean Mean

City Dhaka 122.07 812.60 68.61 199.50 121.69 76.73 109.22 1909.37 1812.66 3722.03 Barisal 89.04 478.83 133.51 125.80 82.43 44.04 58.15 1573.30 1179.77 2753.07 Sirajganj 80.58 294.73 91.60 143.71 72.27 40.27 47.65 1314.87 945.34 2260.21 Gender Code Male 119.43 765.26 69.34 194.98 115.73 64.26 112.27 1887.28 1741.26 3628.54 Female 128.01 1008.24 81.44 208.39 149.55 156.27 66.15 1910.56 2066.90 3977.47 Education completion None 109.83 730.91 80.25 169.26 89.54 47.69 77.66 1718.83 1562.14 3280.97 categories (per DHS + Primary incomplete 119.65 741.00 70.21 207.18 163.81 64.58 110.60 1832.62 1765.20 3597.82 MICS) Completed primary 128.96 767.97 52.83 197.65 94.13 90.35 93.14 1900.88 1733.17 3634.05 Secondary incomplete 136.94 893.08 62.78 237.49 124.95 110.46 146.71 2138.80 2054.56 4193.36 Secondary complete or higher 124.21 1099.05 71.58 215.19 211.82 144.17 186.54 2364.13 2458.89 4823.03 Livelihood Groupings Self-employed 128.17 865.94 78.69 200.01 101.47 90.88 85.45 2020.09 1812.23 3832.32 (self-identified per E07 + Salary 95.49 730.59 55.01 165.93 69.25 12.30 111.57 1726.16 1519.67 3245.83 “work, no pay = self-em- Daily wage 114.67 769.00 66.95 177.13 189.87 42.20 91.88 2000.12 1849.57 3849.69 ployed”) No work, income 121.91 712.33 75.95 215.30 110.72 20.37 113.82 1835.28 1651.19 3486.47 Wealth group Lowest quintile 122.45 808.97 69.56 197.63 127.66 103.81 113.74 1870.00 1845.19 3715.19 Second quintile 116.14 745.94 77.45 206.02 107.86 69.22 127.42 1899.06 1747.98 3647.04 Third quintile 132.37 851.38 65.19 214.55 136.68 82.49 117.55 1958.53 1887.95 3846.48 Fourth quintile 111.95 747.09 56.85 173.69 98.49 38.64 77.31 1810.31 1614.48 3424.79 Highest quintile 122.84 920.51 85.88 159.23 162.62 148.99 57.67 1829.10 1941.71 3770.81 Duration in current city Always in city 72.18 468.13 62.85 92.09 49.37 24.42 28.60 1032.13 931.03 1963.16 <= 2 years 90.32 605.91 71.25 124.17 69.67 41.32 60.75 1392.57 1257.12 2649.69 3-5 years 110.20 725.09 88.41 170.31 71.14 29.47 80.19 1740.79 1511.66 3252.45 6-10 years 133.21 840.87 67.25 235.40 133.15 66.39 124.16 2193.99 1933.57 4127.56 10+ years 195.92 1327.91 64.01 360.11 274.88 213.91 239.48 3083.14 3259.68 6342.82 BBS_1991 Kafrul-Badda 88.24 620.64 92.27 157.91 131.23 31.46 105.46 1659.50 1464.63 3124.13 Dhanmondi Mohammadpur 121.37 762.15 77.68 240.40 173.06 42.70 151.46 1766.61 1862.52 3629.13 Gulshan 123.59 897.37 64.10 235.31 105.55 85.32 142.83 1769.69 1990.65 3760.33 Jatrabari Sabujbagh 143.52 762.18 47.40 197.48 124.88 71.77 122.30 1914.99 1775.99 3690.98 Mirpur Pallabi 117.63 851.63 89.44 194.61 85.74 110.44 77.23 2020.05 1839.55 3859.60 Old Dhaka 172.20 909.34 2.28 184.97 199.04 134.54 89.19 2130.31 1961.98 4092.29 Ramna Tejgaon 108.55 1039.77 54.49 220.80 82.26 31.38 123.22 2191.36 2061.30 4252.66 Table A29: Subjective assessment of food consumption by selected background variables

Concerning your household’s food consumption over the past one month, which of the following is true?

It was too less It was It was just It was It was more than adequate less than adequate adequate for than adequate for household adequate for for house- household for household needs household hold needs needs needs needs

Row N Row N Row N Row N Row N

City Dhaka 4.5% 28.0% 36.7% 30.3% .4%

Barisal 4.1% 25.8% 44.1% 25.8% .2%

Sirajganj 17.1% 50.1% 23.3% 9.4% 0.0%

Gender Code Male 4.0% 28.1% 36.1% 31.4% .4%

Female 10.1% 29.9% 41.5% 18.5% 0.0%

Education comple- None 5.4% 31.9% 39.3% 23.0% .4% tion categories Primary incomplete 4.2% 30.3% 35.4% 30.1% .0%

Completed primary 2.1% 29.5% 35.0% 33.4% 0.0%

Secondary incomplete 5.7% 19.7% 37.2% 36.3% 1.1%

Secondary complete or higher 5.3% 24.0% 26.0% 44.6% .1%

Livelihood Self-employed 4.8% 30.4% 33.9% 30.0% .8% Groupings Salary 5.6% 38.4% 27.8% 28.2% .0%

Daily wage 5.7% 20.9% 40.0% 33.4% 0.0%

No work, income 4.9% 25.1% 36.2% 33.8% 0.0%

Wealth group Lowest quintile 4.4% 28.1% 39.0% 28.1% .5%

Second quintile 5.0% 27.4% 39.1% 28.5% .0%

Third quintile 3.5% 25.6% 34.0% 36.4% .5%

Fourth quintile 2.9% 30.8% 34.5% 30.7% 1.0%

Highest quintile 10.4% 34.5% 38.5% 16.6% 0.0%

Duration in current Always in city 11.6% 47.6% 30.0% 10.8% .0% city <= 2 years 5.2% 37.4% 41.5% 15.2% .7%

3-5 years 3.4% 22.7% 48.0% 25.9% 0.0%

6-10 years 2.5% 23.0% 33.7% 40.5% .4%

10+ years 1.2% 11.3% 30.2% 56.5% .7%

Dhaka zones Kafrul-Badda 7.4% 32.2% 37.9% 22.6% 0.0% Dhanmondi Mohammadpur 3.4% 37.0% 35.8% 23.8% 0.0%

Gulshan 4.6% 25.7% 34.1% 35.6% 0.0%

Jatrabari Sabujbagh 4.3% 30.1% 38.7% 27.0% 0.0%

Mirpur Pallabi 2.0% 26.0% 35.9% 35.3% .8%

Old Dhaka 5.1% 15.3% 44.0% 34.0% 1.7%

Ramna Tejgaon 8.8% 27.4% 30.1% 33.7% 0.0%

92 Table A30: Ownership pattern to dwelling by selected background variables.

What is the ownership pattern of this dwelling?

Owned Being Employer Free, autho- Free, not Rented Squatting purchased provides rized authorized Row N % Row N % Row N % Row N % Row N % Row N % Row N %

City Dhaka 7.5% .1% .4% 10.1% 2.8% 74.9% 4.2% Barisal 31.1% 0.0% 0.0% 5.2% .2% 53.3% 10.2% Sirajganj 38.8% .4% .9% 6.3% 1.3% 6.6% 45.6% Gender Code Male 8.2% .0% .4% 9.1% 2.7% 74.8% 4.8% Female 13.1% .5% .4% 15.8% 2.6% 59.7% 7.9% Education completion None 6.9% .2% .3% 10.5% 3.3% 72.7% 6.1% categories Primary incomplete 7.0% .0% .1% 9.6% 2.7% 73.7% 6.9% Completed primary 9.8% .0% 0.0% 11.2% 1.5% 73.4% 4.1% Secondary incomplete 10.6% 0.0% .3% 9.7% 2.9% 73.4% 3.0% Secondary complete or higher 17.6% 0.0% 2.1% 4.3% 1.3% 71.9% 2.8% Livelihood Groupings Self-employed 7.0% 0.0% .1% 9.2% 3.5% 72.8% 7.4% Salary 9.2% .2% .4% 11.6% 2.3% 73.6% 2.7% Daily wage 4.7% .0% .8% 6.3% 1.9% 82.9% 3.4% No work, income 22.0% 0.0% .4% 14.9% 2.4% 53.8% 6.5% Wealth group Lowest quintile 7.9% .3% .3% 8.1% 1.5% 73.9% 8.0% Second quintile 8.5% .0% .2% 9.9% 4.7% 72.0% 4.8% Third quintile 6.9% .0% .3% 14.4% 4.2% 71.3% 2.9% Fourth quintile 8.3% 0.0% 1.0% 6.7% 2.5% 76.6% 5.0% Highest quintile 12.3% 0.0% .0% 10.2% .8% 71.5% 5.1% Duration in current city Always in city 18.7% .0% 1.0% 17.8% 3.4% 47.0% 12.1% <= 2 years 1.7% 0.0% 0.0% 1.3% .5% 96.3% .1% 3-5 years 1.1% 0.0% 0.0% 1.9% 3.5% 91.2% 2.3% 6-10 years 4.1% 0.0% 0.0% 2.5% 2.5% 89.4% 1.5% 10+ years 9.1% .1% .4% 12.1% 2.8% 70.4% 5.2% Dhaka zones Kafrul-Badda 3.2% 0.0% .4% 5.4% 7.6% 75.1% 8.4% Dhanmondi Mohammadpur 3.3% 0.0% 0.0% 14.1% .4% 82.2% 0.0% Gulshan 9.7% 0.0% 1.4% 11.2% 5.5% 68.5% 3.7% Jatrabari Sabujbagh 8.7% 0.0% 0.0% 7.8% 0.0% 83.4% 0.0% Mirpur Pallabi 8.0% .2% .2% 11.9% 2.0% 69.3% 8.4% Old Dhaka 9.5% 0.0% .5% 8.3% 2.7% 78.9% 0.0% Ramna Tejgaon 14.1% 0.0% .6% 12.7% .9% 71.7% 0.0% Table A31: Type of dwelling by selected background variables.

What is the ownership pattern of this dwelling?

Single Several Apartment/ Room in Improvised Other house separate flat a larger housing structures dwelling

Row N Row N Row N Row N Row N Row N

City Dhaka 76.1% 16.6% .4% 6.8% .2% 0.0%

Barisal 55.7% 39.4% .5% 4.4% 0.0% 0.0%

Sirajganj 75.0% 23.7% 0.0% 1.1% .2% 0.0%

Gender Code Male 75.1% 17.9% .2% 6.6% .2% 0.0%

Female 78.3% 13.5% 1.6% 6.6% 0.0% 0.0%

Education None 79.9% 15.6% 0.0% 4.5% 0.0% 0.0% completion categories Primary incomplete 81.7% 9.8% .2% 8.3% 0.0% 0.0%

Completed primary 72.7% 20.8% 0.0% 5.4% 1.1% 0.0%

Secondary incomplete 68.1% 21.9% 1.0% 9.0% 0.0% 0.0%

Secondary complete or higher 57.4% 30.0% 2.3% 10.2% .1% 0.0%

Livelihood Self-employed 77.5% 16.9% .4% 5.2% 0.0% 0.0% Groupings Salary 68.3% 22.1% .0% 9.0% .5% 0.0%

Daily wage 80.0% 12.2% .0% 7.7% 0.0% 0.0%

No work, income 77.3% 16.7% 2.2% 3.8% .0% 0.0%

Wealth group Lowest quintile 83.4% 10.5% 0.0% 6.1% 0.0% 0.0%

Second quintile 78.3% 14.8% .0% 6.9% 0.0% 0.0%

Third quintile 78.3% 15.0% 0.0% 6.6% 0.0% 0.0%

Fourth quintile 76.2% 17.6% .3% 5.2% .8% 0.0%

Highest quintile 61.3% 28.6% 1.7% 8.4% 0.0% 0.0%

Duration in current Always in city 74.2% 20.7% .8% 4.2% .0% 0.0% city <= 2 years 74.6% 8.8% .0% 14.8% 1.6% 0.0%

3-5 years 79.0% 14.5% .0% 6.4% 0.0% 0.0%

6-10 years 83.4% 11.8% 0.0% 4.8% 0.0% 0.0%

10+ years 73.0% 19.9% .5% 6.6% 0.0% 0.0%

Dhaka zones Kafrul-Badda 89.7% 9.0% 0.0% 1.3% 0.0% 0.0%

Dhanmondi Mohammadpur 83.6% 16.4% 0.0% 0.0% 0.0% 0.0%

Gulshan 55.4% 44.6% 0.0% 0.0% 0.0% 0.0%

Jatrabari Sabujbagh 87.1% 12.6% .3% 0.0% 0.0% 0.0%

Mirpur Pallabi 68.1% 8.7% 1.3% 21.4% .5% 0.0%

Old Dhaka 79.3% 16.8% 0.0% 3.9% 0.0% 0.0%

Ramna Tejgaon 66.7% 31.3% 0.0% 2.0% 0.0% 0.0%

94 Table A32: Outer walls by selected background variables.

The outer walls of the main dwelling of the household are predominantly made of what material?

Concrete Fired brick Mud of Wood Tin/C.I Bamboo Grass/straw Polythene/ Cardboard/ Other (red)/clay tiles unfired mud Sheet Plastic sheet paper brick

Row N % Row N % Row N % Row N % Row N % Row N % Row N % Row N % Row N % Row N %

City Dhaka 16.5% 12.6% .3% .6% 65.3% 3.6% .2% .3% .4% .1% Barisal 3.3% 18.2% .5% 9.5% 66.7% .8% .6% .4% 0.0% 0.0% Sirajganj 4.4% 3.2% .2% 0.0% 90.0% .3% 1.8% 0.0% 0.0% 0.0% Gender Code Male 16.0% 11.9% .3% .9% 66.9% 2.9% .1% .3% .4% .1% Female 15.6% 17.7% .1% .3% 57.2% 7.3% 1.3% .6% 0.0% 0.0% Education completion None 9.2% 12.1% .0% 1.1% 72.4% 3.6% .6% .2% .5% .2% categories Primary incomplete 16.5% 11.9% .8% .5% 64.4% 5.5% .1% .3% 0.0% 0.0% Completed primary 26.0% 10.5% .0% 1.8% 58.3% 3.3% .0% 0.0% 0.0% 0.0% Secondary incomplete 21.3% 13.3% .8% .3% 60.4% 1.9% 0.0% 1.0% .8% .2% Secondary complete or higher 20.8% 18.7% 0.0% .3% 59.5% .7% .0% 0.0% 0.0% 0.0% Livelihood Groupings Self-employed 16.8% 10.3% .4% 1.3% 67.1% 3.2% .2% .1% .5% 0.0% Salary 18.0% 13.7% .0% .3% 63.3% 3.4% .5% .0% .5% .2% Daily wage 13.2% 12.3% .7% 1.1% 69.0% 1.9% .1% 1.3% 0.0% .3% No work, income 12.9% 18.4% 0.0% .1% 61.4% 7.2% .1% 0.0% 0.0% 0.0% Wealth group Lowest quintile 9.6% 8.0% .0% 2.4% 73.1% 4.9% .5% 1.1% 0.0% .3% Second quintile 11.3% 10.3% .0% .6% 71.3% 5.2% .8% .3% 0.0% 0.0% Third quintile 13.9% 12.1% .7% .2% 70.8% 2.3% .0% 0.0% 0.0% 0.0% Fourth quintile 22.3% 13.3% 0.0% .8% 59.8% 2.4% 0.0% .0% 1.1% .2% Highest quintile 22.6% 19.0% .7% .3% 54.2% 2.2% 0.0% .2% .7% 0.0% Duration in current city Always in city 22.1% 12.7% .0% 1.1% 60.4% 2.7% .1% .8% 0.0% 0.0% <= 2 years 10.9% 8.8% 0.0% .0% 79.5% 0.0% .0% 0.0% 0.0% .7% 3-5 years 7.5% 15.7% 1.9% .9% 71.3% 2.8% 0.0% 0.0% 0.0% 0.0% 6-10 years 12.7% 10.5% .0% 1.4% 70.4% 2.9% .1% .7% 1.3% 0.0% 10+ years 17.0% 13.3% .3% .8% 63.0% 4.6% .5% .1% .3% .1% Dhaka zones Kafrul-Badda .3% 4.1% 0.0% .3% 91.0% 2.7% .5% 1.1% 0.0% 0.0% Dhanmondi Mohammadpur 15.4% 12.6% 0.0% 3.9% 62.9% 3.6% 1.4% 0.0% 0.0% 0.0% Gulshan .6% 1.5% 0.0% 0.0% 94.9% .1% 0.0% 1.4% 1.4% 0.0% Jatrabari Sabujbagh 32.5% 20.5% 0.0% 1.0% 38.7% 6.9% 0.0% 0.0% 0.0% .4% Mirpur Pallabi 13.7% 17.7% 1.1% 0.0% 66.1% 1.3% 0.0% 0.0% 0.0% .2% Old Dhaka 43.8% 10.0% 0.0% 0.0% 32.7% 11.7% 0.0% 0.0% 1.7% 0.0% Ramna Tejgaon 22.2% 14.6% 0.0% 0.0% 60.3% 2.0% 0.0% 0.0% .9% 0.0% Table A33: Dwelling size by selected background variables.

What is the total floor area of the dwellinf in square feet? Mean Median

City Dhaka 128 104

Barisal 215 180

Sirajganj 221 180

Gender Code Male 133 108

Female 126 96

Education completion categories None 125 100

Primary incomplete 127 108

Completed primary 135 108

Secondary incomplete 140 120

Secondary complete or higher 163 126

Livelihood Groupings Self-employed 132 108

Salary 139 109

Daily wage 122 100

No work, income 134 108

Wealth group Lowest quintile 128 101

Second quintile 137 108

Third quintile 124 100

Fourth quintile 125 108

Highest quintile 145 113

Duration in current city Always in city 159 120

<= 2 years 115 94

3-5 years 115 108

6-10 years 108 95

10+ years 135 108

Dhaka zones Kafrul-Badda 151 108

Dhanmondi Mohammadpur 109 95

Gulshan 166 113

Jatrabari Sabujbagh 110 95

Mirpur Pallabi 118 102

Old Dhaka 135 120

Ramna Tejgaon 115 100

96 Table A34: Main source of cooking fuel by selected background variables.

What is your main source of cooking fuel? Straw/ Electricty Gas Kerosine Wood Charcoal Leaves/ Animal waste Jute plants Other Husks

Row N % Row N % Row N % Row N % Row N % Row N % Row N % Row N % Row N %

City Dhaka 3.1% 55.5% 1.2% 35.5% 1.9% 2.1% 0.0% .2% .6% Barisal .4% 1.2% .2% 94.8% 2.8% .3% 0.0% 0.0% .2% Sirajganj 2.8% 5.4% .7% 55.5% 2.1% 15.5% 13.4% 4.2% .4% Gender Code Male 3.1% 53.5% 1.3% 37.4% 1.8% 2.1% .3% .3% .3% Female 2.7% 49.3% 0.0% 38.3% 3.0% 4.0% .2% .0% 2.5% Education completion categories None 3.0% 46.2% 1.4% 43.3% 3.1% 1.4% .3% .1% 1.2% Primary incomplete 2.7% 51.7% .5% 39.7% 1.9% 2.2% .3% .9% 0.0%

Completed primary 4.9% 61.6% 2.0% 28.5% .9% 1.8% .3% .0% 0.0% Secondary incomplete 3.2% 59.6% .2% 31.3% .7% 4.4% .2% .0% .3% Secondary complete or higher .2% 63.7% 2.1% 30.2% .3% 3.2% .2% 0.0% 0.0% Livelihood Groupings Self-employed 4.1% 47.9% 2.3% 41.7% 2.0% 1.4% .3% .1% .2% Salary 2.4% 61.0% .5% 32.0% .3% 2.4% .1% .0% 1.3% Daily wage 3.0% 54.5% .3% 36.5% 1.7% 2.1% .4% .8% .7% No work, income .8% 50.1% 0.0% 37.1% 5.9% 5.7% .1% .1% 0.0% Wealth group Lowest quintile 4.0% 41.7% .6% 44.4% 5.0% 2.8% .9% .3% .4% Second quintile 3.5% 47.5% 1.1% 40.4% 1.9% 2.1% .2% .8% 2.4% Third quintile 2.5% 49.1% 2.2% 42.9% 1.1% 2.0% .1% .0% 0.0% Fourth quintile 2.2% 57.7% 1.0% 35.9% .8% 2.4% .0% .0% 0.0% Highest quintile 3.2% 69.3% .7% 23.8% .8% 2.2% .0% 0.0% .0% Duration in current city Always in city 8.0% 35.9% 1.7% 46.8% 1.3% 4.0% 1.1% .4% .9% <= 2 years 1.0% 67.2% 1.5% 27.5% 1.7% 1.0% .1% .0% 0.0% 3-5 years 2.5% 53.6% .1% 40.8% 2.0% .8% .2% 0.0% 0.0% 6-10 years .7% 58.2% 3.0% 34.6% 1.2% 1.3% .0% .9% .0% 10+ years 2.4% 55.2% .4% 36.2% 2.4% 2.4% .1% .0% .8% BBS_1991 Kafrul-Badda 3.6% 36.6% 0.0% 55.8% 2.4% .5% 0.0% 0.0% 1.2% Dhanmondi Mohammadpur 1.4% 55.0% 2.0% 39.4% 2.2% 0.0% 0.0% 0.0% 0.0% Gulshan 0.0% 60.8% .7% 36.4% 2.0% .1% 0.0% 0.0% 0.0% Jatrabari Sabujbagh 0.0% 71.9% 1.0% 23.4% 3.7% 0.0% 0.0% 0.0% 0.0% Mirpur Pallabi 7.6% 38.9% 2.3% 41.3% 1.0% 7.0% 0.0% .5% 1.3% Old Dhaka 1.8% 95.4% .5% .5% 1.8% 0.0% 0.0% 0.0% 0.0% Ramna Tejgaon .6% 70.6% 0.0% 28.8% 0.0% 0.0% 0.0% 0.0% 0.0% Table A35: Type of rubbish disposal by selected background variables.

Type of rubbish disposal by selected background variables.

Collected Personal Burning Public rub- Put in drain / Other None from rubbish rubbish pit bish heap ditch bin or pit Row N % Row N % Row N % Row N % Row N % Row N % Row N %

City Dhaka 33.6% 9.7% .0% 18.5% 34.6% 2.7% .8% Barisal 6.0% 10.0% 0.0% 21.9% 51.6% 10.5% .0% Sirajganj 1.1% 1.5% .1% 2.8% 90.5% 2.4% 1.7% Gender Code Male 31.8% 10.1% .1% 17.5% 36.7% 2.9% .9% Female 36.1% 5.9% 0.0% 23.7% 30.8% 3.3% .1% Education completion categories None 30.7% 6.4% 0.0% 16.3% 41.9% 3.7% 1.0% Primary incomplete 29.8% 10.2% .2% 20.9% 35.6% 2.1% 1.1% Completed primary 34.3% 12.0% 0.0% 17.4% 32.1% 3.6% .5% Secondary incomplete 33.7% 13.4% .0% 20.8% 30.2% 1.3% .7% Secondary complete or higher 41.3% 11.3% 0.0% 17.1% 26.0% 4.2% .0% Livelihood Groupings Self-employed 29.3% 9.7% .1% 17.4% 39.9% 2.6% .9% Salary 36.1% 10.8% 0.0% 18.7% 31.4% 2.8% .3% Daily wage 29.1% 10.3% .0% 18.3% 37.5% 3.1% 1.7% No work, income 40.3% 4.6% 0.0% 20.1% 30.7% 4.2% .1% Wealth group Lowest quintile 26.2% 1.6% 0.0% 22.5% 45.7% 2.5% 1.5% Second quintile 22.8% 6.3% .0% 21.5% 44.6% 3.8% 1.0% Third quintile 33.9% 11.4% 0.0% 15.5% 35.6% 3.0% .6% Fourth quintile 31.0% 15.2% .2% 17.6% 32.6% 2.6% .7% Highest quintile 47.5% 13.3% 0.0% 14.2% 21.9% 2.8% .3% Duration in current city Always in city 28.4% 9.2% .3% 19.8% 38.5% 3.1% .7% <= 2 years 21.4% 7.1% 0.0% 24.6% 41.3% 5.7% 0.0% 3-5 years 29.6% 12.6% 0.0% 18.1% 35.2% 1.5% 2.9% 6-10 years 29.0% 11.1% 0.0% 15.9% 40.1% 2.2% 1.6% 10+ years 37.2% 9.1% 0.0% 17.3% 33.1% 2.9% .4% Dhaka zones Kafrul-Badda 7.0% 3.0% 0.0% 20.2% 61.1% 6.3% 2.3% Dhanmondi Mohammadpur 38.5% 19.5% 0.0% 11.1% 28.3% 2.0% .6% Gulshan 22.5% 14.4% 0.0% 2.6% 59.8% .6% 0.0% Jatrabari Sabujbagh 45.0% 17.8% 0.0% 15.5% 20.9% .4% .3% Mirpur Pallabi 30.2% 6.3% .2% 24.2% 34.8% 3.1% 1.1% Old Dhaka 68.7% .1% 0.0% 19.9% 11.2% 0.0% 0.0% Ramna Tejgaon 49.1% 10.7% 0.0% 31.0% 3.6% 5.6% 0.0% Table A36: Source of drinking water by selected background variables

What is your main source of drinking water?

Piped supply Tube well RinF well/ Pond or river Other water Indara

Row N% Row N% Row N% Row N% Row N%

City Dhaka 91.8% 6.8% .1% 0.0% 1.3%

Barisal 8.9% 91.1% 0.0% 0.0% 0.0%

Sirajganj 4.7% 95.1% .0% 0.0% .1%

Gender Code Male 88.1% 10.7% .1% 0.0% 1.1%

Female 86.6% 10.9% 0.0% 0.0% 2.5%

Education None 86.4% 11.7% .1% 0.0% 1.8% completion categories Primary incomplete 89.2% 10.1% .0% 0.0% .7%

Completed primary 85.9% 11.8% .3% 0.0% 1.9%

Secondary incomplete 89.2% 10.2% .0% 0.0% .6%

Secondary complete or higher 93.6% 6.4% 0.0% 0.0% 0.0%

Livelihood Self-employed 88.7% 10.3% .1% 0.0% .9% Groupings Salary 88.8% 9.9% 0.0% 0.0% 1.3%

Daily wage 85.2% 13.0% .2% 0.0% 1.6%

No work, income 88.0% 10.1% 0.0% 0.0% 1.9%

Wealth group Lowest quintile 79.3% 19.1% .2% 0.0% 1.3%

Second quintile 83.8% 13.1% .2% 0.0% 2.9%

Third quintile 92.0% 7.7% 0.0% 0.0% .3%

Fourth quintile 91.1% 7.9% .0% 0.0% 1.1%

Highest quintile 93.3% 5.9% 0.0% 0.0% .8%

Duration in current Always in city 74.7% 22.9% .3% 0.0% 2.1% city <= 2 years 91.2% 8.3% .5% 0.0% 0.0%

3-5 years 88.5% 10.7% 0.0% 0.0% .8%

6-10 years 92.1% 6.4% 0.0% 0.0% 1.5%

10+ years 90.8% 8.0% 0.0% 0.0% 1.2%

Dhaka zones Kafrul-Badda 87.5% 9.4% 0.0% 0.0% 3.1%

Dhanmondi Mohammadpur 92.7% 4.7% 0.0% 0.0% 2.6%

Gulshan 99.4% 0.0% 0.0% 0.0% .6%

Jatrabari Sabujbagh 88.7% 10.4% 0.0% 0.0% 1.0%

Mirpur Pallabi 91.1% 7.8% .3% 0.0% .8%

Old Dhaka 97.8% 2.2% 0.0% 0.0% 0.0%

Ramna Tejgaon 90.7% 8.4% 0.0% 0.0% .9%

99 Table A37: Toilet facility by selected background variables.

What kind of toilet facility does your household use? Use of this toilet facility

Water- Not water- Pit-latrine Hanging latrine None Other Household Other sealed sealed (katcha ) (katcha, members households temporary) only also Row N % Row N % Row N % Row N % Row N % Row N % Row N % Row N %

City Dhaka 37.7% 46.8% 4.6% 10.7% .2% .0% 8.9% 91.1% Barisal 28.6% 57.1% 11.8% 2.1% .2% .2% 34.9% 65.1% Sirajganj 40.5% 28.7% 21.8% 5.2% 2.9% .8% 43.3% 56.7% Gender Code Male 37.0% 46.8% 5.5% 10.4% .2% .1% 9.1% 90.9% Female 41.9% 45.6% 2.3% 10.0% .1% .0% 18.9% 81.1% Education completion None 34.1% 45.8% 6.8% 13.2% .1% .0% 8.5% 91.5% categories Primary incomplete 43.6% 43.0% 4.9% 7.5% .8% .3% 14.6% 85.4% Completed primary 37.5% 47.9% 4.7% 10.0% 0.0% 0.0% 6.2% 93.8% Secondary incomplete 39.9% 47.0% 3.0% 10.1% 0.0% 0.0% 11.1% 88.9% Secondary complete or higher 34.1% 60.5% 2.6% 2.8% 0.0% .1% 13.6% 86.4% Livelihood Groupings Self-employed 36.8% 45.9% 4.8% 12.0% .4% .1% 9.0% 91.0% Salary 36.5% 49.3% 5.2% 8.9% .0% .0% 10.9% 89.1% Daily wage 36.4% 46.3% 6.9% 10.3% .1% .0% 6.8% 93.2% No work, income 45.1% 44.5% 2.8% 7.7% .0% 0.0% 19.5% 80.5% Wealth group Lowest quintile 42.5% 35.9% 7.1% 13.5% 1.0% .1% 7.2% 92.8% Second quintile 39.8% 39.9% 7.1% 13.2% .0% .0% 9.7% 90.3% Third quintile 39.6% 46.2% 3.0% 11.2% 0.0% 0.0% 7.4% 92.6% Fourth quintile 28.4% 56.6% 5.7% 9.1% .0% .2% 9.1% 90.9% Highest quintile 37.3% 55.1% 2.8% 4.8% 0.0% .0% 17.7% 82.3% Duration in current city Always in city 41.3% 45.5% 5.7% 7.1% .3% .1% 24.0% 76.0% <= 2 years 27.0% 54.9% 3.4% 14.7% 0.0% 0.0% 1.5% 98.5% 3-5 years 35.4% 47.7% 10.2% 6.6% .1% 0.0% 4.4% 95.6% 6-10 years 34.3% 45.8% 4.6% 15.2% .0% 0.0% 6.9% 93.1% 10+ years 39.4% 45.9% 4.6% 9.7% .3% .1% 8.7% 91.3% Dhaka zones Kafrul-Badda 34.2% 33.0% 13.7% 18.8% 0.0% .3% 2.9% 97.1% Dhanmondi Mohammadpur 41.7% 34.2% 2.7% 21.4% 0.0% 0.0% 9.3% 90.7% Gulshan 15.6% 51.2% 8.0% 23.8% 1.4% 0.0% 2.5% 97.5% Jatrabari Sabujbagh 35.9% 62.7% .4% 1.0% 0.0% 0.0% 14.3% 85.7% Mirpur Pallabi 37.8% 54.1% .2% 8.0% 0.0% 0.0% 7.0% 93.0% Old Dhaka 72.8% 24.5% 2.7% 0.0% 0.0% 0.0% 24.8% 75.2% Ramna Tejgaon 32.7% 56.8% 9.6% .9% 0.0% 0.0% 7.6% 92.4% Table A38: Size of dwelling vs cost and household size by selected background variables.

Rent per Square feet per Number of people square feet person (size by per room (hh size (HH renti ng) hh size) by number of rooms)

Row N% Row N% Row N% Row N% Row N% Row N%

City Dhaka 21.99 20.20 38.94 31.50 3.34 3.00

Barisal 7.11 6.17 65.16 54.00 2.56 2.50

Sirajganj 5.46 4.55 62.33 54.00 3.04 3.00

Gender Code Male 21.47 19.84 39.46 32.00 3.36 3.00

Female 23.83 25.00 44.61 34.00 2.91 3.00

Education None 21.60 21.15 37.99 31.00 3.43 3.00 completion categories Primary incomplete 21.41 20.37 37.84 31.50 3.28 3.00

Completed primary 21.75 20.32 38.90 31.50 3.35 3.00

Secondary incomplete 22.04 19.48 43.69 36.00 3.18 3.00

Secondary complete or higher 21.73 18.52 51.31 42.00 2.94 3.00

Livelihood Self-employed 21.02 18.92 39.94 32.00 3.34 3.00 Groupings Salary 22.53 21.43 41.74 33.75 3.24 3.00

Daily wage 21.69 21.16 37.85 31.50 3.34 3.00

No work, income 22.06 21.05 40.69 33.33 3.29 3.00

Wealth group Lowest quintile 19.24 18.52 31.52 25.20 4.18 4.00

Second quintile 20.94 20.00 37.04 30.00 3.57 3.00

Third quintile 21.83 20.37 37.95 31.50 3.23 3.00

Fourth quintile 21.44 20.00 42.82 36.00 2.91 3.00

Highest quintile 25.00 21.73 51.03 42.00 2.65 2.50

Duration in current Always in city 19.52 18.25 49.01 38.40 3.06 3.00 city <= 2 years 20.22 20.00 38.34 32.00 3.28 3.00

3-5 years 22.65 20.83 34.84 31.50 3.40 3.00

6-10 years 21.32 19.84 34.27 31.50 3.41 3.00

10+ years 22.52 21.05 39.77 31.50 3.36 3.00

Dhaka zones Kafrul-Badda 18.28 16.67 42.33 31.00 3.65 4.00

Dhanmondi Mohammadpur 23.42 23.61 33.11 30.00 3.48 3.00

Gulshan 19.35 19.44 47.23 37.50 3.13 3.00

Jatrabari Sabujbagh 23.12 21.73 32.61 30.00 3.54 3.00

Mirpur Pallabi 19.79 19.05 41.64 34.00 2.96 3.00

Old Dhaka 25.12 21.25 37.02 33.33 3.52 3.00

Ramna Tejgaon 32.93 31.00 33.50 31.50 3.47 3.00

101 Table A39: Agriculture and livestock by selected background variables

In this urban area (not a rural home area), do you grow any crops, fruits, or vegetables, keep livestock, or do you own agricultural land of any sort?

Yes

Row N% Unweighted Count

City Dhaka 1.2% 13

Barisal .7% 5

Sirajganj 5.5% 45

Gender Code Male 1.2% 53

Female 2.1% 10

Education None 1.1% 29 completion categories Primary incomplete 2.4% 12

Completed primary 1.6% 4

Secondary incomplete .5% 13

Secondary complete or higher .8% 5

Livelihood Self-employed 3.0% 39 Groupings Salary 0.0% 0

Daily wage .9% 2

No work, income 1.0% 4

Wealth group Lowest quintile 1.0% 18

Second quintile .8% 27

Third quintile 1.1% 9

Fourth quintile 2.3% 22

Highest quintile 1.4% 5

Duration in current Always in city .8% 36 city <= 2 years .9% 10

3-5 years 1.8% 4

6-10 years 1.1% 8

10+ years 1.8% 5

Dhaka zones Kafrul-Badda 2.7% 5

Dhanmondi Mohammadpur 0.0% 0

Gulshan 0.0% 0

Jatrabari Sabujbagh 0.0% 0

Mirpur Pallabi 1.6% 6

Old Dhaka 1.7% 1

Ramna Tejgaon 2.0% 1

102 Table A40: Loans by selected background variables

Can the household What is the Are household members access to total amount of members paying savings in need? outstanding loan regular installments in the household? to pay back loan (s)? If no, why not?

Yes Yes Do not have in- The income project Spend on food/ come to pay back (IGA) failed health/ or other things

Row N % Mean Row N% Row N% Unweighted Row N% Unweighted Row N% Unweighted Count Count Count

City Dhaka 49.0% 26915 68.9% 76.6% 147 8.7% 19 14.6% 27 Barisal 59.9% 24001 89.5% 56.9% 30 11.9% 6 31.3% 16 Sirajganj 62.3% 15457 89.1% 76.2% 34 6.5% 2 17.3% 6 Gender Code Male 51.1% 26575 70.4% 77.6% 195 6.0% 22 16.4% 45 Female 37.3% 27009 69.5% 62.2% 15 36.4% 5 1.3% 4 Education completion None 46.5% 21753 70.6% 88.6% 91 3.9% 7 7.6% 21 categories Primary incomplete 56.1% 28186 71.7% 59.1% 39 8.0% 4 32.8% 13 Completed primary 47.7% 22192 61.8% 70.5% 27 11.2% 6 18.4% 7 Secondary incomplete 51.2% 24361 73.2% 70.2% 38 17.8% 7 12.0% 7 Secondary complete or higher 47.3% 64453 68.0% 90.6% 16 9.2% 3 .3% 1 Livelihood Groupings Self-employed 53.2% 19816 68.8% 63.9% 75 13.4% 10 22.6% 18 Salary 53.8% 20262 54.3% 85.5% 26 8.8% 2 5.7% 6 Daily wage 37.4% 24678 75.1% 86.7% 12 12.9% 2 .4% 1 No work, income 47.5% 23724 68.2% 82.6% 29 4.7% 5 12.7% 8 Wealth group Lowest quintile 49.9% 31552 73.7% 75.7% 69 7.6% 8 16.7% 16 Second quintile 55.5% 24859 74.4% 75.1% 89 4.7% 11 20.2% 23 Third quintile 41.5% 29742 71.6% 72.4% 40 13.2% 7 14.4% 8 Fourth quintile 49.9% 20629 68.4% 82.7% 60 1.9% 3 15.5% 16 Highest quintile 46.1% 39244 50.5% 76.2% 22 21.7% 6 2.1% 2 Duration in current city Always in city 46.4% 20293 65.1% 84.1% 78 3.4% 6 12.5% 20 <= 2 years 50.8% 21847 66.1% 85.7% 51 6.6% 5 7.7% 7 3-5 years 53.8% 22743 73.3% 73.0% 32 14.5% 8 12.5% 7 6-10 years 41.7% 27057 69.9% 77.6% 31 2.2% 4 20.2% 8 10+ years 54.9% 39476 74.8% 58.4% 19 17.5% 4 24.1% 7 Dhaka zones Kafrul-Badda 50.0% 27147 70.4% 89.3% 29 0.0% 0 10.7% 3 Dhanmondi Mohammadpur 53.8% 24403 53.4% 75.5% 28 11.2% 3 13.3% 3 Gulshan 50.2% 28056 69.8% 81.2% 15 4.7% 2 14.0% 3 Jatrabari Sabujbagh 57.7% 30080 79.5% 70.6% 16 4.2% 2 25.2% 5 Mirpur Pallabi 45.1% 25969 68.9% 67.3% 35 20.4% 11 12.3% 6 Old Dhaka 41.7% 27615 60.0% 71.6% 15 3.2% 1 25.2% 6 Ramna Tejgaon 42.7% 23061 73.3% 98.9% 9 0.0% 0 1.1% 1 Table A41: Households benefiting from various social security schemes by selected background variables.

Has anyone in your Has anyone in Over the past 12 months, did Did any children in your Did any children in your household benefited in your household any members of your house- household benefit in the household benefit in the past the past 12 months from benefited the past hold purchase rice or wheat at past 12 months from the 12 months from the Stipend participating in a Public 12 months from cheaper prices from the Open Stipend or school feeding for Secondary Education? Works Programme (food or the Gratuitous Market Sales programmmes of programme in Primary cash-for-work)? Relief Programme? the Government? Education?

No school age No school age Yes Yes Yes Yes No Yes No child in household child in household

Row N% Row N% Row N% Row N% Row N% Row N% Row N% Row N% Row N%

City Dhaka .0% .8% 15.7% 2.4% 34.1% 63.4% .2% 4.7% 95.1% Barisal .3% 1.2% 22.1% 18.3% 23.3% 58.4% 5.1% 3.3% 91.6% Sirajganj 1.0% 2.9% 28.1% 5.1% 40.9% 54.0% 3.1% 8.3% 88.6% Gender Code Male .0% .8% 15.0% 2.8% 34.4% 62.9% .4% 5.2% 94.4% Female .0% 1.4% 24.6% 3.9% 30.4% 65.7% .2% 1.7% 98.2% Education None .0% .6% 19.6% 3.2% 33.2% 63.7% .2% 4.1% 95.7% completion Primary incomplete .0% 1.9% 15.7% 3.1% 33.5% 63.4% 1.0% 2.4% 96.6% categories Completed primary 0.0% .4% 12.7% 1.0% 42.7% 56.3% .1% 5.1% 94.8% Secondary incomplete .0% .8% 13.1% 4.2% 29.3% 66.5% .3% 3.5% 96.2% Secondary complete or higher .0% .1% 11.0% .9% 36.5% 62.6% .9% 18.0% 81.1% Livelihood Self-employed .1% .3% 19.8% 4.0% 36.4% 59.6% .6% 5.5% 93.9% Groupings Salary 0.0% .5% 8.5% .1% 15.4% 84.4% .5% 0.0% 99.5% Daily wage 0.0% .0% 7.9% .3% 33.7% 66.0% 0.0% 2.0% 98.0% No work, income 0.0% 1.2% 14.7% 2.1% 27.9% 70.0% .1% .3% 99.6% Wealth group Lowest quintile .0% 1.1% 17.9% 3.7% 38.5% 57.8% .4% 7.2% 92.3% Second quintile .0% 1.3% 13.6% 2.7% 36.2% 61.2% .6% 6.3% 93.1% Third quintile .0% .8% 15.4% 4.4% 33.9% 61.6% .3% 4.4% 95.3% Fourth quintile .0% .1% 15.3% 2.1% 31.7% 66.3% .3% 2.4% 97.3% Highest quintile .1% .5% 28.7% 1.7% 30.4% 67.9% .2% 4.1% 95.7% Duration in Always in city .1% 1.9% 24.2% 2.7% 37.8% 59.5% .8% 2.6% 96.7% current city <= 2 years .0% .1% 12.3% 3.7% 41.7% 54.5% .2% 3.9% 95.9% 3-5 years 0.0% 1.7% 16.7% 2.4% 33.2% 64.4% .9% 3.1% 96.0% 6-10 years 0.0% .5% 15.3% 2.6% 26.6% 70.8% .0% 6.2% 93.8% 10+ years .0% 0.0% 12.1% 3.1% 30.7% 66.3% .0% 7.9% 92.0% Dhaka zones Kafrul-Badda 0.0% 0.0% 5.0% .4% 31.6% 68.0% .3% 3.4% 96.3% Dhanmondi Mohammadpur 0.0% 6.5% 24.3% 4.7% 37.5% 57.8% 0.0% 2.7% 97.3% Gulshan 0.0% 0.0% 4.1% 2.8% 32.6% 64.6% 0.0% 7.4% 92.6% Jatrabari Sabujbagh 0.0% .3% 29.3% 5.8% 37.1% 57.2% 0.0% 2.5% 97.5% Mirpur Pallabi .0% 0.0% 9.8% 1.7% 30.3% 68.0% .5% 4.6% 94.9% Old Dhaka 0.0% 0.0% 26.6% 0.0% 42.6% 57.4% 0.0% 12.9% 87.1% Ramna Tejgaon 0.0% 0.0% 24.6% 1.9% 35.3% 62.8% 0.0% 2.0% 98.0% Table A42: Membership in UPPR programme by selected background variables

Are you or anyone in your household participating in any UPPR programme?

Yes

Row N%

City Dhaka 2.9%

Barisal 22.1%

Sirajganj 27.1%

Gender Code Male 3.7%

Female 4.8%

Education None 3.0% completion categories Primary incomplete 3.1%

Completed primary 1.7%

Secondary incomplete 4.4%

Secondary complete or higher 13.3%

Livelihood Self-employed 7.0% Groupings Salary .1%

Daily wage .2%

No work, income 1.8%

Wealth group Lowest quintile 4.6%

Second quintile 3.5%

Third quintile 4.7%

Fourth quintile 3.8%

Highest quintile 3.2%

Duration in current Always in city 4.6% city <= 2 years 3.3%

3-5 years 3.2%

6-10 years 4.3%

10+ years 3.8%

Dhaka zones Kafrul-Badda 1.2%

Dhanmondi Mohammadpur 0.0%

Gulshan 6.8%

Jatrabari Sabujbagh 7.8%

Mirpur Pallabi 2.4%

Old Dhaka 0.0%

Ramna Tejgaon 0.0%

105 Table A43: Most effective community leaders by selected background variables

To assist you and your household overcome difficulties in getting enough food, which one of the enlisted com- munity leaders or organizations is the most effective?

Pourshava Ward Mastaan Community Imam or oth- Local National or Other None chairman commissioner organization er religious NGO staff international leader leader NGO staff

Row N % Row N% Row N% Row N% Row N% Row N% Row N% Row N% Row N%

City Dhaka 1.7% 5.3% .0% 3.4% .7% 2.0% 2.0% 1.7% 83.0% Barisal 1.7% 21.8% 0.0% 1.3% 0.0% 5.1% 3.3% 12.9% 53.9% Sirajganj 11.0% 11.4% .3% 18.5% 0.0% 12.5% 5.6% 4.0% 36.7% Gender Code Male 2.0% 6.3% .1% 3.7% .8% 2.5% 2.4% 1.8% 80.7% Female 1.5% 2.4% 0.0% 3.3% .4% .1% .2% 4.4% 87.7% Education completion None 2.0% 4.6% .0% 3.2% .6% 2.2% 1.9% 1.7% 83.8% categories Primary incomplete 1.6% 7.0% .2% 4.9% 2.1% 2.1% 1.0% 1.7% 79.4% Completed primary 2.6% 8.4% 0.0% 4.2% .4% 2.7% 4.4% 2.5% 74.9% Secondary incomplete 1.1% 5.2% 0.0% 2.2% 0.0% 3.2% 1.6% 1.9% 84.8% Secondary complete or higher 3.3% 6.7% 0.0% 5.1% 0.0% .5% 3.3% 5.6% 75.4% Livelihood Groupings Self-employed 2.2% 7.2% .0% 4.5% 1.1% 4.5% 2.8% 2.4% 75.2% Salary 2.4% 3.3% 0.0% 2.5% .1% .7% .7% 2.0% 88.3% Daily wage 3.5% 4.2% 0.0% 2.2% 0.0% 2.0% 1.9% 3.5% 82.6% No work, income 1.7% 4.2% 0.0% 1.7% .7% .2% 3.5% .7% 87.2% Wealth group Lowest quintile 1.5% 6.5% .1% 4.3% .8% 2.6% 1.7% 2.2% 80.3% Second quintile 2.3% 6.4% .0% 4.1% .9% 2.5% 2.8% 1.5% 79.5% Third quintile 1.0% 4.9% 0.0% 2.4% .6% 1.8% 2.3% 2.7% 84.3% Fourth quintile 3.0% 6.9% .2% 3.5% .9% 1.2% .9% 2.7% 80.6% Highest quintile .5% 4.0% 0.0% 5.0% 0.0% 5.1% 1.1% 1.6% 82.7% Duration in current city Always in city 3.1% 5.9% .3% 2.6% .3% 4.3% 3.5% 3.2% 76.8% <= 2 years 1.1% 6.0% 0.0% 3.2% 1.3% 3.0% 2.2% .5% 82.6% 3-5 years 1.6% 6.2% 0.0% 3.6% 1.0% 1.7% .7% 4.3% 80.8% 6-10 years 1.9% 5.2% 0.0% 4.5% 0.0% 1.4% 3.0% 1.1% 82.9% 10+ years 1.8% 5.7% 0.0% 4.1% 1.0% 1.2% 1.1% 1.3% 83.7% Dhaka zones Kafrul-Badda 2.4% 2.3% 0.0% 2.6% .7% 1.8% .1% 1.3% 88.9% Dhanmondi Mohammadpur 1.4% 12.6% 0.0% 0.0% 0.0% 2.1% 4.4% 2.0% 77.5% Gulshan .7% 0.0% 0.0% .5% 0.0% 2.0% .1% 2.0% 94.6% Jatrabari Sabujbagh 2.7% 6.3% 0.0% 4.2% 0.0% 3.2% 4.3% .4% 78.9% Mirpur Pallabi 2.0% 5.2% .2% 5.7% .5% .0% .6% 2.6% 83.1% Old Dhaka 0.0% 7.1% 0.0% 5.2% .1% 6.8% 5.6% .5% 74.6% Ramna Tejgaon 1.1% 4.7% 0.0% 1.7% 6.0% 2.0% 1.8% 2.8% 79.9% SURVEY QUESTIONNAIRE

J32 How much of the products from these livestock (milk, other dairy, meat, skin, manure, None ...... 1 etc.) did you sell or Five away since the [LAST CROPPING SEASON]? Not much ...... 2 Less than half ...... 3 About half...... 4 More than half ...... 5 Almost all or all ...... 6 J33 How much did you earn from the sale of animals and livestock products during and since TAKA [LAST CROPPING SEASON]?

J34 Did you raise any poultry (chickens, ducks, etc.) on this land in [LAST COMPLETED Yes ...... 1 CROPPING SEASON]? No (»NEXT MODULE) ...... 2 J35 How many poultry birds do you now own? J36 How many birds did you sell during or since the [LAST CROPPING SEASON]? J37 How much of the eggs from these poultry did you sell or give away? None ...... 1 Not much ...... 2 Less than half ...... 3 About half...... 4 More than half ...... 5 Almost all or all ...... 6 J38 How much did you earn from the sale of poultry and eggs during and since [LAST TAKA CROPPING SEASON]?

Module K: Loans and Savings (ASK OF HOUSEHOLD HEAD OR OTHER SENIOR MEMBER OF HOUSEHOLD.) K01 Do any of your household members save? Yes……. 1 No ……… 2 K02 Where do you save? 1= At home 2= Bank 3 = NGOs/ CBO/ Society (Samity) 4= Grameen Bank 3= Insurance company 4= With relatives/ friends 5= Postal saving bank 6= Other (specify)______K03 What is the current amount of savings by the household members? TAKA K04 How much did the household members save over the past one month? TAKA K05 Can the household members access to savings in need? Have full access…………………….1(>>K07) Have partial access…………………2(>>K07) No access……………...3

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 21 K06 Why can’t they access their savings? I have a loan from the same NGO where I save……………………………………..1 It is not allowed………………………..2 It is deposited for a longer time (fixed deposit)………………………………..3 Can only access savings when I quit my membership with the NGO/Samity ………………………4 Other (specify)………………………5 K07 Do the members of the household have any outstanding loan? Yes…….1 No…….2(>>next module) K08 What is the total amount of outstanding loan in the household? TAKA

K09 What are the sources of loan (there can be multiple sources) Relative/ friend………………….1 Source 1: Bank or financial institution……..2 Source 2: NGO/ CBO/ Samity….…………..3 Source 3: UPPR loan……………………….4 Source 4: Grameen…………………………5 Money lender...………………………….6 Other (list)…………...... 7 K10 What was the reason of taking loan? (there can be multiple reasons) Meet daily HH needs……………..1 Reason 1: Purchase food………………………..2 Reason 2: Ag. inputs…………………………………..…3 Reason 3: Social events……………………..….4 Reason 4: Medical expenses…………………..5 Buy/ lease/ mortgage in land ………6 Repaid loan………………………………7 Housing/repair expenses…………8 Buy poultry/ livestock……………9 Invest on small business…….…10 Education……………………………….11 Dowry payment……………….…….12 Buy clothes………………………….…13 Buy household items…………….14 Other (list)…………………………...15 K11 Are household members paying regular installments to pay back loan (s)? Yes ...... 1(>>next module) No 2 K12 If no, why not? Do not have income to pay back……….1 The income project (IGA) failed……….2 Spend on food/ health/ or other things…3

Module L: Other Income & Participation in Social Safety Nets (ASK OF HOUSEHOLD HEAD OR OTHER SENIOR MEMBER OF HOUSEHOLD.)

L01 Over the past 6 months, did any members of your household receive any regular income from savings interest or Yes ...... 1 other investment income? No (»L03) ...... 2

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 22 L02 How much does your household in total usually receive in savings interest or other investment income over a TAKA three month period? L03 Over the past 6 months, did any members of your household receive any regular income from rental of property? Yes ...... 1 No (»L06) ...... 2 L04 What sort of property? House; residence ...... 1 Commercial building ...... 2 Agricultural land ...... 3 Other property specify………....4 L05 How much does your household in total usually receive in rental income over a three month period? TAKA L06 Over the past 6 months, did any members of your household receive any remittances? Yes ...... 1 No (»L08) ...... 2 L07 How much does your household in total usually receive from this other income over a six month period? TAKA

L08 Over the past 6 months, did any members of your household receive any regular income of any other type that Yes 1 has not been discussed earlier? No (»L11) ...... 2 L09 What sort of income (specify)? L10 How much does your household in total usually receive from this other income over a six month period? TAKA

L11 Has anyone in your household benefited in the past 12 months from participating in a Public Works Programme Yes ...... 1 (food or cash-for-work)? No (»L14) ...... 2 L12 How much grain did you receive in total, in kilograms? KG L13 How much cash did you receive in total? TAKA L14 Has anyone in your household benefited the past 12 months from the Gratuitous Relief Programme? Yes ...... 1 No (»L17) ...... 2 L15 How much grain did you receive in total, in kilograms? KILOFRAMS L16 How much cash did you receive in total? TAKA L17 Over the past 12 months, did any members of your household purchase rice or wheat at cheaper prices from the Yes ...... 1 Open Market Sales programmmes of the Government? No (»L20) ...... 2 L18 How much grain did you purchase from OMS in total, in kilograms? KG L19 What was the price per kilogram? TAKA PER KG L20 Did any children in your household benefit in the past 12 months from the Stipend or school feeding programme Yes ...... 1 in Primary Education? No ...... 2 No school age child in household…………………3 L21 Did any children in your household benefit in the past 12 months from the Stipend for Secondary Education? Yes ...... 1 No ...... 2 No secondary school age child in household ...... 3 L22 Are you or anyone in your household participating in any UPPR programme? Yes 1 No ……….2 (>>next module) L23 What is the name of your CDC? CDC Name

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 23 L24 What is your group code (CDC)? Group code Not member……..88888888 Does not now……99999999 L25 Do you have any CIP (Ceritified Identification of Poor) number? Yes……….1 No ……….2 L26 What is your CIP number? CIP (Ceritified Identification of Poor) number Does not have CIP………….…..88888888 Does not now……99999999 L27 Have you received SEF (Social Enterprise Fund) loan of UPPR? Yes……….1 No ……….2

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 24 Module M: Food Security Related Information (ASK OF PRINCIPAL HOME MAKER OF HOUSEHOLD.)

I would like to ask you some questions about what you do when you do not have enough food or money to buy food and how frequently this happened in the past one month M01 In the past 30 days, did you worry that your household would not have enough food? Yes ...... 1 Code for M1-10: No ...... 2 (>>M02) Rarely (1/2 times)……1 M01a If answer is yes, then how often did this happen? Code Sometimes (3 to 10 M02 times)………………...2 In the past 30 days, were you or any household member not been able to eat the foods you preferred to eat because Yes ...... 1 Often (more then 10 of lack of resources? No 2 (>>M03) times)…………………3 M02a IfM answer is yes, then how often did this happen? Code M03 In the past 30 days, did you or any household member have to eat a limited variety of foods due to a lack of Yes ...... 1 resources? No 2 (>>M04) M03a If answer is yes, then how often did this happen? Code M04 In the past 30 days, did you or any household member have to eat food that you preferred not to eat because you Yes ...... 1 did not have resources to obtain other food? No 2 (>>M05) M04a If answer is yes, then how often did this happen? Code M05 In the past 30 days, did you or any household member have to eat limited portions at mealtimes because there was Yes ...... 1 not enough food? No 2 (>>M06) M05a If answer is yes, then how often did this happen? Code M06 In the past 30 days, did you or any household member have to eat fewer meals in a day because there was not Yes ...... 1 enough food? No 2 (>>M07) M06a If answer is yes, then how often did this happen? Code M07 In the past 30 days, did you or any household member have no food to eat at all in the household because there Yes ...... 1 were no resources to get food? No 2 (>>M08) M07a If answer is yes, then how often did this happen? Code M08 In the past 30 days, did you or any household member go to sleep at night hungry because there was not enough Yes ...... 1 food? No 2 (>>M09) M08a If answer is yes, then how often did this happen? Code M09 In the past 30 days, did you or any household member have go a whole day without eating anything because there Yes ...... 1 was not enough food? No 2 (>>M10) M09a If answer is yes, then how often did this happen? Code M10 Did your household take any loan for food in the last month? Yes ...... 1 No (»M21) ...... 2 M11 How much taka was the loan for? TAKA

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 25 M12 Now I would like to ask you about your household's food supply during different months of year. In the past 12 months, were there months in which you did not have enough food to meet your family's needs? Yes ...... 1 No (»NEXT MODULE) ..... 2

M13 January M14 February M15 March M16 April M17 May M18 Which were the months in the past 12 months in which you did not have enough food to meet your family's needs? June

M19 DO NOT READ THE LIST OF MONTHS. MARK WITH A CHECK MARK THOSE THAT RESPONDENT MENTIONS. July M20 August M21 September M22 October M23 November M24 December

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 26 Module N: Subjective Assessment of Well-being (ASK OF HOUSEHOLD HEAD OR OTHER SENIOR MEMBER OF HOUSEHOLD.) N1 Concerning your household's food consumption over the past one month, which of the following is true? It was less than adequate for house- N2 Concerning your housing, which of the following is true? hold needs ...... 1 It was just adequate N3 Concerning your household's clothing, which of the following is true? for household needs ...... 2 It was more than adequate for N4 Concerning the standard of health care you receive for household members, which of household needs ...... 3 the following is true? N5 Which of the statement is true? Your current income . . . [READ]: allows you to build your saving …………….1 allows you to save just a little……………... 2 only just meets your expenses ... ……………3 is not sufficient, so you need to use your savings to meet expenses …4 is really not sufficient, so you need to borrow to meet expenses………5 N6 In terms of your household economic well-being, are you better off, the same as, or Much better ...... 1 worse off than this same time a year ago? Better...... 2 No change ...... 3 Worse off ...... 4 Much worse ...... 5 N7 In terms of your household economic well-being, in a year from now do you expect to Much better ...... 1 be better off, the same as, or worse off than now? Better...... 2 No change ...... 3 Worse off ...... 4 Much worse ...... 5 N8 What income level per month do you personally consider to be absolutely minimal - below which you and your household could not fulfil basic needs? TAKA Interviewer: Explain basic needs, example: dwelling house, clothes, sufficient quantity and quality of food etc N9 Overall, how satisfied (content, happy) are you with your life? Are you . . . very unsatisfied ...... 1 unsatisfied ...... 2 neither unsatisfied or satisfied . 3 satisfied ...... 4 very satisfied ...... 5 N10 What do you (HH HEAD) sleep on? Bed & mattress ...... 1 Bed or choki & mat (grass) ..... 2 Bed or choki alone ...... 3 Mattress on floor ...... 4 Mat (grass) on floor ...... 5 Cloth / plastic on floor ...... 6 Floor (nothing else) ...... 7 Other (specify) ...... 8

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 27 Module O: Recent Shocks to Household Welfare (ASK OF HOUSEHOLD HEAD OR OTHER SENIOR HOUSEHOLD MEMBER.) What did you do cope with this CODE Q2: Response to shock shock? Spent savings ...... ………01 Sold assets ...... ………02 Over the past one year, was your household severely YES.1 affected negatively by any of the following events? LIST UP TO 3 most practiced coping Borrowed money from a moneylender ...... ………03 NO .2 Borrowed money from an institution (bank, NGO)………….04

(»NEXT ITEM strategies , Borrowed money from relatives or friends ...... ………05 GO THROUFG ENTIRE LIST BEFORE ITEM) CODE Code-Q1 Workers in HH took on more work ...... ………06 PROCEEDING. O1 O2 O3a o3b O3c Previous non-workers in HH began working…………….…. 07 Sent children to work………………………………………....08 Sent dependents in HH to live with relatives...... ……...09 Household business failure, non-agricultural 101 Moved elsewhere to find work ...... ……...10 Received help from institution (NGO, religious, Govt., etc.)…11 Agricultural crop failure 102 Started begging…………………………………………….….12 Reduced quantity of consumption…………………………….13 Ate less preferred and inexpensive food……………………..14 Loss of employment or non-payment of salary 103 Purchase food on credit……………………………………….15 Pass entire day without eating………………………………..16 Political unrest caused damage to employment and Restrict adult consumption so that children can eat…………..17 104 livelihood Increased purchasing food in credit…………………………..18 Did not do anything ...... ………19 End of regular assistance, aid, or remittances from Other (specify) ...... ….…..20 105 outside household

Price hike of food and essential commodities 106

Major illness or accident of household member 107

Birth in the household 108

Death of working member of the household 109

Death of other family member 110

Break-up of the household 111

Dowry / marriage expenses 112

Loss of property due to theft/robbery, flood, fire, 113 etc.

Eviction from residence 114

Dwelling damage, destroyed 115

Family member arrested, imprisoned 116 Extortion by mastaans, corrupt officials required 117 bribe, etc.

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 28 What did you do cope with this CODE Q2: Response to shock shock? Spent savings ...... ………01 Sold assets ...... ………02 Over the past one year, was your household severely YES.1 affected negatively by any of the following events? LIST UP TO 3 most practiced coping Borrowed money from a moneylender ...... ………03 NO .2 Borrowed money from an institution (bank, NGO)………….04

(»NEXT ITEM strategies , Borrowed money from relatives or friends ...... ………05 GO THROUFG ENTIRE LIST BEFORE ITEM) CODE Code-Q1 Workers in HH took on more work ...... ………06 PROCEEDING. O1 O2 O3a o3b O3c Previous non-workers in HH began working…………….…. 07 Sent children to work………………………………………....08 Cyclone 118 Sent dependents in HH to live with relatives...... ……...09 Moved elsewhere to find work ...... ……...10 Received help from institution (NGO, religious, Govt., etc.)…11

Flood 119 Started begging…………………………………………….….12 Reduced quantity of consumption…………………………….13 Ate less preferred and inexpensive food……………………..14 Water stagnation 120 Purchase food on credit……………………………………….15 Pass entire day without eating………………………………..16 121 Restrict adult consumption so that children can eat…………..17 Other: ______Increased purchasing food in credit…………………………..18 Did not do anything ...... ………19 Other (specify) ...... ….…..20

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 29 Module P: Community Participation (ASK OF HOUSEHOLD HEAD OR OTHER SENIOR MEMBER OF HOUSEHOLD.) P01 Can your household rely on neighbor to help you through difficult periods? Yes ...... 1 No...... 2 P02 Are you or other working members of your household a member of a trade association or labor Yes ...... 1 union? No (»P04) ...... 2 P03 Do you have to pay a fee to be a member of this Group? Yes ...... 1 No...... 2 P04 Are you or other members of your household a member of a women's association? Yes ...... 1 No (»P06) ...... 2 P05 Do you have to pay a fee to be a member of this group? Yes ...... 1 No...... 2

P06 Do you or other members of your household belong to a slum-dwellers association (basti Yes ...... 1 bashi)? No (»P08) ...... 2 P07 Do you have to pay a fee to be a member of this Group? Yes ...... 1 No...... 2 P08 Do you or other members of your household belong to a credit and savings Group? Yes ...... 1 No (»P10) ...... 2 P09 Do you have to pay a fee to be a member of this Group? Yes ...... 1 No...... 2

P10 Do you or other members of your household belong to any other effective community Yes ...... 1 association, such as an NFO project? No (»P15) ...... 2 P11 What kind of Group is it? (Specify)

P12 Do you have to pay a fee to be a member of this Group? Yes ...... 1 No...... 2 P13 Does the membership in the groups/organization help you to overcome your difficult times? Yes ...... 1 No (>>P15) ...... 2 P14 If yes, mention one major assistance that you receive from the group/organization of which you Financial grant……………….1 are a member during your difficult times. Loan………………………….2 Moral support………………..3 P15 To assist you and your household overcome difficulties in getting enough food, which one of the enlisted com-munity leaders or READ Pourshava chairman...... …1 Most organizations is the most effective? Second most effective? Ward commissioner ...... …2 effective Mastaan ...... …3 Community organization leader.... …4 Imam or other religious leader ...... …5 P16 Local NGO staff ...... …6 National or international NGO staff…7 Second Other (SPECIFY) ...... …8 most None ...... …9 effective

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 30 Module Q1: Anthropometric Measurements of Children:

Take information for children under 60 months

Member Name of the Name of the child’s Sex of child Birthdate Age Weight Height Measurement while Results: ID child mother standing or lying: Measures….1 While lying…….1 Absent…….2 Dead…..98 While standing....2 Did not want to Doesn’t live in the Male…..1 (in full (any child under 2 has to be be measured..3 household…99 Female…2 Day Month Year months) (kg) (c.m.) measured as it is lying) Others……..4 MID Name MOTID Q101 Q102 Q103 Q104 Q105 Q106 Q107 Q108 Q109

Module Q2: Anthropometric Measurements of Women: Measure height and weight of women in the household of 14-49 years of age. Measure MUAC of pregnant women in the household. Member Name of the Sex Do you breastfeed? Are you Weight Height Measurement while Results: ID women pregnant? (go to standing or lying: Q208 after Measures….1 measuring While lying…….1 Absent…….2 height) While standing....2 Did not want to Male…..1 Yes…..1 Yes…..1>>Q207 (any child under 2 has to be be measured..3 Female…2 No……2 No……2 (kg) (c.m.) measured as it is lying) Others……..4

MID Name Q201 Q202 Q204 Q205 Q206 Q207 Q208

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 31

Module R: Healthcare Utilization and Nutritional Status of Children Under 5 Years

Question no. R1to R26should be administered only to children within 6-59 months of age. In a household record maximum 3 children within this age group. Serial Questions Child No.1 Child No.2 Child No3 NName Child name Name of the child

MID Child member ID Child member ID

MOTID Mother’s ID Dead…..98 Mother of the child’s ID Doesn’t live in HH…..99 R01 Male…….1 Sex of child - 1= Male/ 2= Female │__│ │__│ │__│ Female…2

R02 Date of birth __/__ _ / ______/__ __ /______/__ __ /__ __ Day/Month/Year

R03 Age in months │____│ months │____│ months │____│ months In full month

R04 Was the child sick in the 2 weeks Yes……...1 │__│ │__│ I___I before the survey? No………2>>R08 If No >> S8 If No >> S8 If No >> S8

R05 1= Fever 2= Repeated coughs/ What was the child’s main breathing difficulties sickness? │__│ │__│ │__│ 3= Diarrhoea

4= Measles 5=Other______R06 Was the child taken to a health │__│ │__I I___I Yes……...1 service? If Yes >> S8 If Yes >> S8 If Yes >> S8 No………2>>R08 R07 1= Not serious 2= Far away/lack transport 3= Lack money If child was not taken, why? 4= Does not like/ │__│ │__│ │__│ distrust 5= Other source of treatment was used 6= Other (specify) R08 Did the child receive a vitamin A Yes……...1 capsule during the past 6 months? │__│ │__│ │__│ No………2

R09 Did the child receive breast milk Yes……...1 │__│ │__│ │__│ yesterday? No………2 R10 Did the child receive animal milk Yes……...1 │__│ │__│ │__│ yesterday? No………2 R11 How many times did the child receive Number breastmilk or animal milk │__│ │__│ │__│ yesterday? R12 Did the child receive solid, semi- Yes……...1 │__│ │__│ │__│ solid or soft foods yesterday? No………2>>SName If No, >> S28 If No, >> S28 If No, >> S28

R13 Number How many times did the child receive │__│ │__│ I___I food yesterday?

Which food (among R14 to R26) did the child eat yesterday? R14 Yes……...1 Wheat, bread, rice, pasta, biscuits │__│ │__│ │__│ No………2 R15 Yes……...1 Potatoes │__│ │__│ │__│ No………2 R16 Yes……...1 Beans, peas, lentils, nuts │__│ │__│ │__│ No………2 R17 Yes……...1 Milk, cheese, yogurt │__│ │__│ │__│ No………2 R18 Yes……...1 Meat, liver, kidney, chicken, fish │__│ │__│ │__│ No………2 R19 Yes……...1 Eggs │__│ │__│ │__│ No………2 R20 Yellow/orange vegetables (pumpkin, Yes……...1 │__│ │__│ │__│ carrots) & fruits (apricots, plums) No………2 R21 Other vegetables and fruits, including Yes……...1 │__│ │__│ │__│ fruit juices No………2 Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 32 Serial Questions Child No.1 Child No.2 Child No3 R22 Yes……...1 Tea │__│ │__│ │__│ No………2 R23 Yes……...1 Plain or sugary water │__│ │__│ │__│ No………2 R24 Yes……...1 Infant formula │__│ │__│ │__│ No………2 R25 Yes……...1 Porridge │__│ │__│ │__│ No………2 R26 Vitamins, mineral supplements, and/ Yes……...1 │__│ │__│ │__│ or any medicine No………2

Only attempt this question when the household has children < 6 Child no. 1 Child no. 2 Child no.3 months SName Child name Name of the child

SMID Child member Child member ID ID Mother’s ID SMOTID Dead…..98 Mother of the child’s ID Doesn’t live in HH…..99 S01 Male…….1 Sex of child - 1= Male/ 2= Female │__│ │__│ │__│ Female…2

S02 Date of birth __/__ _ / __ __ /___/___ __ /__/___ Day/Month/Ye ar S03 │____│ │____│ │____│ Age in months months months months In full month S04 0= No/ 1= 0= No/ 1= 0= No/ Is the child being currently breast fed? Yes Yes 1= Yes S05 Since yesterday was this child given anything to drink including water │__│ │__│ │__│ other than breast milk? S06 Since yesterday was this child given anything to eat including porridge or │__│ │__│ │__│ blended food/cereal other than breast milk?

Survey of Household Food Security and Nutrition in Urban Slum Areas of Bangladesh, 2013 - 33

World Food Programme IDB Bhaban (17th floor) E/8-A, Rokeya Sharani Sher-e-Bangla Nagar Dhaka-1207, Bangladesh Tel: 880 2 9183022-25 Fax: 880 2 9183020 www.wfp.org/countries/bangladesh